The World Scientific Reference of Water Science 9789811245770, 9811245770

Table of contents :
Volume 1: Molecular Engineering of Water Sensors
Contents
Preface
About the Editor
Chapter 1 Field-Effect Transistor Sensors Based on 2D Nanomaterials for Detecting Contaminants in Water
1.1. Introduction
1.1.1. Importance of water safety and the need for high-performance water sensors
1.1.2. Why 2D-nanomaterials-based FET water sensors?
1.1.3. The goal of this chapter
1.2. The Working Principles of FET Water Sensors
1.2.1. Charge transfer and gating effects
1.2.2. Thickness-dependent sensor performance
1.2.3. Passivated FET water sensor
1.3. Two-dimensional Nanomaterials for Water Sensing
1.3.1. Graphene and graphene oxide
1.3.2. Black phosphorus
1.3.3. Molybdenum disulfide
1.3.4. Comparison of typical 2D materials
1.4. Detecting Contaminants in Water with 2D-Nanomaterials-Based FET Sensors
1.4.1. Heavy metal ions
1.4.2. Microorganisms
1.4.3. Phosphates and nitrates
1.5. Summary and Outlook
Acknowledgments
References
Chapter 2 Innovative Optical Fiber Approaches for Water Quality Monitoring
2.1. Introduction
2.2. Evanescent Wave-Based Optical Fiber Sensor
2.2.1. Refractive index modulation
2.2.2. Surface plasmon resonance
2.2.3. Colorimetric
2.2.4. Fluorescence
2.3. Summary: Future Challenges and Opportunities
References
Chapter 3 Liquid-Phase Chemical Sensors
3.1. Introduction
3.2. Liquid-Phase Chemical Sensor Platforms
3.3. Chemically Sensitive Layers for Liquid-Phase Detection
3.3.1. Polymer sensing layers and analyte partition coefficients
3.3.2. Polymer sensing layers and selectivity
3.4. SH-SAW Sensors for Organic Detection in Water
3.4.1. Fundamentals of the SH-SAW sensor platform
3.4.2. Polymer-coated SH-SAW sensors
3.4.2.1. Model of the single-analyte sensor response
3.4.2.2. Model of the multiple-analyte sensor response
3.4.2.3. Applying estimation theory to multiple-analyte sensor response
References
Chapter 4 Strategies for Development of Protein-Based Biosensors for Detecting Aromatic Xenobiotics in Water
4.1. Introduction
4.2. NtrC Protein Family Domain Organization and Biological Function
4.3. MopR: Phenol-Sensing Protein
4.3.1. MopR domain organization
4.3.2. Structure of the phenol sensor
4.3.3. Biosensor design for phenol detection
4.3.4. Aromatic pollutant screening for MopR: Alteration of sensor design
4.3.5. Design and synthesis of a catechol, 3-chlorophenol, and selective phenol sensor
4.4. BTEX Group Pollutant Sensor Design
4.5. Using Evolutionary Approaches to Improve Sensor Design
4.6. Protein Immobilization and Efficient Strip-Based Phenol Sensor Design
4.7. Conclusion
4.8. Future Directions
References
Chapter 5 Functional DNA Sensors for Heavy Metal Ions and Microbial Contaminants in Water
5.1. Introduction
5.1.1. Functional DNA (fDNA)
5.2. Development of Functional DNA Using In Vitro Selection
5.2.1. Functional DNA for metal ions
5.2.2. Functional DNA for microbes
5.3. Conversion of Functional DNA Recognition into Biosensors
5.3.1. Fluorescent sensors
5.3.2. Colorimetric sensors
5.3.3. Electrochemical sensors
5.4. Isothermal Amplification Methods
5.5. Summary and Future Perspectives
Acknowledgments
References
Chapter 6 DNAzyme-Based Biosensors and Their Applications in Monitoring Environmental Water Sources
6.1. Introduction
6.1.1. Functional nucleic acids
6.1.2. Advantages of functional nucleic acids
6.1.3. Functional nucleic acids as biosensor components
6.1.4. Molecular recognition in DNAzyme-based biosensors
6.1.5. Signal transduction and amplification in DNAzyme-based biosensors
6.1.5.1. Peroxidase-mimicking DNAzymes
6.1.5.2. RNA-cleaving fluorogenic DNAzymes (RFD)
6.1.5.3. Modified litmus assay (MLA)
6.1.5.4. Non-DNAzyme ST and SA strategies
6.2. DNAzyme-Based Biosensors for Detecting Heavy Metals
6.2.1. Detection of lead
6.2.2. Mercury detection
6.2.3. Detection of uranium (uranyl ion)
6.2.4. Detecting other metals
6.3. DNAzyme Based Biosensors For Detecting Bacteria And Bacterial Targets
6.4. Using a DNAzyme as the Recognition Element
6.5. Using DNAzymes as the Signal-Transducing and/or Signal-Amplifying Element
6.5.1. PMDs for signal transduction
6.5.2. DNAzyme as recognition and signal-transducing element
6.6. DNAzyme-Based Biosensors for Detecting Small Molecules
6.7. Outlook
References
Chapter 7 Biological and Chemical Sensors for Monitoring Water Quality
7.1. Introduction
7.2. Pollutants
7.2.1. Cyanobacteria
7.2.2. Pathogens
7.2.3. Pesticides/Herbicides
7.2.4. Industrial pollutants
7.3. Methods for Sensing Pollutants in Water
7.3.1. Laboratory testing/assays
7.4. Introduction to Sensors
7.4.1. Bioassays
7.5. Biochemical/Biosensor
7.5.1. Enzymatic
7.5.2. Immunosensors
7.5.3. DNA/aptamers
7.5.3.1. Aptamers
7.5.4. Chimeric proteins
7.5.5. Whole cell
7.5.5.1. Bacterial
7.5.5.2. Algal
7.5.5.3. Viruses
7.6. Signaling and Measurement
7.6.1. Electrochemical/transducer
7.6.1.1. Voltammetric biosensors
7.6.1.2. Potentiometric biosensors
7.6.1.3. Amperometric biosensors
7.6.1.4. Impedance-based biosensors
7.6.1.5. Conductometric
7.6.2. Piezoelectric biosensors
7.6.3. Optical biosensors
7.6.3.1. Surface plasmon resonance (SPR) biosensors
7.6.3.2. Evanescent field-based fiber optic biosensors
7.6.3.3. Infrared (IR) and Raman spectroscopy
7.6.3.4. Fluorescence and chemiluminescence (CL)
7.6.3.5. Colorimetric biosensors
7.6.3.6. White-light reflectance spectroscopy
7.6.4. Thermal
7.7. Future Developments
7.7.1. Opportunities
7.7.1.1. Paper-based probe
7.7.1.2. Nano/graphene biosensors
7.7.1.3. Smartphones
7.7.1.4. Microfluidic
7.7.1.5. Artificial receptors/molecularly imprinted polymers
7.7.1.6. Metagenomics
7.7.1.7. Microarrays
7.7.1.8. Ionic liquids
7.8. Conclusions
References
Chapter 8 Electrochemical Water Sensing Technologies
8.1. Introduction of Water Quality Monitoring
8.2. Introduction of Electrochemical Sensing
8.3. Brief History of Electrochemical Water Sensors
8.4. Heavy Metals Detection
8.4.1. Arsenic detection
8.4.2. Mercury detection
8.4.3. Lead detection
8.4.4. Simultaneous detection of multiple heavy metals
8.4.4.1. Lead and cadmium
8.4.4.2. Lead and copper
8.4.4.3. Multiple heavy metals
8.5. pH Determination
8.6. Phosphate Detection
8.7. Nitrite Detection
8.8. Conclusion
References
Chapter 9 Phosphates Analysis in Water: State-of-the-Art Technologies and Challenges
9.1. Introduction
9.1.1. Water pollution
9.1.2. Phosphate as a pollutant
9.1.3. Phosphate as a nutrient
9.1.4. The phosphorus cycle
9.1.5. Consequences of excess phosphate
9.1.6. Cost of treatment
9.1.7. Forms of phosphate
9.1.8. Challenges of prevention
9.1.9. The challenge of measurement
9.2. State-of-the-Art Technologies for Monitoring Phosphate in Water
9.2.1. Electrochemical techniques
9.2.2. Potentiometric techniques
9.2.3. Amperometric techniques
9.2.4. Voltammetric techniques
9.2.5. Conductance techniques
9.2.6. Capacitive techniques
9.2.7. Optical techniques
9.2.8. Colorimetric/Absorbance techniques
9.2.9. Chemiluminescent techniques
9.2.10. Fluorescent techniques
9.3. Future Challenges
References
Chapter 10 Metal Organic Framework-Based Sensors for Water Contaminants Detection
10.1. Introduction
10.2. MOF-Based Sensors
10.2.1. Fluorescent sensors using MOFs
10.2.1.1. Heavy-metal ion sensing
10.2.1.2. Inorganic-anion sensing
10.2.1.3. Organic-compound sensing
10.2.2. Electrochemical sensors using MOFs
10.2.2.1. Ion sensing
10.2.2.2. Organic-compound sensing
10.2.3. Other MOF-based sensors
10.3. Challenges and Outlook
References
Chapter 11 Smartphone-Based Sensors for On-Site Water Quality Monitoring
11.1. Introduction
11.2. Pesticide Residues
11.3. Microorganisms
11.4. Toxic Ions
11.5. Conclusions and Perspectives
Acknowledgments
References
Chapter 12 Large-Scale Assembly of Nanostructure-Based Sensors for Real-Time Bio-Analysis in Water
12.1. Introduction
12.2. Assembly of Nanostructure-based Devices
12.2.1. Nanostructures
12.2.2. Directed assembly of nanostructures
12.2.3. Properties of assembled nanostructure-based devices
12.3. Label-free Biosensors
12.3.1. Chemical sensors
12.3.2. Biosensors
12.3.3. Integrated advanced sensor devices
12.4. Electrophysiological Monitoring of Cellular Activities
12.4.1. Cell adhesion on nanostructures
12.4.2. Monitoring cellular activities
12.5. Bioelectronic Nose and Tongue
12.5.1. Olfactory receptor proteins in a lipid bilayer as a probe
12.5.2. Olfactory receptors in nanovesicles as a probe
12.5.3. Olfactory receptors in nanodisks as a probe
12.6. Summary
References
Chapter 13 Microfluidic Devices for Water Quality Management
13.1. Introduction
13.2. Different Microfluidic Systems — Electrochemical and Optical
13.2.1. Electrochemical detection systems
13.2.2. Optical detection systems
13.3. Fabrication of Microfluidic Systems
13.3.1. Common materials used for sensor fabrication
13.3.1.1. Inorganic materials
13.3.1.2. Polydimethylsiloxane (PDMS) and plastics
13.3.1.3. Paper
13.4. Different Microfluidic Devices for Detecting Different Pollutants
13.4.1. Heavy-metal detection
13.4.2. Nutrients
13.4.3. Pathogens
13.5. Conclusion
References
Chapter 14 Underwater Self-Healable Materials for Sensing Applications
14.1. Introduction
14.2. Design Strategies for Self-Healing Underwater
14.2.1. Dynamic covalent bonds
14.2.2. Coordination chemistry
14.2.3. Hydrogen bonds
14.2.4. Dipole–dipole interactions
14.2.5. Hydrophobic interaction
14.2.6. Combinatorial interactions
14.3. Underwater Self-Healing Applications
14.3.1. Electrochemical self-healing devices
14.3.2. Electronic self-healing devices
14.4. Perspectives
Acknowledgment
References
Chapter 15 Additive Manufacturing of Graphene-Based Sensors for Environmental Applications
15.1. Introduction
15.2. Sensor Types and Graphene-Based Materials for Water-Sensing
15.2.1. Water sensor types
15.2.2. Conductivity-based sensors
15.2.2.1. Electrochemical sensors
15.2.2.2. Transistor-type sensors
15.2.3. Graphene-based materials for water sensing
15.2.3.1. Graphene oxide/reduced-graphene oxide
15.2.3.2. CVD/grown-graphene materials
15.2.3.3. Sensors based on exfoliated graphene flakes
15.3. Synthesis of Graphene
15.3.1. Introduction
15.3.2. Top-Down exfoliation
15.3.3. Liquid phase exfoliation of graphene
15.3.4. Graphite oxidation, graphene oxide, and RGO
15.3.5. Electrochemical exfoliation of graphene
15.3.6. Chemical exfoliation of graphene
15.3.7. Bottom-up synthesis
15.3.8. CVD of graphene
15.3.9. Laser-induced graphene growth
15.3.10. Hydrothermal synthesis of graphene
15.3.11. Concluding thoughts
15.4. Direct Writing of Graphene-Based Sensors
15.4.1. Shear exfoliation and graphene ink formulation
15.4.2. Direct ink writing of graphene-based sensors
15.4.3. Results and discussion
15.4.3.1. Exfoliated graphene characterization
15.4.3.2. Characterization of graphene-based inks
15.4.3.3. Characterization of printed graphene films
15.4.3.4. Sensor fabrication and characterization
15.5. Conclusions and Future Directions
References
Index
Volume 2: Nanotechnology for Water Treatment and Water Interfaces
Contents
Preface
About the Editors
Chapter 1 Enhancement of Filtration and Adsorption Processes for Water Treatment Using Graphene-Based Nanomaterials
1.1. Graphene-Based Nanomaterials
1.2. Filtration Applications
1.2.1. Introduction
1.2.2. Modification techniques
1.2.3. Microfiltration and ultrafiltration
1.2.4. Nanofiltration and reverse osmosis
1.3. Adsorption
1.3.1. Introduction
1.3.2. Batch adsorption
1.3.3. Continuous adsorption
1.4. Conclusion
Acknowledgments
References
Chapter 2 Engineered Graphene Oxide as Advanced Separation Material for Water Treatment
2.1. Introduction
2.2. GO Material Properties
2.3. GO as a High-Performance Adsorbent
2.3.1. Adsorption mechanisms
2.3.2. Adsorption performance
2.3.2.1. Inorganic contaminants
2.3.2.2. Organic pollutants
2.3.3. Applications in real-world settings
2.3.3.1. Influencing environmental conditions
2.3.3.2. Treatment of realistic waters
2.3.3.3. Comparison of GO and AC
2.4. GO Membrane for Precise Filtration
2.4.1. Assembly of GO membranes
2.4.1.1. Vacuum- and pressure-driven filtration
2.4.1.2. Layer-by-layer assembly
2.4.1.3. Coating
2.4.2. Structural manipulation of GO membranes
2.4.2.1. Adjusting horizontal nanochannels (interlayer spacing)
2.4.2.2. Modulating vertical nanochannels
2.4.2.3. Modifying GO morphology
2.4.3. Environmental separations
2.5. Future Perspectives
2.5.1. Establishing structure–property relationships
2.5.2. Exploring new opportunities at the nanoscale
2.5.3. Taking sustainability issues into consideration
Acknowledgments
References
Chapter 3 Graphitic Carbon Nanomaterial-Based Membranes for Water Desalination
3.1. Introduction
3.1.1. Use of graphene-based materials (2D graphitic carbon nanomaterial) for water desalination membranes
3.1.2. CVD graphene for pressure-driven desalination membranes
3.1.3. CVD graphene for thermal desalination membranes
3.2. Graphene Oxide and Reduced-Graphene-Oxide-Based Membrane for Water Desalination (Graphene-Flake-Based)
3.2.1. Graphene-flake-based membranes for pressure-driven desalination membranes (free-standing membrane)
3.2.2. Graphene-flake-based TFN membranes for pressure-driven desalination systems (graphene embedded in polymer matrix)
3.2.3. Graphene-flake-based membranes for thermally driven desalination
3.3. Use of Carbon-Nanotube (1D Graphitic Carbon Nanomaterial)-Based Materials for Water Desalination Membranes
3.3.1. Carbon-nanotube-based membranes for pressure-driven desalination systems (earlier studies on water transport through CNTs)
3.3.2. Carbon-nanotubes-based TFN membranes for pressure-driven desalination systems (CNTs embedded in active layer of the membrane)
3.3.3. Carbon-nanotubes-based TFN membranes for pressure-driven desalination systems (CNTs as interlayer for TFN membranes)
3.3.4. Carbon-nanotubes-based membranes for thermally driven desalination systems
3.4. Use of Graphitic Carbon Nitride (2D Graphitic Carbon Nanomaterials) for Water Desalination Membranes
3.4.1. GCN-based membranes for pressure-driven desalination systems (freestanding and TFN membranes)
3.5. Use of Graphene and Graphene-Oxide Quantum Dots (0D Graphitic Carbon Nanomaterials) for Water Desalination Membranes
3.5.1. Graphene and graphene-oxide quantum dots for pressure-driven desalination systems (TFN membranes)
3.5.2. Graphene and oxide-quantum-dot-based membranes for thermally driven desalination systems
3.6. Future Perspectives
3.6.1. CVD graphene-based membrane (2D graphitic carbon nanomaterials)
3.6.2. Graphene oxide or reduced-graphene oxide flake-based membrane (2D graphitic carbon nanomaterials)
3.6.3. CNTs-based membrane (1D graphitic carbon nanomaterials)
3.6.4. GCN-based membrane (2D graphitic carbon nanomaterials)
3.6.5. Graphene and graphene-oxide-quantum-dot-based membrane (0D graphitic carbon nanomaterials)
References
Chapter 4 Nanomaterials and Nanotechnology for Waterborne Pathogen Inactivation
4.1. Introduction
4.2. Waterborne Pathogen Inactivation
4.3. Nanomaterials and Nanotechnologies
4.3.1. Antimicrobial metals
4.3.2. Photocatalytic metal oxides
4.3.3. Antimicrobial and photoreactive carbon materials
4.3.4. Catalytic nanomaterials for advanced oxidation processes
4.3.5. Nano-electrodes for electrochemical and electrophysical inactivation
4.4. Challenges and Opportunities
Acknowledgments
References
Chapter 5 Development of Nanostructured Adsorption Materials for Removing Heavy-Metal Ions from Aqueous Systems
5.1. Introduction
5.2. Preparation of Nanostructured Adsorbents
5.3. Characterization of Nanostructured Adsorbents
5.3.1. XRD
5.3.2. FTIR
5.3.3. XPS
5.3.4. SEM and TEM
5.3.5. BET
5.3.6. TGA
5.4. Adsorption of Heavy-Metal Ions
5.4.1. Adsorption kinetic studies
5.4.2. Effect of pH
5.4.3. Adsorption isotherm studies
5.4.4. Selective adsorption behaviors
5.5. Adsorption Mechanism
5.6. Conclusion and Perspective
References
Chapter 6 Low-Dimensional Nanomaterials for Next-Generation Capacitive Deionization Systems
6.1. Introduction
6.2. Early Studies of CDI
6.3. Nanostructured Carbon and Composites for CDI and MCDI
6.3.1. Metal-organic framework-derived carbon
6.3.2. 3D CNT- and graphene-based frameworks
6.4. Faradaic Materials for HCDI
6.4.1. Manganese oxides
6.4.2. Prussian blue analogues
6.4.3. Ternary titanates
6.5. Desalination Battery
6.5.1. NMO desalination battery
6.5.2. NASICON desalination battery
6.6. Outlook
References
Chapter 7 Graphene Oxide and Nanocomposite Electrodes for Capacitive Deionization
7.1. Introduction
7.2. Effects of the Hierarchical Porosity of 3D rGO on Ion Electrosorption
7.2.1. Introduction
7.2.2. Methods
7.2.2.1. Synthesis of 3D rGO
7.2.2.2. Synthesis of 3D rGO with out-of-plane pores
7.2.2.3. Fabrication of electrodes and CDI experiments
7.2.3. Results and discussion
7.2.4. Conclusion
7.3. Pseudocapacitive MnFe2O4/Porous rGO Nanocomposite Electrodes in CDI
7.3.1. Introduction
7.3.2. Synthesis of composite nanostructure
7.3.3. Results and discussion
7.3.3.1. MFO/PrGO nanocomposites structures and compositions
7.3.3.2. Electrochemical properties and CDI performance
7.4. Conclusion
Acknowledgments
References
Chapter 8 Palladium-Based Nanostructured Catalysts for Treatment of Recalcitrant and Problematic Waterborne Pollutants
8.1. Introduction
8.2. Basic Principles and Analytical Methods
8.2.1. Reaction pathways
8.2.1.1. Toxic oxyanions
8.2.1.2. Halogenated compounds
8.2.1.3. Nitrogen-containing organics
8.2.2. Characterization methods of Pd-based catalysts
8.2.3. Data analysis methods for determining catalytic activity
8.2.3.1. Reaction kinetics
8.2.3.2. Turnover frequency
8.2.3.3. Langmuir–Hinshelwood kinetic model
8.2.3.4. Mass transfer considerationMass transfer (i.e., diffusion) is an important process
8.3. Factors Affecting Catalyst Performance
8.3.1. Shape and size of Pd nanoparticles
8.3.2. Secondary metal
8.3.3. Support materials
8.4. Conclusion and Future Outlook
Acknowledgments
References
Chapter 9 Catalytically Reactive Membrane Filtration for Water Treatment
9.1. Introduction
9.2. Photocatalytically Reactive Membrane Filtration
9.2.1. Components and working principles
9.2.1.1. Photocatalysts
9.2.1.2. Light sources
9.2.1.3. Photocatalytic membrane materials
9.2.2. Design and operation of photocatalytic membrane filtration
9.2.2.1. System parameters and configurations
9.2.2.2. Design criteria
9.2.2.2.1. Photocatalyst immobilization
9.2.2.2.2. Control of photo irradiation, influent flux and pollutant-loading
9.2.2.2.3. Quantum efficiencies and energy use efficiencies
9.2.2.2.4. Aqueous/solid mass transfer assessment
9.2.3. Challenges and perspectives
9.3. Microwave-Enabled Reactive Membrane Filtration
9.3.1. Microwave catalysis
9.3.2. Components and working principles
9.3.2.1. Microwave-enabled membrane materials
9.3.2.2. Microwave-enabled advanced oxidation reaction
9.3.3. Design and operation of microwave-enabled membrane filtration
9.3.3.1. System parameters and configurations
9.3.3.2. Design criteria
9.3.3.2.1. Selection of the microwave responsive catalysts
9.3.3.2.2. Porosity assessment of microwave-catalyst coated ceramic membranes
9.3.3.2.3. Surface roughness assessment of microwave-catalyst-coated ceramic membranes
9.3.3.2.4. Model analysis of the permeate water flux
9.3.3.2.5. Assessment of penetration of microwave irradiation in membrane filtration system
9.3.3.2.6. Evaluation of energy consumption and distribution in pollutant degradation and water heating
9.3.4. Challenges and perspectives
9.4. Electrochemically Reactive Membrane Filtration
9.4.1. Electrochemical technologies
9.4.2. Applications of electrochemical oxidation in the removal of different micropollutants
9.4.2.1. Industrial solvent additives-1,4 Dioxane
9.4.2.2. Synthetic dye micropollutants
9.4.2.3. Cyanotoxins and harmful algal bloom (HAB) related micropollutants
9.4.2.4. Removal of NOM and the precursors of disinfection by-products
9.4.2.5. Removal of Antibiotic resistant bacteria (ARB) and antibiotic resistance genes (ARG)
9.4.3. Electrochemically reactive membrane filtration: Characterization and design
9.4.3.1. Configurations
9.4.3.2. Electrode potential measurement in relevant aqueous environments
9.4.3.3. Mass transfer coefficient determination
9.4.3.4. Control of the flow rate based on the measured mass transfer coefficient
9.4.3.5. Determination of mass transfer limiting factors (advection or diffusion) using the Peclet number
9.4.3.6. Determination of the electro-active surface area on the membrane
9.4.3.7. Assessment of the pollutant removal rate and the electric energy utilization efficiency
9.4.3.8. Voltage drop measurement and predictive model calculation
9.4.4. Challenges and perspectives
9.5. Summary and Outlook
Acknowledgments
References
Chapter 10 Surface Mimetics of Water Treatment Membranes by Thin-Films and Self-Assembled Monolayers for Exploring Scaling and Antifouling Mechanisms
10.1. Background and Motivation
10.1.1. Membrane-based water treatment technologies and the importance of thin-film composite (TFC) membranes
10.1.2. Mineral scaling and organic fouling of TFC membranes
10.1.2.1. Membrane scaling and mineral precipitation on the membrane surface in RO processes is a major concern
10.1.2.2. Organic fouling and biofouling
10.1.3. The need for model surfaces for studying scaling and biofouling phenomena on water treatment membranes
10.2. Langmuir Films as Reverse-Osmosis Membrane-Surface Mimetics
10.2.1. Mineralization phenomena measured by surface pressure-area (Langmuir) isotherms
10.2.2. Calcium-phosphate scaling in reverse-osmosis desalination of municipal wastewater effluents
10.2.3. Effects of surface-exposed organic chemical groups on calcium-phosphate mineralization
10.2.4. Biopolymer-induced calcium phosphate scaling
10.3. Self-Assembled Monolayers (SAMs) of Alkyl Thiolates on Gold-Coated Surfaces
10.3.1. Influence of surface chemistry on calcium phosphate scaling in the desalination of municipal wastewater
10.3.2. Organic fouling and the effects of membrane functional groups on the sorption and cleaning of organic foulants
10.3.3. Silica precipitation during brackish water desalination: The role of surface charge and functional groups
10.4. Oligoamide Film that Mimics the Active Layer of RO and NF Membranes
10.4.1. Synthesis of oligoamide coating on gold as a surface mimetic of RO and NF membranes
10.4.2. Organic fouling mechanisms and membrane swelling effects in RO desalination of municipal wastewater effluents
10.4.3. Calcium phosphate scaling on RO and NF membranes in municipal wastewater desalination studied by oligoamide surfaces
10.5. Other Types of Surface Mimetics
10.5.1. Gold nanoparticles (GNPs) coated with specific foulants as models for RO membrane-foulant surfaces
10.5.2. Standalone aromatic polyamide film
References
Chapter 11 The Roles of Nanostructures in Mitigating Pore Wetting and Mineral Scaling in Membrane Distillation
11.1. Introduction
11.2. Fundamental Knowledge on Liquid-Membrane Interaction Regulated by Nanostructure
11.3. Designing Membrane Materials That Resist Membrane Pore Wetting in MD
11.4. Designing Membrane Materials That Resist Mineral Scaling in MD
11.5. Outlook and Research Needs
References
Index
Volume 3: Current Status and New Technologies in Water Desalination
Contents
Preface
About the Editor
Chapter 1 Water Desalination: Current Status and New Developments
1.1. Synopsis
1.2. Introduction
1.3. Potable Water and Brine in Desalination
1.4. Thermal Treatment for Water Desalination
1.4.1. Multi-stage flash
1.4.2. Multi-effect distillation
1.4.3. Vapor compression distillation
1.4.4. Freezing/condensation
1.5. Pressure-Driven Membranes Process in Water Desalination
1.5.1. Reverse osmosis
1.5.2. Nanofiltration
1.6. Concentration-Driven Membranes Process in Water Desalination
1.7. Electro-Driven Membranes Process in Water Desalination
1.7.1. Electrodialysis
1.7.2. Membrane capacitive deionization
1.8. Temperature-Driven Membranes Process in Water Desalination
1.9. Renewable Energy-Drive Process in Water Desalination
1.9.1. Solar energy process in water desalination
1.9.2. Wind energy in water desalination
1.9.3. Nuclear energy in water desalination
1.9.4. Geothermal energy
1.9.5. Hybrid systems
1.10. Conclusions and Outlook
References
Chapter 2 Energy and Environmental Impacts of Desalination
2.1. Synopsis
2.2. Introduction
2.3. Global Brine Production
2.4. The True Cost of Water
2.5. Energy Consumption of Desalination
2.6. Potential Environmental Impacts of Desalination
2.7. Worldwide Experience
2.7.1. Middle East–Ashkelon and Sorek/Palmachim (Israel)
2.7.1.1. Ashkelon
2.7.1.2. Sorek and Palmachim
2.7.2. Persian Gulf
2.7.3. Europe–Mediterranean Coast of Spain
2.7.4. North America — Carlsbad and Tampa Bay (US)
2.7.4.1. Carlsbad
2.7.4.2. Tampa bay
2.7.5. Australia — Sydney and Adelaide
2.7.5.1. Sydney
2.7.5.2. Adelaide
2.8. Summary
References
Chapter 3 Post-Treatment of MBR Effluent by Pressure-Driven Membrane Processes for Agricultural Irrigation
3.1. Synopsis
3.2. Introduction
3.3. Desalination of MBR Effluent by Pressure-Driven Membrane Processes for Agricultural Irrigation
3.4. Concentrate Management in Pressure-Driven Membrane Operations
3.5. Membrane Fouling and Chemical Cleaning
3.6. Conclusions
Acknowledgement
References
Chapter 4 Polyelectrolyte Coagulants and Flocculants in Wastewater Treatment
4.1. Synopsis
4.2. Introduction
4.3. Coagulation and Flocculation in Water Treatment
4.3.1. Colloidal interparticle interactions
4.3.2. Mechanisms for coagulation and flocculation
4.3.3. Factors influencing coagulation and flocculation
4.3.3.1. Solution pH
4.3.3.2. Dosage
4.3.3.3 Temperature
4.4. Polyelectrolyte Coagulants and Flocculants
4.4.1. Polyelectrolyte-colloid interactions: A fundamental perspective
4.4.1.1. Structure of polyelectrolyte-colloid mixtures
4.4.1.2. Phase behavior of polyelectrolyte-colloidal particle mixtures
4.4.2. Biopolymer flocculants
4.4.2.1. Chitosan-based flocculants
4.4.2.2. Starch-based flocculants
4.4.3. Incorporation and performance of polyelectrolytes in wastewater treatment
4.5. Summary and Future Perspectives
References
Chapter 5 Blue Energy: Pressure Retarded Osmosis and Reverse Electrodialysis
5.1. Synopsis
5.2. Reverse Electrodialysis
5.3. Pressure-Retarded Osmosis
5.4. Modeling Elements
5.4.1. Concentration polarization in PRO
5.4.2. Support layer
5.4.3. Modeling of the barrier layer and overall equation
5.4.4. Modeling PRO using the SKK model
5.4.5. State-of-the-art model for PRO
5.5. Prospects for PRO
5.6. Concluding Remarks
References
Chapter 6 Modeling Thermodynamic and Hydrodynamic Effects on Ion Transport in Electrodialysis of Highly Saline Waters
6.1. Synopsis
6.2. Introduction
6.3. Modeling Framework
6.3.1. Modeling ion transport in channels
6.3.2. Modeling ion transport in membranes
6.3.3. Ideal solution model
6.3.4. Modeling flow
6.3.4.1. Spacer-free channels
6.3.4.2. Spacer-filled channels
6.3.4.3. Non-ideal flow
6.3.5. Boundary conditions
6.3.6. Numerical method and mesh structure
6.3.7. Comparison of the model results with the experimental data
6.4. Results and Discussion
6.4.1. Ideal solution — spacer-free cell
6.4.2. Non-ideal solution impacts
6.4.3. Spacer impacts
6.4.4. Non-ideal flow impacts
6.5. Conclusion
Acknowledgment
References
Chapter 7 Membrane Distillation Pilot Units for Seawater Desalination
7.1. Synopsis
7.2. Introduction
7.3. Membrane Distillation Projects
7.4. Commercial MD Modules for Pilot Scale Applications
7.4.1. Scarab Development AB (Sweden)
7.4.2. Solar Spring GmbH (Germany)
7.4.3. Aquastill BV (The Netherlands)
7.4.4. Memsys GmbH (Germany)
7.4.5. Econity, INC (Korea)
7.4.6. Memsift Innovation
7.5. Membrane Distillation Pilot Plants
7.5.1. Membrane distillation pilots for seawater treatment
7.5.2. Membrane distillation pilots for RO brine treatment
7.5.2.1. AGMD and PGMD/LGMD modules
7.5.2.2. VMEMD modules
7.6. Membrane Crystallization in Desalination
7.7. Future Research and Main Conclusions
References
Chapter 8 Performance of Membrane Distillation Technologies
8.1. Synopsis
8.2. Introduction
8.3. MD Energy Efficiency Metrics
8.3.1. Flux
8.3.2. First-law efficiency: GOR, evaporation efficiency, SECth
8.3.3. Thermal efficiency
8.3.4. Recovery ratio
8.3.5. Heat recovery parameter (ɛ-NTU)
8.3.6. Second law efficiency
8.3.7. Accounting for multiple energy sources
8.3.8. Relating performance parameters to one another
8.3.9. Efficiency limits
8.3.10. Performance metric summary
8.4. Considerations for Designing MD Systems
8.4.1. Boiling point elevation (BPE)
8.4.2. Temperature and concentration polarization
8.4.3. Multi-stage implementation of MD
8.4.4. Self-heated membranes
8.4.4.1. Joule heating membranes
8.4.4.2. Induction heated membranes
8.4.5. Solar heat and MD
8.4.6. Heat pump MD
8.4.7. Overall water cost
Acknowledgments
References
Chapter 9 Capacitive Deionization
9.1. Synopsis
9.2. Introduction to Capacitive Deionization
9.3. How CDI Works
9.3.1. Basic CDI architectures
9.3.2. CDI with membranes
9.3.3. Inverted CDI
9.3.4. CDI operating methods
9.3.4.1. Electrical control methods
9.3.4.2. Flow rate control methods
9.4. CDI Performance and Water Cost
9.4.1. Energy consumption and thermodynamic efficiency
9.4.2. Salinity reduction and water recovery
9.4.3. Water cost
9.4.4. Performance indicators: Charge efficiency
9.4.5. Performance indicators: Capacitance and salt adsorption capacity
9.4.6. Performance indicators: Resistance
9.5. Selective Removal of Contaminant Ions
9.5.1. Introduction
9.5.2. Overview of selectivity in CDI and i-CDI
9.5.3. Theory: Intrinsic and cycle ion selectivity
9.5.4. Quantification of intrinsic selectivity
9.5.5. Quantification of cycle selectivity in i-CDI
9.5.6. Effect of regeneration voltage and the ratio of concentrations in cycle selectivity in i-CDI
9.6. Outlook
Acknowledgments
References
Chapter 10 Surface Modified Reverse Osmosis Membranes
10.1. Synopsis
10.2. Overview of Reverse Osmosis (RO)
10.2.1. Water and salt transport in RO membranes
10.2.2. Polyamide (PA) thin film composite (TFC) membrane
10.2.3. Water permeability and water/salt selectivity tradeoff
10.2.4. Polyamide interfacial polymerization
10.2.5. Seawater reverse osmosis (SWRO) membrane fouling
10.3. The Impact of PA Surface Modification on RO Desalination Performance
10.3.1. Overview
10.3.2. Coating (physical adsorption)
10.3.3. Layer-by-layer (LbL) assembly
10.3.4. Initiated chemical vapor deposition (iCVD)
10.3.5. Polymer grafting (“Grafting to”)
10.3.6. Graft polymerization (“Grafting from”)
10.4. Salt Rejection-Permeability Tradeoff
10.5. Stimuli-Responsive Brush Layer
10.5.1. Selective removal of ionic solutes by modified RO membranes
References
Chapter 11 Roll-to-Roll, Scale-up of Nanocomposite Membranes Embedded with Silver Nanoparticles: From Laboratory Scale to Pilot Scale
11.1. Synopsis
11.2. Background and Discussion
11.2.1. Incorporation of AgNPs into polymeric membranes
11.2.1.1. Blending AgNPs into the membrane matrix
11.2.1.2. Surface modification
11.2.1.3. Layer-by-layer (LBL) assembly
11.2.2. Scalable fabrication of AgNPs membranes in a pilot scale
11.2.2.1. Comparison of doctor blade casting and slot-die casting of nanocomposite membranes
11.2.2.2. Advantage of slot-die casting for scale up
11.3. Fabricate AgNPs Membranes from Laboratory to Pilot Scale
11.3.1. Performance of AgNPs membranes fabricated at laboratory and pilot scales
11.3.2. Thiol-based covalent addition of AgNPs to membranes
11.3.3. Scale-up work of thiol-based covalent addition of AgNPs to membranes
11.4. Conclusions
Acknowledgments
References
Chapter 12 Application of MOF Nanomaterials in Water Treatment
12.1. Synopsis
12.2. Introduction
12.3. Nanotechnology for Wastewater Treatment
12.3.1. Nano-adsorbent
12.3.1.1. Carbon-based nano-adsorbents
12.3.1.2. Metal-based nano-adsorbents
12.3.1.3. Polymer-based nano-adsorbents
12.3.2. Membrane and membrane-based processes
12.3.2.1. MOFs
12.3.2.2. MOF and MOF-based membrane application for wastewater treatment
12.4. Conclusions and Futuristic Aspects
References
Chapter 13 Concept Demonstration and Future Developments of Sunlight Transmitting Nanophotocatalyst-Coated Substrates for Sustainable Low Pressure Water Filtration
13.1. Synopsis
13.2. Introduction
13.2.1. Membrane filtration and photocatalysts for water treatment
13.2.2. Combining the two: Photocatalytic membranes
13.2.3. Light transmitting substrates: From concept to demonstrating technical and practical performance
13.3. State-of-the-Art in Titania and Photocatalytic Membranes for Water Treatment
13.3.1. Titania as a photocatalyst in water treatment
13.3.2. Photocatalytic membranes
13.4. Titania and Light Conducting Porous Substrates for PMRs
13.4.1. Titania immobilization methods
13.4.2. Choosing an appropriate substrate
13.4.3. Light transparent substrates for photocatalytic membrane water treatment
13.4.3.1. Sintered glass
13.4.3.1.1. Preparation and properties
13.4.3.1.2. Potential advantages and disadvantages
13.4.3.1.3. Application in membrane water treatment
13.4.3.2. Phase separated porous glass
13.4.3.2.1. Preparation and properties
13.4.3.2.2. Potential advantages and disadvantages
13.4.3.2.3. Application in membrane water treatment
13.4.3.3. Shirasu porous glass
13.4.3.3.1. Preparation and properties
13.4.3.3.2. Practical features for water treatment
13.4.3.3.3. Application in membrane water treatment
13.4.3.4. Other innovative light transmitting substrates
13.4.3.4.1. Glowing optical fibers
13.4.3.4.2. Glowing optical fibers cellulose
13.4.3.4.3. Quartz
13.4.3.4.4. Polylactic acid
13.4.3.4.5. Nylon and single-walled carbon nanotubes
13.4.3.4.6. Silk fibroin
13.4.3.4.7. Poly(hydroxyethyl acrylamide) (PHEAAm) grafted onto polydimethylsiloxane (PDMS)
13.4.3.4.8. Free standing graphene oxide
13.5. Energy Requirements in PMRs
13.5.1. Relative energy consumption for the light source in membrane process
13.5.2. Light emitting diodes
13.5.3. Solar energy
13.6. Potential for Light Conducting Membranes in Other Water Treatment Applications
13.7. Conclusion and Future Perspectives
References
Chapter 14 Water and Ion Transport in Carbon Nanotubes: Implications for Next Generation Water Treatment Solutions
14.1. Synopsis
14.2. Introduction: Biomimetic Nanopore Membranes
14.3. Carbon Nanotubes
14.3.1. Structure
14.3.2. Synthesis
14.3.3. Mechanical properties
14.3.4. Vibrational spectra of CNTs
14.4. Molecular Dynamic Simulations and Early Experimental Observation of Water in CNTs
14.5. Experimental Platforms for Observing Transport in CNTs
14.5.1. Aligned and semi-aligned carbon nanotube membranes
14.5.2. Single CNT channel platforms
14.5.3. Carbon nanotube porins: Biomimetic CNT membrane pores
14.6. Water Transport in Carbon Nanotube Channels
14.7. Ion Transport, Ion Rejection, and Water/Ion Permselectivity in Carbon Nanotube Pores
14.8. Outlook: Carbon Nanotube Pores in the Next Generation Advanced Water Purification Solutions
Acknowledgments
References
Chapter 15 Carbon Nanomaterials in Desalination Process
15.1. Synopsis
15.2. Background
15.3. Desalination Techniques
15.3.1. Temperature
15.3.1.1. Multi effect distillation
15.3.1.2. Multi stage flash
15.3.1.3. Membrane distillation
15.3.1.3.1. Process description
15.3.1.3.2. Configurations
15.3.1.3.3. Challenges
15.3.1.3.4. Novel materials
15.3.2. Pressure
15.3.2.1. Reverse osmosis
15.3.2.2. CNT-based membranes
15.3.2.2.1. CNT-based membrane properties
15.3.2.3. Advantages of CNT-based membranes
15.3.2.3.1. Increased water transport through CNT membranes
15.3.2.3.2. Enhanced chlorine resistance
15.3.2.3.3. Inherent toxicity of CNTs toward bacteria
15.3.2.3.4. Electrochemical reactions at the membrane/water interface
15.3.2.4. Challenges and strategic research prospects of CNT membranes
15.3.2.4.1. Scale up from bench to industrial level
15.3.2.4.2. Effect on environment and human health
15.3.2.4.3. Graphene oxide-based membranes
15.3.3. Electrical potential
15.3.3.1. Electrodialysis
15.3.3.1.1. Carbon nanotubes (CNTs)
15.3.3.1.2. Graphene and its derivatives
15.3.3.1.3. Carbon nanofibers (CNFs)
15.3.3.1.4. Activated carbon
15.3.3.2. Capacitive deionization
15.4. Summary
References
Chapter 16 Nanomaterials for Pressure Retarded Osmosis
16.1. Synopsis
16.2. Introduction
16.3. Energy Generation through Salinity Gradient
16.4. Pressure Retarded Osmosis
16.4.1. Benefits, drawbacks, and challenges in PRO
16.4.2. Internal concentration polarization (ICP) impacts on PRO process
16.4.3. PRO process design at estuaries
16.4.4. PRO for power generation and desalination
16.4.5. The rational design of PRO membrane
16.5. Thin Film Composite (TFC) PRO Membranes
16.6. Thin Film Nanocomposite (TFN) PRO Membranes
16.7. Conclusion and Perspective
References
Index

Citation preview

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World Scientific Series in Nanoscience and Nanotechnology* ISSN: 2301-301X Series Editor-in-Chief Frans Spaepen (Harvard University, USA) Members of the Scientific Advisory Board Li-Chyong Chen (National Taiwan University) Jeff Grossman (Massachusetts Institute of Technology, USA) Alex de Lozanne (University of Texas at Austin) Mark Lundstrom (Purdue University) Mark Reed (Yale University, USA) John Rogers (Northwestern University) Elke Scheer (Konstanz University) David Seidman (Northwestern University, USA) Matthew Tirrell (The University of Chicago, USA) Sophia Yaliraki (Imperial College, UK) Younan Xia (Georgia Institute of Technology, USA) The Series aims to cover the new and evolving fields that cover nanoscience and nanotechnology. Each volume will cover completely a subfield, which will span materials, applications, and devices. Published Vol. 23 The World Scientific Reference of Water Science (In 3 Volumes) Volume 1: Molecular Engineering of Water Sensors Volume 2: Nanotechnology for Water Treatment and Water Interfaces Volume 3: Current Status and New Technologies in Water Desalination editor-in-chief: Matthew Tirrell (The University of Chicago, USA & Argonne National Laboratory, USA) edited by Matthew Tirrell (The University of Chicago, USA & Argonne National Laboratory, USA), Junhong Chen (The University of Chicago, USA & Argonne National Laboratory, USA) and Yoram Cohen (University of California, Los Angeles, USA) Vol. 22 World Scientific Reference on Plasmonic Nanomaterials Principles, Design and Bio-applications (In 5 Volumes) Volume 1: Principles of Nanoplasmonics Volume 2: Plasmonic Nanoparticles: Synthesis and (Bio)functionalization

For further details, please visit: http://www.worldscientific.com/series/wssnn (Continued at the end of the book)

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Published by World Scientific Publishing Co. Pte. Ltd. 5 Toh Tuck Link, Singapore 596224 USA office: 27 Warren Street, Suite 401-402, Hackensack, NJ 07601 UK office: 57 Shelton Street, Covent Garden, London WC2H 9HE

Library of Congress Cataloging-in-Publication Data Names: Tirrell, Matthew, editor. Title: The World Scientific reference of water science / editor-in-chief, Matthew Tirrell, The University of Chicago, USA & Argonne National Laboratory, USA. Other titles: Reference of water science Description: New Jersey : World Scientific, 2023. | Series: World Scientific series in nanoscience and nanotechnology, 2301-301X ; volume 23 | Includes bibliographical references and index. | Contents: v. 1. Molecular engineering of water sensors / volume editor, Junhong Chen, The University of Chicago, USA & Argonne National Laboratory, USA - v. 2. Nanotechnology for water treatment and water interfaces / volume editor, Junhong Chen, Matthew Tirrell, The University of Chicago, USA & Argonne National Laboratory, USA - v. 3. Current status and new technologies in water desalination / volume editor, Yoram Cohen, University of California, Los Angeles, USA. Identifiers: LCCN 2022017930 | ISBN 9789811245756 (v. 1 ; hardcover) | ISBN 9789811245763 (v. 2 ; hardcover) | ISBN 9789811253812 (v. 3 ; hardcover) | ISBN 9789811246104 (set : hardcover) | ISBN 9789811246111 (set : ebook for institutions) | ISBN 9789811245916 (set : ebook for individuals) | ISBN 9789811245770 (v. 1 ; ebook for institutions) | ISBN 9789811245787 (v. 2 ; ebook for institutions) | ISBN 9789811253829 (v. 3 ; ebook for institution) Subjects: LCSH: Water--Purification. | Water quality management. | Water-supply. Classification: LCC TD430 .W74 2022 | DDC 628.1/62--dc23/eng/20220705 LC record available at https://lccn.loc.gov/2022017930 British Library Cataloguing-in-Publication Data A catalogue record for this book is available from the British Library.

Copyright © 2023 by World Scientific Publishing Co. Pte. Ltd. All rights reserved. This book, or parts thereof, may not be reproduced in any form or by any means, electronic or mechanical, including photocopying, recording or any information storage and retrieval system now known or to be invented, without written permission from the publisher.

For photocopying of material in this volume, please pay a copying fee through the Copyright Clearance Center, Inc., 222 Rosewood Drive, Danvers, MA 01923, USA. In this case permission to photocopy is not required from the publisher.

For any available supplementary material, please visit https://www.worldscientific.com/worldscibooks/10.1142/12514#t=suppl Desk Editors: Balamurugan Rajendran/Amanda Yun Typeset by Stallion Press Email: [email protected] Printed in Singapore

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Preface Water is an indispensable resource for our society. Necessary for sustaining life and for economic prosperity, water is needed to manufacture nearly everything society depends upon, including energy, food, clothing, cars, and electronics, among many other examples. It is, thus, an integral part of our lives beyond simply quenching our thirst. In addition, our future economy and security highly depend upon the availability of clean water. Yet given its critical importance, there is a limited supply of renewable freshwater across the globe and there is no substitute. Global population and economic growth, urbanization, and climate change further exacerbate the increasing stress on freshwater supplies. As such, society urgently needs scientific and engineering solutions to more efficiently manage our precious water resources. The need for low-cost, real-time, sensitive, and selective detection of a wide range of analytes in water presents exciting opportunities for innovations in sensing modalities, materials, mechanisms, and systems, and in sensor manufacturing. Real-time and low-cost smart sensors can play a critical role in managing our water resources. Smart sensors can improve the safety of drinking water supplies by providing early warning of contamination, save energy, and protect the environment by monitoring the water quality before and after a water treatment process for the precision dosing of energy and chemicals, and enable fit-for-purpose water treatment to facilitate water reuse. The demand for these types of water sensors is further driven by the rapid advancement of artificial intelligence and data analytics empowered by machine learning. Combined with big data analytics, real-time water sensors will enable the efficient management of precious water resources through the use of sensor networks and distributed information processing for a smart, connected, and resilient next-generation infrastructure. This book volume is a collection of state-of-the-art water sensing research based on molecular engineering and technologies, including field-effect v

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transistors, optical fibers, electrochemical technologies, surface acoustic-wave technologies, DNA and aptamer-based technologies, microfluidic technologies, and smartphone-based technologies. The studies involve zero-, one-, two-, and three-dimensional nanostructured materials, metal organic frameworks, and selfhealing materials, as well as the additive manufacturing of water sensors. The contaminants addressed include heavy metals, bacteria and microorganisms, pesticides, organic compounds, phosphates and nitrates, various biomolecules, and other hazardous materials in water. We hope this book volume will become a valuable reference not only for researchers but also for undergraduate students and graduate students who are entering this exciting field. Junhong Chen Crown Family Professor, Pritzker School of Molecular Engineering, University of Chicago; & Lead Water Strategist & Senior Scientist, Science Leader for Argonne in Chicago, Argonne National Laboratory, USA

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About the Editor Junhong Chen is currently the Crown Family Professor in the Pritzker School of Molecular Engineering at the University of Chicago (Email: [email protected]; Website: https://pme.uchicago.edu/faculty/junhong-chen). He is also Lead Water Strategist & Senior Scientist (Email: [email protected]; Website: https://www.anl.gov/ profile/junhong-chen) and Science Leader for Argonne in Chicago at Argonne National Laboratory (https://www.anl. gov/water). Prior to coming to Chicago, Chen served as a Program Director for the Engineering Research Centers (ERC) program of the National Science Foundation (NSF) and as a Co-chair of the NSF-wide ERC Working Group to design the ERC Planning Grants program and the Gen-4 ERC program. As a representative of NSF’s Engineering Directorate, Chen also served on the NSF-wide Working Groups for the NSF Graduate Research Fellowship and the NSF Research Traineeship programs. Prior to joining NSF in May 2017, he was a Distinguished Professor of Mechanical Engineering and Materials Science and Engineering and an Excellence in Engineering Faculty Fellow in Nanotechnology at the University of WisconsinMilwaukee (UWM), and he was a Regent Scholar of the University of Wisconsin System. He also served as the Director of UWM’s NSF Industry-University Cooperative Research Center on Water Equipment & Policy for six years. He founded NanoAffix Science, LLC to commercialize real-time water sensors based on two-dimensional nanomaterials. Chen received his Ph.D. in mechanical engineering from University of Minnesota in 2002, and he was a postdoctoral scholar in chemical engineering at the California Institute of Technology from 2002 to 2003. His current research focuses on nanomaterial innovation for energy and environmental sustainability, vii

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including real-time sensors for detection of water contaminants. Chen has published more than 260 journal papers and has been listed as a highly cited researcher (top 1%) in the materials science or cross-field category by Clarivate Analytics over the last five years. Chen’s research has led to 9 issued patents, 5 pending patents, and 13 licensing agreements. He is a pioneer in technology commercialization through exemplary industrial partnerships and his university start-up company. Chen is an elected fellow of both the National Academy of Inventors and the American Society of Mechanical Engineers.

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Contents Prefacev About the Editorvii Chapter 1 Field-Effect Transistor Sensors Based on 2D Nanomaterials for Detecting Contaminants in Water Xiaoyu Sui, Guihua Zhou, Haihui Pu, Ganhua Lu, Shun Mao, Xiaoyan Chen and Junhong Chen

1

Chapter 2 Innovative Optical Fiber Approaches for Water Quality Monitoring39 Stephanie Hui Kit Yap, Kok Ken Chan and Ken-Tye Yong Chapter 3 Liquid-Phase Chemical Sensors Fabien Josse, Florian Bender and Antonio J. Ricco

71

Chapter 4 Strategies for Development of Protein-Based Biosensors for Detecting Aromatic Xenobiotics in Water Subhankar Sahu and Ruchi Anand

101

Chapter 5 Functional DNA Sensors for Heavy Metal Ions and Microbial Contaminants in Water Ana Sol Peinetti, Ryan Lake and Yi Lu

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Chapter 6 DNAzyme-Based Biosensors and Their Applications in Monitoring Environmental Water Sources Erin M. McConnell, Meghan Rothenbroker and Yingfu Li

171

ix

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Chapter 7 Biological and Chemical Sensors for Monitoring Water Quality Suresh Neethirajan Chapter 8 Electrochemical Water Sensing Technologies Mohammad Rizwen Ur Rahman, Jiang Yang, Sundaram Gunasekaran and Woo-Jin Chang Chapter 9 Phosphates Analysis in Water: State-of-the-Art Technologies and Challenges Mehenur Sarwar, Jared Leichner, Ghinwa Melodie Naja and Chen-Zhong Li

215 239

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Chapter 10 Metal Organic Framework-Based Sensors for Water Contaminants Detection Shun Mao and Xian Fang

307

Chapter 11 Smartphone-Based Sensors for On-Site Water Quality Monitoring Xiangheng Niu, Nan Cheng, Dan Du and Yuehe Lin

331

Chapter 12 Large-Scale Assembly of Nanostructure-Based Sensors for Real-Time Bio-Analysis in Water Inkyoung Park, Jin-Young Jeong, Viet Anh Pham Ba, Narae Shin, Dong-guk Cho and Seunghun Hong

349

Chapter 13 Microfluidic Devices for Water Quality Management Vivekanandan Palaninathan, D. Sakthi Kumar, Dorian Liepmann, Ramasamy Paulmurugan and Renugopalakrishnan Venkatesan

381

Chapter 14 Underwater Self-Healable Materials for Sensing Applications Muhammad Khatib, Tan-Phat Huynh and Hossam Haick

409

Chapter 15 Additive Manufacturing of Graphene-Based Sensors for Environmental Applications Harrison A. Loh and Konstantinos A. Sierros

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Index471

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Chapter 1

Field-Effect Transistor Sensors Based on 2D Nanomaterials for Detecting Contaminants in Water Xiaoyu Sui*,†,ǁ, Guihua Zhou‡,**, Haihui Pu*,†,††, Ganhua Lu§,‡‡, Shun Mao¶,§§, Xiaoyan Chen¶,¶¶ and Junhong Chen*,†,ǁǁ *

Pritzker School of Molecular Engineering, University of Chicago, Chicago, Illinois 60637, USA

Chemical Sciences and Engineering Division, Physical Sciences and Engineering Directorate, Argonne National Laboratory, Lemont, Illinois 60439, USA

†

Department of Mechanical Engineering, University of Wisconsin-Milwaukee, Milwaukee, Wisconsin 53211, USA

‡

NanoAffix Science LLC, Wauwatosa, Wisconsin 53226, USA

§ ¶

College of Environmental Science and Engineering, Tongji University, Shanghai 200092, China [email protected]

ǁ

[email protected]

**

††

[email protected] [email protected]

‡‡

[email protected]

§§ ¶¶

[email protected]

[email protected]

ǁǁ

1

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1.1. Introduction 1.1.1.  Importance of water safety and the need for high-performance water sensors Water is critical for survival and thus affects nearly every aspect of daily life. Among its many uses, water is vital for every human being, is essential for maintaining a healthy ecosystem, functions as a key element of sustainable agricultural and food production and as a solvent for a wide variety of chemical substances, and facilitates industrial cooling, transportation, and effluent discharge and reuse. However, only 3% of available water is potable, and due to the growing global population and increasing demand for better quality of life, the need for clean and safe drinking water continues to rise. The World Health Organization (WHO) reported that 844 million people were still short of even a basic drinking water service and at least 2 billion people (mostly in under-developed regions) used a contaminated drinking water source in 2015.1 Therefore, it is no surprise that the National Academy of Engineering listed “Provide Access to Clean Water” as one of the 14 Grand Challenges for Engineering in the 21st century.2 Even in well-developed countries, such as the United States of America (U.S.), incidents of contaminated drinking water occur (some even at a large scale) due to various reasons, such as aging infrastructures and inadequate regulations. More than 294 million people in the U.S. rely on public drinking water systems for drinking, cooking, showering, cleaning, and other daily uses.3 However, a large portion of the water pipes in the U.S. public water systems are near or already past the end of their useful life and are in unsatisfactory conditions;4 for example, ~50% of the water pipes in water systems in the U.S. Midwest were installed before 1920.5 One major concern for the aging U.S. water infrastructure is lead contamination caused by the corrosion of lead-containing pipes (especially old ones) and plumbing fittings in the presence of aggressive water and chloramines used for water disinfection. For example, the public water crisis in the city of Flint, Michigan, which began in 2014, has resulted in serious health problems for as many as 8,000 children due to consumption of lead-polluted drinking water.6 Flint is not alone: in 2015 more than 18 million people in the U.S. were served by 5,363 community water systems that violated the federal Safe Drinking Water Act Lead and Copper Rule.7 The U.S. Environmental Protection Agency’s (USEPA) National Drinking Water Advisory Council estimated that ~7.3 million lead service lines deliver water to customers nationwide.8 Various contaminants, including heavy metal ions (e.g., lead, mercury, arsenic) and microorganisms (e.g., viruses, bacteria, fungi) are widely present in water systems.9 Heavy metal ions are toxic and may cause serious damage to human organs, tissues, bones, and the nervous system. For example, lead (Pb) can be found in the soil and atmosphere as the result of natural processes or from pollution, and it can seep into groundwater. In addition, acidic water and water systems that use

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chloramines to disinfect drinking water can erode pipes and plumbing which may contain lead. Lead can accumulate in the body’s soft tissue and bones, and lead poisoning, to which children are particularly vulnerable, damages the brain and internal organs (e.g., heart, kidneys) and disrupts body processes. The maximum contaminant level (MCL) for lead in drinking water defined by the USEPA is 0.015 mg/L.9 Mercury (Hg), an extremely toxic heavy-metal ion, is present in water supplies from improperly discarded devices, as runoff from landfills and farmland, or from natural deposits. Mercury can be absorbed through the skin and inhaled. Mercury poisoning can damage the nervous system; impair hearing, speech, vision, gait, and involuntary muscle movements; and corrode skin and mucous membranes. The current USEPA-regulated MCL for mercury is 0.002 mg/L.9,10 Arsenic (As), another heavy-metal ion, is used in semiconductor electronic devices and in the production of pesticides, herbicides, and insecticides. Arsenic is poisonous to almost all organic life, and prolonged or heavy exposure can lead to arsenicosis (arsenic poisoning), malignant tumors of skin and lungs, heart disease, and affect the nervous system. To protect people served by public water systems from the effects of long term, chronic exposure to arsenic, the USEPA has set the arsenic MCL for drinking water at 0.01 mg/L.9 Lastly, microorganisms, such as viruses, bacteria, and fungi, are frequent causes of water contamination, and many are human pathogens. Nearly 100 million Americans (i.e., one-third of the population) have pathogen-related illnesses each year, with 19.5 million cases caused by drinking waterborne pathogens. To address the grand challenge for water safety, we must not only improve existing technologies for water treatment and water quality monitoring but also develop new advanced technologies. This section discusses how advanced sensing technologies can help safeguard public water, especially drinking water. Existing sensing technologies can specifically and accurately determine contaminants in water (e.g., chemicals, microorganisms), but they are considered inadequate given the challenges inherent in low-concentration, rapid, and onsite detection.11 In the last few decades, many new technologies and sensors have emerged for highly sensitive and selective sensing, such as surface-enhanced Raman spectroscopy (SERS),12 electrochemistry,13 and capacitor sensor.14 Among the new technologies, electronic sensors based on field-effect transistors (FETs) stand out due to their extremely high sensitivity, rapid and in situ detection, and simple device configuration. An FET typically consists of three metallic electrodes (source, drain, and gate) and a channel made of a semiconductor which bridges the source and drain electrodes. The conductance of the FET channel changes upon the adsorption of target molecules, which serves as the functional principle of the FET sensor. The FET sensor is a type of resistor sensor with a gate voltage to tune the current in the FET channel. The concept of FET chemical sensors was first demonstrated using bulk semiconducting materials as channels, such as gas-sensitive metal oxides.15 But FET sensors with bulk channel materials usually suffer from low sensitivity or

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need a specific condition for operation due to the electronic properties inherent in the bulk materials and the limited interactions between target analytes and the channel surface. In contrast, semiconducting nanomaterials have properties suitable for achieving much better performance as sensing channels in FET sensors. In particular, two-dimensional (2D) nanomaterials can form conformal and intimate contact with metal electrodes, and they are easy to manipulate because of their relatively larger lateral dimensions, which provides better control over the channel structure in the FET sensor. 2D nanosheets could also be grown into a designed shape and thickness and then transferred precisely onto the sensor substrate.

1.1.2.  Why 2D-nanomaterials-based FET water sensors? FET sensors with 2D nanomaterials are emerging as a powerful sensing platform for detecting chemical and biological molecules in water. The extraordinary features of this FET sensor, such as ultra-sensitivity, label-free, and real-time response, are comparable with or better than those of conventional sensing techniques. Graphene, which is a single layer of carbon atoms, is the representative 2D nanomaterial, and it has attracted extensive interest as a channel material in FET sensors because of its superior electronic properties, high flexibility and biocompatibility, large specific surface area, and facile chemical functionalization. The discovery of graphene and its fascinating properties have also stimulated extensive interest in other 2D nanomaterials. Transition metal dichalcogenides (TMDCs) are a new family of 2D materials with a layered structure, in which a plane of a transition metal element is sandwiched by two hexagonal planes of a chalcogenide. Another interesting material that has been recently added to the family of 2D materials is black phosphorus (BP), which is the most stable allotrope of phosphorus. Few-layer phosphorene has been demonstrated as an outstanding and reliable transistor material with highly desirable properties, and thus has great potential for use in FET sensors. The use of 2D-nanomaterials-based FET sensors leads to new opportunities for detecting water contaminants (e.g., heavy metals and bacteria), since real-time water quality monitoring in water distribution/treatment systems requires accurate and accessible detection technologies which currently are not fulfilled with the existing methods.16 Therefore, a timely overview focused on 2D nanomaterial FET water sensors is necessary to highlight the advantages of these sensors and to offer insights into strategies that may further improve their performance and fabrication for practical applications.

1.1.3.  The goal of this chapter This chapter provides a brief overview of the newly emerged FET sensors based on 2D nanomaterials. This advanced sensing technology could play a

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game-changing role in advancing water quality monitoring technology by realizing affordable, portable, and rapid detection of contaminants in water. Our goal is that, after reading this chapter, one can comfortably grasp most of the important aspects of 2D-nanomaterials-based FET water sensors, including their basic features, outstanding capabilities for low-concentration and rapid detection, and the opportunities and challenges for further development and commercialization. To achieve this goal, in this chapter we first discuss the working principles of FET water sensors and the fundamental understanding that facilitates the rational design and use of these devices. Then, we examine some of the extraordinary properties of 2D nanomaterials (i.e., graphene and graphene oxide, BP, and molybdenum disulfide) which have been demonstrated as effective channel materials in FET water sensors, followed by a discussion of the key features that make 2D nanomaterials exceptional candidates for channel materials in FET water sensors. Lastly, we illustrate the application of several 2D-nanomaterials-based FET sensors for detecting a set of water contaminants, including heavy metal ions and microorganisms, to shed light on the amazing performance of these novel sensors in terms of their sensitivity, selectivity, and response time. The chapter concludes with a summary and the outlook for the future development and eventual deployment of 2D-nanomaterials-based FET water sensors to help build a safer future with better water security.

1.2. The Working Principles of FET Water Sensors An FET sensor comprises three metallic electrodes (i.e., the source, drain, and gate), the channel material, and the gate oxide. To detect contaminates in water, the FET sensor works by monitoring either the electrical conductance or the resistance change in the channel material before and after exposure to the target analytes (e.g., heavy metal ions and bacteria). In principle, the interactions between an FET sensor and the target analyte can be categorized into three groups: (i) the physical adsorption due to van der Waal’s attraction, (ii) the chemical adsorption through the direct bonding between the adsorbates and the channel material, and (iii) the chemical reaction between the adsorbates and the channel material into new products. Among these three types of interactions, only the physical adsorption and the chemical adsorption are mostly reversible thermodynamically, while the physical adsorption shows a faster recovery rate and shorter recovery time. In this section, both the physics and chemistries required for designing and operating an FET sensor with 2D nanomaterials for detecting heavy metal ions are discussed. First, we discuss the direct contact between channel materials and heavy metal ions and how sensor performance depends on the channel material properties, and then we examine the need for sensor passivation due to the instability of the channel materials in ambient environments.

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1.2.1.  Charge transfer and gating effects As depicted in Fig. 1.1(a), an FET water sensor functions as a result of changes in the drain-source current upon adsorption of target analyte (e.g., heavy metal ions) in solution. The surface-adsorbed metal ions lead to the current change in the sensor. Depending on the semiconductor type of the channel material and the concentration of heavy metal ions, the interactions between them can be categorized into two mechanisms: charge transfer and electrostatic gating. As illustrated in Fig. 1.1(b), these two working principles counteract each other. The transfer of electrons from a p-type semiconductor channel material to metal ions and the positive gating effect cause the current to increase and decrease, respectively. Macroscopically, these two distinctive mechanisms can be described as follows: (1) The positively charged metal ions withdraw electrons from a p-type (n-type) semiconductor upon adsorption, which increases (decreases) the current due to the increase (decrease) of hole (electron) concentration. (2) The surface-accumulated and positively charged metal ions exert an equivalent positive gating, which further modulates the carrier concentration/mobility and results in a variation of the electrical conductance in the sensor. Microscopically, the surface-adsorbed metal ions interact with the channel material progressively as follows: (1) Initially, and especially in low concentrations, the metal ions adsorb in an isolated and non-interacting manner and are screened by the channel material, because their spacing is much larger than the Debye length in the channel material (Fig. 1.1(c)). As a result, the carrier can transport diffusively outside of the Debye sphere (Fig. 1.1(d)) with negligible mobility degradation, and the conductance change in the sensor is dominated by the charge transfer between the metal ions and the channel material. (2) When the distance between the adsorbed metal ions becomes comparable to and ultimately shorter than the Debye length at elevated concentrations of metal ions, the Debye spheres begin to overlap with each other and the metal ions act collectively (Fig. 1.1(e)). Under such circumstances, in addition to the charge transfer, the accompanying effect is two-fold: on one hand, a hole depletion region around the top surface of the channel material is induced due to the Coulomb repulsion of the positive metal ions, thereby decreasing the hole concentration. On the other hand, the carrier transport nearby/within the Debye spheres is frequently scattered by adsorbed metal ions (Fig. 1.1(f)), thereby severely degrading carrier mobility. As a result, the conductance in the channel material is largely governed by the gating effect. Apart from the aforementioned qualitative analysis, the two distinctive sensor response behaviors can also be understood (semi-)quantitatively from a model we have developed.17 In physics, the conductivity (σ) of a semiconductor is defined as the product of its carrier concentration (n) and mobility (μ). For the adsorption of heavy metal ions onto the surface of a p-type 2D semiconducting material,

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(b)

Collective

Isolated

+ e

+

d

+

+ + + + + + + + Hole depletion

De

Charge transfer

(c)

+

+

(e)

+

Gating effect

+ + + + + + + + + + + + + + + +

+ (d)

+

Scattering of charge transport

+

+ + + + + + + + (f)

Enhanced scattering

Figure 1.1.    Working principle of an FET water sensor. (a) Device schematic of an FET sensor for detecting heavy metal ions. (b) Two distinctive mechanisms of current change due to the charge transfer and the gating effect, respectively, when positively charged ions adsorb on the surface of p-type semiconductors. (c–d) and (e–f) are schematics illustrating the sensing mechanisms corresponding to the charge transfer and the gating effect in the side and top views, respectively. e is the charge transfer into a metal ion, d is the distance between the surface-adsorbed metal ions, and De is the radius of the Debye sphere (light violet region) of the channel material. The red balls represent the positively charged metal ions while the red arrows depict the lines of the electric field from the metal ions, which scatter the charge transport in the underlying channel materials. The black arrows mimic the paths of the diffusive transport of charges due to the extrinsic scattering of adsorbed ions. Reprinted with permission from Ref. [17]. Copyright © 2019, Royal Society of Chemistry.

the induced hole concentration (nh), as depicted in Eq. (1.1), depends on both the charge transfer and the gating effect.



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  d  nh =  na ∆qa+ − na ∆qg− exp  −α  / δ + nh 0 . De    

(1.1)

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Each metal ion gains ∆qa+ valence electrons from the sensor material upon adsorption and retains a net positive charge from q − ∆qg− valence electrons (subscripts a and g indicate the effects of the adsorption-induced charge transfer and gating, respectively), where q is the calculated valence charge of free heavy metal ions in water. (Note that it is not necessary for q to be integer for heavy metal ions due to the hydration shell.) The total charge transfer can be expressed as na ∆qa+, where na is the density of the surface-adsorbed metal ions. While this charge transfer elevates the hole concentration, the surface-adsorbed metal ions (still positively charged) decrease the hole concentration by effective gating, the magnitude of which depends on the Debye length De in the sensing material, the distance d between the adsorbed metal ions, and an empirical parameter α. The resulting hole concentration also depends on the thickness (δ) of the sensor material and its initial value (nh0) prior to adsorption of the metal ions. The hole mobility is degraded due to the Coulomb repulsion from the adsorbed metal ions, which is related to De, d, and α, as shown in Eq. (1.2),



  d  µ = µ 0 1 − exp  −α . De    

(1.2)

It should be noted the effective gating effect is distinct from a conventional FET. The distance among the adsorbed metal ions and the Debye length in the channel material depends on the concentration of the metal ions, the metal ion species, and the size of band gap in the channel material. Since the metal ions are in direct contact with the channel material, the metal species with larger values of electron negativity has greater interaction/adsorption strength with the channel material. Therefore, a high concentration of metal ions is necessary but not sufficient for the gating effect to occur. For instance, Hg2+ and Pb2+ are typical toxic heavy metal ions; the electron negativity for Hg2+ is 1.9 and 1.8 for Pb2+.18,19 The bare channel material is thus more sensitive to Hg2+ than Pb2+, and a greater amount of Hg2+ can be adsorbed than Pb2+ at the same initial concentrations; therefore, the gating effect is more likely to occur for Hg2+. Apart from the intrinsic properties of metal ions, the band gap of the channel material also plays a critical role (c.f. detailed discussion in Sec. 1.2.2). Specifically, the surface-adsorption density is higher on a channel material with a smaller band gap (i.e., shorter Debye length) and the gating effect can be dominant over the charge transfer.

1.2.2.  Thickness-dependent sensor performance The performance of FET sensors is governed by the intrinsic properties of the channel materials, such as the work function, carrier mobility, and band gap. The work function plays the role mainly in terms of the contact resistance.

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The mismatch of work functions between the electrode and the channel material will give rise to a Schottky barrier, which degrades the carrier injection efficiency through the interface. Au is commonly used as the electrode metal, but it binds weakly to SiO2 grown on an Si wafer, and a thin adhesion layer is typically deposited between the Au and SiO2 during electrode fabrication. When channel materials are underneath the metallic electrodes, they are in direct contact with the adhesion layer; therefore, the adhesion metals (e.g., Ni, Cr, Ti) should be selected carefully with the matched work function to realize the Ohmic contact with channel material, which can be manifested in the linear output characterization. Carrier mobility also affects the carrier injection efficiency, and larger carrier mobility is beneficial for better sensor performance.20 The roles of both the work function and carrier mobility in manipulating or designing desirable sensing response are very limited. Instead, the band gap is the most important parameter in engineering sensor performance. In our model,17 the metal ion adsorption density na, as indicated in Eq. (1.3), is fundamentally controlled by the metal ion concentration (i.e., number density) ρ, thermal wavelength λ, in-plane partition function qi, Gibbs free energy change ΔEG after adsorption, and temperature T. The ability of a material to attract/lose electrons is indicated by the size of its band gap (i.e., ΔEG depends on the band gap Eg). A smaller band gap is favored for larger surface-adsorption density, in which gas molecules could be dissociated on the surface of the metals due to the large concentration of electrons in the metals.21,22 However, this does not necessarily suggest that a metallic channel material in an FET sensor is better due to the large binding energy. In fact, the binding energy is only necessary but not sufficient to understand and design the performance of sensors, while the performance of a 2D-nanomaterials-based FET sensor is dependent on the band gap/thickness, because the initial conductance of the sensor is inversely proportional to the band gap of the channel material in an exponential form, i.e., σ 0 ∝ exp( − Eg /k BT ). Note that both the ions adsorption density na and the initial conductance σ0 depend on the band gap Eg exponentially but with a different coefficient. Since the sensor sensitivity in Eq. (1.4) is defined as the metal ion adsorption induced conductance change in the channel material with respect to its initial value, there would be an optimum size of the band gap of the channel material for optimized sensor performance.



 2 ∆EG  na = ρ λ 3 ( ∏ qi )exp  − ,  k BT  i = x, y S=

∆I ∆σ nh µ − nh 0 µ 0 = = . I0 σ 0 nh 0 µ 0



(1.3) (1.4)

In addition to the band gap dependence, the direct contact between the metal ions and the sensor materials offers its own unique features. Specifically, the

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∆I/I0

0.5

6.4 nm (0.58 eV) 8.5 nm (0.52 eV)

6 3.2 nm (0.76 eV)

12 nm (0.47 eV)

1.6 nm (1.06 eV)

0.0

-0.5

4

1.1 nm (1.32 eV)

∆I/I0

1.0

2

-1.0 10-16

0

95 nm (0.34 eV)

10-14 10-12 10-10 10-8 Hg2+ Concentration (Mol/L)

10-6

(a)

6.4 nm (0.58 eV) 8.5 nm (0.52 eV)

18 nm (0.43 eV) 29 nm (0.39 eV)

3.2 nm (0.76 eV)

10-10

25 nm (0.40 eV)

12 nm (0.47 eV)

1.6 nm (1.06 eV)

42nm (0.37 eV) 1.1 nm (1.32 eV)

95 nm (0.34 eV)

10-9 10-8 Hg2+ Concentration (Mol/L)

10-7

(b)

Figure 1.2.    Simulated sensor performance in normalized current change of BP toward Hg2+ at ­different BP thicknesses in (a) ultrapure water and (b) tap water. Reprinted with permission from Ref. [17]. Copyright © 2019, Royal Society of Chemistry.

surface-adsorbed metal ions in an FET water sensor influence the conductance in the channel material by balancing the charge transfer and gating effects,17 both of which are dependent on the electronic properties of the channel material. The competition between the charge transfer and gating effect gives rise to different sensing response trends. For example, the simulated current change in a BP (p-type)-based FET sensor toward Hg2+ ions in Fig. 1.2 shows that the charge transfer effect will increase the current after the adsorption of metal Hg2+ ions, while the current can start to decrease if the gating effect becomes dominant when the concentration of Hg2+ ions increases. It can also be seen that prominent current change occurs in the band gap range of 0.4–1 eV in both ultrapure water and tap water. Note that the simulated lower limit of detection in tap water is much larger than in ultrapure water, primarily because the shorter Debye length in tap water greatly screens the electrostatic interactions among the adsorbed ions, and thus their surface potential in tap water is smaller.

1.2.3.  Passivated FET water sensor Direct contact between channel materials and metal ions for sensing applications suffers from two main drawbacks. On the one hand, the channel material is sensitive to the metal ions in the order of electronegativity, thereby limiting its selectivity to target metal ions in the presence of interfering ions with large electronegativity. On the other hand, the channel material can be unstable under ambient conditions, and the oxidation-induced degradation decreases the sensor’s lifetime. To circumvent these problems, Al2O3 thin film is now primarily used to protect the channel materials.23 For sensing applications, Au nanoparticles are deposited onto Al2O3

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and then decorated with the organic ligands (i.e., specific probe) via the thiol group (-SH) for detecting different species of targeted metal ions. The interaction between the specific probes and the metal ions can be either physical (i.e., electrostatic attraction) or chemical (products formation). The developed sensor platform is versatile for detecting various metal ion species. Different specific probes have been identified for detecting target analytes. For example, both the thiolglycolic acid (TGA)24 and DNA25 can be used for detecting Hg2+ ions, while reduced glutathione (GSH)26 and DNAzyme27 are used for detecting Pb2+ ions. Furthermore, ferritin protein28 is reported to be sensitive to phosphate ions (HPO42−), and bacteria such as E. coli can be linked to its specific antibody.29 Unlike the direct interaction between the metal ions and the channel materials, the charge transfer is blocked in this sensor architecture by the insulating Al2O3 layer. The metal ions bind with the chemical probe and exert an effective gate voltage to the underlying channel material. Thin Al2O3 film (e.g., ~3 nm) is preferred for better sensor performance (i.e., lower limit of detection and larger sensitivity), since the capacitance of the Al2O3 film is inversely proportional to its thickness. Nevertheless, the Al2O3 film grown by the atomic layer deposition technique30 cannot be too thin, otherwise its porous nature will reduce the sensor’s lifetime under ambient conditions due to the diffusion of water and oxygen molecules through the voids in the gate oxide. In addition, the ionic strength has to be taken into account, especially at a higher concentration of target ions or when the buffer solution contains a higher concentration of various stray ions.31,32 For higher ionic strength (i.e., smaller Debye length) in the solution, the charged target analytes (either metal ions or bacteria) are electrostatically screened by the large number of stray ions; consequently, the channel material will be less tuned in its conductance due to the weakened gating effect. This implies that several criteria during the sensor design: except the thickness requirement for the gate oxide, both the size of the Au nanoparticles for anchoring the probes and the length of the probes should be moderately small/short, and thus the distance between the adsorbed charged target analytes and the channel material can be largely minimized. Overall, an FET water sensor works by transducing the perturbation in the conductance from the adsorption of target analytes on the channel materials. The sensitivity depends on the target species and the band gap of the channel material. There is an optimal range of band gap (~0.4–1 eV) for the best sensor performance; however, due to selectivity and potential instability of channel materials, the FET channel is often protected by a thin film of Al2O3. Such passivated sensors mainly work via the gating effect through the gate oxide. The sensitivity and selectivity of the sensor must be improved by judiciously selecting specific probes. The following sections describe 2D layered nanomaterials and their detailed sensor performances for detecting various target analytes in water (e.g., metal ions and bacteria).

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1.3. Two-dimensional Nanomaterials for Water Sensing 2D nanomaterials are atomically thin sheets with supreme physical and chemical properties that have attracted tremendous attention in the past two decades. The constituent elements of a 2D nanomaterial exhibit a strong covalent in-plane bonding while a weak interlayer interaction (often through van der Waals forces). As the prototypical 2D material, graphene was first successfully isolated from the layered bulk form of graphite by Geim’s group in 2004.33 Graphene exhibits several fascinating properties, such as a superlative mobility of 15,000 cm2 V−1 s−1, even under ambient conditions,34 and record mechanical strength with a breaking strength of 42 N m−1. The discovery of graphene and its outstanding properties have stimulated extensive research interest in other 2D materials. TMDCs refer to a series of more than 40 layered compounds, in which a layer of transition metal atoms (e.g., Mo and W) is sandwiched between two atomic planes of a chalcogenide (such as S, Se, or Te).35 The renewed interest in TMDCs was sparked by the demonstration of monolayer MoS2-based transistors with an ION/IOFF ratio exceeding 1 × 108 in 2001,36 although the first production of ultrathin MoS2 flakes was reported as early as 196337 and monolayer MoS2 was achieved in 1986.38 Recently, layered BP (or phosphorene, in monolayer) has joined in the family of 2D materials and has gained much attention for its excellent transistor performance. Its mobility can reach 1,000 cm2 V−1 s−1, and the drain current can be modulated on the order of 105 at room temperature for FETs made of ~10 nm-thick phosphorene.39 2D materials are ideal channel materials in FET devices because of their ­superior electronic properties, such as high carrier mobility and versatile band structures. Their unique atomically thin structures endow 2D materials with ­adequate flexibility and maximum surface-to-volume ratio. The fully exposed surface atoms can directly interact with analytes, making 2D materials extremely sensitive to local perturbations; therefore, 2D materials-based FET sensors may promise ultimate sensitivity. In addition, the relatively large lateral size and flexibility of 2D materials facilitate control over the channel structure and form conformal and intimate contact with metal electrodes during FET fabrication.11 Currently, the outstanding sensing abilities of 2D materials-based FET sensors are demonstrated by the continuous progress in their use for detecting water contaminants (e.g., heavy metal ions and bacteria) as well as gas and biomolecules, with high ­sensitivity, excellent low limit of detection (LOD), and rapid response.11

1.3.1.  Graphene and graphene oxide Graphene, the first available 2D atomic crystal, has been the catalyst for many breakthroughs in science since its discovery in 2004. As the mother of all graphitic forms (Fig. 1.3), graphene is a flat monolayer of carbon atoms packed into

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Figure 1.3.    Graphene: the mother of all graphitic forms. Reprinted with permission from Ref. [34]. Copyright © 2007, Springer Nature Publishing.

a perfect honeycomb lattice via sp2 hybridization. The resulting electronic band structure is a gapless, linear dispersion, making the charge carriers to be massless Dirac fermions.34 The strong C–C bonds in graphene give rise to high mechanical strength, and the absence of dangling bonds results in chemical inertness.40 Pristine graphene is a semi-metal, with a zero bandgap and super high carrier mobility at room temperature. Graphene has attracted considerable interest as the channel material in FET sensors owing to its excellent electronic properties, large specific surface area (~2600 m2 g−1),41 and extreme sensitivity to environmental perturbations. This sensitivity is mainly attributed to the superior carrier mobility of graphene and its 2D atomic-thin structure, which means all the carbon atoms are surface atoms that can interact directly with analytes. Despite the lack of dangling bonds in the basal plane, graphene can be functionalized through the strong van der Waals interactions between its hydrophobic surface and long alkyl chains,42,43 widely used ᴨ-ᴨ stacking with aromatic molecules,44,45 or arbitrarily introducing anchors (e.g., Au nanoparticles) for chemical linkage.46 This powers the selectivity to specific analytes of graphene-based FET sensors.

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Micromechanical exfoliation (a)

Physical vapour deposition (b)

Chemical vapour deposition (c)

Figure 1.4.    Typical synthesis methods of 2D materials. Reprinted with permission from Ref. [40]. Copyright © 2017, Springer Nature Publishing.

2D materials are typically produced by three methods: mechanical exfoliation, physical vapor deposition (PVD), and chemical vapor deposition (CVD), as shown in Fig. 1.4. These methods usually can produce high-quality 2D crystals with fewer defects. For graphene, in addition to the aforementioned methods, a widely exploited approach is reducing graphene oxide (GO).47 Although reduced graphene oxide (rGO) contains many more defects than pristine graphene, its much lower cost and massive scalability promise cost-effective sensor fabrication. Unlike semi-metallic graphene, rGO is a typically p-type semiconductor under an ambient environment and its FET properties (e.g., band gap and source-drain ION/IOFF ratio) can be tuned by regulating reduction conductions.48 The band gap of rGO is expected to decrease with fewer oxygen-containing groups.

1.3.2.  Black phosphorus Black phosphorus (BP) is the most stable bulk allotrope of phosphorus.49 It has an orthorhombic crystal structure stacked with phosphorus atom layers (Fig. 1.5). Individual BP layers are puckered, which results in significant anisotropy in various properties.50 Different from graphene (zero-gap semiconductor), BP is a p-type semiconductor with a direct band gap that is tunable from 0.3 eV for bulk structure to 2.0 eV for BP monolayer (phosphorene).11 In 2014, Li et al. peeled ultrathin BP flakes by mechanical exfoliation and used them to fabricate FET devices.39 The BP flakes exhibited attractive electronic characteristics, including an on/off ratio of 105 at room temperature (5 nm sample) and high mobility up to ~1000 cm2 V−1 s−1 (10 nm sample). These characteristics are highly favorable for FET sensors; however, the instability of BP thin layers in air and moisture50 must be overcome before its practical application in water sensors. Encapsulation with AlOx thin film by atomic layer deposition has proven effective for long-term ambient stability.23 Although research on BP FET water sensors is still in its infancy, the tunable band gap and high carrier mobility of BP are great advantages

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Figure 1.5.  Structure of BP. Reprinted with permission from Ref. [39]. Copyright © 2014, Springer Nature Publishing.

for FET sensor applications. With the progress in the stabilization and functionalization of BP flakes,51,52 2D BP has the potential to realize better sensing performance.

1.3.3.  Molybdenum disulfide In the large family of layered TMDCs, MoS2 is the most extensively studied ­material due to its robustness and excellent electronic properties. As shown in Fig. 1.6(a), bulk MoS2 consists of vertically stacked layers with a monolayer thickness of 6.5 Å and are held together by a weak van der Waals force.36 Depending on the coordination configurations of the transition metal atoms, MoS2 can form two common structural phases, octahedral (1T) and trigonal prismatic (2H), which have different properties. Bulk MoS2 is semiconducting with an indirect band gap of 1.29 eV, while monolayer MoS2 has a direct band gap of ~1.9 eV, showing layer-dependent transition (Fig. 1.6(c)).53 Monolayer MoS2 has successfully displayed excellent electrical characteristics when used as the conductive channel of FET, including a room-temperature mobility of 200 cm2 V−1 s−1 and a current on/off ratio exceeding 1 × 108 (Fig. 1.6(b)). The diverse chemical compositions and structural configurations of 2D materials result in a very broad range of electronic properties (Fig. 1.7).50 2D nanomaterials cover a wide range of band gaps from 0 to ~2.5 eV (i.e., zero band gap of graphene and large band gap of TMDs), and exhibit superior mobility up to 105 cm2 V−1 s−1, and a high on/off ratio approaching 108. Bandgap engineering could further expand the horizon for designing 2D FET sensors. This provides tremendous opportunities to realize better sensing performance in FET sensors through carefully selected 2D materials and rational sensor design.

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(A) (a)

(B) (b)

(c)

(d)

(C)

Figure 1.6.    (A) Schematic structure of MoS2. (B) Room-temperature transfer characteristics from the FET with a bias voltage Vds of 10 mV. Back-gate voltage Vbg is applied to the substrate. Inset: Ids–Vds curves acquired for the Vbg values of 0, 1, and 5V. (C) Calculated band structures of bulk, quadrilayer, bilayer, and monolayer MoS2 (a)–(d). Panels (a) and (b): Reprinted with permission from Ref. [36]. Copyright © 2011, Springer Nature Publishing. Panel (c): Reprinted with permission from Ref. [53]. Copyright © 2010, American Chemical Society.

1.3.4.  Comparison of typical 2D materials Table 1.1 summarizes and compares several important properties of the 2D nanomaterials that we have discussed (i.e., graphene, graphene oxide, black phosphorous, and molybdenum disulfide) to highlight the pros and cons of these materials when used as FET channel materials. Among the 2D materials presented in this section, graphene exhibits the highest electron mobility (200,000 cm2 V−1s−1), which is attributed to its zero band gap within the structure. However, the practical application of graphene in FET devices remains hindered by several challenges.

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(b)

Figure 1.7.  (a) Electromagnetic wave spectrum and (b) mobility/on–off ratio spectrum of 2D materials. Reprinted with permission from Ref. [50]. Copyright © 2015, National Academy of Sciences. Table 1.1.    Comparison of 2D nanomaterials as channel materials in FET water sensors. Graphene

rGO

MoS2

BP

Pros Extremely high Tunable band gap Tunable band gap Thickness-dependent mobility (0.2–2 eV) (1.2–1.9 eV) band gap (0.3–2 eV) (~200,000 cm2/Vs) High mobility High carrier mobility High carrier mobility (0.05–200 cm2/Vs) (~200 cm2/Vs) (~1000 cm2/Vs)

Cons Semimetal Synthesis

Easy massive synthesis

High on/off ratio (~108)

High on/off ratio (~105)

Relatively low on/off ratio

Relatively large band gap

Stability

For example, better synthesis methods are needed so that high-quality graphene, i.e., graphene with properties close to or even equal to the intrinsic graphene, can be produced efficiently and at a low cost. The CVD synthesis of graphene on some metal foil surfaces (e.g., Cu)54 is a significant improvement compared with the mechanical exfoliation method (i.e., scotch tape method); however, the CVD approach is time-consuming and has relatively low throughput. In addition,

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graphene cannot reach a low off-state current due to the lack of a band gap, which significantly limits its application in transistor devices. On the other hand, chemical exfoliation methods have been used to produce graphite colloidal suspensions and derivatives of graphite (e.g., graphite oxide), and mostly mono-layer graphene oxide (GO) nanosheets can then be obtained by simple sonication of graphite oxide. This chemical approach to graphene derivatives is more scalable and has exhibited the possibility of high-volume production. Because of the chemical reactions involved during synthesis, GO is rendered with various functional groups (mainly carboxyl and hydroxyl groups), which could be helpful for the immobilization of target materials (e.g., chemical probes for certain analytes). Reduced graphene oxide (rGO), (e.g., obtained by the thermal annealing of graphene oxide), has properties similar to graphene, such as a large surface area, relatively low 1/f noise, and tunable ambipolar field-effect characteristics. Due to the abundant structural defects in rGO, its charge mobility is lower than that of graphene, but rGO typically has a sizable band gap.55 In contrast to intrinsic graphene, which has no band gap, MoS2 is a semiconductor that has a direct band gap of 1.9 eV for a monolayer. MoS2 FETs can achieve a high on-off current ratio of ~108 and high mobility (200 cm2 V−1 s−1) at room temperature.36,56 Many methods have been developed to synthesize MoS2, such as the exfoliation of bulk MoS2 via Li intercalation, scotch-tape-based micromechanical exfoliation, liquid exfoliation, chemical vapor deposition (CVD), physical vapor deposition, and thermolysis of a single precursor containing Mo and S.57 Further developments in the synthesis of thin-layer MoS2 will increase the possibility for its mass production and widespread application. BP nanosheets have thickness-dependent and tunable band gaps, ranging from 0.3 eV for bulk BP to 2 eV for monolayer BP nanosheet (phosphorene).58 Fewlayer BP nanosheets can have excellent carrier mobility up to 1000 cm2 V−1 s−1 and a high on/off current ratio (~105) at room temperature.39 The main challenge in applying BP in the FET sensor is its poor air stability under ambient conditions. However, the use of encapsulation or a passivation layer prevents BP from oxidizing and helps to maintain its electronic properties and stabilize its performance under ambient conditions.23

1.4. Detecting Contaminants in Water with 2D-Nanomaterials-Based FET Sensors This section highlights the use of 2D-nanomaterials-based FET sensors for lowconcentration and rapid detection. Compared with conventional low-concentration analysis (e.g., inductively coupled plasma mass spectrometry (ICP-MS)), these sensors can provide continuous water quality monitoring and early warning of contamination and can save time.

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As discussed in Sec. 1.2, the detection of water contaminants with 2Dnanomaterials-based FET sensors is realized by monitoring the electronic characteristics of the semiconducting channel in the FET, which is pre-functionalized with probes or receptors to capture the target contaminants. Since the amplitude of the FET characteristic change (e.g., conductivity) usually depends on the contaminant concentration, the FET sensors could measure the contaminants in water quantitatively. This section discusses the application of FET sensors based on 2D nanomaterials for detecting various contaminants in water. The contaminants are categorized in three groups: (i) heavy metal ions (Hg2+, Pb2+, As3+, As5+, and Cd2+), (ii) microorganisms (bacteria and virus), and (iii) others (phosphate and nitrate).

1.4.1.  Heavy metal ions Many research studies explore the detection of heavy metal ions in water, particularly Hg2+, Pb2+, As3+, As5+, and Cd2+. The label-free detection is attributed to the interaction between the metal ions and the channel material, which usually requires surface modification for the specific detection of target contaminants. Sydibya and others demonstrated rGO micropattern-based FET sensors for detecting heavy metal ions in water, as shown in Fig. 1.8(a).59 The sensor used a metallothionein type II protein (MT-II) as the probe, which was functionalized onto the rGO films via the pyrene linkers. These devices provided sensitive detection of Hg2+ with a detection limit of 1 nM (Fig. 1.8(b)), and Cd2+ with a detection limit of 1 nM. In control experiments, it was found that the FET sensors with MT-IImodified rGO were unresponsive to K+, Na+, Ca2+, or Mg2+ ions, and the sensors without MT-II probes were insensitive to Hg2+ and Cd2+ ions. This study proposed that the current change was attributed to the doping phenomenon or the conformational change of MT protein due to the binding of the heavy metal ions. They also

1 µA

21 nM

(a)

1 nM

50 s

3 nM

6 nM

10 nM

28 nM

15 nM

(b)

Figure 1.8.  (a) Schematic of solution-gated configuration of rGO-FET. (b) Typical real-time recording of Ids with the addition of Hg2+ ions. The rGO-FET was biased at Vds = 400 mV and Vg = −0.6V. Reprinted with permission from Ref. [59]. Copyright © 2011, American Chemical Society.

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used the rGO FET sensor to test real lake water without heavy metal ions using the rGO FET sensor. The sensor showed no significant responses to bare lake water samples but showed significant responses when Hg2+ was added into the lake water, thereby indicating the high selectivity of the rGO FET sensor. In addition to protein, a DNA aptamer was also used as a probe in 2D nanomaterial-based FET sensors for the selective detection of Hg2+. We fabricated FET sensors with MoS2 thin films functionalized with specific DNA (5’- SH-TCA TGT TTG TTT GTT GGC CCCCCT TCT TTC TTA-3’) for detecting Hg2+.60 Figure 1.9(a) shows the schematic of the platform. Figure 1.9(b) presents the dynamic responses of the as-fabricated devices by monitoring the drain current vs. time during a cumulative addition of Hg2+ solution to the sensor, with a detection limit of 0.1 nM, which is two orders of magnitude lower than the MCL of Hg2+ (9.9 nM) in drinking water as defined by the USEPA. This study proposed that when Hg2+ was added to the sensor, the formation of T-(Hg2+)-T chelates, caused by the reactions between the Hg2+ and the thymidine of the DNA molecule, had changed the conductance of the semiconducting channel material–MoS2. Control experiments were conducted to investigate the critical function of the DNA probe in Hg2+ detection. As shown in Fig. 1.9(b), three groups of sensors (bare MoS2 film, MoS2 film decorated with Au NPs only, and MoS2 film decorated with the DNA-probe-modified Au NPs) were tested, and the MoS2/DNA-Au NP sensor had much higher sensitivities than the two control groups. Therefore, adopting a specific probe is required for the highly sensitive and selective detection of Hg2+. In another study, a flexible graphene aptasensor was reported for Hg2+ detection. Using 1,5-diaminonaphthalene (DAN) and glutaraldehyde (GA) as crosslinking agents, the aptamer (30-amine-TTC TTT CTT CCC CTT GTT TGT-C10 carboxylic acid-50) was linked on the graphene surface as a probe for detecting

(a)

(b)

Figure 1.9.    (a) Schematic of the MoS2/DNA-Au NPs FET sensor platform. Inset: The formation of T-(Hg2+)-T chelates, caused by the reactions between Hg2+ and the thymidine of the DNA molecules on the Au NPs, leading to the change in the MoS2 electrical conductivity as a sensor signal. (b) Real-time detections of Hg2+(nM) in water (Vds = 0.1 V) with platforms of MoS2/DNA-Au NPs (black, solid), MoS2−Au NPs (purple, dash), MoS2 (blue, short dash), respectively. Reprinted with permission from Ref. [60]. Copyright © 2016, American Chemical Society.

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Hg2+.61 Other researchers have used DNA aptamer62 and nucleic acid63 as probes to selectively detect Hg2+ ions due to their high binding affinity to heavy metal ions. DNAzyme also has been used for Pb2+ detection;46 however, the stability of biomolecules in a water environment could be an issue for long-term operation and continuous measurements. Compared with biomolecules, chemical probes have better long-term stability in water and have been used in FET sensors for detecting heavy metal ions. For instance, thioglycolic acid (TGA),24 N-[(1pyrenyl-sulfonamido)-heptyl]-gluconamide (PG),64 and 1-Octadecanethiol42 have been used to detect mercury ions; L-glutathione reduced (GSH) was reported in the detection of lead ions;26 and dithiothreitol (DTT) was employed in the arsenic ion sensing platform.65 We prepared FET sensors based on thermally reduced rGO and TGA using Au NPs as the linker to detect mercury ions in aqueous solutions.24 Thermal annealing is one of most commonly used methods for GO reduction because the reduction level can be easily controlled by adjusting the reduction temperature and duration, and the rGO nanosheet has a relatively high purity compared with the chemical reduction method. Figure 1.10 shows a schematic illustration of the rGO/TGA-Au NP hybrid sensor, where TGA-modified Au NPs are anchored to the rGO sheet surface and function as a specific recognition group for immobilizing Hg2+ ions. After exposure to the Hg2+ ions, the R-COO-(Hg2+)-OOC-R chelate forms via the reaction between Hg2+ ions and the carboxylic acid groups of the TGA molecules. This binding event between Hg2+ and TGA changes the charge carrier concentration in rGO sheets and further alters the drain current in the rGO/TGA-Au NP hybrid sensor. Compared with pure water, the introduction of Hg2+ ions increased the conductance of the rGO/TGA-Au NP hybrid sensor. The sensor showed a lower detection limit of 25 nM and responded to Hg2+ within a few seconds and

Figure 1.10.    Schematic diagram of the rGO/TGA-Au NP hybrid sensor. Reprinted with permission from Ref. [24]. Copyright © 2013, American Chemical Society.

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sensitivity

∆Ι/Ι (%)

6 5.515 3.906

4 2

0.99 0.784 0.23 1.2 0.110.13 0 Cu Zn AgCdAsHg Pbmix

metal ions

(a)

(b)

Figure 1.11.    (a) Schematic diagram of the rGO/GSH-Au NP hybrid sensor fabrication process: (1) A layer of AET coating on the bare interdigitated electrode surface; (2) Self-assembly of GO monolayer sheets on the AET-modified electrodes, which is followed by the thermal reduction of GO to rGO; (3) The assembly of Au NPs onto the rGO film; (4) GSH-modification of Au NPs on the rGO sheet surface to form specific recognition groups to detect Pb2+. (b) At Vds = 0.36 mV comparison of the rGO/GSH-Au NP hybrid sensor in response to various heavy metal ions: Ag+, As5+, Cd2+, Cu2+, Hg2+, Pb2+, Zn2+, and a mixed solution of the six ions (10 μM). Reprinted with permission from Ref. [26]. Copyright © 2014, American Chemical Society.

with excellent selectivity compared with other metal ions. The sensing performance was attributed to the chelation interaction of heavy metal ions with the probe molecules, as well as the high charge mobility of rGO. We also investigated rGO-based FET sensors for Pb2+ detection, in which rGO was deposited through a self-assembly method and GSH was employed as the capture probe for label-free detection.26 The sensor fabrication process is shown in Fig. 1.11(a). The performance of the sensor was measured by monitoring the electrical characteristics of the FET device. A lower detection limit of 10 nM was achieved, and the rGO FET sensor was able to distinguish Pb2+ from other metal ions, as shown in Fig. 1.11(b). Compared with the significant responses to Pb2+ and the ion mixture, the sensor’s responses to Ag+, As5+, Cd2+, Cu2+, Hg2+, and Zn2+ ions were much weaker, because the amidogen and carboxylic groups of the GSH molecule favor binding with Pb2+. A control experiment also was conducted to investigate the sensing responses to Na+ and Ca2+ ions, which were negligible. However, when exposed to Fe3+, the sensor conductivity showed some changes, which may be due to the strong affinity of Fe3+ to carboxylic groups on rGO. To address this issue, A passivation layer is desired to block metal ions from direct contact with the sensing materials. Ionophore films can be an alternative to the sensing probes that are commonly used for selective heavy metal ion detection. Ion selective membranes (ISM) with ionophore were investigated for the selective detection of Pb2+ and As3+ ions.66,67 Li et al. reported an air-stable BP sensor encapsulated with ionophore film.66

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(a)

(c)

(b)

(d)

Figure 1.12.    (a) Schematic view of a BP sensor. (b) Schematic view of BP sensors with lead ionophore. (c) ΔR/R0 vs. Pb2+ concentration (experimental results from seven sensors). Resistance of BP shows saturation at higher concentrations. (d) IDS vs. VGS curves of BP with ionophore protection. Reprinted with permission from Ref. [66]. Copyright © 2015, American Chemical Society.

As shown in Fig. 1.12(a), the BP flake is covered with ionophore film. An ionophore film can effectively reduce the chemical reaction activity of BP from an ambient environment, and it only allows certain types of ions to selectively permeate through it. Figure 1.12(b) shows the schematic view of a BP sensor with lead ionophore, which allows only lead ions to pass and repels other ions. The BP sensor detected multiplex ions with superb selectivity and sensitivity to Pb2+ down to 1 ppb (Fig. 1.12(c)). Experimental results demonstrated a good linear relation between (C/ΔG) and C (where C is the molecule concentration and ΔG is the conductance change) and fitted the Langmuir adsorption isotherm well, as shown in the inset of Fig. 1.12(c). Air instability is a severe problem for the practical application of BP devices; fortunately, the ionophore-encapsulated BP device demonstrated significantly improved air stability. In Fig. 1.12(d), the prepared ionophore-encapsulated BP sensor maintained good performance after one week of ambient exposure with less than 10% IDS variation, suggesting significantly improved air stability.

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Li et al. also investigated ionophore-encapsulated MoS2 FET sensors with different contact metals for sensing arsenite (AsO2−).67 Arsenite ionophore film can discriminate between AsO2− and other heavy metal ions, and the selective detection of AsO2− was achieved with detection limit down to 0.1 ppb. During sensing, AsO2− ions passed through the ionophore and caused a negative gate voltage ­(gating effect) on top of MoS2, thereby decreasing the electron density in MoS2. In the sensing response, the addition of AsO2− ions decreased the conductance of MoS2, and higher ion concentrations led to higher responses. In addition to ionophore film, a BP-based FET sensor with DTT functionalized as the probe was also reported for detecting arsenic (As) in water.65 In this work, the pH value of the prepared As3+ solutions varied from 5.30 to 5.92. As reported, in this pH range, H3AsO3 (As(OH)3) is dominant,68 and thus only As(OH)3 was considered for the schematic representation in Fig. 1.13. During the sensing process, the free end thiol group chelates with As3+ ions, since As3+ has a very high affinity to thiol-containing ligands to form covalent bonding and generate a strong complex. As shown in Fig. 1.13(b), each As3+ can bind with up to three DTT-conjugated Au NPs through an As-S linkage. The binding of the charged As3+ led to the electrical conductivity change in the BP film. The performance of the as-fabricated sensor is outstanding, with a lower limit of detection (LOD) of 1 nM, which is much lower than the MCL for As ions in drinking water (130 nM) as advised by the USEPA. It was found that the platform can also detect As5+ with a comparable LOD of 1 nM, which is attributed to the fact that DTT can reduce As5+ to As3+; therefore, the BP/Au NPs/DTT sensing platform can be used to detect both As5+ and As3+ in water. A concentration prediction test was carried out with real water samples collected from Lake Michigan, which were pretreated with ethylenediaminetetraacetic acid (EDTA) and filtered with a 0.2 mm pore filter to remove potential microorganisms and large particles. Each test was replicated three times, and the average sensitivity was 4.68% with a standard deviation of 0.9, indicating good repeatability and consistent sensor performance. The error of the concentration prediction is 14%, calibrated by ICP-MS measurement. The

Ɛ;///Ϳ ; dd Au NPs

(a)

Vg

(b)

Figure 1.13.    (a) Schematic of the BP/Au NPs/DTT sensing platform for detecting As ions. (b) The reaction between the DTT and the As3+ ion in the detection process. Reprinted with permission from Ref. [65]. Copyright © 2018, Elsevier.

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prediction experiment suggests that the platform has great potential to be developed into a stand-alone senor for detecting water pollutants. Other 2D nanomaterial-based FET sensing platforms reported for Hg2+ ion detection include rGO-polyfuran (a conductive polymer) nanohybrids,69 a graphene-Au NPs cluster structure,70 and bare MoS2.71 Table 1.2 summarizes several representative structures for sensing heavy metal ions in water and their detection performance.

1.4.2.  Microorganisms Besides heavy metal ions, many studies have explored the potential of 2D nanomaterials-based FET devices for detecting microorganisms (i.e., bacteria) in water. Most of the detections were achieved by monitoring the change in the electrical characteristics of the sensing channel when target molecules are introduced and react with the receptors via antigen-antibody reaction, hybridization, or redox

Table 1.2.    Heavy metal ion detection with 2D nanomaterial-based FET sensors. Detection performance

Contaminants and MCL Hg (10 nM)

Sensor structure

Detection limit

Detection range

Ref.

rGO-MT- II

1 nM

1–28 nM

[59]

rGO-TGA

25 nM

25 nM–10 µM

[24]

Graphene-aptamer

10 pM

10 pM–100 nM

[61]

Graphene-Au NP cluster

0.05 ppb

0.05 ppb–60ppb

[70]

Graphene-1-Octadecanethiol

10 ppm

10 pM–1 µM

[71]

rGO-PG

0.1 nM

0.1 nM–30 nM

[64]

rGO-Polyfuran Nanohybrids

10 pM

10 pM–100 nM

[69]

GO-Nucleic Acid

1.25 mM

1.25–13 µM

[63]

ERGO-DNA aptamer

0.5 nM

0.5 nM–1 μM

[62]

MoS2-DNA

0.1 nM

0.1 nM–10 nM

[60]

MoS2 Pb (72 nM)

As (130 nM)

30 pM

30 pM–1 μM

[71]

0.02 nM

0.02 nM–100 nM

[46]

rGO-GSH

10 nM

10 nM–10 μM

[26]

BP-Lead Ionophore

1 ppb

1 ppb–100 ppm

[66]

BP-GSH

5 nM

5 nM–500 nM

[72]

BP-GSH

2.5 ppb

2.5 ppb–20 ppb

[73]

MoS2-Arsenite Ionophore

0.1 ppb

0.1 ppb–100 ppm

[67]

1 nM

1 nM–1 µM

[65]

Graphene-DNAzyme

BP-DTT

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reaction. The receptors were pre-functionalized on the sensing material either by direct modification or with linkers (e.g., nanoparticles and 1-pyrenebutanoic acid succinimidyl ester). The first study using a graphene-based FET device for detecting biomolecules was reported by Mohanty et al. in 2008,74 in which they fabricated a rGO FET device and studied its interactions with single bacterium and DNA. To specify the interaction between the rGO and the bacteria, the rGO sheet was functionalized with amine groups [graphene-amine (GA)]. To perform the conductivity (drainsource current versus drain-source voltage) and transistor (drain-source current versus gate voltage) measurements, the sensing channel (i.e., rGO nanosheet) bridged the Au electrodes and a top or back gate potential was applied to the device, as shown in Fig. 1.14(a) inset. Figure 1.14(a), (b) show the electrical measurements of the GO and GA sheets, in which the GA device had a lower conductivity than the parent GO device, implying that the surface modification of GO sheet could change its conductivity due to the change in the carrier density and mobility of the nanosheet. Both the GO and GA devices were p-type semiconductors, which is typical for GO and rGO FETs. Bacterium attachment on the GA device was characterized with the I–V test, as shown in Fig. 1.14(c). The GA device exhibited a sharp 42% increase in conductivity upon the attachment of a single bacterial cell (Gram-positive Bacillus cereus cell). This could be attributed to the p-type characteristic of GA, in which the attachment of a negatively charged species such as bacteria is equivalent to a negative potential gating, which increases the hole density and thus the conductivity of GA (gating effect). Applying the same idea, Mohanty et al. also studied the interaction of the GO device with DNA. First, the selective tethering of the single-stranded DNA (probe DNA) on GO to form G-DNA was carried out, followed by hybridization with cDNA (target DNA) on the G-DNA device. This second step led to a 71% increase in conductivity (Fig. 1.14(d)). Since the probe and target DNA pair is specific, a highly selective sensor could be designed. The results from this study show the possibility of using graphene nanostructures as a bacteria cell or a label-free DNA detector. Many studies have been carried out to evaluate the potential of graphenebased FETs to detect E. coli bacteria, one of the most prevalent pathogens in the environment. We demonstrated an antibody-modified rGO FET sensor for detecting E. coli.75 As shown in Fig. 1.15(a), the antibody probe was labeled on graphene with a linker molecule to selectively capture E. coli cells. To prevent non-specific binding, a blocking buffer (Tween 20) was used to passivate the uncoated sensing surface. The capture of E. coli cells by the anti-E. coli antibody on the sensor was reflected by the change in the electrical conductivity in the rGO channel, i.e., Isd. Figure 1.15(b) shows the monitoring of E. coli by the sensor, where %R = [(Resistance final-Resistance initial)/Resistance initial] × 100. There

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(a)

(b)

(c)

(d)

Figure 1.14.    (a) The current–voltage (I–V) curves of the GO and GA devices. The insets show the device with GO/GA between the gold electrodes and a schematic of the chemical structure of GO and GA. (b) FET curves of GO and GA show they are p-type semiconductors. The top inset shows the post-deposited gold electrodes on a GO sheet. (c) The conductivity of the p-type GA device increases upon the attachment of a single bacterial cell on the surface of GA (inset 1). LIVE/DEAD confocal microscopy test on the bacteria deposited on GA (insets 2–4). (d) DNA transistor: ss-DNA tethering on GO increases the conductivity of the device. Inset shows a G-DNA(ds) sheet with wrinkles and folds clearly visible. Reprinted with permission from Ref. [74]. Copyright © 2008, American Chemical Society.

is a linear decrease in Isd after the addition of E. coli cells, from 103 CFU/mL (single bacterium in the test 1nL solution) to 106 CFU/mL. In the control experiments, the sensors with the anti-E. coli antibody were tested with E. coli (104 CFU/mL) + heat-killed Salmonella typhimurium (106 cells/mL) and E. coli (104 CFU/mL) + heat-killed Streptococcus pneumonia (106 cells/mL). The presence of the bacteria apart from the E. coli did not influence the signal — this proves the sensor is specific to E. coli, which can be attributed to the E. coli antibodies that selectively capture E. coli cells from the sample to generate the response.

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(a)

(b)

Figure 1.15.    (a) Schematic of the anti-E. coli/Au NPs/Al2O3/rGO FET sensor. (b) The response of the FET sensor to E. coli addition; Inset: Linear fitting from 103CFU/mL to 106CFU/mL of E. coli concentration. Reprinted with permission from Ref. [75]. Copyright © 2018, Elsevier.

1.4.3.  Phosphates and nitrates Reports on 2D-nanomaterial FET water sensors have mainly focused on heavy metal ions and bacteria; however, there are other water contaminants and water quality indicators that could be detected by FET sensors. For example, orthophosphate ions, a representative nutrient for water eutrophication, could be sensitively detected by an rGO FET sensor with a lower LOD comparable to the conventional colorimetric methods. We reported the detection of orthophosphate ions (HPO42−) with an FET sensor based on rGO/ferritin,28 as shown in Fig. 1.16(a). The sensing surface was functionalized with ferritin, which formed a hydrated iron oxide mineral core and acted as sorbent for orthophosphate ions. The detection is realized by monitoring the orthophosphate ions-induced conductance change on the sensing material (rGO) between the source and drain electrodes. The adsorption of HPO42− caused the current increase in the channel, as shown in Fig. 1.16(b) and (c). The sensor showed an LOD of 26 nM (corresponding phosphorus concentration: 0.8 μg/L) and a response time on the order of seconds. The FET sensors also exhibited a good selectivity over Cl−, SO42−, and CO32− ions. Nitrate is another nutrient species that has been detected by FET sensors. Both organic and inorganic semiconductors, including 2D nanomaterial (e.g., graphene), have been reported as channel materials.76,77 Minami et al. reported the first selective nitrate sensor based on an extended-gate-type organic field effect transistor (OFET),76 which is shown in Fig. 1.17(a). In their OFET, an organic semiconductor, solution-processable PBTTT (poly 2,5-bisIJ3-hex-adecylthiophene-2-yl)thienoij3,2-b]thiophene), was used as the sensing channel. The sensor comprised an OFET-based transducer and an extended-gate electrode

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(a)

(b)

(c)

Figure 1.16.  (a) Schematic of the rGO/ferritin sensing platform for HPO42− detection. (b) and (c) Dynamic responses of the rGO/ferritin sensors to HPO42− of different concentrations with and without the probe linker. Reprinted with permission from Ref. [28]. Copyright © 2017, Royal Society of Chemistry.

functionalized by a nitrate reductase with a mediator [i.e., a bipyridinium derivative (BP)]. The nitrate detection mechanism can be explained by an electron-relay on the extended-gate electrode. The addition of nitrate enables the enzyme reaction (BP2+↔ BP+). The reaction leads to the change of the electron mediator (i.e., BP) valence, which changes the potential in the gate electrode and reflects as the shifting of Vth, as shown in Fig. 1.17(b). It was reported that 90% of the sensors responded within 20 sec, with a linear working range of 0–4.0 × 10−6M and a detection limit of 45 ppb, which is much lower than the threshold concentration of total nitrogen (0.3 mg L−1) for water eutrophication. The highly selective response to nitrate was confirmed by testing other anions, including Cl−, SCN−, HPO42−, and HCO3−. Furthermore, nitrate detection in diluted human saliva has also been demonstrated, implying the potential for practical applications of the OFET sensor. The selectivity of nitrate FET sensors is primarily determined by the affinity of the sensing probe to nitrate over other ion species. Besides nitrate reductase, quaternary ammonium groups have been applied in FET sensors as a nitrite recognizer to enhance the sensing selectivity for the nitrate-exchange effect.77 In this study, rGO nanosheets were employed as the FET channel material, and

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(a)

(b)

-

Cl N+

NO N O3

TEBA C

-

Drain in

Source S SiO2

Si wafer rGO

Back gate

(c)

Normalized Current (I/I0)

0.0028 mg L-1 -1

1.00

0.028 mg L

-1

0.28 mg L

0.95

-1

2.8 mg L -1 28 mg L

0.90

NO3- detection

0.85 50

75

100

Time (s)

125

(d)

Figure 1.17.    (a) Schematic illustration of the nitrate-sensing device based on the extended-gatetype OFET biosensor; (b) Transfer characteristics of the OFET sensor upon titration with nitrate. (c) Schematic diagram of the rGO/TEBAC hybrid FET sensor platform. Triethyl amine groups in the TEBAC function as the recognition probe for nitrate ions. (d) Dynamic response of the rGO/TEBAC sensor with the addition of NO3− solutions with different concentrations. I0 is the initial source–drain current and I is the current after ion addition. Panels (a) and (b): Reprinted with permission from Ref. [76]. Copyright © 2016, Elsevier. Panels (c) and (d): Reprinted with permission from Ref. [77]. Copyright © 2018, Royal Society of Chemistry.

benzyltriethylammonium chloride (TEBAC) was modified as a capture probe on the rGO surface. The schematic of the platform is shown in Fig. 1.17(c). The sensor demonstrated a real-time response with an ultra-low detection limit of 1.1 μg L−1 for NO3-N (Fig. 1.17(d)) and good specificity for interfering ions. Studies have reported that sensors with a chemical functional group are less expensive, easier to fabricate, and more chemically stable than that with a biorecognizer. In this work, a chemical passivation is suggested to improve sensing selectivity, considering the physical adsorption of ions on bare channel material through various interactions, FET sensing platforms for nitrate detection are promising due to their miniaturized structure for integration and in situ detection and quick response for

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real-time nitrate monitoring in water. Further studies on structural optimization and performance improvement are required for FET nitrate sensors before achieving practical applications and commercial production.

1.5. Summary and Outlook In recent years, we have seen the development of new technologies and sensors for detecting water contaminants, and the need for rapid and in situ sensing technologies and smart sensors has increased due to environmental pollution concerns and contaminant leakage disasters. Compared with conventional sensing methods, there are many advantages of using 2D-nanomaterials-based FET for sensing water contaminants, including high sensitivity, fast response, miniaturized size, easy sample preparation and detection, and low cost. The FET sensing technology is versatile and flexible and can be engineered to detect many analytes (e.g., heavy metal ions, bacteria, nutrients) by choosing specific probes. Therefore, the FET sensors based on 2D nanomaterials have the potential to serve in a broad range of sensing products and play a significant role in water quality monitoring. The individual FET sensor can also be multiplexed to detect multiple analytes simultaneously. FET water sensors can also be incorporated into water equipment and devices to provide additional functionality to the water user. For example, when integrated into a water filter system, the FET sensor can indicate the water quality after filtration and remind the user to replace the cartridge on a timely basis. In addition to water sensing, the use of FET sensors based on 2D nanomaterials could be extended to detect species in other aqueous environments and used in areas such as food and beverage safety control and biomedical applications, with features outperforming those of current technologies. The microelectronics-based FET platform is conducive to interfacing with smartphones and other portable wireless communication devices for remote sensing and interfacing with big data analytics. Combined with advanced data acquisition and transmission technologies, FET sensors could serve as the building blocks of a sensing network for continuous water quality monitoring and real-time diagnosis of contamination events in the entire water system, as well as contributing to the building of smart cities and smart homes, thereby enhancing public safety and freshwater supplies. Another direction for the research of FET sensors based on 2D materials is to explore other strategies for capturing the sensing signal. The signal from an FET sensor is commonly transduced by measuring the change in the resistance or conductance of the channel when a constant voltage is applied between the source and drain electrodes. One potential disadvantage of this approach is that the continuous current passing through 2D materials could cause heat and affect the intrinsic conductivity, resulting in a possible lengthened stabilization period and signal

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drifting. An alternative strategy is to replace the continuous voltage bias across the sensor with a periodic pulse (e.g., a square wave) to perform sensing.73 We found that in the presence of analytes (e.g., Pb2+), the sensing signal across the sensor quickly changed to stable slanting charge/discharge transients representing a high capacitive influence. We showed that a pulse signal combined with the capacitance measurement of FET sensors might be beneficial for capturing the rapid change in the signal in the presence of analytes.72,73 Impedance spectroscopy is a powerful method for analyzing the electrical properties (resistance and capacitance) of a sensor system, and the impedance analysis could provide detailed information about various physical and chemical events in the sensing system. Compared with DC signals, impedance spectrum measurements could provide more information about the relative contributions of the components in the sensing system to the overall sensing performance.72 Although significant efforts have been made toward the commercialization of these 2D materials-based FET sensors, many are not yet in the market for practical applications due to technical or fabrication challenges. Efforts and breakthroughs are still needed to improve sensor performance/reliability and reduce its cost, develop more adoptable sensor fabrication methods, and offer more convenient operation procedures. The detection of water contaminants is often needed in a natural water system, which is complex and has many components. The suspended solid, organic, and inorganic materials in water may disturb the sensor signal, which may cause uncertainties in the detection and possibly degrade the sensor’s sensitivities and reduce its lifetime. The existence of these interfering species is a challenge for the real-world applications of FET water sensors. In addition, the impact of temperature on water sensor performance should be carefully addressed, since 2D semiconducting nanomaterials are sensitive to temperature variations in the environment. A possible research direction is to design a compensation method for the temperature variations and tune the sensor signal through signal processing with a calibration dataset of sensors that can undergo a wide range of working temperatures. New approaches for exploiting various 2D nanomaterials as the sensing channels in FET sensors are increasingly being explored, but the ability to prepare and manipulate these nanomaterials in reproducible and economical ways is still premature.78 For sensor fabrication, the device-to-device variations and large-scale manufacturing are two major challenges that must be overcome before 2D-nanomaterials-based FET sensors can be used for real-world applications. Furthermore, the underlying sensing mechanism for the observed transducer signals is not always fully understood, which is key to optimizing the sensor design. Thus, there remains plenty of room for investigations and breakthroughs in 2D-nanomaterials-based FET water sensors to further improve sensing performance, enhance sensor reproducibility and reliability, and develop more facile sensor fabrication methods.

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Acknowledgments We are grateful to several agencies and industry partners for their financial support to help us carry out the research on FET water sensors based on 2D nanomaterials. Specifically, Chen acknowledges the financial support provided by the US National Science Foundation (IIP-1434059, IIP-1516207, CBET-1631968, CBET1606057, CMMI-1727846 and CMMI-2039268), NSF I/UCRC on Water Equipment and Policy, A. O. Smith, Badger Meter, Milwaukee Metropolitan Sewerage District, and Baker Manufacturing. Lu acknowledges the financial support from the US National Institute of Environmental Health Sciences (1R41ES028656-01 and 2R44ES028656-02A1). We were inspired to prepare this chapter by some exciting works that we have carried out on water sensors in the past several years. This chapter, to some extent, is a summary of those works, which would not have been possible without the contributions from many individuals. We thank the graduate students and postdoctoral researchers, present and former, in Chen’s group, who contributed to FET water sensor research, and the collaborators in academia and industry for the fruitful discussions and insightful suggestions.

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54. S. Bae, H. Kim, Y. Lee, X. Xu, J.-S. Park, Y. Zheng, J. Balakrishnan, T. Lei, H. Ri Kim, Y. I. Song, Y. -J. Kim, K. S. Kim, B. Özyilmaz, J. -H. Ahn, B. H. Hong, and S. Iijima. Roll-to-Roll Production of 30-Inch Graphene Films for Transparent Electrodes, Nat. Nanotechnol., 2010, 5, 574. 55. S. Agarwal, X. Z. Zhou, F. Ye, Q. Y. He, G. C. K. Chen, J. Soo, F. Boey, H. Zhang, and P. Chen. Interfacing Live Cells with Nanocarbon Substrates, Langmuir, 2010, 26(4), 2244–2247. 56. Q. H. Wang, K. Kalantar-Zadeh, A. Kis, J. N. Coleman, and M. S. Strano. Electronics and Optoelectronics of Two-Dimensional Transition Metal Dichalcogenides, Nat. Nanotechnol., 2012, 7, 699. 57. Y. J. Zhan, Z. Liu, S. Najmaei, P.M. Ajayan, and J. Lou. Large-Area Vapor-Phase Growth and Characterization of MoS2 Atomic Layers on a SiO2 Substrate, Small, 2012, 8(7), 966–971. 58. J. Dai and X. C. Zeng. Bilayer Phosphorene: Effect of Stacking Order on Bandgap and Its Potential Applications in Thin-Film Solar Cells, J. Phys. Chem. Lett., 2014, 5(7), 1289–1293. 59. H. G. Sudibya, Q. He, H. Zhang, and P. Chen. Electrical Detection of Metal Ions Using Field-Effect Transistors Based on Micropatterned Reduced Graphene Oxide Films, ACS Nano, 2011, 5(3), 1990–1994. 60. G. H. Zhou, J. B. Chang, H. H. Pu, K. Y. Shi, S. Mao, X. Y. Sui, R. Ren, S. M. Cui, and J. H. Chen. Ultrasensitive Mercury Ion Detection Using DNA-Functionalized Molybdenum Disulfide Nanosheet/Gold Nanoparticle Hybrid Field Effect Transistor Device, ACS Sensors, 2016, 1(3), 295–302. 61. J. H. An, S. J. Park, O. S. Kwon, J. Bae, and J. Jang. High-Performance Flexible Graphene Aptasensor for Mercury Detection in Mussels. ACS Nano, 2013, 7(12), 10563–10571. 62. F. Tan, L. Cong, N. M. Saucedo, J. Gao, X. Li, and A. Mulchandani. An Electrochemically Reduced Graphene Oxide Chemiresistive Sensor for Sensitive Detection of Hg(2+) Ion in Water Samples, J. Hazard. Mater., 2016, 320, 226–233. 63. E. Sharon, X. Liu, R. Freeman, O. Yehezkeli, and I. Willner. Label-Free Analysis of Thrombin or Hg2+ Ions by Nucleic Acid-Functionalized Graphene Oxide Matrices Assembled on Field-Effect Transistors, Electroanalysis, 2013, 25, 851–856. 64. C. Yu, Y. Guo, H. Liu, N. Yan, Z. Xu, G. Yu, Y. Fang, and Y. Liu. Ultrasensitive and Selective Sensing Of Heavy Metal Ions with Modified Graphene, Chem. Commun., 2013, 49(58), 6492–6494. 65. G. Zhou, H. Pu, J. Chang, X. Sui, S. Mao, and J. Chen. Real-time Electronic Sensor Based on Black Phosphorus/Au NPs/DTT Hybrid Structure: Application in Arsenic Detection, Sensor. Actuat. B Chem., 2018, 257, 214–219. 66. P. Li, D. Zhang, J. Liu, H. Chang, Y.e. Sun, and N. Yin. Air-Stable Black Phosphorus Devices for Ion Sensing, ACS Appl. Mater. Interf., 2015, 7(44), 24396–24402. 67. P. Li, D. Zhang, Y.e. Sun, H. Chang, J. Liu, and N. Yin. Towards Intrinsic MoS2 Devices for High Performance Arsenite Sensing, Appl. Phys. Lett., 2016, 109, 063110.

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68. P. L. Smedley and D. G. Kinniburgh. A Review of the Source, Behaviour and Distribution of Arsenic in Natural Waters, Appl. Geochem., 2002, 17(5), 517–568. 69. J. W. Park, S. J. Park, O. S. Kwon, C. Lee, and J. Jang. High-Performance Hg2+ FETType Sensors Based on Reduced Graphene Oxide–Polyfuran Nanohybrids, Analyst, 2014, 139(16), 3852–3855. 70. A. I. Ayesh, Z. Karam, F. Awwad, and M. A. Meetani. Conductometric Graphene Sensors Decorated With Nanoclusters For Selective Detection of Hg2+ Traces in Water, Sens. Actuators B: Chem., 2015, 221, 201–206. 71. S. Jiang, R. Cheng, R. Ng, Y. Huang, and X. Duan. Highly Sensitive Detection Of Mercury(Ii) Ions With Few-Layer Molybdenum Disulfide, Nano Res., 2015, 8(1), 257–262. 72. J. Chang, A. Maity, H. Pu, X. Sui, G. Zhou, R. Ren, G. Lu, and J. Chen. Impedimetric Phosphorene Field-Effect Transistors for Rapid Detection of Lead Ions, Nanotechnology, 2018, 29(37), 375501. 73. A. Maity, X. Y. Sui, C.R. Tarman, H. H. Pu, J. Chang, G. H. Zhou, R. Ren, S. Mao, and J. H. Chen. Pulse-Driven Capacitive Lead Ion Detection with Reduced Graphene Oxide Field-Effect Transistor Integrated with an Analyzing Device for Rapid Water Quality Monitoring, ACS Sensors, 2017, 2(11), 1653–1661. 74. N. Mohanty and V. Berry. Graphene-Based Single-Bacterium Resolution Biodevice and DNA Transistor: Interfacing Graphene Derivatives with Nanoscale and Microscale Biocomponents, Nano Lett., 2008, 8(12), 4469–4476. 75. B. Thakur, G. H. Zhou, J. B. Chang, H. H. Pu, B. Jin, X. Y. Sui, X. Yuan, C. -H. Yang, M. Magruder, and J. H. Chen. Rapid Detection of Single E. Coli Bacteria Using a Graphene-Based Field-Effect Transistor Device, Biosens. Bioelectron., 2018, 110, 16–22. 76. T. Minami, Y. Sasaki, T. Minamiki, S. -I. Wakida, R. Kurita, O. Niwa, and S. Tokito. Selective Nitrate Detection by an Enzymatic Sensor Based on an Extended-Gate Type Organic Field-Effect Transistor, Biosens. Bioelectron., 2016, 81, 87–91. 77. X. Y. Chen, H. H. Pu, Z. P. Fu, X. Y. Sui, J. B. Chang, J. H. Chen, and S. Mao. RealTime and Selective Detection of Nitrates in Water Using Graphene-Based Field-Effect Transistor Sensors, Environ. Sci.: Nano, 2018, 5(8), 1990–1999. 78. H. Zhang. Ultrathin Two-Dimensional Nanomaterials, ACS Nano, 2015, 9(10), 9451– 9469.

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Chapter 2

Innovative Optical Fiber Approaches for Water Quality Monitoring Stephanie Hui Kit Yap*,†,¶, Kok Ken Chan†,ǁ and Ken-Tye Yong‡,§,** *

Department of Electrical and Computer Engineering, National University of Singapore, Singapore School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore

†

School of Biomedical Engineering, The University of Sydney, Sydney, New South Wales 2006, Australia

‡

The University of Sydney Nano Institute, The University of Sydney, Sydney, New South Wales 2006, Australia

§

¶

[email protected]

[email protected]

ǁ

[email protected]

**

2.1. Introduction Since the first introduction of optical fiber in the 1960s, fiber optic technologies have advanced from benchtop experiments to real-world applications. In the past, the use of optical fibers had been focused on telecommunication industries. However, as time progressed, many research studies have reported on interesting properties of optical fibers that were found to be advantageous for sensing applications. These properties include resistance to electromagnetic interference, ability to operate under harsh environments, compact system design due to their small size and lightweight features, multiplexing, and multifunctional sensing capabilities.1 Following these findings, research related to optical fiber-based sensors (OFS) proliferated quickly, from physical parameter sensing such as strain, temperature, pressure, and displacement to chemical and biomolecular sensing. 39

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Recently, OFS have been attracting significant attention for their prominent use in water-sensing applications. This is mainly driven by the demand for an alternative application to circumvent some of the limitations with currently available technologies. For instance, sophisticated mass spectroscopy technologies, such as inductively coupled plasma mass spectroscopy and inductively coupled optical emission spectroscopy, involve bulky and expensive analysis systems, which hinder its use for on-site or in situ detection. On the other hand, an alternative sensing technology based on electrochemical stripping analysis was found to be simpler and less costly, but it has poor long-term stability and is prone to electrochemically active interference and chemical fouling.2 As a result, other strategies that are able to provide high sensitivity and selectivity, long-term stability, and possibly detect multiple analytes simultaneously are highly desirable. OFS appears to be a good solution to address these challenges. However, for bare optical fibers, the types of molecules that can be detected are very limited, and they are unable to achieve detection specificity.3 One solution to this problem is to incorporate OFS with other detection schemes that are capable of inducing changes to light properties (e.g., refractive index, absorption) with surrounding environment changes. This can be achieved by applying chemical coatings comprising sensing materials such as nanoparticles, dye, polymers, and antibodies to the sensing region of the OFS. Upon interaction with the target molecules, light propagating along the OFS will be perturbated, and the resulting output light can be used to obtain information about the detected target molecules. This chapter discusses the recent development of an optical fiber-based water sensor, which has attracted significant interest. The reported works are based upon refractive index modulation, surface plasmon resonance (SPR), and colorimetric and fluorometric principles which have incorporated innovative sensing strategies to aid in the translation of the sensing system into a higher-level prototype for commercialization. Under this scope, an overview of the sensing fundamentals and future challenges and opportunities for fiber optic technology from the perspective of water quality monitoring will also be discussed.

2.2. Evanescent Wave-Based Optical Fiber Sensor An optical fiber is a cylindrical waveguide comprising a cylindrical core surrounded by cladding. The core is usually doped with special dopants, giving the core a higher refractive index than the cladding. Typically, light that propagates through the fiber comprises a guided field that is well confined in the core, and an evanescent field that decays exponentially to zero within the core-cladding interface.4 This allows incident light to propagate along the fiber by total internal reflection, and the interaction of the light with the surrounding environment is negligible. This condition is unfavorable for sensing applications, which requires exposure of evanescent field in order to interact with target molecules present in the surrounding environment. The

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simplest way to expose the evanescent field to the surrounding environment is by removing the fiber cladding and/or reducing the cladding diameter. This will enable the evanescent wave to penetrate beyond the core-cladding interface and to interact with target molecules present in the s­ urrounding environment. Generally, changes in the output light of the evanescent wave-based OFS can be measured alone due to the refractive index change at the core-cladding interface upon the binding of the target molecules on the surface of the optical fiber. However, an additional optical sensing mechanism is required for signal amplification in cases where the penetration depth of the evanescent wave is relatively small in relation to the target molecules.5 In the following sections we will review several sensing principles that have been used with evanescent wave-based OFS for water-sensing applications. These principles include: • Refractive index modulation • Surface plasmon resonance • Colorimetric • Fluorescence.

2.2.1.  Refractive index modulation Among the reported evanescent wave-based OFS, there is much interest in the tapered optical fiber due to its remarkable sensitivity to the surrounding refractive index change. Generally, tapered fiber can be categorized into non-adiabatic and adiabatic. Adiabatically tapered fiber is fabricated in a way such that the outer diameter of the optical fiber decreases gradually (i.e., small taper angle), while non-adiabatic tapered fiber has a much larger taper angle.6 Incident light first propagates as fundamental mode, HE11, along the non-tapered section of the fiber. As the light enters the down-tapered region, a significant portion of the light power will couple into the next higher-order mode, HE12, due to the sudden geometrical and local refractive index change. This not only exposes the evanescent wave beyond the cladding-air interface but also increases its magnitude and penetration depth. Along the tapered length, some light remains guided in the core while some light becomes cladding-guided. As the light continues to propagate along the taper and reaches the up-tapered, a second coupling of the higher-order mode with the fundamental mode will take place again. Here, the phase difference between HE11 and HE12 modes will result in a modal interference spectrum given as,7–8

I = I1 + I 2 + 2 I1 I 2 cos φ , (2.1)

where I is the total intensity, I1 and I2 are the intensities of HE11 and HE12 modes, respectively, and φ is the phase difference between these two modes. If the effective refractive index of the tapered region changes due to the binding of target

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molecules on the surface, this information will be reflected as a spectrum wavelength shift in the output signal of the tapered fiber. Tapered fiber-based sensors are known to exhibit high detection sensitivity, fast response time, and the ability to integrate with IoT applications. However, the broad use of tapered fiber is still constrained by the fragility of the tapered fiber, cost of the interrogation equipment, and portability of the system. The tapered waist is usually designed in sub-micron diameter, and because this requires extremely delicate handling, it is impractical to handle without a proper holder. Moreover, the conventional spectral shift interrogator, which involves visual interpretation of raw data collated from an optical spectrum analyzer, hinders the flexibility of transporting the system for on-site or in situ detection. For these reasons, the current trend in tapered fiber-based sensors is directed toward new strategies to deliver novel interrogation techniques to facilitate system miniaturization. In a research experiment to detect heavy metal ions in water, Ji and coworkers proposed non-adiabatic tapered fibers that were coated independently with ethylenediaminetetraacetic acid (EDTA) and D-penicillamine (DPA) on the tapered region. These served as metal chelators, which exhibit functional groups to develop a metal–ligand coordination bond upon interaction with Pb2+, Cu2+, Zn2+, and Cd2+9. The study first revealed that EDTA-coated tapered fibers exhibit different coordination capability toward different types of metal ions, given as Pb2+ > Cu2+ > Cd2+ > Zn2+. Next, a threefold improvement in the detection limit was observed when comparing DPA-coated fiber (30 ppm) and EDTA-coated fiber (100 ppm) for Cu2+ detection, both of which had the same taper waist diameter of 7.9 µm. It has been suggested that this finding was attributed to the reduction of Cu2+ to Cu+ caused by the electron transfer reaction between the metal ion and DPA. The reduction process caused Cu+ to be more accessible to the thiol groups of the DPA molecules. Therefore, DPA exhibited a higher chelating capacity for Cu2+ compared with EDTA, and hence captured the Cu2+ onto the sensing region more efficiently. Furthermore, an experiment was conducted to investigate the effect of the tapered waist diameter toward the sensitivity of DPA-functionalized tapered fiber. It was discovered the fiber sensor with a taper waist diameter of 3.9 µm greatly enhanced detection sensitivity and achieved a limit of detection (LOD) of 10 ppb. This systematic study not only compared the detection performance of EDTA and DPA-functionalized tapered fiber but also addressed the interference from surrounding physical parameters such as strain and temperature. The authors characterized the tapered fiber against the influence of strain and temperature and discovered maximum sensitivities of −15 nm/mε and −380 pm/°C, correspondingly. To counter these physical cross-sensitivities, the sensor was sandwiched between two polydimethylsiloxane (PDMS) sheets, sealed, and held in place in a 3D-printed cartridge using a pair of magnetic clamps. The closed environment of the cartridge offered a stable temperature while the magnetic clamps eliminated the undesirable strain effect and perturbation to the sensor

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during the insertion and withdrawal of aqueous samples. Furthermore, the initial tensile elongation of the tapered fiber sensor was fixed at 10 mε prior to measuring each sample. A follow up of this work was recently reported by Yap and coworkers,10 which showed a major advancement in translating the benchtop setup into a portable handheld device. The device incorporates an in-house-developed fiber Bragg grating (FBG)-based interrogator, as shown in Fig. 2.1(a) and (b). The device weighs 0.8  kg and has an overall dimension of 203 mm (L) × 149.50 mm (B) × 65 mm (H), making it convenient for field testing. Similar to Ji and coworkers,9 the ­chelator-modified tapered fiber was sealed in a 3D printed cartridge, except the cartridge was packaged in an additional protective sleeve to avoid exposing the bare fibers. Both ends of the sensor were terminated with a snap-in type of lucent connector (LC) to ease the connection to the portable interrogation system (Fig. 2.1(c)). The schematic diagram of the proposed interrogation system is shown in Fig. 2.2. Briefly, light passing through the non-adiabatic tapered fiber sensor continues to propagate across circulator 1 (C1) — FBG 1 — circulator 2 (C2) — FBG 2, and the free end of the fiber was terminated with a fiber patch cord to reflect light back to the photodetectors (PD1 and PD2). As shown in Fig. 2.2, the FBGs reflection peaks can be understood as line markers that indicate the intensity of the output spectrum in real time. The measured intensities of both reflected peaks will change in accordance with the output spectrum red shift. By monitoring the intensity changes, the device will be able to estimate the output spectrum shift and hence calculate the amount of detectable target metal ion concentration.

Figure 2.1.    (a) Illustration of an aqueous sample introduced to sensor cartridge, (b) schematic of side and front view of the portable sensor device, and (c) design of the sensor cartridge packaged in a protective sleeve. Reproduced with permission from Ref. [10]. Copyright © 2018, American Chemical Society.

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Ambient temperature, 24 ± 1°°C

C1

C2 FBG1

Fiber clamp

PD1

FBG2 PD2

Interrogation system As seen from optical spectrum analyzer Liquid sample

Fiber holder

Figure 2.2.    Schematic diagram of the interrogation system incorporated into a handheld portable device. Reproduced with permission from Ref. [10]. Copyright © 2018, American Chemical Society.

The proposed sensing algorithm was then programmed into the device using a Raspberry Pi microcontroller, and a liquid crystal display showed the user interface. This novel intensity-based interrogation scheme not only provides a new alternative to traditional visual-based wavelength shift interrogation using an optical spectrum analyzer but also circumvents the bulkiness and expensive instrumentation cost for implementing a tapered fiber-based on-site sensor for field applications. The viability of this approach was demonstrated using L-glutathione (GSH)modified tapered fiber for detecting Pb2+. Characterization of the sensor performance showed a detection range from 0 to 50 ppb and an LOD of 5 ppb was achieved. Furthermore, the applicability of the work to be used as a real waterquality monitoring tool was tested against tap water samples. Despite the high complexity of background ions in the samples, the developed sensors showed high detection selectivity to Pb2+ and attained good detection accuracy. Moreover, the sensor exhibited optimal sensing performance under a neutral environment owing to the strong protonation state of GSH within pH 6.5 to 8.3. Although the usability of this device was limited to potable drinking water, the total analysis time required to perform a single sample measurement was less than 5 min and no pretreatment of the water sample was needed.

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The development of tapered OFS for organophosphate has also been actively studied due to the reported water contamination by the organophosphate compound.11 Similar to most of the OFS-based chemical sensors, Arjmand proposed a label-free non-adiabatic tapered fiber for detecting methyl-parathion (MPT) in water by immobilizing Acetylcholinesterase (AChE) enzyme on the tapered region to provide binding sites for the MPT.12 A dedicated flow cell made from a 10 mm-thick acrylic piece was used to seal the fabricated sensor to prevent breakage or bending during measurement. A water sample containing MPT was delivered to the sensing region at a flow rate of 0.5 ml/min and left to incubate for 15 min to allow the binding reaction to occur. MPT binding on the sensing region, which caused a change in the surface refractive index, was monitored in real time through the red-shifting of the spectrum. The LOD of the developed sensor was 2.4 × 10−10 M, with a detection range of 10−10 to 10−5 M. Tai and coworkers recently demonstrated an E. coli-tapered fiber-based sensor using a dielectrophoretic (DEP) trapping approach.13 The single-mode fiber optic tapered tip was fabricated using the wet-etching method, then coated with a thin film of thallium (Ti) and aluminum (Al) to produce a conductive fiber tip to support the DEP effect (Fig. 2.3(b)). The presence of E. coli in close distance to the tip of the probe increased the surrounding refractive index and consequently altered the localized optical distribution, which can be measured through the output transmission intensity change. Moreover, the arrangement of the Ti/Al-tapered tip probe functionalize with anti-E. coli antibody and an indium-tin oxide (ITO) glass substrate as a counter electrode formed a large gradient of electric field, which contributes DEP force to selectively trapped E. coli. Tai proposed an interesting interrogation method based on the defocused image of the fiber tip captured by an objective lens and a charge-coupled device (CCD) which were placed below the ITO glass, as seen in Fig. 2.3(a). The defocused image of the tapered tip probe under various surrounding refractive index exhibited higher sensitivity compared with the images taken at the focal plane (Fig. 2.3(c)–(h)). The as-developed probe demonstrated a detection range from 102 to 106 CFU/ml and an LOD of 100 CFU/ml. Water pollution from agriculture does not only include pesticides and pathogens but also other common agrichemicals such as ammonia, which originates from decaying organic matter, like fertilizers, and excreta from animals. The deleterious effects of ammonia on human health has increased the need for a sensor to quickly evaluate the ammonia concentration in natural water to prevent unnecessary exposure. Liu and coworkers prepared a non-adiabatic tapered fiber that was fabricated using the microheater brushing method, and then coated with solgel silica using the one-pass coating technique.14 To ensure uniformity and controlled coating thickness, each layer of sol-gel silica coating was realized by introducing a single drop of silica sol solution to the tapered fiber, followed by air drying, and repeated until the desired thickness was achieved. The sensing region was placed in an enclosed PDMS mold with a narrow channel for the sample to

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(b)

(c)

(d)

(e)

(f)

(g)

(h)

(a)

Figure 2.3.  (a) Experimental setup of Ti/Al-coated tapered fiber tip for DEP measurement, (b) detection scheme for Escherichia coli (E. coli) using the DEP effect in the vicinity to the fiber tip, and optical images of the fiber tip at the various surrounding refractive index at (c–e) focal plane and (f–h) defocused plane. Reproduced with permission from Ref. [13]. Copyright © 2018, American Chemical Society.

flow through at a constant rate, which was externally controlled using a peristaltic pump. Experimental results from the study demonstrated that a tapered fiber sensor with eight layers of sol-gel silica coating (0.131 nm/ppm) exhibited higher sensitivity than two layers (2.47 nm/ppm). Alternatively, Tiwari and coworkers adopted lossy mode resonance (LMR) generated in the transmission spectrum of a porphyrin-titanium dioxide coated adiabatically tapered fiber for ammonia detection.15 Due to the coupling of core mode to the lossy mode, light propagating through the tapered region experienced attenuation at a particular wavelength depending on the thickness and refractive index of the coating. The interaction of porphyrin with ammonia modified the coating’s refractive index and subsequently induced a change in the center wavelength of the LMR, which can be quantified and related to the ammonia

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concentration. An LOD of 0.16 ppm within the detection range of 0.1 to 5 ppm was achieved. Furthermore, the authors suggested that the sensor can be reused by removing the bound ammonia molecules with hydrochloric acid. The use of a tapered fiber-based sensor for marine applications was reported by Irigoyen for the early detection of oil and hazardous and noxious substance (HNS) spill incidents in seawater.16 The proposed sensing system involved three tapered fiber sensors, Cu/TiO2-coated, Al/TiO2-coated and a bare tapered fiber, which worked together simultaneously. TiO2 coating was applied over the metal film to prevent oxidation caused by water salinity. Each of the tapered fiber was capable of detecting a range of seawater pollutants depending on the refractive indices of the substances. For instance, the bare tapered fiber was found to be responsive toward pollutants with refractive index values greater than 1.44, such as benzene, palm oil, and toluene. Meanwhile, the Al-coated sensor was suitable for substances with refractive index values between 1.329 and 1.37, and the Cu-coated sensor was suitable for substances with refractive index values between 1.37 and 1.44. Even though the sensor might be lacking in terms of selectivity as one or more of the tapered fibers were found responsive to the same analyte, the proposed sensing system appeared to be a good threshold sensor and can be very useful for early detection of seawater pollutants. In addition, the sensor exhibited a self-cleaning property under moving seawater in which the calculated sensor degradation was less than 5%.

2.2.2.  Surface plasmon resonance An SPR-based OFS is typically fabricated by first removing the cladding layer from the optical fiber, followed by deposition of a thin, uniform layer of metal. This creates a core-metal interface that supports charge density oscillation upon excitation from the evanescent wave (Fig. 2.4(a)). The incident light should be of the same polarization state as the surface plasmons, and the excitation will be realized only if the momentum and wave vector of the evanescent wave is identical to (a)

(b)

Figure 2.4.    (a) Typical schematic of SPR-based OFS and (b) SPR spectrum.

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the surface plasmons.17 During the excitation process, a portion of the evanescent wave energy will couple to surface plasmons and generate resonance at a particular wavelength. As a result, a sharp wavelength dip, known as the resonance wavelength, λres, can be observed in the reflectance spectrum, as shown in Fig. 2.4(b). Generally, receptors are functionalized on the metal surface of the SPR-based OFS to specifically detect target molecules. The interaction between the target molecules and the functionalized receptors will cause a change in the refractive index of the outer surface layer, which will be reflected as a shift in the resonance dip wavelength. Therefore, by monitoring the changes in the resonance dip wavelength, information about the presence of the target molecules in the external environment can be obtained and quantified. Like SPR phenomena which are often observed across the metal thin-film layer over the fiber core, localized SPR (LSPR) can also be used for detection when the surface plasmon is confined within a nanoparticle instead. Compared with SPR-based OFS, LSPR-based OFS is less sensitive to bulk refractive index changes in the external medium, but has better sensitivity to refractive index changes on the surface of the metallic nanoparticles due to the confined optical near field.18 In other words, the shorter evanescent field decay length in LSPRbased OFS is beneficial because the sensor probe is less susceptible to interference from bulk signals. However, LSPR-based OFS sensing is limited to the wavelength at which the sample exhibits low light absorption and scattering, since they suffer from the light-scattering effect more prominently than SPR-based OFS. This could be a disadvantage when it comes to flexibility of the components used (e.g., light source and photodetector).19 The advancement of plasmonic-based OFS over the past decades has evolved from a benchtop setup to a widely used optical fiber detection scheme. Recent related works also reported the feasibility of integrating SPR-based OFS with mobile applications to develop a smartphone-based sensing platform. For instance, Bremer and Roth first reported the design of an SPR-based refractive index sensor, which required no additional components or batteries, except for the flashlight and back camera of a smartphone as the input light source and interrogator.20 Approximately 10 mm of the fiber core was coated with thin silver (Ag) film, and both end facets of the sensor were polished to 45° to allow efficient light coupling. The output light, which passed through the SPR sensing region, was then dispersed into a line spectrum using a diffraction grating of 1,200 lines/mm to track the resonance wavelength shift that occurred when there was a change in the surrounding refractive index. Each measurement was recorded in a video format and processed using MATLAB to extract the intensity distribution of the captured image. The information was then correlated to the refractive index of the tested solution. The work further investigated the difference in sensor performance when a 0.3- and a 5-megapixel camera were used. It was found that the sensitivity of the Ag-coated SPR sensor improved by one order, from 1.83 × 10−3 to 5.96 × 10−4 RIU/pixel when a 5-megapixel camera was used as a detector.

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Liu and coworkers reported an SPR-adaptor that also used a flashlight and a back camera as the light source and detector, respectively. The Au-coated sensor was designed so that it could be attached and detached from the smartphone. The sensor has three fiber channels: (1) a reference channel (RC) to compensate for the intensity fluctuation from the LED flashlight, (2) a measurement channel (MC) to detect changes in the surrounding refractive index, and (3) a control channel (CC) comprising an uncoated fiber probe (Fig. 2.5). Light intensities from all three channels were independently captured and analyzed through the customized mobile application to estimate the relative intensities after compensating for the power fluctuations from the LED flashlight and the influence of the bulk refractive index. This work was further extended to demonstrate the capability of the smartphone-based SPR OFS platform to monitor the interaction between lectin concanavalin (Con A) and glycoprotein ribonuclease B (RNAse B), which was immobilized on the surface of the SPR-based OFS, as shown in Fig. 2.6(a).22 The reported system also showed a simplified design, with only two channels, and the flexibility to be integrated with a laptop (Fig. 2.6(b)–(d)). While a similar interrogation method was used for this system, Liu and coworkers also demonstrated (a)

(b)

(c)

(d)

Figure 2.5.    (a) Schematic of SPR-based OFS, (b) actual image of the SPR sensor installed on the smartphone, (c) schematic of sensor’s internal structure, and (d) image captured by the back camera. MC: measurement channel, RC: reference channel and CC: control channel. Images are processed to calculate the relative intensity. Reproduced with permission from Ref. [21]. Copyright © 2019, Springer Nature Publishing AG.

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(b)

(c)

(d)

Figure 2.6.    (a) Schematic of SPR-based RNAse B-modified OFS, (b) actual image of fiber optic SPR (FO-SPR) sensor installed on the laptop, (c) schematic of sensor’s internal structure, and (d) actual image of FO-SPR sensor installed on the smart phone. Reproduced with permission from Ref. [22]. Copyright © 2017, MDPI.

the regeneration capability of the RNAse B-modified OFS by rinsing the modified OFS with urea solution. Having paved the way, the realized smartphone-based SPR sensor turned out to be a low-cost miniature sensor that can also be used for detecting a wide range of chemical analytes for environmental monitoring. In addition to refractive-index sensing, researchers in the field have also found the usability of SPR-based OFS for monitoring water quality. For instance, Verma and Gupta fabricated an SPR fiber probe sensor coated with silver (Ag) and ITO. Pyrrole and chitosan composites were layered over the metal layers to achieve trace-level detection of heavy metal ions in potable water. The fabricated pyrrole/ chitosan/ITO/Ag optical fiber probe was found to detect Cd2+, Pb2+, and Hg2+ ions at a detection limit of 0.256, 0.440, and 0.796 µg/L, respectively. The proposed sensor probe was unable to differentiate a particular metal ion, but it was more suitable for bulk-sensing several metal ions in contaminated water.24 Despite the obvious drawback in this work, the subsequent work from the same research group demonstrated the possibility of sensing two or more heavy metal ions simultaneously by cascading two sensing channels on a single optical fiber, as shown in Fig. 2.7.23 The unique binding sites on the sensor were synthesized using the ionimprinted technique. The metal layer design for both channels were different, which in turn resulted in two resonance dips of two different wavelengths for spectral interrogation. With this approach, the sensor had not only overcome the cross-sensitivity issue but also assured great detection sensitivities of 4.06 × 10−12 and 8.18 × 10−10 g/L for Pb2+ and Cu2+, respectively.

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Figure 2.7.    Schematic of the cascaded sensing channel of an SPR-based OFS for simultaneous detection of Pb2+ and Cu2+ types of heavy metal ions. Reproduced with permission from Ref. [23]. Copyright © 2018, Society of Photo-Optical Instrumentation Engineers (SPIE).

In another study, Raj and coworkers reported an LSPR-based plastic fiber sensor using citrate-capped gold nanoparticles (AuNPs)/poly-vinyl alcohol (PVA) composites as the hybrid film for sensing Hg2+ ions. By measuring the wavelength shift in the resonance dip, a linear detection range from 0 to 25 µM and a detection limit of 1 µM were achieved.25 Meanwhile, Yuan and coworkers incorporated 4-mercaptopyridine (4-MPY) on their AuNP-based LSPR sensor to capture Hg2+ in tap water samples. The 4-MPY played two important roles: providing a binding site for Hg2+ to form a coordination bond with its nitrogen moieties and serving as a signal amplification tag. The proposed sensor exhibited a linear detection range from 8 to 100 nM and an LOD of 3.34 nM, which was much lower than World Health Organization (WHO) standard (10–25 nM) for safe drinking water.26 Unlike other researched works which used chemical-based chelators, Halkare and coworkers reported an interesting LSPR-based OFS for detecting Hg2+ and Cd2+ by using E. coli B40 bacteria as the receptors instead. The thiol functional groups present on the bacteria cell have a strong affinity to the target metal ions, and thus formed metal complexes upon binding and modulated the refractive index surrounding the sensor probe. The E. coli B40-AuNP sensor achieved an LOD of 0.5 µg/L for both Hg2+ and Cd2+ ions. On the other hand, Boruah’s group implemented an oxalic-acid functionalized AuNP LSPR-based U-shaped plastic fiber sensor for the selective detection of Pb2+ in aqueous solution, which delivered an LOD of 2.1 µg/L.27 Aside from heavy metal ions, pesticide and pathogen pollution in water caused by agriculture activities and food industries are equally toxic to humans and may cause severe and chronic water-borne diseases such as gastrointestinal illness, urinary tract infection, typhoid fever, and others. Agrawal and coworkers

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proposed an atrazine sensor using SPR-based plastic-clad silica fiber, where the cladding of the fiber was first removed and replaced with an Ag thin film, followed by deposition of a molecularly imprinted polymer (MIP) layer consisting of an atrazine template.28 The atrazine molecules near the MIP layer bonded to the available binding sites on the probe surface due to the matching shape and size, and consequently altered the dielectric constant of the polymer coating, which directly induced a red shift in the resonance wavelength. Thus, the atrazine concentration surrounding the sensor probe can be determined by monitoring the changes in the resonance wavelength shift. Agrawal and coworkers also explored an alternative method for enhancing the sensitivity of the sensor by adding a 10 nm-thick aluminum (Al) layer in between the Ag and MIP layer. Although an increase of 27% in sensitivity was observed at an atrazine concentration of 10−12 M, the resonance dip wavelength was found to be much broader and hence lowered the accuracy of the sensor. Similarly, Shrivastav and coworkers adopted the SPR-MIP combinational approach to develop a profenofos fiber probe sensor. Besides the promising ability to achieve an LOD of 2.5 × 10−6 µg/L, the proposed MIP-SPR detection scheme demonstrated high specificity to the profenofos molecules compared with other analytes of similar shape and chemical, such as parathion.29 Zhou and coworkers reported an interesting design of an LSPR-based E. coli O157:H7 sensor using Magainin I, an antimicrobial peptide as the binding receptors.30 The sensor probe was developed using AgNP-reduced graphene oxide(rGO) for signal amplification, followed by a thin Au film that served dual roles for SPR signal enhancement as well as to prevent oxidation of AgNPs. The outcome of this experimental study has proved the advantage of employing AgNP-rGO coating to improve the sensitivity by 1.5 times when compared with the Au filmonly sensor probe. Using the proposed sensor, a linear relationship over a broad detection range from 1.0 × 103 to 5.0 × 107 cfu/mL and an LOD of 5.0 × 102 cfu/ mL was attained. The reported sensor marked a significant contribution not only for the development of a label-free pathogen probe sensor but also for a fastresponse sensor, which requires only 30 min of reaction time compared with conventional culture methods. On the other hand, Arcas and his coworkers also developed an E. coli O55 SPR-based sensor using an Au-coated plastic fiber by functionalizing polyclonal anti-E.coli antibody onto the silanized Au-coated fiber via the EDC-NHS crosslinking conjugation technique.31 The authors developed a compact design of the overall sensing system, which incorporated an Arduino microcontroller to generate a voltage readout from the photodetector. The detection capabilities of the sensor using either optical or electronic interrogation methods were compared and the observable LOD were found to be 104 and 108 cfu/ml, respectively. Regardless, the reported work has unveiled an optoelectronic approach to ease the translation of the SPR-based sensor into a compact sensing device.

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The root causes of nitrate pollution in groundwater usually originate from industrial wastewater and agricultural runoff. Unlike other SPR-based OFS, which rely solely on a single receptor layer to selectively capture the target molecules, Zhang and coworkers proposed an Au-coated SPR-based OFS. This OFS used copper nanoparticles (CuNPs) and carbon nanotubes (CNTs) as the coating layers, where CuNPs catalyzed the nitrate molecules while CNTs enhanced the sensitivity of the sensor.32 Briefly, copper is an effective catalyst for reducing nitrate in order to produce ammonia, which subsequently is absorbed by the CNT and thus changes the surface refractive index.33 Zhang and coworkers also developed a strategy to compensate for ambient temperature influences by including an additional layer of PDMS that had a higher refractive index than nitrate solution on the upper-half of the sensing head to generate two independent resonance dip wavelengths in the output reflectance spectrum. For instance, when nitrate concentration and the surrounding temperature are not fixed, these resonant dip wavelengths will shift at different sensitivities, allowing simultaneous nitrate and temperature sensing. The outcome of the study showed that the fabricated sensor was capable of achieving nitrate and temperature sensitivities of 3.25 nm/lg[M] and −2.02 nm/°C, respectively. Recently, Tabassum and Gupta presented a chlorine sensor using an Ag-coated unclad multimode fiber with polyvinylpyrollidone(PVP)-supported zinc oxide(ZnO) film deposited on it.34 When chlorine was added to the water, the formation of hydrochloric acid (HCl) and hypochlorous acid (HClO) changed the dielectric function of the PVP, which was reflected as a resonance shift in the output spectrum. Interestingly, the sensitivity of the sensor was found to decrease with increasing chlorine concentrations at a cut-off limit of 5 ppm. It was deduced that chloride ions reacted completely with the PVP molecules, resulting in only a small repulsion force and thus decreasing the sensitivity. On the other hand, detecting carbonate (CO32−) and bicarbonate (HCO3−) ions in water is closely related to a water-quality parameter known as water hardness. Although hard water has no known deleterious health effects, high levels of calcium and magnesium content may increase the risk of constipation.35 Therefore, the detection of CO32− and HCO3− associated with water hardness is important to ensure that the water hardness is within safe levels for human consumption. A tailored Ag-coated multimode fiber covered with ZnO-polyaniline nanoparticles composites was reported for this purpose, and the sensitivity of the sensor under the optimal ZnO:polyaniline ratio was recorded as 0.094 and 0.065 nm/(µg/L) for CO32− and HCO3, correspondingly.36

2.2.3.  Colorimetric The colorimetric sensing approach is frequently used to achieve direct qualitative measurement of water contaminants without the need for sophisticated instruments. It can be widely found in the market as a tool kit for rapid and simple

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on-site detection where users need not be chemically trained. Generally, colorimetricbased chemosensors are divided into two types of structures: (1) sensors with donor-pi-acceptor (D-π-A) structure and (2) sensors containing Rhodamine.37 Typically, sensors with a D-π-A structure are rich in either electron-donating (ED) or electron-accepting (EA) moieties that are responsive to the target molecules. According to Pearson’s acid-base theory, the binding efficiency of the target molecule to the receptor is dependent on their hardness characteristic. Generally, small, non-polarizable and highly charged species are categorized as hard moieties, while large, polarizable, and less-charged species are tagged as soft moieties.38 The principle deduced that hard acids tend to bind with hard bases through ionic bonds, while soft acids are prone to interact with soft bases through covalent bonds.39 For example, when a metal ion interacts with the ED moieties of a colorimetric sensor with D-π-A structures, the electron-donating capability of the donors will decrease, leading to the conversion of a D-π-A system to an acceptorpi-acceptor structure (A-π-A) system. This will result in a decrease in the conjugation rate and a blue-shift in the absorption spectrum. On the contrary, metal ion coordinates with the EA moiety will fortify the D-π-A structure and increase the push-pull inter/intramolecular charge transfer (ICT) and a red-shift of the absorption spectrum will be observed. These metal–ligand interactions will then contribute to a color change in the sample solution. Despite the vast amount of reported colorimetric-based chemosensors, this method of sensing has its own set of drawbacks. First, colorimetric sensing usually exists in the liquid phase and is troublesome to handle if on-site detection is to be carried out. Second, although efforts are underway to develop a test-strip type of sensor, this sensing mechanism is limited to semi-quantitative detection in which the presence and the concentration of target molecules are represented as a color change. This color change often resulted in poor detection resolution and less accurate measurement. As a result, many groups have integrated colorimetricbased chemosensors with the optical fiber sensing platform to achieve a more accurate quantitative detection. Recently, Xiong and coworkers developed an integrated optical fiber capable of detecting free chlorine in water.40 N,N-diethyl-p-phenylenediamine (DPD), a commonly used active chlorine-sensitive material, was adopted as the colorimetric dye, which changes color in the presence of chlorine. By inserting a decladded silica optical fiber into a transparent capillary waveguide tube, an annular microfluidic channel was created and served both as sample flow and detection cell. A green light-emitting diode (LED) was used as the light source and was coupled into the decladded fiber. The interaction of DPD and free chlorine induced a change in the absorption of the evanescent wave caused by Wurster dye. Thus, by measuring the absorption difference with Beer’s law, the amount of chlorine present can be determined. The total measurement time required by the sensor is only 4.42 sec and recorded a low detection limit of 1.5 ppb using only 1.2 µl of

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sample volume. Xiong and coworkers further improved the colorimetric-based OFS by incorporating sequential injection lab-on-valve for chlorine detection.41 The developed system can be used for online and real-time detection of chlorine in running water, with an LOD of 3.5 µgL−1 and a linear operating range of 10–400 µgL−1. In another work, Xiong and coworkers employed a similar sensor setup for detecting nitrite in water.42 The fabricated sensor used Griess reagent, a commonly used agent for nitrate detection, which yields azo dye due to the Griess–Ilosvay reaction. In this reaction, the nitrite molecules react with the primary aromatic amine under an acidic condition to encourage the growth of diazonium salt, which subsequently reacts with an aromatic compound containing amino groups to form the intensely colored azo dye. Therefore, by measuring the changes in the absorption of the emission from the green LED caused by the azo dye, the proposed sensor detected nitrite in water, with an LOD of 7 µgL−1 and a response time of 6.24 sec. The obtained results were compared with conventional spectroscopy and ion chromatograms. The proposed sensor was found to be consistent with spectroscopy analysis and exhibited a higher sensitivity than ion chromatograms. Moreover, this colorimetric-based OFS platform is extremely versatile as it can be modified by simply changing the light source and the sensing reagents for detecting various pollutant species.43 Apart from using the optical fiber as a detector to measure the color change of a sample, OFS can also be directly coated with sensing reagents. The sensor setup for this approach is much simpler, since it no longer requires a microfluidic system for the continuous mixing of sensing reagents and water samples. Instead, the sensor only requires a light source and a spectrometer for the measurement process. Okazaki employed a simple OFS for detecting sodium dodecyl sulfate based on the attenuated total reflection (ATR) phenomenon.44 Sodium dodecyl sulfate is commonly found as an emulsifying cleaning reagent in many commercially available household cleaning products, and it is known to be toxic to both human and aquatic life if it exceeds safety levels.45 Briefly, the plastic cladding of a multimode fiber was removed with acetone to expose the surface of the fiber core. Next, positively charged ethyl violet, which serves as the sensing reagent was functionalized on the negatively charged fiber core surface. As sodium dodecyl sulfate forms an ionic pair with ethyl violet, the interaction of the evanescent wave and the ethyl violet layer was monitored. It was found the ATR signal decreased with an increasing concentration of sodium dodecyl sulfate. This sensor reported an LOD of 3.3 mg/L with a linear operating range from 4 to 15 mg/L. In another report, Khorami and coworkers developed an optical fiber-based hydrogen peroxide sensor by employing ferric ferrocyanide, also known as Prussian Blue (PB), as the sensing reagent.46 The experimental setup is shown in Fig. 2.8. After cleaving and cleaning the sensing probe with isopropanol, the optical fiber was submerged in the as-prepared PB solution and left undisturbed for a day before annealing it at 100°C for 15 min. Next, the sensing probe was immersed

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Figure 2.8.    Experimental setup of a fiber-optic hydrogen peroxide sensor. Reprinted with permission from Ref. [46]. Copyright © 2013, Elsevier B.V.

in an ascorbic acid solution to induce the reduction of PB to potassium ferrous ferrocyanide (Prussian White). The absorption of Prussian White is low; hence, the output reflected light intensity is minimal. The sensor is then considered ready for sensing hydrogen peroxide. By submerging the sensing probe into a buffered solution of hydrogen peroxide, the Prussian White will undergo oxidation and turn into PB, reducing the transmission of light out of the fiber optics and reflecting more light into the fiber. The amount of reflected light is then collected by a spectrometer, which can be used to correlate with the amount of hydrogen peroxide present. This proposed sensor was found to be robust, with negligible reduction in performance when tested after four and seven months from its fabrication date. Overall, the implementation of a colorimetric-based OFS for detecting water quality requires careful selection of sensing reagents, an input light source with a suitable emission wavelength, and a spectrometer capable of detecting minute changes in the light absorption due to the presence of target molecules.

2.2.4.  Fluorescence An optical fiber-based fluorescence sensor is generally based on the analyteinduced variation of optical properties such as fluorescence intensity, lifetime, and anisotropy of fluorescent agents, like dyes or quantum dots that are functionalized on the surface of the optical fiber or embedded within the fiber core.47 Typically, in the presence of target molecules, the perturbation of the optical properties of the sensing reagent is based on several phenomena, such as fluorescence resonance

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energy transfer (FRET), fluorescence intensity quenching or enhancement, or changes in fluorescence lifetime.48 FRET is an energy-transfer process that originates from the dipole–dipole resonance interactions between a donor and acceptor species. For this process to occur, the absorption spectrum of the acceptor species is required to overlap with the emission spectra of the donor species. When the donor species is excited to the lowest unoccupied molecular orbital (LUMO), it will return to the ground state after transferring the energy to the acceptor species. Other prerequisites for this process to occur are the intermolecular distance between the donor and acceptor species, which is usually in the 60–220 Å range, as well as the relative orientation of the acceptor species with respect to the donor species.47 There are several approaches for implementing FRET-based fluorescence sensing. The first approach involves donor and acceptor species that are both fluorescent and thus the energy transferred to the acceptor species is used to emit its own fluorescence at a much lower energy wavelength. This way, the excitation energy absorbed by the donor species, whose fluorescence is quenched, will be used to excite the acceptor species, giving rise to its fluorescence. Another approach is where the target molecule, which serves as the acceptor species, is non-fluorescent and serves as the quenching moiety. For instance, Yao and coworkers developed a “FRET on fiber” sensor platform capable of detecting Cd2+, dopamine, and single-stranded DNA(ssDNA).49 Each fiber sensor was first coated with partially reduced graphene oxide (prGO), which is hydrophobic in nature, to enhance the stability of the sensing region in an aqueous environment. Next, Rhodamine 6G (R6G) was adsorbed on the surface of the prGO, resulting in the quenching of R6G through the FRET phenomenon. Depending on the target molecule, dopamine and ssDNA, for instance, required the prGO/R6G-coated fiber to undergo additional chemical modification to enhance the sensor’s specificity. In the presence of the target analyte, binding competition will lead to the release of R6G, hence restoring the fluorescence intensity. A pulsed laser of 532 nm was focused on the coated region, and the output fluorescence signals were collected by optical lenses and measured by a spectrometer. Concurrently, the refractive index change surrounding the prGO sensing region due to the binding of target molecules was also reflected in the output spectrum shift at wavelengths between 1,510 and 1,590 nm. This showed that the developed sensor can be used as a fluorometric or an interferometric sensor. The proposed sensor exhibited high sensitivities toward Cd2+, dopamine, and ssDNA, with an LOD of 1.2 nM, 1.3 µM, and 1 pM, respectively. Another type of fluorescence-based OFS is based on the fluorescence-­ intensity quenching of the sensing probe in the presence of target molecules. For instance, the effective use of curcumin as the receptor for fluoride ion detection was reported by Venkataraj and coworkers.50 This type of organic dye-based receptor is advantageous since it is non-toxic, easily available, and economical.51

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Venkataraj adopted tapered fiber as the sensing platform, and through experimental works the fluorescence emission of curcumin at 512 nm was found to decrease with an increase of fluoride ion concentration due to the formation of a hydrogenbonded complex on the fiber surface. The sensor’s usability for fluoride detection recorded a wide linear operating range of 1.08 × 10−6 to 2.005 × 10−4 M, with an LOD of 2.08 × 10−6 M. On the other hand, Long and coworkers developed a DNA-based tapered optical biosensor for monitoring Hg2+ in water resources.52 The DNA receptors comprised sequences of T-T mismatch pairs and short oligonucleotide sequences. The T-T mismatch pairs were capable of selectively binding with Hg2+ and formed THg2+-T complexes by folding the DNA probe segments into a hairpin-like structure, while the oligonucleotide sequences functioned to hybridize with fluorescently labeled complementary DNA. Exploiting the dual-binding capability of the DNA receptors, Hg2+ detection in water was performed by introducing the sensor probe into a water mixture containing a fixed concentration of fluorescently labeled cDNA and different concentrations of Hg2+ solutions. The experimental results showed the output fluorescence signal responded differently depending on the ratio of Hg2+:cDNA in the mixture solution. A larger ratio of Hg2+:cDNA resulted in a higher concentration of Hg2+ and a lower concentration of cDNA trapped on the sensor, which consequently resulted in weaker output fluorescence signal. Thus, it is feasible to identify the amount of detectable Hg2+ on the sensor surface by monitoring the intensity of the output fluorescence signal. This indirect way of Hg2+ quantification offers a tunable operating detection range and a detection limit of the sensor probe by simply adjusting the concentration of cDNA, which influences the surface-binding competition. Furthermore, the response time of the developed sensor was recorded to be 6 min, and sensor regeneration was demonstrated by washing the sensor probe with 0.5% of SDS solution followed by phosphate buffered saline. The fabricated sensor showed good reusability, with only 5% degradation in sensing performance after completing 100 successive assays. Two years after the initial report, this work escalated into a prototype to facilitate on-site and in situ detection of Hg2+ in sludge water, shown in Fig. 2.9(a) and (c). Unlike their previously reported sensing scheme, the improved version of the sensor probe introduced by Long and coworkers was first hybridized with fluorescence-labeled T-rich cDNA. Upon interaction with Hg2+, the T-T mismatch pairs would bind to Hg2+ to form T- Hg2+-T complexes and induced the release of fluorescence-labeled cDNA, thereby weakening the fluorescence signal (Fig. 2.9(b)). This strategy not only simplifies the on-site measurement protocol but also enhances the LOD of the sensor probe to 1.2 nM. Above all, the principle of the proposed sensing scheme is interesting, and it can be extended to other water contaminants simply by replacing the T-T mismatch pairs with other specificity structures that can selectively bind to the target molecules.

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Lock-in amplifier

Embedded computer hybridization

Photodiode

Pulse laser

Regeneration

Hg2+

Single-multi fiber coupler

(b)

Fiber connector

On-site detection

Fiber optic sensor In situ detection

(a)

(c)

Figure 2.9.    (a) Schematic of the proposed DNA-based tapered optical biosensor, (b) Hg2+ detection scheme, and (c) real image of the developed prototype sensor for on-site detection of heavy metal ions. Reprinted with permission from Ref. [53]. Copyright © 2013, Springer Nature.

Another compelling work on water-quality monitoring was implemented by Long and coworkers using a tapered fiber-based sensor for the rapid detection of microcystin-LR (MC-LR), a member of the cyanobacteria family, which may cause liver cancer in humans if consumed via potable water or surface water.54 The sensing principle of the MC-LR-ovalbumin-coated tapered fiber probe is a combinatorial principle of immunoassay and the total internal reflection of fluorescence. Briefly, a known concentration of MC-LR antibodies was incubated with an analyte solution for 5 min; the number of unoccupied antibodies’ binding sites was dependent on the concentration of the MC-LR. Following the incubation, the mixture was delivered to the sensor probe and the free antibodies would bind to the antigen immobilized on the probe’s surface. The output signal was claimed to be dependent upon the concentration of the free antibodies in the mixture solution, i.e., the output signal is inversely related to the concentration of the MC-LR ­present. Figure 2.10(a) illustrates the schematic of the proposed sensor, while Fig. 2.10(b) shows the actual image of the developed rugged-case like prototype. In order to deliver a commercially viable sensing system, Long and coworkers investigated the influence of antibody concentration on the sensing capability. It was observed that increasing the antibody concentration enhanced the output signal, but at the cost of increased LOD and reduced operating detection range.

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Photodiode Pulse laser

Single-multi fiber coupler

Fiber connector

Fiber probe

Sample cell

Pump

Reference signal Lock-in amplifier

Embedded computer

(a)

(b)

Figure 2.10.    (a) Schematic of the proposed MC-LR tapered optical sensor and (b) actual image of the MC-LR sensing platform. Reprinted with permission from Ref. [54]. Copyright © 2009, Elsevier B.V.

Furthermore, the sample measurement time was set to 8 min, which is the typical response time for the sensor to achieve 85% of the maximum value, which is regarded as reliable in order to minimize the overall sample analysis time. Based on the experimental results, the achievable LOD and detection range of the presented sensor was 0.03 µg/L and 0.1–10.1 µg/L, respectively. Many types of fluorescent nanoparticles and quantum dots, such as carbon nanoparticles, metal oxide frameworks, and semiconductor quantum dots, have been developed for detecting various types of pollutants.55–57 In a pioneering work by Goncalves, carbon nanoparticles prepared using direct laser ablation of carbon targets had a dimension of about 20 nm and became fluorescent after functionalization with NH2-polyethylene glycol and N-acetyl-L-cysteine.58 Subsequently, the as-synthesized carbon nanoparticles were immobilized on the tip of an optical fiber using a layer-by-layer deposition technique for detecting Hg2+ in water. The layer-by-layer deposition enhanced the coating stability such that no significant loss of signal was observed after repeated drying and hydration. From their experimental results, a low LOD of 0.01 µM was obtained as a result of the direct interaction of the Hg2+ with the carbon dots without passing through any matrix, thereby increasing the sensor’s availability. Chu and coworkers developed a fluorescence-based optical fiber sensor for detecting hydrogen peroxide and dissolved oxygen.59 The experimental setup is as shown in Fig. 2.11. Briefly, CdSe/ZnS quantum dots were employed as the receptors for hydrogen peroxide, Ru(dpp)3 was used for detecting dissolved oxygen, while ethyl cellulose functioned as a support matrix. The receptors were functionalized on the fiber by dip-coating a plastic fiber into the reagent solution containing both CdSe/ZnS and Ru(dpp)3 before being dried at room temperature and left to stabilize at room temperature for one week before use. The sensor used a 405-nm LED light as the excitation source, and the emissions from CdSe/ZnS and

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Figure 2.11.    Experimental setup of fluorescence-based optical fiber sensor for the detecting hydrogen peroxide and dissolved oxygen. Reprinted with permission from Ref. [59]. Copyright © 2017, Elsevier B.V.

Ru(dpp)3 were observed at 517 nm and 620 nm, respectively. Since the emission wavelengths of both sensing agents did not overlap each other, this sensor can operate simultaneously yet independently for detecting hydrogen peroxide and dissolved oxygen. The capability of this sensor has opened a new avenue, especially in aquaculture applications or hydrogen peroxide-compensated sensing of dissolved oxygen. Other than nanoparticle-based receptors, the generation of synthetic molecular receptors that provide a binding site for selective recognition of a specific target molecule via the molecular imprinting technique has also been extensively developed over the past few decades. Nguyen and coworkers combined the benefits of molecular imprinting and OFS to create a robust cocaine sensor in terms of thermal and chemical stability.60 The MIP, which binds selectively to cocaine, was functionalized to the distal end of the optical fiber. A fluorescent monomer, acrylamidofluorescein (AAF), was employed to indicate where the carboxylate group of the fluorescein will undergo deprotonation in the presence of cocaine and subsequently lead to fluctuation in the output fluorescence intensity. The deprotonated form of AAF had a stronger fluorescence while the protonated form was found to be much weaker. The proposed sensor showed no significant interference

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from other types of drugs such as ketamine, codeine, amphetamine sulfate, ecgonine methyl ester, and buprenorphine, and is capable of detecting cocaine within the concentration range of 0–500 µM. In another interesting work, Ton and coworkers developed an evanescent wave-based OFS that uses fluorescent MIP as the recognition element for detecting herbicide 2,4-dichlorophenoxyacetic acid (2,4-D), and mycotoxin citrinin.61 2,4-D is a widely used herbicide for controlling weeds that can contaminate soil and groundwater, while citrinin is a cytotoxic mycotoxin commonly found in contaminated agricultural products. The MIP was coated on the surface of the fiber, which was then excited by the evanescent wave, and part of the emitted fluorescent was coupled back into the fiber for subsequent signal analysis use. Since the potential interfering molecules do not react with the MIP, the proposed sensor has a lower background interference. In this work, polystyrene fibers were first coated with 2,4-D and citrinin-specific template molecule, which also contained fluorescent monomer N-(2-(6-4-methylpiperazin-1-yl)-1,3-dioxo-1 H-benzo[de]isoquinolin-2(3H)-yl-ethyl)acrylamide (FIM). Next, the template molecule was washed off, leaving imprinted cavities that were complementary to the template molecule. An excitation wavelength of 410 nm was used, and upon binding the 2,4-D to the available binding sites, an enhancement in fluorescence intensity which was linearly proportional to the molecule’s concentration was observed. The sensor showed that herbicide 2,4-D and mycotoxin citrinin could be selectively detected in the nM and µM range, respectively. While it is common that fluorescent sensing reagents are often coated on the external surface of the OFS, this arrangement is often limited by analyteindependent interferences such as unstable light source, environmental surroundings, and instrument efficiency.63–64 To address these challenges, some research groups have developed sensor probes that incorporate the fluorescent-sensing reagents within the optical fiber itself. For instance, Zhou and coworkers developed a novel cadmium telluride (CdTe)-doped hydrogel optical fiber for detecting Fe3+ ions (Fig. 2.12).62 Green-emission thioglycolic acid capped (gQD) and red-emission N-acetyl-L-cysteine quantum dots (rQD) were first prepared before mixing with hydrogel precursors such as PEGDA and 2-hydroxy-2-methyl-­ propiophenone in deionized water. Subsequently, the precursors were injected into a silicone tube mold to form the fiber core. A multimode fiber was then pigtailed to the hydrogel fiber by immersing the short end of the fiber into the precursors and aligning it with the hydrogel fiber before being irradiated with 365-nm ultraviolet light for 5 min to initiate the photo-crosslinking of the hydrogel. Next, the hydrogel core was removed from the tube before dipping in Na-alginate solution and CaCl2 solution to form the cladding. The details of the fabrication of the hydrogel fiber are clearly shown in Fig. 2.12(a) and (b). A 405-nm light source was used as the excitation for both types of quantum dots. In the presence of an increasing concentration of Fe3+ ions, the peak emission

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(a)

(b)

Precursor injection Tube mold PEGDA+PI+QDs Na-alginate Fiber fixing and UV polymerization

CaCl2

rQDs-PEGDA

UV lamp

gQDs-PEGDA

Ca-alginate cladding (c)

3 dB coupler Laser 430 LP filter

Silica MMF Excitation & emission

Silica MMF Waveguide extraction

Spectrometer USB line Computer Samples

Figure 2.12.    (a) Fabrication steps of the fiber core doped with QDs, (b) cladding encapsulation, (c) optical setup for sensing of Fe3+ ions. Reprinted with permission from Ref. [62]. Copyright © 2018, Elsevier B.V.

intensity from the rQD at 628 nm diminished linearly while the emission originating gQD remained unchanged. The proposed sensor exhibited a linear operating range of 0–3.5 µM, with an LOD of 14 nM. The hydrogel fiber sensor had high selectivity toward Fe3+, as negligible fluorescence quenching of both rQD and gQD was observed after immersing the sensor in other metal ions solutions such as Pb2+, Hg2+, Cd2+, Cr6+, As2+, Cu2+, Zn2+, and Ag+. This ratiometric fluorescencebased sensor is highly advantageous compared with single fluorescence-based sensors, since one of the fluorescence emissions can serve as built-in calibration to eradicate the effect of analyte-independent interferences.

2.3. Summary: Future Challenges and Opportunities The studies on evanescent wave optical-fiber-based water sensors have provided us with a better understanding of current optical-chemical sensing schemes. Multidisciplinary efforts between optical fiber technologists and biochemistry groups have led to impressive sensing performances for water pollutants, which require precision monitoring and quantification to govern a safe level of water quality. We also know that limitations with some detection schemes can be

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overcome by the integration of optical fiber as the sensing platform. For instance, the conventional colorimetric detection scheme is limited to qualitative or semiquantitative measurements; however, a definite quantitative measurement is possible when the detection principle is applied to OFS. Similarly, the need for an expensive set up and high-resolution CCD spectrometer to read the changes in fluorescence emission intensity in a fluorescence-based detection scheme can be omitted with the aid of the high-coupling efficiency of the fluorescence signal using the optical fiber waveguide. SPR-based OFS simplified the traditional SPR setup, which involves imaging optics, while the tapered fiber allows for system miniaturization with the novel design of interrogation system. Compared with the currently available technologies for water monitoring industries, the OFS is advantageous due to its physical traits such as small size, light weight, and able to operate under harsh environments. Moreover, the OFS sensing platform is versatile, since the detection of a wide range of water contaminants detection is viable by simply applying an appropriate fiber-surface chemistry to target a particular molecule. Fast response time, self-referencing, and miniaturization are other benefits of the OFS, which opens up opportunities for implementation as a handheld device for on-site or in situ detection. Table 2.1 summarizes the advantages and limitations of an optical fiber for water quality monitoring applications compared with conventional methods such as electrochemical and mass spectroscopy. Despite the fact that OFS has immense commercial potential in water-sensing industries, more research efforts and commercialization initiatives are needed to address the technical concerns in order to develop a competitive advantage against currently available technologies on the market. These technical concerns include cross-sensitivities to physical and chemical parameters, complex interrogation system designs, and inexperienced end-users working with the optical system. To develop a more innovative OFS-based sensor, the following steps are recommended: (1) Development of new sensing agents to enhance signal amplification for higher detection sensitivities; (2) Application of non-toxic and biodegradable biomaterials as sensing agents; (3) Development of an automated detection process, including a microfluidic system; (4) Reduction in analysis time; and (5) Incorporation of self-powered elements to facilitate self-sustainable operations. Furthermore, while the capability of OFS technology at the proof-of-concept stage has been endorsed by the scientific community, future research works should focus on implementing system designs or applications that can benefit from the irreplaceable merits of OFS, such as low development costs for distributed sensing, multiplexing capabilities, and the ability to integrate with IoT. This will not only warrant investment in research efforts to build a comprehensive optical-fiberbased water-sensing platform but also be well positioned for the next wave of sensing technology such as big data collection in large scale wireless water sensor networks.

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Innovative Optical Fiber Approaches for Water Quality Monitoring  65 Table 2.1.    Advantages and limitations of different water-quality monitoring technologies.

Technology

Optical fiber

Electrochemical

Mass spectroscopy

Advantage

• • • •

Small size Long-term cost savings Multiplexing capabilities Distributed sensing capabilities • Multifunctional sensing capabilities • Large dynamic operating range • Self-referencing • Fast response time • Resistant to EMI • Can be used in harsh environments • Versatile sensing platform • Miniaturized and portable sensor • Feasible on-site or in situ monitoring

• Chemical signal converts to electrical signal which can be used immediately for electrical hardware • Fast response time • Miniaturized and portable sensor • Feasible on-site or in situ monitoring

• Ultra-high sensitivity and selectivity • High precision and accuracy

Limitation

• Requires custom design of the interrogation system • Cross-sensitivities to physical parameters

• Poor long-term stability • Prone to electrochemically active interference • Sample pre-treatment is required

• Expensive setup • Bulky • Sample pretreatment is required

References  1. K. T. V. Grattan and T. Sun. Fiber Optic Sensor Technology: An Overview, Sens. Actuat. A, 2000, 82(1), 40–61.   2. M. B. Gumpu, S. Sethuraman, U. M. Krishnan, and J. B. B. Rayappan. A Review on Detection of Heavy Metal Ions in Water — An Electrochemical Approach. Sens. Actuat. B, 2015, 213, 515–533.   3. C. Elosua, F. J. Arregui, I. D. Villar, C. Ruiz-Zamarreño, J. M. Corres, C. Bariain, J. Goicoechea, M. Hernaez, P. J. Rivero, and A. B. Socorro. Micro and Nanostructured Materials for the Development of Optical Fibre Sensors, Sensors, 2017, 17(10), 2312.   4. G. Keiser. Optical Fiber Communications, 5th ed. McGraw-Hill Education, London, 2015.   5. A. Leung, P. M. Shankar, and R. Mutharasan. A Review of Fiber-Optic Biosensors, Sens. Actuat. B, 2007, 125(2), 688–703.

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21. Y. Liu, Q. Liu, S. Chen, F. Cheng, H. Wang, and W. Peng. Surface Plasmon Resonance Biosensor Based on Smart Phone Platforms, Sci. Rep., 2015, 5, 12864. 22. Q. Liu, Y. Liu, S. Chen, F. Wang, and W. Peng. A Low-Cost and Portable DualChannel Fiber Optic Surface Plasmon Resonance System, Sensors, 2017, 17(12), 2797. 23. A. M. Shrivastav and B. D. Gupta. Ion-Imprinted Nanoparticles for the Concurrent Estimation of Pb(Ii) And Cu(Ii) Ions Over a Two Channel Surface Plasmon Resonance-Based Fiber Optic Platform, Vol. 23. SPIE: Washington, D.C., 2018, p. 8. 24. R. Verma and B. D. Gupta. Detection of Heavy Metal Ions in Contaminated Water by Surface Plasmon Resonance Based Optical Fibre Sensor Using Conducting Polymer and Chitosan, Food Chem., 2015, 166, 568–575. 25. D. Rithesh Raj, S. Prasanth, T. V. Vineeshkumar, and C. Sudarsanakumar. Surface Plasmon Resonance Based Fiber Optic Sensor for Mercury Detection Using Gold Nanoparticles PVA Hybrid, Opt. Commun. 2016, 367, 102–107. 26. H. Yuan, W. Ji, S. Chu, Q. Liu, S. Qian, J. Guang, J. Wang, X. Han, J.-F. Masson, and W. Peng. Mercaptopyridine-Functionalized Au Nanoparticles for Fiber-Optic Surface Plasmon Resonance Hg2+ Sensing, ACS Sensors, 2019, 4(3), 704–710. 27. B. S. Boruah and R. Biswas. Localized Surface Plasmon Resonance Based U-Shaped Optical Fiber Probe for the Detection of Pb2+ in Aqueous Medium, Sens. Actuat. B, 2018, 276, 89–94. 28. H. Agrawal, A. M. Shrivastav, and B. D. Gupta. Surface Plasmon Resonance Based Optical Fiber Sensor for Atrazine Detection Using Molecular Imprinting Technique, Sens. Actuat. B, 2016, 227, 204–211. 29. A. M. Shrivastav, S. P. Usha, and B. D. Gupta. Fiber Optic Profenofos Sensor Based on Surface Plasmon Resonance Technique and Molecular Imprinting, Biosens. Bioelectron., 2016, 79, 150–157. 30. C. Zhou, H. Zou, M. Li, C. Sun, D. Ren, D. and Y. Li. Fiber Optic Surface Plasmon Resonance Sensor for Detection of E. Coli o157: H7 Based on Antimicrobial Peptides and AgNPs-rGO, Biosens. Bioelectron., 2018, 117, 347–353. 31. A. S. Arcas, F. S. Dutra, R. C. S. B. Allil, and M. M. Werneck. Fiber-Optic Sensor for Bacteria Detection Based on Intensity-Modulated SPR by Monochromatic Excitation, TechConnect Briefs, 2017, 3, 231–234. 32. Y. S. E. Zhang, B. Tao, Q. Wu, and B. Han. Reflective SPR Sensor for Simultaneous Measurement of Nitrate Concentration and Temperature, IEEE Trans. Instrum. Meas., 2019, 68(11), 4566–4574. 33. S. Parveen, A. Pathak, and B. D. Gupta. Fiber Optic SPR Nanosensor Based on Synergistic Effects of CNT/Cu-Nanoparticles Composite for Ultratrace Sensing of Nitrate, Sens. Actuat. B, 2017, 246, 910–919. 34. R. Tabassum and B. D. Gupta. Surface Plasmon Resonance Based Fiber Optic Detection of Chlorine Utilizing Polyvinylpyrolidone Supported Zinc Oxide Thin Films, Analyst, 2015, 140(6), 1863–1870. 35. P. Sengupta. Potential Health Impacts of Hard Water, Int. J. Prevent. Med., 2013, 4(8), 866–875.

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36. R. Tabassum and B. D. Gupta. Tailoring the Field Distribution of ZnO by Polyaniline for SPR-Based Fiber Optic Detection of Hardness of the Drinking Water, Plasmonics, 2016, 11(2), 483–492. 37. B. Kaur, N. Kaur, and S. Kumar. Colorimetric Metal Ion Sensors — A Comprehensive Review of the Years 2011–2016, Coordin. Chem. Rev., 2018, 358, 13–69. 38. P. W. Ayers, R. G. Parr, and R. G. Pearson. Elucidating the Hard/Soft Acid/Base Principle: A Perspective Based on Half-Reactions, J. Chem. Phys., 2006, 124(19), 194107. 39. P. W. Ayers. An Elementary Derivation of the Hard/Soft-Acid/Base Principle, AIP, 2005, 122, 141102. 40. Y. Xiong, J. Tan, C. Wang, J. Wu, Q. Wang, J. Chen, S. Fang, and M. Duan. A Miniaturized Evanescent-Wave Free Chlorine Sensor Based on Colorimetric Determination By Integrating on Optical Fiber Surface, Sens. Actuat. B, 2017, 245, 674–682. 41. Y. Xiong, J. Tan, S. Fang, C. Wang, Q. Wang, J. Wu, J. Chen, and M. Duan. A LEDBased Fiber-Optic Sensor Integrated with Lab-On-Valve Manifold for Colorimetric Determination of Free Chlorine in Water, Talanta, 2017, 167, 103–110. 42. Y. Xiong, C.-J. Wang, T. Tao, M. Duan, S.-W. Fang, and M. Zheng. A Miniaturized Fiber-Optic Colorimetric Sensor for Nitrite Determination by Coupling with a Microfluidic Capillary Waveguide, Anal. Bioanal. Chem., 2016, 408(13), 3413–3423. 43. Y. Xiong, J. Wu, Q. Wang, J. Xu, S. Fang, J. Chen, and M. Duan. Optical Sensor for Fluoride Determination in Tea Sample Based on Evanescent-Wave Interaction and Fiber-Optic Integration, Talanta, 2017, 174, 372–379. 44. T. Okazaki, K. Imai, A. Sultana, N. Hata, S. Taguchi, and H. Kuramitz. Development of a Fiber Optic Evanescent Wave Sensor for Anionic Surfactants Using Ethyl Violet, Anal. Lett., 2015, 48(14), 2217–2222. 45. C. A. Bondi, J. L. Marks, L. B. Wroblewski, H. S. Raatikainen, S. R. Lenox, and K. E. Gebhardt. Human and Environmental Toxicity of Sodium Lauryl Sulfate (SLS): Evidence for Safe Use in Household Cleaning Products, Environ. Health Insights, 2015, 9, S31765. 46. H. A. Khorami, J. F. Botero-Cadavid, P. Wild, and N. Djilali. Spectroscopic Detection of Hydrogen Peroxide with an Optical Fiber Probe Using Chemically Deposited Prussian Blue, Electrochim. Acta, 2014, 115, 416–424. 47. E. Benito-Pena, M. G. Valdes, B. Glahn-Martinez, and M. C. Moreno-Bondi. Fluorescence Based Fiber Optic and Planar Waveguide Biosensors: A Review, Anal. Chimic. Acta, 2016, 943, 17–40. 48. K. K. Chan, S. H. K. Yap, and K.-T. Yong. Biogreen Synthesis of Carbon Dots for Biotechnology and Nanomedicine Applications, Nano-Micro Lett., 2018, 10(4), 72. 49. B. Yao, Y. Wu, C. Yu, J. He, Y. Rao, Y. Gong, F. Fu, Y. Chen, and Y. Li. Partially Reduced Graphene Oxide Based FRET on Fiber-Optic Interferometer for Biochemical Detection, Sci. Rep., 2016, 6, 23706. 50. R. Venkataraj, V. P. N. Nampoori, P. Radhakrishnan, and M. Kailasnath. Chemically Tapered Multimode Optical Fiber Probe for Fluoride Detection Based on Fluorescence Quenching of Curcumin, IEEE Sens. J., 2015, 15(10), 5584–5591.

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51. P. Anand, A. B. Kunnumakkara, R. A. Newman, and B. B. Aggarwal. Bioavailability of Curcumin: Problems and Promises, Mol. Pharm., 2007, 4(6), 807–818. 52. F. Long, C. Gao, H. C. Shi, M. He, A. N. Zhu, A. M. Klibanov, and A. Z. Gu. Reusable Evanescent Wave DNA Biosensor for Rapid, Highly Sensitive, and Selective Detection of Mercury Ions, Biosens. Bioelectron., 2011, 26(10), 4018–4023. 53. F. Long, A. Zhu, H. Shi, H. Wang, H. and J. Liu. Rapid On-Site/In-Situ Detection of Heavy Metal Ions in Environmental Water Using a Structure-Switching DNA Optical Biosensor, Sci. Rep., 2013, 3, 2308. 54. F. Long, M. He, A. N. Zhu, and H. C. Shi. Portable Optical Immunosensor for Highly Sensitive Detection of Microcystin-LR in Water Samples, Biosens. Bioelectron., 2009, 24(8), 2346–2351. 55. K. K. Chan, C. Yang, Y.-H. Chien, N. Panwar, and K.-T. Yong. A Facile Synthesis of Label-Free Carbon Dots with Unique Selectivity-Tunable Characteristics for Ferric Ion Detection and Cellular Imaging Application, New J. Chem., 2019, 43(12), 4734–4744. 56. L. E. Kreno, K. Leong, O. K. Farha, M. Allendorf, R. P. Van Duyne, and J. T. Hupp. Metal–Organic Framework Materials as Chemical Sensors, Chem. Rev., 2011, 112(2), 1105–1125. 57. Y. H. Chien, K. K. Chan, S. H. K. Yap, and K. T. Yong. NIR — Responsive Nanomaterials and Their Applications, Upconversion Nanoparticles and Carbon Dots: A Perspective, J. Chem. Technol. Biotechnol., 2018, 93(6), 1519–1528. 58. H. M. Gonçalves, A. J. Duarte, F. Davis, S. P. Higson, and J. C. E. da Silva. Layer-byLayer Immobilization Of Carbon Dots Fluorescent Nanomaterials on Single Optical Fiber, Anal. Chim. Acta, 2012, 735, 90–95. 59. C.-S. Chu and C.-J. Su. Optical Fiber Sensor for Dual Sensing of H2O2 and DO Based on CdSe/ZnS QDs and Ru (dpp) 32+ Embedded in EC Matrix, Sens. Actuat. B, 2018, 255, 1079–1086. 60. T. H. Nguyen, S. A. Hardwick, T. Sun, and K. T. Grattan. Intrinsic FluorescenceBased Optical Fiber Sensor for Cocaine Using a Molecularly Imprinted Polymer as the Recognition Element, IEEE Sensors J., 2012, 12(1), 255–260. 61. X.-A. Ton, V. Acha, P. Bonomi, B. T. S. Bui, and K. Haupt. A Disposable Evanescent Wave Fiber Optic Sensor Coated with a Molecularly Imprinted Polymer as a Selective Fluorescence Probe, Biosens. Bioelectron., 2015, 64, 359–366. 62. M. Zhou, J. Guo, and C. Yang. Ratiometric Fluorescence Sensor for Fe3+ Ions Detection Based on Quantum Dot-Doped Hydrogel Optical Fiber, Sens. Actuat. B, 2018, 264, 52–58. 63. J. Guo, M. Zhou, and C. Yang. Fluorescent Hydrogel Waveguide for On-Site Detection of Heavy Metal Ions, Sci. Rep., 2017, 7(1), 7902. 64. P. Wu, X. Hou, J.-J. Xu, and H.-Y. Chen. Ratiometric Fluorescence, Electrochemiluminescence, and Photoelectrochemical Chemo/Biosensing Based on Semiconductor Quantum Dots, Nanoscale, 2016, 8(16), 8427–8442.

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Chapter 3

Liquid-Phase Chemical Sensors Fabien Josse*,‡, Florian Bender*,§ and Antonio J. Ricco†,¶ *

Department of Electrical and Computer Engineering, Marquette University, Milwaukee, WI, USA

Department of Electrical Engineering, Stanford University, Stanford, CA, USA

†

[email protected]

‡

[email protected]

§

¶

[email protected]

3.1. Introduction Unintentional releases of various chemicals into water systems can be difficult to detect and periodically monitor, particularly for large bodies of surface water or extensive underground water systems.1 Notable among the culprits are unintentional releases of fuel and oil products from underground storage tanks and pipelines,2 organic compounds flowing from industrial processes or mixed wastewater, newly emergent contaminants9 such as antibacterials10,11 and polyfluorinated compounds,12,13 and chemicals from agricultural operations, particularly organophosphates (OPs),3–6 which, as the most extensively used of all agricultural pesticides,7,8 have left measurable residues in groundwater, soil, and agricultural end products (i.e., food). Whether the contamination process was wholly unintentional or within ­permissible limits, such contamination can threaten both public health and environmental quality. Timely detection and periodic monitoring can reduce these risks and their associated cleanup costs. For example, in some cases, groundwater systems near underground storage tanks for fuel and oil are subject to legal monitoring requirements by government agencies. Current monitoring practices typically involve manual on-site sample collection, followed by transport to an off-site laboratory for analysis,14 which is a costly and time-consuming process. Moreover, sample integrity is easily compromised 71

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during collection, transportation, storage, and analysis, a sequence that can span days to weeks. Consequently, continuous or frequent monitoring of extensive natural water systems ranges from mildly impractical to prohibitively expensive when using conventional methods. Therefore, there is a high demand for field-appropriate systems that provide rapid, in situ detection and periodic monitoring of water systems, even if such solutions are sometimes limited to chemical-class-based identification combined with quantitation of limited accuracy. Even with these limitations, the sensorbased system should exhibit: (1) a limit of detection (LOD) low enough for regulatory and health-related requirements; (2) adequate selectivity with regard to expected interferences outside the target chemical class; (3) rapid response time, usually seconds to hours, as appropriate for regulatory compliance, including triggering timely remediation; (4) compatibility with an integrated system suitable for deployment in situ, whether down a well or on a floating or submersed platform; and (5) an ongoing operating cost significantly below traditional manual sample collection and laboratory analysis protocols. To realize this combination of performance factors, a number of (bio)chemical detection technologies are under investigation.15 It is not surprising that developing and implementing such systems for effective water monitoring is challenging. For example, identifying and quantifying aromatic hydrocarbons typified by the “BTEX compounds” (benzene, ethylbenzene, toluene, and the three xylene isomers), particularly benzene, is challenging not only because of low relevant concentrations in water but also because of the chemical similarity of these compounds to one another, as well as the presence of other similar aromatic compounds in groundwater. A particularly complicating factor is that the number of chemical isomers increases with the number of carbon atoms and substituent locations around the aromatic ring(s). For example, toluene is the only seven-carbon isomer of the substituted benzenes, but ethylbenzene and the three xylenes are all chemical isomers with the same empirical formula, C8H10. Due to their fairly similar physicochemical properties, and depending on the nature of the sensor used, a given set of isomers often exhibit similar sensitivities and response times. (On the other hand, due to their chemical similarities, it may be adequate or even preferred for some monitoring applications to detect such isomers as a single class.) Additional, more chemically dissimilar interferents may also be present in groundwater, such as dissolved salts, particles and sediments, humic acid, dissolved gases, aliphatic hydrocarbons, ethers, and esters, which should be successfully discriminated by some aspect of the sensor or its supporting system.

3.2. Liquid-Phase Chemical Sensor Platforms Several sensor technologies have been investigated and developed to monitor properties and materials in the aqueous phase. The resulting devices offer the

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opportunity to realize simple yet low cost, robust sensors capable of in situ, rapid detection and characterization of the samples, even if only through class-based identification. Chemical sensors typically have two key components: an appropriate transduction device and a chemically sensitive coating. The coating dictates the sensitivity, the LOD, and the selectivity of the sensor; the device primarily dictates the sensitivity and LOD, although the capability to measure multiple physico-chemical parameters of the sensitive coating can involve the device in selectivity, as well. The sensitivity and selectivity of the coatings are determined by the interactions between the coatings and the contaminant molecules (as well as the interferents). For practical water-monitoring field applications, the sensor with its coating form the heart of a larger multicomponent system that may include features such as active sampling (pumps, valves); provision of blanks, controls, or reference samples; removing insoluble materials; adjusting pH or adding reagents in order to improve LOD or selectivity; capturing, processing, storing, and telemetering data; and fault-detection/state-of-health monitoring capabilities. Liquid-phase chemical sensors have been implemented using various sensing platforms such as optical devices,16–23 electrochemical devices,24–31 capacitive devices,32,33 resistive devices,34–37 micro-electromechanical systems (MEMS),38–42 and acoustic wave devices.15,43–45 The overall sensitivity and LOD of a given chemical sensor are highly dependent on the selected sensing platform. To be more precise, the overall sensitivity of the device is the product of the sensitivity of the chemically sensitive element to the target analytes and the sensitivity of the sensor platform to changes in the sensing element. The role of noise, background, and drift in the sensor signal is also very relevant to its analytical performance: any one or all of these factors can affect the system’s LOD. For example, a sensor system can only reliably measure a target species if the latter generates a signal that is several times (three times as a general rule) the background and/or noise. The choice of sensing platform also plays a significant role in determining the suitable readout technique for a chemical sensor, which, in turn, can greatly affect the overall complexity of the chemical sensor device or system. Optical chemical sensors monitor chemically induced changes in optical parameters such as the index of refraction, the amount of light absorbed, the effective optical thickness of a film (the product of its physical thickness and index of refraction), or the photoluminescence in order to detect and quantify the target analytes.46 Typically, a chemically sensitive layer that is sensitive to the target analytes is placed on the optical device. Interactions between target analytes and the chemically sensitive layer result in changes in a particular optical parameter, which can be related to the concentration of the target analytes.43,46 Optical chemical sensors can also include spectroscopic interrogation of the target molecules, whether bound in/on a coating layer or in a measurement chamber or cell, which provides a high degree of chemical specificity. The systems required to support such sensors, however, often place them in a different category due to their cost, size, and overall complexity.

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For electrochemical sensors, the sensing mechanism is most often based not only on the electrochemical properties of the target molecules or ions themselves but it also can be linked to interactions between the target analytes and the (coated) measurement electrode of the device. In the former case, a measurable signal results when an electrochemically active analyte is either oxidized or reduced at the electrode at a specific potential or across a range of potentials, which generates electrical current; such sensors include various amperometric (current-measuring) and voltammetric (measured current as a function of a time-varying applied potential) devices.47 Potentiometric (potential-measuring) sensors that lack sensing layers do not involve a significant amount of oxidation or reduction of the target species, but small “exchange currents” must flow for direct potentiometric sensing, which is thus limited to electroactive targets. Concentrations of ions, whether electroactive are not, are also routinely measured using ion-sensitive electrodes (ISEs), a form of potentiometric electrode in which the potential is (largely) determined by the chemical activity (closely related to the concentration) of an ion for which a target-specific ionophore layer immobilized on the electrode acts as the sensing interface. A sensing electrode can also be modified using an electroactive film whose redox properties (e.g., the potentials at which current flows and the magnitude of the current) are determined in part by the presence and concentration(s) of the analytical target(s). Capacitive, conductive, and resistive chemical sensors are advantageous because the measured signal is electrical, and thus it does not need further transduction prior to processing and reporting. In capacitive chemical sensors, changes in the capacitance of the sensor device result from the interactions with the target analytes.48,49 Resistive (or conductive) chemical sensors, also known as chemiresistors, monitor changes in their electrical resistance resulting from interactions with the target analytes.50 MEMS-based chemical sensors include devices that have a micromachined structural/mechanical element that enables or improves the transduction process and thus the associated sensing performance. For example, microbridges51 and micro(hot)plates52,53 use MEMS processes to provide superior thermal isolation via suspension of a heated sensing element, thereby reducing power demands and mitigating issues created when the sensor’s packaging is unintentionally heated. Microcantilevers, in contrast, use MEMS methods to fabricate suspended structures with a sufficiently small mass and limited mechanical linkages to a supporting substrate, so that they can be made to oscillate at frequencies sufficient for useful limits of detection; alternatively, the bending of such a micromechanical element can also provide the transduction mechanism.41 More generally, the mechanical or electrical properties of these micro-scaled devices are monitored, and any changes observed in these properties can be related to the concentration of the target analytes.41,54

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One of the most promising MEMS-based devices for chemical sensing is the microcantilever, a diving-board-like structure, usually only a few hundred micrometers in length, which can be operated in either dynamic or static mode. In dynamic mode, the device is driven into oscillation and the resonant frequency and quality (Q) factor of oscillation are monitored; in static mode, deflection of the microcantilever is monitored, typically using either a reflected light beam or strain-dependent changes in the resistivity of the cantilever’s anchoring ­structure.41 In static mode, microcantilever chemical sensors have a chemically sensitive layer; in dynamic mode, the analyte interacts with the coating to cause mass-loading and stress effects, thereby changing the device’s resonant frequency and quality factor41 in relation to the analyte concentration. Initially, all types of dynamic-mode resonant microcantilevers had used outof-plane flexural modes (similar to the motion of a bouncing diving board), which, when immersed in liquid, causes severe damping of the oscillatory motion. Such liquid-phase damping diminishes the Q-factor, which leads to low sensitivity, increased frequency noise, and poor LODs.41 More recent microcantilever work has revealed the benefits of in-plane flexural-mode operation (a finger-wagging sort of motion), which results in significantly less damping upon immersion in liquids and thus improved sensor performance.41,55,56 Another mode of operation immune to the damping issues of out-of-plane oscillation is the static mode, in which differential surface stress from analyte sorption in/on a sensing layer causes the microcantilever to bend by an extent that depends on the concentration of the analyte.41 Acoustic-wave chemical sensors include a wide variety of devices, such as the thickness-shear mode (TSM) resonator,45,57–62 surface acoustic wave (SAW) device,45,61,62 shear-horizontal surface acoustic wave (SH-SAW) device,45,63–65 flexural plate wave (FPW) device,61,62,66 and acoustic plate mode (APM) device.61,62,67 These devices can be used as chemical sensors to detect target analytes in gas and/or liquid phase. Typically, acoustic wave devices are operated at frequencies of 1 MHz to more than 1000 MHz.45 Chemical interactions between target analytes and chemically sensitive coatings placed on the sensor perturb the wave-propagation characteristics, such as frequency and amplitude, generally by amounts related to the concentrations and, in the case of partially selective sensors coatings, the identities of the target analytes. TSM resonators and SAW devices are the most commonly used sensor ­platforms for chemical sensing applications.45,57–62 TSM resonators are effective for both gas- and liquid-phase detection of target analytes; their surface motion is predominantly in-plane and thus they are not excessively damped by liquid contact. SAW devices, in contrast, are suitable only for gas-phase detection; when contacting a liquid phase, the vertical displacement component of the SAW causes unacceptably high attenuation. The SH-SAW device is far superior

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for liquid-phase detection45,63–65 because its surface displacement is in-plane and thus does not cause high attenuation.

3.3. Chemically Sensitive Layers for LiquidPhase Detection Common performance-limiting issues for all the chemical sensor technologies previously discussed in this chapter are the lack of adequate sensitivity (and consequently an insufficiently low LOD) and selectivity. Both sensitivity and selectivity are highly dependent on the chemical interaction between the chemically sensitive layer — which is often a polymer — and the target analytes. Therefore, the selection of the chemically sensitive element is critical to achieve the required sensitivity and selectivity for a particular application. An ideal chemical sensor coating should feature a high sensitivity, conferring a low LOD; a high selectivity to the target analyte, with low cross-sensitivities to other analytes likely to be in the solution and act as possible interferents; and short response and rapid recovery times. A large dynamic range, i.e., measurement output over a wide concentration range, may also be important. The chemically sensitive layer should also be easy to make and apply, its interactions with analytes and interferences should be reversible (i.e., the analyte can be removed/desorbed and the sensor baseline regenerated), and it should not create significant signal drift. Chemical sensitivity is often addressed by selecting a sensor coating that generates an adequate transducer response — at least three times the background and/ or noise, regardless of the source — at the required LOD for a given analyte for a particular application. Chemical selectivity can be far more challenging — unlike biological sensors based on antibody-antigen, DNA hybridization, or other highly molecularly specific interactions, reversible chemical sensors are almost invariably only partially selective; their response sensitivity for a specific analyte or group of analytes may be greater than for other (interfering) species, but there are nearly always cross-sensitivities. For instance, a chemical sensor for methane is likely to respond to ethane, propane, and other similar molecules in the air — it may, in fact, respond to nearly any readily combustible species in air — making it difficult to determine which species caused the response. Thus, a combustible gas sensor is a more appropriate and readily developed device than a methane-only sensor. To date, the applicability of chemical sensors has been significantly limited due to the lack of sufficient selectivity for many real-world applications; this issue can be exacerbated when the target is not a single analyte, but a particular mixture of compounds. Among the various sensing platforms briefly described earlier, only polymercoated acoustic wave-based devices for direct liquid-phase chemical sensing63–65 are addressed in the remainder of this chapter. A key advantage of such coatings

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is the straightforward processing: thin polymer films can be deposited onto ­sensing platforms by spin-coating, dip-coating, drop-coating, spray-coating (often using a mask), or ink-jet and similar printing methods. They are also readily tailored — typically prior to, but sometimes after, their deposition on the ­ ­transducer — to provide many of the desired physical and chemical properties for sensing.68 Selectivity, however, remains a significant challenge for polymer sensing layers, which is addressed in the next section along with multiple promising methods to confer enhanced chemical specificity.

3.3.1.  Polymer sensing layers and analyte partition coefficients Analytes are most often absorbed into the bulk of the polymer volume, and thus relatively large changes in mass and viscoelastic properties can be achieved, resulting in a relatively high sensitivity. The difference between gas and liquid sensing with respect to coating selection can be demonstrated by considering the partition coefficients for the sorption of an analyte from either the gas phase or from water into the polymer coating.69,70 The partition coefficient (or equilibrium constant), K, represents the ratio of the analyte concentration in the polymer to the analyte concentration in the gas or liquid phase in contact with the polymer-coated device, CA. In the limit of only mass-loading contributions — when viscoelastic contributions are low or negligible — the observed frequency shift, ∆f, of a resonant or traveling-wave sensor platform exposed to an analyte can be related to K, CA, the frequency shift due deposition of the polymer layer, ∆fs, and the mass density of the polymer layer, ρs.71,72 Appropriate selection of the (partially) selective coating is critical for sensor design; coatings that are optimal for achieving a high sensitivity to analytes in the gas phase may not necessarily be optimal for liquid-phase detection,69 especially for ionic or polar analytes. For liquid-phase sensing, in place of the generic partition coefficient, K, the polymer/water analyte partition coefficient, KP − L, will be used as the thermodynamic parameter, which characterizes the distribution of organic analytes between the polymer coating and the aqueous solution.69 Calculated KP − L values can predict the relative sensitivity (and inherent selectivity) of a sensor coating material in liquid environments, which is given by



KP − L =

Cp CL

CP

=C

L

CA CA

=

KP − A . KL − A

(3.1)

CP, CA, and CL are the concentrations of analyte molecules in the polymer coating, air, and liquid, respectively. KP − A and KL − A, the partition coefficients of polymerair and liquid-air pairs, can be calculated using a linear solvation energy relationship (LSER)69,73 provided that the appropriate LSER parameters are available.

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78  F. Josse et al. Table 3.1.    Partition coefficients* for selected polymers. Coating

Analyte Benzene

Poly(dimethylsiloxane) PDMS KP − A

KP − W**

Poly(epichlorohydrin) PECH

KP − A

KP − A

KP – W

KP – W

164

30

372

67

Ethylbenzene

3153

772

1672

464

2425

673

Xylenes

2785

743

2137

458

3284

700

Toluene

1164

279

583

140

1040

249

526

1.5

256

0.75

3482

10

3767

0.23

31689

1.98

Nitrobenzene DMMP***

Not Calculated

Poly-(isobutylene) PIB

Not Calculated

Notes: *Data from Refs. [69,70]. **KP − W is polymer-water partition coefficient. ***DMMP is dimethyl methylphosphonate.

Partition coefficients provide insight into the extent of analyte partitioning into the coating, which can be directly related to the mass-loading contribution to sensor response. Examples illustrating the difference in partition coefficient between gas and liquid for several polymers are given in Table 3.1. Changes in the viscoelastic properties of chemically sensitive layers may also contribute to the observed responses upon exposure to aqueous analytes. This is especially relevant for oscillatory sensor platforms, in which the viscoelastic contributions to the frequency shift are defined in terms of swelling-induced modulus changes.74 As analyte partitions into a rubbery polymer matrix, stress internal to the polymer related to the added analyte volume is often (partially) relieved by changes in the arrangements of polymer chains and/or their conformations. In some cases, small analyte molecules within a polymer allow the chains or their segments to move more readily relative to one another, effectively reducing the polymer modulus. This process is referred to as dilution, softening, or plasticization.72,74,75 In such cases, changes in polymer viscoelastic properties, specifically shear modulus, G, are related to the extent of analyte partitioning into the coating, hence the partition coefficient. Note that G = G′ + jG″, where G′ is the shear storage modulus, representing acoustic energy storage, and G″ is the shear loss modulus, representing acoustic energy dissipation or loss.75 In general, absorption of water or analytes into the polymer matrix decreases G′ and increases G″ as the polymer becomes “lossier”. Different analytes typically change polymer viscoelasticity differently, due to a combination of differences in their partition coefficients and the details of their physico-chemical interactions with the polymer. Viscoelastic changes lead to attenuation in the acoustic wave and change in the velocity of

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wave propagation (or resonant conditions) and hence the frequency of operation. Because device response to viscoelastic effects can be significant and it is not necessarily linear with concentration, the sensitivity with respect to mass-loading alone, which is the typical approach with resonant sensors, is not generally appropriate if G is not known to remain constant. Device sensitivity can be defined as frequency shift, Δf, per change in solution concentration, CA, or as relative sensitivity, which considers the operating ­frequency of the device, f:



SR =

∆f

f

∆C A

.

(3.2)

The LOD of a chemical sensor can then be defined as the minimum measurable concentration that corresponds to a frequency shift no smaller than three times the peak-to-peak or three times the root-mean-square noise level. The LOD depends on the coating-plus-transducer sensitivity, the signal stability due to noise, and any background (chemical) signal that cannot be predicted in order to subtract it. Signal noise depends on the type of device, frequency of operation for resonant devices, coating stability in water, and on the measurement system and the electrical circuit.

3.3.2.  Polymer sensing layers and selectivity In general, both sensitivity and selectivity are highly dependent on the chemical interactions between the chemically sensitive layer and the target analytes. Molecular specificity is feasible for large, complex target species, particularly biomolecules. Perfect specificity is very challenging to obtain for small organic molecules in combination with rapidly reversible binding. Thus, chemically sensitive layers for “real-time-reversible” sensing often use a combination of physical and weak chemical interactions, consisting of van der Waals bonds, electrostatic interactions, polar/non-polar interactions, and polarizability-related association. For this approach, using the general principle of “like dissolves like”, the sensitivity, and sometimes the selectivity, of the sensor can be enhanced by selecting or synthesizing sensor coatings that match or mimic the chemical features of the target analyte(s).69,76 However, the resulting sensor coatings are usually not highly selective to any specific analyte, although preference for a chemical class of analytes can result in the most successful cases. For instance, it is difficult to develop a rapidly reversible chemical sensor coating that interacts exclusively with a compound such as benzene. The benzene sensor coating is likely to respond to chemically similar molecules as well, such as toluene, ethylbenzene, and, if aromatic character is not a factor, cyclohexane and other alkanes, making it difficult to accurately determine the concentration of benzene.

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This challenge can be addressed by using an array of sensors coated with different partially selective coatings combined with a signal-processing approach that matches the response pattern of the array to a particular analyte. Another approach is to use a single sensor (or a small sensor array) but evaluate multiple sensor parameters. For example, while it is most common to measure the equilibrium response of a sensor to an analyte, the response time, if measurable, can also be a deterministic characteristic of a given coating/analyte combination. In liquid phase, the rate of sensor response is often limited by analyte transport in the liquid or by analyte diffusion into the polymer, which is much slower than in gas, thus allowing accurate measurement of the sensor response time. Identification in such cases is then based entirely or in part on the dynamics of analyte transport through the liquid and diffusion into the sensitive polymer. Similarly, even multianalyte concentrations in a mixed sample can be accurately measured using a single sensor device combined with a smart, multivariate sensor signal processing approach if equilibrium responses are supplemented by response-time information. Choosing sensor coatings based on the principle of “like dissolves like” can be made quantitative by matching the Hansen solubility parameters of the coating material to those of the target analytes. This maximizes the amount of target analyte absorbed by the sensor coating while minimizing sensor sensitivity to interferents that may be present, assuming their solubility parameters are different. The number of commercially available polymers that can be used in the liquid phase is limited, partly because not every sensor coating that is stable in air is also stable in water. One way to augment the set of polymers suitable for aqueous sensing is to use multicomponent polymers. The copolymerization of different monomers, the mixing of different polymers by deposition from a common solution, or the addition of plasticizers each have their advantages. In general, this strategy not only increases the number of stable polymer materials useful for sensing but it also can improve their performance: the added components can be used to finetune the coating solubility parameters and other physical or chemical characteristics, and thus increase the sensitivity to the target analyte.76 A powerful example of a multicomponent coating strategy that focuses on response time is the addition of a plasticizer to a glassy polymer which has a good solubility parameter match to the target but whose response time is too slow for useful sensor application. Adding sufficient plasticizer renders such a polymer rubbery at room temperature, thus turning it into a much better (more rapid and, often, more sensitive) sorbent for a given target analyte.76

3.4. SH-SAW Sensors for Organic Detection in Water In this section, polymer-coated SH-SAW sensor systems for direct groundwater monitoring, capable of aqueous-phase measurement of organic compounds,

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specifically aromatic hydrocarbons at low concentrations (about 100 parts per billion (ppb)), are described as an example of liquid-phase chemical sensors.1,76,79 As indicated earlier, among the many aromatic hydrocarbon compounds that can contaminate natural water systems, benzene, toluene, ethylbenzene, and xylenes (BTEX) are of particular concern due to their relatively high solubilities in water and their hazard potential.2,77,78 The EPA’s maximum contaminant levels of BTEX compounds in drinking water are in the low ppb (µg/L) to low ppm (mg/L) range.2 The task of identifying and quantifying aromatic hydrocarbons such as the BTEX compounds, particularly benzene, is challenging, not only because of the low relevant concentrations but also the chemical similarity of these compounds as a group, as well as the presence of other similar aromatic compounds in groundwater.79 Additional interferents may also be present in groundwater. A sensor system is presented that makes use of an array of SH-SAW devices, coated with partially selective polymers and matched with customized signal processing in order to detect and quantify BTEX compounds in the presence of interferents typically found in groundwater. As described in Sec. 3.4.2, the use of equilibrium sensor responses alone for identification purposes in the absence of a sufficient number of chemically diverse sensing films results in unacceptably high rates of misclassification. Adding a second measured parameter to the identification process, such as the characteristic transient response time, provides additional chemically specific information. Therefore, the SH-SAW device is a promising sensor platform for this application.

3.4.1.  Fundamentals of the SH-SAW sensor platform Of all acoustic wave devices, SH-SAW devices appear most suitable for detecting chemicals in liquid environments, because shear horizontal surface waves are more sensitive than bulk waves to perturbations produced from the environment without the excessive loss associated with Rayleigh SAWs in liquids. When properly designed, the device propagates an SH-SAW that is closely confined to the solid–liquid interface without suffering prohibitive attenuation, thereby facilitating high sensitivity to analytes dissolved in the liquid phase.80,81 The device sensitivity to mass and viscoelastic loadings can be increased using a thin guiding layer which traps the acoustic energy near the sensing surface,63,65,80 thereby increasing the sensitivity to surface perturbations. The resulting acoustic wave is analogous to a Love wave on an isotropic substrate with an overlayer. The addition of an appropriate thin, chemically sensitive polymer film then enhances sensitivity due to the partitioning of organic analytes from the aqueous phase into the film, and provides (partial) selectivity to the analytes of interest. Figure 3.1 shows the basic configuration of a polymer-coated SH-SAW device as a chemical sensor platform.

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Figure 3.1.    Schematic of an SH-SAW sensor device. IDT: interdigital transducer; PIDT: transducer periodicity.

The sensor device consists of an SH-SAW platform (in this case, a delay line) with an overlayer of thickness, h, having a lower shear wave velocity than the piezoelectric substrate. Perturbation theory, which yields approximate sensitivity expressions, can be used as long as the perturbing film is sufficiently thin in comparison to the acoustic wave wavelength. In general, the device response is a change in the wave velocity, V, (or frequency f ), and/or attenuation, γ, (or loss L), and can be expressed in terms of the mass loading, viscoelastic stiffening, dielectric, and conductivity changes of the contacting coating/liquid medium. Assuming a constant pressure and operation temperature, this change can be expressed as



 ∂V   ∂V   ∂V   ∂V  ∆V =  ⋅ ∆m +  ⋅ ∆c +  ⋅ ∆ε +  ⋅ ∆σ ,     ∂m   ∂c   ∂ε   ∂σ 

(3.3)

where m, c, ε, and σ represent the mass, viscoelastic constant, dielectric constant, and conductivity, respectively, of the liquid/film medium. A similar expression can be written for the attenuation, γ, from which the device loss can be determined. The device sensitivity, such as the mass sensitivity, the viscoelasticity effect, and the sensitivity to electrical loading, can then be determined. However, for the device shown in Fig. 3.1, a thin metal layer is used between the two IDTs, representing an electrical short and eliminating the acoustoelectric interactions with the load; as a result, only the sensing caused by mechanical loading — the ∆m and ∆c terms in Eq. (3.3) — can be considered here. The sensitivity to electrical loading is eliminated to reduce any perturbation that may arise from fluctuation in the electrical perturbation by the liquid, e.g., changes in its ionic conductivity. In the case of a guided SH-SAW, waveguiding by a suitable layer (e.g., oxide coating, polymer65,84) with an appropriate thickness occurs when the shear wave velocity in the layer (VM) is less than that in the substrate (VSH). Assuming that a predominantly shear-horizontal wave is coupled to the IDT, the fractional change in the

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wave velocity, a measure of the sensitivity to surface mechanical perturbations can be obtained from perturbation theory82 as



  V 2 ∆V V 2 = − SH ( ρ h ) 1 −  M   U 2 , V 4 V   SH  

(3.4)

where VSH is the unperturbed velocity of the SH-SAW, ρ is the layer mass density, h is the layer thickness, and U2 the normalized particle velocity displacement amplitude at the surface. Note that when the resulting wave suffers no dispersion and the propagation path length, l, is equal to the sensing path length, ls, Eq. (3.5) also describes the relative frequency shift, ∆f/f, of the SH-SAW sensor:



∆f  ls   ∆V  =   . f  l  V 

(3.5)

As a result, the relative sensitivity of the device to mass and viscoelastic loading, SR, due to the thin guiding layer, can be calculated.83,84 Clearly, the device sensitivity increases as U2 at the sensing surface increases, i.e., as the amount of acoustic energy trapped at the sensing surface increases. Note the effect of the layer viscoelasticity is implicitly represented by the term [1 − (VM /VSH ) 2 ].

3.4.2.  Polymer-coated SH-SAW sensors The design principles and considerations are similar to those of Rayleigh SAW devices.61 The two most commonly used crystal orientations for SH-SAW sensor platforms are the rotated Y-cut 90°-X propagation quartz substrates and multiple orientations of the rotated Y-cut of LiTaO3. The SH-SAW sensor platform described here has been previously reported,1,65,79 which consists of two identical IDTs on a 36° YX–LiTaO3 substrate with a metallized surface region between the IDTs to eliminate acoustoelectric interactions65 with the aqueous sample. The devices are fabricated with 10/80-nm-thick Ti/Au IDTs using a multielectrode design1 which produces an operating frequency for the third harmonic SH-SAW of 103 MHz for polymer-coated devices. The sensor platform uses a dual-delayline configuration, with one line used as the sensor and the other as a reference to measure and correct for changes in temperature, pressure, and other parameters unrelated to sorption of the analyte of interest. The sorbent polymers used in this example are poly(ethyl acrylate) (PEA), poly(epichlorohydrin) (PECH), and poly(isobutylene) (PIB), ranging from 0.6 to 1.0 µm, as indicated below. The polymers are deposited from solution in toluene (PEA) or chloroform (PECH, PIB) by spin-coating and then baked for 15 min at

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55°C, resulting in thicknesses of 1.0 µm for PEA, 0.6 µm for PECH, and 0.8 µm for PIB. Note that the baking step was found to be crucial in order to ensure reproducible results when the polymer-coated sensors are used over extended periods of time (up to a few months). The selected sorbent polymers are known to show partial selectivity for a number of aromatic hydrocarbons in the gas phase. One of the delay lines, used as the reference delay line, was coated with poly(methyl methacrylate) (PMMA) and baked for 120 min at 180°C, resulting in a glassy, comparatively non-sorbent coating 0.5 µm in thickness. LiTaO3 showed a significant temperature coefficient of delay for the SH-SAW of about 3 kHz/°C for the present sensor device. BTEX analytes were diluted to the appropriate concentration with deionized (DI) water or groundwater for the sensing experiments. Concentrations are reported in ppm or ppb by weight. The following interferents, commonly found in groundwater at contaminated sites, were also tested in the experiments: n-heptane, 1,2,4-trimethylbenzene, MTBE (methyl-tert-butyl ether), and ethanol. The maximum contaminant levels for the BTEX compounds in drinking water are in the low ppm to low ppb range,15,85 requiring low limits of detection and high accuracy in the extracted analyte concentrations from multicomponent aqueous samples. This requirement can be addressed by various aspects of the measurement system design, including minimizing sensor signal noise, online data averaging, a flow system designed to minimize variations in temperature and flow rate, and the design of the IDTs of the sensor devices. Data for single-analyte sensor responses were first collected to establish the partial selectivity of each analyte. Typical SH-SAW responses to single-analyte samples are shown in Fig. 3.2. The single-analyte experiments also provide sensitivities (Hz of frequency shift per ppm-by-mass of analyte concentration) for each combination of sensor coating and BTEX compound, which, like the response times, are independent of concentration (and hence the equilibrium frequency shifts can be conveniently used to determine analyte concentration(s)). For the selected coatings, chemical isomers, namely ethylbenzene and the three xylenes, have nearly identical values for their response times and sensitivities; therefore, no attempt was made to distinguish between them. The average values of response-time constants and sensitivities for various coating/analyte combinations are listed in Tables 3.2 and 3.3,79,86 and these results confirm that the selected coatings indicate partial selectivity. Figure 3.3 shows the normalized frequency shifts for all combinations of the aforementioned sensor coatings (PEA, PECH, and PIB) and the BTEX analytes. Ratios of frequency shifts for a given pair of analytes vary for different sensor coatings, indicating partial selectivity of the latter, although the effect is not very large. Use of the response time as a second sensing parameter greatly increases the selectivity of the sensor system. To demonstrate this, characteristic response patterns are generated using six sensor output parameters, specifically, the response times and the pairwise ratios between the steady-state frequency shifts, measured

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(a) Ϭ͘ϱ Ϭ ͲϬ͘ϱ Ͳϭ Ͳϭ͘ϱ ͲϮ ͲϮ͘ϱ Ͳϯ Ͳϯ͘ϱ Ͳϰ

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Figure 3.2.    (a) Response of an SH-SAW sensor coated with 1.0 µm PEA, alternating between DI H2O and various step concentration changes of benzene in water (concentrations are indicated in the graph). (b) Response of an SH-SAW sensor coated with 1.0 µm PEA to various concentrations of ethylbenzene in water.87 Table 3.2.  Measured mean response times, τ (in s), for three different polymer coatings to various BTEX analytes, together with their standard errors (n = 5).79,86 Polymer

τbenzene

τtoluene

τethylbenzene

1.0 µm PEA

36.1(±10.0)

76.7(±6.0)

204(±4.5)

0.6 µm PECH

26.5(±8.4)

77.6(±2.8)

175(±13)

0.8 µm PIB

29.3(±7.8)

84.2(±6.5)

245(±14)

for three different coatings. The results are shown in Fig. 3.4 in the form of a radial plot. Note that an excellent separation of response patterns is achieved for all coordinate axes showing response times, thereby demonstrating the great promise of this approach to achieve selectivity.

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86  F. Josse et al. Table 3.3.    Measured mean sensitivities, σ (in Hz/ppm), for three different polymer coatings to various BTEX analytes, together with their standard errors (n = 5).79,86 Polymer

σbenzene

σtoluene

σethylbenzene

1.0 µm PEA

344(±27)

690(±160)

2240(±460)

0.6 µm PECH

109(±9)

435(±25)

1450(±240)

0.8 µm PIB

63(±5)

344(±43)

1670(±10)

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Figure 3.3.    Normalized frequency shifts for BTEX compounds for sensors coated with 1.0 µm PEA, 0.6 µm PECH, and 0.5 µm PIB. Data have been normalized for the molecular weight and aqueous solubility of each analyte and divided by the value for benzene.88

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Figure 3.4.    Radial plot showing the response times, τ (in units of 100 s), and ratios of the equilibrium frequency shifts, Δf, for various BTEX analytes detected using three different sensor coatings.79

While the results shown in Fig. 3.4 for three individual analytes using an array of three sensing films is very promising, extracting individual analyte concentrations from arbitrary mixtures of the BTEX compounds is more challenging. Indeed, this sort of sensor-array approach often works well for single analytes

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(identification accuracy of 95%–98%89) and for binary mixtures (identification accuracy ~89%89,90), but as the complexity of the mixture increases, the approach often will fail or produce inaccurate results. For instance, in one study90 the identification accuracy of ternary mixtures using sensor arrays was only about 3%. Other drawbacks of the sensor-array approach include potential misclassification and longer time to detection if steady-state features are required to identify and quantify analytes. Misclassification errors are particularly likely if chemical diversity (chemical orthogonality91) and partial selectivity of the sensor coatings in the array are insufficient. Moreover, the use of only one sensing parameter per sensor for classification — though not the case in the Fig. 3.4 results — further increases the chances of misclassification.92

3.4.2.1.  Model of the single-analyte sensor response This section describes a very promising new approach for detection and quantification in a mixture. The first step is to develop an accurate analytical model that describes the sensor response to the analytes. This is not unusual, as nearly all methods for analyzing sensor response data require thorough characterization of the responses in some manner as a starting point. A typical SH-SAW response to single-analyte samples, as shown in Fig. 3.2,87 for step changes in analyte concentration in the 0–10 ppm range, can be modeled using a single exponential fit,87

∆f ( t ) = ∆f o [1 − e −τ ], t



(3.6)

where ∆fo is the equilibrium frequency shift, τ is the response time constant, and ∆f(t) is the frequency shift as a function of time.79,86 Fitting the sensor response to Eq. (3.6) yields a characteristic value of τ for each coating/analyte combination. Experimental results show τ to be independent of analyte concentration in the range of interest (just as shown in the previous section) and, for a given sensor coating, it therefore can be used to identify the analyte. The single-analyte experiments also provide sensitivities (Hz of frequency shift per ppm-by-mass of analyte concentration) for each combination of sensor coating and BTEX compound, which, like the response times, are independent of concentration (and hence the equilibrium frequency shifts can be conveniently used to determine analyte concentration(s)). In order to analytically model the sensor responses to single-analyte samples of BTEX compounds (see Eq. (3.6)), it was assumed the analytes in water obey Henry’s law, as indicated previously in Refs. [79,86,87]. Usually, when the sensor is exposed to a step change in the ambient concentration of an analyte, the sensor responds rapidly at first, with the signal changing more slowly as the coating and analyte approach equilibrium. The process of analyte absorption for the case of a single-analyte sample can then be fit by a first-order model described by Ref. [87].

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γp 1 C ( t ) = − C ( t ) + Camb ( t ) τ τ

(3.7a)

∆f ( t ) = − aC ( t ) ,

(3.7b)

and



where C(t) is the concentration of the analyte in the coating at time t, Camb(t) is the ambient analyte concentration at time t (which for these experiments remains constant due to the constant flow of fresh analyte solution throughout the measurement), τ is the response time constant for a given analyte/coating combination, gp is the polymer/water partition coefficient for a given analyte/coating pair (see Table 3.1), Df(t) is the frequency shift observed at time t, and a is the equilibrium frequency shift or sensitivity, which is a function of the sensor platform, the sensor coating, and the analyte.93 Equation (3.7b) represents the measured SH-SAW device frequency shift for the single-analyte system at time t. Solving the differential Eq. (3.7a) yields an exponential time dependence with decay time τ, which, when substituted into Eq. (3.7b), yields a single exponential expression in the form of Eq. (3.6), with ∆f0 = aC(∞).

3.4.2.2.  Model of the multiple-analyte sensor response The model of single-analyte sensor response can be readily extended to multianalyte responses. It has been shown79,86 that sensor responses to binary analyte mixtures can be well modeled by dual exponential fits, and that the total equilibrium frequency shift in response to a binary mixture is the sum of the frequency shifts for the individual analytes (i.e., Df = Df1 + Df2) given by Ref. [87].

∆f ( t ) = ∆f1 [1 − e

−τt 1

] + ∆f 2 [1 − e

−τt 2

],



(3.8)

where t1 and t2 represent the response time constants of analyte 1 and analyte 2, respectively, and ∆f1 and ∆f2 represent the equilibrium frequency shifts of analyte 1 and analyte 2, respectively. The generic analytical model for the sensor response to a mixture of n analytes can be obtained by making two major assumptions. The first assumption is that the mixture obeys Henry’s law, from which it can be inferred that for a diluted mixture of multiple soluble species, the sorption of any given species into the polymer does not affect the sorption of the other species in any way. Free partitioning of analytes between the polymer and aqueous phase is inferred, including the implication that the sorption process is fully reversible at room temperature (i.e., only physisorption occurs). Based on experimental observations,79,86,87 Henry’s law is

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valid for analyte concentrations in the range of 0 to at least 10 ppm depending on the analyte. Moreover, Henry’s law implies that the concentration of the mixture in the coating at any time, t, is simply the sum of the concentrations of the individual analytes as they would be measured in a single-analyte response. The process of analyte absorption can then be assumed to be first-order (similar to the model of single-analyte sensor response). The second assumption is that the equilibrium frequency shifts are also mutually independent: the frequency shift due to the mixture at any time, t, is the sum of the frequency shifts due to each analyte in the mixture at that time. Based on these two assumptions, the sensor response to a mixture of n analytes can be represented by Ref. [87]:



γ p ,i 1 C i ( t ) = − Ci ( t ) + Camb,i ( t ) τi τi

(3.9a)

∆f ( t ) = − ∑ i =1ai Ci ( t ) ,

(3.9b)

and

n



where all the variables are as previously defined, with subscript i = 1, 2, …, n referring to each analyte in the mixture. Equation (3.9) represents the general analytical model for the sensor response to any number of analytes in the sample, provided that each possible target analyte and all interferents in the sample have been separately characterized for the appropriately selected coating in the relevant concentration range. For the desorption process (which is measured by exposing the sensor to clean (or filtered) water after the sorption process reaches the steady state), it is also observed that the sensor signal changes rapidly at first and then more slowly as it approaches the steady state. Thus, the desorption process can also be described using Eq. (3.9). The difference between absorption and desorption is reflected in the sign of the frequency shift, Df(t). It should be noted that, based on single-analyte experiments for a given analyte, sorption and desorption responses display the same absolute magnitude of equilibrium frequency shift — meaning that the responses are reversible, but they do not necessarily have the same response-time constants. Empirically, it is observed that desorption time constants are slightly different from sorption time constants, but the difference is usually within the experimental error margins. The mechanistic implication of this observation is that the dominant factor in determining the time constants is the rate of diffusion for each analyte through the polymer coating. In order to use the generic model of Eq. (3.9) to detect and quantify target analytes in a mixture and in the presence of interferents, two assumptions are made: first, that there can be an unknown number of interferents present with the target analytes; second, that the sensor response(s) due to these interferents does

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not dominate the response(s) due to the target analytes. From experimental observations for BTEX in groundwater,87 it was found that the interferents either have slower response time constants or lower sensitivities than the four target analytes, or both. In other applications, this condition often can be met by using appropriate sorbent coatings, i.e., coatings with significantly larger partition coefficients for the analytes than interferents. When the selected coatings and the set of interferents are such that the time-dependent absorption for all the interferents is relatively similar, a simple approach is to represent their combined response by a single exponential term. In such cases, Eq. (3.9) can be used to represent the sensor response of n − 1 target analytes (i = 1,2,…, n − 1) and the collection of interferents (i = n).87

3.4.2.3.  Applying estimation theory to multiple-analyte sensor response The model presented in the previous section can be used as the basis for estimation-theory-based sensor signal processing for the detection and quantification of BTEX compounds in the presence of interferents. For this purpose, the sensor response model of the multianalyte mixture (Eq. (3.9)) must be normalized, discretized, and converted into state-space form. Since the frequency shift data are collected at discrete-time instants, t = kTs, where Ts is the sampling period and k is a positive integer, Eq. (3.9a) can be normalized and discretized by dividing by gp,i Cmax,i (where Cmax,i represents the equilibrium ambient concentration), resulting in the following equations:94

mi ,k +1 = (1 − Si ) mi ,k + Si ui ,k



(3.10a)

and

n

∆f k = ∑ i =1ai mi ,k + wk ,



(3.10b)

where mi,k is the normalized concentration of analyte, i, Si = Ts/τi is the sorption rate constant, Ts is the sampling period, and ui,k is the concentration profile. To obtain Eq. (3.10), the term wk is added to represent the measurement noise with variance σ w2 , which is likely to be present during data collection. All other variables are as previously defined. From Eq. (3.10), the state-space form can be used by assigning state variables to the normalized concentrations mi,k, which can then be estimated using the estimation-theory-based technique together with the measured total frequency shift, ∆fk.87,95 Here, results are reported for the detection and quantification of BTEX compounds in groundwater in the presence of interferents using the above-­formulated sensor signal-processing model and measured SH-SAW sensor responses.

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Multiple measurement data with BTEX concentrations ranging from low ppb to low ppm levels were tested using this technique. The estimated ­concentrations obtained from these tests are summarized graphically as shown in Fig. 3.5. A pair of representative estimation results for each coating (i.e., PECH and PIB) are presented first to highlight the effectiveness of the technique. Figure 3.5(a) and Table 3.4 show such results for sensor response data, collected using an SH-SAW

(a)

(b)

Figure 3.5.    (a) Measured response of an SH-SAW sensor coated with 0.6 µm PECH to a groundwater sample containing 390 ppb benzene, 580 ppb toluene, 75 ppb ethylbenzene/xylenes, and unknown concentrations of interferents. Also shown (red dashed line) is the estimated sensor response using Eq. (3.9) with n = 5. (b) Measured response of an SH-SAW sensor coated with 0.8 µm PIB to a groundwater sample containing 400 ppb benzene, 800 ppb toluene, 550 ppb ethylbenzene/xylenes, and unknown concentrations of interferents. The red dashed line is the estimated sensor response using Eq. (3.10) with n = 5.94

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92  F. Josse et al. Table 3.4.  Estimated concentrations of BTEX compounds obtained from measured data on groundwater samples (collected using SH-SAW sensors with the listed coatings) compared to concentrations measured using GC-PID.94

Polymer PECH

Target analyte Benzene

390

353

9

Toluene

580

533

8

75

70

7

Benzene

400

420

5

Toluene

800

890

11

Ethylbenzene and Xylenes

550

517

6

Ethylbenzene and Xylenes PIB

Analyte Estimated analyte concentrations from concentrations GC-PID (ppb) (ppb) % Difference

sensor coated with 0.6 µm PECH, to a sample of contaminated groundwater that contained 390 ppb of benzene, 580 ppb of toluene, and 75 ppb of ethylbenzene and xylenes combined, in the presence of interferents commonly found in groundwater. Figure 3.5(b) and Table 3.4 show the estimation results, obtained using the response of a SH-SAW sensor coated with 0.8 µm PIB, to a sample of contaminated groundwater that contains 400 ppb of benzene, 800 ppb of toluene, and 550 ppb of ethylbenzene and xylenes combined, in the presence of interferents. Figure 3.5 shows that the estimated response curve in each case is in close agreement with the measured data points, which indicates the estimated equilibrium frequency shifts, ai, are close to the actual values. In Table 3.4, the estimated concentrations are in very good to excellent agreement with the concentrations independently determined using a gas chromatograph-photoionization detector (GC-PID) system.94 In this case, all estimated concentrations are within 5%–10% of the GC-PID measurements (measurement error is about ±7% for the GC-PID instrument96). The aforementioned results represent the detection and quantification of individual BTEX analytes in a mixture of multiple aromatic compounds plus interferents using estimation-theory-based signal-processing to analyze the data only from a single polymer-coated sensor device. The multianalyte response model requires no prior knowledge of the concentrations or specific identities of the target analytes, so long as they are among the BTEX group upon which the model is based. The use of both the equilibrium frequency shifts and the time constants of single-analyte responses to the target analytes and the dominant interferents are key to this approach. To make the method robust to chemical interferents — compounds other than the target BTEX molecules — the coated sensors were exposed to four different

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compounds selected to represent the classes of interferents commonly encountered in groundwater at sites of unintentional petroleum release. For the first of these, ethanol, no significant response was found up to concentrations of 100 ppm. For MTBE, a very low sensitivity was found (~1 Hz/ppm); since this compound is usually present at low concentrations, its effect on the sensor response is deemed negligible. For both 1,2,4-trimethylbenzene and n-heptane, high sensitivities (>1 kHz/ppm) and long response times were found. The high sensitivities mean that these compounds cannot be ignored, even at the low concentrations commonly encountered; however, since their response times are significantly longer than those of the BTEX compounds, they can be conveniently modeled by the last term (i = 4) in Eq. (3.9b). The same approach is expected to hold true for other, larger aromatic or aliphatic compounds. In different scenarios where each interferent has its own distinctive response and is present at concentrations high enough to interfere, the model can include further terms in Eq. (3.9): i = 4, i = 5, i = 6 … etc. In the specific scenario of detecting and quantifying BTEX analytes in the presence of interferents commonly found in groundwater, a five-analyte model (i.e., n = 1–5) is sufficient to analyze the six BTEX compounds/isomers along with the two most significant groups of interferents. The results, which compare sensor-extracted identification and concentration to actual concentration (using GC-PID), summarized in Fig. 3.6, demonstrate the capability of estimation-based

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Figure 3.6.    BTEX concentrations estimated from SH-SAW sensor measurements using multivariate signal processing and estimation theory vs. concentrations measured using GC-PID. The dotted line represents ideal estimation. Sensor coatings are PECH and PIB. Both contaminated groundwater samples (GW) and samples mixed in the lab using DI water (DI) were included in the study.

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sensor signal-processing to rapidly detect and quantify mixtures of BTEX compounds at ppb concentrations in the presence of interferents. The approach described in this section allows for more accurate detection and quantification of target analytes in near-real time using the data from only a singlecoated sensor device, with high tolerance of measurement noise and improved chemical speciation. This approach is suitable for field implementation in various applications, including wastewater and groundwater monitoring, monitoring plumes in a sub-surface marine oil spill, and spill clean-ups. Importantly, this approach is sensor-platform independent, i.e., the signalprocessing procedure should perform equally well with other sensing platforms such as chemiresistors, optical sensors, and MEMS-based sensors, provided that the sensor responses using these platforms (1) offer the appropriate limits of detection, dynamic range, and reproducibility, and (2) can be analytically modeled. This approach does not require a sensor array to achieve high accuracy in the individual quantification of multiple target analytes in a mixture, even in the presence of multiple interferents.

References   1. F. Bender, R. E. Mohler, A. J. Ricco, and F. Josse. Analysis of Binary Mixtures of Aqueous Aromatic Hydrocarbons with Low-Phase-Noise Shear-Horizontal Surface Acoustic Wave Sensors Using Multielectrode Transducer Designs, Anal. Chem., 2014, 86, 11464–11471.  2. U.S. Environmental Protection Agency. Semiannual Report Of UST Performance Measures, End Of Fiscal Year 2018. U.S. Environmental Protection Agency: Washington, D.C., 2018. https://www.epa.gov/sites/production/files/2018-11/ documents/ca-18-34.pdf.  3. A. K. Mensah-Brown, M. J. Wenzel, F. J. Josse, and E. E. Yaz. Near Real-Time Monitoring of Organophosphate Pesticides in the Aqueous-Phase Using SHSAW Sensors Including Estimation-Based Signal Analysis, IEEE Sens. J., 2009, 9, 1817–1824.   4. A. K. Mensah-Brown, D. Mlambo, F. Josse, and J. Hossenlopp, Rapid detection of organophosphates in aqueous solution using a hybrid organic/inorganic coating on SH-SAW devices, 2010 IEEE International Frequency Control Symposium, 2010, pp. 232–237, doi: 10.1109/FREQ.2010.5556335.   5. H. Li, J. Li, Z. Yang, Q. Xu, and X. Hu. A Novel Photoelectrochemical Sensor for the Organophosphorus Pesticide Dichlofenthion Based on Nanometer-Sized Titania Coupled with a Screen-Printed Electrode, Anal. Chem., 2011, 83, 5290–5295.   6. H. Li, Z. Wang, B. Wu, X. Liu, Z. Xue, X. Lu. Rapid and Sensitive Detection of Methyl-Parathion Pesticide with an Electropolymerized, Molecularly Imprinted Polymer Capacitive Sensor, Electrochim. Acta, 2012, 62, 319–326.

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 7. W. Pinkham, M. Wark, S. Winters, L. French, D. J. Frankel, and J. F. Vetelino. A Lateral Field Excited Acoustic Wave Pesticide Sensor, Proc. IEEE Ultrason. Symp., 2005, 4, 2279–2283.  8. O. Sadik, W. H. Land, Jr., A. K. Wanekaya, and M. Uematsu. Detection and Classification of Organophosphate Nerve Agent Simulants Using Support Vector Machines with Multiarray Sensors, J. Chem. Inf. Comput. Sci., 2004, 44, 499–507.   9. R. P. Schwarzenbach et al. The Challenge of Micropollutants in Aquatic Systems, Science, 2006, 313, 1072–1077. 10. P. J. McNamara, T. M. LaPara, and P. J. Novak. The Impacts of Triclosan on Anaerobic Community Structures, Function, and Antimicrobial Resistance, Environ. Sci. Technol., 2014, 48, 7393–7400. 11. D. E. Carey, D. H. Zitomer, K. R. Hristova, A. D. Kappell, and P. J. McNamara. Triclocarban Influences Antibiotic Resistance and Alters Anaerobic Digester Microbial Community Structure, Environ. Sci. Technol., 2016, 50, 126–134. 12. M. Houde, A. O. de Silva, D. C. G. Muir, and R. J. Letcher. Monitoring of Perfluorinated Compounds in Aquatic Biota: An Updated Review, Environ. Sci. Technol., 2011, 45, 7962–7973. 13. P. Zareitalabad, J. Siemens, M. Hamer, and W. Amelung. Perfluorooctanoic Acid (PFOA) and Perfluorooctanesulfonic Acid (PFOS) in Surface Waters, Sediments, Soils and Wastewater — A Review on Concentrations and Distribution Coefficients, Chemosphere, 2013, 91, 725–732. 14. Z. Wang, S. A. Stout, and M. Fingas. Forensic Fingerprinting of Biomarkers for Oil Spill Characterization and Source Identification, Environ. Forensics, 2006, 7, 105–146. 15. C. K. Ho, A. Robinson, D. R. Miller, and M. J. Davis. Overview of Sensors and Needs for Environmental Monitoring, Sensors, 2005, 5, 4–37. 16. P. I. Nikitin, A. A. Beloglazov, V. E. Kochergin, M. V. Valeiko, and T. I. Ksenevich. Surface Plasmon Resonance Interferometry for Biological and Chemical Sensing, Sens. Actuators, B, 1999, 54, 43–50. 17. S.-F. Cheng and L.-K. Chau. Colloidal Gold-Modified Optical Fiber for Chemical and Biochemical Sensing, Anal. Chem., 2003, 75, 16–21. 18. J. F. Fernández-Sánchez, A. Segura Carretero, C. Cruces-Blanco, and A. FernándezGutiérrez. Highly Sensitive and Selective Fluorescence Optosensor to Detect and Quantify Benzo[a]pyrene in Water Samples, Anal. Chim. Acta, 2004, 506, 1–7. 19. M. Karlowatz, M. Kraft, and B. Mizaikoff. Simultaneous Quantitative Determination of Benzene, Toluene, and Xylenes in Water Using Mid-Infrared Evanescent Field Spectroscopy, Anal. Chem., 2004, 76, 2643–2648. 20. C. McDonagh, C. S. Burke, and B. D. MacCraith. Optical Chemical Sensors, Chem. Rev., 2008, 108, 400–422. 21. B. Pejcic, M. Myers, and A. Ross. Mid-Infrared Sensing of Organic Pollutants in Aqueous Environments, Sensors, 2009, 9, 6232–6253.

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Chapter 4

Strategies for Development of Protein-Based Biosensors for Detecting Aromatic Xenobiotics in Water Subhankar Sahu† and Ruchi Anand‡,* Department of Chemistry Indian Institute of Technology Bombay Mumbai, India 400076 [email protected]

†

[email protected]

‡

4.1. Introduction Water is the most essential element for the sustenance of life. Yet, despite modern advances in science and technology, access to safe water has become increasingly difficult. Statistics show that one in nine people worldwide lack access to safe drinking water. Contaminated water is often the source of serious health problems and the cause of several hygiene-related diseases. The lack of available sources of clean water is aggravated in certain parts of the world due to unregulated discharge of industrial waste in river and lake systems. Therefore, proper sensing technologies that can continuously monitor effluents in water systems are greatly needed. When combined with appropriate remediation approaches, these sensing technologies can reduce the total volume of unsafe water, thereby helping to uplift the global economy. Xenobiotics, such as benzene, toluene, phenol, and xylenols, enter bodies of water as byproducts of petrochemicals from tanneries, dyes, and packaging  Webpage: http://structuralbioiitb.wix.com/ruchianand

*

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industries. Some of these chemicals have long residence times and pose harmful and long-term effects on human health. This chapter focuses on the development of sensitive, selective, and durable sensors for detecting xenobiotic compounds. The development of sensors with the specificity of interaction between a recognition scaffold and an analyte is a fundamental and urgent problem in the field of sensing.1,2 Since the beginning of the 21st century, the field of sensor design has undergone an explosion due to significant advances in application-based engineering, where inventions in application-based nanotechnology, engineering, and semiconductor electronics have made device development easier and increased device sensitivity by several folds.3,4 Considering their vast applications, one can find plethora of sensors, from gas sensors for monitoring human breath quality5 to electrochemiluminescence sensors for monitoring cancer,6 both of which have been produced as a result of research efforts in this area. In the bioscience field, sensor development has gained even more significance due to its vast therapeutic value and the scientific challenges it poses to harness them. Ever since Clark and Lyons developed a method in 1962 to quantify glucose in a biological sample, based on glucose oxidase activity, there has been a serious effort to use the intelligent biochemical machinery.7 The use and evolution of biosensors have traditionally centered on enhancing their sensitivity and selectivity of protein or enzyme molecules toward their ligand/effector. Immobilization of the enzyme, such as glucose oxidase onto a semipermeable dialysis membrane, further led to its translation as a compact sensor platform.8 The fundamental design of a biosensor has two major components, one associated with the chemistry of sensing and the other with the signal readout via interfacing with appropriate electronics (Fig. 4.1). The idea to develop a biosensor is focused on enhancing the sensitivity and selectivity of biomolecules for their corresponding ligands, both in terms of the chemical and structural determinants. Its binding is shaped by eons of molecular evolution, making the interaction acutely specific. In a typical biosensor, detection occurs when an analyte binds with its complementary unit, referred to as the biological recognition element (BRE). The nature of the BRE can vary in different sensor prototypes, ranging from biosynthetic enzymes to antibodies.9 An effective BRE relies on its conformational specificity due to the presence of particular pockets in enzymes where only a subset of ligands can be accommodated, making the biological recognition even more particular.10,11 Sensor developers have taken advantage of this attribute of biomolecules to develop research methodologies for efficient and sensitive biosensors.12–14 Principally, the binding of the analyte to the enzyme produces a response or signal that is carried out to a transducer module, and the final readout is measured either by spectrophotometric means or electrochemically (Fig. 4.2).1 Protein and enzyme-based biosensors can detect analytes using direct and indirect detection approaches. Direct or label-free detection methods are very attractive because

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Figure 4.1.    Different targeted samples and the principle elements of the biosensor. The transducer can be thought of as a combination of a bio-receptor and an interface to carry out the electrical signal. Potential application of the sensors is focused on the food, pharmaceutical, and environmental samples. Adapted from open access article in Ref. [81].

Figure 4.2.    Design of protein-based biosensor based on the biochemical machinery of the NtrC family of proteins. Upon the binding of the aromatic pollutants (as effectors) to the sensor domain, a conformational change is induced in the consecutive ATPase domain which makes it amenable toward ATP hydrolysis.

only the protein standalone can bind the analyte/ligand and generate detectable signals through some chemical activity that can be quantified.15 With indirect detection methods, a series of enzyme combinations or subsequent chemical tags are introduced which can then produce a measurable signal, thereby, adding steps

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where signal can be compromised.16 The recognition unit frequently is immobilized on a different substrate, such as carbon nanotubes, graphene, and polymers, to create sensors with effective real-time readout in a device setting. Selecting a BRE has always been challenging, and efforts continue toward a suitable substrate combination for designing novel biosensors.17 In the past two decades, the field of synthetic biology has flourished in many ways and thus has significantly affected the momentum of associated fields like biosensor design. Several biosensors have emerged using this technique.14 For protein-based biosensors, sensitivity and selectivity are not major barriers, as many sensors have reached the lowest detection limit (LOD) of ng/ml, some even as low as fg/ml. However, other important attributes include the dynamic range of sensing, reproducibility, and stability.18 Several enzymes fail on these attributes, especially toward stability and long shelf life. Therefore, to some extent, protein engineering, fusion protein, and conjugation approaches have been used to enhance these qualities.14,19 Most of the biomolecules traditionally used for developing biosensors are either commercially available or can be synthesized easily in the laboratory at a reasonable cost. Importantly, it is crucial to ensure minimal batch-to-batch variation. Recombinant biosensors are generally produced in synthetic bacterial systems and thus the protein quality can vary from batch to batch. As a result, procedures have been developed and optimized such that both qualitative and quantitative variation is limited, making the sensors reproducible.17,20,21 Starting from the last decade of the 20th century, oil and natural gas industries grew reasonably fast due to the high demand for these fuels in the global market, indirectly causing the accumulation of abounding wastages, which are byproducts of toxic chemicals. The effluents from these sources and their mishandling have caused severe toxicity in the food chain and the environment, affecting almost all forms of life on earth.22–24 Bulk volume of aromatic compounds like phenol benzene and their major derivatives, such as the BTEX (i.e., Benzene, Toluene, Ethylbenzene, and Xylenes) group of compounds, accumulate as xenobiotics in the environment when discharge from paper, pharmaceutical, pulp and petroleum industries. The seriousness of the contamination of such aromatic compounds can be understood by reports, such as in 2014, when approximately 10,000 people in Ireland were poisoned by phenol-contaminated drinking water. Another incident, in which approximately 2.4 million people in China were severely affected by benzene-contaminated ground water, was reported by the BBC in 2014. As a result of many of such reports of contamination around the globe25,26 and the serious health threats posed by the phenol and benzene group of chemicals, the U.S. Environmental Protection Agency (EPA) categorized these compounds in its list of top-priority pollutants. Considering the major catastrophic effects of these xenobiotics, the dire need to develop sensors for detecting the phenol/benzene group of compounds came

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into the picture. These compounds lack detectable functional groups, making the development of direct detection strategies even more difficult. Traditional HPLC, GC-MS, and LC-MS methods routinely detect organic compounds from environmental samples,27–29 but they have a complicated experimental setup and are quite expensive, and not suitable for the easy, on-site detection of pollutants. Therefore, biosensors are the logical alternatives for this class of compounds. To combat the harm caused by xenobiotics, many bacteria in the natural world have evolved with biochemical machines that can degrade some of the major pollutants. Here, the bacteria activate biochemical detoxification pathways when a threshold is crossed for a particular pollutant. Several microorganisms, like Pseudomonas sp. and Acinetobacter sp., can use pollutants like phenol, catechol, and benzoate as their carbon source and use the metabolic energy by degrading them into non-toxic tricarboxylic acid cycle intermediates.30–33 The biodegradation pathway is under the control of a class of transcriptional activators which activate sigma-54-dependent RNA polymerase and then transcribe the degradation genes in response to stimuli by these pollutants. To induce the transcription and synthesis of the proteins, these transcription factors contain sensor units that sense the very same pollutant that they degrade. One such sensor activator system is the two-component NtrC family of proteins identified by Ray Dixon and coworkers.34,35 The protein members are classified based on a conserved AAA+ ATPase domain, which is present in these two-component signaling systems. MopR,36 DmpR,37 and XylR38 are members of the NtrC family, harboring sensor domains for pollutants such as phenol, bulkier phenolic derivatives like dimethyl phenols, and benzene (Fig. 4.3). Because of the ability of this NtrC subfamily of proteins to detect/bind the aromatic pollutants, there has been a long-standing effort by several research groups to develop protein-based biosensors.39,40 However, working with these proteins has been painstaking due to their low solubility and thus in vitro sensors have been difficult to realize. Most of the developed sensors were based on whole cell approaches for detecting pollutants;39,41 however, due to the lack of knowledge in sensor architecture, intelligent structure-based sensor design could not be pursued. Random mutagenesis and domain-swapping experiments in whole cell systems provided only limited information. A major breakthrough came into the field when, in 2016, Anand and her research team solved the crystal structure of the phenol-sensing domain of the regulatory protein MopR.36 This led to structure-guided design for sensor development and thus a broad spectrum of aromatic pollutant sensors could be created.36 To optimize the biosensors, the active site pocket was tweaked for a pollutant-BRE unit for detecting toxic pollutants like phenol, 3-chlorophenol, catechol, benzene, and m-xylene.42,43 The developed detection assay was a colorimetric, cheap, malachite green-based ATPase assay, which has a robust design for monitoring phosphate released as a byproduct of ATP hydrolysis. The principle design framework and the sensitivity and selectivity of an MopR-protein-based

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Figure 4.3.    Structural comparison of aromatic-compound-sensing NtrC family proteins. Protein like DmpR, XylR, and HbpR are compared with MopR. The sensor domain percentage identity is given along with the ATPase domain identity. It can be seen that the DmpR sensor domain shares 42% residue identity with MopR, whereas the ATPase domain has ~64% identity. The ATPase domain is more conserved in all the NtrC family proteins. The other conserved segment of the protein is the DNA-binding domain (DBD). Mentioned proteins bind to the following contaminants: MopR — Phenol, DmpR — Phenol/Phenolic derivatives, XylR — Xylene/Benzene/Toluene, HbpR — 2-hydroxybiphenyl/2,2′-dihydroxybiphenyl.

sensor is presented throughout this chapter, narrating the pivotal role of the structure and ligand interaction conformation and depicting a novel bioengineering approach toward the substrate specificity alteration of a biosensor.

4.2. NtrC Protein Family Domain Organization and Biological Function Transcriptional activation in proteins by RNA polymerase (RNAP) is tightly regulated by certain adaptor proteins. In typical bacterial systems, a σ factor is essential for initiating the transcription by core RNA polymerase, which consists of four major domains, namely (αββꞌω).44,45 Two types of sigma factors are found in bacterial cell as a control button toward RNAP activation, σ7046 and σ54.47 Sigma 70 (σ70) is the prototypical sigma factor responsible for the transcription initiation from the promoter sequence at position −35 and −10, which helps transcribe most of the housekeeping genes and requires no activation. Sigma 54 (σ54) is the other type of factor that controls the synthesis of genes that are transcribed under particular stimuli, such as those dedicated to stress response.

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These sigma-54-dependent RNAPs are also known to control the expression of different virulence-associated genes in response to different stimuli.47 The activation of the σ54-RNAP complex occurs via an energy-dependent motor protein family broadly known as the bacterial-enhancer-binding proteins (bEBPs).48 The bEBPs bind the DNA around 100 base pairs upstream of the promoter region and bear a signature ATPase domain and a signal activation region, along with another helper protein referred to as integration host factor (IHF).49 Upon activation, bEBPs come into the proximity of the σ54-RNAP complex and, via the conformational changes triggered by ATP hydrolysis, they assist in transcription initiation (Fig. 4.4). Nitrogen regulatory protein C is one of the very first members of the bEBPs for which the structure was decoded, and thus all the homologous proteins with this conserved ATPase domain are henceforth referred to as NtrC family proteins. NtrCs are two-component systems that contain a receptor domain, which then communicates and controls the activity of the AAA+ ATPase domain.50 Variation in family members is mostly in the diverse N-terminal-signal-sensing domain which then determines the mode for activating the proteins. Transcriptional

Figure 4.4.    Transcription activation of σ54-RNAP complex by ATPase activity-dependent energy transfer from the MopR protein through the interaction between σ54 and MopR. Many of the NtrC family proteins hexamerize upon effector binding (here MopR with phenol as activator is shown). IHF is another helping protein which directs the local bending of DNA and assists with the throughspace interaction between the MopR and σ54-RNAP complex. MopR protein upon binding with phenol as the effector molecule undergoes ATP hydrolysis reaction, initiating a conformational change to activate the σ54-RNAP complex.

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regulation and activation of the NtrC family proteins occur via three different modes: (a) chemical modification (e.g., phosphorylation) of a particular residue in the sensor domain, (b) binding of a particular effector molecule to the sensor domain (such as phenol), and (c) protein–protein interaction. Each of these mechanisms subsequently induce a conformation change in the consecutive transcriptional activation domain (i.e., ATPase domain), thereby initiating transcription. Here, we focus on members that operate by mode (b) and bind to a host of diverse aromatic ligands. This group of NtrC family proteins is able to bind to major pollutants such as phenol,31 toluene, and other bulkier phenolic derivatives37,51 which are regarded as top-priority xenobiotics. The modular nature of these two component systems, with an “OFF” ligand free state in which ATP hydrolysis is inhibited, and an “ON” ligand bound state in which ATPase activity is initiated, makes them perfect candidates for biosensors.36

4.3. MopR: Phenol-Sensing Protein 4.3.1.  MopR domain organization MopR, a multidomain NtrC family of transcriptional regulators, activates the σ54-RNAP complex from the −12/−24 upstream site of the promoter and directs the synthesis of proteins, which degrades the phenol through transcription initiation.31,36 Structural organization of MopR includes three major domains, A, C, and D, and a linker connecting the A and C domain referred to as B linker. MopR is a 560 amino acid protein. Based on both structure and function, the first 1-233 amino acid form the A domain which is responsible for the molecular recognition of phenol (i.e., phenol binding). The C domain is the central ATPase domain (where ATP hydrolysis occurs) and harbors the canonical AAA+ fold found in the ATP motors that operate the proteome. Following the C domain connected by a long loop is the 60 amino acid helical D domain, which is responsible for binding to the upstream activating sequences (UAS) DNA recognition element.31 As mentioned earlier, most of the proteins that belong to the xenobiotic subfamily like DmpR and XylR were found to be insoluble; however, in the case of MopR, with extensive tweaking the signal sensing and the ATP hydrolysis domain could be obtained in a soluble form, and thus the sensor module could be designed using this as a model system. Here, as mentioned earlier, the ATPase domain acts as a natural signal transduction domain and the regulation of the ATPase activity is coupled with the binding of the phenol to the sensor domain (Fig. 4.5), thereby conforming to the two “ON” and “OFF” states which are desirable for a sensor. As in other NtrCs, sensor (phenol) binding induces an allostery-based restructuring of the enzymes’ ATPase activity. The mechanical and chemical energy available through the ATP hydrolysis allows the52,53 C domain to interact with the σ54-RNAP complex and activates the synthesis of the downstream genes to degrade the phenol.

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Figure 4.5.    Structural allostery of the MopR protein. In the resting/inactive state of the protein the ATP hydrolysis face is blocked by the A domain. Upon the binding of the phenol to the sensor domain (A domain) a conformational change occurs which makes the ATP hydrolysis surface accessible. In the absence of effector (i.e., phenol) the ATPase activity of the protein is repressed by its own structural stringencies.

4.3.2.  Structure of the phenol sensor One of the key aspects of biosensor design is understanding the basis of the specificity and selectivity of the pocket. This is important because the sensor can then be diversified to accept variants of the parent ligand. In the case of MopR, the primary sensing molecule is phenol, and thus the structure of the phenol-sensing pocket is paramount to delineating the conformational identifiers of its binding. The crystal structure of the MopR sensor domain (A domain) was solved in 2016 and opened many avenues for sensor design. This was the first structure from the DmpR/XylR/MopR subfamily of NtrC family proteins that detected aromatic pollutants.36 The structure revealed that the overall monomeric architecture of the MopR, sensor falls in the “nitric oxide signaling (NOS) and golgi transport” fold. Essentially, the basic sensor A-domain fold consists of 7 alpha helices and 7 beta strands topologically labeled as α1 – α7 & β1 – β7, respectively. Interestingly, a search using the DALI server for similar 3D folds revealed that this fold is common in eukaryotic complexes that tether onto the mitochondria.36 Although the eukaryotic counterparts harbor the same fold, they have very little similarity in function and contain fatty-acid-binding sites.54,55 Moreover, unlike MopR’s, phenol sensor domain, which is dimeric, the eukaryotic counterpart lacks the dimerization N-terminal domain and is monomeric. In MopR, the N-terminal domain and helix 5 (α5) intertwine to form the dimeric interface, which seems to be essential for stability. Furthermore, the crystal structure shows that in each of the

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monomers, four antiparallel β strands, along with the α3, α4, α6, create the pocket to be amenable toward binding the phenol in its concavity. Apart from the phenol-binding pocket, a surprising discovery was the presence of a zinc atom. The zinc-binding site was located 10 Å distant from the phenol-binding pocket and does not seem to play any direct role in phenol sensing (Fig. 4.7). Before the structure solution, the presence of zinc ion was not known, and part of the reason why several constructs of this family were insoluble was because this region, although showing as an unstructured region in secondary structure predictions, is integral for stability and folds compactly to harbor the zinc ion. The zinc atom is anchored by a novel signature sequence comprising three cysteine residues C155, C181, C189, and a glutamic acid E178. EDTA chelation studies showed the zinc atom is very tightly bound and plays an integral role in the structural stability of the protein.36 The phenol-binding site was found to be deeply buried in the protein. Attempts were made to solve the apo form of the sensor, but they were largely unsuccessful because the apo form of the enzyme is highly dynamic and flexible. It appears the pocket is only fully formed in the presence of the sensor molecule. Primary anchoring residues for binding the phenol are histidine 106 (H106) and tryptophan 134 (W134), which stabilize the pollutant through the hydrogen-bonding interaction with the O-H group of phenol; the binding synergy is very specific in nature (Fig. 4.6). The interaction that bonds the phenol with H106 is much stronger in nature, and mutation of this residue (e.g., to alanine) compromises the phenol

Figure 4.6.    Crystal structure of the sensor domain (A domain) of MopR with a zoomed-in view of the phenol-binding pocket. The phenol is anchored by a hydrogen-bonding interaction from H106 and W134 residue. Phenol bound in the concavity is also stabilized by the other residues labeled in the figure. The zinc atom is shown by an orange sphere. Adapted with permission from Ref. [36]. Copyright © 2016, American Chemical Society.

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(a)

(b)

Figure 4.7.  Conformation of the Zn-binding site in the MopR protein sensor domain. (a) The orange sphere is the Zn atom; the residues stabilizing the Zn locally are labeled by their amino acid symbol. (b) Global view highlighting the ligand and the zinc-binding region. The Zn atom is distantly located from the phenol-binding site, and its role is inherently structural in nature. Adapted with permission from Ref. [36]. Copyright © 2016, American Chemical Society.

sensitivity more than that of W134. This is highlighted by the ITC experiments, where the Kd value is almost two times higher with the H106A mutation than that of H134A (Table 4.1). The chemical nature of the rest of the pocket is mostly hydrophobic, with a diameter of approximately ~5Å, where π-π stacking interactions dominate stability of the aromatic ring. Residues F99, F132, Y161, Y165, and Y176 form the wall of the binding pocket and significantly contribute to the stabilization of the phenol moiety (Fig. 4.6). Here, we stress that the determination of the structure was very important for moving the field forward. Understanding of the specific binding residues of the phenol in the MopR sensor domain pocket has led toward the design of a series of aromatic pollutant sensors in which intelligent design can be exploited. Prior to detailed scientific insight into the crystal structure, many research groups have focused on random-site-directed mutagenesis to find effective phenol binding/ interacting residues; however in the absence of a proper structure of the sensor domain in any of the NtrC Group 2 proteins,56 their studies were non-confirmative (Fig. 4.8). Several groups concluded that either dimerization residues or zincbinding residues served as phenol sensor determinants,57,58 because any mutations in these regions rendered the protein unstable and decreased sensor activity.37

4.3.3.  Biosensor design for phenol detection In a good biosensor design, the signal detection is direct and occurs with high sensitivity and selectivity, and the readout should be nearly immediate so as to be tunable for on-site detection. The most desirable properties of the MopR biosensor are the presence of the sensor domain, in-built ATPase domain (which can act as

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112  S. Sahu & R. Anand Table 4.1.  Thermodynamic binding parameters of the MopRAB biosensor unit tabulating the relative binding ability of MopR compared with other similar molecules. Column 2 shows mutations that led to the design of an exclusive xylenol sensor which no longer binds phenol efficiently. The values are obtained from an ITC experiment. MopRAB mutants

Aromatic compounds

MopR

AB

MopRABWHA

ΔG (kcal/mol)

Kd (µM)

phenol

−8.92 ± 0.06

0.46 ± 0.06

o-cresol

−8.22 ± 0.15

0.78 ± 0.05

catechol

−7.19 ± 0.02

4.12 ± 0.08

3-cp

−8.55 ± 0.13

0.55 ± 0.06

m-cresol

−7.83 ± 0.17

1.80 ± 0.03

3,4-dmp

−3.15 ± 0.09

4.02 ± 0.01

resorcinol

−7.74 ± 0.61

2.16 ± 0.03

hydroquinone

−7.56 ± 0.09

3.69 ± 0.17

phenol

−5.88 ± 0.26

38.46 ± 0.13

o-cresol

−6.56 ± 0.29

15.50 ± 0.07

3-cp

−6.98 ± 0.15

9.69 ± 0.06

m-cresol

−7.14 ± 0.13

8.04 ± 0.11

3,4-dmp

−6.87 ± 0.24

4.99 ± 0.06

Figure 4.8.    Mapping of reported DmpR/XylR mutations (green beads) on a single monomer of the MopRAB dimer; the MopR phenol-binding residues are the deep purple beads. The bound phenol is represented as a magenta stick, and the Zn atom as an orange sphere. It can be seen that the residues involved in previously reported mutation studies do not overlap with actual phenol-binding site residues. Adapted with permission from Ref. [36]. Copyright © 2016, American Chemical Society.

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a transducer), and their allosteric regulation in one combined protein, which served as a platform for sensor development. The design of the biosensor based on the ATPase activity of the protein was conceived after the structure was solved and proper phosphate response analysis was performed in the presence of the effector phenol.43 In many biosensor design studies, different MopR domain constructs are designated as MopRA: sensor domain of MopR, MopRAB: sensor domain and the B-linker of MopR, MopRABC/MopRAC: sensor domain along with the ATPase domain of MopR. Previous research works have clearly showed that the extent of ATPase activity of the protein is regulated through the phenol binding and is directly proportional to the pollutant (i.e., phenol) concentration. The greater the ability of the phenol detection/binding in the sensor domain, the larger the extent of the followup ATPase activity of the MopR C domain.36 The assay designed to monitor sensor activity is based on sequestering the inorganic phosphate produced as a result of ATP hydrolysis by any inorganic phosphate detection protocol, in which the phosphate produced is complexed by malachite-green-based dye. The assay based on the complex formation between phosphomolybdate and malachite green was optimized by A. A. Baykov et al. in 1988 and is very sensitive for phosphate detection. The solution used for the phosphate detection was optimized and consisted of a mixture of malachite green dissolved in 6N sulfuric acid, ammonium molybdate, and the detergent TWEEN 20.59 The inorganic phosphate generated in the reaction by degrading the ATP through MopR forms a complex with molybdate. This results in a phosphomolybdate complex, which changes the color of the dye from orange to green. A larger amount of orthophosphate formation results in a higher phosphomolybdate complex, which increases the intensity of the green color. A spectrophotometric detection of the green color at 630 nm is quantified and the amount of phosphate generated through the ATPase activity inferred. A standard phosphate curve was made using the malachite-green-based dye method to directly correlate total phosphate. A standard curve, with a measured amount of phenol at a fixed enzyme concentration and a fixed time, was optimized for the best sensitivity and subsequently made, and phenol in unknown water samples was measured using the interpolation method. It is intriguing to notice that, the MopR biosensor prototype has a lower limit of detection of 0.1 µM (10 ppb), which is less than the most of the previous detection/sensing strategies reported.60 According to the U.S. EPA, phenol is a hazardous water pollutant and lifetime exposure to phenol in drinking water should not exceed 2 mg/L (~2 ppm). According to the U.S. Food and Drug Administration (FDA), the phenol content in bottled drinking water should be less than 0.001 mg/L (~0.9 ppb). The cumulative 8-h total weight average (TWA) permissible exposure limit (PEL) for industries like construction and shipping should not exceed 5 ppm of phenol, as set by the U.S. Occupational Safety and Health

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Administration (OSHA). Our sensor reaches very close to the detection limit, not only for industrial waste waters but also for drinking water samples. The current limit of detection can be easily improved by shifting to an ATPase-based fluorescence assay, which can easily become suitable for minute phenol detection. This is important, as all bottled water is currently screened for phenolic and aromatic xenobiotics, and in the absence of any direct instantaneous detection method, the concentration of samples is followed by chromatographic analysis. Here, the developed biosensor has an advantage: no sample concentration is required and direct instantaneous detection can be achieved. Moreover, the The biosensor’s greatest strength is its very high selectivity for phenol, and this protein-based sensor has minimal off-target effects from the other contaminants (Fig. 4.9(b)). For instance, the biosensor can selectively detect phenol in the (a)

(b)

(c)

(d)

Figure 4.9.    Biosensor design for phenol detection. (a) Model design of the MopRAC protein-based biosensor. (b) Aromatic substrate-binding profile of the MopR protein. The sensor shows the most activity for phenol. (c) Thermostability of the MopR biosensor is represented as a CD-based melting curve for two different MopR variants labeled as Construct 1 and Construct 2. (d) Comparison of the biosensing efficiency of the MopR protein with a standard phenol solution vs. simulated waste water. Adapted with permission from Ref. [60]. Copyright © 2018, American Chemical Society.

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micromolar range, even in presence of wastewater, which has millimolar levels of impurities such as antibiotics and heavy metals. The design of the readout assay is simple, sensitive, and straightforward. Moreover, the source of the protein is microbial and thus can be produced in large amounts with a low cost and higher accuracy. An important quality integrated into the sensor design is the robustness of the protein in use. The initial full-length MopR protein was not very soluble. By carrying out optimal protein-engineering approaches, thermostable sensor units (Fig. 4.9(c)). The sensor and readout domain Combinations were tweaked to deliver sensor units that were stable and could detect phenol in water, even at 70°C, which can be a very attractive feature for any biosensor (Fig. 4.10). The protein

(a)

(b)

(c)

(d)

Figure 4.10.    Stability of the MopR-based biosensor (a), (b) SDS-PAGE gel and assay showing that the stability of the protein is unaltered for a test period of 20 days. (c) SDS-PAGE gel of the MopR, showing thermal stability in the range of 25°C–90°C temperature gradient. (d) ATPase activity of the protein monitored for a similar temperature gradient. The biosensor activity is still retained, even up to 70°C. Adapted with permission from Ref. [60]. Copyright © 2018, American Chemical Society.

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also had an enhanced shelf life and was stable at room temperature for several weeks. The basal activity of the protein is inherently low and helps in the rapid and efficient detection of phenol in water samples, which also makes the phenol sensor commercially attractive. Taken together, a thermostable, selective, and sensitive biosensor for phenol with a cost-effective rapid assay was engineered.

4.3.4.  Aromatic pollutant screening for MopR: Alteration of sensor design To diversify the substrate scope and understand the determinants that control ­sensor design, the biosensor structure in combination with isothermal calorimetry was used to assess the substrate profile of native MopR sensor. The binding constant (Kd), from the isothermal titration calorimetry experiment of the aromatic ligands, targeted to the sensor domain explicitly helped to delineate the interaction profile. The phenol was concluded to be the best binder, with a Kd value of 0.46 ± 0.06 µM. The sensitivity of the 3-cholorophenol binding is comparable with that of phenol, and with a binding constant of 0.55 ± 0.06 µM43 (Table 4.1), 3-cholorophenol is the second-best binder. Several other ligands, such as o-cresol and m-cresol, were found to exhibit Kd values in the nanomolar to low micromolar range, 0.78 ± 0.05, 1.80 ± 0.03, respectively. Aromatic moiety containing bulkier pollutants like resorcinol, hydroquinone, 3,4-dimethylphenol, and catechol were also tested as candidates for the binding experiment with the MopR sensor domain, and their Kd values were found to be in the higher region of 2–4 µM range (Table 4.1). Bulkier compounds with phenolic moieties were not at all accepted in the MopR pocket. Hence, it was concluded from the ITC experiment that bulkier aromatic compounds are not properly accommodated in the sensor pocket, and only small substituents on the basic phenol unit are accepted. To eliminate cross-reactivity with smaller phenolics and to make more ­selective catechol and xylenol sensors, structure-based engineering design was undertaken. Altering the ligand-binding specificity using the MopR protein was a major target for protein-based biosensor design, as it allows for the detection of many related pollutants with high selectivity and sensitivity. One sensor unit, MopR, can be used to design variants with specificity for a particular pollutant. The idea was to modify the sensor pocket to make a plethora of specific sensors. This was important because most of the other natural sensors that can sense aromatic compounds turned out to be insoluble proteins and could not be purified in sufficient quantities in a stable form. Since all these aromatic sensors have a similar structure-based design, the MopR could be redesigned to act as a sensor for this inaccessible group as well as a novel sensor for bio-similar pollutants. To achieve this, an intelligent structureguided design was conceived to mutate the binding residues in the phenol-binding

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pocket of the MopR and change the ligand-binding properties to sense other hazardous aromatic contaminants. The in silico approach was used to design the sensor-pollutant combinations. Essentially, using the crystal structure, in silico point mutations were made in the sensor pocket to fit the pollutant of choice and calculate the binding energy. Competent combinations were then chosen for experimentation. Solving the crystal structure of the protein was crucial because it enables to scrutinize detailed binding interactions between the phenol and the anchoring residues. Such structural knowledge subsequently served as a guide for the selection of residues that can be modified and tweaked for sensing other aromatic hydrocarbons. Docking studies on these mutated versions served as a stepping stone for ­making MopR variants that could act as sensors for a set of pollutants. For instance, methyl-group-substituted phenols, xylenols, such as 2,3-dimethylphenol (2,3-dmp), 3,4-dimethyphenol (3,4-dmp) are a major class of pollutants that can be found in contaminated rivers and lakes, and present a potential health threat due to their very long residence times. In native protein sensing, the bulkier phenolic derivatives (like DmpR) are difficult to prepare in a pure form in-vitro due to their insolubility.41,61 The lacuna in the field of bulkier phenolic sensors was surpassed by the alteration of the MopR phenol-binding pocket targeted to accommodate and sense bulkier dimethylphenols (dmps). It can be easily understood from the structural standpoint that to accommodate a dimethyl-substituted phenol, more conformational space is required in the pocket. In silico screening showed that the best exclusive binding of the xylenol is with the MopR W134A-H106A mutation. Molecular docking showed that the free energy from the binding of 3,4-dmp was −5.79 ± 0.19 kcal/mol, which was considerably greater than that of the native protein. A docking interaction landscape was insightful enough to show that, compared with the native phenol orientation, the molecule reorients and stabilizes itself through hydrogen bonding with A162 residue (Fig. 4.11). Hence, both the hydrogen bond interacting partners of phenol such as W134 and H106 were mutated to alanine to create space in the pocket; after this structural mutation, it was understood that the MopR can bind to 3,4-dmp with a moderate affinity of Kd 4.99 ± 0.06 µM (Fig. 4.11).43 Moreover, this sensor combination does not efficiently bind phenol, and thus it can serve as an exclusive xylenol sensor unit. The subsequent biochemical assay confirmed this variant is indeed a successful xylenol.

4.3.5.  Design and synthesis of a catechol, 3-chlorophenol, and selective phenol sensor Intelligent structure-guided design and conformational insight not only played a frontier role in the generation of different mutant constructs of the MopR protein to create a battery of pollutant sensors but also paved the way to improve

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(a)

(b)

Figure 4.11.    Design of the xylenol sensor (3,4-dmp sensor). The MopRABWHA mutation confers selective binding for the bulkier 3,4-dmp in the pocket. (a) Molecular docking landscape shows the 3,4-dmp interaction with the MopRABWHA pocket. (b) The ITC data depicting the binding of the 3,4-dmp. Adapted with permission from Ref. [43]. Copyright © 2017, American Chemical Society.

selectivity. It is very intriguing to observe that by changing only one, two, or three residues of the phenol-binding pocket it is possible to create a suitable chemical environment so that only a specific desirable pollutant will bind. Such engineering led to the synthesis of a biosensor for detecting catechol, 3-chlorophenol (3-cp), and phenol.43 Binding prediction achieved via in-silico mutation followed by experiments resulted in the creation of very specific sensors that detect only a single compound. Such accuracy can only be achieved by the access of the crystal structure to the sensor domain of MopR. Although native MopR exhibits better phenol sensing, it also could detect a spectrum of smaller phenolics (Fig. 4.9(b)). To reduce the level of background signal from the other pollutants, a selective phenol sensor was designed. The pocket was tailored and conformationally constricted to anchor the phenol more tightly. To achieve the design successfully, anchoring residue H106 was replaced with an asparagine residue, which has a similar pKa value (Table 4.2). Such substitution enhanced selection, and the modified sensor only effectively detected phenol. Here again, docking into the in silico-created MopRHN sensor pocket was a starting point, which was followed by actual sensor construction via mutation of the sensor to H106N, and it was observed that the resultant sensor has the ATPase activity triggered only by phenol (Fig. 4.13). Since chlorophenols are more hazardous and longer-lasting pollutants than phenol, a specific sensor that detects only this moiety and does not sense phenol

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Table 4.2.  Thermodynamic binding parameters of a MopRAB biosensor unit targeted for 3-cp (MopRABHA construct) and selective phenol sensor (MopRABHN construct). The values are obtained from the ITC experiment. (“Not determinable” represents those ITC runs for which some heat change was observed but the data could not be fit using any standard binding curve models; hence, their thermodynamic parameters could not be computed.) Sensor Type

Aromatic compounds

3-cp sensor

Phenol sensor

ΔG (kcal/mol)

Kd (µM)

Phenol

−6.99 ± 0.36

7.87 ± 0.01

o-cresol

−6.81 ± 0.23

13.15 ± 0.06

Catechol

−4.55 ± 0.24

45.01 ± 0.08

3-cp

−7.69 ± 0.15

2.36 ± 0.01

m-cresol

−7.61 ± 0.31

2.68 ± 0.05

3,4-dmp

−2.91 ± 0.09

59.80 ± 0.05

Phenol

−7.28 ± 0.23

1.59 ± 0.23

o-cresol

−6.89 ± 0.14

12.35 ± 0.05

Catechol

Not determinable

65.26 ± 0.18

−4.59 ± 0.32

33.44 ± 0.08

3-cp m-cresol

Not determinable

55.86 ± 0.13

3,4-dmp

Not determinable

Not determinable

was also created. This sensor was designed to exclusively and instantaneously assess levels of 3-cp in waste waters. To achieve this, creating space in the pocket via an H106A mutation was the best approach. This specific mutation changes the interaction pattern in the pocket by flipping the direction of the –OH group toward the A162 residue, which again stabilizes the 3-cp through the H-bond via the main chain C=O atom. The affinity of the 3-cp toward the H106A was calculated to be Kd 2.36 ± 0.01 µM (Fig. 4.12, Table 4.2) and the binding and sensor assays confirmed that, for the mutant H106A sensor, phenol is unable to efficiently bind in the pocket and the sensor module becomes sensitive only toward the 3-cp (Fig. 4.13). Similarly, the designs for a selective and sensitive catechol sensor, ortho substitute, and meta-substituted specific sensors were created. Catechol is a major pollutant, a volatile organic, that causes skin and eye irritation and hypertension by depression of central nervous system, and thus a viable design of a catecholspecific sensor is a useful step forward. In all aspects of sensor development, the use of a single-sensor module for synthesizing a wide spectrum of effective biosensor prototypes is a novel and attractive approach for xenobiotic monitoring. This approach paves the way for an effective structure-based design and possesses immense versatility.

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(c)

(e)

(b)

(d)

(f)

Figure 4.12.    Specific sensing strategies of ortho and meta-directed phenolic pollutants. Panels represent docked ligands and the ITC curves for the following MopRAB mutants. (a, b) 3-cp with MopRABHA. (c, d) o-cresol with MopRABWA. (e, f) phenol with MopRABHN. Adapted with permission from Ref. [43]. Copyright © 2017, American Chemical Society.

(a)

(b)

(c)

Figure 4.13.    Selectivity profile of various selective sensor designs for MopR. Percent (%) ATPase activity of (a) native and (b, c) mutated MopRABC constructs toward pollutants selection was tested using ATPase assay. Adapted with permission from Ref. [43]. Copyright © 2017, American Chemical Society.

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4.4. BTEX Group Pollutant Sensor Design The sensor development strategy described previously is viable only for phenolic pollutants. A challenging task is to develop sensors that detect hydrocarbons, such as the benzene group of pollutants. Due to the lack of adequate function groups present on these molecules, designing detection protocols is a major challenge. BTEX group molecules are major priority pollutants, according to the U.S. EPA,62,63 and have the potential to contaminate subsurface and groundwater from hazardous waste sites due to their vast use in petrochemical and oil industries.64,65 Benzene is one of the most prominent toxins contaminating the air and water; over-exposure to benzene causes immune system dysfunction, cancer, and even central nervous system depression.66,67 Therefore, a selective sensor for benzene is highly desirable. The only techniques currently available for detecting benzyne compounds are expensive chromatographic techniques such as LC/MS, which are difficult to translate on field. Therefore, biosensors are a step forward in this direction for the BTEX series. A protein-based benzene sensor by MopR was successfully achieved by the intelligent replacement of selected residues in the ligand-binding concavity.42 A closer look at the phenol scaffold reveals that phenol harbors benzene like an aromatic ring with a hydroxyl group, which makes it more reactive. In the MopR crystal structure the phenolic OH group is stabilized by two residues, W134 and H106. To convert a phenolic sensor to a hydrocarbon sensor, an essential step is to minimally perturb the environment by satisfying the hydrogen-bonding interactions as well as the steric space occupied by the hydroxyl group. A chemical substitution was made to change the environment of the pocket in order to compensate the effect of the –OH group such that the mutated pocket has enough potential amenable to bind to benzene as a compound. Bringing such design ideas into reality was made possible by the H106Y mutation, as tyrosine bearing a phenolic –OH, its hydrogen bonds with W134 and occupies the same space where the OH group of phenol resided. Molecular docking on the mutated pocket, along with the benzene as an interacting partner, shows a binding free energy of −6.18 ± 0.23 kcal/mol.42 Moreover, the altered pocket can no longer bind to the phenol due to a direct steric clash and is tailored for aromatic hydrocarbons. The sensor was executed via mutagenesis and the response was checked via the ATP hydrolysis assay for the MopR bio-sensing system (described earlier in the chapter). The ATPase activity profile of the MopR H106Y construct clearly shows that the H106Y sensor could sense both benzene and toluene (sensitivity ~0.3 ppm) with very minor interference from other hydrocarbon pollutants (Fig. 4.14). There was no cross-reactivity observed for this mutant sensor for the phenolic class of compounds. Furthermore, the sensor-mutant combination on the H106Y basic benzene-sensing scaffold was screened, and effective sensors for xylene and mesitylene were also designed. For instance, a double mutant of

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(d)

(b)

(e)

(c)

(f)

(g)

Figure 4.14.    Selectivity profile of biosensor designs for benzene and its derivatives on an in-vitro platform. (a–c) Conformation of docked ligands for the following MopR mutants: (a) phenol with native MopRAB, (b) phenol with MopRHY, and (c) benzene with MopRHY. Surface representations are shown for the H106 residue (a) and Y106 in (b) and (c) panels. Panels (d–g) represent in-vitro biosensing ability of the above sensor designs. Adapted with permission from Ref. [42]. Copyright © 2018, American Chemical Society.

MopR can accommodate a bulkier m-xylene and ethylbenzene preferentially. Adequate space is created in the pocket by synthesizing H106Y-Y176F mutants, and such changes led to the binding of the m-xylene and ethylbenzene. Phenol, 3-cp, cresol, and other smaller pollutants are no longer able to recognize the pocket because they lost their OH anchoring ability. Incorporation of a third mutation, F132A, increases the pocket space to 201.8 Å3, and thus the H106YY176F-F132A MopR construct can accommodate a much bulkier pollutant mesitylene (Fig. 4.14).42 The triple-mutant sensor does not show high binding/ activity for benzene/toluene (smaller hydrocarbons). We believe this is because the pocket becomes too big and benzene cannot find appropriate hydrophobic interactions to be stabilized. The triple mutant shows a response only for the bulkier mesitylene pollutant. Here again, the ATPase activity profile from the malachite-green-based spectrophotometric assay corroborated the findings from the docking result, and it can be clearly seen that this strategy works. The developed sensors were also very selective for their target pollutants, as understood from the interference tests. The MopRHY (benzene) sensor signal was affected only by ~3%–5% from the interference of ions, salts, and other phenolics in a simulated water sample (Fig. 4.15). Such effect was only ~5%–10% for the m-xylene sensor. Overall, in the underlying study the authors tweaked the MopR pocket, which was originally selective for phenol sensing based on structural logic, and series of architectural variations of the sensor-pocket binding to BTEX series pollutants could be created.

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(a)

(b)

Figure 4.15.    Interference tests to gauge the selectivity and sensitivity of the biosensor designs. (a) Interference studies on the MopRHY sensor where sensing of benzene (0.5−20 μM) in a simulated wastewater (SWW) sample (having 1 mM each of various noninducing pollutants, black curve) has been compared to standard solutions (SS) of benzene (red curve). Different green curves further represent interference tests in the presence of 10 μM (light green), 100 μM (olive green), and 1000 μM (bottle green) of a structurally homologous compound, m-xylene, mixed with benzene (0.5−20 μM) in the SS. (b) Interference studies on the MopRHY_YF sensor where sensing of m-xylene (0.5−20 μM) in a simulated wastewater (SWW) sample (having 1 mM each of various non-inducing pollutants, black curve) has been compared to standard solutions (SS) of m-xylene (red curve). Different green curves further represent interference tests in the presence of 10 μM (light green), 100 μM (olive green), and 1000 μM (bottle green) of a structurally homologous compound, benzene, mixed with m-xylene (0.5−20 μM) in the SS. Adapted with permission from Ref. [42]. Copyright © 2018, American Chemical Society.

4.5. Using Evolutionary Approaches to Improve Sensor Design By exploiting the structure-based design we were able to make several sensors. However, the design was not perfect and, in several cases, the sensitivity and LOD was compromised. For instance, the xylenol sensor created was in the ppm range, which is 1,000-fold less sensitive than the original phenol sensor, which operates in the low ppb range. Hence, it appears that the sensor design, although viable, was not perfected. To improve the design, we focused on evolutionary information in combination with the structural knowledge because the protein-ligand interaction landscape has been perfected by nature and is much more precise and shaped by eons. To find other sensors that have similar two-component systems, a thorough search of the NtrC common ortholog group members was performed. In addition, by using bioinformatic tools such as BLAST, a sequence similarity network was created and proteins with similar scaffolds and high sequence homology were categorized. Upon construction of a sequence similarity network (SSN) with all

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(b)

(d)

(g)

(c)

(e)

(f)

(h)

Figure 4.16.  Exclusive 2,3-dimethylphenol sensor design using MopRAC as template. (a)  Cytoscape representation of the sequence similarity network of cog1221 (σ54-based AAA+ ATPases) at an e-value cutoff of 10–90. In each group, the nodes represent the proteins and the edges represent the BLASTP linkages. The group names are based on the characterized protein present in each group (Group 1, small molecule sensing regulators; Group 2, cyclic nucleotide binding

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the σ54-based AAA+ ATPase proteins (cog 1221), a large number of clusters (groups) appeared depending on their structural similarities (Fig. 4.16(a)). Among these groups the four major categories are: Group 1 — small molecule sensing regulators; Groups 2 — cyclic nucleotide binding proteins; Group 3 — type VI secretion system regulators; Group 4 — propionate catabolism regulators; Group 5 remains unannotated. It was observed the bulkier phenolic-derivative-binding proteins such as DmpR belong to the same group as the MopR protein, which are denoted as “small-molecule-sensing regulators” (Fig. 4.16(a)). Since the structural information of MopR was already available, a homology model of DmpR was created using this as template and the pocket residues were visualized. Comparison with the phenol-binding pocket shows that several of the features remain the same, and both phenolic anchors H106 and W134 are conserved in both the proteins. However, subtle changes in the hydrophobic residues occur where an isoleucine is replaced by valine in DmpR, a rigid phenylalanine by a methionine, and tyrosine by a phenylalanine residue. These changes are just small enough to create space to efficiently accommodate and stabilize the bulkier xylenol moiety. For instance, nature replaced an aliphatic methionine, which can adopt multiple conformations, from original MopR phenylalanine, which aptly fits the methyl substitution. This combination was perfected by nature, and our in silico experiments, although held good scope, could not match the evolutionary rigor. Based on the evolutionary knowledge the mutations were first tested in-silico in a step-by-step fashion starting from a single-mutant F132M. Docking showed that the potential binding gradually improved toward the double-and triplemutant constructs. Using this knowledge, we synthesized a single-, double, and triple-mutant variant of the MopR protein, substituting I191V-F132M-Y176F for all three residues. The mutation F132M had the most significant effect toward increasing the sensitivity. As mentioned earlier, the rigid aromatic phenylalanine moiety was replaced by the flexible methionine (having many rotamers), which eases the conformational readjustment of bulkier 2,3-dmp. In addition, the I191V and Y176F also created space in the binding pocket. The perfection of the design was also validated by the ITC experiment (Fig. 4.16(d)–(f)) and the colorimetric ATPase assay (Fig. 4.16(g)–(h)), which showed that upon mutation the pocket

proteins; Group 3, type VI secretion system regulators; Group 4, propionate catabolism regulators; Group 5 remains unannotated). The proteins under study, MopR (in red) and DmpR (in pink), falls in Group 1. (b–c) Docked 2,3-dimethylphenol (2,3-dmp), a representative xylenol, in native and mutated (MopRFM_IV_YF) MopR construct, respectively. (d–f) ITC of 2,3-dmp with MopRFM, MopRFM_IV, and MopRFM_IV_YF, respectively. (g) ATPase activity in response to 2,3-dmp upon introduction of mutations in the binding pocket. (h) Substrate profile of the MopRFM_IV_YF selective design. Adapted with permission from Ref. [60]. Copyright © 2018, American Chemical Society.

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geometry gradually becomes more effective toward 2,3-dmp binding. The structural plasticity of the protein in regard to ligand binding was seen to play a major role herein. The increase of the geometric space in binding pocket showed a greater affinity for 2,3-dmp with the triple-mutant (MopRIV_FM_YF) construct than the double mutant one (MopRIV_FM). The decrease of the Kd value (from the ITC experiment) for the triple mutant confirms the higher affinity; the values are MopRIV_FM Kd: 2.01 ± 0.06 µM, MopRIV_FM_YF Kd: 0.12 ± 0.04 µM. It was evident from the mutagenesis studies performed on MopR that the pollutant sensitivity was directly affected and regulated by the structural arrangements of sensor pocket residues. In the final 2,3-dmp design, the sensitivity and selectivity were comparable to the phenol sensor, with an LOD of 12 ppb. This exercise establishes this approach and now opens doors toward the development of hard-to-make/obscure sensors that exist in nature but cannot be extracted. The pocket variation can be visualized using the MopR protein as a template, which can be appropriately modified to achieve specificity at par with nature’s design.

4.6. Protein Immobilization and Efficient Strip-Based Phenol Sensor Design Sensor development and modification require extensive understanding of the ligand sensitivities of different enzymes not only in their isolated solution version but also as an immobilized/anchored system on various substrate materials.68,69 The field of protein-immobilized sensors has advanced significantly, as seen in the development of the electrochemical field effector transistor (FET)-based device for detecting specific enzymes70,71 and cells, and even aptamer-based interactions.72,73 With the biosensor’s portable nature and robustness, synthesizing a biosensor prototype on a device or chip format has immense commercial and industrial importance.69,74 Therefore, to achieve the device stage, the first step was to attempt to make biosensor strips. The objective was to make strips that could be stored and used for aromatic pollution monitoring, and thus the design of a stripbased pollutant sensor by direct, label-free colorimetric methods was undertaken (Fig. 4.17). To develop such a prototype, immobilizing protein without incurring a loss in its functionality is one of the fundamental problems that must be overcome. Many of the major device/strip-based sensor systems use nanoparticles for biomolecule immobilization, and some of these systems use the properties of the nanoparticles toward electrochemical detection for analyte recognition.75,76 An MopR protein construct of 500 amino acid residues harboring an A+C domain was chosen for the immobilization and fabrication of a chip-based phenol sensor (Fig. 4.17).60 This construct, as described earlier in this chapter, possesses

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(a)

(b)

(c)

(d)

(e)

Figure 4.17.    Characterization and design of protein sensor strip. (a) Schematic representation of the MopRABC protein-based biosensor strip design. (b, c) SEM images of MCM-41 (b), MCM-41 with adsorbed protein (c) and (d, e) TEM images of MCM-41 (d), MCM-41 with adsorbed protein (e) showing the external particle morphology of the spherical MCM-41 before and after protein adsorption. Adapted with permission from Ref. [60]. Copyright © 2018, American Chemical Society.

high thermal stability and a long shelf life with unaltered activity, even up to 20 days.60 Such biochemical characteristics are appropriate and desirable for the development of the biosensors, making it a viable candidate for a strip-based format. Silica nanoparticles were chosen as the base substrate for the strips, and several silica nanoparticles were tested. Silica nanoparticles such as SAB-15, KCC-1, and non-porous scaffolds did not show any phenol sensitivity (Fig. 4.18(d));60 however, the immobilized chip format using MCM 41 type silica nanoparticles had very good results. It turned out to be a good scaffold due to its high porosity, and the ease of synthesis makes it scalable. To maintain the functional integrity of the enzyme, a certain level of conformational flexibility is required, as this protein goes through major allosteric transformation upon phenol binding, which the mesoporous silica aptly provided. It should be pointed out that a simple adsorption with the appropriate strip support worked very well. Briefly describing the setup, the protein was immobilized on the air-dried MCM-41 silica nanoparticle solution by simple drop-casting and the desired immobilization was carried out at room temperature (Fig. 4.17). The strips were subsequently left to dry. Such a simple, cost-effective, and easy-to-reproduce design shows that, even at a very low concentration of water, silica pores can maintain a certain level of hydration, thereby giving the proper functional environment to the MopR protein and conserving its phenol-sensing function. It was observed that the immobilized protein can maintain its phenol sensitivity in the ppb range, and the accuracy is similar to that observed for the solution format (Fig. 4.18(a)). The designed strips can selectively sense phenol in a

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(a)

(b)

(c)

(d)

Figure 4.18.    Commercial importance and performance of sensor strip design. (a) Comparative biosensing efficiency and sensitivity of the MopR sensor protein design in solution and strip format with standard phenol solution. (b) Biosensing efficiency of the protein in strip version with standard phenol solution and simulated wastewater. Phenol detection is retained with 97% efficiency. (c) Shelf life of MopRABC sensor strip. The 50% activity is retained up to the 9th day. (d) Comparison of the phenol detection efficiency of the MopRABC in different silica substrates at a fixed concentration of phenol (2 µM). Adapted with permission from Ref. [60]. Copyright © 2018, American Chemical Society.

pollutant-crowded environment and the background effect is minimal. In a simulated wastewater sample (SWW) containing a high concentration of ions, semivolatile organic contaminates, volatile organic contaminates, metal pollutants, and phenol, the MopR protein-based sensor strip selectively detected only the phenol with very high sensitivity, which is comparable to the solution format (Fig. 4.18(b)). Furthermore, since the interference from the other pollutants are minimal in nature and the effect is less that ~5%, the results were cross-checked

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(b)

Figure 4.19.    Detection of phenol in environmental samples by using MopRAC. (a) Testing of environmental samples (tap water and EVS1, EVS2, which are contaminated waters) in solution and immobilized form. (b) Total phenol content (in ppb) represented as mean ± standard deviation of three triplicate readings. The amount of phenol in each sample was calculated based on the slope of the standard phenol sensitivity plot. The overall pollutant concentrations were found to be 90% accurate when compared to a standard chemical analysis of total phenolic content in the samples. Adapted with permission from Ref. [60]. Copyright © 2018, American Chemical Society.

against other standard phenol detection methods directed by agencies such as the U.S. EPA, and the phenol biosensor strip turned out to be one of the best. A very similar performance pattern was also observed when real environmental samples were tested, which established that the sensor setup can detect the level of phenol with optimum accuracy (Fig. 4.19). To reiterate, the biosensor strip setup is advantageous because the protein has not lost activity upon immobilization and both the sensitivity and selectivity was maintained, even in the field samples from highly polluted rivers and lakes in the Mumbai region (EVS1 — environmental sample from Mithi River; EVS2 — sample from Powai Lake). This strip serves as proof of concept, and now the design can be extended to a whole repertoire of pollutants. The only current drawback lies in the shelf life of the protein in the strip format. Once immobilized the protein strips are stable and have an average shelf life of nine days, with 50% of the original activity kept intact on the ninth day (Fig. 4.18(c)). This may be due to the protein drying up on the silica scaffold, which denatures the protein. Ongoing efforts to functionalize the silica and develop moistened strips aim to make improvements in this direction.

4.7. Conclusion The design and development of sensors for detecting the aromatic group of hydrocarbon pollutants is one of the potential areas of research which can effectively

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help in toxicity monitoring of water in order to ensure health safety. According to the U.S. EPA and OSHA, phenol, its derivatives, and BTEX belong to a top-­ priority class of pollutants that contaminate many waterbodies throughout the world. The central focus of this chapter is to demonstrate the strategic and evolutionary logic-guided synthesis of a protein-based sensor module that can detect a wide spectrum of xenobiotics in contaminated water samples. Biochemical machinery of the protein MopR is used to achieve a sensor design that is sensitive to phenol in the low ppb range, highly selective, and non-responsive toward other bulkier phenolic derivatives. This chapter describes how the MopR-protein-based biosensor can be effectively altered and tailored to change the ligand recognition ability and sense pollutants apart from phenol. Here, the key was the solution of the crystal structure, a salient milestone in the direction toward the biosensor design, as it laid the foundation for understanding the specific interaction characteristics between the MopR sensor pocket and phenol. Detailed knowledge of the structural aspects of phenol binding enabled residue-specific mutations, which aided in alteration of the binding determinates. This led to specific sensor development for the catechol, 3-chlorophenol, 2,3-dimethylphenol group of pollutants. This strategy was further refined to engineer selective and exclusive BTEX sensors that can detect these pollutants with high precision in the ranges below the hazardous limits. These sensors can be used for toxicological profile monitoring of xenobiotics in water for monitoring water waste and water management systems. The research advances presented in the chapter incorporate social concerns for protecting human health and providing real-time water pollutant monitoring, which are the primary areas the research community can address toward achieving societal benefit. This study emphasizes the profound importance of the ligandbound crystal structures of enzymes in designing novel sensors, which can aid in hypothesizing fresh approaches toward the optimization of biosensors.

4.8. Future Directions The sensing field is an important arena that has seen a revolution in the types and scale of sensing products. The major impact in the field can be understood by the statistics: the global sensor market was valued at $138,965.0 million in 2017 and is projected to reach $287,002.0 million by 2025, growing at a CAGR of 9.5% from 2018 to 2025. Biological sensing is a very promising area, which is still in its nascency and holds enormous potential. But challenges remain with converting biological information to an easily quantifiable electronic signal, which is at the heart of making protein-based biosensors usable as marketable products. Very low levels of detection are possible for biosensors in their native cellular environment, but the structural allostery and atomic recognition process is often very complex

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and difficult to translate in a device setting. These problems have been somewhat circumvented. Starting with the prominent application of diabetes sensors worldwide, the field of biosensors has drifted in many ambitious areas such as optic spectrum disorders,77 single-molecule sensing of nucleic acid and peptide,78 electrochemical immunosensors for protein detection,79 electrode array for nitrate determination,80 and many more. Therefore, the combined understanding of the sensitive chemistry of a biomolecule and the use of such potential molecules for biosensing purpose is essential. Clear insights into the biochemistry and structure of the molecules involved is paramount toward understanding recognition in order to design real-time sensors. The sensing work for aromatic pollutants represented in this chapter clearly demonstrates the usefulness of chemistry and the structural biology of the protein MopR in monitoring phenol, catechol, benzene, and toluene. The detection approach is colorimetric, with a sensitive quick response. The authors have shown the immense commercial importance and portability of these sensors through the use of the nanomaterial-based immobilization technique. The immediate application of the sensors is targeted to monitoring water quality with a fully functional portable module. Progress in portable spectrophotometry and suitable electronic components that do not compromise sensitivity is the way forward, which will have a direct impact on the evolution of cheap and sensitive biosensors for monitoring toxic xenobiotics in water systems. Currently possessing an LOD of 10 ppb and an optimal dynamic range, the MopR-based phenol sensor60 can even be elevated with the application of suitable semiconductor-based electronics or microfluidics for drinking water applications. This sensor design can be upgraded and translated to the continuous on-site monitoring of effluents and discharge from industrial waste sites, and the levels of specific xenobiotics in river water can be monitored to improve access water quality in a given region. In summary, the protein-based aromatic sensors described in this chapter will have a lasting impact on the measurement and quantification of xenobiotics. Moreover, recent developments can be horizontally moved en route to polycyclic aromatic hydrocarbons sensors. This will open doors to the quick and reliable sensing of a wide range of pollutants that require immediate attention.

References   1. A. P. F. Turner, I. Karube, G. S. Wilson, and P. J. Worsfold. Biosensors: Fundamentals and Applications. Oxford University Press, Oxford, 1987, p. 201.  2. C. Ziegler and W. Gpelt. Biosensor Development, Curr. Opin. Chem. Biol., 1998, 2(5), 585–591.   3. Y. Lin, F. Lu, Y. Tu, and Z. Ren. Glucose Biosensors Based on Carbon Nanotube Nanoelectrode Ensembles, Nano Lett., 2004, 4(2), 191–195.

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52. A. E. Dago, S. R. Wigneshweraraj, M. Buck, and E. Morett. A Role for the Conserved GAFTGA Motif of AAA+ Transcription Activators in Sensing Promoter DNA Conformation, J. Biol. Chem., 2007, 282(2), 1087–1097. 53. S. De Carlo, B. Chen, T. R. Hoover, E. Kondrashkina, E. Nogales, and B. T. Nixon. The Structural Basis for Regulated Assembly and Function of the Transcriptional Activator NtrC, Genes Dev., 2006, 20(11), 1485–1495. 54. Y. G. Kim et al. Crystal Structure of Bet3 Reveals a Novel Mechanism for Golgi Localization of Tethering Factor TRAPP, Nat. Struct. Mol. Biol., 2005, 12(1), 38–45. 55. Y. G. Kim et al. The Architecture of the Multisubunit TRAPP I Complex Suggests a Model for Vesicle Tethering, Cell, 2006, 127(4), 817–830. 56. M. Bush and R. Dixon. The Role of Bacterial Enhancer Binding Proteins as Specialized Activators of 54-Dependent Transcription, Microbiol. Mol. Biol. Rev., 2012, 76(3), 497–529. 57. E. Skärfstad, E. O’Neill, J. Garmendia, and V. Shingler. Identification of an Effector Specificity Subregion within the Aromatic-Responsive Regulators DmpR and XylR by DNA shuffling, J. Bacteriol., 2000, 182(11), 3008–3016. 58. H. Pavel, M. Forsman, and V. Shingler. An Aromatic Effector Specificity Mutant of the Transcriptional Regulator DmpR Overcomes the Growth Constraints of Pseudomonas sp. Strain CF600 on Para-Substituted Methylphenols, J. Bacteriol., 1994, 176(24), 7550–7557. 59. A. A. Baykov, O. A. Evtushenko, and S. M. Avaeva. A Malachite Green Procedure for Orthophosphate Determination and Its Use In Alkaline Phosphatase-Based Enzyme Immunoassay., Anal. Biochem., 1988, 171(2), 266–70. 60. S. Ray, T. Senapati, S. Sahu, R. Bandyopadhyaya, and R. Anand. Design of Ultrasensitive Protein Biosensor Strips for Selective Detection of Aromatic Contaminants in Environmental Wastewater, Anal. Chem., 2018, 90(15), 8960–8968. 61. V. Shingler and T. Moore. Sensing of Aromatic Compounds by the DmpR Transcriptional Activator of Phenol Catabolizing Pseudomonas sp. Strain CF600, J. Bacteriol., 1994, 176(6), 1555–1560. 62. L. H. Keith. The Source of U.S. EPA’s Sixteen PAH Priority Pollutants, Polycycl. Aromat. Compd., 2015, 35(2–4), 147–160. 63. United States Environmental Protection Agency. Priority Pollutant List, 2014, Effl. Guidel., p. 2. 64. J. Kirkeleit, T. Riise, M. Bråtveit, and B. E. Moen. Benzene Exposure on a Crude Oil Production Vessel, Ann. Occup. Hyg., 2006, 50(2), 123–129. 65. C. P. Weisel. Benzene Exposure: An Overview of Monitoring Methods and Findings, Chem. Biol. Interact., 2010, 184(1–2), 58–66. 66. H. Bahadar, S. Mostafalou, and M. Abdollahi. Current Understandings and Perspectives on Non-Cancer Health Effects of Benzene: A Global Concern, Toxicol. Appl. Pharmacol., 2014, 276(2), 83–94. 67. L. Falzone, A. Marconi, C. Loreto, S. Franco, D. A. Spandidos, and M. Libra. Occupational Exposure to Carcinogens: Benzene, Pesticides and Fibers (Review), Mol. Med. Rep., 2016, 14(5), 4467–4474.

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Chapter 5

Functional DNA Sensors for Heavy Metal Ions and Microbial Contaminants in Water Ana Sol Peinetti*,§, Ryan Lake† and Yi Lu‡ Department of Chemistry, University of Illinois at Urbana−Champaign, Urbana, Illinois 61801, USA [email protected]

*

[email protected]

†

[email protected]

‡

5.1. Introduction Water contaminants include an enormous range of toxic chemical and biological species, including heavy metal ions, pesticides, chemical and biological toxins, and viral and bacterial pathogens. They are present in low quantities in media, with significantly large interference from other species such as calcium and ­phosphate.1 Among the targets in water, two stand out as extremely challenging to detect and quantify: (1) metal ions with very similar chemical properties, such as charge, atomic radius, and coordination number; and (2) microbial targets, in which unique conserved epitopes on the surface of pathogens are rare and ­challenging to discover and thus difficult to detect. As a result, on the one hand, monitoring metal ions in the environment is a long-standing analytical task. Current technologies for identifying trace metal ions in water are largely based on analytical instrumentation methods such as inductively coupled plasma mass spectrometry (ICP-MS) and atomic absorption Co-corresponding author.

§ 

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spectroscopy (AAS). While these instruments are highly sensitive and selective, they are usually expensive and require skilled operators and sample pretreatments, making on-site real-time detection difficult. On the other hand, the detection of microbial contaminants is traditionally performed by using microbiology techniques, which are highly accurate, but it can take days or even weeks to obtain the results.2 The entire process is laborious and time-consuming because specific media and differentiation tests are needed. For instance, the current regulations adopted by both the World Health Organization (WHO) and the U.S. Environmental Protection Agency (EPA) to monitor drinking water quality are primarily based on indicator organisms (e.g., total coliform bacteria, Escherichia coli (E. coli) and not necessarily the specific contaminant of interest. The approved methods quantify colony-forming units (CFU) after cultivation in lactose-based medium for up to 96 h. In addition to the long procedure, this method is vulnerable to false-positive results due to the growth of some non-E. coli bacteria in the media,3 and falsenegative results when bacteria are in a dormant state.4 Detecting water-transmitted viruses is even harder since they are obligatory parasites; therefore, their propagation requires the use of tissue cultures, a system that requires increased time, labor, expertise, and expensive equipment.5,6 Furthermore, several of these viruses cannot be grown easily (e.g., diarrhea-causing adenovirus serotypes 40 and 41) or at all (e.g., hepatitis A virus) in cell culture. Consequently, traditional viral growth assays, such as plaque assays, are either unavailable or too lengthy to be practical for water treatment facilities. To overcome the aforementioned limitations, immunological and qPCR-based technologies have been developed to allow faster detection of proteins and genomic sequences, respectively, from both bacteria and viruses. However, these methods require relatively high concentrations of pathogens, involve many analytical steps, or need special equipment. Furthermore, in the case of viruses, these methods lack the possibility of distinguishing infectious vs. non-infectious viral particles.7 Therefore, there is a need to develop rapid, accurate, inexpensive, and sensitive methods to complement existing standard protocols. Recent technological advances have made it possible to develop portable devices that allow users to test the quality of water for known contaminants. However, reliable methods to detect and quantify contaminants in water remain largely constrained to sophisticated, centralized laboratories with costly instrumentation that is time-consuming to use and require significant technical expertise. Major barriers to successful on-site and real-time detection and quantification include the lack of cost-effective and stable agents that can bind strongly and selectively to the contaminants, including emerging contaminants such as new viruses that affect public health, as well as conversion of these agents into signals that can be detected by portable devices. Functional DNA (fDNA)-sensing methods have recently been shown to overcome the aforementioned barriers and become a leading technology in environmental monitoring. For example, fDNA-based sensors have been developed to

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detect many metal ions, with detection limits lower than the maximum contamination concentrations of these metal ions in drinking water set by the EPA.1,8 Moreover, there are already reported examples of commercialized fDNA-based sensors. For instance, ANDalyze Inc. (Champaign, IL, USA), has obtained EPA certification to detect heavy metals based on DNAzyme technology and on a portable fluorometer, for on-site detection in real samples.9 This chapter focuses on the development and application of fDNA technology for these two markedly different targets of metal ions and microorganisms, and highlights the versatility of functional DNA as highly selective and sensitive sensors for a wide range of contaminants in water.

5.1.1.  Functional DNA (fDNA) Functional DNA (fDNA) molecules are short, single-stranded DNA oligonucleotides that can fold into complex secondary and tertiary structures to achieve a specific function, either enzymatic activity, molecular recognition, or both.10–12 The fDNA molecules that display enzymatic functions are called deoxyribozymes or DNAzymes. A prominent example is the RNA-cleaving DNAzymes that can cleave the phosphodiester backbone of a ribonucleotide sequence.13 This activity typically requires the presence of a specific target which acts as a cofactor for enzymatic reactions or for proper folding (Fig. 5.1(a)). As a result, the enzymatic activity can be used as a basis for signaling output for the cofactor, which is often a metal ion, as the target for detection. On the other hand, fDNA molecules that can bind to a target molecule with high affinity and specificity but do not further catalyze a chemical reaction are called aptamers (Fig. 5.1(b)). Based on their recognition capability, DNA/RNA aptamers are often compared with antibodies that are consist of proteins, and used extensively as the recognition element in biosensors. When aptamers are combined with DNAzymes, in which the binding of targets by the aptamer can enhance or inhibit the activity of the DNAzyme, they are called aptazymes or allosteric DNAzymes. Compared with other types of molecules, these fDNAs have several advantages as the recognition element in a sensor.10 First, they are versatile in their synthesis and range of applications. Functional DNA molecules are generated in vitro by a combinatorial selection process called in vitro selection (Fig. 5.1(c)). As described in detail in Sec. 5.2, this process is carried out in test tubes and thus requires less time than obtaining antibodies/enzymes, which require animals or cell cultures. Furthermore, with this method it is possible to select specific fDNAs to selectively bind a broad range of targets, ranging in size from a single metal ion to a whole microbe, under both physiological and non-physiological conditions that are often encountered in environmental monitoring. Second, DNA is highly stable and can be refolded after thermal denaturation without losing its ability to bind or to catalyze reactions. Third, commercial chemical synthesis of DNA is relatively cheap, offering the possibility of incorporating a diverse range of

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(c)

(b)

Figure 5.1.    Schematic representation of two fDNA molecules: (a) DNAzyme, where the substrate strand is cleaved in the presence of the target, and (b) aptamer, where the binding of a specific target produces a conformational change of the DNA. (c) Scheme of the in vitro selection method used for isolation of fDNA.

modifications at essentially any site of choice. The incorporation of various functional groups and specific moieties, such as biotin, carboxyl groups, amines, and thiols, allows signal transduction elements to be coupled to the DNA sequences post-synthetically using chemical conjugation techniques, such that molecular recognition events can be converted into detectable signals. Furthermore, many signal transduction elements, like commonly used fluorophores, have been incorporated into the phosphoramidite monomers that are used in solid-phase synthesis, and thus they can easily be incorporated into the sequence during the synthesis process without further modification, allowing for higher yield and lower cost than post-synthesis modifications. Finally, it is possible to predict secondary structures of fDNA and to design sensors based largely on functional DNA secondary structures with minimal knowledge of their tertiary structures.

5.2. Development of Functional DNA Using In Vitro Selection Functional DNAs are identified and isolated by a high-throughput process called in vitro selection, often called SELEX (Systematic Evolution of Ligands by Exponential Enrichment) when referring to aptamers (Fig. 5.1(c)).14–16 Conceptually, this process is controlled by the ability of relatively small oligonucleotides (on the order of 15–50 nucleotides in length) to fold into unique 3D structures that can interact with a specific target with high specificity and affinity. Because large numbers of random DNA sequences can be synthesized relatively easily (on the order of 1014–1015 molecules), statistically, it is likely that a “pool” of these

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random sequences is likely to contain sequences with the desired function. Therefore, a method to “select” or separate these active sequences from the nonactive sequences is needed. Even though there are differences in the mode of selection and the methodology, depending on the functional properties of the DNA (aptamers or DNAzymes) and the target (i.e., metal ions, microbes), they share the same basic principle of incubation, separation, and amplification. The starting point of this method is the use of a large library (1014–1015) of chemically synthesized DNA, with random sequences flanked by two constant regions (used for PCR amplification primer binding, as described later). In each round of this iterative process, first, the pool of DNA is subjected to a selection pressure by incubation with the target under the desired conditions. Then, in the separation step, the sequences that are not bound to the target (aptamers) or have not reacted in the presence of the target (DNAzymes) are removed. Finally, those sequences that have been selected in the previous step are amplified by PCR, enriching the population with these functional sequences for the next round of selection, and the process is repeated. When the activity reaches a plateau (typically after 10–20 rounds), the library is analyzed by DNA sequencing to identify the “winner” sequences that exhibit the highest activity or affinity. Typically, the candidate sequences are identified in the last step by cloning and sequencing the last round pool with Sanger sequencing with relatively low throughput (on the order of 100 sequences). In the last few years, recently developed high-throughput sequencing technologies have become a popular alternative because of the much higher number of oligonucleotides that can be sequenced at once (on the order of millions of sequences, compared with of hundreds of sequences for Sanger sequencing). Furthermore, multiple selection rounds can be sequenced at the same time using barcoded adapter sequences rather than just a single round with the previous method. Thus, this new sequencing technology enables individual sequences and families of related sequences to be tracked over multiple selection rounds to provide information about the evolution of the selection pool, which can provide more confidence in identifying the “winner” sequences for further testing. Stringency can be introduced during the process of in vitro selection by lowering either the concentration of the target or the incubation time. Also, to gain higher selectivity against compounds similar to the target, counter-selection steps can be incorporated. In a counter-selection step, the pool of nucleic acid sequences is exposed to secondary targets for which activity is not desired, and any sequences that bind are discarded.17 The most powerful aspect of in vitro selection lies in the generality of the concept behind the technique. In principle, it should be possible to select fDNA against any molecular target as long as it has at least one characteristic molecular feature. There are no requirements on the target: single atoms, small molecules, proteins, and even entire cells have been shown to drive the selection process

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successfully (though selections against cells typically result in fDNAs active toward a biomolecule that is specific to that cell rather than to the entire cell itself). Thus, the success of the functional DNA-generation process is primarily determined by the applied selection conditions, and not necessarily on the target molecule chosen for the selection. However, it is still true that the selection mode and the methodology used to generate these fDNAs, as well as the assays used to identify the potential candidates, can largely depend on the target of interest. The most challenging cases require careful design of the combinatorial selection process to obtain functional DNA sequences with specific functionality and selectivity.

5.2.1.  Functional DNA for metal ions DNA is quite versatile in metal binding, using both electrostatic (phosphate backbone) and coordination interactions (bases),18–21 which are key to obtaining functional DNA sequences with high affinity and selectivity to different metal ions. To date, different fDNAs have been developed for more than 15 different metal ions, including highly toxic water contaminants like Pb2+, Hg2+, Cd2+, and UO22+. On the one hand, in vitro selection processes for obtaining metal ion-dependent, RNA-cleaving DNAzymes have been widely applied, with more than 15 metal selective DNAzymes reported: Pb2+,22 Zn2+,23,24 Mg2+,25,26 Cu2+,27,28 Ca2+,25 UO22+,29 Na+,30 Hg2+,31 Cd2+,32 Cr3+,33 Ln3+,34–36 Ce3+,37 and Ag+.38 Here, the catalysis only proceeds in the presence of the target metal ion. The library for DNAzyme selection usually contains a single RNA linkage (cleavage site), and the randomized region is positioned across from this cleavage site by using two base paired stem “binding arms” (Fig. 5.2(a)). The library is incubated with a target metal ion, and a small fraction of the library may bind the metal and consequently cleave the RNA linkage. Following the reaction step, denaturing polyacrylamide gel electrophoresis (PAGE) is used to pull apart the shorter cleaved sequences which will then be regenerated to their full length and further amplified by PCR (Fig. 5.2(a)).13,39 Counter selections can be introduced to remove sequences that also react with competing metal ions to obtain sequences that are more selective for the target metal ion. To demonstrate the effectiveness of the counter-selection strategy, the Lu group carried out two parallel in vitro selections for Co2+-dependent catalytic DNA. When no counter-selection was used in the selection process, the resulting catalytic DNA molecules were more active in solutions of Zn2+ and Pb2+ than in Co2+. On the other hand, when the counter-selection steps were performed between the normal positive-selection steps, the resulting catalytic DNA molecules were much more active with Co2+ than with other metal ions.40 Recently, efforts have been made to improve upon metal sensitivity and binding affinity (typically in the μM-mM range) by introducing modified nucleotides or modifications in the DNA backbone to expand the range of metals that can be

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Figure 5.2.   (a) Scheme of the in vitro selection process for a metal ion DNAzyme using gel-based separation. Adapted with permission from Ref. [39]. (b) A schematic representation of the hairpin structure of the rationally designed Hg-specific aptamer. Metal binding representation of Hg2+ by thymine. (c) Representation of the in vitro selection process of a metal ion aptamer through the immobilization of the DNA library via a short duplex region on beads. Adapted with permission from Ref. [46].

detected or the applications of existing metal-specific DNAzymes. To expand upon the number of different chemical functional groups that can be used in the DNA molecules, chemical modifications have been incorporated into the DNA library to improve the interaction of DNA with some metal ions, particularly for thiophilic metals. These modifications have been introduced in the DNA bases (e.g., with imidazole),31,41 as in the case of Cu2+- and Hg2+-specific DNAzymes, or in the phosphate backbone by replacing one of the non-bridging phosphate oxygen atoms with sulfur (phosphorothioate),32 as in the case of a Cd2+-specific DNAzyme. On the other hand, only a handful of aptamers for metal ions have been reported. The first examples of metal-binding aptamers were based on rational design.42 For example, the rational design of aptamers for Ag+ and Hg2+ has been based on the well-known chelating interactions that some nucleotides have with

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metal ions, such us cytosine for Ag+ 43 and thymine for Hg2+ 42,44 (Fig. 5.2(b)); the aptamers for most of the other metals were obtained by in vitro selection. For small molecules and proteins, aptamer selection typically proceeds by immobilizing the target molecules to separate the bound and unbound DNA to the target. However, it is nearly impossible to immobilize metal ions without significantly masking their coordination sites. To overcome this limitation, a strategy initially used for selecting aptamers against small molecules has been developed (Fig. 5.2(c)).45 Briefly, the DNA library is immobilized via a short duplex region on avidin-coated beads in a way that binding of the metal target by the aptamer would destabilize the aforementioned duplex from immobilization, triggering the release of the aptamer from the avidin-coated beads. Aptamers selective to Zn2+,46 Cd2+,47,48 and Hg2+ 49 have been obtained based on this approach. Furthermore, because of the large number of metal-specific DNAzymes, recent studies have also focused on obtaining metalspecific aptamers based on these DNAzymes by removing the catalytic function of these sequences. This strategy has been effectively used to obtain aptamers for the monovalent targets Ag+ 50 and Na+,51 and it has potential for converting other metal-dependent DNAzymes into aptamers, as well. Despite this creative effort to overcome the limitations of the selection process against small molecules, it is still challenging to obtain high-affinity metal-binding aptamers. Recent efforts involving microfluidic devices and compartmentalized PCR49 have the potential to improve the selection process and increase the number of metals for which highaffinity metal-binding aptamers are obtained.

5.2.2.  Functional DNA for microbes For sensing microbial and viral pathogens, a much wider range of aptamers have been obtained than those for metal ion aptamers. A recent review provides a complete list of all the published aptamers for microorganisms.52 In general, microbes express on their surface many different molecules that constitute possible targets for functional DNA recognition, which facilitate the development of high-affinity aptamers. Two approaches have been used during the aptamer selection process to take advantage of this multiplicity of targets for each microbe. One involves targeting predefined bacterial cell surface targets, or viral proteins. In this case, a specific protein is isolated and used as the target during the in vitro selection process. For instance, aptamers specific to bacteria (Mycobacterium tuberculosis,53 Staphylococcus aureus,54 and E. coli) and viruses (severe acute respiratory syndrome,55 influenza, and Ebola56) have been reported using this method. In principle, this approach simplifies the potentially complex selection against a whole microorganism to a typical selection against a specific protein.

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Alternatively, the whole cell or viral particle can be used as a target in a process named cell-SELEX.14 In this process, oligonucleotides bind to molecules on the extracellular surface of the microorganism. The exact molecular composition of the cell surface is often unknown, but all of the molecules on the surface are potential targets. The key advantage of this approach is that it bypasses the need to identify a specific biomarker as a prerequisite for biosensor development, which is a distinct advantage of cell-SELEX over most other biosensor design strategies. Instead, the specificity is solved by the selection itself, and many potential targets are tested in each round. Moreover, because cell-SELEX occurs essentially in situ, the aptamers obtained will bind to the real folded conformation of the target, representing their natural folding structures as well as their natural distribution, as opposed to fully in vitro selections, in which the isolated biomolecule may not adapt its native conformation and thus the selected aptamers may not be able to bind their intended target in biological systems. Although this technique can significantly simplify the process for obtaining new sensors that are selective for certain populations of microorganisms, the advantages it offers can only be harnessed with smart design of the selection process. A broad range of virus-specific aptamers have been generated using the whole-particle approach, e.g., norovirus,57 and influenza58,59 aptamers. Aptamers for Mycobacterium tuberculosis, Salmonella,60 Staphylococcus aureus,61 and E. coli,62 bacteria have been developed using cell-SELEX, as well. More recently, the first examples of RNA-cleaving DNAzymes showing specificity to bacterial pathogens have been reported. The Li group developed an in vitro selection method to search for RNA-cleaving DNAzymes that respond to an undefined target exclusive to a bacterium of interest.63 Unlike the previously described cell-SELEX process, the unpurified crude extracellular mixture (CEM) from a bacterial pathogen is used as the target to derive the ligand-responsive DNAzymes. In addition, a counter-selection step using the crude cellular mixture from unintended bacterial species is used to remove the DNAzymes that respond to common bacterial targets. This is followed by a positive selection step that uses the crude cellular mixture from the intended bacterium. This method to obtain RNA-cleaving DNAzymes using bacteria CEM as target has been successfully applied to different bacteria: E. coli,63 Clostridium difficile,64 Klebsiella pneumoniae,65 and Helicobacter pylori.66 An additional step has been taken in the case of Clostridium difficile,64 in which the authors show the capability of this approach to differentiate pathogenic strains of bacteria that cause infectious diseases from non-pathogenic strains. Using CEM from the targeted BI/027-H strain of C. difficile in the positive-selection step, and the mixed CEM from E. coli, B. subtilis and a non-BI/027 strain of C. difficile in the negative-selection step, the authors isolated a DNAzyme that is highly reactive in the CEM of BI/027-H, but exhibits minimal activity in the CEMs of other C. difficile strains and other bacterial species.64

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5.3. Conversion of Functional DNA Recognition into Biosensors As discussed in the previous section, the in vitro selection process can obtain fDNA with outstanding selectivity based on the smart design of the selection pressures. This selectivity, along with a relatively low-cost synthesis and intrinsic DNA chemical stability, makes it an ideal recognition element for sensing. Moreover, fDNA is easy to attach to nanomaterials and other molecular-signaling groups to develop biosensors due to the plethora of DNA modifications available. Additionally, fDNA has a unique advantage due to its similarly designed secondary structures, especially for DNAzymes, which enable the same sensing strategy to be applied for different targets simply by changing the sequence that determines the selectivity. For instance, Pb2+ and UO22+ ions have been detected with the same fluorescence approach (catalytic bacon) just by changing the DNAzyme sequences.29,67 Therefore, if a new sensing platform is developed for detecting a certain metal ion using a DNAzyme, it often can be easily adapted to sense other metal ions simply by exchanging the DNAzyme with another one specific to the new target. A vast range of sensing platforms have incorporated functional DNA as the recognition element for a variety of applications, as reviewed elsewhere8,10,68–70 Here, we emphasize three different strategies most commonly used for the on-site and real-time monitoring of contaminants in water.

5.3.1.  Fluorescent sensors Fluorescence detection is a hallmark technique in terms of sensitivity, and a low detection limit can be reached with minimal consumption of materials. Furthermore, nucleic acids can be easily modified with external fluorophores. The possibility of using different fluorophores with emissions at different wavelength provide a general strategy for detecting multiple targets without interference.71 Functional DNAs have been combined with fluorescence detection strategies for detecting metal ion contaminants, with an LOD in the low nanomolar to picomolar range, including UO22+, Pb2+, Hg2+, Cu2+, Ag+, Cr3+, and Ce3+, in tap,72,73 ground,33 pond,74,75 waste, river, and lake water.35,46,72,76 In addition, portable ­fluorometers are now commercially available, allowing the use of field-portable fluorescent sensors. As a result, fluorescent sensors based on DNAzymes are now commercially available.9 The Lu group developed a fluorescence sensor using RNA-cleaving DNAzymes, called the catalytic beacon because of the catalytic reactions involved in generating fluorescent signals. Typically, in these assays, a

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Figure 5.3.    (a) Schematic representation of the catalytic beacon strategy. (b) Different approaches reported to decrease the background signal that arises from incomplete hybridization of the substrate and catalytic strand in a DNAzyme. (c) Fluorescence sensor based on an aptamer using small molecules as a fluorophore and quencher pair. (d) Scheme of an alternative DNAzyme fluorescence sensor design in which graphene oxide is used as a quencher for a fluorophore-labeled DNAzyme.

fluorophore–quencher pair is placed on the adjacent ends of the enzyme and substrate strands.67,77,78 In the presence of the target, the substrate strand is cleaved, thereby decreasing the melting temperature between the catalytic strand and the two shorter pieces of the substrate strand. Then, at room temperature the cleavage strand will dehybridize, allowing the fluorophore to dissociate from the quencher and generating a turn-on fluorescent signal (Fig. 5.3(a)). The catalytic turnovers allow a single target to generate numerous products containing fluorophores or other labels, allowing amplification of the signals. Finally, instead of using the absolute fluorescent intensity at a single timepoint, as is typical for molecular beacon and small molecule sensors, and which is vulnerable to background signal fluctuations due to the autofluorescence by many species in the sample matrices, the catalytic beacon can rely on the rate of fluorescence increase, which is characteristic of target-induced cleavage and is much less prone to interference from signal fluctuation. However, high background fluorescence could be observed in this catalytic bacon approach due to the potential dehybridization of the substrate strand from the enzyme strand in the absence of the target at ambient conditions. To overcome this issue, a second quencher was placed on the other end of the substrate.79 For instance, the Lu group showed a 6-fold fluorescence increase at room temperature for Pb2+ detection using this strategy compared with the use of just one quencher (Fig. 5.3(b)).79 A highly sensitive mercury sensor was developed with this approach, capable of achieving a detection limit of 2.4 nM, which is lower than the permitted level of Hg2+ in drinking water (10 nM) defined by the EPA.80 Moreover, Lu and coworkers reported a UO22+ sensor with a detection limit as low as 45 Pm,28 which is lower than even the corresponding detection limit of ICP-MS. This work demonstrates the promising use of DNAzyme-based sensors for high-performance metal-ion detection.

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Another strategy for decreasing the background signal that arises from incomplete hybridization involves connecting the DNAzyme and substrate with a short oligonucleotide linker to construct a unimolecular form (Fig. 5.3(b)), often called the “cis” form as opposed to the separate substrate and enzyme strands of the more common “trans” form.77 In this case, the ratio between the enzyme and substrate strand is constant at 1:1. A unimolecular DNA-catalytic probe containing both a Pb2+-dependent DNAzyme fragment and a substrate motif has been used for the sensitive monitoring of a single Pb2+ ion.77 In another design to solve the background issue, instead of attaching fluorophores and quenchers to the ends of DNA strands, a fluorophore and a quencher were inserted close together at two nucleotides adjacent to a DNAzyme substrate’s cleavage site (Fig. 5.3(b)). Li and coworkers first demonstrated that it is possible to accommodate a fluorophore-quencher pair near the cleavage site without significant activity loss for a commonly used DNAzyme.81 However, this approach can also decrease DNAzyme activity, and it may not work for all existing DNAzymes. To improve upon this approach, Li and co-workers further incorporated the fluorophore/quencher pair near the cleavage site in the random pool sequences during the in vitro selection process in order to ensure that the DNAzymes obtained with this modification have the maximum possible activity.82,83 With this approach, the fluorogenic DNAzyme obtained can be used as sensors directly after selection without any further labeling or optimization steps. On the one hand, these sensors have low background fluorescence due to the closely localized pairs of fluorophores and quenchers, thereby significantly improving the signal to-noise ratio for more sensitive detection. For instance, an E. coli-specific fluorogenic DNAzyme has been selected and integrated with a cell-culturing step to detect E. coli.63,84 If 1CFU of E. coli is used to initiate cell growth, 12 h of culturing at 37°C is needed to generate a detectable signal, thereby reducing the time required to detect a single live cell from days to hours. On the other hand, a disadvantage of this design is that the fluorophore and quencher rarely can be changed to other fluorophore/quencher pairs for multi-wavelength detection, because they are integral parts of the structure for the DNAzyme reactions. Another approach explored to reduce the background is the immobilization of DNAzymes onto a surface; in this way, it is possible to wash away unhybridized fluorescent substrate DNA, thus overcoming the limited hybridization efficiency issue.85,86 For instance, a Pb(II)-specific DNAzyme has been immobilized on a Au surface by a thiol moiety and hybridized with the complementary fluorophorecontaining substrate strand.85 Upon reaction with Pb(II), the substrate strand is cleaved, releasing a fluorescent fragment for detection. These results demonstrate that, in comparison with solution-based schemes, immobilization of the DNAzyme sensor onto a Au surface lowers the detection limit (from 10 to 1 nM). Moreover, this immobilized DNAzyme can be regenerated after cleavage, allowing multiple sensing cycles, a possibility that is not straightforward in solution-based sensors.

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The simplest aptamer-based fluorescent sensor design consists of aptamers that have been modified with a fluorophore and hybridized with a partially complementary strand labeled with a quencher (Fig. 5.3(c)).87–89 The higher affinity of the DNA to the target induces a conformational change of the aptamer and the release of the partially complementary sequences, and thus turns on the fluorescence signal. This approach has been used to detect metal ions, such as Cd2+ with an LOD of 40nM.47 Alternatively, the two ends of a single stranded DNA hairpin can be labeled by a fluorophore and a quencher, known as a molecular beacon.90,91 The hairpin loop can be designed as an aptamer, which forces the hairpin to open when bound to the target and triggers a fluorescence enhancement.92,93 In addition to small organic molecules, nanomaterials, like graphene oxide (GO), have been explored as quenchers.94 GO is a 2D carbon nanocrystal that is only one-atom thick, with excellent solubility due to the carboxylic acid and hydroxyl groups on the graphene surface. It also has been used as a powerful fluorescence quencher.95 In particular, the sp2-conjugated domains of graphene materials allow the direct interfacing of nucleic acid-based biorecognition ­moieties through non-covalent π–stacking interactions,96,97 thereby laying a very strong foundation for the design of nucleic-acid-based sensing platforms. For instance, graphene was used as an efficient quencher for the development of a Pb2+ sensor based on Pb2+-dependent DNAzymes, in which the cleavage of fluorophore-labeled substrates significantly reduced the affinity of the ­fluorophore-labeled DNA fragment to graphene surfaces and produced the release of this fragment, generating a turn-on fluorescence signal (Fig. 5.3(c)).98 This sensor exhibited a high sensitivity and a detection limit of 300 pM for Pb2+, which is lower than previously reported for catalytic beacons. Using the same approach, a fluorescent biosensor that functions by the direct interfacing of colloidal graphene with an E. coli-specific DNAzyme has also been described.99 Other examples of fluorescence sensors based on nanoparticles and aptamers have been reported for detecting pathogens. One interesting example is an upconversion nanoparticle (UCNP)-based aptasensor (Fig. 5.4). In this case, gold nanoparticles (AuNPs) were functionalized with the aptamers while UCNPs were modified with the corresponding complementary DNA (cDNA).100 Through the  complementary pairing of the aptamers and cDNA, the AuNPs and UCNPs are placed in close proximity, which quenches the green upconversion fluorescence from the UCNPs. When the target bacteria are present, the aptamers preferentially bind to the bacteria, inducing a conformation change of the aptamers and their dissociation from the cDNA. Finally, this dissociation liberates the UCNPs and produces the subsequent recovery of the upconverted fluorescence. This aptasensor was tested with E. coli, which had a detection range of 5–106 cfu/mL in tap and pond water samples.

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Figure 5.4.  Schematic illustration of the UCNP-based aptasensor for detecting bacteria. The aptamer is attached to AuNPs, while a complementary strand (cDNA) is attached to UCNPs. When mixed together, UCNP-cDNAs hybridize with AuNP-aptamers. When the target is introduced (E. coli), the aptamers preferentially bind to the target, resulting in the dissociation of cDNA and recovery of the green fluorescence. Adapted with permission from Ref. [100].

5.3.2.  Colorimetric sensors While fluorophores are commonly used as signal reporter molecules for the development of high-performance sensors, sensing strategies that do not need an instrument are still more convenient for on-site and real-time environmental monitoring. Colorimetric sensors are an attractive option because the change of color can be easily seen with the naked eye. AuNPs have several unique optical properties that make them suitable for colorimetric sensors. For instance, AuNPs have extremely high extinction coefficients, and thus the color of even just a few nanomolars of the AuNPs can be distinguished with the naked eye. In addition, the extinction wavelength of AuNPs can be significantly altered by changing the distances between the nanoparticles. Mirkin and coworkers101 have demonstrated that a color change of DNAfunctionalized gold nanoparticles occurs upon the transformation between discrete

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Figure 5.5.    Representation of two colorimetric designs using DNAzyme and gold nanoparticles. (a) Labeled method: Gold nanoparticles are crosslinked with DNAzyme substrates by DNA hybridization. After the target is added, the substrate strand is cleaved and the AuNPs are disassembled. (b) Unlabeled method: The target induces the cleavage of the substrate strand and the release of short ssDNA, which stabilizes the AuNPs in the presence of NaCl, preventing their aggregation. Adapted with permission from Ref. [106].

and aggregated AuNP states. Taking advantage of these features, a series of colorimetric sensors have been developed based on AuNPs and functional DNA. The Lu group demonstrated the first colorimetric Pb2+ sensor based on AuNPs and DNAzymes (Fig. 5.5(a)).102–105 In this approach, the gold nanoparticles are crosslinked by DNAzyme substrates through DNA hybridization, thereby aggregating the gold nanoparticles and resulting in a blue color. If Pb2+ is present, it catalyzes the cleavage of the DNAzyme and prevents the formation of nanoparticle aggregates, appearing as a red color. The same approach has been used to detect other metal ions, like UO22+.106 In the previous colorimetric approach, AuNPs are chemically functionalized with DNA and their aggregation, and the disassembly status is controlled by the hybridization of the DNA. Another possible approach does not require functionalization of the DNA on AuNPs; instead, it is based on the different adsorption properties of single-stranded DNA and double-stranded DNA on the surface of citrate modified AuNP.107,108 Although AuNPs are stabilized using citrate as the capping agent, they are naturally unstable in the presence of NaCl and thus can be easily aggregated. Single-stranded DNA is flexible and can uncoil its structure to expose the bases and adsorb onto the AuNP surface, thereby enhancing the stability of the AuNPs, even in the presence of NaCl. A colorimetric uranium sensor based on a uranyl (UO22+)-specific DNAzyme and AuNPs has been developed

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with this concept. The presence of uranyl resulted in cleavage of the substrate by DNAzyme, releasing a single-stranded DNA that can be adsorbed on AuNPs, thereby helping to protect them from aggregation (Fig. 5.5(b)) and producing a “turn-off” sensor.106 This label-free approach has also been used with aptamers as the molecular recognition element to detect viruses.109 The different advantages of labeled and unlabeled methods have been exemplified by the Lu group, which compared their performance with UO22+ colorimetric sensors.106 The labeled sensor is simpler to use, as it requires a one-step process once the sensor is prepared and thus is more versatile, and the DNA functionalized on AuNPs provides stability and turn-on sensing. On the other hand, the label-free sensor has better sensitivity, shorter operation time, and lower costs. Regardless of these differences, both sensors have detection limits lower than the maximum contamination level defined by the EPA (130 nM), have excellent selectivity over other metal ions, and operate at room temperature. While the use of colorimetric sensors has been shown to minimize or even eliminate the need for analytical instrumentation, a much simpler method for a single-step procedure that does not require laboratory operation is desired. Storage of the colorimetric sensors over long periods of time is also difficult, because AuNPs aggregate in solution. To overcome these limitations, lateral-flow devices that integrate functional DNA have been reported.110–115 For instance, a Pb2+dependent DNAzyme was coupled with gold nanoparticles to construct an easyto-use dipstick test for Pb2+ detection.114 Well-established colorimetric methods like litmus tests and enzyme-linked immunosorbent assay (ELISA) have also been adapted with functional DNA as a molecular recognition element, which can improve the selectivity for detecting different pathogens by taking advantage of these relatively inexpensive tests. The drawbacks of these assays are the multiple steps required and the amount of time required for obtaining the results. In the case of the litmus test, the molecular ­recognition event must be linked to the pH change of the sensing solution. Using this method, an E. coli DNAzyme has been incorporated in a litmus test.116 The DNAzyme contains a biotin moiety at its 5’ end for binding streptavidin to magnetic beads, and a sequence extension at its 3’ end for hybridization with urease modified with an ssDNA. In the presence of E. coli, the urease is released from the magnetic beads, which, after magnetic separation of the beads, can be used to hydrolyze urea into carbon dioxide and ammonia, generating a pH change and thus a direct color change in the presence of a litmus dye. Frequently, antibodies are replaced by aptamers on ELISA schemes for pathogen detection. In this process, known as enzyme-linked oligonucleotide assay (ELONA), a plate is coated with the target and a biotinylated aptamer binds to the target, followed by the addition of a streptavidin-horse radish peroxidase (HRP) conjugate and an enzyme substrate for color development. Some viruses have been detected with this approach, such as the norovirus,57 and hepatitis C virus.117

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5.3.3.  Electrochemical sensors In electrochemical biosensors, the specific biological interaction is converted into an electrical signal. The functional DNA is supported in a modified layer on the electrode substrate. This modified layer often comprises organic molecules and nanomaterials that enhance the sensitivity. These electrochemical sensors are convenient for environmental on-site detection due to the impressive miniaturization of microelectronics, the lack of electroactive contaminants, and the relative stability of electroactive labels that make them less likely to have potential drawbacks.118 This section addresses several different electrochemical techniques that have been used for this purpose.119,120 In voltammetric sensors, both the potential and the current are measured. Information about the oxidation and reduction potential of the species of interest as well as its concentration can be obtained from this technique. Additionally, multiple redox-active species can be detected simultaneously if their reduction potentials are sufficiently separated. The common approach in this case is the use of an fDNA label with a redox active molecule. The binding of the target analyte changes both the flexibility and conformation of the redox-modified fDNA, which is reflected in a change of the redox current. In a simple design to detect Pb2+ at the parts-per-billion level, Plaxco and coworkers assembled a Pd2+-dependent DNAzyme on a gold electrode (Fig. 5.6(a)).118 The catalytic enzyme strand was modified with an electrochemical tag (methylene blue) on the end farthest from the gold electrode. The presence of the substrate strand forced the enzyme strand into a more rigid, double-stranded conformation in the hybridized binding arm regions such that the methylene blue tag was physically separated from the electrode, and almost no current was observed. After adding Pb2+, the cleavage of the substrate by the Pb2+-dependent DNAzyme allowed the substrate fragments to dehybridize. Thus, the enzyme strand adapted to a more flexible single-stranded conformation, allowing the attached electrochemical tags to move more freely and come into contact with the electrode much more closely, which significantly enhanced the electrochemical signal. Based on this approach, a series of sensors have successfully used DNAzymes for detecting Pb2+,121–123 Cu2+,124,125 and UO22+, as well as aptamers for detecting Hg2+,126–128 Pb2+,129,130 and Cd2+.131 Overall, most of these metal-ion fDNA sensors are highly sensitive, and the LODs are lower than the toxicity levels in drinking water as defined by the EPA (EPA 822-R-04-005). Additionally, label-free aptamers have been attached to an electrode surface and a free electroactive probe has been used to detect the binding between the aptamer and the target. Generally, this approach is successful when the target is large enough to block the surface, as is the case for microbes. A norovirus-specific aptamer was attached by thiol chemistry to gold nanoparticles, which were themselves attached to screen-printed carbon electrodes.132 Binding the virus to the surface-bound aptamers blocked the surface, which limited the access of the

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Figure 5.6.    Scheme illustration of three different electrochemical sensors. (a) A Pb2+-dependent DNAzyme is immobilized on a gold electrode and the catalytic strand is modified with methylene blue (MB). In the presence of the target, the cleavage of the DNAzyme allows the MB to approach the surface and increase the current. (b) An S. enteritidis-specific aptamer is immobilized on AuNPmodified screen-printed carbon electrodes. When the bacteria bind to the aptamer, they block the gold surface and thus prevent an electroactive probe (Fc) in the solution from reaching the electrode surface, which produces the electrochemical signal change. (c) Aptamers are covalently immobilized on a graphene oxide surface. In the absence of a target, the DNA bases π-stack with the carbon nanostructure. Upon binding to a cell, the negatively charged phosphate backbone of the aptamer is removed from the carbon surface, leading to a potentiometric change. Adapted with permission from Ref. [134].

electroactive species (ferrocyanide) to the electrode and thus decreased the current signal. The detection limit was 10 aM (ca. 180 viral particles), with high selectivity against a similar virus. Potentiometric sensors monitor the electrical potential of a solution at very high impedance, meaning there is minimal current flow. In this case, the interaction with the target leads to a structural change in the immobilized aptamer, thereby altering the electron transfer resistance between the electrode surface and the redox-active species in the solution. Many examples of bacterial detection with potentiometric sensors involve carbon nanostructures, like single-walled carbon nanotubes or graphene oxide sheets, modified with aptamers. These sensors function because of the conformational change of the aptamer. In the absence of target, the DNA bases π-stack with the carbon nanostructure. Upon binding to a bacterial cell, the negatively charged phosphate backbone of the aptamer is removed from the carbon surface, leading to a potentiometric change (Fig. 5.6(c)). A device specific for E. coli detection, with no cross-reactivity and displaying a

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linear response to an increase in bacterial concentration up to 104 CFU/mL, has been reported using carbon nanotubes.133 This carbon nanotube potentiometric sensor also demonstrated to be effective for detecting Salmonella typhi, with an LOD of 10 CFU/m.134 Similarly, S. aureus135 was detected by immobilizing its aptamer on a graphene oxide surface, which detected the pathogenic bacteria with impressive sensitivity, with an LOD of 1 CFU/mL. Electrochemical impedance spectroscopy (EIS) is an effective technique for detecting the formation of complexes between biomolecules immobilized on an electrode surface by probing the electrode/electrolyte interface. Impedance is the effective resistance of an electrical circuit in response to an alternating current. If the impedance of the electrode/electrolyte interface changes when the target is bound to the surface-immobilized biorecognition element (e.g., aptamer), EIS can be used to detect that impedance change in a label-free method. An impedimetric sensor was reported for whole bacteria detection based on an aptamer immobilized on AuNP-modified screen-printed carbon electrodes, enabling an LOD of 600 CFU/mL of Salmonella enteritidis (Fig. 5.6(b)).136,137 This sensor also provided a rapid readout, with detection occurring in 10 min. However, a major challenge with impedimetric sensors is avoiding the interference from complex real samples, in which other biomolecules may bind non-specifically.120 Field effect transistor-based biosensors (FET) are increasing in popularity because they generally do not require sample labeling and can be extremely sensitive. Sensing with FETs is based on a change in the source-drain conductivity due to the electrical field in the local environment. Upon the binding of an analyte to the recognition element, the charge distribution, and therefore the surface potential, changes. This change leads to a measurable change in conductance between the source and the drain.138 This platform leads to highly sensitive detection using volumes as small as 1 μL. For example, a graphene FET was developed for the specific capture of E. coli. In this case, a DNA aptamer, which was modified with a pyrene tag to link the DNA and graphene, was used as a specific biorecognition element for the bacteria,139 and the binding of negatively charged cells increased the density of holes in the graphene; the LOD was found to be 100 CFU/mL. Metal ions have also been detected using the FET effect, and a platform that uses single-molecule graphene-DNAzyme junctions to achieve direct electrical detection of paramagnetic Cu2+ with ultra-high femtomolar sensitivity and high selectivity has been reported.140 Despite their great promise, and many years of investigation, only a limited number of portable sensors are commercially available to consumers at this time. Perhaps the most successful example is the personal glucose meter (PGM), which is based on the detection of glucose by the redox reaction catalyzed by glucose oxidase. The Lu group reported a novel methodology that uses a commercially available PGM as a signal readout for fDNA sensors to detect and quantify a broad range of non-glucose targets for environmental monitoring and point-of-care

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medical diagnosis. Functional DNA-conjugated invertase is used to link glucose detection to the detection of other targets, and thus uses the concentration of glucose, which is proportional to the amount of invertase-linked DNA generated by the functional DNA sensor, to quantify the other targets of interest present in the samples.141 This technology has been adapted to detect different metal ions, such as UO22+, with a detection limit of 9.1 nM,141 and Cd2+ in lake water, with a detection limit of 5 pM.142 Similarly, in another recent report, the Lu group made use of the indocyanine green (ICG) released by UO22+-specific DNAzymes to construct a UO22+ sensor readable by a standard thermometer.143 In short, the UO22+-dependent cleavage of the DNAzyme releases a substrate fragment conjugated with phospholipase A2 (PLA2). Upon release, PLA2 can degrade liposomes encapsulated with ICG, which upon release and irradiation with NIR induces a temperature change readable by a thermometer that correlates to the UO22+ concentration, with an LOD of 25.7 nM. Further development of this system with more targets and a simpler single device could allow for a new family of cheap, fully portable sensors for environmental monitoring.

5.4. Isothermal Amplification Methods Ultrahigh sensitivity and low detection limits are highly desired for detecting water contaminants. Moreover, the ability to detect a single live cell or viral particle is the ultimate goal for a microbial detection method with many practical applications, including the detection of waterborne pathogens. Therefore, amplification reactions are often essential components of molecular detection designs that aim to achieve low detection limits and high sensitivity. The best-known and most successful DNA-amplification reaction is the polymerase chain reaction (PCR). PCR is based on a thermal cycling protocol, in which repeated copying of the analyte sequence is achieved using a thermostable polymerase. PCR makes it possible to detect a small number of DNA molecules, down to even a single DNA molecule, of a specific sequence. This makes the PCR reaction unbeatable in terms of sensitivity. However, the PCR technique requires the use of a thermocycler, which is bulky, expensive, and requires a great deal of electricity, and thus it cannot be easily used in applications with on-site and realtime monitoring. In this context, isothermal amplification appears as a crucial alternative for on-site environmental detection. Isothermal amplification methods involve an enzyme or catalyst so that each aptamer binding event or DNAzyme cleavage event can be converted to multiple signal-generating molecules within a relatively short period of time.144,145 These highly efficient in vitro nucleic acid amplification techniques are simple, portable, and do not require costly instruments. Some of

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these isothermal nucleic acid amplification techniques have already been incorporated in commercial products.144 Although many potential isothermal amplification techniques have been developed for other applications145 that could also be applied to fDNA-based sensors, this section describes three of the most commonly used amplification techniques already demonstrated for functional DNA sensors. Strand displacement amplification (SDA)146 is one of the first reported isothermal amplification reactions, with amplification efficiency comparable to PCR without the need of a thermocycler. SDA relies on the ability of several polymerases that have a strong strand-displacing ability to extend the 3’ end of the DNA template and displace the downstream complementary DNA strand, and have a restriction enzyme to nick the primer strand at the recognition site, leaving the complementary strand intact (Fig. 5.7(a)). For instance, SDA was used for detecting S. enteritidis with a capture probe complex formed by a specific aptamer hybridized with a primer. In the presence of the bacterial target, the aptamer binds to the target and releases the primer that will trigger the SDA reaction.147

(a)

(b)

(c)

Figure 5.7.    Schematic representation of fDNA linked with different isothermal amplification reactions: (a) strand displacement amplification (SDA), (b) rolling circle amplification (RCA), and (c) hybridization chain reaction (HCR).

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Among various DNA amplification methods, the rolling circle amplification (RCA) represents an attractive option due to its excellent amplification power. It was revealed that RCA is capable of amplifying a target DNA for up to 109 times in 90 min.148 Furthermore, the reaction mechanism is simple, requiring only a single primer and a productive enzyme. RCA uses special DNA polymerases with strand displacement capability, such as phi29 DNA polymerase. This DNA polymerase can copy the sequence of a circular template going around it multiple times, resulting in long-chain single-stranded DNA products comprising thousands of tandem repeats that are complementary to the circular DNA template.149 In order to use this RCA amplification technique, the activity of functional DNA must be linked to the production of the primer for RCA, which is required for the reaction. For DNAzymes, the primer sequence is typically incorporated into the substrate strand, and thus the target will induce the cleavage of the substrate and release the primer that will trigger the RCA reaction (Fig. 5.7(b)).150 Similarly, the adsorption of aptamer sequences, including the primer sequence on graphene oxide, has been used to control the release of the primer in the presence of the target.151 In addition, RCA products can be conveniently produced with desired repetitive sequences by incorporating the complementary sequence into the circular template. Thus, judiciously designed sequences could fold into functional moieties, such as aptamers and DNAzymes, which can greatly extend the scope of RCA. For instance, the Li group has built a cross-amplification system between an E. coli-specific DNAzyme and RCA. They designed a circular template that codes for an Mg2+-specific DNAzyme, incorporating an extra-amplification step.152 The LOD calculated from the fluorescence measurement was 10 cells mL−1, using a 60 min reaction time. This represents a 1000-fold improvement in LOD over the DNAzyme assay without signal amplification.63 The hybridization chain reaction (HCR) is another isothermal amplification method used to overcome the inherent limitation of one signal output per single functional DNA.153 This method has the advantage of being performed without involving any additional enzymes. It uses a pair of DNA hairpins (H1 and H2) to propagate the chain reaction of hybridization events. The activity of a DNAzyme is linked to the production of the initiator sequence to open a hairpin (H1) (Fig. 5.7(c)). Subsequently, hairpin 2 (H2) binds to H1 to form a nicked dsDNA polymer chain which can be used to generate different amplified signal outputs. One of the most sensitive of these methods employed the highly selective UO22+-dependent DNAzyme.154 In this report, the double-strand DNA polymer chain was produced on the surface of an electrode. After incubation with a redox reporter (methylene blue) that can be intercalated in the double-strand, an electrochemical signal was recorded, and an LOD of 2 pM was achieved. Another example, based on a Cu2+-dependent DNAzyme, was developed for sensing Cu2+

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ions by combining HCR with a fluorescence resonance energy transfer (FRET) technique.73 In the presence of Cu2+ ions, the substrate strands were specifically cleaved and released. The released strands initiated the HCR process of hairpin H1 and H2, labeled with FAM as the donor and TAMRA as the acceptor. Finally, the long dsDNA structures self-assembled to bring the donor and the acceptor in close proximity, resulting in a FRET process. Other enzyme-free amplification techniques have also been reported in recent years for the amplification of aptamer and DNAzyme-based sensors, as reviewed elsewhere.155

5.5. Summary and Future Perspectives This chapter described how fDNA technology has become an active and promising technology for sensing metal and microbial contaminants in water with ultrahigh selectivity and sensitivity. Given the versatility of the in vitro selection method, which allows selection of fDNA for a wide range of new and emerging targets, this fDNA technology will continue to have a major impact for on-site and real-time detection. While a many fDNAs have been obtained that are specific for different metal ions, some water contaminants are still difficult to tackle with this technology, such as metalloids (e.g., arsenate and arsenite). For instance, both an aptamer156 and DNAzyme157 have been reported for arsenate or arsenite; however, recently the aptamer has shown not to be selective for arsenate or arsenite,158 and other labs have not been able to reproduce the DNAzyme, including the Lu lab, and thus selection of these functional DNA remains a significant challenge. Modified DNA bases and backbones with functional groups have shown the capability to improve metal binding, which is a promising strategy for recognizing metal ions that may be difficult for natural DNAs. Furthermore, for some important environmental contaminants, such as Cr ions, it is highly desirable to develop sensors that can differentiate not only different metal ions but also different oxidation states of the same metal ion, because Cr(III) can be beneficial while Cr(VI) is quite toxic. The highly selective fDNAs have the potential to meet this challenge. In terms of detecting microorganisms, the ability to selectively distinguish pathogenic microorganisms from non-pathogenic ones is highly desired. Recent reports have shown that fDNA sensors can differentiate pathogenic bacterial strains from non-pathogenic strains,64 as well as infectious from non-infectious viruses.159,160 These reports highlighted the level of selectivity that fDNA can harness and opening the possibility of developing fDNA that can differentiate infectious states of microorganisms. More generally, this allows fDNA sensors to detect not only target identity but also target functionality. Yet, despite much progress, detection of a single bacterial cell or viral ­particle remains a challenge for complex real samples in portable devices.

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To address this issue, the combinations of fDNAs with nanomaterials and incorporation of isothermal amplification reactions are promising strategies to achieve this goal for portable detection at very low detection limits. Given the progress made so far, we are confident that continued development of fDNA technology and interfacing with other technologies, including nanotechnology, will make possible the on-site and real-time detection of almost any contaminant in water.

Acknowledgments We thank the U.S. National Institutes of Health (Grants GM141931), Jiangsu Industrial Technology Research Institute (JITRI 23965), and KAUST Competitive Research Grant (URF/1/3407-01-01) for financial support. A.S.P. thanks the PEW Latin American Fellowship for financial support.

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Chapter 6

DNAzyme-Based Biosensors and Their Applications in Monitoring Environmental Water Sources Erin M. McConnell*, Meghan Rothenbroker† and Yingfu Li‡ Department of Biochemistry and Biomedical Sciences, McMaster University, 1280 Main Street West, Hamilton, Ontario, L8S 4K1, Canada [email protected]

* †

[email protected] [email protected]

‡

6.1. Introduction 6.1.1.  Functional nucleic acids The central dogma of molecular biology, which describes the biological role of nucleic acids as molecules which store (DNA) and translate (RNA) genetic information into proteins, had remained the prevailing associated functional identity of DNA and RNA until the late 1970s.1 Seminal work done by Thomas Cech and Sidney Altman in the early 1980s revealed an alternative role for RNA in nature: it could function as an enzyme. An enzymatic molecule made of RNA is now known as a ribozyme.2–4 Cech and Altman’s discovery of the catalytic properties of RNA earned them a Nobel Prize in Chemistry in 1989.5,6 Inspired by this work, Breaker and Joyce took advantage of a newly described technique, known as “in vitro selection”, to investigate whether DNA could also exhibit catalytic properties. In 1994, they reported the first synthetic enzyme made of DNA, which in the presence of Pb2+ was able to cleave an embedded ribonucleotide situated in a DNA substrate sequence.7 Since then, many catalytic DNA molecules, also called 171

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DNAzymes or deoxyribozymes, have been derived to catalyze a diverse range of biologically relevant chemical reactions. This chapter focuses on RNA-cleaving DNAzymes (RCD).8 The in vitro selection technique, also called Systematic Evolution of Ligands by EXponential enrichment (SELEX), was independently invented by two research groups, that of Jack Szostak and Larry Gold. SELEX is highly useful for isolating small RNA or DNA molecules, called aptamers, with target-binding activities from a large pool of random RNA sequences.9,10 The selection process is represented schematically in Fig. 6.1. To date, a large number of functional nucleic acids (ribozymes, DNAzymes, DNA, and RNA aptamers) have been selected for diverse targets, ranging from small molecules to whole cells, and have become popular molecular recognition elements (MREs).11–14 In vitro selection of functional nucleic acids begins with a starting library of 1014–1016 unique single-stranded oligonucleotides. The library is generally designed with a central random-sequence domain flanked on each side by a fixed sequence, which is used as the primer binding site to allow amplification. The common libraries use a random-sequence domain of 20–80 nucleotides in length. The simple schematic in Fig. 6.1 represents the necessary steps for the in vitro selection of functional nucleic acids. To select an aptamer or analyte-dependent DNAzyme, the library is incubated with the target of interest. Examples of relevant targets for environmental water source monitoring include heavy metals, bacteria, and bacterial toxins. Following incubation, sequences that interact favorably with the target or activate catalysis are separated from those that do not in a

Figure 6.1.    Schematic representation of in vitro selection of functional nucleic acids. A pool of single-stranded DNA or RNA molecules are used as the starting library. It is incubated with a target of interest for binding (in aptamer selection) or with a substrate (in ribozyme or DNAzyme selection). This is followed by a partitioning step in which a significant portion of inactive molecules are removed and the active molecules are retained. The remaining nucleic acid sequences are amplified to produce an enriched library to begin the next cycle of selective enrichment. The cycle is repeated as many times as needed until the desired functional nucleic acid molecules dominate the pool.

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partitioning step. The active sequences are then amplified by the polymerase chain reaction (PCR) to produce an enriched library, which is subjected to the next round of selection. This process can be tailored to each specific target, with consideration to the downstream application to control the sensitivity and selectivity of the obtained functional nucleic acids.

6.1.2.  Advantages of functional nucleic acids Functional nucleic acids have several advantages over traditional or alternative affinity tools such as antibodies, protein-based receptors and enzymes, and molecularly imprinted polymers (MIPs).15 Functional nucleic acids tend to be smaller in size (5–20 kDa) than antibodies and protein enzymes. They are produced synthetically and can be easily modified post-selection. Functional nucleic acids are biocompatible, inherently more stable than protein-based affinity agents, and can be selected for a wide variety of targets, including those for which traditional antibodies cannot be generated. Finally, once the sequence is known, these versatile tools can be adapted to a multitude of biosensor designs with ease.

6.1.3.  Functional nucleic acids as biosensor components Biosensors are a subclass of sensors that can detect a specific biomolecule or a class of biomolecules and produce a measurable signal that is directly proportional to the concentration of that biomolecule in solution.16 The established criteria for a biosensor include the ability to detect a specific analyte and to translate that molecular recognition event into an observable signal (transduction and signal amplification).16 Biosensors are generally classified by their signal transduction strategy, the most common of which are electrochemical, optical (fluorescent, colorimetric, chemiluminescent), or piezoelectric in nature.17 Several biosensors for human health and environmental-monitoring applications have used DNAzymes as either the MRE, the signal transducer (ST), or the signal amplifier (SA).18–24 In some cases, DNAzymes are used as both the MRE and an ST/SA. The conceptual design of DNAzyme-based biosensors is represented schematically in Fig. 6.2.

6.1.4.  Molecular recognition in DNAzyme-based biosensors In the design of a DNAzyme-based biosensor, the MRE can often be classified into one of the four categories: DNAzymes, aptamers, antibodies, and DNA. In the examples highlighted in this chapter, DNAzymes were employed to specifically recognize either bacterial or metal targets. In the case where a DNAzyme was used as an ST and/or SA, either aptamers/antibodies or DNA were used as the MRE. The aptamer and antibody targets were either bacterial (whole cell, protein, toxin, or unknown), or relatively smaller, simplistic molecules (antibiotics or hemin).

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Figure 6.2.    Hierarchical schematic of DNAzyme-based biosensor components, highlighting common strategies. Abbreviations are as follows: peroxidase-mimicking DNAzymes (PMD), RNAcleaving fluorogenic DNAzyme (RFD), modified litmus assay (MLA), horseradish peroxidase (HRP), magnetic (Mag), SYBR Green I (SG1), methylene blue (MB), 3,3′,5,5′-tetramethylbenzidine (TMB), rolling circle amplification (RCA), catalytic hairpin amplification (CHA), and polymerase chain reaction (PCR).

When DNA was used as the MRE, recognition occurred upon the hybridization of a target sequence (gene) or the formation of a specific metal-stabilized base pair (T-Hg2+-T).

6.1.5.  Signal transduction and amplification in DNAzyme-based biosensors Likewise, in DNAzyme-based biosensors, the ST and/or SA strategy can be classified into one of the three categories: DNAzymes, nanoparticles, and DNA.

6.1.5.1.  Peroxidase-mimicking DNAzymes DNAzymes have been employed as powerful ST and SA elements. Peroxidasemimicking DNAzymes (PMD) are most commonly used for this purpose. DNAzymes with peroxidase-like activity have found widespread use as signaltransducing elements in biosensors, and were first reported by Sen and coworkers in the late 1990s.25–27 In 1997, Li and Sen used in vitro selection for identifying the DNA aptamers for N-methylmesoporphyrin (NMM), a stable transition-state analogue for porphyrin-metallation reactions.28 After 12 rounds of selection and subsequent sequencing, it was determined that a series of guanine-rich motifs (15–30 nucleotides long) were capable of binding not only NMM but also other

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porphyrins and metalloporphyrins, such as hemin. The NMM-binding site of one of the sequences was synthesized as a 24-nucleotide DNAzyme called PS5.ST1, but later simplified to PS5.M. The authors determined that iron (III)-protoporphyrin (hemin) was a good competitive inhibitor for the DNA-catalyzed metallation reaction and appeared to bind strongly to PS5.M. The Sen group followed up with PS5.M the next year by identifying a shorter and more superior catalyst for porphyrin metalation, only 18 nucleotides in length, called PS2.M (Fig. 6.3).26 Like PS5.M, PS2.M is guanine-rich and can form a specific hemin-binding tertiary structure by the formation of an intramolecular guanine (G)-quadruplex. G-quadruplexes are a common secondary structure which result from the canonical and non-canonical interaction of guanine to form stacked G-quartets.29 In nature, G-quadruplexes have been observed in the human genome in gene-promoter regions, and some of these naturally occurring G-quadruplexes have been shown to be catalytically active in the presence of hemin.30 In the previous study Sen and coworkers sought to isolate a DNA aptamer for NMM; however, given that the DNAzyme can bind hemin, they investigated whether this DNAzyme could catalyze peroxidation reactions to a greater extent than hemin itself. They found the DNAzyme complex more closely resembles horseradish peroxidase and other heme proteins as opposed to heme itself. Taken together, these findings revealed a new class of catalytic activity for nucleic acids and the complexes described were representative of novel DNA enzymes. It was not until a few years later, however, in 2001, that the true application of

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Figure 6.3.   Schematic illustration of PS2.M DNAzyme activity. The guanine-rich sequence of PS2.M forms a G-quadruplex that when complexed with hemin can catalyze the oxidation of ABTS in the presence of hydrogen peroxide, producing a blue color visible to the naked eye.

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PS2.M was revealed.31 In this study, Sen and coworkers demonstrated that PS2.M complexed with hemin can catalyze the oxidation of ABTS [2,2′-azino-bis (3-ethylbenzothiozoline-6-sulfonate)] in the presence of hydrogen peroxide, resulting in a visible color change from colorless to blue. The oxidation of TMB (3,3′,5,5′-tetramethylbenzidine) catalyzed by PS2.M complexed with hemin in the presence of hydrogen peroxide to generate a blue color has also been demonstrated multiple times in the work described in this chapter.

6.1.5.2.  RNA-cleaving fluorogenic DNAzymes (RFD) In 2011, the Li group demonstrated for the first time that it was possible to use a DNAzyme as both the MRE and ST simultaneously in a bacterial biosensor design when they carried out an in vitro selection experiment using the crude extracellular mixture (CEM) produced by bacteria.32 For this study, the authors selected E. coli K12, a non-pathogenic strain, as their bacteria target. Common to all in vitro selections, the search began with a random DNA library. This library was first incubated with the selection buffer and the CEM of Bacillus subtilis, representing the “negative selection” step to remove any non-specific and self-cleaving DNAzymes. The purified uncleaved sequences were then incubated with the CEM of E. coli, constituting the “positive selection” step. The negative selection step and positive selection step constituted one round of selection. Twenty rounds of selection were conducted, after which the resulting pool was sent for sequencing. From this, a dominant DNAzyme sequence was identified, called RFD-EC1 (Fig. 6.4). RNA-cleaving fluorogenic DNAzymes (RFD) are intrinsic Turn-ON biosensors derived from a DNAzyme selection library which incorporates quencher and /ŶĂĐƟǀĞZ& >ŽǁŇƵŽƌĞƐĐĞŶĐĞ

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Figure 6.4.    Schematic illustration of RFD-EC1 mode of action. In the absence of the E. coli CEM target, RFD-EC1 is inactive with low fluorescence. RFD-EC1 becomes activated upon the binding of the target molecule present in the E. coli CEM and cleaves at the RNA site, thus generating an enhanced fluorescent signal. F = fluorescein-dT, Q = dabcyl-dT, r = adenosine ribonucleotide.

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fluorophore modifications on the deoxyribonucleotide sites flanking the sessile ribonucleotide cleavage site. Upon cleavage of the embedded ribonucleotide, a fluorescent signal is generated. The specificity of RFD-EC1 was assessed against the CEM of more than a dozen gram-positive and gram-negative bacteria and none were able to activate RFD-EC1, indicating that the DNAzyme is highly specific for the CEM of E. coli. The authors also demonstrated that with an integrated culturing step the DNAzyme can achieve single-live-cell detection. Since its discovery in 2011, RFD-EC1 has been incorporated into many other strategies as a recognition element or as both a recognition element and ST, given its inherent fluorescence.

6.1.5.3.  Modified litmus assay (MLA) In 2014, Li and coworkers developed an all-in-one colorimetric alternative to RFD when they reported a biosensor design that used a DNAzyme as the MRE and an enzyme as the SA.33 The MLA is represented schematically in Fig. 6.5. In this design, the DNAzyme was immobilized on a magnetic bead (MB). The sequence of the DNAzyme was terminally extended to allow for the complementary hybridization of a urease (Ur)-modified oligonucleotide. In the presence of the cognate target (star), cleavage of the DNAzyme released the DNAzyme-urease complex from the MB. Following the molecular recognition event, the cleavage

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Figure 6.5.    Schematic representation of the modified litmus assay.

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complex could be separated from unreacted DNAzyme via an MB and added to a new tube containing the necessary reagents (urea and phenol red) for the modified litmus assay to occur. Li and coworkers applied the MLA design to their E. coli DNAzyme. In the presence of E. coli, the DNAzyme was cleaved and the cleavage complex was separated and added to a tube containing urea and phenol red in buffer. The urease catalyzed the hydrolysis of urea into carbon dioxide and ammonia, and thus increased the pH of the solution. The increase in pH was visualized using phenol red, a pH-sensitive dye. In the presence of E. coli, an obvious color change from yellow to pink was observed. Using this method, Li and coworkers demonstrated a visual limit of detection (LOD) of 500 cells after 2 h. They also showed that inexpensive and commonly used pH paper could be used to visualize the targetspecific change in pH solution. Since the MLA was first described, Li and coworkers have optimized the method and shown its diverse applicability in the DNAzyme-based detection of uranyl ion in water.34

6.1.5.4.  Non-DNAzyme ST and SA strategies In studies where a DNAzyme was employed as the MRE, several non-DNAzyme ST and SA strategies were also reported. Horseradish peroxidase was used for the catalysis of TMB in a colorimetric design, and nanoparticles and fluorescent or redox active reporter probes were used to generate electrochemical and optical signals. Several DNA-based methods have also been reported, most commonly rolling circle amplification (RCA), hybridization chain reaction (HCR), catalytic hairpin assembly (CHA), and PCR. Each of these methods takes advantage of the complementarity of DNA to amplify the generated molecular recognition event. The method of RCA was first reported by Fire and Xu in 1995.35 This isothermal amplification depends on the presence of a circle template DNA and a single complementary primer. The generated amplicon contains tandem repeats of the circular template complement. These tandem repeats can be encoded with functionality, such as a PMD sequence, or exploited to introduce a complementary reporter probe. RCA is a common tool that has been successfully incorporated into many of the biosensor designs described in this chapter. HCR and CHA both describe a process in which a catalyst strand or initiator strand is used to set off a chain reaction of hybridization where the end point is a method of signal amplification by the assembly of a reporter probe or by access to a reporter probe. In this method, the catalyst strand or initiator is usually a short oligonucleotide that hybridizes to a hairpin, which induces a change in secondary structure and allows access to a different part of the sequence. In subsequent steps of this process, the catalyst strand is regenerated by displacement by a more fully complementary oligonucleotide, which returns the catalyst strand to the system for further cycles.

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This approach has been widely applied for designing DNAzyme-based biosensors for detecting contaminants in water and for monitoring water sources. The following sections highlight recent advances in biosensor development for monitoring environmental water sources. For the detection of metal cations, selected publications from 2011–2019 are summarized, highlighting the specific use of these biosensors for detecting metal ions in water sources. The most commonly examined targets are Pb2+, Hg2+, and UO22+, but biosensors for Zn2+, Mg2+, and Cu2+ are also described. If the reader is also interested in the detection of cadmium, chromium, thallium, or the detection of regulated metal ion contaminants in drinking water by DNA-based sensing strategies, please refer to the comprehensive review recently published by Zhou et al.19 Several biosensors have been developed for bacteria known to contaminate water; however, only the E. coli DNAzyme selected by Li and coworkers has been extensively used for detecting bacteria in water sources. For this reason, the DNAzyme-based biosensors for detecting bacteria that are highlighted in this section present the opportunity for the application of these sensors in water monitoring. Similarly, several DNAzyme-based biosensors for detecting small molecules that are known water contaminants are also highlighted. Some of these biosensors have been applied for detecting their small molecular target in water, and others represent potential alternative applications. An advantage of the existing DNAzyme-based biosensors is that, in most cases, the sensors have already been employed to detect their target in a biological matrix, which is technically more complex. Therefore, these sensors could potentially be easily adapted for water monitoring.

6.2. DNAzyme-Based Biosensors for Detecting Heavy Metals The contamination of water sources from heavy metals remains a persistent problem, the monitoring of which has proven challenging when government regulations and access to testing equipment and expertise do not extend to all environmentally available water sources. DNAzyme-based biosensors present an excellent opportunity for detecting metal contaminants in water because they are stable, specific, and catalytically active, and they can be easily adapted to paperbased sensors.34,36 A caveat to metal-ion sensing by DNAzymes is that they are only capable of measuring the soluble, free metal ion at a particular oxidation state, whereas traditional analytical methods like ICP-MS measure the total metal content.19 Considering this limitation, DNAzymes may not replace traditional methods, but the examples highlighted suggest they can closely compete as a semi-quantitative, easier to use, and less expensive alternative.

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6.2.1.  Detection of lead For detecting metal ions in water, most of the work on DNAzyme-based biosensors has been done with lead-dependent DNAzymes. Two DNAzymes, 8-1737 and GR-57, have been used extensively to develop electrochemical, optical, and piezoelectric biosensors, which have been reviewed previously.12,19,21,38 Those which have applications in the monitoring of environmental water sources are highlighted in Table 6.1. Recent examples of fluorescent, electrochemical, and colorimetric deigns are detailed in the following paragraphs. Importantly, for a DNAzyme-based sensor to be an effective alternative to traditional analytical detection methods, it must be able to detect Pb2+ at the maximum contaminant level, which is set at 70 nM by the United States Environmental Protection Agency (US EPA).19 The following paragraphs highlight recent DNAzyme-based biosensors for Pb2+ and their applicability for environmental water sources. Of note, the LOD for all the highlighted biosensors falls below the 70 nM standard set by the US EPA. As recently as 2019, Zhang et al. developed a fluorescent biosensor using the GR-5 lead-dependent DNAzyme as the MRE and the digital PCR as the SA.39 In this design, the catalytic domain of the GR-5 DNAzyme was biotinylated and immobilized on a streptavidin-coated plate. The substrate, which had been extended by 50 bases to act as a PCR template, was allowed to hybridize to the catalytic domain. In the presence of Pb2+, cleavage of the substrate strand released the PCR template into the reaction. Following amplification, an increase in the number of droplets with template DNA was directly proportional to the concentration of Pb2+ in solution. In the absence of Pb2+, the template DNA remained immobilized to the surface through complementary hybridization of the substrate-PCR template and catalytic domains of the DNAzyme. In this digital platform, positive droplets were indicated by an increase in fluorescence. The authors reported a linear dynamic range of 500 pM to 100 nM, with an LOD of 500 pM, which is well within the regulation set by the US EPA. The authors also assessed the specificity of the biosensor against Zn2+, Hg2+, Cu2+, Fe2+, K+, and Ca2+ and determined that a signal was generated only in the presence of Pb2+. Additionally, lake water, which had been centrifuged to remove insoluble impurities, was spiked with 1 nM, 5 nM, 10 nM, or 50 nM of Pb2+ to assess the applicability of the biosensor for monitoring environmental samples. Measured values of Pb2+ were 1.05 nM, 5.08, nM, 10.15 nM, and 48.10 nM, respectively, demonstrating recoveries ranging from 96.2% to 105.0%, with standard deviations of less than 5%. In most DNAzyme-based biosensors, either a single DNAzyme is used for the MRE or two DNAzymes may be used, one as the MRE and one as the ST and/or SA. In one example, an electrochemical biosensor was devised in which two leaddependent DNAzymes were used as the MRE in a dual DNAzyme-feedback amplification strategy.40 In this case, the biosensor design was based on three

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ST and/or SA

Type

8-17

Ru(phen) and electrically heated ITO electrode

ECL

8-17

ST: 2-amino-5,6,7-trimethyl-1,8naphthyridine (ATMND)

2+ 3

LDR and LOD

Detection in water

References

LDR: 0.25–500 pM LOD: 0.2 pM

Not reported, shown in soil samples

[42]

FR

LDR: 100 pM to 10 µM LOD: 50 pM

nM levels in tap, lake, and pond water, which were comparable to LC-MS/MS controls

[43]

FR

LDR: 500 pM to 100 nM LOD: 500 pM

Recovery of Pb2+ from spiked lake water (1–50 nM) was observed, with recoveries ranging from 96.2% to 105%

[39]

8-17 & GR5

Dual-DNAzyme feedback amplification using RCA and methylene blue

EC

LDR: 0.2 pM to 100 nM LOD: 0.048 pM

Pb2+ from 5 river samples was detected between 12.7 and 25.4 nM, which had good agreement with control measurements by AAS

[40]

GR5

CHA product captured on Au electrode surface and labeled with methylene blue

EC

LDR: 4 × 10−11 to 3 × 10−6 M LOD: 2.7 × 0−11 M

Used for detecting Pb2+ in serum

[44]

8-17

PtNP-TiO2/α-Fe2O3 composites

EC

LDR: 1 pM to 0.1 µM LOD: 0.29 pM

Recovery of 21.1 and 83.3 nM Pb2+ in spiked lake water with 105.5% and 104.12%, respectively

[45]

8-17

Methylene blue and ferrocene

EC

LDR: 0.1 nM to 5 µM LOD: 45.8 pM

Detection of Pb2+ in serum shown

[46]

GR5

Horseradish peroxidase enzyme

CR

LDR: 102 to 108 pM LOD: 32 pM

Low nM detection of Pb2+ in tap, river, and waste water comparable to AAS controls

[41]

8-17

PMD

EC

LDR: 0.01–1000 nM LOD: 8 pM

Recovery of Pb2+ ion from spiked tap and pool water was 93% and 92%, respectively

[47]

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(Continued )

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Digital PCR and SYBR Green I

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MRE



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Table 6.1.    DNAzyme-based biosensors for detecting lead (Pb2+) in water samples.

GR5

AuNP-DNA complex captured on paper device

CR

LDR: 0.01–100 µM LOD: 0.05 nM

Used for detecting Pb2+ from diluted soil samples

[48]

8-17

Fe-MOFs/PdPt NP-mediated catalysis of H2O2

EC

LDR: 0.005–1000 nM LOD: 2 pM

Detection of Pb2+ in reservoir, well, and tap water between 1.52 and 24.55 nM by this device was in good agreement with AAS controls

[49]

17E

Loss in dsDNA-dependent conductivity between NPs

RS

LDR: 10 nM to 10 µM LOD: 10 nM

Not reported

[50]

8-17

Raman-dye-labeled DNA immobilized on AuNPs

LDR: 1.0 × 10−13 to 1.0 × 10−7 M LOD: 70 fM

Tap water spiked with 10 nM and 100 nM Pb2+ led to 92% and 95% recovery, respectively

[51]

GR5

Zinc(II)-protoporphyrin IX/ G-quadruplex system

FR

LDR: 5–100 nM LOD: 3 nM

Detection of Pb2+ in river water spiked with 10, 30, and 50 nM of Pb2+ with 98.9%, 99.33%, and 99.4% recovery, respectively

[52]

8-17

Methylene blue-labeled oligo and ITO electrode

EC

LDR: 0.05–1 µM LOD: 0.018 µM

Detection of spiked Pb2+ ion in river and tap water at 200, 400, and 600 nM, with detected concentrations comparable to ICP-MS controls

[53]

8-17

NaCl-induced aggregation of AuNPs in the absence of target

CR

LDR: 0.05–5 nM LOD: 20 pM

Used to detect Pb2+ in spiked river water, with recoveries ranging from 96% to 109.2%

[54]

8-17

Interaction of redox active [Ru(NH3)6]3+ probe with DNA-AuNP complex

EC

LDR: 0.05–100 nM LOD: 0.012 nM

Recovery of Pb2+ from spiked (5 and 10 nM) tap water, river water, and landfill leachate were consistent with AFS controls

[55]

SERS

LDR and LOD

Detection in water

References

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Table 6.1.   (Continued )

Presence of Pb2+ ions in tap, river, and pool water ranging from 10 to 60 nM was detected consistently compared with ICP-MS controls

[56]

GR5

Silver reduction of deposited AuNP-labeled cleavage fragment

CR

LDR: 0.1–1000 nM LOD: 0.3 nM

Tested in 3 different river water samples spiked with 10 or 100 nM of Pb2+ ion. Recoveries between 91.83% and 113.24% were observed.

[57]

8-17

AuNP labeled with DNA and invertase produced glucose

PGM

LDR: 1.0 pM to 0.8 nM LOD: 0.8 pM

Detection of natural and spiked Pb2+ (low nM) in sewage and drinking water, respectively, showed good agreement with ICP-MS controls

[58]

8-17

Absorption of AuNP-DNAzyme CD onto graphene oxide

CR

LDR: 0.1 nM to 1 µM LOD: 100 pM

Pb2+ ion was recovered from tap water and lake water spiked with 100 and 1000 nM of lead, with 105.9% and 98.6% recovery for tap water, and 93.2, and 96.8% recovery for lake water, respectively

[59]

GR5

Cleavage of the AuNP-modified oligo results in QCM-D signal change

QCM-D

Frequency LDR: 46–3000 nM LOD: 14 nM Dissipation LDR: 66–3000 nM LOD: 20 nM

Tap water was spiked with Pb2+ (50–2000 nM) which recovered with 90%–99.9% accuracy compared with ICP-MS controls

[60]

GR5

CF of DNAzyme used as primer for strand displacement amplification reaction and interaction of SYBR Green I

FR

LDR: 200 pM to 20 nM LOD: 200 pM

Underground water was tested and showed 11.11 ± 1.21 nM (2.30 ± 0.25 ppb), which was in agreement with control measurements by ICP-MS

[61]

(Continued )

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Accumulation of AuNPs on control and test zones by complementary hybridization



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17E

Type

LDR and LOD

Detection in water

References

GR5

Interaction of fluorophore-labeled CF with optical fiber

FR

LDR: 2–75 nM LOD: 1.03 nM (0.21 ng/mL)

Spiked (10–40 nM) wastewater and tap water was recovered with values ranging from 88.50% to 98.20%

[62]

8-17

Interaction ZnO-ITO electrode and dsDNA intercalator Ru(bpy)2(dppz)2+

PEC

LDR: 0.5–20 nM LOD: 0.1 nM

Recovery of spiked Pb2+ (2,5, and 10 nM) in tap and lake water samples ranged from 99 to 104%, which had good agreement with ICP-MS controls

[63]

8-17

Hemin/G-quadruplex catalysis of luminol oxidation

CL

LDR: 5 nM to 1 µM LOD: 0.79 nM

Used to detect Pb2+ in lake water, which showed good agreement with ICP-MS controls

[64]

8-17

HCR and ferrocene

EC

LDR: 0.1–75 nM LOD: 37 pM

Not reported

[65]

8-17

Nicking enzyme (Nt.BbvCI)assisted signal cascade amplification

FR

LDR: 8.0 × 10−10 M to 1.0 × 10−7 M LOD: 1.0 × 10−10 M

Recovery of spiked Pb2+ (5, 10, and 100 nM) in river water ranged from 96.1% to 108%

[66]

8-17

Interaction of fluorophore-labeled CF and SWCNTs

FR

LOD: 1 nM

Detection of Pb2+ (nM range) in tap water from two sites showed good agreement with ICP controls

[67]

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Notes: Molecular recognition element (MRE), signal transduction (ST), signal amplification (SA), linear dynamic range (LDR), limit of detection (LOD), Peroxidase mimic DNAzyme (PMD), electrochemiluminescence (ECL), chemiluminescence (CL), electrochemical (EC), photoelectrochemical (PEC), Fluorescent (FR), colorimetric (CR), resistance (RS), personal glucose meter (PGM), catalytic hairpin assembly (CHA), nanoparticle (NP), metal-organic framework (MOF), double-stranded (ds), atomic fluorescence spectrometer (AFS), catalytic domain (CD), cleavage fragment (CF), indium tin oxide (ITO), single-walled carbon nanotubes (SWCNT), surfaceenhanced Raman-scattering (SERS), and quartz crystal microbalance with dissipation monitoring (QCMD). When an alternative reference is not provided, see reference for DNAzyme, PMD, aptamer, or gene sequences.

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MRE

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oligonucleotides, referred to as C1 (circular DNA containing the 8-17 DNAzyme and antisense sequence of the ST G-quadruplex), C2 (circular DNA containing the antisense sequence of the GR-5 DNAzyme and the same antisense sequence of the ST G-quadruplex), and L-DNA (linear DNA containing complementary regions to C1 and C2, and a Pb2+-active substrate region). Initially, these DNA molecules form two complexes L-DNA/C1 and L-DNA/ C2, which can then interact to form a tertiary structure. When Pb2+ is present, cleavage of the substrate region of L-DNA/C2 by the catalytic domain of C1 initiates the first RCA reaction. The product of the first RCA reaction, which contains repeats of the GR-5 DNAzyme and G-quadruplex domain, is used to generate the product of a second RCA reaction. Based on the system design, both RCA products can be used to generate an electrochemical signal in the presence of the methylene blue reporter probe. The authors reported a linear dynamic range of 0.2 pM to 100 nM, with an LOD of 0.048 pM, which also falls well below the limit set by the US EPA. The biosensor showed excellent selectivity for Pb2+ when it was assessed against Fe3+, Mn2+, Ag+, Ba2+, Cu2+, Al3+, Ca2+, Ni2+, Cd2+, Co2+, Cr3+, and Hg2+. The biosensor was used to detect free Pb2+ in five different river water samples collected from various locations in the Yangtze River in Chongqing, China. The values detected ranged from 12.7 nM to 25.4 nM, had recoveries that ranged from 94.7% to 106.7% and had standard deviations of less than 5% when compared with control measurements obtained using atomic adsorption spectroscopy (AAS). One challenge with fluorescent and electrochemical biosensors is that they often require complex equipment for signal processing. Recently, Rong et al. reported the development of a colorimetric biosensor for the detecting Pb2+ based on the GR-5 DNAzyme.41 In this relatively simplistic design, the substrate of the GR-5 DNAzyme and horseradish peroxidase were immobilized on the surface of a gold nanoparticle (referred to as AuNP-HRP-GR5S). The catalytic domain of the GR-5 DNAzyme was immobilized on MBs and, through complementary hybridization, the AuNP-HRP-GR5S was immobilized on the MB. In the presence of Pb2+, cleavage of the substrate region led to the specific release of the AuNP-HRPGR5S complex into solution. The cleaved AuNP-HRP-GRS were then transferred to a multi-well plate, where colorimetric determination of the Pb2+ content was either by AuNP (red color) or oxidation of TMB by the AuNP-HRP to produce a blue color. Using this biosensor, the linear dynamic range was reported over the orders of magnitude 102 to 108 pM, with an LOD of 32 pM, which is environmentally relevant according to the US EPA. The specificity of this biosensor was assessed against common metal cations (Zn2+, Na+, Mg2+ Ca2+, Cu2+, Mn2+, Ag+, Ni2+, Fe2+, Co2+, and Hg2+), as well as six anions (NO3−, CO32−, Cl−, SO42−, HPO42−, and H2PO4−). When assessed both individually and in a mix of ions, the biosensor was selectively active to Pb2+. The applicability of this biosensor for monitoring

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environmental water samples was investigated, both with the recovery of Pb2+ from spiked water samples and the detection of free Pb2+ in water samples ­collected from Wenzhou City, China. In the recovery assay, water was spiked with 103, 105, or 107 pM, and the biosensor showed recovery values of 103 ± 8%, 105 ± 9%, and 98 ± 5%, respectively. Free lead was detected in purified water, tap water, river water, and waste water using the described biosensor and AAS as a reference. Although generally in the same order of magnitude, the amount of lead detected by AAS was consistently higher than that detected by the reported biosensor. The authors proposed that the AAS may have detected lead in metal complexes or bound to colloidal particles, which may not have been detected by the DNAzyme.

6.2.2.  Mercury detection To detect mercury ions (Hg2+) in water, DNAzyme-based biosensors use oligonucleotide probe stabilization by T-Hg2+-T interactions as the MRE and either an Mg2+-dependent DNAzyme or a PMD for the ST and SA. The details of these biosensors are summarized in Table 6.2. This section discusses recent examples of photoelectrochemical, electrochemical, and colorimetric biosensors used to detect mercury in environmental water sources. An important consideration in the design of DNAzyme-based biosensors for detecting mercury in environmental water sources is that the US EPA has defined the allowable detection limit of Hg2+ in drinking water to be 10 nM.19 The following paragraphs highlight examples of biosensors with limits of detection that meet the regulated US EPA standard. Zhang et al. recently reported a highly sensitive photoelectrochemical biosensor for detecting mercury in tap river and lake water using Fe3+/ZnO-Ag as a photocatalyst.68 In this design, Hg2+ detection was based on the ion-dependent formation of T-Hg2+-T to stabilize the terminal ends of a hairpin. Specifically, in the presence of Hg2+, the double-stranded DNA was cleaved by exonuclease III, allowing the formation of the catalytically active hemin/G-quadruplex PMD. Essential to the design, the DNA hairpin was immobilized on the surface of an Fe3+/ZnO-Ag/ITO electrode, in which sensing was triggered by a 4-chloro1-naphthol (4-CN) solution containing H2O2. The authors reported a linear dynamic range of 0.5–100 nM and an LOD of 0.1 nM. Additionally, the specificity of the biosensor was assessed against Cu2+, Mg2+, Co2+, Cd2+, Fe3+, Ag+, and Na+. The biosensor displayed no significant photochemical response compared with the blank for any of the ions investigated. Finally, the performance of the biosensor was assessed in tap, river, and lake water. Three samples of each were spiked with either 1.0, 10, or 50 pM Hg2+. The recovery values ranged from 90% to 100%, 90% to 98%, and 80% to 98% in the tap, river, and lake water samples, respectively.

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ST and/or SA

Type

LDR and LOD

Detection in water

Reference

LDR: 50–1200 pM LOD: 33 pM

Not reported; used to detect Hg in Chinese herbs

[72]

Probe stabilization PMD (EAD230)-based catalysis of luminol by T-Hg2+-T oxidation interaction

CL

LDR: 12–600 nM LOD: 12 nM

Not reported

[73]

Probe stabilization Cleavage of Mg2-dependent DNAzyme by T-Hg2+-T (E671) and complementary fluorophoreinteraction labeled DNA interaction

FR

LDR: 0.1–5 nM LOD: 30 pM

Not reported; used to detect Hg2+ in Chinese herbs

[74]

Recovery of spiked Hg2+ (1, 10, and 50 pM) in tap, river, and lake water, with recoveries ranging from 80% to 100%

[68]

Probe stabilization Oxidative catalysis of 4-chloro-1-naphthol by PEC LDR: 0.5–100 nM by T-Hg2+-T PMD (PS2.M26) LOD 0.1 nM interaction

2+

Probe stabilization Mg2+-specific DNAzyme (E6 truncation75) by T-Hg2+-T cleavage-released trigger strand for HCR, interaction which led to the formation of a hydrogel at the electrode surface

EC

LDR: 0.1 pM to 10 nM Recoveries from drinking water LOD: 0.042 pM spiked with Hg2+ (2.0 pM to 1 nM) ranged from 95.2% to 103.3%

[69]

Probe stabilization Split Mg2+-dependent DNAzyme71 and dualby T-Hg2+-T cycle amplification-induced AuNP interaction aggregation via complementary hybridization of surface-modified probes

CR

LDR: 10 pM–100 nM LOD: 5 pM

[70]

Spiked river water showed recoveries ranged from 88% to 106%; measured Hg2+ concentrations in tap, lake, and pond water were 82.5 pM, 145.6 pM, and 936.8 pM, respectively. These values agreed with controls measured by atomic fluorescence spectrometry

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CR

71

DNAzyme-Based Biosensors and Their Applications  187

Probe stabilization Mg -dependent DNAzyme (E6) cleavage by T-Hg2+-T releases DNA strand, which absorbs to interaction the surface of hemin/GO, preventing aggregation and facilitating TMB oxidation 2+

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MRE



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Table 6.2.    DNAzyme-based biosensors for detecting mercury (Hg2+) in water samples.

ST and/or SA

Type

LDR and LOD

Detection in water

Reference

CR

LDR: 1–30 nM LOD: 0.8 nM

Used to detect Hg in tap water, river water, and industrial wastewater, and showed good recovery (93%–109.2%) of spiked (10 or 12 nM) samples

[76]

Probe stabilization Exonuclease III-assisted target recycling and by T-Hg2+-T hemin/g-quadruplex DNAzyme (PS2.M26)interaction mediated oxidation of ABTS2-

CR

LDR: 50.0 pM to 20 nM LOD: 10.0 pM

Used to detect Hg2+ in spiked (1–10 nM) tap and river water samples, recoveries ranged from 95.2% to 106%

[77]

Probe stabilization Cleavage of an Mg2+-dependent DNAzyme71by T-Hg2+-T based molecular beacon released a FAMinteraction oligo for turn-ON fluorescence, and returned the catalytic complex for cyclic amplification

FR

LDR: 1–20 nM LOD: 0.2 nM

Recovery of spiked (2.5 nM and 10.0 nM) river water samples was 96% and 105%, respectively

[78]

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Probe stabilization Catalysis of the oxidation of ABTS by by T-Hg2+-T hemin/g-quadruplex DNAzyme (PS2.M26) interaction

2+



MRE

188  E. M. McConnell et al.

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Table 6.2.   (Continued )

Notes: Molecular recognition element (MRE), signal transduction (ST), signal amplification (SA), linear dynamic range (LDR), limit of detection (LOD), Peroxidase mimic DNAzyme (PMD), photoelectochemical (PEC), chemiluminescence (CL), electrochemical (EC), Fluorescent (FR), colorimetric (CR), graphene oxide (GO), hybridization chain reaction (HCR), nanoparticle (NP), 3′,5,5′-tetramethylbenzidine sulfate (TMB), and 2,2′-azino-bis(3-ethylbenzothiozoline-6-sulfonate) (ABTS). When an alternative reference is not provided, see reference for DNAzyme, PMD, aptamer, or gene sequences. “9.61 x 6.69”

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Cai et al. recently reported an electrochemical impedance biosensor for detecting Hg2+ which takes advantage of the hybridization chain-reaction-induced formation of a DNA hydrogel for signal generation.69 Molecular recognition is achieved in this design by the use of two DNA probes, D1 and D2, which, in the presence of Hg2+, assemble with a substrate-containing hairpin (H2) to form an active Mg2+ DNAzyme. When Mg2+ is introduced to the system, the H2 is cleaved which releases (i) the single-stranded cleaved fragments which can hybridize with a hairpin DNA (H1) immobilized on a GCE/AuNP surface, thereby exposing a single-strand region for the hybridization-based capture of a DNA-functionalized polymer (P1), and (ii) the assembled Mg2+ DNAzyme to continue the iterative cleavage of H2. Hybridization-based capture of P1 onto the GCE/AuNPs surface facilitates the capture of P2. Continued rounds of the HCR results in the formation of a DNA hydrogel on the surface of the electrode. This method allowed for the detection of Hg2+ within the range of 0.1 pM to 10 nM, and displayed a detection limit of 0.042 pM. Specificity analysis revealed that the biosensor was specific for Hg2+ against nine other metal cations, including Ag+, Mn2+, Al3+, Ba2+, Cu2+, Co2+, Cd2+, Ni2+, and Fe3+. The applicability of this biosensor to detect Hg2+ in real samples was assessed in drinking water spiked with Hg2+ ranging from 1 nM to 2 pM. Reported recoveries ranged from 95.2% to 103.3%, with relative standard deviations below 7%. The final highlighted example in this section describes a colorimetric biosensor reported by Chen and colleagues, which also takes advantage of a split Mg2+ DNAzyme and HCR for signal generation.70 This two-cycle system is initiated by the formation of T-Hg2+-T base pairs. In the first cycle, the split Mg2+-dependent DNAzyme is assembled in the presence of Hg2+ and binding DNA. This complex hybridizes to another duplex, which is made up of a substrate DNA and blocking DNA. In the presence of Mg2+, the substrate strand is cleaved and releases a fragment (E), which enters the second cycle. From the first cycle, a colorimetric signal (red to blue) is generated when two AuNP-modified probes are brought close together, by hybridization, to the remainder of the substrate DNA and blocking DNA duplex. In the second cycle, the cleavage fragment, E, hybridizes with a hairpin DNA to form a second version of the Mg2+-dependent DNAzyme/substrate and binding DNA complex. Once assembled this complex behaves in the same way as that described in Cycle I to generate the cleavage fragment E and the colorimetric AuNP-based probe. The authors reported a linear dynamic range of 10 pM to 100 nM, with an LOD of 5 pM. The selectivity of this system was assessed against Pb2+, Cu2+, Ag+, Cd2+, Sn2+, Cr3+, Co2+, and Mn2+ individually and combined; no cross-reactivity was observed. The applicability of this system to the assessment of real water samples was shown by both recovery and detection assays. The recovery of spiked river water samples ranged from 88% to 106%. More importantly, the detection of Hg2+ in tap, lake, and pond water was measured as 86.5 pM, 137.4 pM, and 985.6 pM, respectively. The measured values were not

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significantly different from control measurements performed by atomic fluorescence spectroscopy.

6.2.3.  Detection of uranium (uranyl ion) In 2007, Liu et al. reported the selection of a DNAzyme and development of a biosensor for the detection of uranium that exhibited parts-per-trillion sensitivity and greater than one million-fold selectivity.79 Specifically, the sensor which utilized the DNAzyme named clone 39, could detect uranyl ion with a linear dynamic range of 0–20 nM and an LOD of 45 pM (11 ppt). Additionally, the reported 39 DNAzyme showed greater than one million-fold selectivity over all other ions assessed, including Ag+, Mg2+, Ca2+, Sr2+, Ba2+, VO2+, Mn2+, Fe2+, Fe3+, Co2+, Ni2+, Cu2+, Zn2+, Cd2+, Hg2+, Pb2+, Tb2+, Eu3+, and Th4+. For reference, the regulated UO22+ level in drinking water has been set at 130 nM and 30 µg/L by the US EPA and World Health Organization (WHO), respectively, and thus the 39 DNAzyme is a powerful tool for sensitive UO22+ detection. The DNAzyme-based biosensors used for the detecting of uranyl ion in water are summarized in Table 6.3. The following paragraphs describe three different colorimetric strategies which meet the regulatory limit. Wu and coworkers recently reported the development of a colorimetric biosensor for detecting uranyl, in which signal generation was based on the photosensitization of SYBR Green I and the subsequent oxidation of TMB.80 In this design, the UO22-specific DNAzyme is incubated with SYBR Green I, which interacts with the double-stranded regions of the secondary structure, and, following double-stranded DNA controlled photosensitization, generates a colorimetric signal. In the presence of UO22+, cleavage of the substrate results in the liberation of the SYBR Green I, and no colorimetric signal is generated. To accommodate for the “turn-off” generation of a colorimetric signal, a background color dye (methyl orange) was added to the reaction to improve visual resolution in the presence of UO22+. The authors reported a linear detection range of 0.5–500 µg/L and a detection limit of 0.08 µg/L, or 0. 33 nM. The specificity of the biosensor for uranyl was tested against multiple metal ions, including Na+, K+, Ca+, Mg2+, Hg2+, Mn2+, Al3+, Fe3+, Zn2+, Pb2+, Co2+, Cd2+, Tl3+, AsO2−, Ba2+, and Ni2+, and no visually appreciable signals were detected. To assess the applicability of the biosensor in detecting UO22+ in salt water, sea water samples were collected from the East China Sea, the Yellow Sea, the South China Sea, Lingdingyang Bay, and the Bohai Sea; concentrations between 0.7 and 1.2 µg/L were detected in these samples. Biosensor performance was validated by the quantitative recovery of spiked UO22+, which ranged from 93% to 104%. Manochehry et al. recently adapted the modified litmus assay, which had previously been shown to be an effective assay for the DNAzyme-based detection of bacteria, in order to detect uranyl both in solution and on paper.34 In this approach,

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MRE

ST and/or SA

Type

LDR and LOD

Detection in water

Reference

LOD: 0.08 µg/L (UV Vis) and 0.5 µg/L by eye LDR: 0.5–500 µg/L

LDR was determined in seawater samples (0.7–1.2 µg/L (2.9–5 nM)). Additionally, spiked (10 or 35 µg/L) recoveries ranged from 93% to 104%

[80]

Uranyl ion DNAzyme Modified litmus assay (39)79

CR

Not reported

15 µg/L in spiked well water, on paper and in solution

[34]

Uranyl ion DNAzyme HCR- and HRP-assisted catalytic (39)79 oxidation of TMB

CR

LDR: 0.5–15 ppb LOD: 2.5 ppb (9.25 nM) by eye and 0.09 ppb (0.33 nM) by UV-Vis

Recoveries of spiked (0–5 ppb) river water ranged from 96 to 106%

[81]

Uranyl ion DNAzyme AuNP-based enzymatic catalytic (39)79 oxidation of TMB

CR

LDR: 0.02–15 ppb (74 pM–56 nM) by Recoveries of spiked (0–5 ppb) river water UV-Vis ranged from 93% to 99% and showed LOD: 0.02 ppb (74 pM) in aqueous good agreement with ICP-MS controls environment by eye and 1.89 ppt (7.0 pM) by UV-Vis.

[82]

Uranyl ion DNAzyme HCR and interaction of methylene (39)79 blue with dsDNA immobilized on Au surface electrode

EC

LDR: 10 pM to 1 nM. LOD: 2 pM At −0.22V (Vs. Ag/AgCl)

Recoveries of spiked (0.02–1 nM) ranged between 85% and 107.5%

[83]

Uranyl ion DNAzyme Fluorescence quenching of TAMRA(39)79 labeled substrate by poly-guanine of DNAzyme alleviated following cleavage event

FR

LDR: 1.12–121.1 nM LOD: 0.41 nM

Recoveries of spiked well (0.25 nM), lake (0.21 nM), and river (20 nM) water were 92.0%, 114.3%, and 98.5%, respectively

[84]

Uranyl ion DNAzyme Quenching of fluorophore-labeled CF (39)79 by molybdenum disulfide (MoS2) nanosheets

FR

LDR: 0–100 nM LOD: 2.14 nM

Recoveries of spiked (0–50 nM) river water ranged from 96% to 102% and had good agreement with ICP-MS controls

[85]

Notes: Molecular recognition element (MRE), signal transduction (ST), signal amplification (SA), linear dynamic range (LDR), limit of detection (LOD), cleavage fragment (CF), electrochemical (EC), Fluorescent (FR), colorimetric (CR), 3,3′,5,5′-tetramethylbenzidine sulfate (TMB), tetramethyl-6-carboxyrhodamine (TAMRA), hybridization chain reaction (HCR), nanoparticle (NP), double-stranded (ds), and horseradish peroxidase (HRP). When an alternative reference is not provided, see reference for DNAzyme, PMD, aptamer, or gene sequences.

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Uranyl ion DNAzyme SYBR green I photosensitization in (39)79 the absence of uranyl-ion-induced TMB oxidation leading to color change

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Table 6.3.    DNAzyme-based detection of Uranyl ion (UO22+)



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the substrate strand of the DNAzyme was immobilized on a MB and then terminally extended to allow for the hybridization of an urease-modified oligonucleotide probe. In the presence of uranyl, cleavage of the substrate strand led to the specific release of the urease-modified duplex. Immobilization of the ureasemodified DNAzyme on MBs allowed for the specific separation of cleaved sequences from intact sequences. Transfer of the urease-modified DNA to a tube containing urea and phenol red resulted in a vibrant color transition from yellow to pink to generate a solution-based colorimetric signal. A paper sensor was also demonstrated, in which the only modification was the exclusion of the phenol red dye in solution. Following the necessary incubation period, the reaction solution was spotted on litmus paper and a clear color transition from yellow to green occurred. In this work, the detection of UO22+ in distilled, well, and lake water spiked with 15 µg/L was observed both in solution and on paper. The value was chosen because it was slightly below regulatory limits. Fu and colleagues designed a biosensor for detecting uranyl ion that takes advantage of the HCR for signal amplification.81 In this design, the DNAzyme is immobilized via the substrate strand on the surface of MBs. In the presence of UO22+, cleavage of the substrate strand results in liberation of the DNAzyme sequence from the MB surface, except for the immobilized cleavage fragment, which is used as an anchor for the HCR to occur. Following HCR, in the presence of TMB and H2O2, MBs coated with dsDNA stretches containing horseradish peroxidase were used to generate a colorimetric signal from colorless to blue. The reported linear dynamic range of the sensor was 0.5–15 ppb and the LOD was 0.09 ppb. The selectivity of the biosensor was assessed against Na+, K+, Ba2+, Ca2+, Mn2+, Zn2+, Cr3+, Mg2+, Cu2+, Fe2+, Fe3+, La3+, Lu3+, and Th3+; no observable signal was generated by any of these examined ions. Finally, the practicality of the biosensor for detecting uranyl in real water samples was examined. River water samples were spiked with 0.0 ppb, 0.5 ppb, or 5.0 ppb of UO22+. The concentrations detected by the biosensor for these samples were 0.2 ppb, 0.73 ppb, and 5.01 ppb, respectively, and showed good agreement with control ICP-MS measurements, which were 0.21 ppb, 0.78 ppb, and 5.18 ppb, respectively.

6.2.4.  Detecting other metals Other common regulated metal contaminants in drinking water include antimony, barium, beryllium, cadmium, chromium, copper, and thallium.19 DNAzyme-based sensors have been reported for some of these metals, as well as for biologically relevant zinc and magnesium, and are summarized in Table 6.4. Recent examples are highlighted in the following paragraphs. Wang et al. recently developed a strategy for detecting magnesium (Mg2+) in water using DNAzyme-templated CdTe quantum dots.86 Quantum dots have been successfully used in biosensor development because of their optical characteristics

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MRE

ST and/or SA

Type

LDR and LOD

Detection in water

Reference

Specific PCR of the substrate strand in the absence of Zn2+ leads to fluorescence due to dsDNA-activated dye (SYBR Green I).

FR

LDR: 80–1280 pM LOD: 58.61 pM

Not reported (tested in human serum)

[100]

Ratiometric fluorescence measurement based on FRET between CdTe QDs and Cy5-DNAzyme

FR

LDR: 0–20 nM LOD: 0.3 nM

Recoveries of spiked (5 mg/L) drinking water ranged from 99% to 102%

[86]

Cu2+-dependent DNAzyme90

HCR generation of PMD concatemers used to catalyze the oxidation of TMB

CR

LDR: 0.05–3 µM LOD: 8 nM

Detection of Cu2+ in bottled water, lake water, and domestic sewage spiked with 0.500 or 2.500 µM showed recoveries ranging from 95.9% to 245.4%, and was in good agreement with ICP-MS controls

[87]

Cu2+-dependent DNAzyme91

Digital PCR in the absence of Cu2+-induced cleavage

FR

LOQ: 0.5 pmol LOD: 50 fmol

Detection of Cu2+ in natural (tap) water spiked with 0.1 or 1.0 µM showed recoveries ranged 101% to 121%

[101]

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Table 6.4.    DNAzyme-based detection of other metal ions. Zinc (Zn2+) ES5 Cis version of 17E99

Copper (Cu2+)

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Mg2+-dependent DNAzyme (E671)

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Magnesium (Mg2+)

Type

LDR and LOD

Detection in water

Reference

Cu -dependent DNAzyme91

CF-induced HCR, and FRET between FAM and TAMRA on the dsDNA product of HCR

FR

LDR: 1–100 nM LOD: 0.5 nM

Recoveries of spiked (40–80 nM) tap water ranged from 92.6% to 1007%.

[102]

Cu2+-dependent DNAzyme91

Loss of SYBR Green I fluorescence due to cleavageinduced secondary structure changes

FR

LDR: 4 × 10−8 to 120 × 10−8 M LOD: 10 nM

Recovery of Cu2+ spiked (0.6–1.0 µM) drinking water ranged from 107.8% to 111.8%

[103]

Hybridization of fluorescently labeled complementary probes in ion-specific zones

FR

LDR: 10 nM to 100 µM LOD: ~10 nM (0.6 ppb for Cu2+ and 2 ppb Pb2+)

Recoveries of spiked (0.05 or 1 µM) river water ranged from 92.3% to 110%

[98]

Multiplex Cu2+ DNAzyme91 and Pb2+ DNAzyme (17E)99

Notes: Molecular recognition element (MRE), signal transduction (ST), signal amplification (SA), linear dynamic range (LDR), limit of detection (LOD), Peroxidase mimic DNAzyme (PMD), Fluorescent (FR), colorimetric (CR), Förster Resonance Energy Transfer (FRET), double-stranded DNA (dsDNA), cleavage fragment (CF), polymerase chain reaction (PCR), quantum dots (QD), 3,3′,5,5′-tetramethylbenzidine sulfate (TMB), tetramethyl-6-carboxyrhodamine (TAMRA), fluorescein amidite (FAM), hybridization chain reaction (HCR), and limit of quantitation (LOQ). When an alternative reference is not provided, see reference for DNAzyme, PMD, aptamer, or gene sequences.

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ST and/or SA

2+



MRE

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Table 6.4.   (Continued )

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and signal transduction properties. In this one-pot preparation, the biosensor was prepared by immobilizing the catalytic strand of the Mg2+-dependent DNAzyme on CdTe quantum dots. Hybridization of the Cy5-labeled substrate strand allowed for ratiometric fluorescence signal generation via FRET. Upon cleavage of the substrate strand, the Cy5-labeled cleavage fragment was released and the fluorescence emission shifted from 664 nm (no cleavage) to 607 nm (cleavage). For this biosensor, the authors reported a linear dynamic range of 0–20 nM and an LOD of 0.3 nM. The specificity of the biosensor was assessed against Cu2+, Ni2+, Fe2+, Ca2+, Cd2+, Ag+, K+, Zn2+, Pb2+, and Na+; the biosensor specifically responded to Mg2+. In addition, the ability of the biosensor to detect Mg2+ in drinking water was assessed. From five measured samples, 4.87–4.93 mg/L was detected. When the water samples were spiked with 5 mg/mL of Mg2+, recovery values ranged from 99% to 102%. In all the other examples illustrated in this chapter, the DNAzymes are RNAcleaving. Xu et al. reported the use of a DNA-cleaving Cu2+-dependent DNAzyme.87 First reported in 1996 by Breaker and coworkers, the DNA-cleaving DNAzyme has been used to develop biosensors for multiple analytes, develop an allosteric DNAzyme with both DNA- and RNA-cleaving properties, and design DNA-based logic gates.88–97 In this application, the Cu2+-dependent DNAzyme was used to detect Cu2+ in environmental water sources. The DNAzyme was immobilized on a polystyrene surface via a streptavidin–biotin interaction. In this “turn-off” design, the absence of Cu2+ led to signal generation, in which the HCR was used to produce horseradish-peroxidase-modified concatemers (HRPc). In the presence of TMB and H2O2, when Cu2+ was not present in solution, the HRPc generated a yellow color; when Cu2+ was present, the DNAzyme-HRPc washed away and the solution remained colorless. The reported linear dynamic range was 0.05–3 µM whereas the detection limit was 8 nM, which is below the lower regulation limit of 20 nM for drinking water.19 The selectivity of the biosensor was assessed in the presence of Co2+, Ti2+, Fe2+, Fe3+, Mn2+, Ca2+, Ni2+ Mg2+, Zn2+, Cd2+, Pb2+, Hg2+, Ba2+, Sr2+, UO22+, Eu3+, Tb2+, Ag+, and Al3+; a minimal biosensor response was observed in the presence of non-target ions. The ability of the biosensor to detect Cu2+ in real water samples was also assessed. Purified bottled water, lake water, and domestic sewage were spiked with 0.0, 0.5, and 1.5 µM Cu2+. The colorimetric biosensor showed a semi-quantitative visual LOD of 0.5 µM. Furthermore, recoveries measured by UV-Visible spectroscopy ranged from 95.9% to 245.4% and showed good agreement with measurements obtained from the ICP-MS controls. Whereas most of the DNAzyme-based biosensors for detecting metal ions in solution report the detection of one type of ion, Zuo et al. reported a microarray for detecting multiple metal-ion analytes.98 In this design, Cu2+ and Pb2+ DNAzymes were immobilized via an amine-modified 5′-extension of the substrate strand on the surface of aldehyde-coated slides. In the presence of the target

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analytes, the catalytic strands dissociated, and complementary fluorescent probes hybridized to the remaining substrate strand. Cleavage-specific differences in fluorescence were indicative of the metal ion concentration. Fluorescence visualization of the slide indicated semi-quantitatively which metal ions were present in solution. The biosensor system was assessed in the presence of Cu2+, Pb2+, Ca2+, Hg2+, Mn2+, Cd2+, Mg2+, K+ and Na+; only Cu2+ and Pb2+ resulted in a strong fluorescence signal. Finally, the microarray used for detecting the target analytes in spiked river water recoveries ranged from 92.3% to 110%.

6.3.  DNAzyme Based Biosensors For Detecting Bacteria And Bacterial Targets Monitoring environmental water sources is critically important for detecting the presence of pathogenic bacteria that can lead to disease outbreaks. Since its inception, the fluorogenic RNA-cleaving DNAzyme selection method has been used to generate RCD which are responsive to a wide range of bacteria and bacterial targets.22 The RCD-based biosensors developed in the past few years for detecting bacteria and bacterial targets, which are relevant for monitoring environmental water sources, are summarized in Table 6.5. Several RCD-based biosensors have been developed for E. coli 104–109 that depend on the use of the RFD-EC1 as first reported by Li and colleagues, but RCD-based biosensors for Klebsiella pneumonia,110 Salmonella,111 and the serotype Salmonella typhimurium,112,113 Helicobacter pylori,114 Vibrio parahaemolyticus,115 and Clostridium difficile116 have also been reported. There is interest in detecting these bacteria, as they have been shown to exist in environmental water sources.117 Developing biosensors to detect pathogenic bacteria in water can be quite challenging, as the WHO stipulates that ideally no bacteria should be detectable in drinking water. For example, the WHO states that no E. coli should be detectable in 100 mL of drinking water.118 In contrast to the metal ion examples, which highlighted the detection of their target analyte in water, the following sections highlight recent developments in biosensors for detecting bacteria in which DNAzymes were used either as an MRE, as ST and/or SA, or as both the ST and SA.

6.4. Using a DNAzyme as the Recognition Element In 2017, the Li group took advantage of their EC1 discovery and showed for the first time that an RNA-cleaving DNAzyme could be uniquely integrated into an RCA process to create a feedback loop for autonomous DNA amplification.106 They called this strategy “DNAzyme feedback amplification” (DFA). In the presence of E. coli, EC1 cleaves an RNA-containing DNA sequence that is in a complex with a circular DNA template. This cleavage event was followed by

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Bacteria

MRE

ST and/or SA

Type of sensor

LDR and LOD

Reference

Fluorogenic RCD

FR

LOD: 10 CFU/mL

[110]

Salmonella typhimurium

Aptamer119

Hemin aptamer-controlled PMD (PS2.M26) catalysis

FR

LDR: 10-5.0 × 105 CFU/mL LOD: 8 CFU/mL

[112]

Helicobacter pylori

PCR of UreA gene

PMD catalysis (PS2.M26)

CR

100 pg/reaction by eye

[114]

Escherichia coli

DNAzyme (EC1)

Fluorogenic RCD adsorbed on graphene

FR

Single cell when combined with culturing

[104]

Escherichia coli

DNAzyme33

Magnetic NP-DNAzyme Ag nanocluster complex

FR

LOD: 60 CFU/mL LDR: 1 × 102 to 1 × 107 CFU/mL

[105]

Escherichia coli

DNAzyme (EC1)32

Fluorogenic RCD printed onto paper

FR

LOD: 100 cells/mL

[120]

Escherichia coli

DNAzyme (EC1)32

DNAzyme feedback amplification combining RCA and RCD

FR

LOD: 10 cells/mL (1 h)

[106]

Escherichia coli

DNAzyme (EC1)33

DNAzyme-integrated plasmonic nanosensor

CR

LDR: 50 CFU/mL to 2 × 103 CFU/mL LOD: 20 CFU/mL

[107]

Salmonella

invA gene

Loop-mediated isothermal amplification using PMD catalysis (PS2.M)121

CR

7), the hydrolysis of Hg2+ interferes the deposition steps and thus decreases the stripping current. The stripping peak signal is linearly increased with the increase in the concentration of Hg2+. The selectivity of the developed sensor was tested by introducing other metal ions, such as Zn2+, Cd2+, Pb2+, and Cu2+, in the solution. As shown in Fig. 8.6, the PPy-rGO-modified sensor showed a distinctively higher peak current from the stripping of Hg2+ compared with peak currents of Zn2+, Cd2+, Pb2+, and Cu2+.

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Figure 8.5.    A schematic drawing of Hg2+ electrochemically selective detection by the PPy–rGO nanocomposite. Reproduced with permission from Ref. [91]. Copyright © 2012, Royal Society of Chemistry.

Figure 8.6.    The peak current from the SWV from other metal ions using the PPy/GCE (black) and PPy–rGO/GCE (red) in the presence of Hg2+, Zn2+, Cd2+, Pb2+, and Cu2+ ions. Reproduced with permission from Ref. [91]. Copyright © 2012, Royal Society of Chemistry.

The rGO nanocomposites played an important role in the selective detection of Hg2+, as shown in red and black bars. This sensor proved to be a better fit for one-time use because the nanocomposite material could not be regenerated for multiple uses.

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8.4.3.  Lead detection Lead is a major global concern among all the heavy metals because of its high toxicity, non-biodegradability, and accumulation in various organisms. It is a strong neurotoxin and carcinogen that causes lung diseases, stroke, kidney problems, and high blood pressure.74,92–94 In 2015, the Institute for Health Metrics and Evaluation (IHME) reported that lead exposure accounted for 494,550 deaths and loss of 9.3 million disability-adjusted life years due to its long-term effects on human health.95 In the same report, lead exposure was estimated to account for the global burden of 12.4% of idiopathic developmental intellectual disability, 2.5% of ischemic heart disease, and 2.4% of stroke. In the US, lead contamination in drinking water has been an issue since 2014, because in the city of Flint, one of the largest cities in the state of Michigan, very high levels of lead was detected in drinking water due to leaching from lead pipes in the city’s municipal water infrastructure. In one water sample, lead level reached a staggering 13,200 ppb, which is almost 900 times higher than the 15-ppb regulatory limit set by the USEPA.74,96 The WHO established a guideline limiting lead in drinking water as 10 ppb. According to the USEPA, 10%−20% of adults and 40%−60% of infants are exposed to lead contamination via drinking water.6 Seenivasan et al. reported a highly sensitive electrochemical sensor for detecting and removing lead ions in water using cysteine-functionalized graphene oxide (sGO) and polypyrrole (PPy).29 The sGO/PPy nanocomposite film was grown electrochemically on the surface of the working electrode of an SPE, which was then used to detect lead ions (Pb2+) in water using the DPASV (Differential Pulse Anodic Stripping Voltammetry) technique. Figure 8.7 shows the working methodology of the developed sensor along with the accumulation and stripping steps. In the accumulation step, the target analyte (i.e., Pb2+ ions) from the electrolyte solution is electroplated on the working electrode with a suitable negative potential. Then the accumulated analytes are oxidized from the electrode during the stripping step with the potential sweeping toward the opposite range of the accumulation step. Accumulation: (sGO/PPy) working surface + ( Pb 2+ )solution

+ 2e − → ( Pb 0 …sGO/PPy ) working surface

Anodic stripping: ( Pb 0 …sGO/PPy ) working surface → ( Pb 2+ )solution

+ (sGO/PPy) working surface + 2e −



GO was activated by adding carbonyldiimidazole (CDI) and functionalized with cysteine. Cysteine and PPy were reported to bind to the electrode surface with high affinity through the metal–ligand interaction. The microporous structure of PPy

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Figure 8.7.    Illustration of reactions occurring at the working electrode surface of the SPE (accumulation and stripping of Pb2+). Reproduced with permission from Ref. [29]. Copyright © 2015, American Chemical Society. (a)

(b)

(c)

(d)

Figure 8.8.    Scanning electron microscopy (SEM) images of (a) bare (b) PPy-, (c) GO-, and (d) sGO/Ppy-modified SPEs. Reproduced with permission from Ref. [29]. Copyright © 2015, American Chemical Society.

offers a large surface area as binding sites for the target molecules. As shown in Fig. 8.8, the sGO/PPy-modified SPEs provided more porosity (Fig. 8.8(d)) than the bare (Fig. 8.8(a)) and GO modified SPEs (Fig. 8.8(b)). The important parameters for the electrochemical detection of Pb (II) were studied and optimized for the developed sGO/PPy sensor. To study the effect of pH and ionic compositions and to optimize the operating condition, the sensor was

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tested with 14 ppm Pb2+ in the sample and several supporting electrolytes, including 0.1 M sodium acetate (pH 4.5), 0.1 M HCl (pH 2.0), and 0.1 M phosphate (NaH2PO4/Na2HPO4) (pH 7.0). The deposition potential and time were also optimized for better sensitivity in Pb (II) detection. The developed sensor was tested in a solution containing from 0.28 to 280 ppb of Pb (II) to determine the linearity and LOD. Three ranges were selected to construct the calibration curve, and the measurable detection limit of the sensor was 0.07 ppb, as shown in Fig. 8.9. The feasibility of the developed sensor was confirmed by inductively coupled plasma mass spectrometry (ICP-MS) using real samples collected from industry and waste water. The sensor showed good reproducibility and robustness in long-term measurements tested for up to 30 days. The selectivity of the sensors was tested by

(A)

(B)

(C)

(D)

Figure 8.9.    (A) DPASV results of various Pb2+ concentrations (a–j 0, 0.28, 1.4, 7, 14, 28, 70, 140, 210, and 280 ppb) using the sGO/PPy-SPE. Calibration plots between peak current (Ipa, μA) vs. Pb2+ (ppb) in three concentration ranges of (B) 1.4–28, (C) 28–280, and (D) 280–14,000 ppb obtained without (line a, blue color) and with (line b, red color). A 1 cm × 1 cm paper pad was placed over the SPE under optimal experimental condition. Reproduced with permission from Ref. [29]. Copyright © 2015, American Chemical Society.

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introducing other metal ions individually, including Na+, K+, Ag+, Cd2+, Cu2+, Hg2+, Ca2+, Mg2+, Fe3+, Co2+, Ni2+, Zn2+, and Ba2+. The developed sensor provides individual/separable stripping signal for cadmium, lead, copper, and mercury ions from one test. The ability to analyze multiple heavy metals in one test is an advantage of electrochemical sensors.

8.4.4.  Simultaneous detection of multiple heavy metals 8.4.4.1.  Lead and cadmium As the electrochemical trace analysis of heavy metal ions is based on the redox potential of the metals, several studies were conducted to investigate the simultaneous detection of heavy metal ions. For example, bismuth nanoparticles along with graphene and other carbon-based structures have been used. The bismuth film electrode (BFE) can form alloys with many metals, and it has a low toxicity with a wide potential window for redox analysis.97,98 Li et al. reported electrochemical sensors based on a composite film of Nafion–graphene (Nafion-G) in conjunction with a bismuth film.99 The developed sensor exhibited improved sensitivity for two heavy-metal ions (Cd2+ and Pb2+) and improved resistance to interference from the synergistic effect of graphene nanosheets and Nafion. The DPASV technique was employed to determine multiple metal ions through single stripping step. The LOD was reported as 1.5–30 ppm for Cd2+ and 0.5–50 ppm for Pb2+ under optimum operating conditions. Acetate buffer (0.1 M) at pH 4.5 was used as the supporting electrolyte in the measurement. The cation exchange capacity of Nafion was reported to enhance electron conduction of GO, which resulted in high sensitivity.100–102 Well-defined plots were obtained for both heavy metals at different stripping potentials (Fig. 8.10(a)). The stripping current increased linearly with increasing concentrations for both metals (Fig. 8.10(b)). The developed sensor was investigated using lake water samples spiked with Pb2+ and Cd2+. The LOD of Pb2+ and Cd2+ was determined as 0.47 ± 0.05 ppb and 0.37 ± 0.09 ppb, respectively, using the Nafion-G-BFE sensor, while the LOD was confirmed as 0.52 ± 0.03 ppb for Pb2+ and 0.45 ± 0.06 ppb for Cd2+ using ICP-MS. To detect trace heavy metals (Pb2+ and Cd2+) by SV (Stripping Voltammetry), Chen et al. modified porous SPEs with a bismuth film.103 The porous-SPEs were fabricated by dissolving powders of CaCO3 uniformly with the graphite-based printing ink. The porous-SPE was reported to have a large surface area and low background current compared with the solid carbon paste SPE. The sensor exhibited better sensitivity in heavy metal analysis compared with the bismuthcoated SPE and the bismuth-coated GC developed by Hočevar et al. (2002) and Arduini et al. (2010), respectively.31,104 The electroanalytical performance of the developed electrode was enhanced by optimizing the operation parameters, i.e., the deposition potential, deposition time, pH of the supporting electrolytes, and

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(a)

(b)

Figure 8.10.    (a) DPASV response from the different concentrations of Cd2+ and Pb2+ on an in situ plated Nafion-G-BFE in solution containing 0.4 mg L−1 Bi3+. From bottom to top, 0.5 μg L−1, 1.5 μg L−1, 4 μg L−1, 6 μg L−1, 10 μg L−1, 20 μg L−1, 30 μg L−1, and 40–50 μg L−1. (b) The calibration curve of Cd2+ and Pb2+. Reproduced with permission from Ref. [99]. Copyright © 2009, Elsevier.

the stripping sweep parameters. A total of 300 s and −1.2 V were determined as the optimum deposition time and deposition potential, respectively. For better ion mobility and lower noise, 0.1 M acetate buffer (pH 4.5) containing 500 ppb bismuth (III) was used as the supporting electrolyte. The bismuth-coated porousSPE showed good linearity for Cd (II) when tested in the range of 1–30 ppb and 0.05–30 ppb for Pb (II). Maria-Hormigos et al. modified the working electrode of SPE via the ionexchange mechanism using the composites of carbon nanopowder (CnP), bismuth and polystyrene sulfonate (PSS) to quantitate Pb (II) and Cd (II) by DPASV.105 An acetate buffer solution (0.2 M) at pH 4.5 was used as the supporting electrolyte for noise reduction and better ion mobility in the test solution. The LOD was reported as 0.029 ppb for Pb (II) and 0.012 ppb for Cd (II). The interference from other ions, such as Na+, K+, Ca2 +, Mg2 +, Fe3 +, NO3−, and SO42 – was tested. The peak height of the stripping curve was slightly reduced when tested in the presence of Cu (II) because of the formation of intermetallic compounds between copper and other metal ions, as well as the competition between the copper ions and electrodeposited bismuth ions for limited binding sites.31,33,106 Zhu et al. reported an electrochemical sensor based on a gold nanoparticlegraphene-cysteine composite modified glassy carbon electrode (bismuth-film) for the simultaneous determination of Cd2+ and Pb2+ using SWASV.107 Under the optimum operating condition, the LOD was reported as 0.10 ppb for Cd (II) and 0.05 ppb for Pb (II).

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8.4.4.2.  Lead and copper Wang et al. developed an electrochemical micro-sensor for the simultaneous detection of Cu2+ and Pb2+ using an L-aspartic acid/L-cysteine/gold nanoparticlemodified microelectrode using the micro electro-mechanical system (MEMS) technique.108 The fabrication of this electrochemical sensing platform toward Pb2+ and Cu2+ detection was prepared by combining the unique properties of nanomaterials with the specific capability of amino acids. Gold nanoparticles were introduced to enlarge the surface area and to increase the amount of loaded L-Cys; the L-Asp molecules were cross-linked with L-Cys to increase the available binding sites with metal ions. The LOD was determined as 1 ppb for both Cu2+ and Pb2+, and the developed sensor showed negligible interference from Co2+, Cd2+, Ca2+ and Ni2+, in solution. Morton et al. developed a cysteine-functionalized multi-walled carbon nanotubes (MWCNT)-modified GCE as a sensor to improve the detection of lead (Pb2+) and copper (Cu2+) dissolved in water.109 The stripping current (from DPASV) was analyzed to determine the concentrations of Pb2+ and Cu2+ in solution, with 0.1 M acetate buffer (pH 5.5) as the supporting electrolyte. Herein, L-Cys was employed to provide active sites for toxic metal ions. The metal complex formed between the immobilized cysteine and metal ions on the electrode surface was reduced and then oxidized to generate stripping current. The sensor was reported to have good linearity when tested for Pb2+ and Cu2+ under the optimum operating condition. The LOD was reported as 1 ppb and 15 ppb for Pb2+ and Cu2+, respectively.

8.4.4.3.  Multiple heavy metals Emmanuel et al. also reported a highly sensitive electrochemical platform for the determination of Zn2+, Cd2+, Pb2+, and Cu2+ via SWASV by using a Nafion-G nanocomposite solution in combination with an in situ plated mercury film electrode.110 The electrode modified with the Nafion-G nanocomposite was reported to be appropriate for both simultaneous and individual metal ion detection. The LOD was reported as 0.07 ppb for Pb2+, 0.08 ppb for Zn2+, 0.13 ppb for Cu2+, and 0.08 ppb for Cd2+. Spiked water samples were tested and the concentration was validated by ICP-MS.111,112 Wei et al. reported an SnO2/reduced GO nanocomposite-modified glassy ­carbon electrode for the simultaneous and selective electrochemical detection of an ultra-trace amount of Cd2+, Pb2+, Cu2+, and Hg2+ ions in drinking water.30 Simultaneous analysis by SWASV against Cd2+, Pb2+, Cu2+, and Hg2+ ions using the SnO2/reduced GO nanocomposite-modified GC electrode showed LODs of 0.102, 0.184, 0.227, and 0.279 nM, respectively, and all well below the guideline values specified by the WHO.

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8.5. pH Determination The level of pH in drinking water recommended by the USEPA and the WHO is in the range from 6.5 to 8.5.9,113 Even minor deviations from this recommended range of pH in a water distribution system can increase the solubility and toxicity of detrimental chemicals and metals dissolved in water and it can make water corrosive. Changes in the pH not only affects the taste and color of water but also initiates leaching and nitrification.96 Two widely used methods to measure the pH of water or liquid samples are pH meters and pH strips. Pharmaceutical companies, food industries, and researchers typically use pH meters, and pH strips are most commonly used for environmental monitoring, pool water treatment facilities, and in small-scale facilities in farming. The electrode of the pH meter needs to be soaked in liquid solution to maintain stability of the electrode resistance. Most of the pH measuring electrodes are fragile, expensive, and need special maintenance and periodic calibration. While the pH test strips are inexpensive and easy to use, their values are interpreted through the color change of the test strip and need to be subjectively compared against standard color chart, which is prone to errors. Manjakkal et al. designed a pH sensor using a mixture of two metal oxides, i.e., ruthenium (IV) oxide (RuO2) and tin (IV) oxide (SnO2).49 A homogeneous mixture of chemically inert SnO2 and active RuO2 at 30:70 (% w/w) ratio was dissolved in isopropyl alcohol, dried to powder form, and then used for electrode surface treatment. A printable paste was produced with the powder using terpineol as a solvent and ethyl cellulose as a binder. Screen-printing technology was used to develop thick-film electrodes for both conductimetric (Fig. 8.11(I)) and potentiometric (Fig. 8.11(II)) pH sensors. Figure 8.11 shows the detailed scheme of the pH sensors. A commercial glass electrode was used to validate pH reading of the developed sensors. The potential difference between the working and reference electrode was studied to determine the pH of the solution using the potentiometric pH sensor. When tested with standard pH buffers, the sensors successfully determined the pH. The RuO2–SnO2 sensors were reported to provide nearly perfect Nernstian behaviors over the pH range from 2 to 12. The reported response time was 5 and 9 s, from acidic to basic and from basic to acidic, respectively. To test the selectivity of the developed sensors, H+, Li+, Na+, and K+ ions were mixed in a solution; and there were no notable changes in the detection of pH. To check the repeatability of the sensors, the authors fabricated several type II-potentiometric sensors and tested simultaneously; as shown in Fig. 8.12(a), there was no significant potential difference between two different sensors. The sensors were reported to show a very small hysteresis when tested repeatedly, which was noticed to be higher in the basic solutions compared with the acidic solutions, as in Fig. 8.12(b). The RuO2–SnO2 pH sensors were tested with lemon juice, water from different

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Figure 8.11.    Schematic view of the RuO2–SnO2 based pH sensors (I: conductimetric; II: potentiometric). Reproduced with permission from Ref. [49]. Copyright © 2015, Elsevier.

Sensor-II E = 630.8 – 56.5 ¸ 0.7 pH

basic

Sensor-I

acidic

E = 611.7 – 55.9 ¸ 1.2 pH acidic

(a)

basic

(b)

Figure 8.12.    (a) Repeatability of the pH sensor (comparison of the lot I and lot II) and (b) hysteresis effect of the potentiometric RuO2–SnO2 (70:30 % w/w) thick-film pH sensor for different pH values of the solution. Reproduced with permission from Ref. [49]. Copyright © 2015, Elsevier.

sources (river, tap, and distilled water), and the results were verified within a −3% to 9% deviation from the results using a conventional glass pH electrode. Fog et al. explained the limitations of electronically conducting oxides as pHsensing devices.114 Different metal oxides (PtO2, IrO2, RuO2, OsO2, Ta2O5 and

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TiO2) were tested, and it was determined that reducing and oxidizing agents, as well as other interfering ions, can cause shift/drift over time for the same pH when using sensors modified with metal oxides. Sensors modified with PtO2, OsO2, and TiO2 were reported to show larger drifts. Iridium oxide (IrOx) is a non-toxic, low-impedance material that has received considerable attention in recent years for a high-performance pH electrode.20,23,115–117 The advantages of IrOx over conventional glass electrodes and other metal oxides include good stability over a wide pH range, extraordinary sensitivity, and minimal electrochemical irreversibility.19,20 IrOx has outstanding mechanical strength, tolerance to ultrasonication and repeated dehydration/hydration processes, and stability even at a high temperature of up to 250°C. The structures and compositions of IrOx is generally very sensitive to synthesis conditions, and thus fabrication methods and conditions play an important role in IrOx-based pH sensors. Furthermore, anodically electrodeposited iridium oxide thin films (AEIROFs) are widely used as high-performance pH electrodes.21,115,118 One of the main reasons for their limited practical application is the poor adhesion to the underlying substrate.22 Carbon scaffolds can be used to support AEIROF deposition for better stability and adhesion. Carbon nanotube hybrid materials are known for faster electron transfer rates,115 and functional GO thin films are used as scaffolds to anchor IrOx and enhance electrode−electrolyte electron transfers. Kampouris et al. designed and fabricated a disposable electrochemical pH sensor based on screen-printing technology and potential difference between dime-thylferrocene and phenanthraquinone.119 The sensor was reported to have good linearity over the wide pH range from 1 to 13. Yang et al. designed and fabricated an inexpensive electrochemical pH sensor, which is easy to use and able to read the pH of water consistently and accurately.19 SPE was used as a base sensor and the surface of the electrode was modified with rGO and IrOx. After initial cleaning, GO was drop-casted on the sensor surface and then reduced electrochemically. Electrochemical reduction was adopted because chemically reduced GO provides a low surface area, and thermally reduced GO often has defects. The sensor structure, comprising electrochemically reduced GO, was very similar to pristine graphene, with a high carbon-to-oxygen ratio. The rGO surface was composed of ultrathin graphene sheets wrinkled on the surface of the working electrode, as shown in Fig. 8.13. The cracks and defects shown in the SEM image proved the IrO2-rGO thin film dispersed surface and anchored IrO2 nanoparticles. The developed hybrid thin film of the IrO2-rGO produced additional surface area, which served as binding sites for additional electrochemically active particles (Fig. 8.13(A), (B)). The hydrophobicity of the developed surface was studied and compared with bare, rGO-modified, and IrO2-modified surfaces. From the contact angle, it was evident that the hydrophilicity was almost similar to the rGO and better than the

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(A)

(C)

(B)

(D)

Figure 8.13.    SEM images of (A) rGO; (B) IrO2-rGO. (C) Static water contact angles on bare graphitic carbon, rGO-, IrO2-, and IrO2-rGO-SPEs. (D) CVs of (a) rGO- and (b) IrO2-rGO-SPEs in de-aerated 11.9 mM pH 7.4 PBS with redox peaks indicated. The area defining CSCc for (b) is highlighted by dotted lines. Scan rate: 0.05 V s–1. Reproduced with permission from Ref. [19]. Copyright © 2016, American Chemical Society.

IrO2-modified surface, as shown in Fig. 8.13(C). The cyclic voltammograms (CV) of the developed IrO2-rGO-modified sensor and rGO-modified sensor showed redox behaviors (Fig. 8.13(D)). Based on the redox peaks, it was concluded that the IrO2-rGO-modified sensor has a large active surface area and better conductivity than other sensors. The open-circuit potential (OCP) method was used to determine the potential between the reference electrode and the working electrode. The measured potential changed proportionally with the change in pH, i.e., the negative logarithm of the H+ concentration in solution. The IrO2-rGO pH sensor was reported to provide a well-defined linear trend over the pH range from 2 to 12 when tested by dipping in the bulk solution. In Fig. 8.14, the slight deviation in OCP values (black line) is shown due to the super-Nernstian slope, which was close to nearly perfect Nernstian behavior of anodic electrodeposited iridium oxide film (AEIROF). Nernstian behavior refers to the linear response with respect to

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Figure 8.14.    pH responses using the OCPT technique from testing the IrO2-rGO SPEs at varying pHs (2–12) of B-R buffers in bulk solution and μ-PADs. Reproduced with permission from Ref. [19]. Copyright © 2016, American Chemical Society.

change in pH, i.e., concentration of hydrogen ions (H+). A paper-based hydrophobic-patterned micro-pad (μPAD), was developed and tested to test pH with this sensor using only few drops of sample liquid. The response slope in Fig. 8.14 (red line) is very similar to the response slope in the bulk solution test method, and the negligible deviation is due to the ohmic effects from the fibrous cellulose matrix in paper. Four different buffer solutions, namely phosphate buffered saline (PBS), NaOH/HCl adjusted Millipore water, B-R buffer, and commercially available pH calibration buffers, were tested, as shown in Fig. 8.15. The pH signal from the PBS buffer showed a significant potential drift compared with the other three buffer solutions, but the pH readings were nearly identical for all four buffers. For potential applications using electrochemical sensors, the response time and signal stabilization are important in analytical testing. For the developed IrO2-rGO sensor, the response time is mainly dependent on the capillary forces and diffusion of the ions in mPAD. The typical response time was reported to be less than 250 sec from pH 2 to 12. External stirring might reduce the response time by accelerating mass transfer in the bulk solution. Marzouk et al. and Yang et al. (1998) reported the response time of IrO2-modified pH electrodes usually have longer response time in the pH range of 4–10 than below 2 and over 12.120,121 For pH sensors, hysteresis is known to occur over repeated use, which originates from the delayed responses of the sensors. The developed sensor was tested cyclically from acidic to basic and basic to acidic buffers and showed minor hysteresis from repetitive measurements.

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Figure 8.15.    pH responses using the OCPT technique with the IrO2-rGO SPEs at varying pH solutions (from 2–12). (B) in different buffer systems. Reproduced with permission from Ref. [19]. Copyright © 2016, American Chemical Society.

8.6. Phosphate Detection Phosphate (PO4−3) is a chemical compound containing phosphorus, and a nonmetallic essential element as plant nutrients necessary for growth of plants and animals. As the major component of many fertilizers, calcium hydrogen phosphate (CaH4P2O8) is widely known as “Superphosphate”;122 however, high content of nitrogen and phosphorus entering the environment usually generated from a wide range of human activities.123 Excessive nitrogen and soluble reactive phosphate, i.e., phosphorus, in the water are often responsible for eutrophication, i.e., aquatic plant overgrowth, in lakes, reservoirs, and streams. Substantial growth of algae also deteriorates water quality and damages the food supply and habitats of fish and other aquatic life. Most importantly, eutrophication severely reduces or eliminates oxygen in water, leading to illnesses and death of fish.122,123 In the past several decades, nutrient pollution has impacted many streams, rivers, lakes, bays, and coastal waters, resulting in serious heath, environmental, and economical problems.123 Algal bloom may contain blue-green algae, i.e., cyanobacteria, which releases toxins into water. This happened in Lake Erie in the US during the summer of 2014,124,125 making water from the Toledo water treatment plant unhealthy to drink for several days. Soluble phosphorus in natural water may present in four states based on the pH: H3PO4 (phosphoric acid), H2PO4─ (dihydrogen phosphate), H2PO42─ (hydrogen phosphate), and PO43─ (orthophosphate).126 The USEPA and the state Department of Natural Resources (DNR) have set specific guidelines for effluent

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water stream from industries into rivers, lakes, and watersheds.127–129 Around 0.05 mg/L of soluble reactive phosphate may trigger an algal bloom.130 Dissolved phosphate is another concern in drinking water quality. Phosphate levels greater than 1.0 ppm may interfere the coagulation in water treatment plants.131 Public water systems (PWSs) frequently add phosphates to the drinking water as a corrosion/ rust inhibitor to prevent the leaching of lead and copper from metal pipes and fixtures.130 Cobalt has been used over the past 10 years as an electrode coating material for sensitive phosphate detection,132–135 because cobalt shows a potentiometric response when exposed to an aqueous solution that contains phosphate.136 Pure cobalt wire was first used by Xiao et al. to detect phosphate (H2PO4─) in an acidic medium.137 Meruva et al. also developed an inorganic phosphate sensor using surface oxidized cobalt,138 and reported that the slow oxidation of cobalt and cobalt oxide ions and oxygen at the surface of the electrode formed measurable potential responses. The developed sensor was reported to show very good sensitivity toward phosphate in a wide range of pH. Chen et al. and Parra et al. also developed potentiometric systems for determining phosphate compounds using cobalt oxide nanoparticles.139,140 Wang et al. developed a microelectrode for phosphate detection and removal using cobalt as the sensing material for the microelectrode.141 The formation of cobalt phosphate caused potential shift, which was proportional to the logarithm of the phosphate concentration. The sensor was reported to have a good linear behavior with the logarithm of phosphate ion concentration, and showed excellent selectivity toward H2PO4−, HPO42−, and PO43− ions. As the phase formed between cobalt and phosphate varies depending on the pH of the solution, the potential response from this cobalt-based phosphate sensor was affected by pH. The electrode was developed for testing activated sludge flocs in waste water phosphorus reactor (with a typical pH range of 7.5–8.0), and the effect of pH of the solution from 7.5 to 8.0 did not have a significant impact on the sensing of the orthophosphate ions. Zou et al. developed a disposable microsensor inside a microfluidic channel based on a cobalt microelectrode.142 This sensor was reported to show good selectivity and high sensitivity toward organic and inorganic phosphate compounds. The scheme of the microsensor (Fig. 8.16) shows the sensing array and tiny electrodes, which only need 2–3 drops of sample solution to determine the phosphate concentration. For phosphate sensors based on cobalt oxides, the redox couple of Co2+/Co0 plays an important role on the signal generation. The formation of complex compounds of cobalt and phosphate at the surface of the electrode generates electrical potential change. The change in potential at the electrode is determined by the change of the phosphate concentration. A linear potential response related to the logarithm value of the phosphate concentration was explained by Chen et al.143

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Figure 8.16.    Schematic view and working principle of the on-chip phosphate sensor with planar cobalt oxide electrodes on polymer substrates. Reproduced with permission from Ref. [142]. Copyright © 2007, Elsevier.

The LOD of the phosphate was determined as 1 × 10−6 M at pH 4.0 using a cobalt wire electrode. The entire size of the fabricated microelectrode device developed by Zou et al. was 1.5 cm × 2 cm.142 Figure 8.17 shows the details of the microelectrode with the reaction chamber of 10 mm × 2 mm × 100 μm (L×W×D). The developed sensor was tested with a dynamic range of inorganic phosphate solution (KH2PO4) from 5 × 10−5 to 5 × 10−2 M. Due to its miniaturized size, the sensor reached the equilibrium within 60 s for 10−5 M and in less than 30 s for higher concentrations for above 10−5 M. The LOD of the phosphate was 10−5 M, and a good log-linear fit was reported, with an R2 value of 0.9804. The sensor was reported to show steady-state potential responses for more than 30 min after the equilibrium was reached and minor chip-to-chip deviations. Song et al. reported a cobalt-based phosphate sensor using SPEs, with a detection limit of 10−4 M. The detection mechanism is based on the potential generation due to the oxidation reaction between cobalt oxide and phosphate. The cobalt phosphate layer was formed on the surface of the electrode as a result of the reaction.134 A three-electrode sensor based on screen-printed technology was used as the basic sensor, and the working electrode was modified with cobalt oxide nanoparticles. The LOD of the developed sensor was reported as 10−5 M at pH 4.5,

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Figure 8.17.    Image of the fabricated device and the microscopic image of the on-chip phosphate sensor (the entire chip size is 1.5 × 2 cm) composed of Co working electrodes (WE) and Ag/AgCl reference electrodes (RE). Reproduced with permission from Ref. [142]. Copyright © 2007, Elsevier.

with a very good linear fit in respect to the concentration of phosphate, as shown in Fig. 8.18. The sensor response remained stable for over 1600 sec continuously (Fig. 8.18(c)]. The authors also tested reproducibility, selectivity, and recovery of phosphates. The developed sensor was also tested repeatedly for 10 times in the same phosphate solution and showed negligible deviations. The SEM images of the tested sensor (before and after testing) showed a significantly different morphology on the surface of the electrode. Due to the change in the oxidation corrosion potential, the surface of the electrode was corroded and became more porous with use (Fig. 8.19). Song et al. tested the sensor with waste water samples and reported an accuracy of within 10% compared with the conventional analytical method, i.e., spectrophotometry. Ryu et al. investigated the promise of GO along with cobalt oxides nanoparticles to improve the sensitivity of SPEs as phosphate sensors.144 Pyrrole (Py) and polypyrrole (PPy) were also included to verify the viability of improving the LOD and the sensitivity of the developed phosphate senor. Different layers and mixtures of cobalt oxide nanoparticles, GO, Py, and PPy were tested to optimize the phosphate sensor structure. The OCP technique was used to analyze the potential change in response to the change in the concentration of phosphate in water. The authors reported that the SPE sensor modified with GO, Py, and Co showed the best sensitivity and log-linear behaviors when tested over a wide range, i.e., from

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(a)

(b)

(c)

Figure 8.18.    Potentiometric responses of the phosphate sensor in different concentrations of phosphate solutions (KH2PO4) at pH 4.5: (a) dynamic measurement; (b) calibration curve in the range from 1 to 1 × 10−5 mol/L H2PO4–, (c) the potential response over prolonged time in 10−5 mol/L in a phosphate solution (KH2PO4 at pH 4.5). Reproduced with permission from Ref. [134]. Copyright © 2014, Springer Nature Publishing.

Figure 8.19.    SEM images of the cobalt-based phosphate sensor before (left) and after (right) continuous measurements for more than 50 times. Reproduced with permission from Ref. [134]. Copyright © 2014, Springer Nature Publishing.

10−9 M to 10−2 M, of KH2PO4 in water at pH 4.5. A low LOD was reported as 10−9 M, and only a minor potential deviation was reported for interference from other anions such as Cl─. For decades, ammonium molybdate has been used for colorimetric detection and analytical investigation of phosphate in water.145–147 Ammonium molybdate reacts with phosphate compounds and produces molybdophosphoric acid. The formation of molybdophosphoric acid generates potentials at the electrode and thus is a promising material for the selective detection of phosphate.148 In a strong acidic medium, ammonium molybdate reacts with phosphate compounds and produces a complex of molybdate phosphate. This complex can be reduced to a mixed molybdenum oxidation state and can be detected at a low applied potential by amperometry.149

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Ryu et al. (2016) employed ammonium molybdate along with GO and PPy to enhance selectivity of the SPE-based phosphate sensor.144 The authors reported that the LOD was 10−11 M when using the pyrrole and ammonium-molybdatemodified SPE. The calibration curve (logarithmic fit) from the OCP responses was evaluated in two ranges of phosphate concentrations, with R² = 0.9781 and R² = 0.9864 for 10−11 to 10−8 (mol/L) and 10−8 to 10−2 (mol/L), respectively. The SPE sensor modified with pyrrole and ammonium molybdate showed minimum interference from Cl─ ions.

8.7. Nitrite Detection Nitrite (NO2−) is another inorganic pollutant that must be monitored to maintain water quality. The main sources of nitrite are human activities and waste from fertilizers used in farming. The maximum limit of nitrite in drinking water approved by WHO is 3 ppm.3 When drinking nitrite-contaminated water, the nitrite ions interact with amines, potentially causing stomach cancer (due to the production of N-nitrosamines), blue baby syndrome, or methemoglobinemia.150–152 Some of the analytical methods used for determining nitrite in water156 include spectrophotometry,153 chemiluminescence,154 capillary electrophoresis,155 and chromatography. Zhang et al. employed the super-conductive properties of GO to develop a nitrite sensor.157 Reduced-graphene-oxides and copper nano-dendrites were deposited on the surface of the GC electrode. The pH of the supporting electrolyte (PBS) was determined to be 2.0 as the optimized operating condition. The LOD of the nitrite was reported as 0.4 μM, with good sensitivity and stepwise increasing amperometric responses with respect to nitrite concentrations under different potentials (Fig. 8.20). The developed nitrite sensor showed no noticeable interference in the presence of K+, Na+, Ca2+, Mg+, NH4+, NO3−, SO42−, HPO42−, and H2PO4− (in PBS solution pH 2.0), and was successfully used to determine nitrite in river water. Similarly, a composite film which contains graphene nanosheets and carbon nanospheres for the electrochemical determination of nitrite was developed by Cui et al.158 Radhakrishnan et al. developed a highly sensitive nitrite sensor via the hydrothermal deposition of iron oxides (Fe2O3) over reduced-graphene oxide (rGO) on a GC electrode.159 The dispersed metal oxide nanoparticles increased the surface area and improved electron transfer rates. Upon analysis of the field-emission scanning electron microscopy (FESEM) images of the GO-, rGO-, and Fe2O3/ rGO-modified electrodes, the authors confirmed the Fe2O3 nanoparticles were attached on corrugated wrinkle-shaped reduced-graphene-oxide sheets. Typical CVs of (a) bare GC, (b) rGO, (c) Fe2O3/rGO, and (d) Fe2O3-modified electrodes recorded in 3.0 × 10−4 M nitrite in 0.1 M PBS (pH 7.0) demonstrated that the largest peak was obtained with the Fe2O3/rGO-modified electrode

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Figure 8.20.   Amperometric responses of Cu-NDs/RGO/GCE at different electrode potentials in pH 2.0 PBS with a dropwise addition of 0.5 mM NaNO2 (detection potential: 0 to −0.3 V). Reproduced with permission from Ref. [157]. Copyright © 2013, Elsevier.

(Fig. 8.21(A)). Due to the decomposition of NO2− into NO3, the nitrite anions are unstable in an acidic medium.160 At a higher pH (>7.0), the oxidation process of nitrite usually becomes more difficult due to the lack of protons in the solution. The Effect of pH on the developed sensors was also investigated, and the optimized pH was determined as 7.0 for the best amperometric responses (Fig. 8.21(C)). Linear sweep voltammograms (LSVs) were used for the amperometric detection of nitrite when tested over the concentration range of 0–150 μM, with the GC electrode modified with Fe2O3/rGO. With an increase in the concentration of nitrite in water to 10 μM, the oxidation current of nitrite concomitantly increased, and the oxidation peak potential also shifted slightly toward the negative potential. Figure 8.21(D) shows the linear relationship of the oxidation current with the concentration of nitrite. The DPV technique was also employed to check the sensitivity and the LOD of the developed sensor for nitrite. The Fe2O3/rGO-modified sensor displayed a detection limit of 0.015 μM, with an excellent sensitivity toward nitrite ions. The DPV response shows a stable and well-defined anodic oxidation peak at 0.78 V, as reported by authors when tested with 0.05–780 μM nitrite. The Fe2O3/rGO composite sensor was also tested for potential interference from other ions, such as Cu2+, Ca2+, Na+, and Mg2+, and some common physiologically co-existing species, such as urea (the most common fertilizer), hydrogen peroxide, and glucose, when detecting nitrite. There were no noticeable variations reported for the DPV response when 0.03 mM nitrite was tested in the presence of

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Figure 8.21.    (A) CVs obtained for nitrite (3.0 × 10−4 M) at the (a) bare GC, (b) rGO, (c) Fe2O3/ rGO and (d) Fe2O3 modified electrodes recorded in PB solution (pH 7.0) at a scan rate of 50 mV s−1 at a potential sweeping between −0.2 and 1.2 V. Inset: bar diagram of the peak current acquired with different modified electrodes (B) CVs obtained for nitrite (3.0× 10−4 M) with the Fe2O3/rGO-modified electrode at different scan rates (10–100 mV s−1) in PBS (pH 7.0). Inset shows the resulting calibration plot. (C) Plot of oxidation peak current of nitrite vs. pH (D) LSVs obtained for nitrite in the concentrations ranging from 0 to 150 μM. Nitrite was added in steps of 10 μM each for the Fe2O3/ rGO-modified electrode in PB solution (pH 7.0). Reproduced with permission from Ref. [159]. Copyright © 2014, Elsevier.

3 mM of CuCl2, CaCl2, NaCl, and MgCl2, as well as 0.3 mM of urea, hydrogen peroxide, and glucose. The sensor was also reported with a minor deviation of about 2.20% for 15 repetitive measurements. When tested for 10 days, the current was decreased by about 1.94%, and no significant fouling was occurred over the repetitive testings.

8.8. Conclusion Electrochemical analysis has been proven to be a promising method for developing sensors to detect organic/inorganic ions, molecules, gases, and other

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bio-particles in water, air, and body fluids. Electrochemical sensing technologies offer the following advantages. • Sensing mechanisms can be explained elaborately with theoretical electrochemistry. • Required instrumentation is comparatively inexpensive and simple to use. • The sensors and tests are inexpensive. • Testing can be done quickly. • Developed method can be used over a wide range of analytes. • Sensors are suitable for in situ analysis and real-time monitoring. • Highly sensitive and selective when appropriate electrochemical system is applied. • Sensors can be miniaturized and disposable. While some electrochemical sensors are already commercialized, there are still some drawbacks as follows: • • • •

Possible interference from other matrices with similar chemicals. Limited shelf-life and the need for re-calibration in some cases. Use of harmful/toxic materials to develop certain sensors. Limited repeatability and stability in some cases.

The ever-increasing need to monitor water for safe consumption and use has boosted researches toward the development of fast, accurate, and efficient sensors that do not require extensive laboratory preparation or equipment. The development of high-performance electrochemical sensors to test most of the major materials to determine quality of water can overcome the challenges of conventional analytical testing methods and serve as effective alternatives. Electrochemical sensors have already demonstrated the possibility for use at different stages in water monitoring and thus will play an important role in redefining water quality testing methodologies, from individual households to industrial use.

References    1. 2016 Treated Water from Water Treatment Plants Water Quality Report, Milwaukee Water Works, 2016.    2. 2016 Distribution System Water Quality Report, Milwaukee Water Works, 2016.   3. World Health Organization. Guidelines For Drinking-Water Quality, Vol. 1: Recommendations (3), 1. World Health Organization, Albany, 2003.    4. US Environmental Protection Agency — Framework for Metals Risk Assessment, EPA 120/R-07/001, Office of the Science Advisor, Risk Assessment Forum, Washington, D.C., 2007.

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   5. C. B. Gordon and C. Vickers. WHO Guidelines for Drinking-Water Quality, WHO Chron., 2008, 38(3), 564.    6. US EPA, U.S. EPA to Strengthen Protection from Lead in Drinking Water, J. Environ. Health, 2005, 68(2), 42.   7. O. US EPA. Types of Drinking Water Contaminants, US EPA, 23 June 2014. Available from: https://www.epa.gov/ccl/types-drinking-water-contaminants.    8. O. US EPA. National Primary Drinking Water Regulations, US EPA, 30 November 2015. Available from: https://www.epa.gov/ground-water-and-drinking-water/nationalprimary-drinking-water-regulations.    9. WHO. pH in Drinking Water, WHO Guidelines for Drinking-Water Quality. Geneva, World Health Organization, 2007.   10. NSF/ANSI. Drinking Water, Treatment Chemicals — Health Effects, 2013.   11. Wisconsin Department of Natural Resources, Wisconsin DNR Drinking Water data, Lead/Copper Samples, 2017.  12. O. US EPA. Municipal Wastewater, US EPA, 22 October 2015. Available from: https://www.epa.gov/npdes/municipal-wastewater.   13. O. US EPA. Green Infrastructure, US EPA, 24 April 2015. Available from: https:// www.epa.gov/green-infrastructure.   14. O. US EPA. Ground Water and Drinking Water, US EPA, 20 February 2013. Available from: https://www.epa.gov/ground-water-and-drinking-water.   15. US EPA. Why Do Water Systems Add Phosphate to Drinking Water? What are the Health Effects of Drinking Water Containing Phosphates: Ground Water and Drinking Water. Available from: https://safewater.zendesk.com/hc/en-us/ articles/211406008-Why-do-water-systems-add-phosphate-to-drinking-water-Whatare-the-health-effects-of-drinking-water-containing-phosphates-.   16. US Environmental Protection Agency. Analytical Methods Approved for Drinking Water Compliance Monitoring of Inorganic Contaminants and Other Inorganic Constituents. EPA: Washington D.C., p. 66.   17. US Environmental Protection Agency. Analytical Methods Approved for Drinking Water Compliance Monitoring of Organic Contaminants. EPA: Washington D.C., 1995, p. 36.  18. US Environmental Protection Agency (EPA). Analytical Methods Approved for Drinking Water Compliance Monitoring of Radionuclides. EPA: Washington D.C.  19. J. Yang, T. J. Kwak, X. Zhang, R. McClain, W.-J. Chang, and S. Gunasekaran. Digital pH Test Strips for In-Field pH Monitoring Using Iridium Oxide-Reduced Graphene Oxide Hybrid Thin Films, ACS Sens., 2016, 1(10), 1235–1243.   20. M. Wang, Y. Yao, and M. Madou. A Long-Term Stable Iridium Oxide pH Electrode, Sens. Actuat. B, 2002, 81(2–3), 313–315.  21. E. Prats-Alfonso, L. Abad, N. Casañ-Pastor, J. Gonzalo-Ruiz, and E. Baldrich. Iridium Oxide pH Sensor for Biomedical Applications. Case Urea-Urease in real Urine Samples, Biosens. Bioelectron., 2013, 39(1), 163–169.   22. R. D. Meyer, S. F. Cogan, T. H. Nguyen, and R. D. Rauh. Electrodeposited Iridium Oxide for Neural Stimulation and Recording Electrodes. IEEE Trans. Neural Syst. Rehabil. Eng., 2001, 9(1), 2–11.

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Chapter 9

Phosphates Analysis in Water: State-of-the-Art Technologies and Challenges Mehenur Sarwar*, Jared Leichner*, Ghinwa Melodie Naja† and Chen-Zhong Li*,‡,§ Nano Bioengineering/Bioelectronics Laboratory, Department of Biomedical Engineering, Florida International University, 10555 West Flagler Street, EC2600, Miami, FL 33174, USA *

†

Everglades Foundation, 18001 Old Culter Road, Palmetto Bay, FL 33157, USA

Department of Biochemistry and Molecular Biology, Tulane University, J. Bennett Johnston Building, 1324 Tulane Ave, New Orleans, LA 70112, USA ‡

[email protected]

§

9.1. Introduction 9.1.1.  Water pollution Water is an indispensable element for living organisms. Despite this, water pollution is a rampant problem. Monitoring water pollution is additionally complex, as the dissolution of pollutants within large bodies of water reduces their concentration to extremely low levels, complicating their accurate measurement. In addition, the biochemical diversity of aquatic environments introduces numerous interfering agents, which may further skew these measurements. While extremely diluted pollutants may not pose immediate threats, the ability to measure and monitor their concentration both spatially and temporally is critical for understanding the specific sources of pollution present and identifying ways to reduce or eliminate them before they cause further harm. 281

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One type of pollutant that is receiving widespread scrutiny due to its contribution to global algae blooms is phosphate. When uncontrolled, phosphate can pose significant threats to marine, terrestrial, plant and human life. This chapter examines the fundamental nature of phosphate, discusses the challenges in its measurement, and characterizes the state-of-the-art technologies emerging to track its concentration in aquatic environments. To examine the pollution phenomenon, it is first necessary to understand the underlying typology. According to the United States Environmental Protection Agency (USEPA), water can be polluted in two ways: by point sources or nonpoint sources. USEPA defines point sources as “any discernible, confined and discrete conveyance, including but not limited to any pipe, ditch, channel, tunnel, conduit, well, discrete fissure, container, rolling stock, concentrated animal feeding operation, or vessel or other floating craft, from which pollutants are or may be discharged.” The USEPA defines non-point source as “Any source of water pollution that does not meet the legal definition of ‘point source’.”1 Point source pollutants usually remain confined and treated in a facility before release into nature, while non-point sources (including runoff from agricultural or residential areas) are introduced into these aquatic environments by factors such as excess rainfall. Nonpoint source pollution is a primary driver of natural water contamination, and it emerges as a side effect of overpopulation and excess crop fertilization.

9.1.2.  Phosphate as a pollutant Phosphorus is considered as a non-point source pollutant (NPS-P).2,3 Phosphate is currently well-established as a source of aquatic pollution, and its excess has been directly linked to the overproduction of phytoplankton, macroalgae, cyanobacteria,4 and other plants. High levels of phosphate are one of the main causes for the growth and spread of environmentally detrimental algae.5 According to a report by the National Oceanic and Atmospheric Administration (NOAA), up to nine inches of heavy blue-green algae bloom has been found in Lake Okeechobee in Florida after a heavy rain.6–8 Due to the rising atmospheric temperature, it is expected that the problems associated with algae blooms will only worsen.9–11 Furthermore, high levels of phosphate can cause bio-deterioration of archaeological sites due to the growth of cyanobacterial films on historical structures.12 The USEPA considers any region that has phosphate levels greater than 0.1 mg/l to be a heavily polluted site;13 therefore, phosphate measurement and remediation are powerful strategies that environmental agencies can use to control their domestic water quality.

9.1.3.  Phosphate as a nutrient Despite its deleterious effects in aquatic environments, phosphorus is an important biological growth-limiting factor, which serves a variety of critical functions with

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Figure 9.1.    Importance of phosphate. (Left) Phosphate serves a well-known role within the DNA of all cells as the backbone of nucleotide binding.18 (Right) The interplay of ATP/ADP/AMP serves a critical role in the body generating catabolic reactions to release needed energy.19 These works are licensed under Creative Commons 2.0 (https://creativecommons.org/licenses/by/2.0/).

an atomic number of 15 and an atomic mass of 30.9738 g/mol.14 As a molecule, phosphate is the structural backbone of hereditary material DNA15 and RNA,16 and is the primary component in the cell membrane (phospholipid bilayer) and in the human body; calcium phosphate is the second-most abundant mineral.19 The fundamental energy currency of a cell, Adenosine Tri-phosphate (ATP), comprises three phosphate groups17 and provides substantial energy release in organisms due to the modification of its phosphate bonds; this process is demonstrated in Fig. 9.1. Lastly, phosphate remains a critical component in marine ecosystems as long as it is carefully controlled.

9.1.4.  The phosphorus cycle While phosphorus plays an important role as a nutrient, the environmental phosphorus cycle is much different from other nutrient cycles due to its substantially slower pace.20 Specifically, after the nutrient is consumed by organisms such as plants and animals as a source of nutrition, it is returned to the environment following the digestion or death of the organism. The soil-bound phosphorous slowly leaches into surrounding bodies of water, and eventually sediments and transforms into rock over millions of years. This slow cycle has been substantially disturbed by loading the ecosystem with an excess amount of soluble or reactive phosphate, which is further exacerbated by the growing demand for phosphate-based fertilizer every year. This process is documented in Fig. 9.2.

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Figure 9.2.    Demonstration of the phosphorus cycle. (Top) Weathering and erosion processes drive phosphates into aquatic environments. Over long periods of time, after numerous cycles of use by marine organisms, the phosphate sediments. (Bottom) Reducing the quantity of agricultural runoff is extremely challenging, as global demand for fertilizer usage grows every year due to population growth.21

9.1.5.  Consequences of excess phosphate In addition to agricultural leaching, phosphates in the environment also originate from discharges of untreated industrial wastewater,22,23 sewage,24 detergents, food

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Figure 9.3.    Formation of dead zones. Growth of algae on the water surface reduces the subsequent supply of oxygen, killing living organisms below.28 This work is licensed under the Creative Commons 2.0 (https://creativecommons.org/licenses/by/2.0/).

waste,25 and food additives. Combined with agricultural runoff, these factors result in eutrophication26 and subsequent algae blooms. Furthermore, the eutrophication and subsequent algae blooms create dead zones in the water that kill aquatic creatures in the region. In freshwater systems, excess phosphate has been identified as the leading cause of eutrophication.27 A graphical depiction of this process is shown in Fig. 9.3. In South Florida, excess phosphate has even been connected with the loss of native plant species, such as the iconic Sawgrass. These native plants are being replaced by Cattails due to shifts in nutrient levels, which changes the ecosystem of the area.29,30 Subsequently, the future of native animals, such as wading birds and other species that live alongside Sawgrass, becomes threatened, and thus it is important to consider the dangers to native plant species in addition to the wellunderstood harm to aquatic life. Historically, water in the Florida Everglades is known to have phosphate levels below 10 ppb, accelerating pollution has caused the level to rise above 50 ppb.

9.1.6.  Cost of treatment The cost of treating water with high levels of contamination31 and eutrophication is extremely expensive, reflecting the need to detect and resolve the problem

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earlier. For instance, each year the state of Ohio spends $3–$4 million to purify drinking water systems after finding that microcystins, a cyanobacterial toxin, exceeded the safe threshold of 1 ug/L which is set by the World Health Organization (WHO).32 Cyanobacteria release brain and liver toxins that can kill both terrestrial and marine animals, create dead zones in water, and, subsequently, harm tourism industries. Overall, the United States spends approximately $2 billion per year to purify water due to aquatic pollution.

9.1.7.  Forms of phosphate When attempting to measure phosphate levels, it is critical to understand the various pH-dependent protonated forms that exist, since sensor designs may not have equivalent sensitivity to each form. In natural water, where the pH is between 6 and 7, a combination of H2PO4 and HPO4 can be found.33 Figure 9.4 demonstrates the distribution of different forms of phosphate at different pH levels. When at more extreme pH levels, other forms of phosphates that may predominate include H3PO4 and PO43−. This pH-dependent distribution is highlighted in Fig. 9.4.

Figure 9.4.    Distribution of various phosphate species. The specific forms of protonated or deprotonated phosphates found in water is pH-dependent. The figure represents these distributions at 25° C.33

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9.1.8.  Challenges of prevention In order to prevent current water pollution problems from worsening, it is critical to have both a strong legal framework that prohibits polluting activities and a dedicated enforcement team to identify and litigate individuals and industries that violate environmental laws. For example, the US Clean Water Act prohibits discharges of pollutants from a point source unless a carefully scrutinized permit process is undertaken.34 While developed economies were some of the earliest proponents of creating these laws and enforcing them, international efforts have fortunately led to a 38-fold increase in environmental laws around the world since 1972.35 However, this same report makes it clear that, despite this tremendous growth in environmental legal frameworks, there is a widespread failure to enforce laws and punish violators, which prevents the reduction of environmental pollution rates. Hence, when considering modern sensing technologies, it is necessary to keep in mind that their effectiveness in preventing larger problems will be highly dependent on the enforcement of the environmental laws in the country in which they are used.

9.1.9.  The challenge of measurement In addition to pollution prevention challenges, there is the additional challenge of measuring pollution levels accurately, which modern sensors aim to resolve. This challenge encompasses a variety of sensor criteria that must be optimized in parallel for an ideal phosphate sensor to emerge. For example, while historical conventional colorimetric phosphate sensors could only sense within a narrow range at a high concentration, next-generation devices must be capable of extremely sensitive measurements over a broad range in order to drive widespread adoption. Even though measurements in the sub-ppb or low-ppb level may not be indicators of substantial pollution, it is very important to monitor these ranges so that sudden, unexpected increases in phosphate concentration can be readily recognized and resolved before it becomes a larger problem. An additional measurement challenge that must be carefully overcome is the requirement for remote and continuous sensing over a long period of time. Many existing phosphate sensors require complex laboratory procedures to be carried out for each sample, and even sensors that can be submerged for repeated measurements over time often lose their measurement sensitivity quickly. By creating a network of submerged sensors to overcome these hurdles, it will be possible to monitor phosphate levels at a spatial and temporal resolution never before seen, which will undoubtedly help protect sensitive ecosystems. Furthermore, fieldbased measurements are invaluable for monitoring phosphate levels in the environment36 because samples do not need to be transported back to the lab and there are no associated transportation costs. With these considerations in mind, the following section describes some of the modern advances in phosphate sensing, each with their own advantages and limitations.

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9.2. State-of-the-Art Technologies for Monitoring Phosphate in Water Fundamentally, biochemical sensors have a recognition component and a transduction element.37 The recognition component is designed to bind specifically with the analyte of interest, while the transduction component converts the binding event into a measurable signal. This signal can take numerous forms; however, for biochemical sensors, it can be most conveniently subdivided into either an optical or an electrical signal. This fundamental architecture is true for both historic and more modern sensors, as well. To measure phosphate levels in bodies of water, governmental agencies have typically relied on two unique assays that have been used for decades. The first, the malachite green assay, is based on the recognition element of malachite green molybdate, which binds with high specificity to free orthophosphate in liquid samples.38 The resulting binding event is then transduced into an optical signal, due to its modification of optical absorbance between 620 and 640 nm, allowing phosphate measurements between 0.02 and 40 uM. The second assay uses ascorbic acid to reduce this complex in the presence of antimony potassium tartrate, which can be subsequently measured by absorbance at 880 nm.39 While these techniques have historically been useful to understand phosphate concentrations in natural bodies of water, they lack specificity in the presence of interfering agents such as detergents40 and fluoride.41 In addition to these natural interfering agents, both assays are susceptible to ethanol and a variety of other common chemical compounds. For this reason, numerous state-of-the-art technologies have been developed in recent years that aim to expand the available detection range, specificity, and reliability of measurements while minimizing the impact of common interfering agents Table 9.1. In order to organize the plethora of technologies that have emerged in the previous decade, the following sections divide these advances into two major classes: electrochemical and optical techniques. Electrochemical techniques share the commonality of generating an electrical signal after binding with phosphate but differ in their specific measurement strategy, whether potentiometric, amperometric, voltammetric, conductive, or capacitive. Similarly, optical techniques modify the fundamental optical properties of the substrate upon phosphate binding and can encompass a wide variety of phenomena, including colorimetric, absorbance, chemiluminescent, or fluorescent techniques.

9.2.1.  Electrochemical techniques Electrochemical techniques encompass a variety of different measurement ­techniques, each with its own unique instrumentation requirements. Each of the following electrochemical techniques has been implemented in recent years to

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Detection Technique

Sensor Description

Range (ppm)

LOD (ppm)

Interferences

Response Time

Reference

39 to 3096

31

Dissolved Oxygen

40 seconds

[44]

Electrochemical (Amperometric)

Cobalt-Copper Electrode

39 to 3096

Not reported

Oxygen interference reduced through use of copper

Less than 30 seconds

[79]

Electrochemical (Voltammetric)

Paper-based ScreenPrinted Electrode

0.31 to 9.3

0.12

Not Reported

Not Reported

[58]

Electrochemical (Voltammetric)

Metallic Molybdenum Electrodes

Mode 1: 0.0031 to 0.031 Mode 2: 0.008 to 0.12

Mode 1: 0.0015 Mode 2: 0.0031

No silicate interference

Mode 1: 60 mins Mode 2: 30 mins

[59]

Electrochemical (Conductance)

Molecularly Imprinted Polymer

0.66 to 8

0.16

Alkalinity & Anions can Interfere

2 Minutes

[61]

Electrochemical (Capacitance)

Phthalocyanine-acrylate polymer adduct

0.000003 to 0.3

0.00003

Low interference by chloride, sulfates, carbonates

Not reported

[62]

Optical (Colorimetric)

Molybdenum Phosphate-Blue Assay

0.004 to 0.31

0.001

Significant arsenic & silicate interference

5 minutes

[68]

Optical (Fluorescence)

Aluminium-morin Microspheres

0.1 to 1

0.1

Low interference by nitrate, carbonate, sulfate

5 minutes

[80]

Optical (Fluorescence)

Graphene Quantum Dots

0.031 to 0.4

0.003

Low interference by nitrates, F- & chloride

Not reported

[78]

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Electrochemical (Potentiometric)

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Table 9.1.    Summarizes and compares the various tradeoffs of the sensors described in this chapter.



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improve phosphate monitoring in water. Metric techniques measure the electrical potential between two electrodes using a high-impedance electrical meter, and they use the specific chemical reactivity between the analyte of interest and the electrode material to generate electrical potential. Amperometric techniques take advantage of oxidization or reduction reactions to generate an electrical current, which, when traveling through an immersed electrode, will be proportional to the concentration of the analyte in the solution. Voltammetric techniques combine the preceding two strategies and measure current flow as the voltage potential is swept over a unique range of voltages, measuring the half-cell reactivity of the analyte within the electrode-immersed solution. Conductance techniques are simpler and measure the ability of the electrolyte solution to conduct electricity. Finally, capacitive techniques measure the charge-storage behavior of an electrochemical solution and the degree to which it is affected by the presence of an analyte.

9.2.2.  Potentiometric techniques Recent potentiometric techniques have focused on the reactivity between phosphate and cobalt, which leverages the formation of a Cobalt (II) phosphate hydrate precipitate on a cobalt-coated electrode when immersed in a phosphate solution.42 The formation of this oxide film shifts the electrical potential to be more electronegative, allowing the Nernst equation to be used for calculating the concentrations of phosphates in solution. The electrodes are fabricated using MEMS techniques, using a microelectrode array (MEA) created through meniscus etching processes. The fundamental design characteristics of this MEA are shown in Fig. 9.5. While this technique provides phosphate measurements over a far broader range than historic techniques (10 uM–1 mM), it is substantially affected by the presence of dissolved oxygen, which is especially problematic during ­measurements in aquatic bodies. Advances in the use of cobalt as a phosphate-sensing transducer have emerged through use of cobalt–iron alloys, which substantially enhance sensor stability, response time, and linear response range.43 Furthermore, the related costs have been further reduced by screen-printing these cobalt electrodes, which allows for the mass-production of biosensors in many flexible configurations.44 Unfortunately, the use of cobalt has fundamental limitations due to the interfering effects of ­dissolved oxygen and chloride ions, which make their use problematic in environmental bodies of water. In addition to cobalt, there are alternative materials that can interact with phosphate to generate electrochemical signals. For example, bisthiourea ionophores have demonstrated substantial selectivity for phosphate and have been screen-printed to generate a low-cost sensing platform;45 however, the thiourea moiety has similar binding functionality for acetates, halides, and sulfates.

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Figure 9.5.  Representative image of phosphate sensing microelectrode array. Glass probes are polished and tapered, after which electrodeposition is used to sandwich an electrically conducting element (Ti/Au) within an insulating element (Polypyrrole). The tip is coated with cobalt to generate the phosphate-sensitive capabilities.

Other ionophores, such as cerium acetylacetonate complex (CAA), have also been found to be selective for phosphate when combined with a nanocomposite carbon paste electrode,46 but they are only applicable over a narrow pH range from 8.0 to 9.5. An interesting advancement in potentiometric detection occurred through the development of a molybdenum acetylacetonate membrane sensor.47 This sensor leverages the historical molybdate phosphate-sensing moiety and incorporates it into a membrane sensor that can be used continuously for up to 10 weeks in environmental monitoring scenarios. The use of the molybdenum-binding moiety also eliminates the concern for chloride and dissolved oxygen interference, thereby enhancing the suitability of this sensor for use in environmental samples.

9.2.3.  Amperometric techniques While potentiometric techniques allow concentration measurements through changes in electrical potential, amperometric techniques take advantage of the concentration-dependent electrical flow through a solution when a constant potential is applied. This potential creates oxidation or reduction, which generates current between the working and counter electrodes. Since the introduction of an extra analyte is designed to enhance the electrical current, a requirement for amperometric techniques is its restriction to electroactive species. The chosen potential controls the selectivity of the technique and, when optimized, amperometry has the benefit of extremely fine detection limits. Typical amperometric sensors use an enzyme to help catalyze the oxidation or reduction reaction of the analyte of interest. To take advantage of this technology, an early technique used immobilized pyruvate oxidase on a polyion complex membrane in order to generate hydrogen peroxide, an electroactive species that generates the resultant current flow.48

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While the sensor was designed to measure pyruvate, the capability of phosphoric acid to serve as a co-substrate within the chemical reaction allowed the electrode to be similarly used for measuring phosphoric acid at a very fine detection limit, 0.2 uM. However, while the polyion membrane prevented electrochemical interferents, the interfering effect of pyruvate was unavoidable due to the fundamental sensor design. Despite these limitations, the fundamental design has been incorporated into screen-printed sensors for phosphate determination in wastewater.49 However, these sensors are unsuitable for long-term use due to the substantial degradation occurring after one week, even when stored −20°C.A representative diagram of this screen-printed sensor is shown in Fig. 9.6. In order to overcome this shelf-life limitation, a more recent group immobilized the pyruvate oxidase onto a poly-5,2′:5′,2″-terthiophene-3′-carboxylic acid, poly-TTCA-layered glassy carbon electrode.52 While the detection limit of 0.3 uM and linearity up to 100 uM was not significantly different from that of the original design, the updated design can be reused for up to 1 month when refrigerated. Emerging work in amperometric phosphate detection has abandoned the use of pyruvate oxidase in place of a more specific biorecognition element, purin nucleoside phosphorylase (PNP).53 This element is entrapped with xanthine oxidase (XO) and potassium ferrocyanide within a polypyrrole (PPY) film. While the resulting design of the sensor made it highly specific only for inorganic phosphate, the resulting stability degraded rapidly after 24 h. In contrast with these enzymatic approaches, other groups have taken advantage of unique phenomena to identify the presence of inorganic phosphate. For example, a recent research team found that barrel-plated nickel electrodes following activation in alkaline media would selectively trigger inorganic phosphate adsorption onto the electrode surface.54 The presence of this inorganic phosphate layer effectively suppressed the amperometric measurement of the electrocatalytic oxidation of glucose. This suppression became a useful source of contrast and allowed for measurements of inorganic phosphate at levels as low as 0.3 uM, with linearity up to 1 mM and very little interference from other common ions. Furthermore, the electrode could be effectively regenerated by a combination of applied potential and continuous hydrodynamic flow in the presence of glucose, allowing long-term use. Hence, this improved design clearly enhanced the shelf life and linear range of previous amperometric configurations.

9.2.4.  Voltammetric techniques Unlike the passive potentiometric and amperometric strategies previously described, voltammetric techniques do not simply measure potential or current under a fixed condition. In contrast, this active technique varies electrical potential in a predefined waveform while the current is monitored; therefore, these techniques provide results that are a simultaneous function of applied potential,

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Figure 9.6.    Representative demonstration of an amperometric screen-printed electrode. The simplistic design and orientation of the three electrodes can be readily controlled during the screenprinting process to maximize the efficiency of batch manufacturing and hence reduce the end cost of the sensor. Representative of an amperometric data showed on the top left.50,51

measured current, and time. While this undoubtedly creates additional technological cost and complexity, voltammetric techniques have numerous advantages. Typically, these techniques provide results over a large linear concentration range over a wide range of temperatures, are compatible with numerous solvents, and provide fast response. Furthermore, due to the additional measurement complexity, a variety of additional mechanistic and kinematic parameters can be extracted, and multiple analytes can be measured simultaneously.

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To measure phosphate, early voltammetric techniques relied upon the sensitivity of bisthiourea ionophores to phosphate, a phenomenon discussed previously in the context of potentiometry. These techniques used self-assembled monolayers of a bis-thiourea receptor attached to a gold electrode.55 The fundamental characteristics of this approach mirrored that of a ligand-gated ion-channel protein, since the phosphate binding could modify the electron transfer rate while being insensitive to common potentially interfering anions. While the detection limit was not precisely calculated in this early work, the response was shown to linearly span an extremely broad range, from 10−4 to 10−1 M. Akin to previously described works that found phosphate measurement was possible due to its suppression of electrocatalytic activity, a recent approach found that nano-porous iron hydroxide electrodes’ intrinsic Fe(III)/Fe(II) redox coupling was modified by the presence of phosphate on the electrode surface.56 While this sensor design was capable of measuring an extraordinarily low concentration range, from 10−9 to 10−4 M, it had substantial hysteresis due to the strong binding between the phosphate and iron oxide surface. This required numerous cleaning cycles to ensure that the electrode did not retain substantial phosphate between successive measurements. More recent work has also leveraged this iron redox coupling through the creation of ferrocene-functional thiophene compounds and styrene copolymers,57 which shift the redox potential of the iron cathodically upon the introduction of phosphate. This has even been incorporated into paper-based screen-printed electrodes, although the resulting linear range, from 4 to 300 uM, was far less than the range of the preceding strategies.58 One of the most recent examples of voltammetric detection in the literature emerged from the historic colorimetric malachite green assay. Perceiving the benefit of the binding affinity of phosphate with molybdate, the researchers chose to use a solid molybdenum electrode instead of a liquid substrate.59 Phosphate detection was carried out using square-wave voltammetry because it is more sensitive than typical cyclic voltammetry. Furthermore, by controlling the frequency the team was able to control the linearity. At a high 250 Hz frequency the detected range spanned from 0.1 to 1 uMol; at a lower 25 Hz frequency the range was 0.25–4 uMol. While the developed system was powerful due to its tunability, the complexation waiting time unfortunately required between 30 and 60 min, depending on the frequency.

9.2.5.  Conductance techniques Conductance-based techniques typically leverage the biorecognition events on the surfaces of modified electrodes, such as those produced by immobilized proteins or antibody/antigen reactions;60 however, these techniques are rarely used to measure phosphate. In one recent application, a molecularly imprinted polymer which

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used thiourea receptors was used as a sensing agent;61 this same strategy has been described in earlier sections. Integration of these receptors into an electrolytic chamber generated measurable changes in conductance (by measuring resistance) over a linear range, spanning 0.66 to 8 mg/L of phosphorus. Furthermore, the substantial specificity of the molecularly imprinted polymer technique allowed for the direct measurement of unfiltered wastewater.

9.2.6.  Capacitive techniques The capacitive techniques measure stored charge through pairs of conductors separated by an insulator. These techniques are generally favorable due to their low cost and simple construction; however, capacitive technologies are highly sensitive to environmental factors, such as temperature and humidity, and are more complex in their measurement strategy than a simpler conductive sensor. A group recently has taken advantage of the binding properties of metallo-­ phthalocyanine (MPc) materials, which can selectively target specific ions by tuning the central metal material.62 This sensor further transduced the signal using a capacitive silicon nitride substrate with an AlCu/Si-p/SiO2/Si3N4 structure. Despite the general disadvantages of capacitive-sensing technologies, this technique was found to have one of the broadest detection ranges studied, from 10−10 to 10−5 M of phosphate ions.

9.2.7.  Optical techniques While electrochemical techniques can be favorable due to their simpler miniaturization and lack of requirements for optical transparency and turbidity, electrical techniques in general are readily affected by electromagnetic interference and electrical cross-talk.63 Optical techniques, in contrast, play an important niche in phosphate detection technologies due to their resilience to external electrical noise and the generally higher selectivity of its techniques. For detecting phosphate, the major optical techniques used include colorimetric, absorbent, chemiluminescent, and fluorescence detection. Colorimetric strategies are designed to use chromogenic substrates to create visible changes upon introduction of the analyte. Absorbance techniques use the analyte to change the transmission intensity of a specific wavelength of light when passing through a sample in a concentration-dependent manner for the analyte. Chemiluminescent approaches use the formation of a light-emitting substrate, in which its intensity is proportional to the analyte concentration due to a carefully managed sequence of intermediate chemical reactions. Finally, fluorescence techniques measure the emission of a substrate upon excitation with a controlled source.

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9.2.8.  Colorimetric/Absorbance techniques Colorimetric techniques have tremendous promise due to their ease of measurement. In most cases, end users simply place their sample into a prepackaged reservoir, after which the concentration of an analyte can be measured without the need for expensive and sophisticated equipment. However, it is important to acknowledge that colorimetric measurements are often only semi-quantitative and thus may require a look-up table, similar to a color wheel, to provide a rough estimate of the sample concentration. Despite this, colorimetric strategies are by far the most simplistic for enabling non-technical personnel to conduct sophisticated measurements. As previously described, historic phosphate measurement techniques were colorimetric in nature, although a more precise quantitative assessment was possible using absorbance detection techniques. Absorbance techniques are described here as well, as they are typically used to validate colorimetric sensors. The chromogenic changes that characterize colorimetric sensors often affect the substrate’s wavelength-dependent absorption qualities. This feature allows for the dual and parallel measurement of colorimetric and absorbance qualities, which is common in most published works demonstrating a colorimetric response to an analyte. Many of the recent attempts to provide an autonomous colorimetric/­ absorbance measurement device have relied upon the simple automation of historic measurement techniques. For example, modern attempts have combined the typical phosphomolybdate detection strategies with a microfluidic flow device, which mixes reagents and measures absorbance autonomously.64 Other laboratories have chosen to incorporate a lab-on-chip design, which uses phosphomolybdate detection to further miniaturize the composite system.65 Field-flow systems have been created with the same underlying detection method for the simultaneous measurement of phosphate, nitrite, and nitrate in natural and wastewater.66 Finally, a group recently developed a CYCLE phosphate sensor, which uses four micropumps and a mixing manifold to carry out the phosphomolybdate measurement scheme every few seconds.67 While these approaches do not improve upon the existing detection ranges for these techniques, they do enhance the capability for continuous measurement. In contrast with these approaches, which simply automate existing optical methods, other groups have attempted to modernize these historic methods. For example, a group that recently used the common phosphomolybdate detection scheme found that using polyvinylpyrrolidone instead of typical surfactants enhanced sensitivity.68 This improvement decreased the detection limit to 40 nM, with a linear range from 140 nM to 10 uM and enhanced resilience to temperaturedependent effects. While the availability of colorimetric substrates has long been limited to the reuse or incremental advancement of historic techniques, recent work has

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identified the novel application of a ytterbium-pyrocatechol violet (Yb-PV) complex for phosphate detection.69 Upon interaction with phosphate, a ligand exchange process is carried out which releases pyrocatechol violet freely into solution. This process simultaneously shifts the color of the solution from blue to yellow, which can be readily assessed through spectrophotometric analysis of the ratios of each molecule’s absorption peak. While the colorimetric effect could be assessed semiquantitatively using a color wheel, the measurement range was between only 10 and 50 uM, and no detection limit was reported. Furthermore, due to the nature of the binding event, the ATP acted as a strong interfering agent for inorganic phosphate measurement.

9.2.9.  Chemiluminescent techniques The use of chemiluminescent techniques for measuring phosphate is extremely rare. However, chemiluminescence is a promising technique for improving upon the poor quantifiability of colorimetric approaches without necessitating the expensive instrumentation to carry out an absorbance measurement. Fundamentally, chemiluminescent techniques use a sequence of chemical reactions to activate a chemiluminescent substrate, with the concentration of the analyte serving as an amplifier for the chemiluminescent reaction. Chemiluminescence degrades over time and must be measured immediately; however, since the reaction generates an optical signal without requiring an input optical excitation, measurement can be carried out simply and quantitatively with consumer equipment. For example, common consumer cameras and smartphones allow users to carefully tune the exposure time of the shutter — a parameter that can be readily tuned to collect chemiluminescent information within a dark environment. Therefore, chemiluminescent approaches provide a useful combination of quantifiability and ease of measurement. The most modern application of this technology to measure phosphate was carried out by constructing a phosphate sensor plasmid by fusing an E. coli alkaline phosphatase gene with bioluminescent genes from the Vibrio fischeri bacteria.70 This construct was subsequently incorporated within E. coli and was able to distinguish bioavailable and bio-unavailable (insoluble) phosphate. While this technique showed promise with further optimization, it was able to produce a dose-dependent response between only 40 and 60 uM for exogenous phosphate.

9.2.10.  Fluorescent techniques Most modern optical phosphate measurement schemes have relied on advancements in fluorescence techniques. Fluorescence techniques share many fundamental instrumentation components in common. First, an excitation light source with

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a narrow spectral bandwidth is used to drive an absorption event within a specific component of the substrate. Once the energy is absorbed, a radiative relaxation event takes place and photons with a lower energy (i.e., higher wavelength) are released. To ensure the detection optics can distinguish between this excitation and emission energy, a narrow-band filter is often placed in front of the detection optics to reject excitation photons and accept emission photons. Fluorescence techniques are characterized by a gain or loss of fluorescence when binding to the analyte of interest. Quantification of the resulting signal requires careful calibration of the optical system to ensure the spatial and temporal stability of all components across multiple measurements. One of the earlier attempts to use fluorescence as a measurement tool took advantage of intramolecular charge transfer (ICT). Fluorophores, which contain both an electron-donating and electron-withdrawing group, can undergo this procedure upon excitation but the presence of cation binding can reduce the strength of the red-shift caused by ICT. One group took advantage of this physical process by using a pyrene–terpyridine–Zn conjugate, in which zinc acted as the electron acceptor and created a red-shift more than 100 nm.71 A selective response to phosphate anions further allowed for the development of a ratiometric detection scheme by comparing the red-shifted ICT fluorescent peak with the original one. Ratiometric detection is extremely useful in quantitative fluorescence, as it reduces the need to perform multiple calibrations due to the smaller impact of temporal variability in the light source intensity. While the resultant system was able to successfully measure soluble phosphate, its fundamental binding characteristics also made it highly responsive to pyrophosphate. Thus, pyrophosphate was clearly an interfering agent if the desired action for the sensor was to measure inorganic phosphate. The use of zinc as a fluorescence substrate was further advanced by a separate group, which synthesized a unique dinuclear chiral zinc complex.72 Unlike the previous group, this group’s substrate fluorescence detection scheme used turn on-off signaling instead of ratiometrics. This form of signaling reduces the substrate’s existing fluorescence intensity proportionally to the quantity of the analyte introduced; therefore, quantification of the resulting signal is more complex than the ratiometric approach previously described. Additionally, the sensor was found to have the strongest selectivity for pyrophosphate, ATP, and ADP than inorganic phosphate. Alternative approaches for fluorescence measurement have emerged through the use of fluorescent microspheres functionalized with uranyl salophene derivatives, which are established dihydrogen phosphate carriers.73 These microspheres were combined with chromoionophores in order to establish a composite fluorescence sensing system. While the specificity for dihydrogen phosphate is extremely high, the system suffers from a poor shelf life, with lifetimes ranging from hours for a microsphere-based sensor to days for a film-based sensor.

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Xanthenic derivatives have similarly emerged as potentially suitable targets for measuring phosphate concentrations. For example, researchers have generated a fluorescein derivative dye (2-Me4-OMe TG) which can measure phosphate concentration by leveraging the excited-state proton-transfer (ESPT) phenomena;74 with this strategy, phosphate in its protonated form acts as a suitable proton donor. However, to measure the outcomes of this reaction it is necessary to use a technique known as Fluorescence Lifetime Imaging Microscopy (FLIM). With FLIM, the alteration in the nanosecond-scale decay time influenced by ESPT can be readily quantified and used to predict the concentration of phosphate in solution. While a powerful technique, the technological complexity and cost associated with the extremely high temporal resolution required makes this approach out of reach for many laboratories. Fortunately, alternative fluorescent-sensing strategies have emerged in recent years that have enhanced specificity compared with zinc-derived fluorophores, and reduced measurement complexity because FLIM-based measurements are not needed. For example, novel fluorophores have emerged that combine tetraamide, isoquinolyl, and quinolyl groups to selectively recognize dihydrogen phosphate anions.75 The quinoline and isoquinoline derivatives are used to generate the fluorescent signal while the tetraamide-based receptors show phosphate specificity. Fluorescence quenching occurs following the addition of anions, including dihydrogen phosphate. Fluorescent strategies for sensing phosphate have also been carried out using immobilized aluminum-morin complexes on pGMA microspheres.76 This strategy leverages the ability of the flavonoid morin to detect the phosphate group and the capabilities of an aluminum-morin complex to generate a fluorescent signal. The final sensor was capable of measuring between 0.1 and 15 ppm of dihydrogen phosphate, had little interference from common ions, and generated a measurable result through a fluorescent quenching effect. Another fluorescent strategy that has found recent use in phosphate detection uses functionalized quantum dots. Quantum dots are highly fluorescent particles that are generally resistant to photobleaching (Fig. 9.7), a process in which

Figure 9.7.    The figure demonstrates the process of phosphate-induced turn-on fluorescence. The binding of europium nitrate (grey) to the surface of the spherical quantum dots prevents its intrinsic fluorescence. However, the introduction of phosphate into solution separates the europium nitrate from the quantum dots, restoring fluorescent properties.

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repeated excitation light exposure degrades the fluorescent emission over time. Due to this photobleaching resistance, quantum dots are powerful tools for generating sensors and can be used continuously over long periods of time. An example of the use of quantum dots for phosphate sensing can be found in the development of thiol-capped cadmium telluride quantum dots.77 These fluorescent nanoparticles form a complex with europium nitrate in solution, which quenches the potential of these quantum dots. However, upon the addition of phosphate solution the europium, nitrate detaches from its complex and restores fluorescence intensity in a manner proportional to the concentration of phosphate in solution; this solution was also found to be highly specific for inorganic phosphate. Similar strategies have been carried out incorporating europium with graphene quantum dots, which similarly reverse the quenching behavior following the addition of phosphate.78 Overall, quantum dot-based approaches are most desirable due to their long-term measurement stability.

9.3. Future Challenges Despite the plethora of available detection methods, numerous challenges still exist in sensor design that are simply intrinsic to the unique detection methodology chosen. Potentiometric methods are highly susceptible to electrical interference, amperometric methods have poor outcomes for trace sub-uM concentrations, voltammetry can induce unintended background reactions that can impact signal quality, and conductance methods generate weak signals.62,63 Furthermore, the chemical reactions underlying absorbance techniques are often non-specific, and the induction of photobleaching following fluorescent measurements reduce the capability for long-term probe usage. Fortunately, many incremental advances in recent decades have reduced the impacts of these factors, although further work is necessary to optimize each technology. Regarding sensor deployment, there is a problematic delay of multiple decades between technique development and fully automated deployment. Of the various manuscripts assessed in this review, the published works that provided a professionally assembled and long-term measurement scheme that is ready for market were all simply automated versions of historical phosphate measurement techniques developed many decades ago. Following this logic, it can be expected that in another decade or two the more modern techniques described here may find themselves in widespread commercial use. However, this time frame is unacceptable due to the growing threat of climate change on the aquatic health of our planet, and thus it is critical that governmental agencies pursue and fund existing methodologies that show promise order to expedite their deployment and preserve the health of our ecosystems. At the birth of phosphate measurement technologies, very few techniques were available for use. However, it is fortunate that in the modern age a variety of

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biorecognition elements have been identified to aid in the detection and quantification of phosphate levels. Many of these motifs have been incorporated into various measurement technologies, as molybdenum-based sensing has been used within potentiometric-, voltammetric-, and absorbance-based techniques. The use of parallel technologies to leverage biorecognition elements will prove critical, as each technique has a unique measurement range and limitations. Simultaneous measurement with multiple recognition elements and detection strategies can allow for detection over an extremely wide range, and recent advances in biofilm fabrication can maximize measurement accuracy over a long duration. Continued optimization of existing technologies in conjunction with exploratory research on novel recognition elements must coexist for commercial-ready sensors to eventually emerge.

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56. R. E. Moss, J. J. Jackowski, M. deSouza Castilho, and M. A. Anderson. Development and Evaluation of a Nanoporous Iron (Hydr)Oxide Electrode for Phosphate Sensing, Electroanalysis, 2011, 23(7), 1718–1725. 57. O. Karagollu, M. Gorur, F. Gode, B. Sennik, and F. Yilmaz. Phosphate Ion Sensors Based on Triazole Connected Ferrocene Moieties, Sensors Actuat. B Chem., 2014, 193, 788–798. 58. S. Cinti, D. Talarico, G. Palleschi, D. Moscone, and F. Arduini. Novel Reagentless Paper-Based Screen-Printed Electrochemical Sensor to Detect Phosphate, Anal. Chim. Acta., 2016, 919, 78–84. 59. C. Barus, I. Romanytsia, N. Striebig, and V. Garçon. Toward an In Situ Phosphate Sensor in Seawater Using Square Wave Voltammetry, Talanta, 2016, 160, 417–424. 60. D. Grieshaber, R. MacKenzie, J. Vörös, and E. Reimhult. Electrochemical Biosensors — Sensor Principles and Architectures, Sensors, 2008, 8(3), 1400–1458. 61. C. Warwick, A. Guerreiro, E. Wood, J. Kitson, J. Robinson, and A. Soares. A Molecular Imprinted Polymer Based Sensor for Measuring Phosphate in Wastewater Samples, Water Sci. Technol., 2014, 69(1), 48–54. 62. L. Barhoumi, A. Baraket, N. M. Nooredeen, M. Ben Ali, M. N. Abbas, J. Bausells et al. Silicon Nitride Capacitive Chemical Sensor for Phosphate Ion Detection Based on Copper Phthalocyanine — Acrylate-Polymer, Electroanalysis, 2017, 29(6), 1586–1595. 63. M. Bariya, H. Yin, Y. Nyein, and A. Javey. Wearable Sweat Sensors, Nat. Electron. 2018, 1(March), 944–952. 64. J. Cleary, S. Slater, C. McGraw, and D. Diamond. An Autonomous Microfluidic Sensor for Phosphate: On-Site Analysis of Treated Wastewater, IEEE Sens J., 2008, 8(5), 508–515. 65. M. M. Grand, G. S. Clinton-Bailey, A. D. Beaton, A. M. Schaap, and T. H. Johengen, M. N. Tamburri et al. A Lab-On-Chip Phosphate Analyzer for Long-Term In Situ Monitoring at Fixed Observatories: Optimization and Performance Evaluation in Estuarine and Oligotrophic Coastal Waters, Front Mar. Sci., 2017, 4(August), 1–16. 66. I. A. Tsoulfanidis, G. Z. Tsogas, D. L. Giokas, and A. G. Vlessidis. Design of a Field Flow System for the On-Line Spectrophotometric Determination of Phosphate, Nitrite and Nitrate in Natural Water and Wastewater, Microchim. Acta., 2008, 160(4), 461–469. 67. A. H. Barnard, B. Rhoades, C. Wetzel, A. Derr, J. Zaneveld, C. Moore et al. RealTime and Long-Term Monitoring of Phosphate Using the In-Situ CYCLE Sensor. Paper presented at Oceans 2009, March, 1–6. 68. G. S. Clinton-Bailey, M. M. Grand, and A. D. Beaton, A. M. Nightingale, D. R. Owsianka, G. J. Slavik et al. A Lab-on-Chip Analyzer for In Situ Measurement of Soluble Reactive Phosphate: Improved Phosphate Blue Assay and Application to Fluvial Monitoring, Environ. Sci. Technol., 2017, 51(17), 9989–9995. 69. E. Gaidamauskas, K. Saejueng, A. A. Holder, S. Bharuah, B. A. Kashemirov, D. C. Crans et al. Metal Complexation Chemistry Used for Phosphate and Nucleotide Determination: An Investigation of the Yb3+-Pyrocatechol Violet Sensor, J. Biol. Inorg. Chem., 2008, 13(8), 1291–1299.

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70. M. A. Dollard and P. Billard. Whole-Cell Bacterial Sensors for the Monitoring of Phosphate Bioavailability, J. Microbiol. Method., 2003, 55(1), 221–229. 71. X. Peng, Y. Xu, S. Sun, Y. Wu, and J. Fan. A Ratiometric Fluorescent Sensor for Phosphates: Zn2+-Enhanced ICT and Ligand Competition. Org. Biomol. Chem., 2007, 5(2), 226–228. 72. S. Khatua, S. H. Choi, J. Lee, K. Kim, Y. Do, and D. G. Churchill. Aqueous Fluorometric and Colorimetric Sensing of Phosphate Ions by a Fluorescent Dinuclear Zinc Complex, Inorg. Chem., 2009, 48(7), 2993–2999. 73. K. Wygladacz, Y. Qin, W. Wroblewski, and E. Bakker. Phosphate-Selective Fluorescent Sensing Microspheres Based on Uranyl Salophene Ionophores, Anal. Chim. Acta, 2008, 614(1), 77–84. 74. J. M. Paredes, M. D. Giron, M. J. Ruedas-Rama, A. Orte, L. Crovetto, E. M. Talavera et al. Real-Time Phosphate Sensing in Living Cells Using Fluorescence Lifetime Imaging Microscopy (FLIM), J. Phys. Chem. B, 2013, 117(27), 8143–8149. 75. S. I. Kondo and R. Takai. Selective Detection of Dihydrogen Phosphate Anion by Fluorescence Change with Tetraamide-Based Receptors Bearing Isoquinolyl and Quinolyl Moieties, Org. Lett., 2013, 15(3), 538–541. 76. A. Ahmad, S. A. Hanifah, S. A. Hasbullah, K. Suhud, N. M. Zaini, L. Y. Heng. Phosphate Sensor Based on Immobilized Aluminium-Morin in Poly (Glycidyl Methacrylate), Microspheres, 2014, 486(February), 486–491. 77. V. Borse, P. Jain, M. Sadawana, R. Srivastava. “Turn-On” Fluorescence Assay for Inorganic Phosphate Sensing. Sens. Actuat. B Chem., 2016, 225, 340–347. 78. S. Zhuo, L. Chen, Y. Zhang, and G. Jin. Luminescent Phosphate Sensor Based on Upconverting Graphene Quantum Dots, Spectrosc. Lett., 2016, 49(1), 1–4.

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Chapter 10

Metal Organic Framework-Based Sensors for Water Contaminants Detection Shun Mao* and Xian Fang† College of Environmental Science and Engineering, Tongji University, 1239 Siping Road, Shanghai 200092, China [email protected]

*

[email protected]

†

10.1. Introduction Water pollution and habitat degradation has led to increasing water scarcity and public health risks. Many types of pollutants, e.g., heavy metals, anions, organic compounds and microorganisms, are associated with health risks that induce a burden of diseases in almost all organ systems, such as diarrheal diseases, respiratory diseases, neurological disorders, cardiovascular disease, etc.1 Monitoring pollutants is critical for identifying water pollution and reducing its adverse effects on human health and the environment. Conventional analytical techniques, e.g., high-performance liquid chromatography (HPLC), capillary electrophoresis (CE), and spectrometry methods, have been routinely used for the analysis of water pollutants and demonstrated high sensitivity and selectively.2–5 However, most of these techniques suffer from the disadvantages of high cost and the need for sophisticated instruments, complex pretreatment, and trained technicians, making it difficult for the real-time and rapid monitoring of water contaminants. Currently, realizing the facile, visual, online, and quantitative detection of trace pollutants remains a major challenge in environmental monitoring. As a result, there has been tremendous interest in developing highly sensitive and rapid chemical sensors to detect contaminants in water. For chemical sensors, the 307

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selection and design of the sensing materials used in sensor platforms are key to enabling advanced sensor performance to meet sensing application requirements.6,7 Metal-organic frameworks (MOFs) are a fascinating class of highly ordered crystalline polymers formed by the coordination bonds of metal ions/clusters and organic bridging linkers/ligands,8 which combine the intrinsic merits of the rigid inorganic materials and flexible organic materials. Because of their high porosity and high internal surface area, MOFs can possess a huge Langmuir surface area (>10,000 m2 g−1),9 which is several times higher than that of activated carbon (1200 m2 g−1).10 MOFs also exhibit other inherent advantages, including high porosity, good absorbability, high catalytic activity, structural diversity, and easy structure functionalization.11–13 Benefiting from these merits, MOF is a favorable candidate. The permanent porosities within MOFs not only can potentially preconcentrate the analyte for higher sensitivity but also offer an ideal environment to accommodate substrates and thus induce specific recognition via intermolecular interactions between the MOF skeleton and analyte.14 Based on different analytic strategies, the chemical, physical, or structural changes in an MOF upon the adsorption of guest molecules have been used in recent years for monitoring water contaminants such as heavy metals, anions, and organic compounds.15,16 This chapter explores recent progress in the use of MOF-based sensors for detecting water pollutants. Specifically, we discuss the development of MOF-based optical and electrochemical sensors and their specific features and performance in sensing water pollutants. The challenges in using these sensors in real water environment applications are also discussed, and future directions in MOF-based sensor research are proposed.

10.2. MOF-Based Sensors 10.2.1.  Fluorescent sensors using MOFs Fluorescent sensors employ spectra signals, such as fluorescence enhancement, quenching, or the shift of the fluorophore emission wavelength, to qualitatively and quantitatively detect the analyte or target condition. Fluorescent sensors are among the most desirable sensing techniques due to its relative ease of use, technical simplicity, rapid sensing, and broad adaptability.17,18 Various luminescent materials (e.g., fluorophore) have been used as the sensing element in fluorescent sensors, including inorganic quantum dots,19–22 metal nanoclusters,23–25 and fluorescent organic dyes.26,27 Compared with traditional inorganic and organic fluorescent materials, MOFbased materials show promise as multifunctional and designable fluorescent materials, since both organic and inorganic components in MOFs can potentially

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generate fluorescent signals. Moreover, guest molecules can be easily incorporated in MOFs to emit or induce fluorescence. In addition, the large specific surface area of MOFs is beneficial for concentrating the analytes, which improves sensitivity. Another merit of MOF-based sensors is its tunable pore size for selective recognition of small molecules/ions and specific combination sites (e.g., Lewis basic/acidic sites and open metal sites for different interactions). Analysis within porous fluorescence MOFs will certainly enhance their sensing sensitivity.14,28,29 Over the past few years, a wide range of MOF-based fluorescent probes have been reported for environment pollutants sensing, including metal cations, anions, and organic molecules.6,16,30–33

10.2.1.1.  Heavy-metal ion sensing Heavy metals are highly toxic, non-biodegradable pollutants, and their contamination in water is one of the most serious concerns to human health. Although heavy metals such as copper (Cu), iron (Fe), and zinc (Zn) at trace levels are nutritionally essential for a human health, they are toxic at higher concentrations. In addition, some heavy metals, such as lead (Pb), arsenic (As), mercury (Hg), chromium (Cr), and cadmium (Cd) are not biologically essential and are harmful to organisms, even at low concentrations.34,35 Many MOF-based fluorescence sensors have been reported for detecting heavy-metal ions in water.36–41 Recently, Jiang’s group reported a Pd(II)porphyrinic MOF (PCN-222-Pd(II)) fluorescence sensor for Cu2+ determination based on the catalytic Heck reaction system.42 The Pd(II) is preinserted into the porphyrin center of PCN-222 to produce PCN-222-Pd(II), which is highly sensitive to Cu2+, and the Pd2+ is easily replaced by Cu2+ due to a stronger binding affinity between porphyrin and Cu2+ than that of Pd2+. Simultaneously, the isolated Pd2+, in an equal amount to the inserted Cu2+, can be reduced in situ to Pd nanoparticles (NPs). Based on this strategy, the non-fluorescent aniline was employed as the sensing probe, which could be converted to a fluorescent indole product via the Heck cross-coupling reaction over the catalysis of Pd NPs. Therefore, the amount of Cu2+ can be monitored based on the difference in the fluorescence intensity of indole product. The “turn-on” fluorescence strategy realized highly selective and sensitive detection of Cu2+ in aqueous solution. Liu and coworkers synthesized a Lanthanide (Ln)-MOF [Tb(L)(H2O)5]n (Tb-MOF, H2L-=3,5-dicarboxyphenol anion ligand) with a 2D network structure for Pb2+ sensing.38 The as-synthesized Tb-MOF exhibited an intense green luminescence of Tb3+, which was induced by the “antenna effect” from the ligands under UV irradiation. The antenna effect indicates that the lanthanide ions suffer from weak light absorption due to the forbidden f-f transitions, making the direct excitation of metals very inefficient unless high-power laser excitation is used.

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This problem can be overcome by coupling species that can participate in energy transfer processes.14 The principle of the antenna effect with MOFs can be described as follows. Light is first absorbed by the organic ligands and then energy is transferred to the lanthanide ions from organic ligands, leading to the luminescence generated from the lanthanide ions. In this case, when Pb2+ is added in Tb-MOF suspension, the green emission signal is quenched significantly. This is primarily attributed to the Lewis basic sites of phenolic oxygen in Tb-MOF that exhibit a weak coordination to Pb2+, which evidently can affect the efficient energy transfer from the ligand to Tb3+ ion center. Hence, the sensitive tracing of Pb2+ ions could be realized by the Ln-MOF probe, which possesses a low detection limit of 10−7 M. Li’s group designed two MOFs, namely [Zr6O4(OH)8(H2O)4(L1)2] (BUT-14) and [Zr6O4(OH)8(H2O)4(L2)2] (BUT-15), for detecting Fe3+ in water; the detection principle is shown in Fig. 10.1.43 The two new ligands used in the MOFs, 5′,5‴-bis(4-carboxyphenyl)-[1,1′:3′,1″:4″,1‴:3‴,1⁗-quinquephenyl]-4,4⁗dicarboxylate (L1) and 4,4,44‴-(4,4′-(1,4-phenylene)bis(pyridine-6,4,2-triyl))tetrabenzoate (L2), are structurally similar; the only difference is the latter is functionalized by pyridine N. In this study, these two MOFs, with the Lewis basic sites’ functionalized−conjugated organic carboxylic ligands, are employed as the sensing probe. Zr(IV)-MOFs possess strong fluorescent emission with π-conjugated organic ligands, and Zr4+ ion tends to coordinate with hard basic sites, typically metal ions. Thus, both BUT-14 and BUT-15 show intense fluorescence in water, which is mainly due to the emissions of ligands. The fluorescence could be

(a)

(b)

Figure 10.1.  (a) Working principle of Zr(IV)-MOFs-based sensors for Fe3+ sensing. (b) Fluorescent spectra of BUT-15 in the presence of different concentrations of Fe3+ under an excitation at 320 nm. Reproduced with permission from Ref. [43]. Copyright © 2017, American Chemical Society.

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selectively quenched by trace amounts of Fe3+ in water, with low detection limits of 212 and 16 ppb, respectively. The fluorescent quenching effect is due to the photo-induced electron transfer between Fe3+ ions and ligands in MOF. Moreover, the introduced pyridine N in the ligand of BUT-15 donates its lone-pair electrons to Fe3+, which enhances sensing performance. In addition, hexavalent chromium ion (CrVI) is one of the highly toxic heavy metals classified as a human respiratory carcinogen.44 The level of CrVI (Cr2O72−/ CrO42−) regulated by World Health Organization (WHO) is 50 μg L−1 in groundwater.45 Recently, Bu’s group designed a fluorescent MOF [Zn3(bpanth)(oba)3]·2 N,N-Dimethylformamide (NUM-5) for determining CrVI in water, which possessed a three-dimensional (3D) network with one-dimensional (1D) triangular channels.46 The fluorescence of NUM-5 comes from the organic ligand in the structure, which exhibited a selective luminescence quenching response toward CrVI (Cr2O72− and CrO42−) in aqueous solution. The spectra study revealed two sensing mechanisms: (1) the UV absorption bands of NUM-5 and CrVI anions strongly overlap, and thus the competitive absorption from the excitation energy CrVI leads to the quenching effect of NUM-5; (2) the absorption bands of CrVI anions from 250–525 nm also cover a large part of the emission band of NUM-5 (400–650 nm), and thus the resonance energy transfer from NUM-5 to Cr2O72−/ CrO42− could result in fluorescence quenching. Based on this strategy, the NUM-5 could detect 0.7 ppm Cr2O72− and 0.3 ppm CrO42−, respectively, in water.

10.2.1.2.  Inorganic-anion sensing Fluoride (F−) is one of the most toxic and dangerous inorganic anions, even at trace levels. Yin and coworkers developed a novel boric-acid-functional Ln-MOFbased ratiometric fluorescence strategy for F− determination.47 As shown in Fig. 10.2, the Ln-MOF (Eu-MOF 1) was synthesized with 5-boronoisophthalic acid (5-bop) as the functional ligand and Eu3+ ions as the metal node. In commonly used Ln-MOFs, the organic ligand is excited by UV photons to produce its triplet state, which then excites Ln-ions for their emission. However, in this study, the introduced boric acid group is electron-deficient and could control the energy level of π-conjugated ligand, thereby decreasing the energy transfer efficiency from the triplet state of 5-bop to Eu3+ ions. With boric acid, Eu-MOF 1 showed dual emission centers from both 5-bop (320−500 nm) and Eu3+ ions (590−750 nm) at the single excitation of 275 nm. Moreover, boric acid is used to recognize F− as a free accessible site, because it can identify diol and Lewis base specifically, such as F−.48 The sensing probe Eu-MOF 1 can detect F− ions using the ratiometric fluorescence strategy, in which the ratio of emission intensity of 366 nm and 625 nm gradually increases when the F− concentration increases. The proposed fluorescence detection strategy exhibits a low detection limit of 2 μM, and it is successfully used to determine F− in river and underground water samples.

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Figure 10.2.    Schematic of the F− sensing mechanism. The inset represents the spectra and fluorescence signal. Reproduced with permission from Ref. [47]. Copyright © 2017, American Chemical Society.

Monitoring and controlling free ClO− ions in drinking water is greatly important, because high levels of ClO− ions may produce disinfect byproducts (DBPs), which are harmful to human health. Recently, Lu and coworkers proposed a novel fluorescent sensing platform to detect free chlorines based on MOF hybrid materials.49 The NH2-MIL-53(Al) was synthesized by a facile one-step hydrothermal treatment of AlCl3·6H2O and NH2-H2BDC in water, which used urea as a modulator. The as-synthesized NH2-MIL-53(Al) nanoplates exhibited excellent water solubility and stability. The strong fluorescence of nanoplates was significantly suppressed after the addition of ClO−. The mechanism study suggested that the energy transfer through the N−H···O−Cl hydrogen-bonding interaction between the amino group and ClO− ions plays the key role in the fluorescence suppression. The sensor has a good detection limit of 0.04 μM and a wide detection range of 0.05–15 μM. The recovery test using tap water and swimming pool water showed recoveries in the range of 97%−101%. Cyanide ion (CN−) is another highly toxic and lethal contaminant, which occurs in nature and can pollute water. The Ghosh group developed a hydrolytically stable MOF-based fluorescence sensor for CN− recognition.50 In this report, an MOF with porous channels was used as the loading platform of the guest molecule, such as dye. As shown in Fig. 10.3, the synthesized anionic bio-MOF-1 is incorporated with a cationic dye (3, 6-diaminoacridinium cation (DAAC)) inside

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Figure 10.3.    Reaction-based approach with bio-MOF-1⊃DAAC prompted by CN− induces a turnon fluorescence response. Reproduced with permission from Ref. [50]. Copyright © 2017, Royal Society of Chemistry.

the porous channel through the conventional cation exchange process. After encapsulation in bio-MOF-1, the fluorescence emission of DAAC is significantly quenched due to the aggregation-caused quenching affect. However, in the presence of CN− ions, the electrophilic position (C9 position) of the DAAC molecule is attacked by CN− via the Michael Addition to form a covalent bond, rendering the cationic dye neutral. This neutral dye, which is also highly fluorescent in the aqueous phase, then escapes from bio-MOF-1 to the solution, which induces a “switch on” signal. Thus, the pure aqueous phase recognition of highly toxic CN− could be achieved by bio-MOF-1⊃DAAC.

10.2.1.3.  Organic-compound sensing Organic compound pollutants in water include various hydrocarbons, aromatic substances, chlorine compounds, and surfactants.1 Recently, a Zn-based MOF of pyromellitic acid (Zn-BTEC) was developed and used as a platform for the selective and quantitative detection of chlortetracycline (CTC), one in the broad spectrum antibiotics.51 As shown in Fig. 10.4, Zn-BTEC exhibits a weak fluorescence and the addition of trace CTC greatly enhances the emission, which shows bright fluorescence. The turn-on detection mechanism was based on the interaction between CTC and Zn-BTEC. The compound CTC assembles into the rigid MOF frame and aggregate induces a greatly enhanced aggregation-induced emission (AIE) of CTC molecules. This AIE effect, first reported by the Tang’s group, is described as a photophysical phenomenon in which a series of materials that are non-emissive in dilute solutions become highly luminescent when their molecules

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Figure 10.4.    Schematic of CTC sensing based on Zn-MOF material. Reproduced with permission from Ref. [51]. Copyright © 2019, American Chemical Society.

are aggregated in concentrated solutions or casted into solid films.52 When CTC is defused into the porous channels of Zn-BTEC, the coordination between the zinc atoms and CTC molecules could trap CTC molecules, and then the AIE of the CTC effect was induced by π−π stacking. Thus, the amount of CTC could be determined by the fluorescence enhancement. Furthermore, a paper sensor based on the combination of Zn-BTEC and a polyvinylidene fluoride microporous filter membrane was prepared for the visual and rapid detection of CTC. Zhang and coworkers reported a nanosize IRMOF-3, which exhibited high fluorescent properties for detecting 2,4,6-trinitrophenol (TNP).53 The IRMOF-3 was prepared by an electrochemical strategy, and the reaction system contained a zinc plate as the anode, a copper plate as the cathode, tetrabutylammonium bromide (TATB) as the supporting electrolyte, and DMF-ethanol as the solvent. The fluorescence emission of IRMOF-3 could be quenched by TNP due to the π−π interactions between them, which effectively fastened the TNP on the surface of IRMOF-3. This is beneficial for the electron transport from the conduction band of the IRMOF-3 to the lowest unoccupied molecule orbital (LUMO) energy of the TNP. Furthermore, the UV absorption bands of TNP and the fluorescence spectrum of IRMOF-3 partly overlap and thus induce the fluorescence filtration effect, which is another possible mechanism for the fluorescence quenching of IRMOF-3. Zhang’s group recently reported a novel MOF-based luminescent sensor for sensing water pollutants, including pesticides, antibiotics, and Fe3+ ion.54 In this report, the luminescent MOF [Mg2(APDA)2(H2O)3]·5DMA·5H2O (MgAPDA) is synthesized by amino-decorated bridging ligand H2APDA and Mg2+, which forms

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a 3D structure with hexagonal channels. The microporous channels are decorated with Lewis-base amino sites and uncoordinated O atoms, facilitating the anchor and recognition of various analytes. The 2,6-dichloro-4-nitroaniline (DCN) and nitrofurantoin (NFT) molecules, which belong to the pesticide and the antibiotic, respectively, could effectively quench the emission of MgAPDA. Moreover, the UV−vis absorption spectrum of Fe3+ and the emission spectrum of Mg-APDA largely overlap, indicating the energy transfer effect may lead to luminescence quenching. To realize convenient and cyclic fluorescent determination of nitrobenzene (NB), Han’s group reported a stable fluorescent nanofibrous membrane (NFM) by growing in situ Eu-MOF crystals on electrospun polyacrylonitrile (PAN) nanofibers modified with g-aminobutyric acid (GABA) (Fig. 10.5(a)).55 The PAN is employed as a matrix due to its good mechanical strength, excellent water insolubility, good hydrophilicity, and high chemical stability. The modification of GABA can introduce many coordination sites (carboxyl groups) onto the PAN NFM to further anchor the Eu-MOF particles. The prepared PAN–GABA@ Eu-MOF NFM was fabricated as a fluorescent test paper for detecting NB. Under excitation at 234 nm, the Eu-MOF NFM exhibited a red color, with four typical emission peaks at 593, 618, 650, and 699 nm, which originated from the characteristic emission 5D0-7FJ (J=1–4) transitions. Upon the addition of NB, the emission is quenched due to the energy competition between Eu-MOF and NB. The existence of NB makes the energy absorbed by BTC3− transfer to Eu3+, which inhibits the antenna effect and results in emission quenching. The test paper can be used multiple times (10 cycles) with constant quenching efficiency and a fast “off–on” fluorescence-switching process (Fig. 10.5(b) and (c)). (a)

(b)

(c)

Figure 10.5.    (a) Fabrication process of a PAN–GABA@Eu-MOF NFM. (b) Reversibility test of the fluorescent test paper with NB and methanol. (c) Performance of the test paper with ‘‘off–on’’ fluorescence-switching (10 cycles). Reproduced with permission from Ref. [55]. Copyright © 2019, Royal Society of Chemistry.

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10.2.2.  Electrochemical sensors using MOFs Electrochemical sensing is based on the redox reaction of the analyte in an electrochemical system. The virtues inherent in this system include high sensitivity and selectivity, facile miniaturization, quick responses, and low cost, thereby presenting significant potential for in situ detection.56,57 The electrochemical measurement is carried out using a three-electrode system consisting of a working electrode, a counter electrode, and a reference electrode. The number analytes involved in the reaction can be determined by measuring the current, electric potential, or other electrical signals.16 The electrochemical-sensing performance of an electrode can be enhanced by modifying the surface chemistry, and thus the selection of the sensing material on the sensing platform interface is key to improving its sensitivity and selectivity. Various nanomaterials with different characters have been employed in electrochemical sensors,58 including carbon materials (e.g., graphene, carbon nanotubes),59–62 metals and metal oxides,63–65 and polymers.66 By taking advantage of their high surface area, porous structure, good absorbability, excellent compatibility, and high catalytic activity, MOFs show great potential as electrochemical-sensing interface modifiers;6,67,68 however, most MOFs are considered to be wide band gap semiconductors with limited conductivity, and they exhibit relatively low stability in aqueous solution due to the reversible nature of the coordination bonds.16 Hence, improving their conductivity and stability is necessary for their application in electrochemical sensors. One common approach for overcoming these defects is to combine MOFs with other functional materials such as carbon or a metal-based material that has high conductivity or stability in aqueous media.69–72 Since the rapid development of MOFs and their composite materials, many MOF-based electrochemical sensors have been reported for environmental monitoring.16,73–75

10.2.2.1.  Ion sensing Lead ion (Pb2+) is one of the most toxic and commonly found heavy metals in water systems. Monitoring Pb2+, especially in trace amounts, is important for protecting public health and ecosystems. Recently, Ju’s group designed a DNAfunctionalized iron-porphyrinic MOF (GR−5/(Fe−P)n-MOF) composite as a probe for the electrochemical determination of Pb2+.67 As illustrated in Fig. 10.6, the DNA GR–5 is linked to a (Fe−P)n-MOF with a robust Au–S bond with surface-deposited AuNPs. In the presence of Pb2+, GR−5 could be specifically cleaved at the ribonucleotide (rA) site, which produces short (Fe−P)n-MOFlinked oligonucleotide fragments that can be hybridized with hairpin DNA (HP) immobilized on the electrode surface. Due to the mimic peroxidase property of (Fe−P)n-MOF, an enzymatically amplified electrochemical signal would be

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(a)

(b)

Figure 10.6.    Schematics of (a) preparation of the GR−5/(Fe−P)n-MOF probe, and (b) the electrochemical sensing of Pb2+. Reproduced with permission from Ref. [67]. Copyright © 2015, American Chemical Society.

obtained in 3,3′,5,5′-tetramethylbenzidine (TMB) and H2O2. The Pb2+ sensing with the GR−5/(Fe−P)n-MOF showed both the recognition behavior of GR−5 to Pb2+ and excellent mimic peroxidase ability. This sensor showed an LOD of 0.034 nM to Pb2+. Wang and coworkers developed an electrochemical-sensing platform based on a 3D graphene aerogel (GA) and UiO-66-NH2 composites for the simultaneous detection of multiple heavy-metal ions in water.76 The composites were synthesized by in situ growth of UiO-66-NH2 crystals on a 3D porous GA structure. GA not only serves as the backbone for MOF growth but also improves the conductivity of the composite. UiO-66-NH2 acts as a binding site for heavymetal ions (Cd2+, Pb2+, Cu2+, and Hg2+) due to the interaction between hydrophilic groups and metal cations. Based on the signal amplification effect of GA/ UiO-66-NH2, the detection limits, as measured by the differential pulse

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voltammetry (DPV) technique, of each metal ion were 9 nM for Cd2+, 1 nM for Pb2+, 8 nM for Cu2+, and 0.9 nM for Hg2+, respectively. The proposed electrochemical sensor was also employed to examine Cd2+, Pb2+, Cu2+, and Hg2+ amounts in real samples, including river water, soil, and vegetables, with recoveries from 86.7% to 104.7%. A highly porous titanium-based MOF (NH2-MIL-125) was reported for the determination of Mn2+ ions.77 First, the Mn2+ ions were oxidized by the MOF under the visible light condition, followed by cathodic stripping voltammetry (CSV)-based detection of the oxidized manganese product. The proposed sensing platform was used to measure Mn2+ in Chinese tea samples, and the results (Mn2+ content of 623.4 μg g−1) were in good agreement with those obtained by destructive wet analysis using the flame atomic absorption spectrometric (FAAS) method (625.5 μg g−1). Nitrite ion (NO2−) is an important contaminant in water, because excessive amounts may cause blue baby syndrome, hypertension, and stomach cancer, among other diseases. Mobin group designed a hybrid MOF/reduced-graphene oxide (rGO) electrode for the electrocatalytic oxidative determination of nitrite; the scheme graph is shown in Fig. 10.7.78 In this work, Cu-MOFs were stacked with rGO using a simple ultrasonication method. The electrode modified with Cu-MOF/rGO composites exhibited better electrocatalytic performance for nitrite oxidation (LOD: 0.033 mM) than that of the MOF electrode or bare electrode. The improved sensing performance was due to the increased conductivity of MOF with rGO. Additionally, this sensor showed good selectivity toward nitrite in the presence of common salts like CH3COONa, KCl, MgSO4, CaCl2, NaClO4, and

(a)

(b)

Figure 10.7.  (a) Schematic of the sensor electrode structure. (b) Amperometric test for the Cu-MOF/rGO/GCE at different concentrations of nitrite in 0.1 M PBS. The inset shows the calibration plot of current vs. concentration. Reproduced with permission from Ref. [78]. Copyright © 2016, Royal Society of Chemistry.

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NH2-UiO-66/RGO

GO

Zr4+

Ligand ion

Thermal reduction NH2-UiO-66 Anodic Stripping

Cu2+

Anodic

Cu

Stripping

Ciprofloxacin(Cip)

н Cip

Cu2+

Cu(Cip)2

Figure 10.8.   Schematics of the NH2-UiO-66/RGO synthesis procedure and the electrochemical detection of Cip with the aid of Cu2+. Reproduced with permission from Ref. [80]. Copyright © 2019, American Chemical Society.

KNO3. This sensor was also tested with NO2− spiked pond water with recoveries of 100%–120%.

10.2.2.2.  Organic-compound sensing Antibiotics are common organic contaminates that are difficult to be degraded in water environment.79 Our group recently developed a Zr(IV)-MOF and RGO ­composite (NH2-UiO-66/RGO)-modified electrochemical sensor for detecting ciprofloxacin (Cip) based on the complexation reaction of Cip and Cu2+.80 Although Cip belongs to electroactivity substances, the electro-oxidation of Cip normally require the high catalytic activity of electrode, and thus the relative low sensitivity limits the direct electrochemical determination for Cip. In our detection strategy, as shown in Fig. 10.8, the Cu2+ exhibits a strong oxidization current on the NH2-UiO-66/RGO-modified electrode using the anodic stripping voltammetry (ASV) technique, which decreased after the addition of Cip due to the formation of complex between Cip and Cu2+. The change of stripping peak current of Cu2+

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is employed as the signal for Cip determination, which shows much higher sensitivity than the direct electrochemical oxidation of Cip. The NH2-UiO-66/RGO composite possessed a large specific surface area, high active sites, and electrical conductivity. Meanwhile, the amino group of the composite was beneficial to the absorption of Cu2+, and thus, the composite effectively enhanced the sensing performance of the electrochemical sensor. Upon the optimal condition, the sensor could recognize trace amounts of Cip as low as 6.67 nM. The 2,4-dichlorophenol (2,4-DCP) molecule, which belongs to chlorinated phenolic compounds, is toxic even at the trace level. These compounds enter the environment by degradation and metabolism of agricultural and food chemicals such as pesticides, insecticides, plant growth regulators, and food preservatives.81 A copper-1,3,5-benzenetricarboxylic acid [Cu3(BTC)2]-modified carbon paste electrode (CPE) was reported for the sensitive determination of 2,4-DCP via the electrochemical technique.82 Using cyclic voltammetry (CV) and DPV methods, the oxidation peak at 0.694V, corresponding to the oxidation of 2,4-DCP, was used to analyze the presence of 2,4-DCP. The reported sensor exhibited high selectivity toward 2,4-DCP from a mixture of other anions, cations, phenolic complexes, and other organic pollutants containing chlorine and hydroxyl groups. However, the sensor experienced interference problems in the presence of phenol and resorcinol. When the concentrations of phenol and resorcinol were greater than 1 ppm, the oxidation peak current decreased; the limit of detection for 2,4-DCP was 9.0 nM. The sensor was also applied in reservoir water samples, with recoveries of 80%–90%. The bisphenol A (BPA) is an endocrine disruptor which has been linked to many health concerns, including cancer and reproduction problems.83 Rinaldi’s group fabricated a BPA electrochemical sensor based on a Cu-MOF@AuNp composite,64 which effectively increased the electroactive surface area of the bare CPE electrode by 2.3 times. Meanwhile, when compared with bare CPE and Cu-MOF/ CPE electrodes, the AuNp@Cu-MOF/CPE electrode exhibited the largest oxidation current of BPA. It is worth mentioning the presence of AuNp plays an important role in enhancing the analyte signal due to their high electrocatalytic activity and electro conductivity. Using the DPV method, the electrochemical sensor could quantify the BPA amount as low as 37.80 μM. Catechol (CT), resorcinol (RS), and hydroquinone (HQ) are three typical dihydroxybenzene isomers (DBIs) of phenolic compounds, which are highly toxic and can cause serious harm to the water environment and human body.84 HQ, CT, and RS have similar chemical structures and often coexist; thus, it is difficult to analyze them simultaneously. Therefore, constructing an electrochemical sensor for HQ, CT, and RS discrimination and detection is major challenge. Recently, Li et al. reported a sandwich MOF structure synthesized on rGO (M@Pt@M-rGO) for simultaneous determination of CT, RS, and HQ.85 The MOF is synthesized by Cr3+ and ligand H2BDC using the hydrothermal method and the M@Pt@M-rGO

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is prepared by an in situ synthesis process. The combination of Pt NPs and rGO effectively improves the adsorption capacity and conductivity of MOF. Upon the optimal condition, the M@Pt@M-rGO-modified electrode could realize selective and sensitive determination of CT, RS, and HQ, with detection limits of 0.015 μM, 0.032 μM, and 0.133 μM, respectively.

10.2.3.  Other MOF-based sensors Colorimetric sensors are commonly used to determine water contaminants based on the chromogenic reaction of colored compounds. The component and amount of target compounds can be determined by measuring the color change.86,87 MOFbased colorimetric sensors for detecting water contaminants have been also reported. Xian’s group synthesized a Pt nanoparticle (Pt NP)-decorated UiO66-NH2 composite for the colorimetric sensing of Hg2+.88 As shown in Fig. 10.9, the UiO-66-NH2 offers a stable substrate for loading Pt NPs and the porous structure of UiO-66-NH2 leads to uniformly dispersed Pt NPs. The Pt NPs possess highly peroxidase-like activities and can oxidize 3,3′,5,5′-tetramethylbenzidine (TMB) to oxTMB in the presence of H2O2, which exhibits a blue color. However, after the addition of Hg2+, it shows an inhibitory effect on the catalytic oxidation of TMB due to the interaction between Hg2+ and Pt. As a result, partial Pt NPs changed to Pt2+ and thus the amount of oxTMB decreased, inducing a faded blue color. Based on the strategy, the colorimetric probe could quantitatively measure Hg2+ in the linear range from 0 to 10 nM, with a detection limit of 0.35 nM. Another MOF-based colorimetric sensing platform for monitoring Hg2+ was developed by Gu’s group.89 In this assay, a chromophoric Ru complex was doped

Figure 10.9.    Measurement of Hg2+ based on the peroxidase-like activity of Pt NP@UiO-66-NH2 nanocomposites and the inhibition effect of Hg2+. Reproduced with permission from Ref. [88]. Copyright © 2017, American Chemical Society.

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in the porous MOF structure RuUiO-67 and the composite was exploited as the sensing probe. It was found that thiocyanate-bearing dyes in an Ru(H2bpydc)(bpy) (NCS)2 (H2L) complex exhibit strong absorption in the wavelength range of visible light and could specifically interact with Hg2+ due to the strong affinity between Hg2+ and thiocyanate groups in the dye. Thus, upon the addition of Hg2+, a concomitant red-to-yellow color change of the probe suspension can be recognized by naked eye, and the detection limit for Hg2+ was as low as 0.5 μM with UV-vis spectroscopy. In this case, the RuUiO-67 served as the recognition site and signal indicator. The developed probe also exhibited high selectivity toward Hg2+, even in the presence of interfering metal ions. Surface-enhanced Raman-scattering (SERS) is a promising spectroscopic technique for biological and chemical sensing due to its high sensitivity and highly informative spectra characteristics.55 It is well known that the SERS signal strongly relies on the interaction and distance between the analyte molecule and metallic nanostructure. Li’s group recently reported an Au NP-embedded MOF structure for the SERS detection of organic pollutant p-phenylenediamine in environmental water and tumor marker alpha-fetoprotein in human serum. The MOF material (MIL-101) possessed high specific surface area, large pore diameter, and high stability in solution and served as a stabilizing host material for Au NPs. The Au NP/MIL-101 composites combined the localized surface plasmon resonance properties of Au NPs and the high adsorption capability of MOF. A highly sensitive SERS substrate was constructed by the effective preconcentration of analytes near the electromagnetic fields at the SERS-active metal surface. The substrate also showed high stability and reproducibility due to the protective shell of the MOF. This sensor showed good linearity of 1–100 ng mL−1 for p-phenylenediamine (recoveries: 80.5%–114.7%) and 1−130 ng mL−1 for alpha-fetoprotein.

10.3. Challenges and Outlook In this chapter, we discussed various fluorescent and electrochemical MOF-based sensors for monitoring water contaminants. As discussed, MOF-based materials have shown outstanding sensor performance, which could be improved by combining it with other functional materials. Although MOF-based materials have shown significant potentials in sensors, the facile, visual, and accurate detection of trace pollutants remains challenging for field applications and real-water sample analysis. Therefore, much work is still needed to improve sensor performance and applicability. New water contaminants such as persistent organic pollutants (POPs), DBPs, and micro plastics are rapidly emerging, but we still lack accurate and rapid detection methods for these and many water other contaminants. The use of MOF materials with highly designable and controllable structures and properties show great potential for use in chemical sensors for such emerging contaminants.

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On the other hand, for MOF material, its function in each sensing platform should be better understood to take advantage of its benefits and to assist in sensor design and performance optimization. Meanwhile, it is worthwhile to broaden the MOF category with new useful properties such as plasmonic, electrical, thermo-chromic properties, which will create more opportunities in sensor design and integration. For electrochemical sensors, the sensing materials not only need to have high activity but also acceptable conductivity. Therefore, the conductivity of MOF should be increased by either developing conductive MOF materials or combining MOF with other conductive substrates. Moreover, the stability of MOF-based sensing materials needs improvement, especially for sensors that work under an acid condition. Finally, most of the sensor demonstrations in this chapter were conducted with lab-prepared samples, and thus sensor performance in a complex environmental media (e.g., real water) needs to be evaluated, with special attention on the selectivity and reliability of the sensors.

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86. J. Wang, Y. Fan, H.-W. Lee, C. Yi, C. Cheng, X. Zhao, and M. Yang. Ultrasmall Metal–Organic Framework Zn-MOF-74 Nanodots: Size-Controlled Synthesis and Application for Highly Selective Colorimetric Sensing of Iron(III) in Aqueous Solution, ACS Appl. Nanomater., 2018, 1, 3747–3753. 87. C. C. Chang, G. Q. Wang, T. Takarada, and M. Maeda. Iodine-Mediated Etching of Triangular Gold Nanoplates for Colorimetric Sensing of Copper Ion and Aptasensing of Chloramphenicol, Acs. Appl. Mater. Inter., 2017, 9, 34518–34525. 88. H. P. Li, H. F. Liu, J. D. Zhang, Y. X. Cheng, C. L. Zhang, X. Y. Fei, and Y. Z. Xian. Platinum Nanoparticle Encapsulated Metal-Organic Frameworks for Colorimetric Measurement and Facile Removal of Mercury(II), ACS. Appl. Mater. Inter., 2017, 9, 40716–40725. 89. Z. Wang, J. Yang, Y. S. Li, Q. X. Zhuang, and J. L. Gu. Zr-Based MOFs Integrated with a Chromophoric Ruthenium Complex for Specific and Reversible Hg2+ Sensing, Dalton T., 2018, 47, 5570–5574.

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Chapter 11

Smartphone-Based Sensors for On-Site Water Quality Monitoring Xiangheng Niu*, Nan Cheng†, Dan Du§ and Yuehe Lin‡ School of Mechanical and Materials Engineering, Washington State University, Pullman, WA 99164, USA [email protected]

*

[email protected]

†

[email protected]

‡

[email protected]

§

11.1. Introduction There is no doubt that our living environment is facing increasing threats from various pollutants. In particular, our water environments are especially at high risk of contamination by pollutants such as pesticide residues, microorganisms, and toxic ions. Monitoring water quality is critical not only for the global ecosystem but also for human health, and thus we urgently need to develop feasible principles, methods, and devices to detect these pollutants in water. Various analytical techniques such as chromatography, spectroscopy, and electrochemistry have been explored for environmental analysis, and various strategies and instruments have also been fabricated for laboratory measurement. However, portable devices that can be used expediently for on-site rapid detection are still very limited. Facilitating laboratory methods and instruments to in-field environmental monitoring is highly desired. The emergence of smartphones brings new opportunities for the on-site and rapid detection of contaminants in water. Statistically, there were more than 2 billion smartphone users worldwide in 2016, and it is estimated this number will reach 6.1 billion by 2020.1 A common smartphone is usually equipped with camera, 331

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photoflash lamp, Bluetooth, and USB port, providing an ideal terminal for optical measurement. Thanks to the popularity and multi-functionalization of smartphones, scientists are combining feasible sensing strategies with portable smartphones to develop easy-to-use optical systems for various analytical applications.2 This chapter provides a summary of the sensors that can be integrated with smartphones for monitoring water quality. Recent advances in portable devices for detecting pesticide residues, microorganisms, and toxic ions will be discussed. Smartphone-based devices combine the advantages of high-performance sensing methods and portable readers, offering an emerging class of tools for rapid, lowcost, and on-site environmental monitoring.

11.2. Pesticide Residues To enhance the output and quality of agricultural products, pesticides are used in many countries throughout the world. However, uncontrolled use of these pesticides causes serious environmental pollution. Developing rapid, low-cost, and easy-to-use devices to efficiently monitor pesticide residues has been a hot research area since the 1980s. Although many feasible principles and strategies have been explored for pesticide detection, fundamental and technological advances are still required to promote the practical applications of these approaches from the laboratory to the field. Since smartphones can provide a high-quality camera for signal reading and high-speed computing ability for data processing and transmission, their integration with pesticide sensors shows great promise for on-site environmental monitoring. In this regard, our lab developed an aptasensor for in-field quantification of multiple pesticides using a fluorophore-quencher pair and a smartphone spectrum reader.3 In our design, quantum dot (QD) nanobeads with high fluorescence quantum yield and excellent stability were employed as a fluorophore, and gold nanostars (AuNSs), which have a broader surface plasmonic absorbance than common gold nanoparticles, were selected as a powerful quencher for the QD fluorophore. As illustrated in Fig. 11.1, the positive sample (A), which contained target pesticides and AuNSs-aptamer conjugates, was first incubated at room temperature to form complexes; afterward, the solution was dropped onto the sample pad and further driven by capillary force to flow through the three branch pads. When the complexes reached the test lines, they could not be captured by the corresponding biotinylated complementary sequences (BCSs) that were immobilized on the test lines, and thus the fluorescence of the QDs fixed on the test lines was observable. When target pesticides were present (B), independent AuNSs-aptamer conjugates were caught on the test lines by the hybridization interaction between the target aptamers and corresponding BCSs, and thus the gathering of AuNSs on the test lines quenched the fluorescence of the immobilized QDs. To further

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Figure 11.1.    The aptamer-based lateral flow biosensor for detecting multiple pesticides. Adapted with permission from Ref. [3]. Copyright © from the Elsevier.

improve the biosensor’s detection performance, a 3D-printed smartphone reader with zero background was exquisitely designed to read the full emission fluorescence spectra. The resulting portable biosensor integrated the merits of aptamer, fluorophore-quencher pairs, and smartphone readers to simultaneously detect chlorpyrifos, diazinon, and malathion. Other pesticides could also be detected by the system by using different aptamers. Apart from aptasensors, specific antibodies have been employed to fabricate enzyme-linked immunosorbent assays (ELISAs) for pesticide sensing, as well.4–6 Our group built a smartphone-based optical platform for 2,4-dichlorophenoxyacetic acid (2,4-D) monitoring by using ELISA.5 The assay was based on the competitive binding of 2,4-D and horseradish peroxidase (HRP)-labeled 2,4-D toward an

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anti-2,4-D antibody fixed on the wells of a microliter plate. Here, 2,4-D and HRPlabeled 2,4-D competed for the binding sites in the antibody and were competitively bound onto the wells. After washing and further adding an HRP substrate solution containing H2O2 and 3,3′,5,5′-tetramethylbenzidine (TMB), a remarkable chromogenic reaction changing from colorless to blue occurred under the catalysis of the HRP. After adding a stop reagent to terminate the reaction, the solution color could be evaluated. Since the color intensity was inversely proportional to the level of 2,4-D, the target was able to be quantitatively determined by the kit. For the facile measurement of multiple samples, we further designed and fabricated a smartphone-based colorimetric reader for the immunoassay of 2,4-D. Compared with laboratory microplate readers, the smartphone reader, with its lower cost, lighter weight, and smaller size, was more convenient for on-site monitoring. Instead of using relatively expensive aptamers or antibodies to recognize ­target pesticides, unique interactions between certain pesticides and optical labels can also be used for sensing. Interestingly, Mei and coworkers proposed a paper sensor for thiram detection based on the specific interaction between the analyte and Cu2+-decorated NaYF4:Yb/Tm upconversion nanoparticles.7 In their research, Cu2+-doped NaYF4:Yb/Tm upconversion nanoparticles with strong emission fluorescence at around 475 nm were first fixed onto a common filter paper to form the test strip. After adding thiram, the pesticide could react with the Cu2+ species capped on upconversion nanoparticles via a coordination mechanism. It was further revealed that the disulfide bond in thiram would break and a resonated bidentate structure would form through the coupling of two S–C–S groups and one Cu2+ ion. The coordination reaction between thiram and Cu2+-doped NaYF4:Yb/Tm upconversion nanoparticles resulted in the fluorescence quenching of the latter via resonance energy transfer. The fluorescence variations could be monitored by a common smartphone, and the fluorescence intensities of their paper strips could be employed to quantitatively detect thiram using an application program installed on the smartphone. With this device, the thiram was accurately detected, with a limit of detection (LOD) down to 0.1 μM. Furthermore, the sensing system was selective to thiram and other pesticides containing N and/or S atoms, including imidacloprid, nicosulfuron, thifensulfuron, and 2,4-D, and did not lead to any quenching toward the upconversional label. Raman spectroscopy is a powerful approach that can be used to directly measure targets without the need for foreign labels or signaling molecules. The characteristic molecular fingerprints provided by this method are usually used for molecular identification and quantification. Due to its weak molecular response, many surface-enhanced Raman-scattering (SERS) substrates have been developed to monitor pesticide residues at trace or lower levels. As a typical example, Mu and coworkers developed a smartphone-based SERS system for fast and on-site detection of pesticides.8 Their system could provide different combinations of molecular fingerprints for multiple pesticides (fipronil, azoxystrobin, triazophos,

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dimethomorph, tebufenpyrad, and difenoconazole). Using the advantages of algorithms, cloud databases, and deep learning, the accurate and fast identification of these pesticides could be realized by the SERS system. More attractively, the smartphone-based system was much smaller than traditional Raman instruments, and thus showed great potential for on-site pesticide monitoring. Molecular imprinting is well known as a technology that can artificially create binding sites for template molecules (target molecules). Usually, molecularly imprinted polymers (MIPs) are produced by co-polymerizing functional monomers and cross-linkers in the presence of template molecules. After removing template molecules, recognition cavities that are complementary to template molecules in shape, size, and chemical functionality are formed in the polymer matrix, which can be employed to rebind the template molecules from external environment.9 With the “key-and-lock” feature, the artificial recognition sites formed in MIPs can offer excellent selectivity for target molecules. Due to their robustness and cost-effectiveness, MIPs are very attractive for analytical applications10 rather than biological recognition materials like antibodies and aptamers. Recently, Merkoçi’s group reported an MIP-based electrochemical device for detecting chlorpyrifos using the electrochromism property of IrOx.11 As a typical electrochromic material, IrOx turns to blue-black upon oxidation and becomes transparent upon reduction, providing reversible and persistent changes in its optical properties. In their design, IrOx NPs were first decorated onto an ITO working electrode by screen-printing, and then an MIP film covering these IrOx NPs was assembled on the surface of the electrode by thermal polymerization. If no target existed, the color of the working electrode changed rapidly when positive and negative potentials were alternately applied onto it. When chlorpyrifos molecules were present, they could be recognized by the MIP film and bind onto the surface of the working electrode, thereby increasing its electrical resistance. As a consequence, the color change of the electrode under the stimulation of a certain applied potential became much slower. Thus, both visual observation and smartphone imaging of the working electrode could be conducted to detect and quantify the pesticide. With this sensor, chlorpyrifos, ranging from 100 fM to 1 mM, was linearly determined, offering a very low LOD (0.1 pM). Thanks to the specific recognition of the MIP film toward chlorpyrifos, this sensor presented good selectivity to the target over other pesticides. It is well known that pesticides are a great risk to human health because of their interactions with enzymes, proteins, and nucleic acids in the body. Typically, organophosphorus (OP) pesticides harm human health by inhibiting the biological activity of cholinesterase. Therefore, certain biomarkers, including relevant enzymes, hydrolyzed metabolites, and phosphorylated adducts can be used to evaluate pesticide exposure.13–15 Thus, our lab designed a nanozyme-based smartphone platform for the simultaneous detection of active butylcholinesterase (BChE) and its total amount.12

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Figure 11.2.    (A) Illustration of the principle of the simultaneously immunodetection of active and total amount of BChE. (B) PtPd NPs for signal amplification. (C) Smartphone-based ambient light sensor for on-site detection. Adapted with permission from Ref. [12]. Copyright from the American Chemical Society.

As shown in Fig. 11.2, the detection of active BChE was achieved by combining a competitively immune recognition process and an Ellman assay. In detail, a BChE primary antibody was first immobilized on an immunochromatographic test strip. After adding a mixture of active BChE and OP-deactivated BChE, the two species competed for the primary antibody and then bound onto the strip. Active BChE hydrolyzed the butyrylthiocholine (BTCh) substrate to thiocholine (TCh), and the TCh product specifically interacted with colorless 5,5′-dithiobis-(2-­ nitrobenzoic acid) (DTNB) to form yellow thio-nitrobenzoic acid (TNB). For the total amount of BChE, we employed a PtPd NPs-labeled secondary antibody for a response. When all the BChE, including active BChE and OP-deactivated BChE, was recognized and captured by the primary antibody, the labeled secondary antibody was further bound onto all the BChE that were captured. The PtPd NPs labeled on the secondary antibody catalyzed the oxidation of catechol to o-quinone in the presence of H2O2, finally forming a brown polymer. Both color changes could be read by a common smartphone. The proposed platform gave excellent linear signals for active BChE, ranging from 0.1 to 6.4 nM, and the total amount of BChE, ranging from 0.05 to 6.4 nM. Due to its low cost, portability, fast response, and high sensitivity, this device has great potential for use in environmental monitoring, food security, and clinical diagnosis. To satisfy the requirements stated in the US Affordable Care Act (ACA),17 we developed a smartphone optosensing platform (SOP) for the low-cost and

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Figure 11.3.  (a) Illustrates the optical path of our SOP, (b) presents the assembled SOP, and (c) shows the SEM image of the DVD grating surface used in our SOP. Adapted with permission from Ref. [16]. Copyright © from the American Chemical Society.

convenient determination of neurotoxins16 and organophosphorus poisoning,18 in which a DVD diffraction grating was explored as a miniature spectrometer. In our SOP, we used an Apple iPhone 5 built with a rear camera. The SOP device comprised four parts (Fig. 11.3): a DVD diffraction grating, a holder for the smartphone and diffraction grating, a holder for sample locating, and an optical tube assembly. Using paraoxon as the pesticide model, the platform could linearly detect the target in the range of 5 nM–25 μM with the help of the Ellman method, providing an LOD as low as 2.9 nM. Surprisingly, we found that the low-cost DVD diffraction grating integrated with the iPhone 5 could be used as an on-site diagnosis platform, with analytical performance as good as laboratory instruments.

11.3.  Microorganisms Microbiological parameters in water rely on the detection of various bacteria.19,20 According to the EU Drinking Water Directive, indicator bacteria Escherichia coli and Intestinal enterococci should not be detected in 100 mL water.21,22 The development of water monitoring technologies has prompted increasing awareness of the harm of microorganism contaminants to human health and the environment.23 In our group, efforts for developing smartphone-based biosensing platforms for bacteria detection can be divided into cuvette-, plate-, and paper-based strategies.

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Cuvette-based strategy: We demonstrated a cuvette-based lightweight device equipped with a common smartphone for detecting Escherichia coli O157:H7.24 As depicted in Fig. 11.4, the portable fluorescent imager consisted of three parts: excitation light source, sample chamber, and signal collection module. Instead of employing complicated and expensive optical components, we used a simple filter and a low-cost focusing lens to collect the imaging data. Using 3D-printing technology, these parts were assembled together in a compact bulk. Experimentally, 100 μL of magnetic beads-conjugated primary antibody was added to 1 mL tube, followed by the addition of 50 μL of Escherichia coli O157:H7 and 10 μL of FITC-labeled second antibody (3.75 mg mL−1). The mixture was intensively mixed and incubated for 1.5 h. A magnet was used to separate the sandwich immunoassay. After washing with PBST several times, the supernatants were removed and the remaining magnetic beads were re-dispersed in 50 μL PBS and then transferred to a cuvette for

(a)

(b)

Figure 11.4.    The smartphone-based sensor with a cuvette-based strategy for detecting Escherichia coli O157:H7. (a) shows the sandwich immunosensor for E. coli O157:H7 detection, and (b) illustrates the configuration of the portable smartphone-based device. Adapted with permission from Ref. [24]. Copyright © from the Elsevier.

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measurement. The fluorescence intensity was recorded by the 3D-printed smartphone cuvette device, with an excitation wavelength of 405 nm. A Tecan Safire2 microplate reader was employed as a reference in the detection process. Compared to commercial microplate readers, our device provided lower background noise for imaging. With the sandwich ELISA, specific and fast determination of Escherichia coli O157:H7 was obtained, providing an LOD of 1 CFU mL−1. Our smartphone device based on the cuvette bioassay provided a facile, fast, and sensitive platform for fluorescent analysis of pathogens. Plate-based strategy: We also developed a smartphone-based immunosensor for fast analysis of Salmonella enteritidis using a plate-based strategy (Fig. 11.5).25 We first conjugated the streptavidin magnetic beads with a biotin-labeled capture antibody, and further integrated them with the HRP-inorganic calcium nanoflower composite. In a plate that was fitted over a magnetic separator, 50 μL of streptavidin magnetic beads and 50 μL of biotin-labeled anti-Salmonella antibody were added to 200 μL PBSA and then incubated at 37°C for 90 min. The wells were washed with PBST several times to remove the unbounded antibody. After that, 50 μL of Salmonella enteritidis was added to each well and incubated at 37°C for 60 min. Then, 50 μL of antibody-nanoflower composite was added and incubated at 37°C for 40 min. The unbound antibody-nanoflower composite was washed off, and then 50 μL of TMB substrate was added and incubated at 37°C. After reacting for 15 min, the solution

Figure 11.5.    Illustration of the smartphone-based device with a plate-based strategy for detecting Salmonella enteritidis. Adapted with permission from Ref. [25]. Copyright © from the Elsevier.

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color was monitored. The proposed bioassay could detect Salmonella enteritidis with an LOD of 1.0 CFU mL−1. The smartphone device had similar sensitivity and accuracy to common microplate readers, offering a handy platform for sensing pathogens without expensive microplate readers. Paper-based strategy: In the portable smartphone-based device using a paper-based strategy,26 Pt-Pd nanoparticles were employed as a signal amplifier in a dual lateral flow immunoassay (LFIA) for simultaneous sensing of Salmonella enteritidis and Escherichia coli O157:H7 (Fig. 11.6). The design of such a system was based on the

Figure 11.6.    Illustration of the smartphone-based device with a paper-based strategy for determination of Salmonella enteritidis and Escherichia coli O157:H7. Adapted with permission from Ref. [26]. Copyright © from the American Chemical Society.

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limitation of the Washburn’s theory and the cross-reactivity. The dual LFIA could offer the same sensitivity for multi-channel detection as single LFIA. The antibodymodified Pt-Pd nanoparticles had double recognition and signal amplification functions. Thanks to the peroxidase-mimicking activity of Pt-Pd nanoparticles, the antibody-nanoparticle conjugate produced a deep-blue color in the presence of H2O2 and TMB. To establish a portable and low-cost platform with favorable analytical performance, a smartphone-based device was further fabricated to record the results. The device consisted of a 3D-printed cover, a holder, and a smartphone accessory. After optimization, the LODs provided by the smartphone-based device were determined to be ~20 CFU mL−1 for Salmonella enteritidis and ∼34 CFU mL−1 for Escherichia coli O157:H7. The significantly enhanced sensitivity originated from the peroxidase-mimicking activity of the Pt-Pd nanoparticles for signal amplification and the design of the dual detection system with no interference.

11.4. Toxic Ions Toxic ions, including metal ions like Hg2+ and non-metal ions like F−, is a typical group of pollutants in the water environment that can cause serious damage to ecosystems and to human health, and thus various principles have been explored for monitoring them. By integrating various sensing principles with a portable smartphone, on-site environmental analysis of toxic ions can be easily achieved. Hg2+ is one of the most poisonous inorganic species which can damage the human brain, kidneys, nervous system, and endocrine system at a very low concentration. Therefore, several smartphone-based sensors have been fabricated to detect this dangerous species.27–29 Our group introduced a barometer-based sensor for Hg2+ determination.27 As illustrated in Fig. 11.7, the device is based on recording the pressure change made by O2 production in a confined chamber. We employed Au@Pt nanoparticles (Au@PtNPs) as a catalytic label to trigger the decomposition of H2O2 to O2. In the competitive aptasensor, an aptamer for Hg2+ recognition was conjugated with Au@PtNPs (aptamer-Au@PtNPs). Meanwhile, magnetic beads were labeled with some complementary chains of the aptamer (CC-MBs). With respect to the low levels of Hg2+, the aptamer-Au@PtNPs probe was bound to the CC-MBs. As a result, high pressures were detected due to the release of O2 originating from the catalytic decomposition of H2O2 by the Au@PtNPs. For high levels of Hg2+, the aptamer labeled with Au@PtNPs would bind with the Hg2+ and thus could not bind onto the CC-MBs. With this strategy, a linear correlation was gained between the pressure and the Hg2+ level within the scope of 0.25~16 ng mL−1, and the LOD was determined to be 0.22 ng mL−1. To check the selectivity of our barometerbased aptasensor, the sensor was also applied to probe other ions. The results revealed that our aptasensor had high selectivity for Hg2+ determination.

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Figure 11.7.  Illustration of the barometer-based biosensor for Hg2+ monitoring. Adapted with permission from Ref. [27]. Copyright © from the American Chemical Society.

Furthermore, the analyte was spiked into practical water with various levels and then detected by the aptasensor. The detection results from our aptasensor were in agreement with those from ICP-MS, verifying that our aptasensor was feasible for environmental analysis. In addition to Hg2+, several smartphone-based sensors have also been explored for detecting toxic chromium and uranyl ions.30–33 A controlled level of fluoride in drinking water is useful for human health. However, when its concentration is beyond the limit, long-term drinking of water contaminated by high-level fluoride will lead to a chronic disease called fluorosis. For this reason, several smartphone-based systems have been established to monitor fluoride in drinking water and in underground water.34,35 Nath’s group developed an easy-to-use, low-cost, and portable fluoride sensing system using a smartphone as the light intensity detector and its LED flashlight as the optical source (Fig. 11.8). The detection process was based on the SPADNS colorimetric strategy, in which fluoride reacted with the colored zirconyl-SPADNS reagent and formed a colorless and stable complex anion (ZrF62−). As the fluoride concentration increased, it bleached the colored reagent and made it lighter in color.

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Figure 11.8.    The configuration of the smartphone-based sensor for fluoride analysis in drinking water. Adapted with permission from Ref. [34]. Copyright © from the American Chemical Society.

A  specific wavelength from the LED flashlight was used to interact with the sample, and the transmission response was detected by the system. Furthermore, an Android application was fabricated to convert the optical response into readable data. The portable smartphone-based sensing system could be operated by an inexperienced person, making it very convenient, accessible, and easy-to-use for in-field water monitoring. In addition, Wu’s group used a smartphone to detect thiosulfate.36 They employed tannic acid (TA) as a stabilizer of the AgNPs label. As illustrated in Fig. 11.9, when S2O32− existed, a redox reaction first occurred between the phenolic hydroxyl of TA and the target. Then, the reaction between S2−, produced from the reduction of S2O32−, and Ag+ formed a layer of Ag2S on the AgNPs surface, making a notable color change from yellow to brown. With the mechanism, S2O32− was monitored rapidly and with good selectivity. The detection limits were 1.0 μM by visual observation and 0.2 μM by UV-Vis spectroscopy. Furthermore, a smartphone system was fabricated to make the sensor accessible for in-field monitoring. In Wu’s study, a sample containing S2O32− was added to the AgNPs solution, and a remarkable color change was recorded by an APP, which could directly output the RGB values. Thus, the RGB values could be used as functions to determine the level of S2O32− in the sample.

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Figure 11.9.    The principle of the AgNPs-based colorimetric sensor for sensing of S2O32−. Adapted with permission from Ref. [36]. Copyright © from the American Chemical Society.

11.5. Conclusions and Perspectives This chapter discussed the advances in smartphone-based sensors for monitoring water quality. The popularization of smartphones has brought an obvious change from traditional laboratory measurements that require expensive and bulky instruments to fast, on-site monitoring using portable smartphone readers. Corresponding hardware and software accessories, including optical path systems and application programs, have been designed and integrated, making it possible for rapid, convenient, low-cost, and in-field environmental monitoring. Despite the progress that has been made thus far, additional work is still needed to advance the practicability of these smartphone-based sensors. First, most smartphone-based sensors are currently based on the measurement of optical changes (e.g., colorimetric, fluorescent), and very few smartphone devices are based on other detection techniques, such as electrochemistry. Exploring new measurement patterns with portable smartphones may bring some unexpected outcomes in performance and applicability, Second, many external factors affect the use of currently developed smartphone-based optical sensors during on-site monitoring. Typically, different results may be obtained for the same sample when detected by smartphones with different light sources, camera pixels, scanning distances and angles; therefore, standardizing the operation conditions for smartphonebased sensors is necessary. Last, but not least, cloud computing, big data analysis, and remote controls are expected to be integrated with smartphone-based sensors for better water monitoring and management of the water environment.

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Overall, the combination of feasible detection principles and methods with ­smartphones and accessories is opening a broad avenue for the on-site and highperformance measurement of environmental pollutants in water.

Acknowledgments The work was financially supported by the Centers for Disease Control and Prevention/National Institute for Occupational Safety and Health (CDCP/NIOSH) (Grant No. R01OH011023-01A1).

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Chapter 12

Large-Scale Assembly of Nanostructure-Based Sensors for Real-Time Bio-Analysis in Water Inkyoung Park*,‡, Jin-Young Jeong*,§, Viet Anh Pham Ba†,¶, Narae Shin*,ǁ, Dong-guk Cho*,** and Seunghun Hong*,†† Department of Physics and Astronomy, and Institute of Applied Physics, Seoul National University, Seoul 08826, Korea

*

Department of Environmental Toxicology and Monitoring, Hanoi University of Natural Resources and Environment, Hanoi, Vietnam

†

[email protected]

‡

[email protected]

§ ¶

[email protected] [email protected]

ǁ

[email protected]

**

[email protected]

††

12.1. Introduction Recent developments in nanotechnology allow one to build advanced functional devices based on novel nanoscale structures such as carbon nanotubes, nanowires, nanoparticles, and graphene. An important application of such nanostructurebased functional devices can be highly sensitive and selective biochemical sensors operating in water environments.1–5

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Nanostructure-based biosensors take advantages of versatile novel properties of nanostructures. For example, the large surface-to-volume ratio of nanostructures such as carbon nanotubes enables one to build highly sensitive biosensors which can overcome the sensitivity limitations of conventional biosensors.4,5 In addition, the combination of biomolecules with inorganic nanostructures allows one to build highly selective biosensors that can provide a real-time response to specific analytes in water. However, a stumbling block holding back its practical applications can be a lack of mass production methods for such nanostructure-based biosensors. Since most of nanostructures were first synthesized in a solution or a gas environment, one should assemble individual nanostructures onto a specific location of a device substrate to build functional devices, which is a time-consuming task and can significantly increase the fabrication cost of nanostructure-based devices. Considering that price can affect the practical application of biosensors, such a fabrication problem can be a critical issue. In this review chapter, we will introduce versatile advanced bio- and chemical sensors based on nanostructures and explain their advantages over conventional sensors for water analysis. Furthermore, we will also discuss mass production methods for nanostructure-based sensors for practical applications.

12.2. Assembly of Nanostructure-based Devices 12.2.1.  Nanostructures Nanostructures refer to materials or structures that have a nanoscale size, ranging from 1 to 100 nm in at least one dimension. According to their dimensions, nanomaterials can be classified into zero-dimensional (0D), one-dimensional (1D), two-dimensional (2D), and three-dimensional (3D) nanostructures (Fig. 12.1).7 zero-dimensional nanostructures refer to round-shaped nanoparticles, such as core-shell, hollow, and composite. 1D nanostructures are in the form of nanotubes and nanowires, with lengths ranging from 100 nm to tens of microns. 2D nanostructures are films with nanometer thickness. Nanostructures in 0D, 1D, and 2D can be dispersed in a liquid solution, which enables further practical applications. 3D nanostructures usually include powders, fibers, multilayers, and polycrystalline materials in which the 0D, 1D, and 2D structural elements are in close contact with each other and form 3D structures. Nanostructures have unique properties, particularly due to their large surfaceto-volume ratio, which make them more reactive than the bulk forms of the same material. An important direction of current research on nanostructures is the development of novel applications to enhance the performance of conventional devices in a broad range of engineering fields. For example, the use of nanostructures in a biosensor could improve its sensitivity and response speed.3,8,9 Moreover, the

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Figure 12.1.    Various nanostructures according to their dimensions. Reproduced with permission from Ref. [7]. Copyright © from the Royal Society of Chemistry.

small size of nanostructures makes it possible to miniaturize the size of final devices. Ironically, due to their small size, the precise control of nanostructure location and arrangement is often very difficult and thus hinders the further development of nanostructure-based device applications. Recently, various techniques have been developed to control nanostructures for practical applications. Among them, recently developed techniques for manipulating nanostructures have shown interesting results.10,11 In this section, we will discuss the representative techniques for manipulating nanostructures for device applications.

12.2.2.  Directed assembly of nanostructures For nanostructure-based device applications, it is essential to assemble the ­nanostructures onto the desired location of a substrate with precise orientation. Various methods have recently been developed for the massive fabrication of nanostructure-based devices,10–12 including the versatile surface-programed assembly method. Figure 12.2(a) shows a schematic diagram depicting the representative processes for the assembly of carbon nanotubes (CNTs).13 In the method, a self-assembled monolayer (SAM) of organic molecules with non-polar groups was used to create patterns on the substrate.

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Figure 12.2.   Directed assembly of nanostructures. (a) Schematic diagram depicting “self-­ programmed assembly” of CNTs on a solid substrate. Reproduced with permission from Ref. [13]. Copyright © from the American Chemical Society. (b) SEM image of aligned SWNTs on an Au film-coated substrate. Reproduced with permission from Ref. [14]. Copyright © from Springer Nature. (c) SEM image of aligned V2O5 nanowires on a SiO2 substrate. Reproduced with permission from Ref. [15]. Copyright © from John Wiley and Sons. (d) SEM image of aligned CdS nanowires on an Au film-coated substrate. Reproduced with permission from Ref. [10]. Copyright © from the Royal Society of Chemistry.

Many studies have relied on octadecyltrichlorosilane (OTS) or octadecanethiol (ODT) molecules to form an SAM, depending on the type of substrates.12,14 A conventional photolithography technique can be utilized for SAM patterning. Firstly, in the process, a photoresist layer is coated on the substrate, and the patterns are heat-treated with a 10 min baking time. Note that long-time heating of a photoresist layer can result in residual photoresists on the substrate, which can interrupt the formation of a high-quality SAM on the substrate. Then, the substrate is immersed in the OTS (or ODT) solution for a few minutes so that a SAM of

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OTS molecules is formed selectively on the bare surface region of the substrate. The photoresist patterns are removed with acetone, and a high-quality SAM pattern on the substrate can be obtained. For assembling nanostructures onto the desired location, a well-dispersed nanostructure suspension is dropped on the substrate. The nanostructures in the suspension, such as CNTs, have a strong affinity to polar surfaces. Therefore, the patterns of non-polar molecules on the substrate can be used to guide the adsorption of CNTs on the polar surface regions of the substrate. Different SAM layers can be used depending on the surface properties of nanowires and substrates. Figure 12.2(b), (c), and (d) show the SEM images of aligned carbon nanotubes, V2O5, and CdS nanowires, respectively.10,14,15 The insets show the highmagnified SEM images of assembled nanowires, where well-aligned nanowires are clearly observed. By controlling the width of the pattern, the number of absorbed nanowires and their orientation can be controlled. Note that the alignment of nanostructures often affects the performance of nanostructure-based devices. Surface-programed assembly methods enable the large-scale assembly of practical nanostructure-based devices such as biosensors.

12.2.3.  Properties of assembled nanostructure-based devices The directed assembly method was used to fabricate various nanostructure-based devices such as field-effect transistors (FETs).8,9,14 Figure 12.3(a) shows a waferscale array of single-walled carbon nanotube (SWNT) devices fabricated using the directed assembly method.14 Since all the processing steps were performed by photolithography, including molecular patterning, it was easy to fabricate a waferscale integrated device based on nanostructures. Figure 12.3(b) shows the distribution of the conductance of multiple SWNT junctions with Au/Pd contacts.14 The graph shows the relatively uniform distribution of the conductance over 100 SWNT junctions. This result indicates the direct assembly method could be used for the reliable mass production of nanostructure-based devices. However, devices based on random networks of nanostructures have suffered from various fundamental limitations. For example, FETs based on randomly oriented SWNT networks usually have a low on–off ratio due to some metallic SWNTs in the network channels. Moreover, percolated network channels of nanotube/nanowires in devices generally show lower mobility and conductivity than the electrical characteristics of single nanowire-based devices. To solve these fundamental problems, some researchers have suggested “aligned-network” SWNT channels which have thin multiple parallel line-shape patterns.16 Figure 12.3(c) shows AFM topography images of a single 800-nm-wide channel (i) and four 200-nm-wide channels (ii) in SWNT devices. In this case, 800-nm and 200-nm channels formed random and aligned SWNT network channels,

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Figure 12.3.    SWNT-based devices and their electrical characteristics. (a) Wafer-scale fabrication of SWNT devices on a SiO2 wafer. (b) Distribution of the conductance of 100 SWNT junctions. The inset shows the optical micrograph image of the junctions. Reproduced with permission from Ref. [14]. Copyright © from Springer Nature. (c) AFM topography images of a single 800-nm-wide channel (i) and four 200-nm-wide channels (ii). (d) Distribution of electrical characterization of random and textured SWNT network channels. A total of 83 out of 100 FET devices based on random network channels exhibited a metallic behavior while 74 out of 84 devices based on textured channels had a semiconducting behavior. Reproduced with permission from Ref. [16]. Copyright © from John Wiley and Sons.

respectively. Both types of devices had a 10-μm channel length and identical total channel widths of 800 nm. Figure 12.3(d) shows the distribution of the electrical characteristics of random and aligned SWNT network channels. Among chips with a single 800 nm wide channel, only 17% chips showed semiconducting characteristic with an offcurrent lower than 10−10 A. On the other hand, ~88% of those with four 200-nm-wide channels showed an FET characteristic, which is more than a five-fold improvement in device yield. These results indicate that the orientation of nanostructures strongly affects the performance of nanostructure-based devices.

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12.3.  Label-free Biosensors 12.3.1.  Chemical sensors Chemical sensors are analytical devices used to detect the presence or concentration of given target molecules. Chemical sensors can be applied to detect versatile target analytes such as metal ions, toxic gases, and organic compounds. These sensors consist of two main parts, a target-probing system and a signal transducer.8,17–22 The target-probing system can selectively interact with given targets and transmit the information of the interaction to the signal transducer. Therefore, the sensitivity of a chemical sensor often depends on materials used to build the transducer. Recently, CNTs have been extensively used as a semiconducting material for the fabrication of chemical and biological sensors. For example, FET sensors based on SWNT networks were reported as nanoscale sensors for the selective, sensitive, and fast detection of mercury ions (Hg2+) in aqueous solutions.17–19 Figure 12.4(a) depicts a nanoscale sensor based on SWNT networks. Here, the SWNT networks were used as the main parts of signal transducers to detect Hg2+ in water via the redox reactions between SWNTs and Hg2+. When SWNTs give electrons to Hg2+, the consequential hole injection increases the conductance of SWNT networks due to the p-type characteristics of SWNTs. Figure 12.4(b) shows the increased source-drain current of a SWNT sensor after the addition of Hg2+ solutions at concentrations from 1 pM to 10 mM. In this work, a significant change (ΔG/G0 ∼19%) in the source–drain current was detected at 1 pM, which was defined as the detection limit of the sensors. This is even below the allowable limit of Hg2+ concentration (~10 nM) in the standards for drinking-water quality

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Figure 12.4.    Detection of Hg2+ in aqueous solutions using nanoscale SWNT sensors. (a) Schematic diagram depicting an SWNT sensor and the mechanism for Hg2+ detection. (b) Real-time current changes of a 100-nm-wide SWNT sensor during the addition of Hg2+ solutions at various concentrations. Reproduced from Ref. [18] with permission from IOP Publishing. (c) Response of a 2-μm-wide SWNT sensor to various metal ions at concentrations from 1 nM to 1 mM. Reproduced with permission from Ref. [17]. Copyright © from the American Chemical Society.

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established by the US Environmental Protection Agency (EPA). Figure 12.4(c) presents the selectivity of SWNT sensors for the detection of Hg2+. This result shows that only the addition of Hg2+ solutions increased the conductance of the sensor, indicating the high selectivity of the sensor. On the other hand, the decreased conductance of the sensor in the presence of Pb2+ at high concentrations (10 µM to 1 mM) was due to the field effect by the Pb2+ adsorption on the SWNTs. This induces additional negative charges in the SWNTs, thereby changing the conductance of p-type SWNTs. In addition, SWNT networks were also exploited to build refreshable and flexible gas sensors.20,21 Kim et al. developed SWNT-FETs functionalized with colorimetric dyes for the selective detection of various hazardous gases such as SO2, NH3, and Cl2 (Fig. 12.5).20 Figure 12.5(a) shows the sensing mechanism of the SWNT-dye hybrid gas sensor. A CNT-FET device was functionalized with a sol-gel matrix layer including dye molecules, which selectively reacted with specific gas molecules. The sol-gel matrix layer can absorb moisture due to its porous structure. When the gas sensor was exposed to target molecules, they dissolved in the sol-gel matrix and changed the charge of the dye molecules. This results in the conductance changes of the underlying SWNT channels via a field effect. Moreover, owing to the porous structure of the sol-gel, the target gas molecules were easily removed

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Figure 12.5.  Gas sensors based on a dye-functionalized sol-gel matrix on SWNT networks. (a) The sensing mechanism of an SWNT-dye hybrid gas sensor. (b) Optical image of sheet-shaped (i and ii) and tube-shaped (iii and iv) gas sensors before (i and iii) and after (ii and iv) the SO2 gas exposure. (c) Real-time conductance changes of a methyl red-functionalized SWNT-dye hybrid gas sensor obtained during the introduction of SO2 gases with various concentrations. (d) Real-time conductance changes of an SWNT-dye hybrid gas sensor during the introduction of SO2 and NH3 gases. Reproduced with permission from Ref. [20]. Copyright © from Springer Nature.

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from the layer by the N2 flow, resulting in the conductance recovery of the sensor. Figure 12.5(b) shows the optical images of SWNT-dye hybrid gas sensors in sheet and tube shapes before and after exposure to SO2. Since the gas sensors were composed of flexible materials such as PET films, copper tapes, and SWNTs, they could be easily bent to build flexible sensors. The color of channel areas changed from yellow to red after the exposure to gas, indicating the reactivity of the dye molecules, even in the sol-gel matrix or under a highly bent condition. Figure 12.5(c) shows the real-time changes of the sensor conductance during exposure to SO2 gases, with various concentrations from 0.5 ppm to 10 ppm. It shows that the conductance of the sensor decreased by the SO2 gas exposure and recovered after purging with the N2 gas. Significantly, the conductance variation increased with the increase of applied SO2 concentrations, indicating the sensor could be used to evaluate the gas concentration. Moreover, the rather quick recovery of conductance supports the fact that SO2 gases did not chemically degrade the sensor. Also, the sol-gel matrix, like a liquid environment, provided ions for the gas-dye interactions and then exerted gating effects on the SWNT networks for efficient electrical signal transduction. Figure 12.5(d) shows the real-time response of the sensor to SO2 (pink region) and NH3 (yellow region) gases. Note that the conductance of the sensor decreased during the repeated exposure to SO2 gases, whereas, the exposure to NH3 gases caused the increase of conductance. This difference can be explained by the acid-base property of SO2 and NH3. Furthermore, this gas sensor could be immediately applied to detect NH3 gases, indicating that it could be reusable without any heat treatment. Wei et al. reported another application of SWNT sensors.22 Here, SWNT networks were functionalized with 1-pyrenemethylamine (PMA) for the sensitive and rapid detection of 2,4,6-trinitrotoluene (TNT) in water. The sensing mechanism of the sensor is depicted in Fig. 12.6(a). The amino substituent of PMA bound to SWNTs could selectively interact with TNT to form a negatively charged complex on the SWNT surface. This charged complex could exert a gating effect on the underlying SWNTs, resulting in current changes during the addition of TNT solutions. Figure 12.6(b) exhibits the current changes of the PMA-functionalized sensor during the addition of TNT solutions at various concentrations. This sensor showed rapid conductance changes even at 10 ppm of TNT, indicating a high sensitivity to TNT in water. At high concentrations, the response of the sensor was saturated, which can be explained by the saturated TNT adsorption on PMA. The selectivity of the PMA-functionalized sensor was also tested for nitroaromatic derivatives such as 1-nitrobenzene (1-NB), 1,3-dinitrobenzene (1,3-DNB), 2,4-dinitrotoluene (2,4-DNT), 2,6-dinitrotoluene (2,6-DNT), and TNT (Fig. 12.6(c)). The presence of TNT caused larger current changes than those of other derivatives, indicating the high selectivity of the PMA-functionalized sensor to TNT.

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12.3.2.  Biosensors Since their discovery, CNTs have been studied intensively for application in biosensors because of their remarkable mechanical, electrical, and structural properties.23 Recently, CNT-FET sensors have been exploited for the label-free and real-time monitoring of biomaterials.24,25 Lin et al.6 developed biosensors based on CNTs for the detection of glucose (Fig. 12.7(a)). They electrodeposited Ni nanoparticles on a Cr-coated Si substrate and grew CNT arrays on the Si substrate using chemical vapor deposition. An epoxy-based polymer was coated to cover half of the CNTs. Then, glucose oxidase was attached to the tip of the CNTs. By each successive addition of 2 mM glucose solutions, the current in the sensor decreased and reached a steady state (Fig. 12.7(b)). The limit of detection was 0.08 mM. The CNT sensors enabled the selective and sensitive detection of glucose.

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Figure 12.7.    Biosensor based on CNTs for the detection of glucose. (a) Fabrication of the glucose sensor. (b) Responses of the sensor to 2 mM glucose solutions. Reproduced with permission from Ref. [6]. Copyright © from the American Chemical Society.

Pham Ba et al.8 used CNT-FET sensors for monitoring the release of dopamine from cells. The fabrication and monitoring procedures of a Nafion-radical hybrid film-based CNT (NRC) sensor are depicted in Fig. 12.8(a). In the work, CNTs were first assembled on an SiO2 wafer and electrodes were deposited using a thermal evaporator and a lift-off process. To eliminate the leakage currents in aqueous environments, the electrodes were insulated by a passivation layer. They functionalized the CNT-FET sensors with a Nafion film that contained 2,2′-azino-bis (3-ethylbenzothiazoline-6-sulfonicacid) (ABTS) radicals. They used the Nafion film as a catalytic layer and holding matrix for the ABTS radicals to enhance the sensitivity of the sensors. Next, PC12 cells were cultured to measure the dopamine release from the living cells. Then, the cells were loaded on NRC sensors using a pipette and then stimulated by K+ solutions to measure the dopamine release. Figure 12.8(b) illustrates the sensing mechanism of the sensor for the dopamine release. When the cells were treated with K+ solutions, cellular plasma membranes were depolarized to induce a Ca2+ influx, and then the dopamine molecules were released from the cells by exocytosis and diffused on a Nafion film. The interaction between the dopamine molecules and ABTS radicals generated H+. The released H+ permeated the Nafion film and decreased the electric current, and thus the NRC sensor could monitor the dopamine release from living cells. Figure 12.8(c) shows the decrease in the electric current of the NRC sensor induced by the dopamine release from the cells in real time. Furthermore, the authors8 evaluated the effects of an antipsychotic drug pimozide on the response of PC12 cells. The binding of pimozide to the dopamine’s receptors inhibited

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Figure 12.8.    Floating electrode-based CNT sensor with a Nafion-radical hybrid film to monitor the release of dopamine from living cells. (a) Schematic diagram of preparatory processes of an NRC sensor. (b) Mechanism and real-time monitoring of dopamine release from PC12 cells. (c) Real-time current changes during additions of a cell suspension solution and K+ solutions at various concentrations. (d) Monitoring of dopamine release from PC12 cells treated with pimozide at different concentrations. Reproduced with permission from Ref. [8]. Copyright © from American Chemical Society.

the dopamine release. Figure 12.8(d) shows the inhibitory effect of pimozide on the dopamine release from the cells. The data demonstrate a decrease in dopamine secretion with increasing pimozide concentrations. Son et al.1 developed multiplexed bioelectronic sensors for the fast and on-site detection of target odorant or taste molecules. They used olfactory and taste receptor proteins produced from Escherichia coli for selective sensing. The receptors were immobilized on a CNT-FET sensor. The multiplexed sensor system consisted of four separated CNT channels with different receptor proteins for the simultaneous detection of different target molecules (Fig. 12.9(a–d)). They used four sensory receptors for detecting the indicators of food contamination, namely OR2J2 and OR2W1 for octanol, OR2W1 for hexanal, TAAR5 for trimethylamine, and TAS2R38 for goitrin. Octanol, hexanal, trimethylamine, and goitrin are produced

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Figure 12.9.  (a) Schematic diagram of a sensory receptor-functionalized multi-channel sensor platform. (b) Optical image of the sensor. (c) Optical image of the sensing area, including interleaved-electrodes connected by CNT channels. (d) SEM image of CNT channels (yellow arrow). (e) Photograph of the portable sensor connected to a laptop. (f) Selective detection of each target molecules by the sensor. Reproduced with permission from Ref. [1]. Copyright © from Elsevier.

during the rotting processes of meat, dairy products, seafood, and vegetables, respectively. The portable sensor was connected to a computer with a USB connector (Fig. 12.9(e)). As shown in Fig. 12.9(f), the sensor selectively recognized different odors and taste molecules. The results show that the portable and multiplexed biosensor system is suitable for rapid sensing in various applications.

12.3.3.  Integrated advanced sensor devices The integrated biosensor device (IBD) can be defined as a portable biosensor array for sensitive, real-time, and multiplexed detection.26 Recently, the IBD has gained much attention for its ability to detect the beginning of diseases at or near the point of care. Until now, however, only a few biosensors have successfully been converted from benchtop to portable devices. The most important issue is not only to build a sensitive detector but also to maintain a small system space while integrating signal processing circuits and fluidic systems. Two major efforts to help solving these problems are complementary-metal-oxide-semiconductor (CMOS) technology and microfluidics. Biosensor arrays fabricated with CMOS technology have smaller device footprints and can conduct multiple analyses at the same time,26 while microfluidic systems control sample throughput, improve the flow and the mixing rate of various reagents, and increase the sensitivity of detection.27 The integration of microfluidics and CMOS technology can be a useful way to replace bulky conventional instruments.28,29

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Figure 12.11.    (a) Schematic illustration of fabrication and packaging procedures for the flexible CMOS/microfluidic hybrid system. Reproduced with permission from Ref. [28]. Copyright © from Springer Nature. (b) Schematic diagram of integrated FET biosensors with digital microfluidics. (c) Cross-sectional view of the hole in a FET biosensor with digital microfluidics. Reproduced with permission from Ref. [29]. Copyright © from the Royal Society of Chemistry.

Figure 12.10(a),(b) shows the schematic diagram of a CMOS-based sensor array.26 In CMOS-based, uniformly integrated biosensor arrays, each sensor electrode can be functionalized with various probes to enable the parallel/­multiplexed detection. CMOS circuitry allows the integration of logic circuits on-chip and the control of all the electrodes in an array. Figure 12.10(c) depicts some examples of microfluidic systems, which consist of microchannels, micropumps, and microvalves.27 To reduce sample

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consumption, droplet-based microfluidic systems have been suggested, which produce separate droplets with immiscible fluids. Digital microfluidics (DMF), which are more advanced droplet-based microfluidic systems, produce water droplets on electrostatically actuated electrode arrays instead of constantly flowing droplets in microchannels. Figure 12.11(a) illustrates elastomer-based packaging procedures to fabricate a flexible CMOS/microfluidic hybrid system.28 In this system, liquid samples were delivered via microfluidic channels. Through the same channels, a liquid gallium-indium-tin eutectic alloy was used for the electrical connection. This flexible system continued to work even after bending at a radius of curvature of 1 cm and a uniaxial strain of 15%. Figure 12.11(b), (c) demonstrates the integration of FET biosensors with DMF.29 The FET biosensor could measure the real-time current change induced by the interaction between antigens and antibodies. The electro-wetting-on-dielectric technique was used to inject the droplet containing antigens to the sensing region. The system enables improved transportation and the real-time detection of biomolecules without fluidic channels, pumps, or bulky transducers. The success of such an integrated system with biosensors, microfluidics, and CMOS technology holds a promising potential for portable, sensitive, faster, and multiplexed detection. However, significant effort is still required to attain crucial parameters in cost, power efficiency, and biocompatible packaging.

12.4.  Electrophysiological Monitoring of Cellular Activities 12.4.1.  Cell adhesion on nanostructures Recent advances in nanotechnology have presented new biocompatible materials to create controllable microenvironments for cell growth. Among them, CNTbased scaffolds can provide a favorable surface for cell adhesion because their dimension is similar to that of collagen molecules.30–33 For example, the differentiation and growth direction of human mesenchymal stem cells (hMSCs) can be controlled simply by aligning individual SWNTs (Fig. 12.12).30,31 Figure 12.12(a) depicts experimental procedures to reveal the effect of the SWNT alignment on hMSC behaviors. In this work, SWNTs were assembled on gold substrates with a low density in aligned or randomly oriented formations. Then, bare surface areas between the SWNTs were functionalized with thiol-­ terminated polyethylene glycol (PEG-SH) to resist cell adhesion. The hMSCs were cultured on the substrates, and their behaviors on differently oriented SWNT networks were studied.

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Figure 12.12.   Adhesion and differentiation of hMSCs on aligned or randomly oriented SWNT networks. (a) Schematic diagram showing the experimental procedures for the hMSC elongation along the SWNTs. (b) Fluorescence images of actin filaments (TRITC-phalloidins, red) and nuclei (DAPI, violet) in hMSCs elongated along SWNTs in aligned (i) and randomly oriented (ii) networks. (c) Large-area phase-contrast images of hMSCs on the aligned (i) and the randomly oriented (ii) SWNT networks for 2 weeks. The white arrows show the radial direction of the SWNT spincoating. (d) Plausible explanation of the hMSC responses to the aligned (top) and the randomly oriented (bottom) SWNT networks. Reproduced with permission from Ref. [30]. Copyright © from the American Chemical Society.

Figure 12.12(b) is a set of images reconstructed by the combination of 10 microscope images to show the growth of hMSC over a large surface area. On the large surface region (~2 cm), the hMSCs on the aligned SWNT networks grew along the radial directions (white arrows) corresponding with the alignment directions of the SWNTs. On the other hand, the cells on the randomly oriented SWNT networks were aggregated and spread in a random direction. Statistical analyses showed that about 70% of cells on the aligned networks were arranged within 20° from the directions of the SWNTs. Given that the diameters of individual SWNTs were smaller than 2 nm and the SWNT density was low, it is remarkable that cells could recognize and grow along the direction of SWNTs. Figure 12.12(c) shows the fluorescence images of actin filaments in the hMSCs on the aligned and randomly oriented SWNT networks. Actin filaments are essential factors that support morphology changes and cell movement. Actin filaments in the hMSCs cultured on the aligned SWNT networks were also aligned

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along the direction of individual SWNTs, which induced the stretched morphology of the hMSCs. The plausible mechanism of the different cell responses to the alignment of individual SWNTs is presented in Fig. 12.12(d). A high binding affinity between the extracellular matrix proteins and SWNTs induces the formation of focal adhesion contacts between cells and SWNTs. Therefore, the cells can selectively adhere to the SWNTs and grow along the direction of individual SWNTs. In this work, the cells on the aligned SWNT networks stretched along the direction of individual SWNTs. On the other hand, the cells on the randomly oriented SWNT networks spread out randomly. Moreover, the cell morphology formed by the underlying SWNTs structure might modulate a cytoskeletal tension to the cells, possibly affecting the proliferation and osteogenic differentiation of the hMSCs. In addition, surface-functionalized CNTs have provided new opportunities for controlling cell adhesion and growth. It was reported that surface functionalization improved the attachment of proteins, DNA, and aptamers to CNTs.34,35 For example, laminin-hybrid multi-walled carbon nanotube (MWNT) networks were used as scaffolds for the selective adhesion and growth of human neural stem cells (hNSCs).34 The laminin-MWNT hybrid structures enhanced the growth and adhesion of hNSCs much better than conventional cell-culture substrates (Fig. 12.13). Figure 12.13(a) shows a schematic diagram illustrating the experimental procedures of the selective adhesion of hNSCs on the laminin-hybrid MWNT networks. MWNT networks were prepared by patterning ODT SAM onto thin gold films deposited on glass substrates. When the patterned substrate was immersed in MWNT suspensions, monolayer MWNTs were selectively adsorbed onto the bare Au regions. Then, laminin molecules were selectively adsorbed onto the MWNTcoated regions. Laminin, which is helpful for hNSC adhesion and growth, is one of the extracellular matrix components. After cell seeding, the hNSCs were found to grow preferentially along the laminin-hybrid MWNT patterns in the culture media with basic fibroblast growth factor (bFGF) and epidermal growth factor (EGF). Subsequently, the hNSCs were continuously grown in a culture media without a bFGF and also an EGF for 2 weeks to monitor the differentiation of the hNSCs on laminin-hybrid MWNT patterns. For hNSCs, such growth factors are known to enhance proliferation and growth but block differentiation. The structural-polarization-controlled differentiation of individual hNSCs could be achieved by the laminin-hybrid MWNT patterns, which comprised one square and one line-shape (Fig. 12.13(b)). The width of the line-shape region was much smaller than an individual hNSC. After cell seeding, a selective adhesion of the cell was observed inside the square regions (Fig. 12.13(c-i)). Then, the hNSCs on the square regions outgrew the narrow line-shape regions during the growth and differentiation stages (Fig. 12.13(c-ii)). The neuronal differentiation was

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Figure 12.13.  MWNT network patterns for the selective adhesion and polarization of hNSCs. (a) Schematic diagram depicting the experimental procedures for the structural-polarization-­ controlled neuronal differentiation of individual hNSCs using MWNT patterns. The absorption of laminin on the MWNT-coated regions induced the preferential adhesion of hNSCs. (b) SEM image of MWNT patterns with a single narrow strip. (c) Phase contrast images of hNSC adhesion (i) and differentiation (ii) on the MWNT patterns. The hNSCs attached within the MWNT regions (dotted square) and grown along the narrow strip MWNT regions during the differentiation. (d) Immunofluorescence image of growth-associated protein 43 (GAP 43, green) and nuclei (Hoechst, blue) of hNSCs distributed along the narrow strip region. (e) Immunofluorescence image of the differentiated neuronal cells (TUJ1, red) surrounded by astroglial cells (GFAP, green) on the structural-polarization controlled MWNT pattern. Reproduced with permission from Ref. [34]. Copyright © from the American Chemical Society.

identified by the growth-associated protein 43 (GAP 43, green in Fig. 12.13(d)), which is known to be expressed in the growth cone regions of neural cells. The high expression of GAP 43 on the line shape regions indicates large growth over the line-shape regions. Immunocytochemistry was conducted to check the neural lineages of the differentiated cells on the laminin-hybrid MWNT patterns ­ (Fig. 12.13(e)). The fluorescent images of TUJ1 and GFAP from the cell nuclei indicate astroglial and neural cells, respectively. It should be noted that the differentiation of the hNSCs was influenced by controlled structural polarities on the laminin-hybrid MWNT patterns while preserving their differentiation capabilities in the nervous system, such as astroglial or neuronal cells. Although two-dimensional substrates have exhibited notable characteristics related to the development and proliferation of cells, they are not able to mimic cellular environments for the cellular functions of three-dimensional (3D) tissues. Hu et al. reported a graphene (GF) network functionalized with 3-­aminophenylboronic acid monohydrate (APBA) as a 3D scaffold for the adhesion and response monitoring of cells (Fig. 12.14).36 Figure 12.14(a) depicts the growth of HeLa cells on the biomimetic 3D scaffold of APBA/GF and the mechanism of the H2S release from the cells stimulated

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Figure 12.14.    Biomimetic 3D scaffold based on graphene for long-term cell culture and real-time electrochemical monitoring. (a) Scheme depicting the growth and H2S release of HeLa cells cultured on a 3D graphene scaffold. (b) SEM images of cells cultured on a 3D scaffold of APBA/GF; inset shows the enlarged image of a single cell attached on the skeleton (red rectangle). (c) Amperometric responses of HeLa cells cultured on a 3D scaffold electrode under stimulation with 2 mM cysteine (blue), with 2 mM cysteine after the addition of 0.2 mM PAG (line) and with PBS buffer (black). Reproduced with permission from Ref. [36]. Copyright © from the American Chemical Society.

by cysteine. Here, APBA could interact with the diols of carbohydrates on the cell membranes, promoting the adhesion and proliferation of cells on the surface of the 3D scaffold. Moreover, the adhesion of the cells on the scaffold could improve the electrochemical monitoring of real-time cellular activities. Figure 12.14(b) shows the SEM image of HeLa cells cultured on an APBA/ GF scaffold. The image clearly shows that a large number of HeLa cells grew on the scaffold surface, indicating the ability of biomimetic 3D scaffolds for cell adhesion. To demonstrate the applicability of the biomimetic 3D scaffolds, the electrodes on the APBA/GF were used to monitor the real-time release of H2S from HeLa cells stimulated by cysteine (Fig. 12.14(c)). After the addition of a cysteine solution, a sharp spike in current was observed (blue line). However, there was a negligible increase in current when the cells were pretreated with an inhibitor (PAG) before the addition of a cysteine solution (red line). Also, there was no change in the current after the addition of a PBS solution (black line). These results clearly indicate the release of H2S from HeLa cells stimulated by cysteine, suggesting the promising capabilities of nanostructure substrates for cellular adhesion.

12.4.2.  Monitoring cellular activities The standard methods used to assay cellular activities include electrophysiological patch clamp techniques. Despite their high temporal and spatial resolution, conventional patch clamp techniques have several disadvantages, including complicated cell manipulation steps which require a skilled operator. On the other hand, nanoscale FETs or electrode sensors can simply measure the activities of adherent cells cultured on the devices;37,38 however, they usually exhibit inconsistent

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Figure 12.15.  (a) Schematic diagram illustrating the fabrication procedures for the CNT-FETbased RSP. (b) Microscopic image of a single HeLa cell located on the CNT junction with a microcapillary. The scale bar represents 20 µm. (c) Schematic drawing of the Ca2+ influx through the SOCCs and VDCCs stimulated by histamine. (d) Real-time responses of the CNT-FET-based RSP with HeLa cells under the addition of 100 µM histamine solution with and without a cetirizine pretreatment. Reproduced with permission from Ref. [9]. Copyright © from Elsevier.

qualities from one device to another, and the direct growth of adherent cells on the devices might affect their electrical properties. Therefore, it is often difficult to compare the data measured by different devices quantitatively or to obtain statistically significant results. In this section, we discuss the methods to overcome these limitations using CNT-FET-based reusable sensor platforms (RSPs) and to quantitatively monitor electrophysiological cellular activities in various environments.9,39 Figure 12.15(a) shows a schematic diagram illustrating the fabrication procedures for the CNT-FET-based RSP.9 In this work, CNTs were dispersed on a glass substrate by a spin coater. The electrodes were then fabricated by thermal evaporation after conventional photolithography. After that, these platforms were passivated by an Al2O3 layer (Fig. 12.15(a˗i), (ii)). Also, HeLa cells that possess histamine receptors were detached from the plate in culture media using a trypsin solution (Fig. 12.15(a-iii), (iv)). The HeLa cells were transferred onto the RSP, and the aggregate of the cells was adjusted with a microcapillary to place a single cell on the CNT junction (Fig. 12.15(a-v)). Next, the electrophysiological responses of the cell under histamine stimulations were measured with these platforms (Fig. 12.15(a-vi)).

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Figure 12.15(b) shows the microscopic image of a single HeLa cell located on the CNT junction with a microcapillary. Note that these procedures were noninvasive and rather simpler than the conventional patch clamp technique. Furthermore, the measured cells did not change the electrical properties of CNTFETs, since they were not directly cultured on the surface of them. Significantly, the cells could be simply detached from the surface. Thus, these platforms could be reused with other cells, minimizing device-to-device errors. Figure 12.15(c) illustrates the Ca2+ influx through the ion channels. When histamine molecules are bound to histamine receptors (H1Rs), they activate a second messenger system mediating the opening of ion channels such as store-operated calcium channels (SOCCs) and voltage-dependent calcium channels (VDCCs) on a cell membrane. Since the Ca2+ influx through the ion channels induces a negative potential between the cell and the CNT junction, it increases the conductance of p-type CNT junction. The conductance change can be used to measure the electrophysiological responses of the cell under histamine stimulations. Figure 12.15(d) shows the real-time responses of the CNT-FET-based RSP with HeLa cells under histamine stimulations. To evaluate the effect of an antihistamine drug, the cells were incubated with 50 µM cetirizine in a bath solution before they were placed on the CNT junction. Next, a bath solution with 100 µM histamine was injected, which causes a Ca2+ influx into the cells. The relative conductance change of the RSP with a cetirizine pretreatment (~5%) was smaller than that of the RSP without a pretreatment (~13%). This indicates that the CNT-FET-based RSP can be used to evaluate the effect of anti-histamine drugs in real time. Figure 12.16(a) illustrates another example of quantitative monitoring of the electrophysiological cellular activities in which the above-described RSPs were applied. Recently, a high level of nicotinic acetylcholine receptors (nAChRs) were found to be expressed on non-adherent cells such as small-cell lung cancer (SCLC) cells. In this work, the CNT-FET-based RSP was used to identify the overexpression of nAChRs in non-adherent SCLC cells and to evaluate the effects of phytoestrogens.39 Figure 12.16(b),(c) shows the real-time responses of the CNT-FET-based RSP with either an SCLC cell or a normal lung cell under the addition of nicotine. The interaction between nicotine and nAChR causes the influx of Ca2+ into the cell, which increases the conductance of CNT junction in the RSP as described previously. Under the addition of 5 μM nicotine, the CNT-FET with the SCLC cell exhibited a noticeable current increase of 6.2 nA (Fig. 12.16(b)). However, in the case of the normal lung cell, there was no significant change (Fig. 12.16(c)). This result indicates that the CNT-FET-based RSP can selectively measure the interaction between nicotine and nAChRs overexpressed in the SCLC cell. Therefore, this method can be used to study the electrophysiological activities of non-­ adherent cells as well as adherent ones.

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Figure 12.16.    (a) Schematic diagram that illustrates the procedures for capturing a non-adherent SCLC cell (i) and locating the cell on the CNT junction of a RSP (ii). Steps (i) and (ii) can be repeated for different cells with a single RSP. (b) Real-time responses of the CNT-FET-based RSP with either an SCLC cell (i) or a normal lung cell (ii) under the addition of 5 μM nicotine. (c) Realtime responses of the CNT-FET-based RSP with an SCLC cell under the introduction of 5 μM nicotine when the cell was pretreated with 100 μM daidzein (i), 100 μM tamoxifen, and 100 μM daidzein (ii). Reproduced with permission from Ref. [39]. Copyright © from the American Chemical Society.

Figure 12.16(d), (e) demonstrates the quantitative evaluation of the drug effects on the SCLC cell under the addition of nicotine. To evaluate the effects of phytoestrogens, the cell was pretreated with 100 μM daidzein (Fig. 12.16(d)) or both 100 μM tamoxifen and 100 μM daidzein (Fig. 12.16(e)), and then 5 μM nicotine was injected. Notably, all the data were obtained from a single RSP, which enabled the quantitative comparison without device-to-device errors. Significantly, the SCLC cell pretreated with daidzein exhibited the current increase of 2.9 nA, which was much smaller than that of the cell without a pretreatment (∼6.2 nA in Fig. 12.16(b)). This result indicates that the daidzein worked as an inhibitor. On the other hand, when the SCLC cell was pretreated with both tamoxifen and daidzein, the current change of ∼4.5 nA was the value between those of the cells with only a daidzein pretreatment (∼2.9 nA) and without a pretreatment (∼6.2 nA). These results indicate that tamoxifen, an anti-estrogen, could not completely block the effect of daidzein. The ability of the CNT-FET-based RSPs to quantitatively monitor the activities of non-adherent cells without deviceto-device errors could be a major breakthrough for both electrophysiological researches and biomedical applications.

12.5.  Bioelectronic Nose and Tongue 12.5.1.  Olfactory receptor proteins in a lipid bilayer as a probe Kim et al.40 built a hybrid structure of a human olfactory receptor (hOR2AG1) and CNT-FET for the discrimination of odorant molecules with a single-carbonatomic resolution. Figure 12.17(a) shows the schematic diagram depicting a

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Figure 12.17.    Olfactory receptor-based bioelectronic nose. (a) Schematic diagram depicting odorant binding on a CNT-FET-based olfactory sensor. (b) Real-time conductance measurements obtained from the hOR2AG1-functionalized CNT-FET sensor after the injection of AB with various concentrations. (c) Selective responses of an hOR2AG1-functionalized CNT-FET sensor to various odorants. Reproduced with permission from Ref. [40]. Copyright © from John Wiley and Sons.

bioelectronic nose and the specific odorant detection mechanism using the bioelectronic nose sensor. The bioelectronic nose comprised olfactory receptors with a lipid membrane and a CNT-FET. Here, the lipid membrane provides a stable environment for the functionality maintenance of the receptors. When a specific odorant molecule binds to its corresponding receptor, the electrical state of the receptor changes from a neutral state to an active state with negative charges.41 The negative charges on the receptors increase the Schottky barrier heights of the CNT channels, which reduces the conductance of the CNT channels. Figure 12.17(b) shows the real-time responses of an hOR2AG1-based bioelectronic nose to amylbutyrate (AB), a fruity odorant, with various concentrations. A PBS buffer solution was placed on the hOR2AG1-immobilized CNT transducers, and the AB solutions were then introduced. The bioelectronic nose device exhibited significant changes in the conductance after the introduction of a 100 fM AB solution. This result shows that a receptor-based bioelectronic nose could detect biomolecules with a high sensitivity. Figure 12.17(c) shows the real-time responses of an hOR2AG1-based bioelectronic nose after the injection of various odorants. Note that pentyl valerate (PV), propyl butyrate (PB), and butyl butyrate (BB) have similar molecular structures to AB but differ only by the carbon atom number. The addition of PB, PV, and BB solutions did not change the channel conductance of the hOR2AG1-based bioelectronic nose device, even at a high concentration (100 μM). On the other hand, the injection of the 1 pM AB solution resulted in the decrease of channel conductance, indicating that the biosensor device based on hOR2AG1-functionalized CNTFETs has a high selectivity with a single-carbon-atomic resolution. Wu et al.42 reported a biomimetic olfactory receptor (ODR-10)-based surface acoustic wave (SAW) chip with better performance by enhancing the immobilization efficiency of odorant molecule sensing detectors. Figure 12.18(a) shows the schematic diagram of an olfactory receptor-based SAW chip for detecting odorant molecules. The lipid membrane with ODR-10 receptors was attached to the

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Figure 12.18.    Bioelectronic nose based on olfactory receptors in a lipid bilayer. (a) Schematic diagram depicting odorant molecules binding on a surface acoustic wave sensor functionalized with olfactory receptors. (b) Responses of sensors functionalized with ODR-10 to diacetyl with various concentrations. (c) Statistical data of devices functionalized with various molecules in response to different odorant molecules at a concentration of 100 nM. Reproduced with permission from Ref. [42]. Copyright © from Elsevier.

sensitive area of a SAW chip. The lipid membrane facilitated the ODR-10 receptors to preserve their native functionality. When a specific odorant such as diacetyl binds to its corresponding receptor, ODR-10, the mass loading changes on the sensitive area of the SAW chips induced the resonance frequency shifts. Figure 12.18(b) shows the statistical results of the resonance frequency shifts of SAW chips with the injection of diacetyl solution from 100 fM to 100 nM. The responses were measured by monitoring the shift of resonance frequency during the introduction of diacetyl solution. The responses of the chips to various concentrations of diacetyl solution were normalized by the response of the chips to the diacetyl solution at 100 nM. The limit of detection is 100 fM, indicating that lipidbilayer-stabilized receptors-based bioelectronic sensors could detect biomolecules with high sensitivity. Figure 12.18(c) shows the responses of SAW chips functionalized with various membrane fractions to different odorant substances. Note that butanone and 2,3-pentanedione are sweet odorants. The SAW chips with a bare sensitive area were used as control sensors. When diacetyl solutions were applied to SAW chips with ODR-10, the responses were significantly higher than those of the membrane without ODR-10. On the other hand, when the other odorant solutions were applied to biosensors, the responses were negligible. These results from the specific binding of diacetyl to ODR-10 show the highly selective detection of lipid-bilayer-stabilized receptors-based bioelectronic sensors.

12.5.2.  Olfactory receptors in nanovesicles as a probe Human olfactory systems can detect tens of thousands of odorants with a high selectivity and sensitivity because there are many receptors on the cell

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Figure 12.19.    Schematic of the fabrication of nanovesicles with AQP4 receptors from HEK293T cells. Reproduced with permission from Ref. [44]. Copyright © from Elsevier.

membranes. Recently, CNT-FET sensors with nanovesicles were developed to build olfactory-based biosensors.43 Nanovesicles are small liposomes extracted from cells with specific receptors that can respond to target molecules like cells, and thus they can be used for sensing biomolecules. Moreover, nanovesicles can be stored for a longer time than cells, which further enhances the stability of sensors. The nanovesicles were extracted from mammalian cells. The preparation procedures of nanovesicles from human embryonic kidney (HEK) 293T cells are depicted in Fig. 12.19. Here, the HEK 293T cells were cultured with aquaporin 4 (AQP4) genes to express AQP4 receptors in the cell.44 Then, the cells were centrifuged to extract cell membranes, receptors, and ion channels. As a result, the nanovesicles that contain receptors and ion channels were fabricated. The structure of a nanovesicle-based CNT-FET sensor is presented in Fig. 12.20.45 The CNT-FET sensor contained a CNT channel between two ­electrodes. The nanovesicles were immobilized onto the CNT channel using a linker such as pyrenebutyric succinimide ester. The plausible mechanism of the nanovesicle sensor could be explained by a gating effect on the CNT channel.

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Figure 12.20.    Structure of the nanovesicle-based CNT-FET sensor. Reproduced with permission from Ref. [45]. Copyright © from Elsevier.

The specific binding of antibodies and receptors could trigger a signal pathway in the nanovesicles. The signal induced a Ca2+ influx inside the nanovesicles. Then, the concentrated calcium ions exerted a field effect onto the CNT, resulting in the decreased conductance of the sensor. Lee et al.46 developed CNT-FET sensors with honeybee umami taste receptors to discriminate umami taste. They prepared nanovesicles containing umami taste receptors AmGr10 of honeybees. The nanovesicles containing the receptors were immobilized on the sensor. The sensor could detect umami tastants, such as monosodium glutamate (MSG), with a very high sensitivity and selectivity. The scanning electron microscopy image of nanovesicles is presented in Fig. 12.21(a). The nanovesicles were immobilized and uniformly distributed on the surface. The size of nanovesicles was about 20 nm. Figure 12.21(b) shows a schematic diagram of the nanovesicle-based sensor. The sensor comprised CNTs and Au electrodes on a silicon substrate. The nanovesicles containing AmGr10 receptors and Ca2+ channels were immobilized on the gold floating electrodes. The authors measured the responses of the sensor to MSG at various con­ centrations in real-time (Fig. 12.21(c)). A bias voltage was applied between the source and drain. The current of the sensor was monitored during electrical measurements. The relative conductance of the sensor increased after the injection of MSG solutions from 100 pM. Sensor responses were attributed to the binding of MSG to the AmGr10 receptor, which triggered an influx of Ca2+ into the nano­ vesicles. The accumulation of calcium ions modulated the Schottky barrier between the CNT channels and electrodes, resulting in the conductance changes of the sensors. The real-time monitoring of various tastants was also performed to check the selectivity of nanovesicle-based sensors (Fig. 12.21(d)). There were negligible conductance changes during the introduction of sucrose and phenythiocarbamide (PTC) solutions. These data imply that nanovesicle sensors can determine umami tastants with a high selectivity.

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Figure 12.21.   (a) Nanovesicles image taken using a FE-SEM. (b) Schematic diagram of a CNTFET sensor with nanovesicles. (c) Real-time measurements of various concentrations of MSG ­solutions. (d) Real-time responses of the biosensor for selectivity. Reproduced with permission from Ref. [46]. Copyright © from the American Chemical Society.

12.5.3.  Olfactory receptors in nanodisks as a probe Nanodisks (NDs) have been proposed as appropriate tools for G protein-coupled receptor reconstitution. The NDs consist of a receptor, a lipid bilayer, and ­membrane-bound proteins. The membrane-bound proteins tightly wrap the edge of the lipid bilayer and stabilize the receptor in aqueous environments. Yang et al.47 developed an oriented nanodisk-based bioelectronic nose (ONBN) platform which can imitate the signaling pathways of human olfactory systems. The structure of nanodisk-based sensors is depicted in Fig. 12.22(a). The ONBNs were comprised of a floating-electrodes-based CNT-FET and nanodiskembedded TAAR13c olfactory receptors (T13NDs). The conformation of T13NDs on gold substrates was confirmed by the FE-SEM image shown in Fig. 12.22(b). The diameter of T13NDs was about 20 nm. Figure 12.22(c) shows the real-time responses of an ONBN to various concentrations of cadaverine (CV), an odor that is extremely repulsive to humans. While adding various concentrations of CV solutions, the drain-source current of the ONBN was measured. The CV solutions were added from a low concentration, 10 pM, to a high concentration, 100 μM. The conductance of the ONBN

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Figure 12.22.    Bioelectronic nose based on olfactory receptors in nanodisks. (a) Schematic diagram of an oriented nanodisk-functionalized bioelectronic nose. (b) FE-SEM image of TAAR13cembedded nanodisks (T13NDs). (c) Real-time responses of an ONBN to the various concentrations of cadaverine (CV). (d) Real-time responses of the ONBNs to various molecular species with amine functional groups. Reproduced with permission from Ref. [47]. Copyright © from the American Chemical Society.

increased rapidly after the introduction of CV solutions. The ONBN exhibited a conductance increase by the addition of 10 pM CV solutions, indicating the high sensitivity of the bioelectronic nose. The plausible mechanism for the responses of the ONBNs is that the binding of positively charged odorant molecules to olfactory receptors increased the work function of the floating electrodes on the CNT channels. Therefore, the binding of odorant molecules decreased the Schottky barriers for hole carriers in the CNT channels and increased the device conductance. Based on this mechanism, the ONBN detected CV with a high sensitivity. Figure 12.22(d) shows the real-time conductance measurements from an ONBN after the addition of various odorants such as diaminodecane (DD), ethanolamine (EA), trimethylamine (TMA), glutamine (Glu), and CV. The changes of conductance were negligible after adding 1 µM DD, TMA, EA, and Glu solutions.

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On the other hand, a significant conductance change was observed after the injection of 1 nM CV. This result demonstrates that ONBNs are highly selective to target molecules, in the presence of similar molecular species.

12.6. Summary The combination of nanostructures with biomolecules and conventional electronics allows one to build advanced biosensors that can overcome the fundamental limitations of conventional sensors. An example is label-free, flexible biosensors that can selectively detect specific analytes in real time. These label-free biosensors based on nanostructures take advantage of the novel properties of nanostructures, such as high flexibility and large surface-to-volume ratio. Importantly, many biosensors can be integrated into a small portable system using the mass production method based on the directed assembly strategy, which allows us to envision portable sensor systems for point-of-care applications. On the other hand, the direct combination of biosystems with nanostructures enables advanced biosensors that are not possible with conventional technology. Examples include bioelectronic noses and tongues that can imitate the exact response of human sensory systems. In this case, carbon nanotubes were combined with olfactory or taste receptors to build bioelectronic noses or tongues with the same response as human noses or tongues. Overall, nanostructure-based biosensors take advantage of the novel properties of nanostructures to overcome the fundamental limitations of conventional sensors working in water environments. Considering the recent breakthroughs, nanostructure-based biosensors should be a powerful and convenient tool for basic research and practical applications for analyses of the analytes in water.

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21. J. Y. Kim, J. Lee, S. Hong, and T. D. Chung. Formaldehyde Gas Sensing Chip Based on Single-Walled Carbon Nanotubes and Thin Water Layer, Chem. Commun. (Camb.), 2011, 47, 2892–2894. 22. L. M. Wei, D. J. Lu, J. Wang, H. Wei, J. Zhao, H. J. Geng et al. Highly Sensitive Detection of Trinitrotoluene in Water by Chemiresistive Sensor Based on Non­ covalently Amino Functionalized Single-Walled Carbon Nanotube, Sens. Actuat. B Chem., 2014, 190, 529–534. 23. C. N. R. Rao, B. C. Satishkumar, A. Govindaraj, and M. Nath. Nanotubes, ChemPhysChem., 2001, 2, 78–105. 24. J. Wang. Carbon-nanotube Based Electrochemical Biosensors: A Review, Electroanalysis, 2005, 17, 7–14. 25. S. Iijima. Helical Microtubules of Graphitic Carbon, Nature, 1991, 354, 56–58. 26. S. K. Arya, C. C. Wong, Y. J. Jeon, T. Bansal, and M. K. Park. Advances in Complementary-Metal-Oxide-Semiconductor-Based Integrated Biosensor Arrays, Chem. Rev., 2015, 115, 5116–5158. 27. G. Luka, A. Ahmadi, H. Najjaran, E. Alocilja, M. DeRosa, K. Wolthers et al. Microfluidics Integrated Biosensors: A Leading Technology towards Lab-on-a-Chip and Sensing Applications, Sensors (Basel), 2015, 15, 30011–30031. 28. B. W. Zhang, Q. Dong, C. E. Korman, Z. Y. Li, and M. E. Zaghloul. Flexible Packaging of Solid-State Integrated Circuit Chips with Elastomeric Microfluidics, Sci. Rep., 2013, 3, 830. 29. K. Choi, J. Y. Kim, J. H. Ahn, J. M. Choi, M. Im, and Y. K. Choi. Integration of Field Effect Transistor-Based Biosensors with a Digital Microfluidic Device for a Lab-OnA-Chip Application, Lab Chip, 2012, 12, 1533–1539. 30. S. Namgung, K. Y. Baik, J. Park, and S. Hong. Controlling the Growth and Differentiation of Human Mesenchymal Stem Cells by the Arrangement of Individual Carbon Nanotubes, ACS Nano, 2011, 5, 7383–7390. 31. J. Park, D. Hong, D. Kim, K. E. Byun, and S. Hong. Anisotropic Membrane Diffusion of Human Mesenchymal Stem Cells on Aligned Single-Walled Carbon Nanotube Networks, J. Phys. Chem. C, 2014, 118, 3742–3749. 32. S. Y. Park, B. S. Kang, and S. Hong. Improved Neural Differentiation of Human Mesenchymal Stem Cells Interfaced with Carbon Nanotube Scaffolds, Nanomedicine (Lond.), 2013, 8, 715–723. 33. K. Y. Baik, S. Y. Park, K. Heo, K. B. Lee, and S. Hong. Carbon Nanotube Monolayer Cues For Osteogenesis of Mesenchymal Stem Cells, Small, 2011, 7, 741–745. 34. S. Y. Park, D. S. Choi, H. J. Jin, J. Park, K. E. Byun, K. B. Lee et al. PolarizationControlled Differentiation Of Human Neural Stem Cells Using Synergistic Cues from the Patterns of Carbon Nanotube Monolayer Coating, ACS Nano, 2011, 5, 4704–4711. 35. S. Namgung, T. Kim, K. Y. Baik, M. Lee, J. M. Nam, S. Hong. Fibronectin-CarbonNanotube Hybrid Nanostructures for Controlled Cell Growth, Small, 2011, 7, 56–61.

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Chapter 13

Microfluidic Devices for Water Quality Management Vivekanandan Palaninathan†,**, D. Sakthi Kumar†,††, Dorian Liepmann‡,‡‡, Ramasamy Paulmurugan§,§§ and Renugopalakrishnan Venkatesan*,¶,ǁ,¶¶ Bio-Nano Electronics Research Centre, Graduate School for Interdisciplinary New Science, Toyo University, 2100 Kujirai, Kawagoe, Saitama 350-8585, Japan †

Department of Bioengineering, University of California Berkeley, Berkeley, CA 94720, USA

‡

§

Cellular Pathway Imaging Laboratory, Department of Radiology, Stanford University School of Medicine, 3155 Porter Drive, Suite 2236, Palo Alto, CA 94304, USA ¶

Boston Children’s Hospital, Harvard Medical School, Boston, MA 02115, USA Department of Chemistry, Northeastern University, Boston, MA 02115, USA

ǁ

[email protected]

**

[email protected]

†† ‡‡ §§ ¶¶

[email protected]

[email protected]

[email protected]; [email protected]

13.1. Introduction Water is critical for life to exist on Earth. Approximately two-thirds of the Earth surface is covered in water, but only a small portion of it is available as freshwater. With the rise in global population and urbanization, the demand for freshwater for drinking, agriculture, and beverage production has skyrocketed. In addition, the  Corresponding Author: Renugopalakrishnan Venkatesan.

*

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global increase in urbanization has depleted water reserves, and pollution has widely deteriorated the quality of water bodies due to various contaminants. Primary sources of water pollution include: • Heavy metals from industrial discharges and wastes. • Nutrients from the use of fertilizers, which causes eutrophication. • Pathogenic microbials (e.g., bacteria, viruses, protozoa, and parasitic worms) from the leakage or discharge of sewage that contains human and animal excreta. Contaminated water and poor sanitation often result in epidemic diseases that directly affect the health of humans and livestock and have a negative impact on socio-economic growth and the environment. Hence, routine water quality assessment and monitoring are crucial for protecting water supplies.1 Every country across the globe has environmental laws, standards, and protocols in place to routinely monitor the quality of their water resources. Despite the precautionary measures taken by respective governments, water pollution is alarmingly high. In response to the COVID-19 virus pandemic in 2020, many countries issued a mandatory quarantine to help slow the spread of the virus; as a result, the immediate decrease in industrial production, automobile use, and air travel significantly reduced air and water pollution and their impact on the environment. However, industrialization is critical for the economic growth of developed or developing countries, and governments need to adopt new measures and technologies to support industrialization while minimizing its impact on the environment. Governments and environmentalists use various methods to measure pollution levels in water bodies, but they often must be performed by specialists and in highly specialized labs, which is costly and time-consuming. Therefore, the development of a simple, portable, user-friendly, cost-effective, and environmentally benign disposable or reusable water testing device is greatly needed. The ideal device will allow for water quality monitoring on a large scale and the results can be interpreted even by a novice. Furthermore, the acquired monitoring data should be integrated and stored in a cloud-based or public domain with open access so that information about the source and/or cause of pollution can be retrieved and mapped. Policymakers can also use this information to take appropriate steps to control water pollution, while the general public can understand the necessity for water quality monitoring to keep water bodies pollution-free. In line with this strategy, a simple portable device that can indicate pollutants in water bodies should possess high sensitivity, selectivity, and throughput in ­addition to being low-cost and user-friendly. In the quest for such a device, researchers have developed numerous devices using microfluidic technology, which converges disciplines such as chemistry, physics, engineering, nano­ technology, microtechnology, biotechnology, electro-, and surface chemistry.2

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Figure 13.1.    Key developments surrounding microfluidics over the past six decades.

Figure 13.1 shows a timeline underlining key developments in the evolution of the microfluidics field over the past six decades. Microfluidic devices are miniature constructs that are fabricated using stateof-the-art technology at the micron-scale in order to scale-down and simulate the often-complex chemical laboratory processes, such as a chemical separation/ analysis. Such devices are highly sought because they outperform classical biomedical and chemical research techniques.3 Furthermore, these low-cost devices are simple, compact, and easy to use, and achieve rapid yet reliable analyte detection and identification with higher sensitivity, precision, and repeatability with only a small amount of sample. Microfluidic devices can be grouped into three major categories: (1) Continuous flow (2) Droplet-type (3) Digital microfluidics Continuous flow devices have fixed microchannels and peripherals, whereas the droplet-type and digital microfluidic devices manipulate small quantities of liquid inside the channels to create droplets.4 Today, microfluidics is rapidly evolving into a sophisticated technology that may cater to future needs for point-of-care (POC) devices and lab-on-a-chip (LOC) systems, with integrated sensors for automation and specific applications in biomedical, chemical, food, and environmental research.

13.2. Different Microfluidic Systems — Electrochemical and Optical Microfluidic systems that monitor water pollution levels can be categorized as electrochemical and optical systems, depending on the technologies used for

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384  V. Palaninathan et al. Table 13.1.    Categories of microfluidic devices based on their analyzing technologies. Microfluidic Devices Electrochemical ■ Electrochemical Impedance Spectroscopy (EIS) ■ Cyclic Voltammetry (CV) ■ Square Wave Anodic Stripping Voltammetry (SWASV)

Optical ■ Colorimetric ■ Fluorescent ■ Chemiluminescence (CL) ■ Surface Enhanced Raman Spectroscopy (SERS) ■ Surface Plasmon Resonance (SPR)

analyzing the samples (Table 13.1). With the advent of nanotechnology, microfluidic devices have been significantly upgraded and have improved in quality and sensitivity. Here, we discuss various microfluidic devices used to detect the levels of contaminants (e.g., heavy metals and nutrients) and pathogens in water samples. Diverse factors, including the monitoring of pollutant levels in the environment, have contributed to the emergence of optical and electrochemical systems. Water pollution requires urgent attention due to its adverse effects on public health, agriculture, and the environment, and thus the rapid monitoring of pollutant levels to meet national and international standards is critically important. Recently, numerous reports have hinted at an increase in the use of microfluidic devices in environmental analysis.5 There are many analysis techniques used in such systems, including electrochemical analysis,6 chromatography,7 fluorescence,8 and mass spectrometry.9 The following section discusses the basic principles and developments surrounding electrochemical and optical microfluidic systems for water analysis.

13.2.1.  Electrochemical detection systems This past decade has seen the active use of electrochemical reactions in LOC systems. Conventional electrochemical analysis is reliable, selective, and sensitive, but it requires large electrolytic cell and electrodes, which are neither portable nor convenient for POC applications and thus a portable POC device is greatly needed. Electrochemical detection is well-suited for LOC integration due to the inherent miniaturization in which a variety of electrodes can be integrated within a microfluidic platform. Other benefits of microfluidic devices include reliability, selectivity, and higher sensitivity. A typical electrochemical sensor consists of two components: a receptor and a transducer. The receptor recognizes the test species with high specificity and selectivity, while the transducer transposes that recognition into an electrical signal. For instance, an electrochemical sensor designed to detect DNA uses a

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single-stranded DNA that is complementary to the target DNA; when the receptor recognizes the target DNA, the transducer indicates an electronic signal.10 Figure 13.2(a) illustrates a typical design of an electrochemical sensor. The essence of electrochemical systems resides in the electrochemical detection in chip-based capillary electrophoresis, in which this miniaturized device does not incur a loss of analytical performance. Such electrochemical systems use amperometry, voltammetry, and potentiometry as detection methods, in which the electrode materials are either metal- or carbon based. It is also noteworthy that, in terms of the integration and alignment of the electrodes, printable electrodes are more affluent than microwire or microfabricated types.5

(a)

(b)

Figure 13.2.    Illustration of typical electrochemical (a) and (b) optical sensors.

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Water polluted with heavy metals is considerably affecting human health worldwide with potential impairment to brain, kidneys, and blood. Among the numerous microfluidic platforms used for detecting heavy metals, the electrochemical (EC) platform has attracted significant interest due to its higher sensitivity and selectivity and rapidity for sample analysis. In a microfluidic EC system, modifying the working electrode with active electrocatalysts (organic and inorganic) is essential because they attract the target metal ions of trace heavy metals present in the water samples. Previously, mercury (Hg) or its precursors was used to modify the surface of electrodes for heavy metal detection due to its high sensitivity and reproducibility. However, the toxicity associated with mercury limits its applications for continuous monitoring of pollution in target water bodies.11 Bismuth (Bi) was substituted for mercury as an electrode surface modifier due to its lower toxicity and ability to form multicomponent alloys with heavy metals.12–14 The Bi-coated electrodes exhibit fuse alloys’ formation upon reaction with the heavy metals in the analyte, which is akin to the Hg amalgams. However, Bi-coated electrodes also have certain drawbacks, such as the need for pretreatment for activation and maintenance of sample media with pH below 5 to avoid hydrolysis of the Bi ions.11,15 Recently, there has been a surge in the use of metallic nanoparticles, biological materials, and carbon materials as electrode modifiers. Kudr et al. have exhaustively reviewed specific applications of EC systems for detecting pH, metals, nitrate and nitrite ions, phenols, pesticides, herbicides, and bacteria.16,17 We will discuss several EC systems based on their detection principles, including square-wave anodic stripping voltammetry (SWASV), potentiometric, cyclic voltammetry, and amperometry, as shown in Fig. 13.3. (A)

(B) (a) DESALINATOR

100 µm

100 µm Ag WE WE Contact pad

(b)

POTENTIOMETRIC READOUT 10

(ii) CLOSED CELL

(i) OPEN CELL

11

3 mm Ag CE/QRE

CE/QRE Contact pad

12

13

SWASV

14

8 9 5 6 7

1 2 3 4

(D)

(C) Counter Electrode (C)

Electrical Contact

Working Electrode (Au) Analysis Well Vol. = 50 µL

Reference Electrode (Ag/AgCl) Working Electrode (Ag)

4 mm

Analysis Well Vol. = 50 µL Plexiglas

Reference/Counter Electrode (Ag)

Figure 13.3.  Electrochemical systems based on different detection principles. (A) squarewave anodic stripping voltammetry,17 (B) Potentiometry,18 (C) Cyclic voltammetry,17 and (D) Amperometry.19 Reproduced with permission from respective publications.

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Jung et al. presented a reusable polymer-based EC sensor based on the SWASV detection principle for continuous and on-site lead Pb (II) monitoring. As depicted in Fig. 13.3(A), this EC system had a silver (Ag) working electrode, an integrated silver counter, a quasi-reference electrode (CE/QRE), and microfluidic channels on a polymer substrate. This solid-state, reusable EC sensor provided stable peak potentials for the onsite detection of heavy metal ions in non-deaerated solutions without generating toxic wastes, with a limit of detection of 0.55 ppb at 300 s deposition time.20 In another study, nitrate detection in seawater was performed through direct potentiometric determination using an in-line coupling to an electrochemical desalination module. The all-solid-state potentiometric sensor comprises lipophilic carbon nanotubes (f-MWCNTs) as a working electrode and a miniaturized Ag/AgCl reference electrode on a lipophilic carbon nanotube substrate. Nitrate levels were determined in desalinated seawater, with a limit of detection of 5 × 10−7 M. The schematic representation of this all-solid-state potentiometric sensor is shown in Fig. 13.3(B).18 In another interesting study, a microfluidic arsenic sensor was developed by Kim et al.21 This EC sensor uses a three-electrode system, with carbon as the working electrode, Ag as the counter electrode, and Ag/AgCl ink as the reference electrodes on a disposable polyethylene terephthalate (plastic) substrate, as shown in Fig. 13.3(C). This low-cost microfluidic arsenic sensor combined with a portable EC analyzer is fast and accurate, with a detection limit of 50 μM. Figure 13.3(D) shows a whole-cell-based amperometric sensor for detecting arsenite (As III) in tap and groundwater sources. This unique approach uses a bioreporter in Escherichia coli (E. coli) with the electrode system. The plan involved exploiting the E. coli resistance mechanism against the toxic arsenite via the selective intracellular recognition of arsenite and its removal from the cell using electrochemical signals, which was then detected by a two-electrode cell. In this case, gold (Au) was the working electrode, Ag was the counter, and the reference electrode had E. coli on a plastic substrate. This arsenic-sensitive electrochemical biochip had a detection limit of 0.8 ppb.19

13.2.2.  Optical detection systems Optical-based microfluidic detection systems for monitoring water quality are simple, low-cost systems that are either label-free or labeled to generate signals for the analyte concentration present in a sample. In the label-free method, a direct optical signal is generated when an analyte-transducer interaction occurs. In the labeled method, optical detection involves calorimetric, fluorescence, and luminescence signals emitted from the samples upon excitation with light due to the molecular recognition and interaction between the analyte and the label bound to the transducer.

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Figure 13.2(b) shows a schematic representation of an optical detection system, including the sensing unit (biorecognition element), signal-transducing unit, and analyzer. Typically, biomolecules such as nucleic acids, proteins, cells, antibodies, enzymes, and receptors, which have a high affinity to the analytes via catalysis or affinity bonding, are bound to the signal transducer. Upon biorecognition with the specific analyte, the bonding is converted to a detectable signal through a transducing unit which exploits the evanescent field near the surface of the biosensor to detect the biorecognition event.22 Notably, microfluidic devices have a smaller path length which significantly narrows the sensitivity and detection limits of absorbance, posing hurdles to this system.23 However, efforts have been made to overcome these limitations, as optical signals have other traits such as a low signal-to-noise ratio and insusceptibility to external instabilities.24 Like EC-detection based microfluidic systems, optical-detection based ­systems with different signal-transducing units have been extensively explored. Table 13.2 summarizes the various detection methods that use a microfluidic Table 13.2.    Summary of detection methods in microfluidics.25 Detection Method

Chemicals

Limit of Detection

Laser-induced fluorescence

Sulfite and Nitrite in aqueous solution

1 × 10–6, 0.4 × 10–6 M

UV-LED excitation, fluorescence detection

NO2

30–200 ppbV

On-chip microfluorescence detection

H2S

1 ppbV

Absorbance detection with optofluidic modulator

Methylene blue

7 × 10–3 M

Griess method for nitrite detection on chip

Nitrite in potable water

14 × 10–9 M

Miniaturized chemiluminescence detection on paper device

Chromium (III)

0.02 ppm

Gas-liquid chemiluminescence of luminol-chlorine system

Chlorine

0.2 ppm

MS

Pulsed gas sampling in ion trap assembly on palm portable mass spectrometer

Toluene and dimethyl methylphosphonate (DMMP)

6.4, 52.9 ppm, respectively

SERS

SERS with droplet-based microfluidics

Mercury ions

100–500 ppt

SERS with micropillar array microchannel

Dipicolinic acid and Malachite green

200–500 ppb

Optical

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approach and details the different core technological components employed in optical detection-based systems.25 Li et al. developed a 3D microfluidic paper-based analytical device (3D μPAD) consisting of two layers, pre-treatment and colorimetric detection, to identify six metal ions: Fe(III), Ni(II), Cr(VI), Cu(II), Al(III), and Zn(II). The colorimetric detection ions were numbered 1–6 for Fe(III), Ni(II), Cr(VI), Cu(II), Al(III), and Zn(II) for the pre-treatment zones, and 1–8 for Fe(III), Ni(II), Cr(VI), Cu(II), Al(III), Zn(II), pH, and blank reference, respectively, for the detection zones. Figure 13.4(A) shows the fabrication of μPAD. This method is rapid, simple, selective, sensitive, and user-friendly, and it has excellent prospects for multiplexed on-site analysis. The device has an L-type circuitous flow route design, which can prevent random diffusion of the chromogenic reagents. In addition, the two-layered architecture allows multiple pretreatment and chromogenic reactions for one sample to be applied individually on the same device. Improved color perception, enhanced sensitivity, and an extended detection range were obtained using an LED lamp as an upward lighting source and a smartphone as a color detector. The detection limits were as low as 0.2, 0.3, 0.1, 0.03, 0.08, and 0.04 mg/L for Fe(III), Ni(II), Cr(VI), Cu(II), Al(III), and Zn(II), respectively.26 In a similar study, a 3D origami ion-imprinted polymer microfluidic paper-based chip device was developed to detect Cu2+ and Hg2+ ions with high precision and sensitivity. Here, the surface of the paper was grafted with CdTe QDs by amino processing and the formation of Cu2+ or Hg2+ IIPs. The CdTe QDs complex led to the fluorescence quenching of QDs, thereby revealing the Hg(II) and Cu(II) detection zones, respectively, under UV light, as shown in Fig. 13.4(B). Furthermore, this device demonstrated a shallow detection limit of 0.035 mg/L.27 Figure 13.4(C) shows an SERS-based microfluidic device for detecting heavy metals. The authors of this study claim this is a rapid, one-step preparation of SERS substrate in the microfluidic channel, achieved by synthesizing Ag nanostructures using a one-step electrodless galvanic replacement reaction, which could be completed in less than 10 min. This reaction is made possible by the Cu base in the channel, which reduces Ag ions to nanoparticles in the presence of AgNO3 solution and thus allows the sensing of both chemical and biomolecules. This SERS sensor detected Hg ions in aqueous solution, with an LOD of 1 × 10–7 M and good selectivity against other metal ions.29 In another exciting development, a microchannel capillary flow assay (MCFA) platform was developed by Ghosh et al. to conduct chemiluminescence-based ELISA with lyophilized chemiluminescent reagents to detect the malaria biomarker PfHRP2.30 Figure 13.4(D) shows the schematic design of the MCFA chip, with detailed labeling of the microfluidic components and the developed smartphone-based POCT analyzer. This platform exploits the ultra-high sensitivity of chemiluminescent detection to eliminate handling limitations. Furthermore, the architecture of MCFA platform

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Figure 13.4.    Optical-detection-based microfluidic systems with different core technologies. (A) Fabrication of μPAD,28 (B) Fluorescence sensor,27 (C) SERS-based sensor,29 (D) Chemiluminescence sensor,30 and (E) SPR-based sensor.31 Reproduced with permission from respective publications.

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allows for a sequential flow of assay reagents and an independent execution of the highly sensitive chemiluminescence-based ELISA to detect target biomarkers. This platform interfaces to a smartphone with a USB-OTG port using a customdesigned optical detector. The authors reported an LOD of 8 ng/mL by the smartphone analyzer for this device. Surface plasmon resonance (SPR) optical detection systems also have been used to monitor water quality. In a study by Tokel et al., a microfluidic integrated plasmonic platform was developed to detect pathogens.31 Figure 13.4(E) shows the portable microfluidic SPR-based platform to detect pathogens. (a) The surface-activated disposable microfluidic chip with the inlet and outlet ports and the 50 nm-thick gold-coated glass substrate is mounted on the device. (b) The electronic setup of the device. (c) Scheme for the microfluidic integrated SPR platform, where the activated gold surface with anti-Lipopolysaccharide (LPS) antibody captures the E. coli in the microchannel. This recognition alters the local refractive index captured by the sensor and transferred to a computer for analysis. This SPR-based platform detected E. coli at concentrations ranging from 1 × 105 to 3.2 × 107 CFU/mL in phosphate-buffered saline and peritoneal dialysis fluid. Microfluidic devices were developed using different techniques and diverse materials for detecting different target groups to identify pollutants in water. Figure 13.5 illustrates the divisions based on the sensing unit, the substrates used, and the detection units.

13.3. Fabrication of Microfluidic Systems The demand for micromachine technologies in manufacturing and industrial segments, including biotechnology and pharmaceutical processes, has increased ­significantly over the last two decades due to their immense potential at the micron scale. The history of fabricating microsystems devices begins when the first transistors were made in the 1950s with semiconductors fabricated from photolithography and wet etching methods. Since then, many semiconductor devices, with both simple and sophisticated electronic components, have been fabricated.32 Typically, the fabrication of a microsystem is carried out by forming channels on substrates, punching holes on them, and subsequently bonding them with another cover to seal the channels.33 Performance is enhanced as a result of the miniaturization of devices, which serves as the driving force for developing different analytical systems using different materials and methods.34

13.3.1.  Common materials used for sensor fabrication The first generation of microfluidic devices were fabricated on inorganic materials such as silicon and glass, followed by fabrication on PDMS, plastics, and paper.

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Figure 13.5.    Microfluidic systems developed based on sensing unit, substrate, and detection unit. Reproduced with permission from Ref. [17].

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The following section discusses the different materials and methods employed for fabricating sensors.

13.3.1.1.  Inorganic materials Silicon and glass are among the inorganic materials used in the fabrication of microfluidic devices. In the semiconductor family, silicon gained top priority in microsystem production for its easy availability, and its excellent chemical and thermal stability properties can be used to make complex 2D and 3D microstructures using photolithography and etching methods. Microelectromechanical systems (MEMS) have been developed with silicon substrates since the 1980s. Microfluidics branched from MEMS due to the use of microtechnology to control fluids under given conditions. Figure 13.6(A) illustrates a scheme of the silicon device fabrication process. In an interesting study, researchers made a micromixer device with deep channels with a bonded silicon wafer pair by using a combination of conventional reactive-ion etching (RIE) and wet etching.35 Glass is another material used for microstructure fabrication due to its superior mechanical properties and good optical and insulation properties, as well as its solvent compatibility and stable surface. Six typical techniques for fabricating microstructures on glass are shown in Fig. 13.6(B): wet etching, dry etching, laser, mechanical fabrication, photostructuring, and molding. The glass molding process is a promising method owing to its high efficiency and low cost, which enables faster prototyping.36 Although silicon and glass are heavily used for device fabrication, their rigid nature and high production costs, which include sophisticated techniques or dangerous chemicals for etching, limit (A)

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Figure 13.6.    (A) Fabrication process of a silicon device [39] and (B) microstructures on glass substrate fabricated using six typical techniques [36]. Reproduced with permission from respective publications.

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their vast application in microfluidics. To overcome this challenge, Qi et al. recently fabricated a very low-cost Si-glass microfluidic device with a reduced chip area and a whole-chip manifolding strategy. This device achieved a reliable high-pressure, high-temperature fluid connectivity validated at 130 bar and 95°C, showing its potential use for energy and carbon-capture applications.37 Similarly, to reduce the complexity, high cost, and extensive time required to fabricate conventional glass microfluidic devices, several researchers have developed maskless rapid-production glass-based devices with integrated miniature sensors that can sense pressure, temperature, and pH changes inside the channels during the fluid flow. A picosecond pulsed laser system is used to bore holes and draw complex patterns directly onto the glass, which significantly reduces the fabrication time and chemical usage. This glass-based system enables the study of CO2 storage, water remediation, and hydrocarbon recovery processes.38

13.3.1.2.  Polydimethylsiloxane (PDMS) and plastics The use of polymers for device fabrication was introduced in the 1990s due to their great flexibility, easy availability, and low-cost. Polymers such as elastomers and plastics have become more widely used in the fabrication of microfluidic devices. Elastomers are highly elastic amorphous polymers that are loosely crosslinked and have glass transition temperatures well below room temperature. Polydimethylsiloxane (PDMS) is the most prevalent elastomer in microfluidic device fabrication due to its low-cost, high elasticity, ease in microfabrication, and surface compatibility for cell culture. This encouraged Kaigala et al. to fabricate a high-resolution PDMS microfluidic device in a simple “print, pour, peel, press, and run” procedure. The fabrication involved a consumer-grade wax printer which preserved enough resolution of the channels to demonstrate the sizing and separation of DNA fragments using capillary electrophoresis.40 PDMS also facilitates gas permeability, which is an essential factor for the long-term storage of cells in closed devices.41 However, PDMS has inevitable hitches, such as absorbing small molecules and allowing protein adsorption from the solution. Figure 13.7 shows a scheme that depicts the front-end microchannel fabrication and back-end microfluidic bonding and interfacing polymer strategies such as PDMS and ­ thermoplastics. PMMA, PS, PET, and PVC are other well-known polymers used to fabricate microfluidic devices. These plastics can be reshaped multiple times because they soften at the glass transition temperature (Tg), show better solvent compatibility than PDMS, and have reasonable tolerance to alcohols. However, these plastics are not compatible with ketones and hydrocarbons, and they barely allow the air to permeate, thereby limiting long-term cell culture in a closed chamber.41 A summary of the properties of the materials used for microfluidic device fabrication is shown in Table 13.3, including some of the vital properties of the inorganic

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Figure 13.7.  Fabrication of a microfluidic device using PDMS and plastics. Reproduced with permission from Ref. [45]. Table 13.3.    Summary of materials with various properties that are commonly used in microfluidic device fabrications.41,45 Thermoplastics Material Mechanical property

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materials, PDMS/plastics, and paper that are desirable for selecting a material for device fabrication. Recently, Donohoe et al. fabricated inexpensive microfluidic chips for autonomous environmental analyzers using PMMA substrates that were highly reliable and possessed a rugged design. Here, fluidic chips were integrated into prototype nutrient analyzers which sensed phosphate leakage through the vanadomolybdophosphoric acid method. A 28-day field trial comprising 188  in situ autonomous phosphate measurements showed a commendable LOD of 0.09 μM.42 Polystyrene has been considered an alternative to PDMS substrates owing to its superior biological properties suited for a band of analytical and cell culture

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applications. A low-cost method presented an all-polystyrene device fabrication with integrated electrodes and fluidic tubing to analyze dopamine and NO. This microchip-based flow injection analysis showed an LOD of 130 nM and 1.8 μM for dopamine and NO, respectively.43 Similarly, a PET/paper chip assay platform was recently fabricated in an attempt to sense sulfur dioxide (SO2) concentrations. Upon the addition of a sample, a redox reaction on the chip produced a color change that could be captured and processed using an analytical smartphone app. This attempt presented exciting results, with a high measurement resolution equal to 1.45 ppm/a.u., while the measured results did not deviate over 3.82%.44 In another exciting development by Gao et al., nine materials were tested for compatibility with concentrated nitric acid media to build microcolumns for a microfluidic platform, and PVC was found to possess the highest compatibility. Here, 100 μL and 20 μL solid-phase separation columns in microfluidic devices were constructed with polyvinyl chloride, which provided an efficient actinide matrix removal from trace element impurities. The use of 100 μL AG MP-1 microcolumn and 20 μL microcolumns reduces the overall solution volume by 90% and 98%, respectively.7

13.3.1.3.  Paper Paper is a cheap, abundant, readily available, and renewable source that is increasingly used as a substrate for device fabrication. Microfluidic devices developed from the paper aim to provide low-cost diagnostic tools that are affordable, portable, and disposable.46 Most microfluidic channels must be sealed, but a paperbased microfluidic device does not require a seal. Figure 13.8 illustrates different fabrication techniques for paper-based devices, such as wax printing, photolithography, flexographic printing, and inkjet printing. Paper-based microfluidic devices offer significant benefits, including a robust platform, low fabrication cost, energy-efficiency, tiny sample quantity, and simultaneous assays. Paper-based microfluidic devices have garnered significant attention in water quality monitoring. For instance, one study discussed using miniaturized wax printing to produce higher-resolution and high-fidelity microfluidic paper devices.47 This highly tunable method is easily controlled by varying the concentration of periodate or the reaction time. The miniaturized surface area allowed for the fabrication of functional channels, with widths as small as 301 μm and hydrophobic barriers widths as small as 387 μm. In another study, photolithography was used to develop a very stable fabrication method for sensing iron in water samples with high resolution and precision using calorimetry. A photomask printed with a 3D printer was used to create hydrophilic and hydrophobic zones on chromatography paper. The hydrophilic channel and hydrophobic barrier had a width of 500 μm and 100 μm, respectively.48 In another study, screen printing was used for

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Figure 13.8.    Different fabrication techniques used for paper-based microfluidic devices. Reproduced with permission from Refs. [46, 51, 52]. (A) Scheme outlining the fabrication through wax-screen printing technique, (B) Paper plates fabricated using a photolithography process. (Ba) 96-zone plate, (Bb) 384-zone plate, (Bc) Alternative design of a 96-zone paper plate (Bd) 96-zone plate with volumes of liquid up to 55 μL, (Be) 384-zone plate with volumes of liquid up to 10 μL and (Bf) Time-lapse images of the details shown in part C, illustrating the mixing of two solutions and reaction, (C) Scheme showing the fabrication of paper microfluidics using nitrocellulose membrane through the wax printing process, (D) Scheme describing the fabrication of microfluidic multianalyte chemical sensing paper using an inkjet printing method, and (Ea) Flexography unit scheme and (Eb) Relief patterns in the printing plate depicting the hydrophobic regions anticipated on paper.

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microfluidic paper-based device fabrication, as demonstrated by Sameenoi et al.49 A one-step approach using polystyrene and a patterned screen was adopted to fabricate an analytical device to detect the analysis of H2O2 and antioxidant activity. This method yielded the smallest hydrophilic channel width of 670 ± 50 μm and a hydrophobic barrier of 380 ± 40 μm. The results of H2O2 in real samples using distance-based measurement showed a 95% confidence level to a conventional titration.49 Contact stamping is another method for fabricating paper-based microfluidic devices. One report showed a handheld stamping process to fabricate a microfluidic device on paper. The authors designed a pattern on a stainless-steel stamp, which was stamped on an oxidized paper surface to convert hydroxyl into aldehyde groups at a high temperature. Upon applying a pressure of ca. 0.1 MPa for 2 s, the channel and barrier widths of 2.6 ± 0.1 and 1.4 ± 0.1 mm, respectively, were obtained. This allowed for the immobilization of enzymes on the surface, which was useful for detecting glucose, UA, bovine serum albumin (BSA), and nitrite, with an error rate less than 4%.50 Although paper-based microfluidic devices have immense potential, their limitations, such as sample evaporation/ retention, more complex and/or multiple steps for analysis, must be remedied for a viable future.47

13.4. Different Microfluidic Devices for Detecting Different Pollutants This section presents different microfluidic devices based on the electrochemical and optical methods used to detect pollutants in water.

13.4.1.  Heavy-metal detection Heavy metals are harmful pollutants found in drinking water. Many microfluidic devices have been developed to detect the presence of heavy metals in water bodies. Based on the microfluidic electrochemical system, after incorporating a threeelectrode system (Au-Ag-Au) into the microfluidic channel, Chen et al. developed an Hg2+ detector with high sensitivity and reproducibility.53 This sensor could sense as low as 3ppb using anodic stripping voltammetry and differential pulse voltammetry electrochemical analysis. A glassy electrode modified with gold nanoparticles was used for As detection.54 The detection limit for this sensor has been improved55 by modifying microfluidic devices using gold nanoparticles and single-walled carbon nanotubes (SWCNTs). As(III) could be detected up to 4.5 ppb by employing SWASV measurements, which provides very quick and sensitive results. Recently, EC sensors were screen-printed, and this technique makes it very easy to develop microfluidic channel-based sensors. By using

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metalized carbon and silver-based inks for printing electrodes, CV-based microfluidic sensors were developed for detecting Pb2+ and Hg2+.56 Very recently, paperbased microfluidic electrochemical sensing devices (µPED) have been gaining more attention because they are inexpensive, easy to develop, and environmentally friendly. µPEDs are devoid of pumps and tubes due to its capillary forcebased functioning. By using this µPED, Shi et al. detected Pb2+ and Cd2+.57 To detect heavy metals, many microfluidic channel-based sensors have been developed using optical methods such as calorimetry, fluorescence, SERS, and SPR, but the most predominate method is calorimetry. Most calorimetric analyses are conducted using paper-based calorimetric devices (µPADs). Ion contaminants such as Fe, Cu, Ni-Cd, and Cr were analyzed using µPADs, which is highly sensitive and reliable. Wang et al. developed a paper-based sensor by using wax printing, tape, and stacking supported by a smartphone camera that was fast, inexpensive, and user-friendly.58 To integrate nanotechnology into the sensor technology, researchers introduced gold nanoparticles (AuNPs) as the sensor material in the calorimetric probe.59 In the presence of As, this nanoparticle-based nanochannel develops a visible bluish-black color precipitate at the zone of the interface. One problem related to this analysis is that it provides only qualitative results. To provide both quantitative and qualitative results, Chen et al. used platinum nanoparticles, which oxidize tetramethylbenzidine in the presence of Hg2+ ions to detect Hg2+ and could detect the concentration up to 0.01 mM.60 Another exciting development was the invention of portable and power-free microfluidic devices that can detect Pb2+ based on the work by Kim et al.61 In this study, the researchers used 11-mercaptoundecanoic acid (MUA)-modified AuNPs, which changed the color from red to purple after detecting the presence of Pb2+.62 Bioassays based on bacteria, particularly E. coli, have been used to detect As (III) and many other heavy metals in drinking water.63 After reacting with As (III), E. coli emitted green fluorescent due to its natural defense system, and this property was used to detect As; however, it had a low detection limit of about 50 mg/L.64 In addition, SERS has been widely used for detecting As in water.65 Silver nanoparticles modified with glutathione/4-mercaptopyridine are used to detect As, which have a high affinity toward glutathione.66,67 As a result, silver nanoparticles aggregated in the presence of As produce SERS signals. Although sensors made using bioassays and modified silver nanoparticles are fast and good for detecting pollutants, most do not have quantitative analytical capabilities.

13.4.2.  Nutrients Nutrients are a major cause of water pollution, and thus sensors that can detect nutrients quickly and accurately are indispensable. Scientists have developed many types of sensors based on microfluidic devices. For example, microsensors have been fabricated to detect nitrate in groundwater.68 In a remarkable invention,

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an electrochemical device powered by a mobile phone was developed to detect nitrate concentrations of up to 0.2 ppm using an app installed on the phone.69 The advantage of such a mobile application is that the data can be directly uploaded to cloud-based servers, making it possible to map a water body over time.70 Nuria Lopez Ruiz et al. developed a smartphone-based paper microfluidic device for detecting nutrients in water.71 A screen-printed sensor was developed using Ag/ AgCl to detect nitrate and potassium ions, respectively, with a detection limit of 0.81 mg/L and 9.56 mg/L.72 Many researchers have tried different approaches to enhance the transfer of electrons between the electrode and the electroactive analytes, such as multi-wall carbon nanotubes, single-wall carbon nanotubes, graphene, and other metal nanoparticles for modification. For example, detecting nitrogen contamination in seawater using any detection method is challenging due to the presence of NaCl; however, this issue was solved by adding a special compartment to the microfluidic detector for desalination. A combination of CuNPs, MWCNTs, and reduced graphene oxide can be used to detect nitrate levels in water.73 For instance, a graphene foam (GF) is used to develop an electrochemical electrode to detect nitrate with the help of the EIS technique.74 Furthermore, TiO2 electrospun on GF was used to increase nitrate selectivity by incorporating nitrate reductase enzymes. For optical detection of nitrate, the nitrate must be reduced to become more reactive. This can be achieved using hydrazine, zinc, nitrate reductase, and UV light. For example, a microfluidic device was coated with PMMA layers in varying thicknesses on a microfluidic channel to detect nitrate ions in water.75 A paperbased microfluidic channel loaded with zinc microparticles was developed to detect both nitrite and nitrate, with a detection limit of 1.0 mM and 19 mM, respectively. In an LED-based optical detection system developed by Cogan et al. to detect nitrite and nitrate in normal and wastewater, a wireless unit was used for communication between the detection and analysis units.76 In another study, Xiong et al. used azo dye to develop a cost-effective calorimetric fiber optic chemical sensor.77 A change in light intensity was observed upon interaction with azo dye, and the sensor had a detection limit of 7mg/L.

13.4.3.  Pathogens To protect public health, pathogens drinking water must be detected. Microfluidic electrochemical devices that can quickly detect pathogens such as E. coli.78 are especially important. Generally, a DNA/protein/cell probe can detect pathogens in water using a direct or amplification method. Each of these methods immobilizes the probe DNA on the working electrode, which specifically captures E. coli and thus provides a qualitative analysis of a water sample.79 Likewise, a biochip was developed to detect E. coli, where the surface of the sensor was modified with a

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self-assembled monolayer of mercaptoundecanoic tagged with polyclonal rabbit anti-E. coli antibody and sandwiched by horseradish peroxidase.80 This device had a detection limit of 50 colony-forming units. In another study, a device to detect the hepatitis B virus was developed by using a paper-based biosensor functionalized with AgNP-DNA.81 Electrochemical impedance spectroscopy (EIS) can be used to detect pathogens through various electrochemical techniques such as adsorption, capacitance, diffusion coefficients, and charge transfer resistances. Positive dielectrophoretic uses impedance measurement to detect E. coli in samples, where a change in impedance is recorded when cells are trapped on the sensor’s electrodes.82 As the next stage development of this system, Jiang et al. used a Bluetooth-enabled portable microfluidic system connected to a smartphone, using an app that functions as an LCR meter.83 In this sensing system, water samples pass through a nanoporous filter paper to trap E. coli cells, which would change the system’s impedance that can be detected very quickly. Attempts have been made to monitor E. coli in water bodies in real-time.84 One such attempt by Gosh et al. integrated antiSalmonella antibodies on the electrode arrays to detect Salmonella typhimurium qualitatively and quantitatively.85 Calorimetric optical detection is predominantly used for detecting pathogens due to its cost-effectiveness and simplicity.86 The advantage of calorimetric optical detection is the ability to use a smartphone or a digital camera as the detection unit to analyze and interpret data. Using a paper-based microfluidic sensor, Wang et al. demonstrated the detection of E. coli with the help of a smartphone camera to detect the color changes.87 Park et al. developed a highly effective sensor by incorporating a multichannel pre-loaded with antibody-conjugated beads to detect multiple pathogens in one step. The samples are analyzed using a smartphone with an appropriate app.88 Fluorescence is another standard method used in optical sensing systems to detect bacterial contamination in water. One such device was connected to a cloud-based data management system, which can be highly useful for mapping water bodies89 over time. For real-time pathogen detection, magnetically labeled bacteria were developed. With the help of streptavidin-coated magnetic markers, the bacteria were brought into the microfluidic channels and placed in a gradient magnetic field. This enables the unique acceleration of the magnetic markers based on the difference in the mass of different bacteria, which allows pathogens to be detected in real time. Researchers have also attempted to integrate the polymerase chain reaction into microfluidic sensors.90 Other serious waterborne pathogens include viruses (e.g., hepatitis A & E, enteroviruses) and protozoans (e.g., cryptosporidium and giardia) which contribute to the large global disease burden. Currently, only a few technologies can identify such pathogens at an early stage, such as a single protozoan cyst, for example. Some researchers have recently demonstrated the identification of live

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and inactive C. parvum oocysts with more than 90% certainty using a microfluidic impedance cytometry device,91 which also detected damaged and excysted oocysts. Interestingly, a sensor that used the RT-PCR technique and fluorescence was integrated with a grooved copper heating block for heating the system for the amplification; as a result, the device could detect the rotavirus RNA within one hour.92 In another study, Connelly et al. developed an integrated microfluidic sensor to detect feline calicivirus (FCV) as a model organism for norovirus, with the detection limit of 1.6 × 105 PFU/mL.93 Likewise, Chung et al. developed a smartphone-based microfluidic paper analytic device to detect norovirus by directly imaging and counting the aggregation of fluorescent antibody.94 An extremely low limit of detection was achieved, in which 1 genome copy/μL in deionized water and 10 genome copies/μL was reclaimed in wastewater.

13.5. Conclusion Due to the increasing scarcity of fresh drinking water worldwide, monitoring the quality of all bodies of water is urgently needed to safeguard from pollution and contaminants. Frequent water quality examination is required on a timely basis to ensure potable water quality, for which the microfluidic devices are indispensable. Currently, the detection of water contaminants is carried out in labs using specialized equipment, which is time-consuming and costly. The introduction of microfluidic devices has relatively eased this process through numerous platforms that can rapidly detect potential contaminants in water accurately and sensitively. The ultimate goal is to develop a one-stop solution that can detect all types of water contaminants in a single test in the form of an integrated, all-in-one chip that is portable, highly specific, and inexpensive. Such a device would be a great solution to most health and environmental problems caused by water pollution and ensure that water bodies are free from pollution.

References   1. G. Tuna, O. Arkoc, and K. Gulez. Continuous Monitoring of Water Quality Using Portable and Low-Cost Approaches., Int. J. Distrib. Sens. Netw., 2013, 9(6), 249598.   2. J. Wang et al. Microfluidics: A New Cosset for Neurobiology, Lab Chip, 2009, 9(5), 644–652.   3. B. K. Gale et al. A Review of Current Methods in Microfluidic Device Fabrication and Future Commercialization Prospects, Inventions, 2018, 3(3).   4. B. C. Dhar and N. Y. Lee. Lab-on-a-Chip Technology for Environmental Monitoring of Microorganisms, BioChip J., 2018, 12(3), 173–183.  5. H.-F. Li and J.-M. Lin. Applications of Microfluidic Systems in Environmental Analysis, Anal. Bioanal. Chem., 2009, 393(2), 555–567.

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Chapter 14

Underwater Self-Healable Materials for Sensing Applications Muhammad Khatib*,§, Tan-Phat Huynh†,¶ and Hossam Haick*,‡,ǁ The Department of Chemical Engineering and The Russell Berrie Nanotechnology Institute Technion — Israel Institute of Technology Haifa 3200003, Israel

*

Laboratory of Physical Chemistry, Faculty of Science and Engineering Åbo Akademi University Porthaninkatu 3-5, FI-20500 Turku, Finland

†

School of Advanced Materials and Nanotechnology, Xidian University, Shaanxi, 710126, P. R. China

‡

[email protected]

§

¶

[email protected]

[email protected]

ǁ

14.1. Introduction Much attention has been devoted to self-healing due to its potential to enhance the lifetime and performance of multiple synthetic systems.1–4 This ability is akin to the miraculous survival capability found in all living organisms that allows them to recover from structural and functional damage incurred during their lifetime. The use of self-healing has also been pursued for underwater applications even though this presents a major challenge due to the harsh underwater conditions (Fig. 14.1). The behavior of polymers and the damage they incur in underwater ­environments has been investigated for many years, yet their application still faces 409

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^ǁĞůůŝŶŐ

Figure 14.1.  Illustration of the possible internal and superficial damages caused by water. Reproduced with permission from Ref. [9]. Copyright © 2014, Springer.

several challenges. The first challenge is the plasticization effect of water molecules on polymers. Polymeric structures, the main building blocks of self-healing applications, tend to absorb huge amounts of water, which leads to a detrimental decrease in their mechanical strength, i.e., acceleration of degradation. The amount of water absorbed at equilibrium depends greatly upon the chemical structure (e.g., hydrophilicity) and morphology (e.g., porosity) of the polymer. The second challenge is the degradation of most self-healing materials, which are based on water-sensitive bonds, by hydrolysis under a wide range of conditions.5 Thus, designing self-healing materials requires dedicated approaches that avoid or neutralize the continuous damaging effects of water. This task is not trivial since the capabilities of ambient self-healable materials cannot be forecast under water. Solving these challenges will lead to new materials with improved durability, not only for under water applications, but also for any system that might be exposed to wet conditions (e.g., rain and sweat, in the case of wearable skin patches). Progress in material development will also facilitate advancements in underwater soft robotics.6–8 This chapter describes the current progress in combining water-insensitivity and underwater self-healing ability in polymers. The main advantages and disadvantages of each approach are addressed and carefully discussed. This chapter continues with a demonstration of how the chemistries apply in the fabrication of underwater self-healing devices. Perspectives and ideas regarding the orientation of this field in the future are also discussed.

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14.2. Design Strategies for Self-Healing Underwater Self-healing materials are divided into two main groups: intrinsic and extrinsic.3,10,11 Intrinsic materials are self-healing systems based on reversible bonds that break upon stress and then reform to recover the properties of the material. Extrinsic materials are obtained by introducing and integrating some additives into the structure of the polymer in the form of capsules or vesicles, which perform the healing process.3 These miniature containers include the required healing agents for recovering structural damage. Once the material is damaged, capsules or vesicles break open to release their contents and begin the process of recovery. Extrinsic self-healing materials have many more advantages than intrinsic materials, such as the ability to recover large-volume damage by controlling the reaction kinetics and vascular delivery rate.12 However, the mechanical properties of these systems are inferior due to the high content of (encapsulated) healing agents and they relatively have complicated design and use (i.e., it is difficult to disperse additional active materials). Therefore, extrinsic self-healing materials do not seem to be the candidate of choice for self-healing devices, especially for underwater applications, because water molecules will deactivate the encapsulated healing agents or inhibit polymerization of the monomers released from ruptured containers. Moreover, slow self-healing is another limiting factor. The only undoubtedly plausible choice is to impart intrinsic self-healing ability into water-insensitive polymers, which will ensure high structural dynamicity and provide fast and multi-time self-healing. Accordingly, for underwater self-healing materials, researchers have focused mainly on intrinsic self-healing, which is based on the reversible formation of molecular interactions/bonding between functional groups (or “active sites”) designed intrinsically on the polymer backbone. The main challenge in developing new underwater self-healing materials is the need to stabilize the polymeric structure and the reversible bonds required to complete the self-healing process, even in the presence of water. Most of the failure with existing self-healing materials is caused by the interactions between the water molecules and the functional groups, i.e., those involved in the reversible bonds between the polymer chains. In this case, water molecules can be seen as interferents. For example, most self-healing materials are based on reversible hydrogen bonds that can be interrupted by strong hydrogen bond formations of water molecules; therefore, the choice of chemistries for molecular interactions under water is relatively limited compared with those in air, because the interaction between functional groups, which leads to self-healing, has to surpass those of the functional group and water molecules.13 Consequently, it is important to look for strong bonding to overcome any interference of the water molecules. To address the many challenges of underwater self-healing, researchers have turned to the blue mussel (Mytilus edulis, common mussel), which can achieve

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rapid, tough, and long‐lasting adhesion in seawater environments, where most synthetic glues fail.14–16 The blue mussel’s outstanding adhesion system is connected to the abundance of L-3,4-dihydroxyphenylalanine (DOPA) in mussel foot proteins, which allows self-healing of the byssal thread under seawater.14,16–18 Chemistries of DOPA, particularly the catechol moiety, have exceptional chemical versatility which allows for the on-demand design of the material’s chemical and physical properties. So far, the catechol-containing natural molecules have been used for applications such as (i) preparing stable surface anchors, adhesives, and multifunctional coatings onto almost any material; (ii) developing biocompatible materials with multiple clinical advantages; and (iii) integrating them into polymer chemical structures as reversible cross-linkers to obtain self-healing materials.19 In addition, catechol-functionalized polymers have shown remarkable selfhealing ability underwater by relying on varying bonds, including metal coordination, hydrogen, and reversible boron ester (B-O). This chemistry creates a successful example of the use of chemical design to obtain underwater self-healing materials, which has inspired many researchers to continue searching for and developing alternatives. We present the main chemistries and strategies that are based not only on catechol but also on other synthetic chemistries that have been successfully applied in the development of underwater self-healing materials. This includes several chemistries, such as covalent bonds, hydrogen bonds, coordination, dipole–dipole, host–guest, and hydrophobic interactions, along with their combinations, which have been successfully applied in different studies (Fig. 14.2).

14.2.1.  Dynamic covalent bonds For designing self-healing materials, dynamic bonds, either covalent or noncovalent, have been considered.4,10 Non-covalent interactions are relatively weak underwater, with few exceptions, such as quadruple hydrogen bonding, highvalence metal chelation, and host–guest interaction,25 whereas dynamic covalent bonds usually have greater strength and more controllable reversibility, e.g., by changing the pH.26 Many types of dynamic covalent bonds or structures, including urea bonds,25 disulfide bonds,27 and Diels–Alder chemistry,28 have been reported. However, for underwater self-healing materials, the use of covalent bonds has been limited to only a few examples. Recalling the mussel-inspired chemistry, catechols can participate in reversible covalent B−O bonding with boronic acids and their derivatives (Fig. 14.2(a)).29–31 Few examples of underwater self-healing polymers based on B−O reversible bonds have been introduced. The resulting materials are often in the form of hydrogels, prepared by using either boronic acids as cross-linkers or boronic acid-derived polymers to react with catechol-functionalized polymers.32–34 These hydrogels have self-healing ability over a wide pH range.

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(a)

(b)

(c)

(e) (d)

Figure 14.2.    Examples of dynamic reversible bonds used and applied in self-healing underwater materials. (a) Underwater self-healing covalent bond based on boronic acid and catechol hydroxyls. Reproduced with permission from Ref. [20]. Copyright © 2018, ACS Publications. (b) Metalcoordination between metal ions and catechols. Reproduced with permission from Ref. [21]. Copyright © 2016, WILEY‐VCH. (c) Host guest interactions used for underwater self-healing. Reproduced with permission from Ref. [22]. Copyright © 2013, WILEY‐VCH. (d) An example of hydrogen bonds between two catechol groups used for underwater repair. Reproduced with permission from Ref. [23]. Copyright © 2014, Nature Publishing Group. (e) Combinatorial interactions: hydrogen bonds, disulfide bonds, and metal coordination used together to prepare a very efficient self-healing material. Reproduced with permission from Ref. [24]. Copyright © 2018, AAAS.

Kim et al.34 have shown that dynamic cross-linking of catechol-functionalized polymers with p-phenyldiboronic acid (PDBA) through B−O bonds is the key to realizing non-swellable (12 months), mainly due to the inclusion of EC that provides steric

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Figure 15.12.    Schematic of general approach for this work.64

stabilization.63 Figure 15.13 shows the Raman spectra of shear-exfoliated graphene dispersions on SiO2/Si wafers. The data exhibit distinct graphene peaks122 compared to that of bulk graphite. Also, low D/G peak ratios are observed, indicating quality low-defect graphene. In addition, the small D peak can be related to flake edge effects, as suggested by the lack of a correlation between the intensity ratio of the D/G peaks and the FWHM of peak G (as shown in Fig. 15.13(c)).123,124 Furthermore, X-ray photoelectron spectroscopy (XPS) analysis of the ink confirms the small number of defects present on the exfoliated flakes. For the XPS analysis, the inks were printed on 6H-SiC substrates so that the 282.5 eV C-Si peak can be used as a reference. Another six peaks were identified (Fig. 15.14), namely the primary graphene peak (283.4 eV), four carbon defect peaks (D1, D2, D3, and D4), and the π–π* loss peak. In addition, AFM characterization of individual graphene flakes indicates an average thickness of 4–6 nm, suggesting that a majority of flakes consist of many-layer graphene. Some graphene flakes with monolayer and bilayer thicknesses were also observed.

15.4.3.2.  Characterization of graphene-based inks The inks were characterized mainly by their thermal and rheological properties (e.g., viscosity) as shown in Fig. 15.15. Thermogravimetric analysis (Fig. 15.15(a)) of the prepared inks suggests that all inks exhibit similar behavior

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Figure 15.13.    (a) Representative spectra of graphene dispersed in EC/EtOH (blue) compared with bulk graphite (orange). Histogram of 20 spectra collections of (b) D to G peak intensity ratio and (c) I(D)/I(G) ratios as a function of the FWMH of the G peak.64

with two main regimes: initially terpineol evaporation and decomposition of the EC. However, it was observed that for inks with higher EC concentrations, decomposition initiates at higher weight% due to the larger mass of EC. Also, as graphene concentration increases, the initial temperature for the decomposition onset was observed to decrease (see 1:1 and 1:2 data, no EC).

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Figure 15.14.    XPS analysis exhibiting the C1s peak of exfoliated graphene deposited on SiC and heated to 415°C. Due to the low total contribution and wide distribution of states, the defect peak positions are approximate to ±0.2 eV.64

Viscosity measurements were taken conducted to characterize the rheological behavior of the formulated inks. The general behavior exhibited by the inks with increasing EC content is non-Newtonian shear thinning. We further studied the results by fitting Bingham, Herschell Bulkley, and Power Law models125 to the acquired data. It was found that the Herschel Bulkley and the Power Law models fit better, with the Power Law exhibiting higher R2 values in most cases. Therefore, we propose that the graphene-based inks exhibit shear-thinning/pseudoplastic behavior.

15.4.3.3.  Characterization of printed graphene films The printed graphene films using the 8:1 ratio ink were characterized morphologically using scanning electron microscopy (SEM) and electrically using two- and four-point probe measurements. The films were characterized with respect to the number of printed layers, which varied from 1 up to 10. Figure 15.16 depicts falsecolor SEM images of one printed layer (1 PL) and 10 PL films, respectively. The 1 PL films exhibited a poorly connected graphene network, with r­ elatively large-sized EC clumps on top. However, the 10 PL films exhibited a coherent graphene flake network, with additional graphene flakes covering the EC residual clumps, thereby demonstrating a scaffolding or “tent-pole” effect.

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100

Initial Weight (%)

80

60

40 Solvent Evaporation

Polymer Decomposition

20

0 0

50

100

(b)

150 200 250 300 Temperature (°C)

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1:1, no EC 1:1, 10%EC

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2:1, no EC 2:1, 10%EC 8:1, no EC

400

200

0

0

50

100

150

200 250

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Shear Rate

(sec–1)

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Figure 15.15.    (a) TGA of various inks. The transition from solvent evaporation to polymer decomposition regime is indicated by the dotted line. (b) Viscosity curves for varying ratios of graphene dispersion and terpineol, with and without EC.64

This out-of-plane orientation of the graphene sheets can be beneficial for sensor applications, as it enhances the surface area available for sensing. From the electrical characterization experiments, the 10 PL film exhibited satisfactory conductivity and a linear I-V response, suggesting Ohmic behavior (Figure 15.17). On the other hand, the 1 PL film exhibited poor conductivity, and

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(b)

(c)

(d)

(e)

(f)

Figure 15.16.    SEM of 1 PL(a–c) and 10PL(d–f). (a, d) are viewed normal to the surface. (b, c, e, f) are shown at a shallow angle. 10 PL samples show complete substrate coverage and increased ­surface roughness, while 1 PL samples exhibit gaps in flake layering on the underlying glass substrate.64

(a)

(b)

Figure 15.17.    (a) 2-point IV measurements showing better electrical conductivity of the 10PL film compared with the 1PL film. (b) Sheet resistance as a function of the number of printed layers, as determined by TLM analysis.64

thus confirms the SEM observations of a poorly connected graphene flake network. It is worth noting that the graphene films were printed onto SiO2/Si wafers while the Ti/Au contacts were wire-bonded to them afterward. The characterization took place in an Ar environment, using both two-point probe IV and

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four-point transmission line measurements (TLM). TLM analysis suggested a decrease in sheet resistance as the number of printed layers increased. In particular, a noted transition was observed for 1 PL and 2 PL films. We conclude that this significant resistance decrease is due to the attainment of a critical graphene sheet concentration (for 2 PL films) that leads to a continuous network formation. We further observed from SEM analysis that each subsequent printed layer tends to fill gaps between the graphene flakes, and after 2–3 printed layers, no underlying substrate locations are visible. However, since only a fractional layer is printed each time (due to filling preexisting gaps) the film resistance change per printing pass is minimal beyond the 2–3 PL threshold.

15.4.3.4.  Sensor fabrication and characterization Graphene-based sensors for detecting acetone in water were prepared through shear-exfoliation of graphite flakes in a solution of ethanol and ethyl cellulose. We focused on sensing acetone because it is water-soluble and can easily leach into ground water from various sources (e.g., solvents, paints, cleaners). Optimized printing of the sensor patterns was performed using an extrusion pressure of 207 kPa and a nozzle speed of 3 mm s−1. In addition, 1 cm2 SiO2/Si wafers (oxide layer 300 nm-thick) were used as substrates during printing. Following each printed layer, the films were dried in an ambient air oven at 70°C for 10 min, followed by 250°C for 1 h. This process was repeated to form a 10-printed layer device (10 PL). Electrical contacts made up of 30 nm Ti and 300 nm Au were deposited by e-beam evaporation, and wiring to external posts from the contacts was done using a West Bond Model 74776E wire bonder (Fig. 15.18).

Figure 15.18.    (left) Close up image of sensor device for measuring water-acetone samples. White scale bar is 1cm. (right) Testing bench with sensor used for measuring. The white and red jigs were 3D-printed using PLA in an Ultimaker 2+ printer.

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Figure 15.19.    (left) Normalized resistance change vs. time with the addition of DI water with varying acetone concentrations. The percentages shown represent volume % of acetone in DI water. (right) Film morphology and delamination following multiple measurements. White scale bar is 0.5 cm.

Measurements of the sensor were taken by first recording the 2-point measured current passed through the printed film at a 1V bias for a given period of time (typically around 200 s) to allow the resistance to stabilize before pipetting a 20 μL drop of a prepared water-acetone mixture to the center of the film. The change in current through the film was monitored for approximately 200 s post-addition of the liquid. From the measured current and applied voltage, the normalized resistance change during the course of the tests was determined (Fig. 15.19). At lower concentration of acetone, the sensor response generally consisted of an initial rise and then a plateau of the film’s resistance change. At higher acetone concentrations, the gradual recovery of the sensor’s initial resistance (i.e., a decrease in the resistance change) is due to the evaporation of the acetone over the course of data collection. Throughout the testing the sensor’s electrical responses remained relatively stable, indicating the potential reusability of the device setup. However, film delamination was observed after numerous measurements (Fig. 15.19, right); this suggests the device may be appropriate for a limited-use, disposable-type sensor. The film delamination can be attributed to the adsorption of water into the film’s structure during the course of testing, with the film swelling and reducing the adhesion between the graphene flakes and polymer used during the printing process. Therefore, improving the lifespan of this type of resistive sensor requires incorporating the exfoliated graphene into a matrix material suitable for continuous water/solvent exposure.

15.5. Conclusions and Future Directions This study focuses on the potential of additive manufacturing (direct ink writing) to provide a scalable means for fabricating low-cost environmental sensors.

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Specifically, we showed the step-by-step process, from formulating starting inks to pneumatically direct writing them into functional sensor patterns. Direct writing is an enabler, because it combines pattern design freedom with the ability to deposit various functional nanomaterial-based inks with minimal waste in a toolfree, lithography-free, environment. In particular, the ability to formulate various multifunctional inks using inorganic and organic components adds to the versatility of this additive manufacturing process. Ink formulation and characterization studies are of paramount in terms of controlling ink rheology and therefore printing fidelity. In this chapter, we demonstrate the versatility and potential of graphene as a functional nanomaterial ink component for direct writing water sensors for detecting acetone. However, there is still considerable work to be conducted for the direct writing of the next generation environmental sensors. Specifically, improving ink-substrate compatibility is very important for enhancing sensor lifetime and avoiding adhesive and/or cohesive failure after repeated use. Also, the ability to improve the quality of printed electrical contacts and their compatibility with the functional sensor material is another area that further fundamental studies are needed. Furthermore, the investigation of multicomponent functional colloidal inks (e.g., metal nanoparticle — graphene composite) for direct writing is needed in order for designed sensors with improved performance. In addition to these areas, the development of new printers with multimaterial printing capabilities and in situ monitoring diagnostics should be considered. This will not only allow the printing of more complex compositions and structures but also will enable industrial scaling up which in turn is expected to reduce manufacturing costs. In summary, we believe that direct ink writing of graphene-based functional materials for environmental sensors is a method with high potential and it may hold the key for developing the next generation of large-area, low cost, devices on demand.

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 71. Y. Arao, F. Mori, and M. Kubouchi. Efficient Solvent Systems for Improving Production of Few-Layer Graphene in Liquid Phase Exfoliation, Carbon N. Y., 2017, 118, 18–24.   72. S. Liang et al. Effects of Processing Parameters on Massive Production of Graphene by Jet Cavitation, J. Nanosci. Nanotechnol., 2015, 15(4), 2686–2694.  73. A. E. Del Rio Castillo et al. High-Yield Production of 2D Crystals by Wet-Jet Milling, Mater. Horizons, 2018, 5(5), 890–904.   74. J. Park, Y. S. Kim, S. J. Sung, T. Kim, and C. R. Park. Highly Dispersible EdgeSelectively Oxidized Graphene with Improved Electrical Performance, Nanoscale, 2017, 9(4), 1699–1708.   75. L. Wei et al. Spontaneous Intercalation of Long-Chain Alkyl Ammonium into EdgeSelectively Oxidized Graphite to Efficiently Produce High-Quality Graphene, Sci. Rep., 2013, 3, 1–9.   76. I.-Y. Jeon et al. Edge-Carboxylated Graphene Nanosheets Via Ball Milling, Proc. Natl. Acad. Sci., 2012, 109(15), 5588–5593.   77. T. Lin et al. Facile and Economical Exfoliation of Graphite for Mass Production of High-Quality Graphene Sheets, J. Mater. Chem. A, 2013, 1(3), 500–504.   78. W. S. Hummers and R. E. Offeman. Preparation of Graphitic Oxide, J. Am. Chem. Soc., 1958, 80(6), 1339.  79. K. Parvez, S. Yang, X. Feng, and K. Müllen. Exfoliation of Graphene Via Wet Chemical Routes, Synth. Met., 2015, 210, 123–132.   80. G. Goncalves, P. A. A. P. Marques, C. M. Granadeiro, H. I. S. Nogueira, M. K. Singh, and J. Grácio. Surface Modification of Graphene Nanosheets with Gold Nanoparticles: The Role of Oxygen Moieties at Graphene Surface on Gold Nucleation and Growth, Chem. Mater., 2009, 21(20), 4796–4802.   81. M. P. Lavin-Lopez, J. L. Valverde, L. Sanchez-Silva, and A. Romero. Solvent-Based Exfoliation via Sonication of Graphitic Materials for Graphene Manufacture, Ind. Eng. Chem. Res., 2016, 55(4), 845–855.   82. S. Aslam, T. H. Bokhari, T. Anwar, U. Khan, A. Nairan, and K. Khan. Graphene Oxide Coated Graphene Foam Based Chemical Sensor, Mater. Lett., 2019, 235, 66–70.   83. Y. Su et al. Direct Writing of Graphene Patterns and Devices on Graphene Oxide Films by Inkjet Reduction, Nano Res., 2015, 8(12), 3954–3962.  84. J. D. Fowler, M. J. Allen, V. C. Tung, Y. Yang, R. B. Kaner, and B. H. Weiller. Practical Chemical Sensors from Chemically Derived Graphene, ACS Nano, 2009, 3(2), 301–306.   85. H. G. Sudibya, Q. He, H. Zhang, and P. Chen. Electrical Detection of Metal Ions Using Field-Effect Transistors Based on Micropatterned Reduced Graphene Oxide Films, ACS Nano, 2011, 5(3), 1990–1994.   86. Y. Guo et al. General Route Toward Patterning of Graphene Oxide by a Combination of Wettability Modulation and Spin-Coating, ACS Nano, 2010, 4(10), 5749–5754.   87. G. Lu, L. E. Ocola, and J. Chen. Reduced Graphene Oxide for Room-Temperature Gas Sensors, Nanotechnology, 2009, 20, 1–9.

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  88. G. Williams, B. Seger, and P. V. Kamt. TiO2-Graphene Nanocomposites. UV-Assisted Photocatalytic Reduction of Graphene Oxide, ACS Nano, 2008, 2(7), 1487–1491.  89. Z. Lu, G. Chen, W. Hao, G. Sun, and Z. Li. Mechanism of UV-Assisted TiO2/ Reduced Graphene Oxide Composites with Variable Photodegradation of Methyl Orange, RSC Adv., 2015, 5(89), 72916–72922.  90. K. Zhou, Y. Zhu, X. Yang, X. Jiang, and C. Li. Preparation of Graphene-TiO2 Composites with Enhanced Photocatalytic Activity, New J. Chem., 35(2), 353–359, 2011.  91. Z. Y. Xia et al. The Exfoliation of Graphene in Liquids by Electrochemical, Chemical, and Sonication-Assisted Techniques: A Nanoscale Study, Adv. Funct. Mater., 2013, 23(37), 4684–4693.   92. P. Yu, S. E. Lowe, G. P. Simon, and Y. L. Zhong. Electrochemical Exfoliation of Graphite and Production of Functional Graphene, Curr. Opin. Colloid Interface Sci., 2015, 20(5–6), 329–338.  93. K. Parvez et al. Exfoliation of Graphite into Graphene in Aqueous Solutions of Inorganic Salts, J. Am. Chem. Soc., 2014, 136(16), 6083–6091.   94. H. Tao, Y. Zhang, Y. Gao, Z. Sun, C. Yan, and J. Texter. Scalable Exfoliation and Dispersion of Two-Dimensional Materials-An Update, Phys. Chem. Chem. Phys., 2017, 19(2), 921–960.   95. V. Sivasankar, E. Senthilkumar, R. Vivekananth, R. A. Kalaivani, and T. Sivakumar. Electrochemically Exfoliated Graphene for Nanosensor Applications, J. Nanosci. Nanotechnol., 2019, 19(11), 7097–7104.   96. K. S. Rao, J. Senthilnathan, Y. F. Liu, and M. Yoshimura. Role of Peroxide Ions in Formation of Graphene Nanosheets by Electrochemical Exfoliation of Graphite, Sci. Rep., 2014, 4, 4237.  97. M. Liu et al. One-Step Chemical Exfoliation of Graphite to ∼100% Few-Layer Graphene with High Quality and Large Size at Ambient Temperature, Chem. Eng. J., 2019, 355, 181–185.   98. T. Liu et al. One-Step Room-Temperature Exfoliation of Graphite to 100% FewLayer Graphene with High Quality and Large Size, J. Mater. Chem. C, 2018, 6(31), 8343–8348.   99. P. Wang et al. Chemically Exfoliated Highly Conductive Layer-Tunable Graphene by Simply Controlling the Exfoliating Temperature, Nanotechnology, 2019, 30(46), 465602. 100. B. Deng, Z. Liu, and H. Peng. Toward Mass Production of CVD Graphene Films, Adv. Mater., 2018, 31, 1800996. 101. S. Naghdi, K. Y. Rhee, and S. J. Park. A Catalytic, Catalyst-Free, and Roll-To-Roll Production of Graphene Via Chemical Vapor Deposition: Low Temperature Growth, Carbon N. Y., 2018, 127, 1–12. 102. M. Gautam and A. H. Jayatissa. Ammonia Gas Sensing Behavior of Graphene Surface Decorated with Gold Nanoparticles, Solid. State. Electron., 2012, 78, 159–165.

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103. S. Roscher, R. Hoffmann, and O. Ambacher. Determination of the GrapheneGraphite Ratio of Graphene Powder by Raman 2D Band Symmetry Analysis, Anal. Methods, 2019, 11(9), 1180–1191. 104. W. Lu, S. Cheng, M. Yan, Y. Wang, and Y. Xia. Selective Soluble Polymer–Assisted Electrochemical Delamination of Chemical Vapor Deposition Graphene, J. Solid State Electrochem., 2019, 23(3), 943–951. 105. S. Bae et al. Roll-to-Roll Production of 30-Inch Graphene Films for Transparent Electrodes, Nat. Nanotechnol., 2010, 5(8), 574–578. 106. P. R. Kidambi et al. A Scalable Route to Nanoporous Large-Area Atomically Thin Graphene Membranes by Roll-to-Roll Chemical Vapor Deposition and Polymer Support Casting, ACS Appl. Mater. Interfaces, 2018, 10(12), 10369–10378. 107. Z. Chen, Y. Qi, X. Chen, Y. Zhang, and Z. Liu. Direct CVD Growth of Graphene on Traditional Glass: Methods and Mechanisms, Adv. Mater., 2018, 1803639, 1–18. 108. D. Wei, Y. Lu, C. Han, T. Niu, W. Chen, and A. T. S. Wee. Critical Crystal Growth of Graphene on Dielectric Substrates at Low Temperature for Electronic Devices, Angew. Chemie — Int. Ed., 2013, 52(52), 14121–14126. 109. P. K. Nayak. Direct Growth of Graphene on Insulator Using Liquid Precursor Via an Intermediate Nanostructured State Carbon Nanotube, Nanoscale Res. Lett., 2019, 14, 107. 110. M. Li, D. Liu, D. Wei, X. Song, D. Wei, and A. T. S. Wee. Controllable Synthesis of Graphene by Plasma-Enhanced Chemical Vapor Deposition and Its Related Applications, Adv. Sci., 2016, 3(11), 1–23. 111. W. A. De Heer et al. Large Area and Structured Epitaxial Graphene Produced by Confinement Controlled Sublimation of Silicon Carbide, Proc. Natl. Acad. Sci. U. S. A., 2011, 108(41), 16900–16905. 112. S. Chadhari, A. R. Graves, M. V Cain, and C. D. Stinespring. Graphene-based Composite Sensors for Energy Applications, Proc. SPIE, 2016, 9836. 113. R. Ye, D. K. James, and J. M. Tour. Laser-Induced Graphene, Acc. Chem. Res., 2018, 51(7), 1609–1620. 114. K. Rathinam, S. P. Singh, Y. Li, R. Kasher, J. M. Tour, and C. J. Arnusch. Polyimide Derived Laser-Induced Graphene as Adsorbent for Cationic and Anionic Dyes, Carbon N. Y., 2017, 124, 515–524. 115. R. Ye et al. Laser-Induced Graphene Formation on Wood, Adv. Mater., 2017, 29(37), 1–7. 116. D. Wei et al. Laser Direct Synthesis of Graphene on Quartz, Carbon N. Y., 2013, 53, 374–379. 117. J. B. Park et al. Fast Growth of Graphene Patterns by Laser Direct Writing, Appl. Phys. Lett., 2011, 98(12), 123109. 118. J. B. Park et al. Transparent Interconnections Formed by Rapid Single-Step Fabrication of Graphene Patterns, Appl. Phys. Lett., 2011, 99(5), 1–4. 119. L. Tang et al. Bottom-Up Synthesis of Large-Scale Graphene Oxide Nanosheets, J. Mater. Chem., 2012, 22(12), 5676.

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120. M. Choucair, P. Thordarson, and J. A. Stride. Gram-Scale Production of Graphene Based on Solvothermal Synthesis and Sonication, Nat. Nanotechnol., 2009, 4(1), 30–33. 121. X. Huang, X. Qi, F. Boey, and H. Zhang. Graphene-Based Composites, Chem. Soc. Rev., 2012, 41(2), 666–686. 122. A.C. Ferrari, J. C. Meyer, V. Scardaci, C. Casiraghi, M. Lazzeri, F. Mauri, S. Piscanec, D. Jiang, K. S. Novoselov, S. Roth, and A. K. Geim, Raman Spectrum of Graphene and Graphene Layers, Phys. Rev. Lett., 2006, 97(18), 187401. 123. A. Capasso, A. E. Castillo Del Rio, H. Sun, A. Ansaldo, V. Pellegrini, F. Bonaccorso, Ink-Jet Printing of Graphene for Flexible Electronics: An Environmentally-Friendly Approach, Solid State Commun., 2015, 224, 53–63. 124. C. Casiraghi, K. S. Novoselov, D. M. Basko, H. Qian, A. Hartschuh, C. Georgi, A. C. Ferrari, A. Fasoli, S. Piscanec, Raman Spectroscopy of Graphene Edges, Nano Lett., 2009, 9(4), 1433–1441. 125. Malvern Instruments. Understanding Yield Stress. Inform White Paepr, 2012. https:// www.malvernpanalytical.com/en/ (accessed April 30, 2019).

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Index A accumulation and stripping steps, 250 acetate buffer, 254 acoustic plate mode, 75 acoustic-wave chemical sensors, 75 acoustic wave devices, 73, 81 actin filaments, 364 additive manufacturing, 433–435, 460–461 adiabatic, 41 adsorption, 127 adsorption capacity, 321 affinity, 51 Al2O3 thin film, 10 algae blooms, 282 allosteric regulation, 113 alpha helix, 109 ammonium molybdate, 265 amperometric, 290, 385 amperometric biosensors, 224 amperometric sensors, 291 analytical investigation of phosphate in water, 265 analytical methods, 240 analytic strategies, 308 antibiotics, 115 antibodies, 333 antigen-antibody reaction, 25 antioxidant activity, 398 application-based engineering, 102 aptamer, 139–141, 143, 145, 149, 152–159, 172–175, 184, 188, 191, 194, 197–199, 201–203

aptasensor, 332, 342 aptazymes, 139 aromatic compounds, 104, 116 array of sensors, 80 arsenic, 3, 24, 244 arsenic ion, 21 arsenite, 24 ATP hydrolysis assay, 121 AuNP, 149–152, 155, 322 B background signal, 118 bacteria, 144–145, 149, 172, 176–177, 179, 190, 196–200, 204 bacteria detection, 154–155 bacterial-enhancer-binding proteins, 107 bacterium, 26 band gap, 9, 14–15 band structures, 12 barometer-based sensor, 341 basic fibroblast growth factor, 365 benzene-sensing scaffold, 121 beta strand, 109 better ion mobility and lower noise, 254 big data, 344 binding free energy, 121 binding prediction, 118 binding sites, 45, 50, 52, 59, 62 binding synergy, 110 bioassays, 217, 399 biochemical/biosensor, 218 biochemical sensors, 219, 288 471

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472  Index

biochip, 400 biocompatibility, 395 bioelectric nose, 373 bioelectronic nose, 370–371, 375–376, 377 bioengineering, 106 bioinformatic tools, 123 biological recognition element, 102 biomarker, 389 biomolecules, 26, 388 biorecognition, 388 biosensor, 173–174, 176–180, 185–186, 189–190, 192, 195–196, 199–201, 204, 217, 219, 222–223, 227, 232, 388, 442 biosensor design, 104, 109, 111, 113, 116, 130 biosensor strip, 126, 129 black phosphorus (BP), 4, 14, 18, 23–24 BTEX (Benzene, Toluene, Ethylbenzene, and Xylenes), 72, 81, 104, 122, 130 bulkier pollutant, 116, 122 butylcholinesterase (BChE), 336 C Ca2+ influx, 359, 369, 374 calorimetry, 387 capacitive, conductive, and resistive chemical sensors, 74 capacitive devices, 73 capillary, 389 carbon-based SPE, 244 carbon materials, 386 carbon nanostructures, 154 carbon-nanotube field-effect transistor, 355–356, 358–360, 368–370, 373–375 carbon nanotubes, 349–353, 355, 358, 365, 368, 374, 377, 387 carrier injection efficiency, 9 carrier mobility, 12 catalyst, 53 catalytic beacon, 146–147 catalytic oxidation, 321 catalytic turnovers, 147 cation exchange capacity of Nafion, 253

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Cd2+-specific DNAzyme, 143 cell adhesion, 363, 365 cell-SELEX, 145 cellular activities, 363, 367, 369 central nervous system, 119, 121 challenge of measurement, 287 challenges, 300 charge mobility, 18, 22 charge transfer, 6, 8 chelator, 43 chemical adsorption, 5 chemical exfoliation, 448 chemical functional groups, 143 chemical interferents, 92 chemically sensitive coating, 73 chemical reaction, 5 chemical sensors, 215, 307 chemical vapor deposition (CVD), 14 chemiluminescence, 297, 388 chemiluminescent, 295 chemistry of sensing, 102 chlorine, 53–55 chromatographic analysis, 114 chromatographic techniques, 121 chromatography, 384 chromogenic reactions, 389 Cip determination, 320 circular template, 158 clean water, 2 cloud computing, 344 CMOS technology, 363 coagulation in water treatment plants, 262 cobalt as the sensing material, 262 cobalt microelectrode, 262 color change, 321 colorimetric, 40–41, 53–56, 64, 296 colorimetric biosensors, 227 colorimetric malachite green, 294 colorimetric phosphate sensors, 287 colorimetric sensors, 152, 321 colorimetric uranium sensor, 151 combinatorial selection process, 142 commercial importance, 131 compact sensor platform, 102

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comparatively inexpensive, 269 complementary-metal-oxidesemiconductor (CMOS) technology, 361 complex ligand on the sensor surface, 248 complex of molybdate phosphate, 265 conducting polymer, 242 conductive substrates, 323 conformational flexibility, 127 conformational space, 117 contact resistance, 8 contaminants, 384 continued optimization, 301 continuous sensing, 287 coordination bond, 51 copper ions, 246 counter-selection, 141–142, 145 COVID-19, 382 crosslinking, 52 cross-reactivity, 116, 121 cross-sensitivity, 50 crude extracellular mixture, 145 crystal structure, 105, 109, 111, 117–118, 121, 130 CTC assembles, 313 Cu2+-dependent DNAzyme, 158 Cu2+ determination, 309 CVD graphene, 441, 442, 449–450 Cyanide ion (CN−), 312 cyclic voltammetry (CV), 247, 320 D Debye length, 6, 10–11 desalination, 400 detecting Fe3+, 310 detection limit, 104 detection of glucose, 358 detect organic/inorganic ions, 268 determination of Mn2+ ions, 318 diabetes sensors, 131 dielectrophoretic, 45 differentiate pathogenic strains, 145 differentiation of cells, 363, 365 dihydroxybenzene isomers (DBIs), 320 directed assembly, 351–353, 377

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Index  473

direct ink writing, 434, 453, 460 direct writing, 461 disinfect byproducts (DBPs), 312 dissolved oxygen, 434, 438–439 DNA, 26, 385 DNA-amplification reaction, 156 DNA aptamer, 20–21 DNA backbone, 142 DNA bases, 143, 154 DNA hairpins, 158 DNA library, 143–144 DNAzyme, 11, 21, 139, 141–148, 151–152, 155–156, 158–159, 171–175, 177–181, 183–204 DNAzymes (RFD), 176 dopamine release, 359–360 drop-casting, 127 DTNB, 336 E E. coli, 11, 26 E. coli DNAzyme, 152 E. coli-specific DNAzyme, 149, 158 E. coli-specific fluorogenic DNAzyme, 148 effect of pH, 248 elastomers, 394 electrical signal, 288 electroactive surface area, 320 electroactivity substances, 319 electrocatalysts, 386 electrocatalytic oxidative determination, 318 electrochemical, 288 electrochemical analysis, 240, 384 electrochemical detection systems, 126, 384 electrochemical device, 73, 335 electrochemical exfoliation, 447–448 electrochemical impedance spectroscopy (EIS), 401 electrochemical-sensing platform, 316–317 electrochemical sensors, 74, 153, 222, 234, 241

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474  Index

electrodes, 385 electromagnetic interference, 39 electronegativity, 10 electronic band structure, 13 electronics, 419–422, 424 electron mobility, 16 electron negativity, 8 electron transport, 314 electrophysiological monitoring, 363, 368–369 emission peaks, 315 environmental analysis, 384 environmental monitoring, 138, 155–156, 307 enzymatic biosensors, 219 enzyme, 291 enzyme-linked immunosorbent assay, 152, 333, 389 epidermal growth factor, 365 epitaxial growth, 442, 450 equilibrium frequency shift, 87 Escherichia coli, 337, 387 Escherichia coli O157:H7, 338 estimation theory, 93 estimation-theory-based sensor signal processing, 90 eutrophication, i.e., aquatic plant overgrowth, 261 evaluation of drug effects, 359, 369–370 evanescent field, 40, 48 evolutionary information, 123 evolutionary knowledge, 125 excellent sensitivity toward nitrite ions, 267 extended-gate-type, 28 extracellular matrix, 365 F F− determination, 311 fDNA-based sensors, 157 fDNA sensors, 155 ferritin, 28 ferritin protein, 11 FET sensor, 3, 19, 31 field applications, 322 field effect transistor, 3, 126, 438–441

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field effect transistor-based biosensors, 155 field sample, 129 flexible electronics, 433, 446 flexible sensor device, 356–357, 363, 377 flexographic printing, 396 flexural plate wave, 75 Florida, 282 fluorescence, 295, 384, 387 fluorescence detection, 146 fluorescence emission, 313 fluorescence filtration effect, 314 fluorescence quenching, 63 fluorescence resonance energy transfer, 56 fluorescent nanofibrous membrane (NFM), 315 fluorescent probes, 309 fluorescent properties, 314 fluorescent quenching effect, 311 fluorescent sensor, 149, 227, 308 fluorescent signal, 299 fluorescent test paper, 315 fluoride, 57–58 fluoride in drinking water, 342 fluorometric, 40, 57 fluorophore–quencher, 147 fluorophore-quencher pair, 148 forms of phosphate, 286 free ClO− ions, 312 free lead ions, 243 frequency shift, 83, 86–87 functional DNA, 138–140, 142, 144, 146, 151–153, 156, 158 functionalized nanomaterials, 242 functional materials, 316 functional nucleic acids, 172–173 G gas sensors, 433, 451, 457 gating effect, 6, 8, 11, 24, 356–357, 373 gold and iron-oxide nanoparticles, 247 gold nanoparticles, 51, 246 good linearity, 322 G protein-coupled receptor, 375

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G-quadruplex, 175, 184–186, 188, 201 GR-5, 180, 185 graphene, 4, 12, 149, 155, 158, 242, 349, 366, 400 graphene ink, 453 graphene oxide (GO), 12, 18, 434, 440, 446–448, 451 graphene synthesis, 443–445 groundwater, 121 groundwater monitoring, 80 growth of cells, 363–365, 368 H H2PO4, 286 Hansen solubility parameters, 80 hazardous, 113, 117–118, 121, 130 heavy metal ions, 2, 19, 21, 115, 309, 382, 434, 437, 440 heavy-metal species, 243 Henry’s law, 87–88 herbicides, 386 Hg2+, 19, 20–21, 341 Hg2+ ions, 11 Hg2+-specific DNAzymes, 143 higher selectivity, 241 highly sensitive electrochemical sensor, 250 highly sensitive nitrite sensor, 266 high sensitivity and selectivity, 316 high-throughput sequencing, 141 histamine receptors, 368–369 housekeeping genes, 106 HPO4, 286 human health, 102, 130, 307 hybridization chain reaction, 158 hydrocarbon, 117, 121–122, 129, 131 hydrocarbon recovery, 394 hydrogen-bonding, 110, 121 hydrophobic, 111, 122, 125 hydrophobic-patterned micro-pad, 260 hydrothermal, 451 I iconic Sawgrass, 285 immobilization technique, 131 immunosensors, 219 impedance spectroscopy, 32

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Index  475

impedimetric sensor, 155 important physical parameters, 244 indirect detection, 102–103 inductively coupled plasma mass spectrometry (ICP-MS), 252 industrial waste, 101, 114, 131 inexpensive electrochemical pH sensor, 258 infectious vs. non-infectious viral particles, 138 in-field environmental monitoring, 331, 344 inhibitory effect, 321 inkjet printing, 396 inorganic-anion sensing, 311 inorganic or organic complexes, 243 inorganic phosphate, 298 in silico, 117–118, 125 in situ analysis, 269 in situ detection, 72 integrated biosensor device, 353, 361–362, 363 interaction pattern, 119 interdigitated electrode, 435, 439 interference, 40–41, 48, 61–62, 65, 246 intermetallic compounds, 254 intramolecular charge transfer, 298 in vitro selection, 139–142, 144–146, 148, 159, 171–172, 174, 176, 200, 204 ion contaminants, 399 ionic strength, 11 ionophore, 22, 24 ionophore film, 23 ion selective membranes (ISM), 22 Iridium oxide (IrOx), 258 IrO2-rGO thin film, 258 isothermal amplification, 156–157, 160 ITC experiment, 111, 116, 125–126 L label-free, 355, 358, 377 label-free detection, 19, 102 lab-on-a-chip (LOC) systems, 383 lab on a disc, 231 lab-on-chip, 296 lake Okeechobee, 282

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lanthanide ions, 310 laser, 394 laser-induced growth, 450 lateral-flow devices, 152 lateral flow immunoassay, 340 layer-by-layer, 60 leaching and nitrification, 256 lead, 2, 180–181, 183, 186 lead contamination, 2 L-glutathione, 44 limitations of electronically conducting oxides, 257 limit of arsenic, 244 limit of detection (LOD), 72, 113–114, 355, 358, 372 linear solvation energy relationship, 77 lipid bilayer, 370, 372, 375 lipopolysaccharide, 391 liquid crystal display, 44 liquid exfoliation, 442, 444–445, 447, 452, 454, 459 liquid-phase chemical sensors, 73 lithography, 435, 450, 461 litmus tests, 152 Ln-MOF probe, 310 localized SPR, 48 lossy mode resonance, 46 low cost, 241 low detection limit, 310 luminescence, 387 M magnetic markers, 401 major parameters, 239 malachite green assay, 288 mass-loading, 77 mass production method, 350, 353, 377 mass spectrometry, 384 mechanical exfoliation, 14 MEMS-based chemical sensors, 74 mercury (Hg2+), 3, 186–187, 248 mercury sensor, 147 metal, 173–174, 179, 184–186, 189–190, 192–193, 195–196, 204

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metal ion, 137, 139, 141–144, 146, 149, 152–153, 155–156, 159 metal–ligand interactions, 54 metalloid complex formation, 248 metallothionein type II protein (MT-II), 19 metal nanoparticles, 400 metal-organic frameworks, 308 methylene blue, 153, 158 Mg2+-specific DNAzyme, 158 microbe, 141, 144, 153 microbial contaminants, 138 microbial targets, 137 microcantilever, 74–75 microcapillary, 368 microchannels, 383 microelectrode array, 290 microelectromechanical systems (MEMS), 73, 255, 393 microfabrication, 394 microfluidic, 54–55, 64 microfluidic devices, 381 microfluidic impedance cytometry, 402 microfluidic paper-based analytical device, 389 microfluidics, 131, 361–362 micromachine, 391 microorganisms, 3, 25, 331, 337 micropillar array, 388 microsensor, 225 microspheres, 299 microsystems devices, 391 microtechnology, 382 miniaturized electrochemical cell, 241 minor hysteresis, 260 modified litmus assay (MLA), 174, 177–178 modified nucleotides, 142 molecular assays, 218 molecular beacon, 147, 149 molecular docking, 117, 121 molecular fingerprints, 334 molecular imprinting, 335 molecularly imprinted polymer (MIPs), 52, 335

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molecular orbital, 57 molecular recognition, 139–140, 152 molecular recognition elements (MREs), 173–174, 176–178, 180–182, 184, 186–188, 191, 193–194, 196–198, 201–203 molecular-signaling groups, 146 molybdenum disulfide, 15 molybdenum electrode, 294 MopR sensor, 109, 111, 116 MoS2, 18 MSG detection, 374 multianalyte mixture, 90 multianalyte responses, 88 multiple heavy-metal ions, 317 multiple times, 315 multiplexed detection, 361–362, 363 multiplexed sensor device, 360 multivariate sensor signal processing, 80 multivariate signal processing, 93 multi-walled carbon nanotube networks, 365–366 mutagenesis, 105, 111, 121, 126 N 2D nanomaterials, 12 Nafion film, 359 Nafion–graphene, 253 nanodisk-based biosensor, 375–376 nanodisks, 375–376 nanomaterials, 146, 149, 153, 160 nanoparticles, 126, 349–350, 358, 386 nanotechnology, 102, 234, 242, 382 nanovesicle-based biosensor, 373–374 nanovesicles, 372, 374–375 nanowires, 349–350, 352–353 nanozyme, 335 native conformation, 145 neutral dye, 313 nicotinic acetylcholine receptors, 369 nitrate, 28–29, 53, 55 nitrite-contaminated water, 266 nitrite ion, 318 nitrogen contamination, 400

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Index  477

non-adherent cells, 369–370 non-adiabatic, 41, 43, 45 non-adiabatic tapered fiber, 45 novel biosensors, 104 NtrC family proteins, 107–109 nucleic acid, 21 nutrients, 399 O odorant detection, 360, 371–372, 376 off-target effect, 114 Ohmic contact, 9 olfactory receptor, 370–372, 375–376 on-site, 146 on-site and real-time detection, 138, 159–160 on-site detection, 105, 111, 153 on-site environmental detection, 156 on-site monitoring, 344 open-circuit potential (OCP) method, 259 optical, 288 optical biosensors, 225 optical changes, 344 optical chemical sensors, 73 optical detection systems, 387 optical devices, 73 optical labels, 334 optical spectrum analyzer, 44 optical techniques, 295 optimal condition, 320 optimization of biosensors, 130 optimized pH, 267 optimum operating condition, 247 organic compound pollutants, 313 organic contaminates, 128, 319 organic ligand, 311 organophosphate, 45 organophosphorus (OP) pesticides, 335 oxidation corrosion potential, 264 oxidation reaction between cobalt oxide and phosphate, 263 oxidation-reduction potential (ORP), 434–435 oxidization current, 319

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P 3D-printed smartphone reader, 333 3D printer, 396 3D-printing technology, 338 15-ppb regulatory limit, 250 pandemic, 382 paper-based, 340 paper-based probes, 228 paper-based screen-printed electrodes, 294 paper sensor, 314 partially selective, 76, 80 partial selectivity, 84 partition coefficient, 77, 78 Pathogenic microbials, 382 Pb2+, 21–23 Pb2+-dependent DNAzyme, 149, 152 Pb2+ ions, 11 Pb2+ sensing, 309 Pb(II)-specific DNAzyme, 148 Pd2+-dependent DNAzyme, 153 penetration depth, 41 performance optimization, 323 permissible exposure limit, 113 peroxidase-mimicking DNAzymes (PMD), 174, 178, 181, 184, 186–188, 191, 193–194, 197–204 personal glucose meter, 155 pesticide residues, 331 pesticides, 45, 51, 332, 386 phenol-binding pocket, 110, 117–118, 125 pH meters and pH strips, 256 pH of the supporting electrolyte, 266 phosphate, 28, 282 phosphate backbone, 142–143, 154 phosphate ions, 11 phosphorene, 12 phosphorus cycle, 283 photobleaching, 300 photo-induced electron transfer, 311 photolithography, 352–353, 368, 396 photoresist, 352–353 physical adsorption, 5 physical vapor deposition (PVD), 14 plaque assays, 138 plasmonic, 48 plasticizer, 80

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point of care, 155, 361, 377 point-of-care (POC) devices, 383 point sources, 282 pollution monitoring, 126 polydimethylsiloxane (PDMS), 394 polymer coating, 77 polymer substrate, 387 polymer/water analyte partition coefficient, 77 portable detection, 160 portable devices, 138, 159 portable fluorometer, 139, 146 portable sensor device, 361, 363, 377 portable sensors, 155–156 possible interference, 269 potentiometric, 290 potentiometric biosensors, 224 potentiometric sensor, 154–155, 387 potentiometry, 385 priority pollutants, 104, 121 proliferation of cells, 365–366 promising method, 268 protein-based biosensors, 104–105, 130 protein-engineering, 115 protein-immobilized sensors, 126 protein-ligand interaction, 123 protozoans, 401 PS2.M, 175–176, 187–188, 197–198, 202–203 p-type semiconductor, 355–356, 369 public health, 400 public water systems, 2 Pyrrole (Py) and polypyrrole (PPy), 264 Q qPCR-based technologies, 138 quantification, 298 quantum dots, 56, 60, 62, 299 R Raman spectroscopy, 334 reactive-ion etching (RIE), 393 real samples collected from industry, 252 real-time monitoring, 146, 350, 355–358, 360–361, 363, 367, 369, 371, 374–376 real-time pathogen detection, 401

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real-water sample analysis, 322 recent progress, 242 recombinant biosensors, 104 redox behaviors, 259 redox couple of Co2+/Co0, 262 redox-modified fDNA, 153 redox potential of As (III), 247 redox reaction, 243, 396 reduced glutathione, 11 reduced-graphene oxide (rGO), 14, 18–19, 22, 266 refractive index modulation, 40–41 regeneration, 50 remediation, 394 remote controls, 344 repeated dehydration/hydration, 258 resistive devices, 73 resonance dip wavelength, 48, 52 response time, 72, 80, 85–86 response time constant, 87 reusable sensor platform, 368–370 reversible bonds, 411–413, 416–417 RGB values, 343 rGO FET sensor, 28 rGO nanocomposites, 249 rGO nanosheets, 29 river, tap, and distilled water, 257 RNA, 402 RNA-cleaving DNAzymes (RCD), 172, 196–198, 200, 204 RNA-cleaving fluorogenic DNAzyme (RFD), 174, 176–177, 196, 198, 200 RNA polymerase, 105–106 rolling circle amplification (RCA), 158, 174, 178, 181, 185, 196–199 RT-PCR, 402 S (UO22+)-specific DNAzyme, 151 p-p stacking, 13 safe drinking water, 240 safe water, 101 salinity, 47 Salmonella typhimurium, 401 scaffolds for cell adhesion, 363, 365–367 Schottky barrier, 371, 374, 376

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Index  479

screen-printed carbon electrodes, 153, 155 screen-printed sensors, 292 secondary structure prediction, 110 selection rounds, 141 selective detection of Hg2+, 249 selectivity, 72, 73, 76 SELEX (Systematic Evolution of Ligands by Exponential Enrichment), 140, 172 self-assembled monolayer, 351–353, 365 self-healing devices, 410–411, 419, 421 self-healing material, 410–415, 417, 420, 422, 425 semiconductor-based electronics, 131 semiconductor devices, 391 sensing materials, 323 sensing platform, 323 sensing pollutants, 216 sensing signal, 31 sensing technologies, 101 sensitivity, 11, 73, 76, 79, 86 sensor, 215, 217, 223, 226, 234, 287, 399 sensor activity, 111, 113 sensor module, 108, 119, 130 sensor performance, 322 sensor platform, 73 sensor pocket, 116–118, 122, 126, 130 sequence similarity network, 123 shear-horizontal surface acoustic wave, 75 shear modulus, 78 sheet resistance, 458–459 shelf life, 104, 116, 127, 129 SH-SAW devices, 81 SH-SAW sensor platform, 83 signal-processing, 80 signal transducer, 355 signal transduction, 108 signal transduction elements, 140 significant potentials, 322 Silica nanoparticles, 127 simulated wastewater sample, 128 simulated water sample, 122 simultaneous and selective electrochemical detection, 255 simultaneous detection, 253 simultaneous measurement, 301 single-molecule sensing, 131

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480  Index

single-mutant, 125 single-walled carbon nanotubes (SWNTs), 363 single-walled carbon nanotube networks, 352–357, 363 smartphone-based devices, 332 smartphone-based immunosensor, 339 smartphone-based sensors, 344 smartphone optosensing platform, 336 smartphones, 331 soft electronic, 421–422 sol-gel matrix, 356–357 solubility and toxicity, 256 South Florida, 285 spectrometer, 55–57, 64 spectrophotometric assay, 122 spectrophotometric detection, 113 spectrum shift, 43, 57 standards for drinking-water quality, 355 stomach cancer, 266 strand displacement amplification, 157 structural allostery, 130 structural biology, 131 structural information, 125 structural mutation, 117 structural plasticity, 126 structure-based design, 116, 119, 123 structure-guided design, 105, 117 super-Nernstian slope, 259 surface acoustic wave, 75 surface acoustic wave chip, 371–372 surface chemistry, 316 surface-enhanced Raman-scattering, (SERS), 322, 334, 389 surface plasmon resonance (SPR), 40–41, 47, 391 surface-programed assembly, 351, 353 surface-to-volume ratio, 12, 350, 377 T tapered fiber, 41, 43–44, 46–47, 58–59, 64 target molecules, 251 target-probing system, 355 taste receptor, 360, 377

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temperature variations, 32 the negative logarithm of the H+ concentration, 259 thermogravimetric analysis, 454 thermoplastics, 394 thermostable, 116 thickness-shear mode, 75 thiol chemistry, 153 thiolglycolic acid, 11 three-electrode system, 316 titanium-based MOF, 318 toxic heavy metals, 240 toxic ions, 331, 341 toxicity, 386 toxicological profile, 130 transcriptional activation, 106, 108 transcriptional regulators, 108 transducer module, 102 transition metal dichalcogenides (TMDCs), 4, 12, 15 two-dimensional (2D) nanomaterials, 4 two steps, 244 U umami taste receptor, 374 underwater self-healing, 410–419, 423, 425–426 underwater self-healing materials, 412 unimolecular DNA-catalytic probe, 148 UO22+ colorimetric sensors, 152 UO22+-dependent DNAzyme, 158 UO22+ sensor, 147 UO22+-specific DNAzymes, 156 upconversion nanoparticle, 149 uranium, 190 uranyl, 178, 190–192 US Environmental Protection Agency, 356 V van der Waals interactions, 13 vapor deposition, 435, 437, 441 viral pathogens, 144 virus, 138, 144–145, 152, 154, 382 viscoelastic properties, 78 viscosity, 452, 454, 456–457

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visible light, 322 volatile organic, 119, 128 voltammetric, 290 voltammetric biosensors, 224 voltammetric sensors, 153 voltammetry, 385 W wastewater, 400 water, 402 water and waste water treatment, 240 water conductivity, 434–435 water contaminants, 19, 137, 142, 156, 159, 308 water distribution systems, 246 water environments, 331 water hardness, 53

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Index  481

water monitoring technologies, 337 water pH, 434, 438, 440 water pollution, 281, 382 water quality, 332, 344 water quality for drinking, 240 water quality management, 381 wave velocity, 83 wax printing, 396 wide range of analytes, 269 wireless communication devices, 31 work functions, 9 working principles, 5 X xenobiotic monitoring, 119 xenobiotics, 101, 104–105, 108, 114, 130–131

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page vi

World Scientific Series in Nanoscience and Nanotechnology (Continuation of series card page)



Volume 3: Self-Assembly of Plasmonic Nanostructures Volume 4: Nanoparticle-Cell Interactions Volume 5: Plasmonics in Diagnostics and Therapy editor-in-chief: Luis M Liz-Marzán (CIC biomaGUNE, Spain) edited by Jwa-Min Nam (Seoul National University, Korea), Jianfang Wang (The Chinese University of Hong Kong, China), Zhihong Nie (Fudan University, China), Kimberly Hamad-Schifferli (University of Massachusetts Boston, USA & Massachusetts Institute of Technology, USA) and Sebastian Schlücker (University of Duisburg-Essen, Germany)

Vol. 21 Advanced Characterization of Nanostructured Materials Probing the Structure and Dynamics with Synchrotron X-Rays and Neutrons edited by Sunil K Sinha (University of California San Diego, USA), Milan K Sanyal (Saha Institute of Nuclear Physics, India) and Chun K Loong (The Chinese University of Hong Kong, Hong Kong) Vol. 20 Soft Matter and Biomaterials on the Nanoscale The WSPC Reference on Functional Nanomaterials — Part I (In 4 Volumes) Volume 1: Soft Matter under Geometrical Confinement: From Fundamentals at Planar Surfaces and Interfaces to Functionalities of Nanoporous Materials Volume 2: Polymers on the Nanoscale: Nano-structured Polymers and Their Applications Volume 3: Bio-Inspired Nanomaterials: Nanomaterials Built from Biomolecules and Using Bio-derived Principles Volume 4: Nanomedicine: Nanoscale Materials in Nano/Bio Medicine editor-in-chief: Oleg Gang (Columbia University, USA and Brookhaven National Laboratory, USA) edited by Jin-Woo Kim (University of Arkansas, USA), D Keith Roper (Utah State University, USA), Wen J Li (City University of Hong Kong, Hong Kong), Patrick Huber (Hamburg University of Technology, Germany), Seung-Wuk Lee (University of California Berkeley, USA), Irina Zvonkina (University of Houston, USA) and Alamgir Karim (University of Houston, USA) Vol. 19 Soft Nanomaterials edited by Ye Zhang (Okinawa Institute of Science and Technology, Japan) and Bing Xu (Brandeis University, USA)

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Vol. 18 Handbook of Synthetic Methodologies and Protocols of Nanomaterials (In 4 Volumes) Volume 1: Solution Phase Synthesis of Nanomaterials Volume 2: Gas Phase Synthesis of Nanomaterials Volume 3: Unconventional Methods for Nanostructure Fabrication Volume 4: Characterization Methods for Nanostructures editor-in-chief: Yadong Yin (University of California, Riverside, USA) edited by Yu Lu (University of California, Riverside, USA), Yat Li (University of California, Santa Cruz, USA), Yiding Liu (Southwest Petroleum University, China), Le He (Soochow University, China), Yihan Zhu (Zhejiang University of Technology, China) and Yu Han (King Abdullah University of Science and Technology, Saudi Arabia) Vol. 17 World Scientific Reference of Hybrid Materials (In 3 Volumes) Volume 1: Block Copolymers Volume 2: Devices from Hybrid and Organic Materials Volume 3: Sol-Gel Strategies for Hybrid Materials editor-in-chief Mato Knez (CIC Nanoscience Research Center, Spain) Vol. 16 World Scientific Handbook of Metamaterials and Plasmonics (In 4 Volumes) Volume 1: Electromagnetic Metamaterials Volume 2: Elastic, Acoustic, and Seismic Metamaterials Volume 3: Active Nanoplasmonics and Metamaterials Volume 4: Recent Progress in the Field of Nanoplasmonics edited by Stefan A Maier (Imperial College London, UK) Vol. 15 Molecular Electronics: An Introduction to Theory and Experiment Second Edition by Juan Carlos Cuevas (Universidad Autónoma de Madrid, Spain) and Elke Scheer (Universität Konstanz, Germany) Vol. 14 Synthesis and Applications of Optically Active Nanomaterials by Feng Bai (Henan University, China) and Hongyou Fan (Sandia National Laboratories, USA) Vol. 13 Nanoelectronics: A Molecular View by Avik Ghosh (University of Virginia, USA) Vol. 12 Nanomaterials for Photocatalytic Chemistry edited by Yugang Sun (Temple University, USA) Vol. 11 Molecular Bioelectronics: The 19 Years of Progress Second Edition by Nicolini Claudio (University of Genoa, Italy)

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Vol. 10 Pore Scale Phenomena: Frontiers in Energy and Environment edited by John Poate (Colorado School of Mines, USA), Tissa Illangasekare (Colorado School of Mines, USA), Hossein Kazemi (Colorado School of Mines, USA) and Robert Kee (Colorado School of Mines, USA) Vol. 9 Handbook of Biomimetics and Bioinspiration: Biologically-Driven Engineering of Materials, Processes, Devices, and Systems (In 3 Volumes) Volume 1: Bioinspired Materials Volume 2: Electromechanical Systems Volume 3: Tissue Models edited by Esmaiel Jabbari (University of South Carolina, USA), Deok-Ho Kim (University of Washington, USA), Luke P Lee (University of California, Berkeley, USA), Amir Ghaemmaghami (University of Nottingham, UK) and Ali Khademhosseini (Harvard University, USA & Massachusetts Institute of Technology, USA) Vol. 8 Polyoxometalate Chemistry: Some Recent Trends edited by Francis Sécheresse (Université de Versailles-St Quentin, France) Vol. 7 Scanning Probe Microscopy for Energy Research edited by Dawn A Bonnell (The University of Pennsylvania, USA) and Sergei V Kalinin (Oak Ridge National Laboratory, USA) Vol. 6 Plasmon Resonances in Nanoparticles by Isaak D Mayergoyz (University of Maryland, USA) Vol. 5 Inorganic Nanomaterials from Nanotubes to Fullerene-Like Nanoparticles: Fundamentals and Applications by Reshef Tenne (Weizmann Institute of Science, Israel) Vol. 4 Plasmonics and Plasmonic Metamaterials: Analysis and Applications edited by Gennady Shvets (The University of Texas, Austin, USA) and Igor Tsukerman (The University of Akron, USA) Vol. 3 Molecular Cluster Magnets edited by Richard Winpenny (The University of Manchester, UK) Vol. 2 Nanostructures and Nanomaterials: Synthesis, Properties, and Applications Second Edition by Guozhong Cao (University of Washington, USA) and Ying Wang (Louisiana State University, USA) Vol. 1 Molecular Electronics: An Introduction to Theory and Experiment by Juan Carlos Cuevas (Universidad Autónoma de Madrid, Spain) and Elke Scheer (Universität Konstanz, Germany)

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World Scientific Series in Nanoscience and Nanotechnology* ISSN: 2301-301X Series Editor-in-Chief Frans Spaepen (Harvard University, USA) Members of the Scientific Advisory Board Li-Chyong Chen (National Taiwan University) Jeff Grossman (Massachusetts Institute of Technology, USA) Alex de Lozanne (University of Texas at Austin) Mark Lundstrom (Purdue University) Mark Reed (Yale University, USA) John Rogers (Northwestern University) Elke Scheer (Konstanz University) David Seidman (Northwestern University, USA) Matthew Tirrell (The University of Chicago, USA) Sophia Yaliraki (Imperial College, UK) Younan Xia (Georgia Institute of Technology, USA) The Series aims to cover the new and evolving fields that cover nanoscience and nanotechnology. Each volume will cover completely a subfield, which will span materials, applications, and devices. Published Vol. 23 The World Scientific Reference of Water Science (In 3 Volumes) Volume 1: Molecular Engineering of Water Sensors Volume 2: Nanotechnology for Water Treatment and Water Interfaces Volume 3: Current Status and New Technologies in Water Desalination editor-in-chief: Matthew Tirrell (The University of Chicago, USA & Argonne National Laboratory, USA) edited by Matthew Tirrell (The University of Chicago, USA & Argonne National Laboratory, USA), Junhong Chen (The University of Chicago, USA & Argonne National Laboratory, USA) and Yoram Cohen (University of California, Los Angeles, USA) Vol. 22 World Scientific Reference on Plasmonic Nanomaterials Principles, Design and Bio-applications (In 5 Volumes) Volume 1: Principles of Nanoplasmonics Volume 2: Plasmonic Nanoparticles: Synthesis and (Bio)functionalization

For further details, please visit: http://www.worldscientific.com/series/wssnn (Continued at the end of the book)

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Published by World Scientific Publishing Co. Pte. Ltd. 5 Toh Tuck Link, Singapore 596224 USA office: 27 Warren Street, Suite 401-402, Hackensack, NJ 07601 UK office: 57 Shelton Street, Covent Garden, London WC2H 9HE

Library of Congress Cataloging-in-Publication Data Names: Tirrell, Matthew, editor. Title: The World Scientific reference of water science / editor-in-chief, Matthew Tirrell, The University of Chicago, USA & Argonne National Laboratory, USA. Other titles: Reference of water science Description: New Jersey : World Scientific, 2023. | Series: World Scientific series in nanoscience and nanotechnology, 2301-301X ; volume 23 | Includes bibliographical references and index. | Contents: v. 1. Molecular engineering of water sensors / volume editor, Junhong Chen, The University of Chicago, USA & Argonne National Laboratory, USA - v. 2. Nanotechnology for water treatment and water interfaces / volume editor, Junhong Chen, Matthew Tirrell, The University of Chicago, USA & Argonne National Laboratory, USA - v. 3. Current status and new technologies in water desalination / volume editor, Yoram Cohen, University of California, Los Angeles, USA. Identifiers: LCCN 2022017930 | ISBN 9789811245756 (v. 1 ; hardcover) | ISBN 9789811245763 (v. 2 ; hardcover) | ISBN 9789811253812 (v. 3 ; hardcover) | ISBN 9789811246104 (set : hardcover) | ISBN 9789811246111 (set : ebook for institutions) | ISBN 9789811245916 (set : ebook for individuals) | ISBN 9789811245770 (v. 1 ; ebook for institutions) | ISBN 9789811245787 (v. 2 ; ebook for institutions) | ISBN 9789811253829 (v. 3 ; ebook for institution) Subjects: LCSH: Water--Purification. | Water quality management. | Water-supply. Classification: LCC TD430 .W74 2022 | DDC 628.1/62--dc23/eng/20220705 LC record available at https://lccn.loc.gov/2022017930 British Library Cataloguing-in-Publication Data A catalogue record for this book is available from the British Library.

Copyright © 2023 by World Scientific Publishing Co. Pte. Ltd. All rights reserved. This book, or parts thereof, may not be reproduced in any form or by any means, electronic or mechanical, including photocopying, recording or any information storage and retrieval system now known or to be invented, without written permission from the publisher.

For photocopying of material in this volume, please pay a copying fee through the Copyright Clearance Center, Inc., 222 Rosewood Drive, Danvers, MA 01923, USA. In this case permission to photocopy is not required from the publisher.

For any available supplementary material, please visit https://www.worldscientific.com/worldscibooks/10.1142/12514#t=suppl Desk Editors: Balamurugan Rajendran/Amanda Yun Typeset by Stallion Press Email: [email protected] Printed in Singapore

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© 2023 World Scientific Publishing Company https://doi.org/10.1142/9789811245787_fmatter

Preface Water is an indispensable resource for our society. Necessary for sustaining life and for economic prosperity, water is needed to manufacture nearly everything society depends upon, including energy, food, clothing, cars, and electronics, among many other examples. It is, thus, an integral part of our lives beyond simply quenching our thirst. In addition, our future economy and security highly depend upon the availability of clean water. Yet given its critical importance, there is a limited supply of renewable freshwater across the globe and there is no substitute. Global population and economic growth, urbanization, and climate change further exacerbate the increasing stress on freshwater supplies. As such, society urgently needs scientific and engineering solutions to more efficiently manage our precious water resources. Water treatment is imperative for the recycling, reclamation, and reuse of wastewater, which is a critical strategy for addressing the present global water challenge. In particular, the selective separation of water contaminants is necessary to deliver fit-for-purpose water. Although activated carbon and various polymeric materials have been widely used as efficient adsorbents and filtration membranes, respectively, many water treatment challenges still remain, such as scaling and fouling, selective separation, energy-efficient desalination, and the effective removal of micropollutants at extremely low concentrations (e.g., parts per trillion), among many others. Therefore, many opportunities exist for exploring and understanding emerging nanomaterials and nanotechnologies for water treatment and water interfaces. This book volume is a collection of state-of-the-art nanotechnology-based water treatment research. Key water remediation technologies covered include adsorption, membrane filtration (e.g., microfiltration, ultrafiltration, nanofiltration, and reverse osmosis), capacitive deionization, and catalytic degradation, along with methods to minimize scaling and fouling. Materials and interfaces v

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discussed include carbon nanotubes, graphene, graphene oxide, reduced graphene oxide, graphene oxide quantum dots, carbon nitrides, metal-organic frameworks, polymers, metals, metal oxides, composite sponges, and hydrogels. The contaminants addressed include heavy metals, radioactive metals, pathogens, organic pollutants (e.g., dyes, antibiotics, aromatic compounds, and natural organic matter), and other micropollutants in water. We hope this book volume will become a valuable reference not only for veteran researchers in the field of water treatment but also for undergraduate students and graduate students who are entering this exciting field. Matthew Tirrell Robert A. Millikan Distinguished Service Professor and Dean Pritzker School of Molecular Engineering University of Chicago Junhong Chen Crown Family Professor, Pritzker School of Molecular Engineering, University of Chicago & Lead Water Strategist & Senior Scientist, Science Leader for Argonne in Chicago, Argonne National Laboratory

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About the Editors Junhong Chen is currently the Crown Family Professor in the Pritzker School of Molecular Engineering at the University of Chicago (Email: [email protected]; Website: https://pme.uchicago.edu/faculty/junhong-chen). He is also Lead Water Strategist & Senior Scientist (Email: [email protected]; Website: https://www.anl.gov/profile/ junhong-chen) and Science Leader for Argonne in Chicago at Argonne National Laboratory (https://www.anl.gov/water). Prior to coming to Chicago, Chen served as a Program Director for the Engineering Research Centers (ERC) program of the National Science Foundation (NSF) and as a Co-chair of the NSF-wide ERC Working Group to design the ERC Planning Grants program and the Gen-4 ERC program. As a representative of NSF’s Engineering Directorate, Chen also served on the NSF-wide Working Groups for the NSF Graduate Research Fellowship and the NSF Research Traineeship programs. Prior to joining NSF in May 2017, he was a Distinguished Professor of Mechanical Engineering and Materials Science and Engineering and an Excellence in Engineering Faculty Fellow in Nanotechnology at the University of Wisconsin-Milwaukee (UWM), and he was a Regent Scholar of the University of Wisconsin System. He also served as the Director of UWM’s NSF Industry-University Cooperative Research Center on Water Equipment & Policy for six years. He founded NanoAffix Science, LLC to commercialize real-time water sensors based on two-dimensional nanomaterials. Chen received his Ph.D. in mechanical engineering from University of Minnesota in 2002, and he was a postdoctoral scholar in chemical engineering at the California Institute of Technology from 2002 to 2003. His current research focuses on nanomaterial innovation for energy and environmental sustainability, including real-time sensors for detection of water contaminants. Chen has vii

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published more than 260 journal papers and has been listed as a highly cited researcher (top 1%) in the materials science or cross-field category by Clarivate Analytics over the last five years. Chen’s research has led to 9 patents, 5 pending patents, and 13 licensing agreements. He is a pioneer in technology commercialization through exemplary industrial partnerships and his university start-up company. Chen is an elected fellow of both the National Academy of Inventors and the American Society of Mechanical Engineers. Matthew Tirrell is the Dean of the Pritzker School of Molecular Engineering and the Robert A. Millikan Distinguished Service Professor at the University of Chicago (E-mail: [email protected]; Website: https://pme. uchicago.edu/faculty/matthew-tirrell). Before becoming dean in 2011, Tirrell served as the Arnold and Barbara Silverman Professor and Chair of the Department of Bioengineering at the University of California, Berkeley, and as Professor of materials science and engineering and chemical engineering and Faculty Scientist at Lawrence Berkeley National Laboratory. Prior to that, he was Dean of engineering at the University of California, Santa Barbara for 10 years. Tirrell began his academic career at the University of Minnesota as an Assistant Professor in the Department of Chemical and Materials Engineering and later became Head of the Department. He also served as Deputy Laboratory Director for Science at Argonne National Laboratory, where he was responsible for integrating the laboratory’s research and development efforts and science and technology capabilities. Tirrell is a pioneering researcher in the fields of biomolecular engineering and nanotechnology, specializing in the manipulation and measurement of the surface properties of polymers, which are materials that consist of long, flexible chain molecules. His work combines microscopic measurements of intermolecular forces with the creation of new structures. His work has provided new insight into the properties of polymers, especially surface phenomena such as adhesion, friction, and biocompatibility, and new materials based on the self-assembly of synthetic and bioinspired materials. Tirrell has received many honors, including the Polymer Physics Prize by the American Physical Society and election to the National Academy of Sciences, National Academy of Engineering, and the American Academy of Arts and Sciences.

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Contents Prefacev About the Editorsvii Chapter 1 Enhancement of Filtration and Adsorption Processes for Water Treatment Using Graphene-Based Nanomaterials Ali Ansari and Debora F. Rodrigues Chapter 2 Engineered Graphene Oxide as Advanced Separation Material for Water Treatment Delai Zhong, Lihong Gan and Yi Jiang Chapter 3 Graphitic Carbon Nanomaterial-Based Membranes for Water Desalination Dong Han Seo, Mitchell Barclay, Myoung Jun Park, Chen Wang, Kostya (Ken) Ostrikov and Ho Kyong Shon Chapter 4 Nanomaterials and Nanotechnology for Waterborne Pathogen Inactivation Cecilia Yu, Jianfeng Zhou, and Xing Xie

1

31

63

89

Chapter 5 Development of Nanostructured Adsorption Materials for Removing Heavy-Metal Ions from Aqueous Systems Dan Zhang and Chuanyi Wang

115

Chapter 6 Low-Dimensional Nanomaterials for Next-Generation Capacitive Deionization Systems Zhi Yi Leong and Hui Ying Yang

141

ix

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Chapter 7 Graphene Oxide and Nanocomposite Electrodes for Capacitive Deionization Linda Zou

173

Chapter 8 Palladium-Based Nanostructured Catalysts for Treatment of Recalcitrant and Problematic Waterborne Pollutants Xiaopeng Min and Yin Wang

195

Chapter 9 Catalytically Reactive Membrane Filtration for Water Treatment Wen Zhang, Shan Xue, Qingquan Ma, Fangzhou Liu, Weihua Qing, Shaobin Sun and Hong Yao Chapter 10 Surface Mimetics of Water Treatment Membranes by Thin-Films and Self-Assembled Monolayers for Exploring Scaling and Antifouling Mechanisms Swati Sundararajan, Karthik Rathinam and Roni Kasher Chapter 11 The Roles of Nanostructures in Mitigating Pore Wetting and Mineral Scaling in Membrane Distillation Tiezheng Tong

221

303

323

Index341

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Chapter 1

Enhancement of Filtration and Adsorption Processes for Water Treatment Using Graphene-Based Nanomaterials Ali Ansari† and Debora F. Rodrigues*,‡ Department of Civil and Environmental Engineering, University of Houston, Houston, TX-77004, USA [email protected]

† ‡

[email protected]

1.1. Graphene-Based Nanomaterials Graphene-based nanomaterials (GBNMs) such as graphene (G), graphene oxide (GO), reduced graphene oxide (rGO), and graphene oxide quantum dots (GOQDs) are among the most promising materials in nanotechnology due to their special properties and wide range of applications.1,2 G and GO have a similar carbon structure, with the only difference being the presence of oxygen functional groups such as hydroxyl, carboxylic acid, carbonyl, and alkoxy in the GO structure (Fig. 1.1).3 Therefore, graphene contains only sp2 orbitals while graphene oxide has some sp3 orbitals as well. rGO is an intermediate between G and GO, with more functional groups than G but less than GO.4 While G, rGO, and GO have a plane-like two-dimension structure, GOQDs are quasi-spherical, zero-dimension nanoparticles (Fig. 1.1).2 Various methods, with some overlaps, are used to synthesize G, rGO, and GO. Graphene sheet can be obtained by simply peeling off graphite using an adhesive Corresponding author.

* 

1

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2  A. Ansari & D. F. Rodrigues (a)

(b)

O

COOH OH

HO

O

O

OH COOH

HO (c)

(d)

HOOC

COOH

O

OH O

HO

O

OH OH

COOH

Figure 1.1.    (a) Graphene, (b) graphene oxide, (c) graphene oxide quantum dot, and (d) reduced graphene oxides structures.

tape;4 however, practical methods to obtain graphene on a large scale can be categorized into bottom-up and top-down methods.1 In bottom-up methods, ­ molecular organic precursors, usually benzene rings, have been used to grow graphene sheets either by organic synthesis methods or on catalyst substrates like copper, nickel, or iron. Techniques such as chemical vapor deposition (CVD), arc ­discharge, or silicon carbide epitaxial growth can be used to form graphene on a catalyst.1,3 In top-down methods, chemical and mechanical exfoliation methods have been used to overcome the van der Waals forces between graphene sheets in graphite to obtain a single sheet of graphene. One of the primary intermediate products to chemically exfoliate graphite is GO.1 The reduction of GO can be accomplished using microorganisms; chemical methods using reducing agents such as hydrazine, hydroquinone, ascorbic acid, or electrochemical methods; ­thermal methods; microwave radiation; ion bombardments; and ultraviolet radiation methods.1,5 Depending on the degree of reduction, the final product can be either G or Rgo.1 Various methods have been explored to synthesize GO via the oxidation and exfoliation of graphite,6 including the Hummers’ method, which is widely used because it is safer and more scalable.3 Different versions of the Hummers’ method have been proposed, but in these methods, graphite in the form of powder or flakes

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is oxidized in a protonated solvent-like sulfuric acid or phosphoric acid by an oxidizing agent, usually KMnO4. Oxidation follows by treating the mixture with H2O2 to remove metal ions, and then the solution is washed several times with water using a centrifuge to neutralize the pH.3 Various researchers have suggested improvements to the Hummers’ method, such as pretreating the graphite with K2S2O8 and P2O5 before oxidation or increasing the interlayer space of graphite using thermal methods.3 Like graphene, top-down and bottom-up approaches have been used to synthesize GOQDs. In the top-down methods, the GOQDs are fabricated by breaking down larger pieces of carbon structures, such as GO, using methods such as arcdischarge, electrochemical oxidation, and laser ablation. However, these methods are expensive and require complex set-ups, and thus bottom-up methods are more popular among researchers. In these methods, molecular precursors such as citrate, carbohydrates, and polymer-silica nanocomposites are converted to GOQDs by hydrothermal, solvothermal, combustion treatment, and microwave irradiation. The pyrolysis of citric acid is one of the most popular techniques for GOQDs fabrication because it is simple, cheap, has high yield, and produces nanoparticles with narrow size distribution.2 Graphene has been a candidate for water treatment due to its unique properties, which include mechanical resistance, low weight, flexibility, large surface area, and for its easy functionalization.1 In addition, GO can easily attach to various types of materials due to abundance of functional groups. For instance, GO has been used in polymer nanocomposites to increase the mechanical strength and adsorption capacity toward different pollutants.3,7 Adding to these features, antimicrobial property, cost-effective production, and excellent antifouling as a membrane coating have made GO popular in water treatment.7,8 GOQDs share important features of their respective nanosheet for water treatment, such as hydrophilicity, being negatively charged, and antifouling.9,10 Moreover, they have excellent solubility and lower cytotoxicity compared with GO.10,11 This chapter discusses the use of GBNMs in filtration and adsorption, along with various modification techniques and operation conditions. At the end of each section, a summary of how the GBNMs affect the water treatment process will be presented. In the conclusion, the role of these materials in filtration and adsorption processes will be discussed, with possible directions for future studies.

1.2. Filtration Applications 1.2.1.  Introduction Membranes play an important role in limiting the transport of chemicals and microorganisms by forming a barrier between two phases, such as seawater or  brackish water and freshwater.12 Membrane filtration can be used to treat

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greywater, raw and pretreated municipal wastewater, industrial wastewater, highpurity water production, and desalination.13,14 One of the primary membrane filtration processes is pressure-driven filtration.13 In this process, the feed water is separated into permeate (pure water) and retentate (concentrate) by applying pressure on one side of the membrane.15 The retention of particles or dissolved materials occurs based on size, shape, and charge of the components.15 Compared with conventional methods for wastewater treatment, such as coagulation/flocculation, biological treatment, and sand filtration,15 pressure-driven membrane filtration has superior permeate quality, high recovery, a small footprint, low CO2 emission, and low energy consumption.13 Moreover, the rejected water with concentrated organics and nutrients can be processed to produce renewable energy such as methane or converted to fertilizers.13 Pressure-driven membrane filtration is more practical than other techniques for decentralized wastewater treatment systems, such as in remote areas and on ships.13 For desalination, membrane technology use is growing and is anticipated to share 65% of the worldwide capacity in the near future, because of its small environmental footprint and low energy consumption compared with thermal methods.16 Pressure-driven filtration by membranes can be categorized into four categories: microfiltration (MF), ultrafiltration (UF), nanofiltration (NF), and reverse osmosis (RO).12 By 2023 the market value of MF is expected to reach $3.7 billion, a 9% increase from that in 2018. For UF, NF, and RO, by 2025 the market value is expected to be $2.9 billion, $845 million, and $12.15 billion, respectively, with a 3.6%, 5.3%, and 8.7% increase, respectively, from that in 2018.12 It should be noted that the biggest share of the market is related to water and wastewater treatment, including desalination.17 MF removes insoluble particles ranging from 0.1 to 10 μm, such as bacteria and clay particles, while UF removes materials ranging from 0.002 to 0.1 μm, such as proteins, large natural organic matters (NOM), and colloids.15,18 The removal mechanism for both types of membranes is sieving, i.e., retention of the components depending on their size and the pore size of the membrane used.15,18 Common materials used to fabricate MF membranes are hydrophobic materials, such as polyvinylidene fluoride (PVDF), polytetrafluoroethylene (PTFE), polypropylene (PP), polyethylene (PE), and hydrophilic materials like cellulose esters, polycarbonate (PC), polysulfone (PSF), polyethersulfone (PES), polyimide/ polyetherimide (PIPEI), aliphatic polyamide (PA), and polyetheretherketone (PEEK).12,15,19 The most common methods for fabricating these kinds of membranes are sintering, electrospinning, track-etching, stretching, and phase inversion.15,20 MF and UF membranes have been used in biotechnology, food industries, municipal water treatments, and wastewater treatment.12,15,19 UF membranes are prepared using phase inversion from various materials such as PSF, PES, PVDF, polyacrylonitrile (PAN), cellulose acetate (CA), polyimide, polyether imide (PI),

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aliphatic PA, and PEEK.15 In some cases, however, the same material and fabrication techniques are used to prepare the different membranes: the size and morphology of the pores in the membrane are determined by diverse parameters during fabrication, such as the types of solvents, polymer concentration, presence of additives in polymer solutions, and temperature. An excellent review on the effects of different parameters on membrane morphology has been presented by Tan and Rodrigue.20 NF membranes, with a pore size ranging from 0.5 to 2 nm, have been used to remove color from water; relatively small organic molecules, with a molecular weight around 300 g/mol, such as organic micropollutants; and molecules like sugars and divalent ions.15,18 These membranes usually are made of polymeric materials such as aromatic PA, PSF, PES, CA, and polypiperazine amide.12,15 NF membranes are used in food and beverage industries, chemical and petrochemical industries, pharmaceutical, biomedical, and textile industries, agriculture, and water and wastewater treatment.12 The most common fabrication methods are phase inversion and interfacial polymerization.21 The separation mechanism in this category is based on sieving and charge effects.15 Due to the presence of ionizable groups, such as carboxylic or sulfonic acids on the surface of polymeric NF membranes, these membranes can remove ions smaller than the pore size by a mechanism known as Donnan exclusion.15 Besides polymers, inorganic materials that are made by sintering or sol/gel process, such as ceramic membranes, consist of alumina (Al2O3), TiO2, silica (SiO2), and zirconia (ZrO2) along with organomineral membranes, which are a combination of polymeric and inorganic materials, such as a PSF matrix containing zirconia grains as filler, that can also be used in MF, UF, and NF.15 Lastly, RO membranes can remove monovalent ions,18 making them suitable for brackish and seawater desalination and water purification. Despite the other pressure-driven membranes, there is no pore size defined for RO membranes, and the removal mechanism is solution-diffusion rather than sieving.15 Two common types of membranes have been used for RO operation, namely, acetylated cellulose (CA) and thin-film composite (TFC).12 CA membranes that emerged before TFC membranes consist of a support layer, usually non-woven polyester, covered by CA.22 TFC membranes typically have a base layer, nonwoven polyester23 or PP,24 a microporous support layer made of PSF, and an ultrathin active layer made of polyamide.23,25 The active layer, which has the main role in membrane permselectivity, usually is formed on the PSF layer by interfacial polymerization between an aromatic polyamine, such as m-phenylenediamine (MPD), and an aromatic polyacyl halide, such as TMC.23,25 Figure 1.2 summarizes the pressure-driven filtration process. In spite of the wide application of pressure-driven membranes in water treatment, three problems associated with this filtration approach have generated

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1Å

1 nm

0.1 µm

10 nm

1 µm

10 µm

Pore size

Solutes, particles

proteins

salts

macromolecules

hormones

humic acids

Filtration process

bacteria

viruses

clay particles

reverse osmosis nanofiltration ultrafiltration microfiltration

Figure 1.2.    Filtration process, solutes and particles, and pore size.20

extensive research in academia and industry: high energy consumption, fouling, and susceptibility to disinfecting agents.6,23,25–27 First, to be practical for large-scale use, membrane filtration systems should produce high volumes of fresh water at relatively low hydraulic pressure.6,23 For instance, for RO membranes, although years of research have reduced energy consumption for seawater desalination from 8 kWh/m3 to 3.4 kWh/m3, this number is still considerably higher than the theoretical value of 1.06 kWh/m3 for 50% recovery of seawater with 35,000 mg/L TDS.28 Second, fouling on the membranes can also increase energy costs and decrease the operating lifetime of the membranes.25,29,30 Fouling is defined as the accumulation of inorganic materials (scaling) such as gypsum, organic materials such as proteins, microorganisms (biofouling), and colloids such as colloidal silica, inside the pores or on the surface of the membranes.25,29,31 Third, polymeric membranes are highly prone to degradation from oxidizing agents such as chlorine, chlorine-based chemicals, and ozone.29,32,33 These materials currently are used as disinfectants, especially for drinking water purification24 to prevent biofouling,23,25,33 and for washing the fouled membranes.24 Free chlorine can attack the aromatic rings of polyamide and amide nitrogen, leading to degradation of the active layer of TFC membranes and eventually membrane failure.23,25,26 These shortcomings have motivated researchers to develop methods to improve the membrane filtration system performance, among which modification of the membranes is very common.25,33

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1.2.2.  Modification techniques Modification of the pressure-driven membranes can be accomplished by changing the polymerization parameters, such as the type of solvent, curing condition, type of monomer, and reaction time for the active or support layer.23,33 Another approach is to improve the performance of the membranes by adding a new material either by modification “on-membrane” or “in-membrane”. In the onmembrane modification, a commercial membrane with no additional coating is used, or the membrane is fabricated via conventional methods and then the new material(s) are added on top of the membrane via three different methods: coating, in which the new material(s) is attached by non-covalent bonding to the surface of the membrane; grafting, in which the new material(s) is attached by covalent bonding to the surface of the membrane; and layer by layer (LbL), which involves alternate deposition of two layers, usually with opposite charges on the surface of the membrane.34 In the in-membrane modification, the material(s) are incorporated in the support layer or active layer of the membrane during fabrication.35 Table 1.1 summarizes the advantages and disadvantages of these techniques. Various materials have been used for membrane modifications, such as TiO2,23,26 alumina,26 zeolite,22,24,26 biocidal materials like silver and copper,22,29,36 silica,24 and zwitterionic polymers.37,38 While these materials have disadvantages, such as aggregation,23 leaching into the environment,29 and ineffectiveness for increasing permeability and solute/particle rejection,36 GBNMs have become popular because they do not have the aforementioned problems and they are chemically stable,25 compatible, mechanically stable, and have a high surface Table 1.1.    Comparison between different modification techniques for pressure-driven membranes. Modification method

Advantages

Disadvantages

On-membrane • Facile processability (coating)

• May decrease permeability by adding a layer, which increases hydraulic resistance26

On-membrane • Stability and durability10 (grafting) • Full use of the modifier, less usage of the material29

• May decrease permeability by adding a layer, which increases hydraulic resistance29 • Not robust for large scale29

25

On-membrane • Facile processability25 • May decrease permeability by adding a (LbL) • Not decreasing water layer, which increases hydraulic resistance29 permeability in some cases25 • Disintegration in harsh environments like • Fine control and tunable high/low pH or high ionic strength29 fabrication of the coating layer34 In-membrane

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• Significant enhancement in mechanical strength and water permeability35

• Embedment of most of the modifier inside the membrane, which does not enhance the surface properties10

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area.23 Moreover, GBNMs are known to be antimicrobial, which can help to improve antibiofouling properties.39–42 The use of GBNMs by either on-membrane or in-membrane approaches to improve the performance of the pressure-driven membranes will be discussed separately in the subsequent sections of this chapter.

1.2.3.  Microfiltration and ultrafiltration As previously discussed, MF and UF membrane performance can be improved by using the on-membrane and in-membrane approaches. In this section, onmembrane approaches are first discussed, followed by in-membrane approaches using GBNMs to modify MF and UF membranes. On-membrane modification: On-membrane modification can mitigate problems associated with MF/UF membranes. One of the primary uses of MF membranes is to remove bacteria from water to prevent biofouling on the membrane.15 To improve the antibiofouling property of a cellulose MF membrane, Ibrahim et al.16 used the coating method. GO nanosheets were coated by spraying a GO dispersion in deionized water on a cellulose membrane fixed on a vacuum filtration setup. GO was attached to the surface of the membrane via hydrogen bonding between -OH groups of cellulose and functional groups such as -COOH on GO. The comparison between pristine and GO-coated membranes in a continuous crossflow MF setup showed that GO decreases the water permeability by filling the cellulose pores. However, filtration tests with seawater samples showed that after 24 h the transmembrane pressure increased 55% for the pristine membrane and 6% for the GO-coated membrane, which means less fouling occurred on GO-coated membranes. This result can be explained by the antibacterial property of GO and the strong electrostatic repulsion between negatively charged bacteria and the negatively charged surface of GO-coated membranes. Comparing different types of GBNMs can help select the one that is most effective for modification. Alam et al.43 coated a commercial UF PES membrane with GO and rGO to improve fouling resistance and then compared the two nanomaterials. The hydrophilicity of the coated membranes, measured by the water contact angle, increased for GO and decreased for rGO. For the GO coating, the water permeability was lower than the PES membrane due to the relatively thick coating of the nanomaterials. However, it was observed, by thermal reduction of GO to rGO that C–O bonds and oxygen content reduced by 32% and 35%, respectively, which led to 300% restoration of water permeability, but the water permeability of rGO-coated membranes was still lower than the pristine membranes. This could be due to the disordered layers of GO during pressurized filtration and the side-pinning effect of GO’s functional groups, which impeded water transport through the GO nanochannels. The fouling test was performed using synthetic wastewater containing sodium alginate (SA) and BSA as organic matters.

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The foulant rejection rate increased from 23% for the PES membrane to 66% for the GO-coated membranes and increased to 63% for the rGO-coated membranes. Although all the membranes’ pore sizes were larger than SA and BSA, a reasonable rejection was achieved, indicating that separation is not only based on size exclusion but also by the electrostatic repulsion between negatively charged GO and rGO with the negatively charged SA and BSA. Despite the lower water permeability, it was concluded that GO-coated membranes were more resistant to fouling, had lower irreversible fouling, and had higher flux recovery after backwash with water compared with rGO-coated membranes because of higher hydrophilicity. A thinner coating of GO might provide antifouling properties without a major negative effect on water permeability. Another comparison between GBNMs was done by Zeng et al.,10 in which GO and GOQD were grafted onto the surface of a UF PVDF membrane to reduce biofouling. The water permeability did not significantly change for the membranes. GOQD showed better anti-biofouling properties compared with GO and CNT, according to the literature.10 The better performance could be due to the uniform dispersion of GOQD on top of the membrane, which generates a high fraction of active edges to insert/cut bacteria and facilitates oxidative stress formation. Besides improving antifouling properties, modification with GBNMs can also improve the stability of the MF/UF membranes. Zhang et al.44 grafted GO on MF PAN membranes using diethylenetriamine as a crosslinker. The modified membranes showed high stability in filtering oil–water emulsion at a broad range of pH and high salt concentrations. Moreover, the GO-modified membrane showed high water permeability, rejection, and antifouling performance for oil– water emulsion separation, which could be attributed to large pores of the membrane and the hydrophilicity of small GO nanosheets attached to the fibers and large GO nanosheets connected to multiple fibers of the polymer. Some studies have used composites of GBNMs to further benefit from the positive effects of GBNMs on MF/UF membranes. A thorough study was done by Musico et al.45 on the antibacterial properties of G and GO and their composite with poly(N-vinylcarbazole) (PVK) as a coating on cellulose nitrate membranes. Filtration of water containing E. coli and Bacillus subtilis by gravity filtration through the membranes showed that G and GO and their composites with PVK enhanced log bacterial removal from ~1.5 for unmodified and PVK-modified membranes up to 3 and 3.2 for G and PVK-G and up to 4.2 and 4.5 for GO and PVK-GO, respectively. The same trends were observed when DNA concentration, as indicator of the number of damaged cells, % metabolic active cell, and % glutathione loss were measured. GO was better than G, and polymer composites performed better than their respective GBNMs. This could be due to the increase in the G and GO dispersion through the π–π interaction between the carbazole group of PVK and the carbon rings in G and GO. Therefore, G and GO became more available to damage cells.

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The role of GBNMs nanocomposites in enhancing membrane performance for pollutant removal was also investigated. In two different studies, Bandara et al.7,46 optimized the composition of a nanocomposite comprising PEI, glutaraldehyde (GLA), CS, and GO using response surface methodology. The optimum nanocomposite was coated on eight different commercial MF membranes, among which the glass-microfiber-coated membranes were found to be the most stable. This membrane showed promising performance in removing a range of contaminants, including three types of heavy metals, two types of bacteria, and nitrate. The membrane was also effective in a wide range of pH, salinity, and turbidity, as well as complex water chemistries such as seawater and wastewater, demonstrating its potential for real-world applications. In-membrane modification: Mixing nanomaterial into the polymer solution can be done before membrane fabrication to incorporate the nanomaterial in the membrane matrix. During thermally induced phase separation fabrication of an isotactic PP MF membrane, various amounts of GO were added to the polymer solution to improve membrane performance.47 By introducing hydrophilic groups into the membrane, GO decreased the water contact angle from 125○ to 52.33○ and increased the water permeability by 353% and mechanical strength by 123%.47 Preparing GBNMs composites with polymers is not the only approach for enhancing their positive effect on membrane performance. The functionalization of GBNMs can also improve their effect. Ayyaru and Ahn48 incorporated GO and sulfonated GO (SGO) in UF PVDF membranes to study and compare their effect on transport properties and the fouling behavior of the membranes. The presence of the nanomaterials in the polymer matrix of the membrane increases water permeability without losing rejection by increasing the pore size of the membrane and the hydrophilicity of the surface. Interestingly, the SGO-modified membranes (P-SGO) had higher water permeability than GO-modified membranes (P-GO), which could be due to the stronger hydrogen bonding between the -SO3H group on SGO to water compared with -COOH or -OH groups on GO to water. This characteristic, as well as more negative surface charges on P-SGO than P-GO, caused better flux recovery ratio (FRR) and less irreversible fouling in fouling tests using BSA. A summary of this section is presented in Table 1.2. The effect of GBNMs on MF and UF membranes can be analyzed from various perspectives. Based on the studies reviewed in this chapter, different approaches can be decrease suggested depending on the purpose of the modification. For instance, on-membrane modification usually decreases water permeability, especially when the modification is done by coating and not grafting.10,16,43,44 This could be due to the addition of transport resistance by introducing additional layers. On the other hand, in-membrane modification seems to be the best approach for increasing water permeability, since the pore structures and size can be manipulated by this technique.47,48 In terms of fouling resistance, G and rGO

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Table 1.2.    Summary of the notable studies in G and GO modification in MF and UF membranes. Material(s) used and modification method

Membrane type and material

Main finding(s)/importance

Ref.

GO coated

MF Cellulose • When coated by vacuum filtration, GO reduces water permeability • GO reduces biofouling

[16]

G and GO coated

MF Cellulose • GO is a better antimicrobial material compared with G nitrate • Polymer composites of GO and G have better antimicrobial properties than GO and G due to enhanced dispersion

[45]

GO coated

MF Glass microfiber

• GO polymer composite can remove a range of contaminants due to functional variety

GO grafted

MF PAN

• GO layer was stable at broad pH ranges and high salt concentrations • Grafting decreased the water permeability by ~30%

[44]

GO incorporated

MF iPP

• Water permeability increased by five times • GO increased flux recovery after backwash • GO incorporation increased mechanical strength

[47]

GO and rGO coated

UF PES

• When coated by vacuum filtration, GO reduces water permeability more than rGO • GO was more effective in reducing fouling and increasing flux recovery after backwash than rGO

[43]

GO and GOQD UF PVDF grafted

• Grafting did not decrease water permeability • GOQD was more effective in reducing fouling and increasing flux recovery after backwash than GO

[10]

GO and SGO incorporated

• SGO was a better filler for improving transport and antifouling properties than GO by making stronger hydrogen bonds with water

[48]

UF PVDF

[7, 46]

showed poor performance compared with GO.43,45 This may be attributed to the functional groups on GO, which enhance hydrophilicity and ROS production. GO’s functional groups reduce fouling by forming a water layer, which protects the surface of the membrane from foulant attachment,46 while ROS production gives GO better antimicrobial properties compared with G, since ROS can be deadly to cells by inducing oxidative stress.45 GO performance can be improved further by combining GO with polymers,7,45,46 functionalizing,48 and changing its physical characteristics.10 These techniques usually result in better performance due to better dispersion of GO or the special characteristics of the new functional groups.

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1.2.4.  Nanofiltration and reverse osmosis GBNMs can be used to improve the performance of NF and RO membranes. This section discusses the use of on-membrane and in-membrane techniques for modifying these membranes. On-membrane modification: TFC membranes can operate in a wide range of pH and temperatures, have high salt rejection, and are chemically stable.24 Adding to these properties, their high-water flux, ease of large-scale fabrication, and operation at lower pressure have made TFC membranes the first choice for RO systems. However, they have one major drawback compared with CA membranes: susceptibility to chlorine.23,24,33 To enhance chlorine resistance, Choi et al.25 used the alternative deposition of aminated GO, positively charged, and GO, negatively charged, on a lab-fabricated TFC membrane to develop a dual-functional protective layer. A chlorine resistance test showed a ~4% reduction in salt rejection for the GO-modified membranes, as opposed to ~50% for pristine membranes. This result is justified by the fact that GO layers retard the diffusion of active chlorine species (OCl−) due to their transport resistance, thereby protecting the PA layer from coming into contact with OCl−, which is responsible for the membrane degradation. LbL modification using layers with opposite charges may provide an unstable layer that might be removed in harsh conditions.29 To improve this technique, Hu and Mi8 used the LbL approach to coat a PSF membrane by GO. However, despite using layers with alternate charges, they used TMC as a cross-linking agent between the GO layers. The stable GO layers decreased water permeability by 300% but increased the rejection for monovalent and divalent ions, as well as for cationic and anionic dyes. Yet, the rejection was still low for ions, since the space between the GO layers is estimated to be around 1 nm because the TMC molecules are around 0.7 nm, and thus the low rejection of ions occurred. Due to its instability and addition to transport resistance, which decreases water permeability, some researchers do not consider LbL to be the best approach and prefer grafting for surface modification. Perreault et al.49 used the grafting approach to functionalize the surface of a TFC membrane with GO. They observed no detrimental effect on permeability and salt rejection, as well as 65% inactivation of E. coli after 1 h for GO-functionalized membranes. To make the grafting procedure simple, the GBNMs were functionalized before grafting. Huang et al.29 functionalized GO with azide (AGO) then used AGO to modify a commercial TFC membrane by a simple grafting reaction using UV irradiation. While water permeability and salt rejection changed negligibly, biofouling with E. coli and fouling with BSA decreased 17 and 2 times, respectively. In-membrane modification: As previously discussed, CA and TFC membranes are the most common RO membranes.23 Their smoothness and uncharged surface, which does not attract foulants, have made CA membranes resistant to fouling.

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Moreover, these membranes have a high tolerance to chlorine and other oxidants.22 On the other hand, the market for CA membranes has shrunk over the years due to their limited pH and temperature operation range, low salt rejection, and susceptibility to hydrolyzation, which decreases operation life.22,24 You et al.22 used rGO and GO to improve CTA membrane performance. The effect of rGO on water permeability was small, but for GO the water permeability showed a five-fold increase. This may be due to an increase in water sorption and the formation of water channels within the GO nanosheets. For salt permeability, both nanomaterials showed an approximately ten-fold decrease. This may be due to the densification of the membrane, since the fillers occupy free volume in CTA film and fill the cavities, as well as interactions of fillers with Na+ and Cl−, such as electrostatic interaction of Na+ and COO− of GO, or the cation–π interaction between Na+ and GO nanosheets. Considering the results for both water and salt permeability, GO was selected as the best filler. The incorporation of GBNMs in the TFC membrane during fabrication has attracted significant attention. This approach can be categorized into the incorporation in the active layer or support layer. While incorporation into the active layer is more popular, several studies have focused on the support layer. Zhang et al. incorporated GOQD into the polyamide active layer of a NF membrane.50 The presence of GOQD increased water permeability by making short flow paths for the water through the nanosheets. GOQD made the surface smoother, more hydrophilic, and more negatively charged, all of which helped to reduce fouling. As a result, in the fouling test with BSA, FRR for GOQD incorporated membranes was 91.7% compared to 64.5% for the control membranes. The modified membranes showed good performance for rejecting four different dyes and four different salts. The rejection was explained by steric hindrance and electrostatic interactions. The concentration of the GBNMs in the polymer matrix is an important parameter that determines the performance of the membrane. Chae et al.26 incorporated GO into the PA layer by adding different dosages of GO to an MPD aqueous solution. For the three dosages of GO investigated in this study, the highest dosage was the best. Generally, incorporation of GO into the PA layer led to an increase in hydrophilicity and a decrease in the layer thickness, which resulted in up to an 80% increase in water permeability for the highest dosage. The antibiofouling property increased for all the GO-embedded membranes, due to the increase in hydrophilicity, antimicrobial property of GO, decrease in roughness, which provided fewer sites for foulants adhesion, and decrease on surface charge. Resistance to chlorination also increased in the presence of GO. Besides the fact that GO can shield free chlorine to reach to PA, the hydrogen bonding between GO and PA can prevent the replacement of amidic hydrogen with chlorine. By forming nanocomposites, GBNMs can leverage the beneficial characteristics of other materials. Safarpour et al.23 prepared rGO/TiO2 and incorporated it to

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the PA layer of an RO membrane by dispersing the nanocomposite in an MPD solution before interfacial polymerization. In this study, by attaching the TiO2 to rGO, the aggregation problem associated with TiO2 nanoparticles was avoided and the nanocomposite was properly dispersed throughout the active layer. Incorporation of 0.02% wt. rGO/TiO2 in the PA layer increased water permeability, antifouling property against BSA, and chlorine resistance. For the bare TFC membrane, the salt rejection decreased by 30% after chlorination, while this number for the membrane containing 0.02% wt. rGO/TiO2 was 3%. Since most of the salt rejection and hydraulic resistance happen in the active layer of the TFC membrane, most studies have focused on the active layer. The role of the support layer is considered to only help the active layer to endure high pressure; however, there is some room for improving the TFC membrane’s performance by modifying the support layer.6 To enhance water permeability and mechanical strength, Lee et al.6 embedded different amounts of GO platelets with different levels of exfoliation (i.e., singlelayer, 5-layer, 14-layer) to the PSF layer of TFC RO membranes. Their first finding was that the presence of GO increases the pore size in the PSF layer, which led to an increase in water permeability without any significant decrease in salt rejection. Both single- and 14-layer GO enhanced the mechanical properties of the support layer up to 1% wt. because of the sp2 carbon bonding network in GO and interfacial interaction between GO and PSF. The authors also found that singlelayer GO is more effective than 14-layer GO for improving mechanical properties. Because of the higher number of exposed oxygenated functional groups for singlelayer GO with 14-layer GO, there is a higher electrostatic repulsion among the sheets of the single-layer GO, which leads to better dispersion and less aggregation. Moreover, it is known that a large specific surface area increases the load transfer of the nanoplatelets, and thus single-layer GO provides higher strength at the same concentration as 14-layer GO. Table 1.3 presents the use of GBNMs for NF and RO membranes. From the literature review on the effect of GBNMs on NF and RO membranes, it can be concluded that GO has more positive effects on the performance of the membranes and thus it is more popular among researchers.22 The effect of graphene-based materials on salt rejection, whether incorporated into the membranes or coated onto the membranes, was mostly none or negligible.6,23,25,26,29,49 Therefore, it can be concluded that graphene-based materials are not the best candidates to improve salt rejection. In terms of water permeability, similar to the trend that was observed for MF and UF membranes, water permeability did not increase with on-membrane modification.25,29,49 However, when GO or rGO modification was performed using in-membrane techniques either in the support layer or active layer of the membranes, water permeability improved.6,22,23,26,50 For increasing chlorine resistance, both of the nanomaterials and both on-membrane and in-membrane techniques are effective, but the in-membrane techniques can

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Enhancement of Filtration and Adsorption Processes  15 Table 1.3.    Summary of the notable studies in GBNMs modification in NF and RO membranes.

Material(s) used and modification method

Membrane

GO LbL

NF TFC

• Stable, covalently bond GO layer • Distance between GO layer determines pollutant rejection

[8]

GOQD incorporated into the active layer

NF TFC PA

• Using TA instead of TMC and IPDI instead of MPD to form PA layer • Effective in rejecting various pollutants

[50]

GO LbL

RO TFC PA

GO layers increased chlorine resistance

[25]

AGO grafted

RO TFC PA

Simple grafting of functionalized GO by UV irradiation

[29]

GO grafted

RO TFC PA

Simple grafting method for GO

[49]

GO incorporated into the active layer

RO TFC PA

Increased water permeability and chlorine resistance

[26]

rGO incorporated into active layer

RO TFC PA

Increased water permeability, fouling resistance, and chlorine resistance

[23]

GO and rGO incorporated

RO CTA

GO increased water permeability while rGO did not change water permeability

[22]

GO incorporated into the support layer

RO TFC PA

Increased water permeability and mechanical strength

[6]

Main finding(s)/importance

Ref.

also enhance mechanical strength.6,23,25,26 If the goal of the modification is to decrease fouling susceptibility, both modification techniques can be used; however, on-membrane techniques might be a better choice since they use fewer materials.50

1.3. Adsorption 1.3.1.  Introduction Various methods for water and wastewater treatment, such as screening, crystallization, sedimentation, and gravity separation, flotation, oxidation, coagulation, precipitation, ion exchange, filtration, electrolysis, evaporation, solvent extraction, and distillation have been used, with costs ranging from $10 to $450 dollars per million liters of water.51 However, adsorption stands out among methods for water treatment because it is flexible, has a wide range of applications, is easy to operate, and is the most cost-effective method compared with other methods, with costs ranging from $10 to $200 per million liters.51–53 Defined as a surface phenomenon, adsorption occurs when substances attach onto a solid surface.51

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Thermodynamically speaking, the substances reach a lower level of free energy by attaching to the surface. The adsorption can be physical, based on non-chemical bonding such as van der Waals, or it can be chemical, in which the substances chemically bond to the surface.54 In the adsorption process, the substance is called adsorbate and the surface is called adsorbent.51 A vast number of pollutants can be removed by adsorption, such as biological pollutants, soluble and insoluble organics such as NOM and volatile organic carbons, and inorganics such as metal ions. From a design standpoint, adsorption can be done in batch or continuous (column) mode.51,54 Usually, the results of the adsorption in batch mode are used to design the column.51 The adsorbent choice primarily depends upon the type of pollutant, and the primary criteria are specific surface area, pore size, and pore volume.54 In addition, a suitable adsorbent must be cheap, available, efficient, and easy to regenerate.51 Over the years numerous materials have been used for adsorption, such as activated carbon, waste materials, clay, chitosan, resins, metal oxides and hydroxides, and zeolites.51 However, the use of nanomaterials for adsorption recently has attracted attention due to their small size, high porosity, high pollutant-binding capacity, and high surface-area-to-volume ratio, as well as their capability to sequester various contaminants with different molecular size, speciation behavior, and hydrophobicity.52,55 Moreover, the catalytic potential and high reactivity further make nanomaterials superior to conventional materials for adsorption.52 Nanomaterials that have been used in adsorption process for water treatment include metal oxides, including ferric oxides, magnesium oxides, cerium oxides, manganese oxides, aluminum oxides, and titanium oxides; silsesquioxane-based nanomaterials, which are organosilicon compounds with the chemical formula [RSiO3/2]n; GBNMs; and various nanocomposites.52 With the fundamental problems associated with the use of other nanomaterials for adsorption, GBNMs are known as superior sorbents for water treatment. For instance, in the case of metal oxides, the high surface energy causes the nanomaterials to aggregate, which leads to a decrease or loss of adsorption capacity and selectivity. Moreover, these nanomaterials cannot be used in fixed beds due to excessive pressure drop and poor mechanical strength.52 On the other hand, GBNMs can be used by themselves or in composite forms to remove pollutants from water due to their high capacity and stability.52 In the following sections, the use of GBNMs in batch and continuous adsorption processes will be discussed.

1.3.2.  Batch adsorption As previously described, it is important to examine different nanomaterials in order to identify the most effective one for a certain application. Smith et al.56 compared the G and GO adsorption capacity for lysozyme, a positively charged protein, in various water chemistries. GO showed higher adsorption capacity than

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G while both nanomaterials were more effective than activated carbon. For both nanomaterials, adsorption was found to be independent of pH. In terms of ionic strength (IS), G adsorption capacity did not change with the presence of monovalent and divalent ions, but GO adsorption capacity decreased significantly with the increase in IS. Since GO adsorption is mainly through an electrostatic mechanism by –COOH and –OH functional groups, the decrease in adsorption capacity caused by the increase in IS could be due to the fact that the zeta potential for GO decreases in the presence ions, hence the adsorption capacity decreased. But for G, the governing mechanism is van der Waals forces and thus the presence of ions does not affect the removal. Although fundamental studies, like the one presented by Smith et al., seek to understand the adsorption mechanisms of nanomaterials and the impact of water chemistry, most of them combine graphene-based adsorbents with polymers to facilitate their removal from water, with the goal to improve contaminant removal or to add new properties to the nanomaterial. For instance, in an attempt to use graphene for Hg2+ removal, Yap et al.57 prepared two polyamine-modified rGO adsorbents. The adsorbents showed a high capacity toward Hg2+ in pH, ranging from 5 to 9. The kinetics study showed a pseudo-second-order model and the equilibrium was reached in 10 min for both nanocomposites; Freundlich was the governing isotherm model. Compared with a commercial-activated carbon (16%), polyamine rGO adsorbents showed better selectivity (>70%) for removing mercury ions in the presence of competing ions Cd2+, Co2+, Cu2+, and Pb2+. The nanocomposites showed high mercury ion removal in real water samples from a river and seawater. Furthermore, the adsorption capacity for both adsorbents did not significantly change after three cycles.57 In another study, Musico et al.58 prepared a PVK-GO nanocomposite to remove Pb2+ from an aqueous phase. The adsorption followed the Langmuir isotherm model, and the metal-ion removal increased by increasing the GO content in the nanocomposite. Analyzing the adsorbents before and after the adsorption showed that functional groups such as -COOH and -OH play a major role in metal ion removal. In two different studies59,60 nanocomposites beads containing CS, PEI, GO, and GLA were synthesized for metal, Cr(VI) and Cu(II), and not-metal, Se, removal. In both studies the concentration of different materials was optimized using RSM based on the target pollutant. The beads were effective in removing pollutants in both studies. Investigation of the N2 adsorption/desorption isotherms by Brunauer–Emmett–Teller (BET) showed the beads had a porous structure, which played a major role in removing pollutants. Furthermore, XPS analysis of the beads before and after adsorption highlighted the importance of functional groups originating from GO and the polymers on the adsorption of the pollutants. These studies showed how combining GO with polymers can make practical adsorbents with multiple functional groups that can effectively remove various pollutants simultaneously.

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All these studies have emphasized the importance of the functional groups in the adsorption process. However, are these groups always able to improve adsorption? Multiple studies have investigated the effect of a number of functional groups on GO, i.e., the oxidation degree of GO on its adsorption performance.61–64 It was found that by increasing the oxidation degree, the number of oxygenated functional groups such as O–C=O, C=O, C–O, C–OH, and C–O–C increases, which enhances the exfoliation degree of GO63 and increases GO dispersion in water due to the strong hydrogen bonding between the functional groups and water molecules.61 These enhancements in exfoliation and dispersion make GO more readily available for pollutant removal.61,63 Highly oxidized GO was found to be more effective than moderately oxidized GO for Cd2+ and Cu2+ removal61,64 and MB removal.62,63 However, increasing the oxidizing degree of GO is not always desirable. Zolezzi et al.62 observed a decrease in the removal of methyl orange (MO), an anionic dye, by increasing the GO oxidization degree. Therefore, the GO synthesis process should be adjusted based on the application of the adsorbent. GBNMs can improve other adsorbents’ performance. For instance, are effective adsorbents for heavy-metals removal due to formation of inner-sphere complexation to specific metals such as lead, cadmium, and zinc. However, aggregation, difficulties with separating from the aqueous phase in batch adsorption, or excessive pressure drop in a continuous adsorption process have made the use of manganese oxides impractical.65 By attaching the hydrated manganese oxide (HMO) to GO as a host matrix, Wan et al.65 made a nanocomposite (HMO@ GO) that has several advantages over HMO, including better dispersion of HMO through electrostatic repulsion due to charged groups on GO; faster sorption kinetics, since GO reduces pore diffusion; higher capacity for metal removal; and easier separation from water. A batch adsorption test on lead showed that the adsorption capacity for HMO@GO is nearly four times higher than HMO. The increase in adsorption capacity could be due to the better dispersion of HMO on GO compared with aggregated HMO. Moreover, adsorption and pre-enrichment of lead ions on negatively charged functional groups on GO make the ions more available for HMO. The presence of GO has made the adsorption process faster, as the equilibrium was reached nearly one order of magnitude faster than that for the porous hosts for HMO presented in the literature. HMO@GO showed a great selectivity for removing lead ions in synthetic industrial wastewater containing Ca2+, Mg2+, Na+, and humic acid, as 1 kg of the nanocomposite was able to decrease the Pb2+ concentration of 22 m3 of wastewater from 5 mg/L to below the discharge limit for the electroplating industry.65 Magnetite (Fe3O4) nanoparticles can easily be removed from water by using magnetic forces, but their low adsorption capacity makes them inappropriate for water treatment. However, GBNMs can improve magnetite adsorption capacity. Yao et al.66 synthesized a Fe3O4@graphene nanocomposite by chemical deposition of Fe3O4 nanoparticles onto GO sheets and then reduced the GO to G by chemical

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reduction. The nanocomposite had the convenience of being separated from water by an external magnet. Moreover, Fe3O4@graphene showed good adsorption capacity for MB and Congo Red, which was higher than for many other adsorbents reported in the study, but the authors believed that GO would perform better for a similar application. A later study by Othman et al.67 showed that magnetic GO is more effective toward MB removal, as the maximum adsorption capacity calculated by the Langmuir isotherm model increased by 37 times. This comparison may explain why most studies on using GBNMs for adsorption purposes prefer to use GO instead of G; as a result, the number of studies using G is very low. Table 1.4 summarizes the main findings in the use of graphene-based adsorbents in batch mode. Table 1.4.    Summary of graphene-based adsorbents in batch adsorption. Adsorbent

Pollutant

Main finding(s)/importance

Ref.

G and GO

Lysozyme

• Both adsorbents are better than activated carbon • GO has a higher adsorption capacity than G

[56]

PVK-GO

Pb2+

Oxygenated functional groups play a major role in metal adsorption

[58]

CS-PEI-GO beads

Cr6+, Cu2+, and Se

• Bead synthesis facilitates practical use of the adsorbents • The porous structure of the beads and the functional groups of GO and polymers contribute to adsorption

[59, 60]

GO

MB

• GO exfoliation increases with an increase in the degree of oxidation • Adsorption capacity increases with an increasing degree of oxidation

[63]

GO

MB and MO

An increase in the degree of oxidation increases MB removal but decreases MO removal

[62]

GO

Cu2+ and Cd2+

• GO dispersion increases with an increasing degree of oxidation • Adsorption capacity increases with an increasing degree of oxidation

[61]

Polyamine modified rGO

Hg2+

The adsorbent was more effective and selective than the commercial-activated carbon

[57]

HMO@GO

Pb2+

• GO increases HMO dispersion • GO makes pollutants more available for HMO by pre-enrichment

[65]

Magnetic G and GO

MB

• G and GO make Fe3O4 stable and increase adsorption capacity • GO is more effective than G in increasing adsorption capacity

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For years activated carbon has been the primary adsorbent for water treatment, but recently graphene-based adsorbents have shown promise for wide applications in water treatment by outperforming activated carbon in lab-scale batch adsorption.56,57 GBNMs can be used individually for adsorption as well as help to improve the performance and stability of other adsorbents.57,65–67 Among different types of graphene-based adsorbents, GO has shown more potential as an effective adsorbent than G and rGO.56,66,67 Functional groups on GO significantly contribute to its adsorption behavior, and studies have found the adsorption capacity of GO increases with an increase in the number of functional groups.61–64 However, this is true primarily for the adsorption of positively charged pollutants, as a negative effect on the adsorption capacity of a negatively charged pollutant was observed with increasing numbers of functional groups.62 Therefore, the type of target pollutant for the adsorption process must be considered in the design and synthesis of the adsorbent. Although adsorption can be performed in batch systems, a more practical way is to perform adsorption in the continuous form in a column packed by adsorbents. A continuous adsorption study is easier to scale up, and it may provide information, such as adsorption curves and column breakthrough, that could be used to design a real system.68 In the next section, nanocomposites containing GBNMs in continuous adsorption mode will be discussed.

1.3.3.  Continuous adsorption To use the adsorption potential of GBNMs in a real system, composites must be prepared by combining GBNMs to other materials in order to help with their collection and the regeneration of the adsorbent to increase reusability. Moreover, in the case of a continuous system, composites must be developed to avoid excessive pressure drop.69,70 To address these problems, two different studies prepared layered double-hydroxide (LDH)/GO nanocomposites.69,70 LDHs are anionic exchangers, which makes them a perfect choice for anion removal. LDHs have an [M(II)1−x M(III)x (OH)2]x+ (An−)x/n.mH2O structure, in which M(II) is divalent metal ions, M(III) is trivalent metal ions, and An− is the n-valent anion.69 In the first study, Guo et al.69 prepared LDH/GO alginate beads to be used in a column for Sr2+ and SeO42- removal. The prepared beads had an average diameter of 2 mm. Batch adsorption experiments showed Sr2+ adsorption follows the Freundlich isotherm model, in which Sr2+ ions primarily interact with functional groups on GO or alginate, and SeO42− adsorption follows the Langmuir isotherm model, in which SeO42− removal is due to the ion exchange on LDHs. Comparison of the adsorption capacity of LDH/GO and LDH/GO alginate beads showed that the removal of Sr2+ increased because of the large number of carboxyl groups in alginate; however, the SeO42− removal decreased because the alginic acid’s functional groups occupy the anion exchange sites on LDH. On the other hand, the alginate

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decreased Al and Mg leaching from LDH and made the adsorbent stable. Continuous co-sorption of Sr2+ and SeO42− was carried out. The LDH/GO beads reduced the concentration of both pollutants below the standard level for synthetic wastewater. In the second study, to facilitate separation and avoid high pressure drop for practical applications, Jana et al.70 immobilized LDH/GO in PAN beads by phase inversion in a dopamine solution. The beads were used for lead removal. Comparison of the lead adsorption capacity of the GO/LDH nanocomposite with GO and LDH showed a 30% and 20% increase, respectively, in batch adsorption. The adsorption capacity for GO/LD H was almost five times higher than the GO/ LDH beads; however, immobilization of the nanocomposite on beads enhanced the adsorption kinetics and prevented leaching of the nanomaterials in the aqueous phase. Continuous adsorption of lead was performed in a column using GO/LDH beads. The adsorption data fit well with the Thomas model, which assumes axial dispersion is insignificant. Calculating the maximum adsorption capacity from the model showed this parameter increases with an increase in flow rate, which suggests the overall rate of adsorption is dominated by external mass transfer. However, the column breakthrough time decreases with an increase in flow rate and increases with an increase in bed height. The breakthrough time was lower when a real battery effluent containing Pb2+ was used. This could be due to the presence of suspended solids and organics; however, the adsorbent remained mostly selective toward lead, as the concentration of Fe2+ and Zn2+ did not change significantly. Most continuous adsorption studies have the same type of experiments, as thoroughly presented in these two studies. Therefore, to avoid redundancy in the next studies, only distinguished findings are presented. In an attempt to design an effective system to remove triclosan (an antibacterial and antifungal agent), magnetic porous rGO was synthesized by Li et al.68 The nanocomposite was tested under various conditions for triclosan removal. Most importantly, the nanocomposite performed better than the powdered-activated carbon in terms of adsorption capacity and breakthrough time, primarily because the nanocomposite had a larger surface area than the powder-activated carbon. A larger surface area provides more adsorption sites for triclosan, and thus number of triclosan molecules per nanocomposite adsorption sites ratio decreases and results in a flatter breakthrough curve. The performance of GO- and rGO-containing beads were compared in an study by Zhuang et al.71 They compared GO alginate single-network (GAS) beads and rGO alginate double-network (GAD) beads for MB removal. In terms of physical properties, the difference between the two beads was that the GAD had a wrinkled structure and GAS had a flat structure. This gave GAD a higher surface area, which is beneficial for adsorption. This advantage was further demonstrated in the continuous removal of MB, where GAD showed a larger breakthrough time and a higher adsorption capacity. These results demonstrate that although

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functional groups are important in adsorption, physical properties can compensate for lower functional groups and result in better adsorption performance. An important parameter in the performance of a packed column is the size of the packings. To investigated this, a PVA-alginate-encapsulated Prussian blue-GO hydrogel bead was prepared by Jang and Lee72 and tested in a continuous system for cesium removal. It was observed that the breakthrough time decreased and the adsorption capacity increased with a decrease in bead size. This could be attributed to the fact that smaller beads provide a shorter diffusion path for cesium ions to the adsorbent, and that small beads have higher surface area per volume, thereby offering more adsorption sites. On the other hand, it should be noted that small beads increase pressure drop and slow down water flow. Therefore, an optimized bead size should be determined to provide a balance between adsorption capacity and pressure drop. Another important parameter in a continuous adsorption process is the sample volume. Manasi et al.73 fabricated a nanocomposite comprising microbially reduced GO and Halomonas BVR 1 bacteria to remove metal ions from water. The rGO-microbe showed good adsorption capacity toward metal ions in a batch process. When the adsorbents were packed in a column, at first the removal was the same as the batch process, but the removal decreased when the sample load volume exceeded 500 ml due to an increase in the void throughout the packed bed. As previously discussed, GBNMs can help to solve problems associated with different types of adsorbents. For this purpose, an rGO-MnO2 nanocomposite was fabricated by Yusuf and Song74 for Co2+ and Cr3+ removal. The rGO helped with the dispersion of MnO2 by acting as spacer which prevented aggregation of MnO2. The column studied showed the breakthrough time increased with an increase in the bed height and decrease in feed flowrate. Table 1.5 summarizes GBNMs used in continuous adsorption. To have a societal impact, all the studies that have been performed in the lab should be applied to real-world applications. For adsorption, this transition happens when stable adsorbents with a high adsorption capacity for continuous process are prepared. GBNMs have the potential to become the mainstream adsorbents, thereby replacing activated carbon.68 They can help stabilize structures such as LDHs or nanoparticles.69,70,74 In addition to batch adsorption, a noticeable number of studies have been performed on rGO-based nanocomposites for column packing, especially porous rGO.68,71,74 However, more research is needed to better understand the relationships among the properties of the GBNMs, such as the degree of oxidation, size, and exfoliation with column performance. Furthermore, understanding the chemical and mechanical stability of the adsorbents are crucial for a real continuous application, which seems to have been neglected in most studies.

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Enhancement of Filtration and Adsorption Processes  23 Table 1.5.    Summary of graphene-based adsorbents in continuous adsorption.

Adsorbent

Pollutant

Main finding(s)/importance

Ref.

LDH/GO alginate beads

Sr and SeO

• Forming beads with alginate made LDH stable • Diverse adsorption mechanisms provided by different materials led to the removal of different pollutants

[69]

LDH/GO PAN beads

Pb2+

• Incorporating LDH/GO in PAN to form beads decreased adsorption capacity, but increased stability and the adsorption rate • Breakthrough time increased with an increase in bed height and decrease in flowrate

[70]

Magnetic porous rGO

Triclosan

Magnetic porous rGO had better adsorption performance than powder-activated carbon

[68]

PB-GO hydrogel beads

Cs

By decreasing the bead size, breakthrough time decreased but the adsorption capacity increased

[72]

rGO-Halomonas BVR 1

Cd2+, Pb2+, and Zn2+

Removal dropped drastically when the sample load exceeded a certain amount due to the formation of voids in the column

[73]

GO alginate and rGO alginate

MB

rGO alginate was better than GO alginate

[71]

rGO-MnO2

Co2+ and Cr3+

rGO prevents MnO2 aggregation and increases adsorption capacity

[74]

2+

2− 4

1.4. Conclusion GBNMs have become popular for water treatment due to their special characteristics. Both filtration and adsorption processes have benefited from these materials. In filtration, GBNMs were found to effectively reduce fouling in the use of both on-membrane and in-membrane approaches. An increase in water permeability was frequently observed when these materials were used with in-membrane approaches, while pollutant rejection was not affected by their presence in most cases. GO was found to be a better choice than G and rGO for filtration enhancement because of its abundance of functional groups and superior antimicrobial property. The same order was also observed for adsorption, in which GO was found to be more popular and effective than the other GBNMs. The physical and chemical properties of GO play a role in its performance. It was observed, in most cases, that GO with a higher exfoliation and oxidation degree is more effective at improving filtration and adsorption, respectively. One of the primary advantages of GBNMs in filtration and adsorption is the increase in mechanical strength and long-term stability, which is vital for practical applications but most studies have

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not investigated these properties. It is suggested that in addition to removal performance, the durability of membranes and especially adsorbents for continuous adsorption processes are further investigated in future studies. Another area for future research in this field could be on the effects of the physical and chemical properties of the GBNMs on the performance of columns in continuous adsorption process.

Acknowledgments This publication was made possible by NPRP 12S-0307-190250 from the Qatar National Research Fund, NSF BEINM Grant Number 1705511, and the Robert A. Welch Foundation award numbers E-2011-20190330.

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11. J. P. Melo et al. Graphene Oxide Quantum Dots as the Support for the Synthesis of Gold Nanoparticles and Their Applications as New Catalysts for the Decomposition of Composite Solid Propellants, ACS Omega, 2018, 3(7), 7278–7287. 12. F. Yalcinkaya et al. A Review on Membrane Technology and Chemical Surface Modification for the Oily Wastewater Treatment, Materials, 2020, 13(2), 493. 13. S. Hube et al. Direct Membrane Filtration for Wastewater Treatment and Resource Recovery: A Review, Sci. Total Environ., 2020, 710, 136375. 14. R. Singh. Water and Membrane Treatment, in Membrane Technology and Engineering for Water Purification, 2nd ed., Ed. R. Singh. Butterworth-Heinemann: Oxford, 2015, pp. 81–178. 15. B. Van Der Bruggen et al. A Review of Pressure-Driven Membrane Processes in Wastewater Treatment and Drinking Water Production, Environ. Prog., 2003, 22(1), 46–56. 16. Y. Ibrahim et al. Surface Modification of Anti-Fouling Novel Cellulose/Graphene Oxide (GO) Nanosheets (NS) Microfiltration Membranes for Seawater Desalination Applications, J. Chem. Tech. Biotech., 2020, 95(7), 1915–1925. 17. F. Lipnizki. 4.06 — Basic Aspects and Applications of Membrane Processes in AgroFood and Bulk Biotech Industries. In Comprehensive Membrane Science and Engineering, Eds. E. Drioli and L. Giorno, Elsevier: Oxford, 2010, pp. 165–194. 18. H. Eccles. Ion Exchange — Future Challenges/Opportunities in Environmental Clean-Up. In Progress in Ion Exchange, Eds. A. Dyer, M. J. Hudson, and P. A. Williams. Woodhead Publishing: Cambridge, 1997, pp. 245–259. 19. L. N. Gerschenson, Q. Deng, and A. Cassano. Conventional Macroscopic Pretreatment. In Food Waste Recovery, Ed. C. M. Galanakis, Chap. 4. Academic Press: San Diego, 2015, pp. 85–103. 20. X. Tan and D. Rodrigue. A Review on Porous Polymeric Membrane Preparation. Part I: Production Techniques with Polysulfone and Poly (Vinylidene Fluoride), Polymers, 2019, 11(7), 1160. 21. M.-B. Wu et al. Thin Film Composite Membranes Combining Carbon Nanotube Intermediate Layer and Microfiltration Support for High Nanofiltration Performances, J. Membr. Sci., 2016, 515, 238–244. 22. M. You et al. Water/Salt Transport Properties of Organic/Inorganic Hybrid Films Based on Cellulose Triacetate, J. Membr. Sci., 2018, 563, 571–583. 23. M. Safarpour, A. Khataee, and V. Vatanpour. Thin Film Nanocomposite Reverse Osmosis Membrane Modified by Reduced Graphene Oxide/TiO2 with Improved Desalination Performance, J. Membr. Sci., 2015, 489, 43–54. 24. S. Inukai et al. High-Performance Multi-Functional Reverse Osmosis Membranes Obtained by Carbon Nanotube·Polyamide Nanocomposite, Sci. Rep., 2015, 5(1), 13562. 25. W. Choi et al. Layer-by-Layer Assembly of Graphene Oxide Nanosheets on Polyamide Membranes for Durable Reverse-Osmosis Applications, ACS Appl. Mater. Interf., 2013, 5(23), 12510–12519.

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26. H. -R. Chae et al. Graphene Oxide-Embedded Thin-Film Composite Reverse Osmosis Membrane with High Flux, Anti-Biofouling, and Chlorine Resistance, J. Membr. Sci., 2015, 483, 128–135. 27. E. F. Diogo Januário et al. Functionalization of Membrane Surface by Layer-ByLayer Self-Assembly Method for Dyes Removal, Proc. Saf. Environ. Prot., 2020, 134, 140–148. 28. Y. Baek et al. Evaluation of Carbon Nanotube-Polyamide Thin-Film Nanocomposite Reverse Osmosis Membrane: Surface Properties, Performance Characteristics and Fouling Behavior, J. Indus. Eng. Chem., 2017, 56, 327–334. 29. X. Huang et al. Low-Fouling Antibacterial Reverse Osmosis Membranes via Surface Grafting of Graphene Oxide, ACS Appl. Mater. Interf., 2016, 8(23), 14334–14338. 30. H. J. Kim et al. The Improvement of Antibiofouling Properties of a Reverse Osmosis Membrane by Oxidized CNTs, RSC Adv., 2014, 4(62), 32802–32810. 31. E. Celik, L. Liu, and H. Choi. Protein Fouling Behavior of Carbon Nanotube/ Polyethersulfone Composite Membranes during Water Filtration, Water Res., 2011, 45(16), 5287–5294. 32. J. Glater et al. Reverse Osmosis Membrane Sensitivity to Ozone and Halogen Disinfectants, Desalination, 1983, 48(1), 1–16. 33. A. Tiraferri, C. D. Vecitis, and M. Elimelech. Covalent Binding of Single-Walled Carbon Nanotubes to Polyamide Membranes for Antimicrobial Surface Properties, ACS Appl. Mater. Interf., 2011, 3(8), 2869–2877. 34. L. Liu et al. Fabrication of Ultra-Thin Polyelectrolyte/Carbon Nanotube Membrane by Spray-Assisted Layer-By-Layer Technique: Characterization and Its Anti-Protein Fouling Properties for Water Treatment, Desalination Water Treat., 2013, 51(31–33), 6194–6200. 35. V. Vatanpour et al. Fabrication and Characterization of Novel Antifouling Nanofiltration Membrane Prepared from Oxidized Multiwalled Carbon Nanotube/Polyethersulfone Nanocomposite, J. Membr. Sci., 2011, 375(1), 284–294. 36. E.-S. Kim et al. Development of Nanosilver and Multi-Walled Carbon Nanotubes Thin-Film Nanocomposite Membrane for Enhanced Water Treatment, J. Membr. Sci., 2012, 394–395, 37–48. 37. J. Wang et al. Improving the Water Flux and Bio-Fouling Resistance of Reverse Osmosis (RO) Membrane through Surface Modification by Zwitterionic Polymer, J. Membr. Sci., 2015, 493, 188–199. 38. M. L. Marré Tirado et al. Assessing Biofouling Resistance of a Polyamide Reverse Osmosis Membrane Surface-Modified with a Zwitterionic Polymer, J. Membr. Sci., 2016, 520, 490–498. 39. C. M. Santos et al. Antimicrobial Graphene Polymer (PVK-GO) Nanocomposite Films, Chem. Comm., 2011, 47(31), 8892–8894. 40. C. M. Santos et al. Graphene Nanocomposite for Biomedical Applications: Fabrication, Antimicrobial and Cytotoxic Investigations, Nanotechnology, 2012, 23(39), 395101.

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41. C. M. Santos et al. Carbon-Based Polymer Nanocomposites: From Material Preparation to Antimicrobial Applications, Polymeric Materials with Antimicrobial Activity: From Synthesis to Applications, 2013, 13, 327–350. 42. I. E. Mejías Carpio et al. Toxicity of a Polymer–Graphene Oxide Composite against Bacterial Planktonic Cells, Biofilms, and Mammalian Cells, Nanoscale, 2012, 4(15), 4746–4756. 43. I. Alam et al. Pressure-Driven Water Transport Behavior and Antifouling Performance of Two-Dimensional Nanomaterial Laminated Membranes, J. Membr. Sci., 2020, 599, 117812. 44. J. Zhang et al. Graphene Oxide/Polyacrylonitrile Fiber Hierarchical-Structured Membrane for Ultra-Fast Microfiltration of Oil-Water Emulsion, Chem. Eng. J., 2017, 307, 643–649. 45. Y. L. F. Musico et al. Surface Modification of Membrane Filters Using Graphene and Graphene Oxide-Based Nanomaterials for Bacterial Inactivation and Removal, ACS Sustain. Chem. Eng., 2014, 2(7), 1559–1565. 46. P. C. Bandara, E. T. Nadres, and D.F. Rodrigues. Use of Response Surface Methodology To Develop and Optimize the Composition of a Chitosan– Polyethyleneimine–Graphene Oxide Nanocomposite Membrane Coating to More Effectively Remove Cr(VI) and Cu(II) from Water, ACS Appl. Mater. Interf., 2019, 11(19), 17784–17795. 47. F. Wang et al. Preparation and Evaluation of iPP/GO Microfiltration Membrane with Enhanced Antifouling Property, Chem. Res. Chin. Uni., 2018. 34(5), 833–838. 48. S. Ayyaru and Y.-H. Ahn. Application of Sulfonic Acid Group Functionalized Graphene Oxide to Improve Hydrophilicity, Permeability, and Antifouling of PVDF Nanocomposite Ultrafiltration Membranes, J. Membr. Sci., 2017, 525, 210–219. 49. F. Perreault, M. E. Tousley, and M. Elimelech. Thin-Film Composite Polyamide Membranes Functionalized with Biocidal Graphene Oxide Nanosheets, Environ. Sci. Tech. Letters, 2014, 1(1), 71–76. 50. C. Zhang et al. Graphene Oxide Quantum Dots Incorporated into a Thin Film Nanocomposite Membrane with High Flux and Antifouling Properties for LowPressure Nanofiltration, ACS Appl. Mater. Interf., 2017, 9(12), 11082–11094. 51. I. Ali. Water Treatment by Adsorption Columns: Evaluation at Ground Level, Sep. Purif. Rev., 2014, 43(3), 175–205. 52. C. Santhosh et al. Role of Nanomaterials in Water Treatment Applications: A Review, Chem. Eng. J., 2016, 306, 1116–1137. 53. Y. A. J. Al-Hamadani et al. Stabilization and Dispersion of Carbon Nanomaterials in Aqueous Solutions: A Review, Sep. Purif. Tech., 2015, 156, 861–874. 54. P. L. Cloirec. Adsorption in Water and Wastewater Treatments. In Handbook of Porous Solids, Eds. F. Schüth, K. S. W. Sing, and Jens Weitkamp, WILEY-VCH GmbH: Weinheim, 2002, pp. 2746–2803. 55. P. Kumari, M. Alam, and W. A. Siddiqi. Usage of Nanoparticles as Adsorbents for Waste Water Treatment: An Emerging Trend, Sustain. Mater. Tech., 2019, 22, e00128.

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56. S. C. Smith et al. A Comparative Study of lysozyme Adsorption with Graphene, Graphene Oxide, and Single-Walled Carbon Nanotubes: Potential Environmental Applications, Chem. Eng. J., 2014, 240, 147–154. 57. P. L. Yap et al. Polyamine-Modified Reduced Graphene Oxide: A New and CostEffective Adsorbent for Efficient Removal of Mercury in Waters, Sep. Purif. Tech., 2020, 238, 116441. 58. Y. L. F. Musico et al. Improved Removal of Lead(ii) from Water Using a Polymer-Based Graphene Oxide Nanocomposite, J. Mater. Chem. A, 2013, 1(11), 3789–3796. 59. J. V. D. Perez et al. Response Surface Methodology as a Powerful Tool to Optimize the Synthesis of Polymer-Based Graphene Oxide Nanocomposites for Simultaneous Removal of Cationic and Anionic Heavy Metal Contaminants, RSC Adv., 2017, 7(30), 18480–18490. 60. P. C. Bandara et al. Graphene Oxide Nanocomposite Hydrogel Beads for Removal of Selenium in Contaminated Water, ACS Appl. Polym. Mater., 2019, 1(10), 2668–2679. 61. M. M. Kadam et al. Impact of the Degree of Functionalization of Graphene Oxide on the Electrochemical Charge Storage Property and Metal Ion Adsorption, RSC Adv., 2014, 4(107), 62737–62745. 62. C. Zolezzi et al. Effect of the Oxidation Degree of Graphene Oxides on their Adsorption, Flocculation, and Antibacterial Behavior, Indus. Eng. Chem. Res., 2018, 57(46), 15722–15730. 63. H. Yan et al. Effects of the Oxidation Degree of Graphene Oxide on the Adsorption of Methylene Blue, J. Hazard. Mater., 2014, 268, 191–198. 64. P. Tan et al. Effect of the Degree of Oxidation and Defects of Graphene Oxide on Adsorption of Cu2+ from Aqueous Solution, Appl. Surf. Sci., 2017, 423, 1141–1151. 65. S. Wan et al. Rapid and Highly Selective Removal of Lead from Water Using Graphene Oxide-Hydrated Manganese Oxide Nanocomposites, J. Hazard. Mater., 2016, 314, 32–40. 66. Y. Yao et al. Synthesis, Characterization, and Adsorption Properties of Magnetic Fe3O4@Graphene Nanocomposite, Chem. Eng. J., 2012, 184, 326–332. 67. N. H. Othman et al. Adsorption Kinetics of Methylene Blue Dyes onto Magnetic Graphene Oxide, J. Environ. Chem. Eng., 2018, 6(2), 2803–2811. 68. Y. Li et al. Effective Column Adsorption of Triclosan from Pure Water and Wastewater Treatment Plant Effluent by Using Magnetic Porous Reduced Graphene Oxide, J. Hazard. Mater., 2020, 386, 121942. 69. B. Guo et al. Co-sorption of Sr2+ and SeO42− as the Surrogate of Radionuclide by Alginate-Encapsulated Graphene Oxide-Layered Double Hydroxide Beads, Environ. Res., 2020, 187, 109712. 70. A. Jana et al. Tuning of Graphene Oxide Intercalation in Magnesium Aluminium Layered Double Hydroxide and Their Immobilization in Polyacrylonitrile Beads by Single Step Mussel Inspired Phase Inversion: A Super Adsorbent for Lead, Chem. Eng. J., 2020, 391, 123587.

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71. Y. Zhuang et al. Batch and Column Adsorption of Methylene Blue by Graphene/ Alginate Nanocomposite: Comparison of Single-Network and Double-Network Hydrogels, J. Environ. Chem. Eng., 2016, 4(1), 147–156. 72. J. Jang and D. S. Lee. Enhanced Adsorption of Cesium on PVA-Alginate Encapsulated Prussian Blue-Graphene Oxide Hydrogel Beads in a Fixed-Bed Column System, Biores. Tech., 2016, 218, 294–300. 73. Manasi, V., Rajesh, and N. Rajesh. Biosorption Study of Cadmium, Lead and Zinc Ions onto Halophilic Bacteria and Reduced Graphene Oxide, J. Environ. Chem. Eng., 2018, 6(4), 5053–5060. 74. M. Yusuf and K. Song. Removal of Co(II) and Cr(III) from Aqueous Solution by Graphene Nanosheet/δ-MnO2: Batch and Column Studies, Chem. Eng. Res. Des., 2020, 159, 477–490.

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Chapter 2

Engineered Graphene Oxide as Advanced Separation Material for Water Treatment Delai Zhong†, Lihong Gan‡ and Yi Jiang*,§ Department of Civil and Environmental Engineering, The Hong Kong Polytechnic University, Kowloon, Hong Kong, China [email protected]

†

[email protected]

‡

[email protected]

§

2.1. Introduction Nanotechnology has the potential to become a key enabler for a wide range of water treatment technologies, such as coagulation, adsorption, catalysis, and membrane separation, among others. Such advancements have been generally achieved by the development and integration of new, advanced nanomaterials into current water treatment processes. Among these, carbon nanomaterials, including graphene (oxide) and carbon nanotubes (CNTs), have attracted significant attention, as exemplified by a rapidly increasing number of publications and patents. Such engineered materials (and enabled treatment technologies) may eventually prove economically advantageous, as they are primarily composed of carbon, which is an abundant, available, and relatively low-cost elemental component. Furthermore, the production costs for engineered carbon nanomaterials continue to decrease with the ongoing development of industrially scalable manufacturing processes.  Corresponding author.

*

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In particular, graphene-based materials hold considerable potential for broad use in water treatment applications. The graphene “gold rush” began with the discovery of a free, two-dimensional (2D) atomically-thick carbon “film”, which earned Geim and Novoselov the 2010 Nobel Prize in Physics. Isolated graphene crystals, which demonstrate exceptional electronic properties, extreme surface area-to-volume ratio, and broad (chemical) functionalization possibilities, are now being explored for a wide variety of applications. Graphene oxide (GO), the oxidized form of graphene, is considered to be the most important graphene derivative, and its potential in water treatment applications has been well documented, especially in water separation processes including adsorption and membrane filtration. Adsorption is the process in which ions/molecules adhere onto a surface, where the material properties of an adsorbent, such as specific surface area and surface charge, play a key role. Adsorption is a widely used process in water/ wastewater treatment, and activated carbon (AC) has been the conventional choice of material in practice due to its low cost and high efficiency. GO is regarded as a promising alternative to AC due to a number of advantages, such as its extremely high theoretical specific surface area (2630 m2/g) for contaminant interactions, tunable surface chemistries (e.g., hydrophilicity, high negative charge density, and functionalization possibilities), and ease of mass production (i.e., synthesized from the abundant natural graphite).1 Filtration is the process that separates contaminants from water by using a filter medium which allows water to pass but rejects contaminants. Current filtration processes in water treatment include sand filtration and membrane filtration. Membrane filtration is increasingly being adopted, because a wide range of membrane types ranging from microfiltration to reverse osmosis can be used for targeted separation purposes. Membrane separation provides several competitive and unique advantages, including a small plant footprint, energy efficiency, and costeffectiveness. GO as a separation material is particularly attractive due to the existence of nanoscale channels when assembled. For example, GO “paper” can be nearly impermeable to liquids, vapors, and gases, including helium (He). However, GO paper (i.e., the assembly of GO nanosheets) allows for the unimpeded permeation of water (e.g., H2O permeates through the membranes > 1010 times faster than He), highlighting the material as a promising candidate for lowenergy and low-cost separation and purification processes.2 This chapter primarily focuses on applications of engineered GO as an advanced material for separation processes in water treatment. We first present a brief introduction to GO material properties, followed by an up-to-date overview of its applications as a high-performance adsorbent and water separation membrane. Our objective is to reveal how GO material properties, which vary considerably, affected the performance in reported studies. We conclude the chapter

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by presenting our thoughts on the challenges and opportunities for the further development and scale-up applications of GO in water treatment.

2.2. GO Material Properties Since its discovery in 2004, graphene has triggered an exponential rise in relevant research in disciplines ranging from physics, chemistry, and materials, to environmental science worldwide. Graphene is an allotrope of carbon comprising a single layer of atoms arranged in 2D honeycomb lattice (Fig. 2.1). Graphene demonstrates many unique physicochemical properties, including extraordinary electron transport capability, strong mechanical strength, excellent pliability, impermeability, and ultrahigh thermal conductivity. GO is arguably the most interesting and important derivative of graphene. GO can be easily and cost-effectively obtained in mass production via the (modified) Hummer’s method without catalyst residues and additional purification steps.1 The detailed chemical structure (surface chemistry) of GO has not yet been completely resolved, due to the pseudo-random chemical functionalization of each layer in addition to the variations in composition and size. In principle, GO partially remains as a one-atom-thick planar sheet with a sp2-bonded carbon structure and is derivatized with oxygen functional groups (OFGs) both on the basal plane (e.g., hydroxyl (-OH) and epoxy (-O-) groups) and at the sheet edges (e.g., carboxyl (-COOH) and carbonyl (C=O) groups) (Fig. 2.1).1 GO material dimensions can span from < 20 nm, described as quantum dots, to μm sizes of nanosheets. Moreover, GO materials can have a broad variety of surface chemistries resulting from the synthesis and/or subsequent chemical functionalization. Compared with

Figure 2.1.    Depicted generic structures and primary properties of graphene-based materials.

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graphene, GO has the distinctive feature of being water-dispersible due to the electrostatic repulsion between deprotonated –COOH groups at the edges.3 This feature allows for the facile processing of GO materials in water instead of organic solvents. For technical applications, GO’s oxygen-based functionality endows enhanced surface hydrophilicity and aqueous stability. GO can be thermally or chemically reduced to produce reduced-graphene oxide (rGO), with partial removal of the OFGs and restoration of the graphene regions (Fig. 2.1). Furthermore, GO can undergo various physical transformations. For example, 2D GO was structurally engineered to have various crumpled morphologies with specific properties (e.g., aggregation-resistance).3 These morphologies include paper ball-like spheres and corrugated surfaces, among others. Therefore, GO can be regarded as a large, diverse family of materials. Its size, surface chemistry, morphology, and cost can drastically differ, even by orders of magnitude (e.g., size by 106 and surface oxidation degree by 102) (Fig. 2.1).4

2.3. GO as a High-Performance Adsorbent GO has been used for the adsorption/concentration of toxic compounds in numerous studies, owing to its many material advantages including high surface-area-toweight ratio, rich surface OFGs, and ease of scalable production. In this section we discuss the mechanisms and performance of GO adsorbent for a wide range of common pollutants in water/wastewater, as well as the effects of environmental conditions on its performance and real-world adsorption applications.

2.3.1.  Adsorption mechanisms The molecular-level adsorption mechanisms of various toxic pollutants by GO include π bonding interactions, electrostatic interaction, hydrogen bonding, and Lewis acid–base interaction (Fig. 2.2). The π bonding interactions mainly include π–π, n–π, and cation–π interactions. The π–π interaction is the direct interaction between two π-electron systems, i.e., between C=C double bonds or benzene rings of adsorbed organic pollutant and benzene rings on the GO surface.5 The π–π interaction is suggested to be an important mechanism, contributing to GO interaction with aromatic compounds (e.g., polycyclic aromatic hydrocarbons (PAHs), biphenyl, and phenol) and macromolecules (e.g., humic acid, DNA, and protein).6 The n–π interaction occurs between GO electron-depleted sites and n-electron donors of an organic pollutant that contains oxygen (e.g., 1-naphthol) or nitrogen (e.g., 1-naphthylamine) with lone electron pairs.7 The cation–π interaction involves the GO π–electrons and protonated amino groups (e.g., tetracycline)8 or metal ions.9

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Figure 2.2.    A schematic of the adsorption mechanisms of various contaminants by GO.

Electrostatic interaction arises from the forces that the electric charges of GO and adsorbate exert upon each other. The isoelectric point of GO is usually lower than pH 2,10 which endows the net negative charge of GO in a typical aquatic environment with a pH value of 5–9. As a result, GO exhibits electrostatic attraction to positively charged adsorbate. Hydrogen bonding plays a significant role when organic molecules contain – OH, –COOH, and amines (–NH2) groups.1 Strong hydrogen bonding may be present between the –OH and –COOH of GO and the –OH and –NH2 of organics, such as humic acid and doxorubicin hydrochloride.11 Lastly, the delocalized π electron systems of graphene layer can act as Lewis base to form electron donor–acceptor complexes with metal ions.12 This strong surface complexation facilitates the adsorption of metal ions onto GO. For the adsorption of organic pollutants, GO can also act as a Lewis acid under acidic conditions due to the presence of abundant acidic functional groups. For example, 1-naphthylamine with an –NH2 group is a strong Lewis base and thus can interact with GO via a strong Lewis acid–base interaction.13

2.3.2.  Adsorption performance Not only do the material properties of GO vary greatly, but different contaminants interact with GO via one or more mechanisms. This can lead to a considerable discrepancy in reported values of the maximum adsorption capacity. The adsorption capacities of GO toward various types of contaminants, including inorganic and organic contaminants, span from a few mg/g to more than 1,000 mg/g, as summarized in Fig. 2.3.

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Figure 2.3.    A summary of the reported adsorption capacities of various kinds of contaminants by GO materials.

2.3.2.1.  Inorganic contaminants Heavy metals: Heavy metals in industrial wastewater can enter natural aquatic environments and threaten aquatic life and human health. GO has been proven as an efficient adsorbent for various heavy metal ions, including Pb2+,14 Cu2+,15 Zn2+,16 Cd2+,17 Hg2+,18 and CrO42-.19 The reported adsorption capacities ranged from ca. 20 mg/g for Cu2+ 15 to as high as ca. 1100 mg/g for Pb2+.14 The adsorption of metal ions on GO is considered to be monolayer coverage, and it is controlled by chemical adsorption involving strong surface complexation of metal ions with the OFGs on the surface of GO.20 Furthermore, the electrostatic attraction between the positively charged heavy metal ions and the negatively charged GO contributes to the adsorption, but is not regarded as the primary driving force.16 The affinities of divalent metal ions toward GO followed the order: Pb2+ > Cu2+ > Cd2+ > Zn2+,14,20 which is consistent with the order of metal electronegativity and the first stability constant of the associated metal hydroxide. Radioactive metal ions: GO has many advantages in removing radionuclides compared with conventional adsorbents, such as good chemical durability in strong acidic nuclear wastes and greater radiation resistivity than organic exchange resins.21 The adsorption of UO22+,22,23 Th4+,24 and Eu3+ 25 is strongly dependent upon solution pH, but not ionic strength. This indicates that the adsorption of radioactive metal ions on GO is primarily dominated by inner-sphere surface complexation. The adsorption was also recognized to be spontaneous and endothermic. Furthermore, isothermal adsorption experiments showed that the adsorption capacities of UO22+ followed the order: GO > carboxylated GO > rGO, which is in agreement with the richness of the total OFGs. This highlights the roles of the –OH, –O–, and –COOH

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groups in the adsorption process.26 Also, the chemical affinity of UO22+ toward the –COOH group is much higher compared with the –OH or –O– groups, and thus it is more difficult for UO22+ to desorb from the –COOH group.

2.3.2.2.  Organic pollutants Dyes: Dye wastewater can lead to serious environmental and health problems owing to its ecotoxicological hazardousness. According to the ionic state of dyes in water, dyes can be divided into three categories: cationic dyes (e.g., methylene blue and rhodamine B), anionic dyes (e.g., methyl orange), and neutral dyes (e.g., acridine orange). Because of the electrostatic interaction between negatively charged GO and cationic dyes, GO commonly shows better performance in the adsorption of cationic dyes than anionic dyes.27 The π–π stacking is also responsible for the adsorption of dyes that contain a benzene structure such as malachite green and brilliant green.28 The adsorption capacity of cationic dyes by GO is generally higher than that by other adsorbents (e.g., clay and CNTs), being a few hundred mg/g (Fig. 2.3). This is because electrostatic attraction plays the major role in the adsorption process by GO, and this interaction is considered to be stronger than the π–π interaction, van der Waals interaction, and hydrogen bonding associated with the other adsorbents.29 Antibiotics: The wide application of antibiotics results in a significant percentage of antibiotics excreted into natural waters. Many kinds of antibiotics have been studied for their adsorption onto GO, such as sulfamethoxazole,30 tetracycline,8 and doxorubicin,31 among others. The adsorption capacities of antibiotics by GO were reported to range from ca. 20 mg/g for ciprofloxacin32 to ca. 1500 mg/g for doxorubicin (Fig. 2.3).31 Most antibiotics encompass benzene ring-like cyclic components, thus enabling effective π–π interaction. Using tetracycline as an example, the π–π interaction was considered to be the determining mechanism for its adsorption onto GO, although the hydrogen bonding played a role.33 In addition, the cation–π interaction likely formed between the dimethylamino group on the ring of tetracycline and the π–electron-rich structures of GO.8 Aromatic compounds: Industrialization and urbanization result in the discharge of aromatic pollutants into natural surface water bodies, which can accumulate in aquatic organisms through the food chain. The adsorption mechanisms of aromatics by GO materials at the molecular level are highly dependent upon the structure of the adsorbate and the surface properties of the GO nanosheets. The high affinity of aromatics toward GO is largely a result of the hydrogen bonding and π–π interaction, the degree of which depends upon the physiochemical nature of GO nanosheets (e.g., the prevalence of the sp2 region).34,35 Reduction of GO can enhance the

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adsorption capacity of phenolics by a factor of 2.35 (from 49.6 to 116.9 mg/g for the adsorption of 4-chlorophenol), due to the increased π–π interaction; and the adsorption capacity generally increased with the increasing reduction degree of GO.36 Similar results were found for the adsorption of pyrene, phenanthrene, and naphthalene.34 rGO exhibited an adsorption capacity more than one order of magnitude higher than that of GO (e.g., 127.7 vs. 2.6 mg/g for the adsorption of naphthalene). This was attributed to the dominant π–π interaction and the (more) active interaction sites in the groove regions formed by wrinkles on the rGO surface. In contrast, for GO, the adsorption sites changed to the –COOH groups at the edges, and the normalized adsorption coefficient for three aromatics followed the order of pyrene > phenanthrene > naphthalene, in agreement with the number of their benzene rings (i.e., size and hydrophobicity).34 The larger size of pyrene is considered to facilitate its access to the C=O groups on the scrolled or flat edges. In another study, chemically reduced GO was found to have the strongest adsorption capability for nitroaromatic compounds, followed by thermally reduced GO; and GO displayed the lowest adsorption capability.37 The order of the adsorption coefficient for nitroaromatic compounds followed: p-nitrotoluene < nitrobenzene < m-dinitrobenzene, which is in accordance with the ranking of electron deficiency potential. The electron deficiency potential is the largest for m-dinitrobenzene among the three nitroaromatic compounds due to the presence of two electronwithdrawing groups (i.e., –NO2). The electron deficiency potential of p-nitrotoluene is lower than that of nitrobenzene due to the presence of the electron-donating methyl group in p-nitrotoluene. They hypothesized that the nitro-functional groups not only enhance the π–π interaction between the π-electron-deficient phenyls of nitroaromatic compounds and the π-electron-rich regions of rGO nanosheets, but also interact with the OFGs/edges/defects of rGO through electrostatic interaction. These studies clearly highlight the interplay between GO material properties and adsorbate characteristics in the adsorption of aromatic compounds by GO. Natural organic matter (NOM): NOM is ubiquitous in the natural environment, and thus it is important to remove them from water sources to produce safe drinking water. Multiple mechanisms have been proposed for the adsorption of NOM onto sp2 carbon-dominated nanostructures (e.g., graphene-based materials and CNTs), including electrostatic interaction, hydrophobic interaction, hydrogen-bonding, and π-π interaction. The reported adsorption capacities of NOM such as humic acid (HA) and fulvic acid (FA) range from ca. 20 to 600 mg/g.15,38 In one study, Suwannee River fulvic acid (SRFA) was found to have increased interaction with GO compared with Suwannee River humic acid (SRHA), which was attributed to the higher –COOH content of SRFA.39 In our earlier study, the highest adsorption occurred in the presence of Aldrich HA (AHA), which implicates the π–π interaction is a (major) adsorption mechanism, as AHA is the most aromatic among the three types of NOM

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evaluated (i.e., SRHA, SRFA, and AHA).38 The discrepancy may be due to different GO material properties, primarily the degree of oxidation (i.e., the richness of OFGs). Our study further revealed that the adsorption of NOM onto GO would likely result from a trade-off between increased aromatic regions and decreased accessible area (steric hindrance).38 Electrostatic, hydrophobic, and hydrogenbonding interactions are dependent upon solution pH, while π–π interaction is not. The –COOH of HA begins to dissociate at pH 4–6, and the phenolic -OH dissociates at relatively high pH values (commonly pH ~10). As a result, the increase of pH likely decreases the adsorption of NOM onto GO, as both becomes (more) negatively charged, which increases the electrostatic repulsion and reduces hydrogen bonding and hydrophobic interactions.

2.3.3.  Applications in real-world settings 2.3.3.1.  Influencing environmental conditions Solution pH: Solution pH is recognized as an influential parameter for the adsorption process. The change of solution pH affects not only the surface properties of GO (e.g., charge, ionization), but also those of the adsorbates in the solution (e.g., speciation of heavy metal ions). With increasing pH, the OFGs are progressively deprotonated, which leads to a greater (more) negative surface charge of GO. The electrostatic interaction between heavy metal ions and negatively charged GO nanosheets becomes stronger with the increase of solution pH, which provides the driving force for increasing adsorption.40 However, in some cases metal hydroxides exist in the form of negatively charged species at high pH values, and the negatively charged species are difficult to be adsorbed onto the negatively charged GO because of electrostatic repulsion.41 Solution pH also affects the adsorption of organics onto GO. For example, a low pH was preferred for the adsorption of tetracycline onto GO due to enhanced cation–π interaction and hydrogen bonding.33 Unlike tetracycline, the removal efficiency of cationic dye via GO adsorption was observed to correlate positively with the increase in solution pH due to the enhanced electrostatic interaction.27,29,42 Temperature: Temperature is an important factor affecting adsorption kinetics.43 Generally, the adsorption capacity of metal ions on GO increases with increasing temperature, indicating its spontaneous and endothermic nature.9 The increase in temperature also enhances the diffusion rate of metal ion from the solution to the GO surface, thereby favoring an increase in the adsorption rate.9 Furthermore, increasing temperature may have a swelling effect on the porosity and the pore volume of the adsorbent (i.e., GO aggregates), which enables organic molecules to rapidly diffuse across the external boundary layer and within the internal pores of the GO aggregates.44

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Ionic strength: The presence of ions influences the surface charge and aggregation states of GO in solution, as well as the properties of contaminants such as organics. On one hand, the presence of salts can affect the electrostatic nature, configuration, and/or activity coefficient of organic pollutants, for example, by decreasing their solubility (from a “salting out” effect), which is favorable for organics adsorption. On the other hand, salt ions may infiltrate into the diffuse double layer over the GO surface, eliminate the repelling interaction between the adsorbents, and then cause the GO to aggregate, all of which are unfavorable for organics adsorption due to the decrease in accessible surface area and in the number of interaction sites.

2.3.3.2.  Treatment of realistic waters The composition of real wastewater is much more complicated than that of simulated wastewater. For example, to test the removal efficiency of methylene blue from real water by rhamnolipid-functionalized GO, Wu et al. prepared and compared four water matrices, which were ultrapure water, tap water, river water, and dye wastewater.44 The adsorption increased with increases in the adsorbent dose, pH, temperature, and initial methylene blue concentration, but it was insensitive to variations of ionic strength. In river water and dye wastewater with 200 mg/L methylene blue, the adsorption capacity could reach 476 and 498 mg/g, respectively. The corresponding removal efficiency reached up to 95.2% and 99.6%, both of which were higher than that in ultrapure water (66.2%). The authors attributed the high removal efficiency in wastewater to a higher pH value (8.3) compared with that (6.0) of ultrapure water, which promoted electrostatic attraction. For the adsorption of organic pollutants, however, another study conducted by Lin et al. in real wastewater found a decrease in the removal efficiency. In their study, the adsorption of chlorpheniramine by GO-Fe3O4 particles decreased in real wastewater compared with deionized water,45 possibly because the other organic substances and NOM in wastewater compete for adsorption sites on the GO-Fe3O4. These two studies show that it might be difficult to directly predict real-world performance from laboratory findings, because many factors including GO properties, adsorbate properties, and environmental conditions, come into play. As for metal ions, Dai et al. evaluated the adsorption performance of UO22+ from radioactive mining wastewater using polyamidoxime/polydopaminedecorated GO (GO/PDA/PAO).46 Their results indicate that the synthetic GO/ PDA/PAO can efficiently remove UO22+ from real radioactive mining wastewater. The concentration of UO22+ in the wastewater reduced from 100.8 to 7.3 μg/L, which is far below the allowable uranium concentration for drinking water as stipulated in the standard by the World Health Organization (30 μg/L). In addition, the removal efficiency of UO22+ is much higher than that of other metal ions (e.g., Ca2+, Mg2+, Zn2+) in the wastewater, showing the selective adsorption capability for UO22+ by this GO composite.

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2.3.3.3.  Comparison of GO and AC There have been a number of studies that compare the adsorption performance of GO and AC in wastewater treatment, with methylene blue, biphenyl, and phenanthrene as the target contaminants.47,48 For example, the adsorption capacities for methylene blue onto AC and GO were reported to be 270.3 and 243.9 mg/g, respectively.47 Similarly, the adsorption capacities (represented by Freundlich isotherm coefficients) for biphenyl and phenanthrene by AC were 228.8 and 422.4 (mg/g)/(mg/L)n, respectively, almost four times those of GO.48 In these studies, the surface areas of the AC were higher than those of the GO (e.g., 1688 vs. 32 m2/g47 and 706 vs. 576 m2/g48), indicating its important role in determining the adsorption performance. As for metal ions, Li et al. evaluated copper removal by AC and GO and found that the maximum Cu2+ adsorption capacities were 75.5 mg/g for GO and 14.8mg/g for AC.49 In contrast with the results from the organic contaminants, GO showed better Cu2+ removal performance than AC, owing to its higher content of surface functional groups. Overall, these studies showed the respective advantages of using AC and GO in removing different contaminants in wastewater. Furthermore, for GO to be competitive in practical applications, its material advantage of high specific surface area needs to be maintained, for example, in the form of sponge.50

2.4. GO Membrane for Precise Filtration Strong hydrogen bond, aromatic π–π interaction, and attractive van der Waals forces between the neighboring GO nanosheets allow GO nanosheets to be stacked in an orderly laminated form.2,51 In water treatment, such unique features of GO nanosheets allow GO membranes to be developed by engineering GO structures and performing various membrane assembly strategies. The GO membranes have distinctive features such as high water permeability and excellent selectivity compared with traditional polymeric membranes. In the following section we discuss their assembly approaches and structural engineering, and their use in environmental separations.

2.4.1.  Assembly of GO membranes 2.4.1.1.  Vacuum- and pressure-driven filtration The presence of OFGs on GO endows excellent hydrophilicity and good dispersion of GO in water. These merits allow for the assembly of GO membranes in a solution-processable fashion using vacuum- or pressure-driven filtration. In the filtration process, GO dispersion can be readily separated by a substrate, which retains and deposits GO to form a laminated GO membrane (Fig. 2.4(a)).

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(c)

(b)

Figure 2.4.    Schematic diagram of GO membranes assembled via (a) vacuum- and pressure-driven filtrations, (b) layer-by-layer strategies, and (c) coating techniques.

The thickness of GO membranes can be precisely regulated by filtrating the desired mass (i.e., concentration × volume) of the GO dispersion. The assembling time for GO membranes highly depends upon the introduced amount of GO in water. In general, one can expect an increase in the deposition time as the amount increases if the introduced GO amount is not too large (i.e., deposited thickness below several micrometers). In addition, the excessive use of GO might result in the formation of imperfect GO membranes with a randomly stacked structure (e.g., defects). Hence, a reasonable amount of GO is desired to create a welllaminated GO membrane. Apart from the amount of GO, filtration conditions, particularly the GO deposition rate and the driving force, can affect the order degree of layered structure as well as surface roughness of the GO membranes. For example, Tsou et al. ­fabricated three GO membranes by depositing GO onto a modified polyacry­ lonitrile support using pressure-, vacuum-, and evaporation-driven techniques.52 Subsequently, they examined the microstructures of the resulting GO membranes and found that the GO membrane made by pressure-driven filtration had the highest degree of orderliness of the stacked layers and the smallest surface roughness. Tsou et al. attributed this achievement in well-defined microstructure to the stable driving force during the pressure-driven filtration compared with the other two approaches. With respect to the influence of the GO deposition rate, Yu et al. found that the GO membrane, fabricated by vacuum-driven filtration at a slow deposition rate, showed an increase in water permeability that was 2.5–4 folds higher than

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that of the fast-deposited membrane, and with, counterintuitively, a salt rejection efficiency that was 1.8–4 times higher (i.e., KCl, NaCl, or MgSO4).53 Yu et al. determined that OFGs regions in the neighboring (face-to-face) GO nanosheets likely create a more thermodynamically favored structure by self-assembling with each other when prepared at a slow deposition rate. In contrast, a fast deposition rate only allows the OFGs regions to form a poor structure of GO membranes in a highly random manner.

2.4.1.2.  Layer-by-layer assembly In typical layer-by-layer assembly, the substrate surface is first charged by introducing commonly used polyelectrolytes (i.e., polycations and polyanions) and then the charged substrate is alternately deposited by GO nanosheets and polyelectrolytes via electrostatic attraction, hydrogen bonding, and/or π–π interaction (Fig. 2.4(b)). The desired thickness of GO membranes can be realized by controlling the number of deposition cycles. For instance, Hu and Mi performed layerby-layer assembly to fabricate GO membranes via electrostatic interaction by alternately depositing negatively charged GO nanosheets and positively charged poly(allylamine hydrochloride).54 Each deposition cycle on average increased the membrane thickness by ca. 16.5 nm (corresponding to a GO-poly(allylamine hydrochloride) bilayer). In addition to electrostatic attraction, several attempts, including cross-linking, have been made to prepare GO membranes via layer-bylayer assembly. Using 1,3,5-benzenetricarbonyl trichloride, Hu and Mi crosslinked GO nanosheets via anhydride or ester bonds on the polydopamine-coated polysulfone support to fabricate GO membranes.55 This cross-linking strategy not only greatly enhanced the stability of the stacked GO nanosheets, but also precisely adjusted the surface charge, functionality, and interlayer spacing of the GO membranes. In addition, the layer-by-layer assembly allows us to regulate the interlayer spacing of GO membranes by intercalating nanomaterials such as CNTs (as discussed in detail in later section). The assembly is stabilized by the interaction between the GO and polyelectrolytes, which can be impacted by the pH and electrolytes during the assembly. Also, compared with those prepared by pressure- and vacuum-driven filtration, layerby-layer assembly of GO membranes have a relatively rougher membrane surface and lower orderliness of the stacked layers, due to the random and multiple manipulations/interactions that cannot be easily controlled.56

2.4.1.3.  Coating Coating, such as spin-coating and spray-coating, is an alternative and facile method for fabricating GO membranes (Fig. 2.4(c)). Generally, the substrate

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possesses one or more favorable properties, such as opposite surface charge and/ or abundant functional groups acting as active/reactive sites, for interacting with GO and for further assembly. These features are prerequisites for the precise assembly of GO membranes with uniformity, continuity, and stability. For the spin-coating method, solvent (or water) evaporation, spin speed, and deposition rate are pivotal factors that control quality of GO membranes. For example, a fast evaporation may induce structural changes of GO nanosheets, which affects the orderliness of the assembly.57 In contrast, the spray-coating technique has the potential to become a scalable method for the scale-up production of GO membranes (Fig. 2.4(c)). Chen et al. prepared large-area GO membranes with a diameter of 100 mm via the electro-spraying technique.58 More impressively, Ren et al. produced highly aligned, compact, and meter-scale GO films via continuous centrifugal casting.59 In their study, GO dispersion was continuously casted/sprayed on the inner surface of a rotating hollow tube (RHT) at 80°C, and the continuous high-speed rotation of the RHT induced the centrifugal force along the radial direction, resulting in a shear force along the tangential direction for GO membrane production. Such spray-coating strategy is a universal, scalable, and efficient method for further commercial production.

2.4.2.  Structural manipulation of GO membranes GO membranes have a delicate and unique structure. The horizontal nanochannels consist of interlayer capillaries formed between adjacent (face-to-face) GO nanosheets, the vertical nanochannels have gaps between the edges of the GO nanosheets in the same layer (or level), and intrinsic porous defects appear in the membranes. These nanochannels interconnect with each other to form numerous pathways for mass transport. Such channels can be well tuned to realize precise molecule sieving via two important mechanisms, i.e., size sieving and Donnan exclusion.60 Depending upon the material engineering and synthesis approaches, the reported water permeability could span over two orders of magnitude.61 A typical GO membrane exhibits a water permeability of 3–70 L/(m2 h bar), and the intercalation of nanomaterials can increase this to 10–200 L/(m2 h bar), while the layer-by-layer GO membrane has a (relatively) lower permeability of 1–30 L/(m2 h bar).61

2.4.2.1.  Adjusting horizontal nanochannels (interlayer spacing) For a GO membrane, horizontal nanochannels (i.e., interlayer spacing between GO nanosheets) play a significant role in determining water permeability and rejection performance. To date there have been various approaches, including thermal reduction, chemical reduction, cation cross-linking, intercalation of

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Figure 2.5.    Adjustable interlayer spacing of GO membranes via chemical/thermal reduction, intercalation of external components, and physical confinement.

nanomaterials, and physical fixation, to tune the interlayer structure of GO membranes.62 Below we roughly categorize them as reduction, intercalation of external components, and physical confinement. These facile methods can adjust the interlayer spacing from a few angstroms to dozens of nanometers, as summarized in Fig. 2.5. Reduction: The presence of OFGs on the surface of GO nanosheets makes it highly hydrophilic. Under humid or aqueous conditions, the adsorption of water molecules into the GO nanochannels makes them swell with enlarged interlayer spacing. This significantly deteriorates the molecule-sieving performance of the laminated GO membrane. In addition, the presence/change of OFGs affects both the surface charge and the oxidized region distribution (or area ratio) of GO nanosheets. The use of reduction to remove (partial) OFGs has been suggested to be an effective strategy for narrowing the interlayer spacing of GO membranes. Two typical reduction methods, thermal treatment and chemical reduction, have been extensively studied to adjust the interlayer spacing.63,64 Moreover, reduction can be introduced on either GO nanosheets (i.e., before the assembly of GO membranes) or GO membranes itself (i.e., after the assembly of GO membranes). For example, by precisely controlling the reduction level of GO nanosheets via a hydrothermal treatment, an uniform rGO membrane was fabricated via vacuum-driven filtration, which achieved higher

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water permeability and enhanced NaCl rejection.65 Han et al. delicately narrowed the interlayer spacing of pristine GO membrane from 8 to 7 Å by thermally reducing the membrane at 162 °C.60 Interestingly, the rGO membrane, with a smaller interlayer spacing, had a higher rejection of oppositely charged dyes (i.e., methyl blue, Congo red, and rhodamine B) but exhibited a lower rejection of metal ions (i.e., Cu2+, Pb2+, and Cd2+) than the pristine membrane. Further investigations clarified that the physical size sieving dominated the former, while the Donnan effect was primarily responsible for the latter. In thermal reduction treatment, thermal conditions such as heating temperature, heating rate, and heating dwell time are recognized influencing factors. Several studies have demonstrated that the interlayer spacing generally decreases due to gradual removal of OFGs as the temperature and treatment time increase, thus leading to a decreased water permeability but increased salt rejection.66,67 In contrast, chemical-assisted reduction offers a more efficient method for manipulating the interlayer spacing and it can be generally completed within several minutes. Zhang et al. fabricated an ultrathin (down to 20 nm in thicknesses) freestanding rGO membrane via reduction of the GO membrane by hydriodic acid (HI) vapor.64 After the reduction, the interlayer spacing narrowed greatly, from 8.67 to 3.50 Å due to the elimination of OFGs, leading to both higher water permeability and NaCl rejection than the GO membrane without reduction. In addition to the chemicals commonly used (e.g., HI and N2H4), several natural organic acids (e.g., theanine amino acid and tannic acid) have been used to reduce GO membranes and adjust interlayer spacing. Recently, Ren et al. used theanine amino acid and tannic acid to fabricate rGO membranes.68 Interestingly, the obtained membranes exhibited unprecedented water permeability higher than 10,000 L/(m2 h bar), which is 10–1,000 times higher than that of previously reported GO membranes and commercial membranes, with the dye (e.g., rhodamine B and methylene blue) rejection efficiency around 100%. These membranes could withstand harsh acidic and alkaline environments and experience no damage or delamination for several months. This exceptional performance was attributed to the multiple roles of the two acids in the preparation of the rGO membranes: (1) increasing graphitic domains by chemical reduction, (2) cross-linking the adjacent (face-to-face) rGO nanosheets, and (3) enlarging the interlayer spacing by intercalating the acids. Obviously, the reduction of a GO membrane does not necessarily lead to a lower water permeability. It is noteworthy that manipulating only the interlayer spacing of GO membranes by controlling the contents of OFGs using reduction methods may fail to achieve higher rejection. This is because multiple mechanisms besides physical size sieving commonly account for the rejection performance, as shown in the aforementioned cases.60 Intercalation of external components: The well-developed layer structure of GO nanosheets, with plentiful OFGs, allows for the intercalation of various

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external components. This intercalation strategy not only enlarges the interlayer spacing between GO nanosheets, but also adjusts the hydrophilicity, charge, morphology, thickness, and roughness of the target GO membranes. Such engineered GO membranes can exhibit higher water permeability, enhanced salt rejection, improved antifouling, and antibacterial properties. Nanomaterials, including but not limited to copper hydroxide nanostrands,69 CNTs,70 and titanium dioxide,71 have been used as spacers. Peng et al. developed a novel GO membrane structure with microscopic wrinkles (3–5 nm) by first intercalating and then dissolving copper hydroxide nanostrands.69 The enhanced water permeability for this GO membrane was 10 times higher than that for the virgin GO membrane, without sacrificing the rejection performance. Excitingly, its water permeability was ca. 144 times beyond that of state-of-the-art commercial ultrafiltration membranes, while having a similar rejection performance (e.g., 83%–84% of rejection efficiency for Evans blue). Also, multiwalled CNTs (MWCNTs) can be intercalated between GO nanosheets via vacuum-driven filtration. The intercalation of MWCNTs enlarged interlayer spacing and thus enhanced water permeability to 11.3 L/(m2 h bar), more than two times higher than that of the virgin GO membrane, while maintaining a high rejection of dyes (> 99% for direct yellow and > 96% for methyl orange).70 This membrane can work well under high driven pressure and ionic strength conditions. Aside from the ex-situ intercalation of nanomaterials, the in situ growth and intercalation of nanomaterials inside/into the interlayer of GO membranes is suggested to be an alternative approach. It is important to note that compared with ex-situ intercalation, in situ intercalation may be superior, because it allows for the more controllable and uniform introduction of nanoparticles into (r)GO membranes. The presence of OFGs allows cations such as monovalent (e.g., K+ and Na+),72 bivalent (e.g., Mg2+ and Ca2+),72,73 and trivalent cations (e.g., Al3+ and Fe3+)74,75 to coordinate and thus tune the interlayer spacing between GO nanosheets. Fang et al. used common cations, including K+, Na+, Li+, Ca2+, or Mg2+ ions, to realize precise control of the interlayer spacing of GO membranes via tuning predominant cation–π interactions at one angstrom level.72 More surprisingly, the K+-controlled GO membrane can reject itself while allowing water to penetrate the membrane, because the hydrated K+ enters the interspace between the GO nanosheets and then dehydrates and cross-links the GO nanosheets to narrow the interlayer spacing. Inspired by this work, a number of works on cation-controlled GO membranes have rapidly emerged.73,75,76 Recently, Sun et al. inserted trivalent cations, Al3+ or Fe3+, into the interlayer of GO membranes by filtrating a GO solution with Fe3+ or Al3+.74 The introduction of Al3+ or Fe3+ brought a 0.6–1.5 Å enlargement of interlayer spacing, which improved the water permeability but deteriorated the rejection/removal of organic contaminants. The water permeability for the Fe3+-cross-linked GO membrane was around 1.1–2.3 times higher than the Al3+introduced GO membrane. This was due to the larger interlayer spacing induced

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by Fe3+ than Al3+ (73 vs. 49 L/(m2 h bar) for the GO membranes cross-linked by Fe3+ and Al3+, respectively). The intercalation of cations simultaneously increased the stability of GO membrane via cross-linking. Organic molecules capable of reacting with GO nanosheets have been proven to successfully adjust the interlayer spacing between GO nanosheets. According to the difference in the molecular weight of an organic molecule, we can roughly divide them into two categories: small-weight organic molecules and large-weight organic molecules. Small-weight organic molecules refer to amines, carboxylic acid, diisocyanate, and so forth. Upon cross-linking using theanine amino acid and tannic acid, the interlayer spacing of the obtained GO membrane increased from 7.6 to 8.5 and 9.9 Å, respectively.68 This not only enhanced the water permeability with excellent separation performance for dyes (e.g., methyl blue and Evans blue) but also enhanced the stability such that the membranes experienced no damage or delamination for months under harsh conditions, including strong acidic or alkaline solutions. Recently, Qian et al. studied a series of aliphatic terminal diamines with different kinetic diameter lengths.77 Impressively, the ­ 1,4-diaminobutane (A4)-GO membrane exhibited the best performance in water permeability and ion rejection due to the combined effects of increased interlayer spacing and decreased hydrophilic property. Similarly, large-weight organic molecules such as polyethylenimine, boronic acid polymer, dendrimers, and poly(ethylene glycol) have been studied. The interlayer spacing can be precisely manipulated over a wide range, from subnanometer to several nanometers, which highly depends upon the entangled conformation of the polymer chains. Jiang et al. applied primary amine-­ terminated polyamidoamine (PAMAM) dendrimers, with regularly branched structures and multiple –NH2 groups, to manipulate the interlayer spacing of GO membranes.78 After the insertion of PAMAM, precise control of the effective interlayer spacing (obtained by interplanar spacing minus the thickness of the graphene monosheet) can be realized in the range of 4.3–7.6 Å in the wet state. The resulting GO membranes displayed excellent water permeability and NaCl rejection efficiency (>99.99%) in simulated seawater desalination, along with ultrastability. Overall, the intercalation of components allows for the adjustment of interlayer spacing between GO nanosheets, but it does not necessarily improve both water permeability and rejection performance. For example, the excessive introduction of components, such as organic molecules as backbones between GO nanosheets, might reduce the free volume in GO nanochannels and thus create a barrier for molecule mass transport. However, it is promising that a desired amount of component, with favorable structures and multiple functional properties (e.g., surface charge and functional groups), is introduced into the interlayer, which synergistically improves the water permeability, rejection, and stability, among other properties.

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Physical confinement: Anisotropic-stacked GO membranes swell in a nearly vertical orientation when interacting with water molecules, and thus confining/regulating the interlayer spacing that can be achieved by applying external/vertical resistance. Nair et al. confined GO films into an epoxy structure to realize ultrastable and desirable interlayer spacing control, subsequently achieving accurate and tunable ion sieving.79 Li et al. sandwiched GO membranes between polyethersulfone hollow fiber substrates and polysulfone coatings, which confined the swelling of GO membranes.80 The composite membrane structure showed high water permeability (up to 14.7 L/(m2 h bar)) and over 94.7% of salt rejection (i.e., NaCl, MgCl2, MgSO4, and Na2SO4) at 2.0 bar, far beyond those of unconfined GO membranes (less than 40%). Furthermore, external pressure can be used to precisely manipulate interlayer spacing. Li et al. applied a counteractive force opposite to the direction of swelling of the GO membranes in order to adjust the interlayer spacing.81 At different levels of external pressure, the interlayer spacing was shown to be delicately adjusted to a desired value at the angstrom level.

2.4.2.2.  Modulating vertical nanochannels Nanopores/defects in GO membranes as vertical nanochannels serve as important mass transport pathways and play an equally important role in water permeability and size sieving. Reasonable control of the nanopores/defects, discussed as follows, can greatly enhance the separation performance of the membrane. Creating nanopores: The methods for creating more porous GO nanosheets/ membranes include chemical oxidation and thermal reduction, which allow for the facile manufacture of large-area nanoporous stacked GO membranes. Peng et al. first reported the mesoporous GO membrane, which was achieved by the reoxidation of GO nanosheets using KMnO4, followed by vacuum-driven filtration.82 The as-synthesized GO membrane showed enhanced water permeability by a factor of nearly three, while effectively rejecting large molecules but not common salt ions, since the nanopores were in the size range of 3–5 nm in the resulting GO membrane. Also, Zhang et al. engineered high-density nanopores in GO nanosheets via mild oxidation with H2O2.63 By introducing nanopores with an average diameter of ca. 3 nm and a high density of 2.89 × 1015 m−2, the GO membrane showed higher water permeability (ca. 40 L/(m2 h bar)) than the previously reported GO-based membranes and commercial nanofiltration membranes. Moreover, the GO membrane showed comparable Na2SO4 rejection (85%) and similar or higher NaCl rejection (40%). Such promising salt rejection performance was attributed to both the Donnan effect and size exclusion by narrow interlayer spacing. Thermal reduction is another method for creating nanopores on GO nanosheets by thermally removing some atoms, particularly carbon atoms. Lin and Grossman

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established relationships between rGO synthesis parameters (i.e., total oxygen content, the ratio of –O– to –OH of virgin GO nanosheets, and reduction temperature) and the size of the nanopores formed in the resulting rGO.83 In their simulation results, a higher total oxygen content of GO was generally in favor of producing larger-sized nanopores in the resulting rGO structure; a similar phenomenon was also observed for a higher reduction temperature.

2.4.2.3.  Modifying GO morphology In addition to 2D nanosheets, GO can be engineered with wrinkles or to become 3D in shape.84 These modified structures can be then assembled for filtration purposes.85,86 This unique crumpled structure with high free volume also allows for the encapsulation of (multi)functional engineered nanoparticles (e.g., nanosized Fe3O4, TiO2, and Ag) inside its structure.87 As a result, the crumpled GO and assembled membrane can have (multi)functionality. In our earlier studies, we prepared a series of crumpled GO with engineered nanoparticles (i.e., TiO2 or Ag) and assembled them into water filters via vacuum-driven filtration.85,86 The resulting membranes not only allowed high water permeability (246±11 L/(m2 h bar)) through vertically tortuous nanochannels, but it also maintained excellent separation performance for model organic and biological foulants. The membranes could efficiently degrade methyl orange in situ under UV irradiation when incorporated with TiO2 nanoparticles and inactivate Escherichia coli with the addition of Ag nanoparticles. In addition, Zhang et al. tuned nanowrinkles by chemical/thermal reduction or solvent-evaporation and found that the nanowrinkles could function as additional mass transport channels within the GO membrane.67,88,89 As shown above, the GO membrane structure can be tuned in various ways, from the molecular/structural engineering of GO nanosheets and the hierarchical design of the laminar structure to varying assembly approaches. The resulting membranes can perform outstanding separation functions, mostly in the nanofiltration and ultrafiltration range, the important application of which is the separation/removal of salts/contaminants to produce clean water. These GO membranes have shown an overall rejection efficiency of 20%–80% and 60%–95% of for NaCl and Na2SO4, respectively. For dyes, these membranes can usually reach a higher efficiency (e.g., 70%–100% for methylene blue and 90%–100% for rhodamine B). Overall. the strategies discussed present multiple opportunities for improving GO performance (e.g., water permeability, rejection, and stability) and developing applications for GO membranes in water treatment.

2.4.3.  Environmental separations Thus far, not many studies have focused on the real-world applications of GO membranes. It is recognized that there is a gap between laboratory studies and

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real-world applications because the composition of real-world waters is considerably more complicated than simulated waters. Zeng et al. explored the removal of Cu2+ or methylene blue in drinking water and wastewater treatment plant (WWTP) effluent using isophorone diisocyanate-cross-linked GO (GO-IPDI) membranes.90 The GO-IPDI membranes showed a higher rejection efficiency (56.3%) for Cu2+ in WWTP samples than that (47.1%) in drinking water, while both had close rejection efficiencies (97.2%–98.1%) for methylene blue. The water permeability of WWTP effluent was considerably lower than that of drinking water due to the complicated compositions of the WWTP sample. Recently, Sun et al. prepared two GO membranes by filtering a mixed solution of GO and Fe3+ (or Al3+) and subsequently evaluated the removal of NOM in both simulated water and actual river water.74 They found that NOM in the simulated water could be more efficiently removed than in the river water, because the surface water consisted of a substantial quantity of small molecule-weight organic components (lower than 2 kDa), which were difficult to be removed using the GO membranes. In their more recent study,91 which cross-linked GO membranes with polyaluminum chloride, a lower NOM removal was observed in real-world river water samples than in simulated NOM-containing water. In addition, Wang et al. employed GO membranes to treat actual secondary effluents which contained refractory organics. After the treatment, the chemical oxygen demand of the effluents decreased from 176 to 42 mg O2/L (by 76%) and the rejection efficiency of the inorganic salts was low (13.2%).92 In addition, GO membranes have been widely applied to desalinate seawater. Abdelkader et al. examined the desalination performance of GO membranes using real seawater,93 and the GO membranes had higher rejection efficiencies of Mg2+ and Ca2+ than commercial nanofiltration membranes (NF270) when using real seawater as the feed solution. Despite many endeavors like these, most of the research remains at bench scale.

2.5. Future Perspectives This chapter has clearly shown that GO is a promising candidate material for advanced water treatment, particularly as an adsorbent and in membranes. Like many other engineered nanomaterials, however, there are both exciting opportunities and challenges for its further development and eventual commercial application. We present a few of our initial thoughts in the following section.

2.5.1.  Establishing structure–property relationships As previously discussed, the separation performance of GO is highly connected to the physical and chemical properties of the monomer (and its assembly as a GO membrane). The structural properties of GO can vary by orders of magnitude,

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from one manufacturer to another, or even from batch to batch. An improved understanding of the underlying mechanisms in the production and functionalization of GO nanomaterials is crucial to achieving a reliable material supply with maintained quality. For practical uses, the degree to which the variations in the material properties of GO will affect the real efficacy of the function must be determined in order to develop guidelines for proper quality control of the materials. While a tremendous number of papers have been published, a summarization of the so-called structure-property relationships remains scarce. In this chapter, we have qualitatively described some important aspects of these relationships, but quantitative relationships are lacking. For example, for all membranes, including GO membranes, water permeability and selectivity are considered the most important performance indexes. Analysis for the trade-off between permeability and selectivity has been performed for many types of membranes including ultrafiltration94 and reverse osmosis,95 but it remains unknown if such trade-off exists for GO membranes. There remains a lack of a standard analysis approach on this type of new membrane assembled with 2D nanosheets. How to leverage the different transport and solute rejection mechanisms to enhance both permeability and selectivity remains to be answered. A feasible strategy might be to enlarge the differences in water and solute transport by simultaneously tuning/manipulating two or more key factors (e.g., a combination of pinholes and surface charge and/or a combination of interlayer spacing and hydrophilicity/hydrophobicity).53,96 For GO as adsorbents, the contribution of each mechanism (π bonding interaction, electrostatic interaction, hydrogen bonding, and Lewis acid–base interaction) to overall adsorption has not yet been resolved and thus needs further investigation. A combined experimental and computation approach has become more common to reveal the molecular insights. This is important for predicting the real performance when multiple types of contaminants co-exist in realistic water/wastewater.

2.5.2.  Exploring new opportunities at the nanoscale New opportunities exist in making new forms of GO materials. For example, it is desirable to construct GO into 3D arrangements, including balls, wrinkles, and folds to prevent restacking. The practical application of bare GO as an adsorbent is also limited, due to the difficulty in its recovery and reuse from aqueous solutions. As a result, a multifunctional GO-based platform is more desirable (e.g., the combination of magnetic nanoparticles for easy recovery). Furthermore, GO or GO membrane as nanoscale platforms have provided opportunities to better understand the basic physicochemical processes/reactions of matters (e.g., water molecule). Their basic physicochemical properties, such as dielectric constant, molecular orientation, viscosity, diffusion rate, and activation

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energy, inside the nanoconfined space (e.g., interlayer of GO membrane) may fundamentally differ from what is commonly observed in bulk water.97–99 For example, Nair et al. demonstrated that ion permeation rates decreased exponentially with a decrease in sieve size (i.e., subnanometer-sized interlayer), but the water transport rate was only slightly impacted.79 Of particular interest is the introduction of external (reactive) components, such as engineered nanoparticles (e.g., Fe3O4 and Fe2O3) and (reactive) organic molecules (e.g., tannic acid) as spacers between GO nanosheets. The enhanced reactions might occur by the nanoconfinement, which will greatly facilitate organic pollutant removal in waterrelated systems. Furthermore, the potential interactions of (r)GO itself with  redox-sensitive pollutants (e.g., arsenic, chromium, polyphenols, and p-­nitrobenzene) in the nanoconfined space of GO membrane will be interesting to study and may provide new directions for designing highly efficient nanoreactors for contaminant removal.

2.5.3.  Taking sustainability issues into consideration In general, a number of recent studies have suggested that the risks related to nanotechnology application in water treatment are likely to be low.100 However, a multi-barrier system is the principle of design in drinking water/wastewater reuse treatment, which aims to minimize risk exposure. Instability caused by undesirable water-induced effects, i.e., swelling, redispersion, and delamination of stacked GO membranes, has not only seriously impeded its performance but it also increases the potential for harming the environment and human health. To address the instability, various strategies, including cross-linking by molecules/ ions, chemical/thermal reduction, physical confinement, and intercalation of external components (e.g., polyelectrolytes), have been adopted. Nevertheless, these attempts usually cause additional effects on the water permeability and selectivity of GO membranes, because such stability strategies induce changes in the structure (e.g., interlayer spacing) and surface properties (e.g., surface charge) of GO membranes, as exemplified by many cases in Sec. 2.4.2.1. Therefore, sustainability and safety considerations must be in place, even at the cost of sacrificing the performance to some degree. The structure and stability of GO membranes are also affected by environmental factors such as solution pH, light irradiation, temperature, among others. For example, UV irradiation induces GO reduction and even degradation with the production of PAHs. In addition, potential redox effects (e.g., residual chlorine and unintentional Fenton-like reactions) should be carefully considered when applying GO in water treatment applications, because the GO can be chemically unstable in these scenarios.101 It is also worthy to note that some filtration ­operations (e.g., applied pressure) and cleaning processes (e.g., vibration and

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sonication) often have negative effects on the (physical and chemical) stability of GO or GO membranes in practical separation processes. These issues should be considered before any practical applications can be implemented.

Acknowledgments The authors acknowledge financial support from the Hong Kong Research Grants Council (25209819, T21-711/16-R), the National Natural Science Foundation of China (51908479), and from the Research Institute for Sustainable Urban Development, The Hong Kong Polytechnic University (1-BBWG).

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  66. J. Kim, S. E. Lee, S. Seo, J. H. Woo, and C.-S. Han. Near-Complete Blocking of Multivalent Anions in Graphene Oxide Membranes with Tunable Interlayer Spacing from 3.7 to 8.0 Angstrom, J. Membr. Sci., 2019, 592, 117394.   67. Y. Li, S. Yuan, Y. Xia, W. Zhao, C. D. Easton, C. Selomulya, and X. Zhang. Mild Annealing Reduced Graphene Oxide Membrane for Nanofiltration, J. Membr. Sci., 2020, 601, 117900.   68. K. H. Thebo, X. Qian, Q. Zhang, L. Chen, H.-M Cheng, and W. Ren. Highly Stable Graphene-Oxide-Based Membranes with Superior Permeability, Nat. Commun., 2018, 9, 1486.   69. H. Huang, Z. Song, N. Wei, L. Shi, Y. Mao, Y. Ying, L. Sun, Z. Xu, and X. Peng. Ultrafast Viscous Water Flow through Nanostrand-Channelled Graphene Oxide Membranes, Nat. Commun., 2013, 4, 2979.  70. Y. Han, Y. Jiang, and C. Gao. High-Flux Graphene Oxide Nanofiltration Membrane Intercalated by Carbon Nanotubes, ACS Appl. Mater. Interfaces, 2015, 7, 8147–8155.  71. Z. Wu, C. Zhang, K. Peng, Q. Wang, and Z. Wang. Hydrophilic/Underwater Superoleophobic Graphene Oxide Membrane Intercalated by TiO2 Nanotubes for Oil/Water Separation, Front. Environ. Sci. Eng., 2018, 12, 15.   72. L. Chen, G. Shi, J. Shen, B. Peng, B. Zhang, Y. Wang, F. Bian, J. Wang, D. Li, Z. Qian, G. Xu, G. Liu, J. Zeng, L. Zhang, Y. Yang, G. Zhou, M. Wu, W. Jin, J. Li, and H. Fang. Ion Sieving in Graphene Oxide Membranes via Cationic Control of Interlayer Spacing, Nature, 2017, 550, 380–383.  73. Y. Mo, X. Zhao, and Y.-X Shen. Cation-Dependent Structural Instability of Graphene Oxide Membranes and Its Effect on Membrane Separation Performance, Desalination, 2016, 399, 40–46.   74. T. Liu, B. Yang, N. Graham, W. Yu, and K. Sun. Trivalent Metal Cation Cross-Linked Graphene Oxide Membranes for NOM Removal in Water Treatment, J. Membr. Sci., 2017, 542, 31–40.   75. C.-N. Yeh, K. Raidongia, J. Shao, Q.-H Yang, and J. Huang. On the Origin of the Stability of Graphene Oxide Membranes in Water, Nature Chem., 2015, 7, 166–170.   76. S. Park, K.-S. Lee, G. Bozoklu, W. Cai, S. T. Nguyen, and R. S. Ruoff. Graphene Oxide Papers Modified by Divalent Ions — Enhancing Mechanical Properties via Chemical Cross-Linking, ACS Nano, 2008, 2, 572–578.   77. Y. Qian, X. Zhang, C. Liu, C. Zhou, C. and A. Huang. Tuning Interlayer Spacing of Graphene Oxide Membranes with Enhanced Desalination Performance, Desalination, 2019, 460, 56–63.   78. Y. Song, R. Li, F. Pan, Z. He, H. Yang, Y. Li, L. Yang, M. Wang, H. Wang, and Z. Jiang. Ultrapermeable Graphene Oxide Membranes with Tunable Interlayer Distances via Vein-Like Supramolecular Dendrimers, J. Mater. Chem. A, 2019, 7, 18642– 18652.   79. J. Abraham, K. S. Vasu, C. D. Williams, K. Gopinadhan, Y. Su, C. T. Cherian, J. Dix, E. Prestat, S. J. Haigh, I. V. Grigorieva, P. Carbone, A. K. Geim, and R. R. Nair.

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Tunable Sieving of ions Using Graphene Oxide Membranes, Nat. Nanotechnol., 2017, 12, 546–550.   80. W. Wu, J. Su, M. Jia, W. Zhong, Z. Li, and W. Li. Ultrastable Sandwich Graphene Oxide Hollow Fiber Membranes with Confined Interlayer Spacing, J. Mater. Chem. A, 2019, 7, 13007–13011.  81. W. Li, W. Wu, and Z. Li. Controlling Interlayer Spacing of Graphene Oxide Membranes by External Pressure Regulation, ACS Nano, 2018, 12, 9309–9317.   82. Y. Ying, L. Sun, Q. Wang, Z. Fan, and X. Peng. In-Plane Mesoporous Graphene Oxide Nanosheet Assembled Membranes for Molecular Separation, RSC Adv., 2014, 4, 21425–21428.  83. L.-C. Lin and J.-C. Grossman. Atomistic Understandings of Reduced Graphene Oxide as an Ultrathin-Film Nanoporous Membrane for Separations, Nat. Commun., 2015, 6, 8335.   84. J. Luo, H. D. Jang, T. Sun, L. Xiao, Z. He, A. P. Katsoulidis, M. Kanatzidis, G. G. Gibson, and J. Huang. Compression and Aggregation-Resistant Particles of Crumpled Soft Sheets, ACS Nano, 2011, 5, 8943–8949.   85. Y. Jiang, W.-N. Wang, D. Liu, Y. Nie, W. Li, J. Wu, F. Zhang, P. Biswas, and J. D. Fortner. Engineered Crumpled Graphene Oxide Nanocomposite Membrane Assemblies for Advanced Water Treatment Processes, Environ. Sci. Technol., 2015, 49, 6846–6854.   86. Y. Jiang, D. Liu, M. Cho, S. S. Lee, F. Zhang, P. Biswas, and J. D. Fortner. In situ Photocatalytic Synthesis of Ag Nanoparticles (nAg) by Crumpled Graphene Oxide Composite Membranes for Filtration and Disinfection Applications, Environ. Sci. Technol., 2016, 50, 2514–2521.   87. S. Mao, Z. Wen, H. Kim, G. Lu, P. Hurley, and J. Chen. A General Approach to OnePot Fabrication of Crumpled Graphene-Based Nanohybrids for Energy Applications, ACS Nano, 2012, 6, 7505–7513.   88. S. Yuan, Y. Li, Y. Xia, Y. Kang, J. Yang, M. H. Uddin, H. Liu, C. Selomulya, and X. Zhang. Minimizing Non-Selective Nanowrinkles of Reduced Graphene Oxide Laminar Membranes for Enhanced NaCl Rejection, Environ. Sci. Technol. Lett., 2020, 7, 273–279.   89. Y. Kang, R. Qiu, M. Jian, P. Wang, Y. Xia, B. Motevalli, W. Zhao, Z. Tian, J. Z. Liu, H. Wang, H. Liu, and X. Zhang. The Role of Nanowrinkles in Mass Transport across Graphene-Based Membranes, Adv. Funct. Mater., 2020, 30, 2003159.   90. P. Zhang, J.-L Gong, G.-M. Zeng, C.-H. Deng, H.-C. Yang, H.-Y. Liu, and S.-Y. Huan. Cross-Linking to Prepare Composite Graphene Oxide-Framework Membranes with High-Flux for Dyes and Heavy Metal Ions Removal, Chem. Eng. J., 2017, 322, 657–666.   91. T. Liu, L. Tian, N. Graham, B. Yang, W. Yu, and K. Sun. Regulating the Interlayer Spacing of Graphene Oxide Membranes and Enhancing Their Stability by Use of PACl, Environ. Sci. Technol., 2019, 53, 11949–11959.

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  92. J.-L. Han, X. Xia, M. R. Haider, W.-L. Jiang, Y. Tao, M.-J. Liu, H.-C. Wang, Y.-C. Ding, Y.-N. Hou, H.-Y. Cheng, and A.-J. Wang. Functional Graphene Oxide Membrane Preparation for Organics/Inorganic Salts Mixture Separation Aiming at Advanced Treatment of Refractory Wastewater, Sci. Total Environ., 2018, 628–629, 261–270.   93. B. A. Abdelkader, M. A. Antar, T. Laoui, and Z. Khan, Z. Development of Graphene Oxide-Based Membrane as a Pretreatment for Thermal Seawater Desalination, Desalination, 2019, 465, 13–24.   94. A. Mehta and A. L. Zydney. Permeability and Selectivity Analysis for Ultrafiltration Membranes, J. Membr. Sci., 2005, 249, 245–249.   95. Z. Yang, H. Guo, and C. Y. Tang. The Upper Bound of Thin-Film Composite (TFC) Polyamide Membranes for Desalination, J. Membr. Sci., 2019, 590, 117297.   96. H. Zhang, X. Quan, S. Chen, X. Fan, G. Wei, H., and H. Yu. Combined Effects of Surface Charge and Pore Size on Co-Enhanced Permeability and Ion Selectivity through RGO-OCNT Nanofiltration Membranes, Environ. Sci. Technol., 2018, 52, 4827–4834.   97. J. Pan, S. Xiao, Z. Zhang, N. Wei, J. He, and J. Zhao. Nanoconfined Water Dynamics in Multilayer Graphene Nanopores, J. Phys. Chem. C, 2020, 124, 17819–17828.   98. S. Zhang, M. Sun, T. Hedtke, A. Deshmukh, X. Zhou, S. Weon, M. Elimelech, and J.-H. Kim. Mechanism of Heterogeneous Fenton Reaction Kinetics Enhancement under Nanoscale Spatial Confinement, Environ. Sci. Technol., 2020, 54, 10868– 10875.   99. Y. Chen, G. Zhang, H. Liu, and J. Qu. Confining Free Radicals in Close Vicinity to Contaminants Enables Ultrafast Fenton-like Processes in the Interspacing of MoS2 Membranes, Angew. Chem. Int. Ed., 2019, 58, 8134–8138. 100. P. Westerhoff, A. Atkinson, J. Fortner, M. S. Wong, J. Zimmerman, J. GardeaTorresdey, J. Ranville, and P. Herckes. Low Risk Posed by Engineered and Incidental Nanoparticles in Drinking Water, Nat. Nanotech., 2018, 13, 661–669. 101. S. An, J. Wu, Y. Nie, W. Li, and J. D. Fortner. Free Chlorine Induced Phototrans­ formation of Graphene Oxide in Water: Reaction Kinetics and Product Characterization, Chem. Eng. J., 2020, 381, 122609.

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Chapter 3

Graphitic Carbon Nanomaterial-Based Membranes for Water Desalination Dong Han Seo*,§, Mitchell Barclay†,¶, Myoung Jun Park‡,||, Chen Wang‡,**, Kostya (Ken) Ostrikov†,†† and Ho Kyong Shon‡,‡‡ Institute of Energy Materials & Devices, Korea Institute of Energy Technology (KENTECH), Naju, Republic of Korea

*

†

School of Chemistry and Physics and QUT Centre for Materials Science, Queensland University of Technology (QUT), Brisbane, QLD 4000, Australia Centre for Technology in Water and Wastewater, School of Civil and Environmental Engineering, University of Technology Sydney (UTS), P. O. Box 123, 15 Broadway, NSW 2007, Australia ‡

[email protected]

§ ¶

[email protected] || **

[email protected]

[email protected] [email protected]

††

[email protected]

‡‡

3.1. Introduction The use of graphitic carbon nanomaterials has been intensively investigated for various applications, including electronic, thermal, and mechanical applications, as well as for energy storage, and conversion devices. This interest has been driven by the important discovery of famous graphitic nanomaterials such as carbon nanotubes (CNTs) and graphene, which exhibit excellent electrical, thermal, and mechanical properties.1,2 In the past decade, researchers have begun to explore the use of these 63

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graphitic carbon nanomaterials for water desalination and purification membranes. Some of the advantageous features of using graphitic carbon-based nanomaterials in water desalination membranes include (1) providing ultra-low resistance pathways for water transport and permeation, (2) tunable ion selectivity, arising from the tunable D spacing or the pores of graphitic-carbon-based nanomaterials, (3) facile incorporation and good compatibility with polymer matrix, (4) low cost compared with other inorganic nanomaterials and synthesis at large scales, (5) anti-fouling to many water-soluble contaminants, and (6) providing additional mechanical strength to membranes. These advantages make graphitic carbon nanomaterials excellent candidates for water desalination and purification membranes.3–7 Membrane-based water desalination is often performed via two primary desalination processes which rely on different mechanisms for ion-sieving and water transport, namely pressure-driven processes (i.e., reverse osmosis, nanofiltration) and thermally driven processes (i.e., membrane distillation). A pressure-driven process for water desalination relies on the physical ion-separation mechanism via a hydrophilic semi-permeable membrane, which consists of a dense ion-sieving layer, known as an active layer, and the support layer, which provides an efficient water pathway and mechanical support for the active layer. For a thermally driven water desalination process, the water permeation and ion-sieving mechanism differs vastly from the pressure-driven process, in which water undergoes a phase change from water (liquid phase) to water induced by a temperature gradient across the hydrophobic, permeable membranes (vapor phase). Therefore, only the pure water vapor transports through the hydrophobic membranes while the bulk saline or contaminated water remains in the feed side.8–11 Depending upon the mode of water desalination, the right material and its properties must be carefully selected to optimize membrane performance. There are several approaches in which nanomaterials are incorporated as a membrane, including: (1) Using the nanomaterials as a self, freestanding active layer without the incorporation of any polymeric materials on top of a support layer, (2) incorporating nanomaterials as a part of polymeric active layer (often known as thin film nanocomposite membrane), and (3) using nanomaterials as a linker between the active layer and the support layer (interlayer approach).5,12 In this chapter, we will discuss the use of various graphitic carbon-based nanomaterials in pressure-driven and thermally driven desalination membranes in its various incorporation approaches, as well as offer recommendations for future research.

3.1.1.  Use of graphene-based materials (2D graphitic carbon nanomaterial) for water desalination membranes The use of graphene-based materials for water desalination membranes can be broadly classified into two types, depending upon the processes used to synthesize

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the graphene. One type of graphene-based material is synthesized via a chemical vapor deposition (CVD) process known as CVD graphene (in form of a thin film). The other type is a powder form of graphene that is typically synthesized by the chemical or mechanical exfoliation of graphite, which form flakes of graphene that usually disperse in solvents.13 These two forms of graphene have different characteristics and ion-sieving mechanisms, which will be explored in greater detail in the following section.

3.1.2.  CVD graphene for pressure-driven desalination membranes Graphene produced from CVD processes that use high-purity, gaseous carbon, and hydrogen sources often results in a film that is impurity-free, high-quality, lowdefect, and has single to few layers. However, pristine, defect-free graphene is water impermeable, as a hexagonal atomic arrangement of graphene leaves geometric pores that are much smaller than that of water molecules. Therefore, to use a CVD graphene film as a desalination membrane an additional process is required to generate the nanopores in the graphene lattice for water permeation as well as generate the ion-sieving effect from the saline feedwater. These pore-generation techniques involve energy-intensive processes such as plasma, UV, gamma ray, or other source of ionizing radiation to remove the carbon atoms from the graphene lattice.14,15 Surwade et al. first demonstrated the successful use of nanoporous CVD graphene film as a water purification membrane in 2015. The defect-free single-crystal graphene film suspended on a 5 µm diameter hole was treated with oxygen plasma for a short time, where upon it generated the nanopores in the graphene lattice. Then, the desalination tests were performed, which achieved nearly 100% salt rejection of 1M NaCl solution with an ultra-high water flux of 1,000 LM−2s−1 at 40°C.16 In addition to this outstanding desalination performance in µm scale, other notable studies include the work by Kidambi et al., which demonstrated the use of nanoporous graphene film as a molecular sieving layer at centimeter scale.17 The nanoporous graphene synthesized in this work showed that the selective pores were 5) conditions. The use of catalytic nanomaterials, however, could mitigate these negative effects, with an added advantage over bulk catalysts due to their smaller size and higher specific surface area for increased reactivity. Since the reduction of Fe3+ is necessary but slow, catalysts that could coordinate the Fe ions to facilitate this step, or prevent the conversely fast but unfavorable Fe2+ oxidation step, would greatly increase the activation of H2O2 and thus (a)

(b)

Figure 4.9.    Mechanisms of (a) the Fenton process and (b) the activation of persulfates, with and without nanocatalysts.

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the production of disinfecting radicals. As an example, Fenton reactions catalyzed by MoS2 nanosheets have been used for the 100% inactivation of E. coli in just 30 min (pH = 3.63).72 Some composite iron oxide nanocatalysts can also enable the Fenton process to occur at near-neutral pH conditions (i.e., iron oxide modified with silica and carbon, or with TiO2 and rGO).73,74 Fenton processes catalyzed by dissolved iron complexes have also enabled the total disinfection of E. coli in less than 60 min at near-neutral (pH = 6.5) conditions, indicating that a Fenton-based disinfection with an iron-based nanocatalyst at near-neutral conditions is theoretically possible, although the presence of bacteria seems to discourage any solid Fe from forming and has thus limited studies on this specific topic.75 An alternative to peroxide is the use of persulfates, such as HSO5− and S2O82−, which are more stable than H2O2 but can be activated by weaker oxidants (see Fig. 4.9(b)).71 The transfer of an electron to these persulfates breaks the O–O bond, yielding strongly oxidizing sulfate radicals, as can be seen in Fig. 4.9(b). Such sulfate radical-producing AOPs are commonly catalyzed by transition metal oxide nanoparticles (i.e., Fe3O4, Co3O4, and Co on TiO2).76–78 Although most of these studies are concerned with the catalytic degradation of organic pollutants rather than microbial inactivation, the complete disinfection of two species of phytoplankton has been catalyzed with zerovalent iron particles, indicating great promise for further applications of these persulfate-based AOPs in other microbial inactivation mechanisms.79 Despite the support that catalytic nanomaterials have to offer for these radicalforming reactions, the practicality of these AOPs remains questionable at best due to their need for externally supplied chemicals (e.g., hydrogen peroxide). The storage, transport, and continual dosing of these chemicals create cost and efficiency problems for the use of AOPs, even in decentralized systems. A proposed solution to this dilemma is the in situ electrochemical generation of H2O2 using other nanotechnologies, such as hydrogen-evolving graphene cathodes or Pd-composite nanoparticles that adsorb H2 and O2 molecules and then coordinate them for reaction.80,81 Such methods may not be energy-efficient or cost-effective enough to promote the use of AOPs for water disinfection, but alternative solutions are still forthcoming.

4.3.5.  Nano-electrodes for electrochemical and electrophysical inactivation Non-catalytic nanomaterials can also be used to enhance electrochemical reactions which then effectively inactivate microbes, although the exact process by which this occurs has greatly evolved. In general, electrochemical inactivation involves the application of a potential across two electrodes (an anode and a cathode), with the microbially contaminated water serving as the electrolyte in between. The inactivation mechanism is either the direct oxidation of the microbes

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attached to the electrode surface or the generation of ROS species (indirect oxidation), depending upon the material and structure of the electrodes.36,82 The nanomaterials used for these electrodes have varied widely, ranging from flow-through electrodes (i.e., Ag nanowire or CNT-modified membranes) to doped metal oxide structures (blue/black TiO2 nanotube arrays).83–85 Boron-doped diamond (BDD) thin film electrodes have shown particular promise, with an Si/ BDD anode equipped in a continuous flow reactor achieving complete (>90%) inactivation of Legionella pneumophila, a chlorine-resistant bacteria responsible for Legionnaire’s disease, after 1 h of contact time and with only 150 mA/cm2 applied current.86 This treatment time was at least three times faster than that of conventional chlorine disinfection alone when conducted with typical tap water (concentration of 3.5 mg/L Cl−), which allows the BDD electrode to generate chlorine as well as ROS and other oxidants to inactivate the bacteria. Despite such promising laboratory results for disinfecting bacteria, a persistent problem emerges when attempting to use these electrodes to treat real drinking water: the formation of Cl2 from the residual disinfectants of chlorination will effectively disinfect microbes, as mentioned earlier, but this Cl2 will also go on to form harmful DBPs upon contact with the naturally occurring organic matters in water.7 While many studies have attempted to use this process (termed electrochlorination) for supplementing or enhancing chlorination disinfection, their transfer to practical disinfection applications has remained limited.86 After all, increasing these DBPs in our waters is undesirable, and thus the inevitable DBPforming reactions of these electrochemical inactivation methods have continued to plague the studies in this field. A more modern electrode-based approach relies on a phenomenon called electroporation, an electrophysical process which theoretically does not require or generate new chemical species. Originally studied to induce changes in membrane permeability, electroporation is a inactivation method that potentially produces zero DBPs.87 When cells are exposed to an external field, their lipid bilayer is rearranged according to the potential gradients that were formed across the membrane, as shown in Fig. 4.10. Under a sufficiently strong external field (>1 kV/cm), pores will forcibly be formed on the cell membrane to alleviate the potential difference. With prolonged exposure to the strong electric field, the pores will become permanent, thereby leading to intracellular damage and cell death. The role of nanomaterials in achieving electroporation inactivation is crucial, since electroporation itself is an energy-intensive process that carries risks of arcing at a large scale due to the strong electric field strength required. Locally enhanced electric field treatment (LEEFT) is a form of nanowire-assisted electroporation that circumvents these technical weaknesses.89 LEEFT relies on the “lightning-rod” effect, which is observed when a conductive high-aspect-ratio nanostructure (such as a metal-based nanowire) is exposed to an electric field; the field strength at the nanowire tip is then greatly enhanced (see Fig. 4.11).90 One study using Nernst–Planck equations to simulate the surrounding electric

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Figure 4.10.  The mechanism of electroporation and SEM images of electroporated E. coli. Schematic adapted from ref. [89] © 2020 is licensed under a Creative Commons Attribution 4.0 International License, SEM images reprinted with permission from ref. [90] © 2013, American Chemical Society.

Figure 4.11.  Schematic of LEEFT treatment and electric field enhancement by nanowires. Schematic adapted from ref. [33] © 2020 with permission from the Royal Society of Chemistry. Electric field simulation reprinted with permission from ref. [94] © 2014, American Chemical Society.

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field strength found that the supplied voltage was enhanced by as much as five orders of magnitude at the nanowire surface.91 Thus, LEEFT enables a strong inactivation effect to be achieved, even at safer and more cost-effective operating conditions. Impressive results have been achieved with the current state-of-the-art LEEFT technology. For example, 6-log inactivation of E. coli has been realized with several different metal-based nanowires despite the short contact times (a few microseconds) and the extremely low voltage supplying the electric field (only 1 V).32 Flow-through porous copper electrodes covered with field-enhancing Cu3P nanowires were synthesized from the reaction of electrochemically grown Cu(OH)2 nanowires and heated sodium hypophosphite. For increased nanowire stability and electrode durability, the Cu3P nanowires can be further coated with a layer of self-polymerizing polydopamine, which extends the electrode lifespan from 12 h to 2 days.92 Under optimal operating conditions to suppress unwanted electrochemical reactions (i.e., using high frequency alternating current), the nanowire-modified electrodes can continue to inactivate model bacteria for as long as 15 days.

4.4. Challenges and Opportunities While these categories of nanomaterials have each shown unique capability and promise for novel water treatment methods, challenges remain for their widespread adoption and real-life usage. One major challenge lies in the nanomaterial recovery after bulk mixing, i.e., retrieval of the inherently antimicrobial metal nanoparticles after disinfection treatment. While some materials (i.e., metal or metal-based nanoparticles) may be recovered effectively with the use of an ultrafine membrane or a strong magnet, the problem remains for other classes of materials, some of which may also tend to dissolve under aqueous conditions (i.e., ZnO into Zn2+ ions), thus rendering such physical retrieval methods inapplicable. Other methods for nanomaterial retention also include their immobilization on resin surfaces or flow-through membranes, some examples of which have been given previously.48,94 If the nanomaterial is highly toxic (i.e., heavy-metal-based nanoparticles such as CdS), its recovery and/or retention also becomes an important issue for the potential adverse environmental impacts it may have. Although some nanomaterials that pose no known adverse effects on human health and the environment can be deemed safe for use without recovery at low levels, this is only feasible if the nanomaterial is both well-studied and inexpensive.95,96 Further studies on the long-term and sublethal health effects of these nanomaterials and others which are being considered for drinking water treatment are still necessary. The cost of a nanomaterial increases significantly if it cannot be recovered, and thus another major obstacle is the cost-effectiveness of nanomaterials,

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especially when compared with chlorine disinfection or other established alternatives. Currently, most of the nanomaterials described in this chapter and of those proposed for novel water treatment applications are still far too expensive for commercial use, except for some TiO2, iron oxide, and polymer-based nanomaterials. For example, the cost of TiO2 means that just 200 g must be capable of treating 1,000 gallons of water in order for it to be comparable to chlorine, a feat which is still difficult to replicate reliably.97 Strategies to improve cost-effectiveness do exist, including the use of cheaper, less-pure nanomaterials that are still reactive, or the reuse of recovered and recyclable nanomaterials, which would increase their cost-effectiveness even without decreasing the initial cost of synthesis.94 Overall, the potential for feasible nanomaterial pricing surely exists, but must be further developed and demonstrated at larger scales. Finally, a number of difficulties may arise during the application of a nanomaterial for water treatment, including nanoparticle aggregation, the impact of realworld water matrixes, and reliable long-term usage. An issue that has been briefly touched upon in this chapter is the aggregation of nanoparticles and CNTs in water, an undesirable tendency which generally increases with decreasing material size (a parameter that is usually minimized in order to achieve higher reactivity rates). This tendency to reduce the available surface area for necessary ROSgenerating reactions and inactivation mechanisms is also aggravated under reallife conditions, where humic acids or other organic molecules in the water can coat aggregates and further increase the size of the clusters (see Fig. 4.12).46 Even without the problem of increased nanoparticle aggregation, the formation of such an inactive coating layer itself usually decreases the lifetime of an inherently

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Figure 4.12.    ZnO nanoparticle aggregates in (a) pure water and (b) 10 mg/L humic acid. Reprinted with permission from ref. [47] © 2011, American Chemical Society.

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antimicrobial or catalytic/photocatalytic nanomaterial, and the impact of organic matter attachment and adsorption in real waters remains a difficult issue to address.98 Although nanomaterials still retain their promising potential (e.g., zero DBP formation, enhanced physical inactivation mechanisms, and green or off-grid energy sources), the true effective lifetime of a nanomaterial outside the controlled conditions of a laboratory setting is still too theoretical. Studies demonstrating the long-term efficacy of these nanomaterials in real-world waters are thus of top priority and should become the focus of future research.

Acknowledgments The authors acknowledge the U.S. National Science Foundation [grant number CBET 1845354].

References 1. World Health Organization. Guidelines for Drinking-Water Quality, 4th ed., Incorporating the 1st Addendum. WHO Press: Geneva, Switzerland, 2017.   2. Chlorine Chemistry Division. Drinking Water Chlorination: A Review of Disinfection Practices and Issues. American Chemistry Council, Washington, D.C., 2016.  3. R. D. Morris et al. Chlorination, Chlorination By-Products, and Cancer: A MetaAnalysis, Am. J. Public Health, 1992, 82(7), 955–963.   4. D. L. Sedlak and U. von. Gunten. The Chlorine Dilemma, Science, 2011, 331(6013), 42.   5. G.-Q. Li et al. Comparison of UV-LED and Low Pressure UV for Water Disinfection: Photoreactivation and Dark Repair of Escherichia Coli, Water Res., 2017, 126, 134–143.   6. Y. Chang et al. Evaluation of Dynamic Energy Consumption of Advanced Water and Wastewater Treatment Technologies. AWWA Research Foundation & California Energy Commission, Denver, 2008.  7. X.-F. Li and W. A. Mitch. Drinking Water Disinfection Byproducts (DBPs) and Human Health Effects: Multidisciplinary Challenges and Opportunities, Environ. Sci. Technol., 2018, 52(4), 1681–1689.   8. P. J. J. Alvarez et al. Emerging Opportunities for Nanotechnology to Enhance Water Security, Nat. Nanotech., 2018, 13(8), 634–641.   9. A. J. van Asselt and M. C. te Giffel. Pathogen Resistance to Sanitisers. In Handbook of Hygiene Control in the Food Industry. Ed. Food Science, Technology and Nutrition. Woodhead Publishing Limited, Abington Hall, Cambridge, England, 2005. 10. L. Li et al. The Importance of the Viable but Non-Culturable State in Human Bacterial Pathogens, Front. Microbiol., 2014, 5(258), 1–20. 11. Center for Disease and Control. Drinking Water Treatment Methods for Backcountry and Travel Use. CDC, Atlanta, 2009.

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Chapter 5

Development of Nanostructured Adsorption Materials for Removing Heavy-Metal Ions from Aqueous Systems Dan Zhang* and Chuanyi Wang† School of Environmental Science and Engineering, Shaanxi University of Science and Technology, Xian 710021, P.R. China [email protected]

*

[email protected]

†

5.1. Introduction Due to rapid industrialization and urbanization, water sources are being polluted with toxic heavy-metal ions such as mercury, copper, cadmium, and lead.1,2 These heavy metals can cause serious problems to human, animal, and plant life when released into the environment, even if their concentrations are within permissible limits, because they can accumulate within biological systems.3 Water polluted with heavy-metal ions can be harmful to human health, causing diseases such as cancer, kidney damage, autoimmunity, and even death in extreme cases.4–6 The removal of heavy-metal ions from industrial wastewater and other polluted waterbodies is urgently needed because they are not only toxic, but also can be reused in various industrial applications.7 Therefore, effective separation methods are needed to efficiently remove heavy-metal ions. Methods such as precipitation, membrane processes, adsorption, and ion exchange have been employed for this purpose.8–11 However, adsorption has attracted extensive attention due to its simple and easy operation, high economic value, low pollutant removal concentration, and high efficiency. 115

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Adsorbents that are commonly used to remove heavy-metal ions include clay, activated carbon, chitosan, cellulose, and other two- or three-dimensional materials,12–16 as well as carbon nanotubes (CNT), nanocomposites, and nanoparticles.17–19 The adsorption mechanism includes establishing equilibrium between both the adsorbent surface and heavy-metal ions using various processes, such as complexation, ion exchange, electrostatic interactions, and precipitation, thereby allowing the separation of toxic metal ions from wastewater solutions.20–22 Here, we present a short survey on the recent developments in nanostructured adsorbents for the removal of heavy-metal ions from aqueous systems.

5.2. Preparation of Nanostructured Adsorbents A large number of studies have reported on traditional and new methods for ­preparing nanostructured adsorbents, such as hydrothermal carbonization, coprecipitation, ultrasonic chemical, cross-linking, and microwave-assisted methods. The hydrothermal carbonization method refers to a process of forming carbon nanomaterials through a series of complex reactions in a hydrothermal reactor, with organic sugars or carbohydrates as a raw materials and water as the reaction medium a certain temperature and pressure.23,24 The advantage of hydrothermal carbonization are low-cost, mild reaction conditions, uniform particle size, and controllable morphology. In our previous work, a montmorillonite/carbon composite material was synthesized by hydrothermal carbonization.25 D-glucose monohydrate was first used as a precursor to carbon nanoparticles, and then mixed with sodium montmorillonite to form a homogeneous dispersion. The mixture was then transferred to a Teflon-lined stainless-steel autoclave for heating. The nanocomposites was then washed, dried, and ground by centrifugation. Finally, the mixture of montmorillonite/carbon material and hydrogen peroxide was vigorously stirred to obtain a black powder named MMT/C-COOH. The results showed that the prepared material has good adsorption properties for Pb2+ ions. The hydrothermal method is one of the most common synthetic methods. It is easy to operate and can be used to prepare nano-adsorbents with good dispersibility and stability and high purity. However, the hydrothermal reaction is carried out under relatively higher temperature and pressure conditions, and thus it is relatively expensive and difficult to use for large-scale sewage treatment. Compared with the hydrothermal method, the cross-linking method has lower cost and shorter reaction time, which is suitable for industrial production. Cross-linking refers to the cross-linking reaction of linear polymers with a certain degree of polymerization under the cross-linking action of cross-linking agent, which makes the linear polymers interconnect to form a network

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structure polymer with a certain degree of cross-linking density.26 The reaction converts linear or slightly branched macromolecules into a three-dimensional network structure, thereby improving strength, heat resistance, and solvent resistance properties.27 The cross-linking reaction is generally divided into physical and chemical cross-linking.28 Physical cross-linking is formed by the combination of physical forces such as hydrogen bonds and polar bonds; chemical cross-linking is formed by covalent bonding.29 In general, crosslinking agents include polyethylene glycols, crown ethers, aldehydes, and epichlorohydrin.30,31 In our previous work,32 we first physically mixed chitosan and lignin, and then added a specific amount of polyvinyl alcohol as a direct pore former to improve the flexibility of the material (Fig. 5.1). After 10 min of high-speed homogenization, polyethyleneimine was added for further homogenization, and finally glutaraldehyde was added as a crosslinking agent to form a polymeric composite material. The composite mixture was mechanically stirred at room temperature until a yellow-brown sol formed. Following that, sponge adsorbent with a three-dimensional network structure is obtained using a lyophilization method. The adsorbent has the advantages of rapid response and strong selectivity to Hg(II) ions in a solution in which multiple ions coexist. In the reaction designed by Mao et al.,33 the ammonium persulfate molecule decomposes into two ammonium sulfate molecules when heated and generates free radicals. Free radicals can form hydroxyl free radicals on the surface of sodium alginate or attapulgite. The hydroxyl radical initiates the double bonds of acrylic monomer, which promotes the cleavage of the double bond to form long polyacrylic acid chain. N, N’-methylene-bis-acrylamide cross-linked polyacrylic acid forms a cross-linked system. The detailed reaction process is shown in Fig. 5.2.

Figure 5.1.    The synthesis process of a PEI-CS-L composite sponge and its chemical structure. (The chitosan, glutaraldehyde, polyethyleneimine, and poly (vinyl alcohol) are abbreviated to CS, GA, PEI, and PVA, respectively.)32

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Figure 5.2.   A schematic expression of the synthesis of nanocomposite hydrogels. (i) Free radical production, (ii) chain initiation, (iii) chain growth, and (iv) chain cessation.33

5.3. Characterization of Nanostructured Adsorbents To characterize the formation and structures of nano-adsorbents, X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FTIR), X-ray photoelectron spectroscopy (XPS), and other spectroscopic techniques are often employed. Scanning electron microscope (SEM), transmission electron microscope (TEM), and other electron microscopy techniques can be used to characterize the morphology, porosity, and size of nano-adsorbents. In addition, the Brunauer–Emmett– Teller (BET) test can analyze the specific surface area and surface adsorption characteristics of nanoscale adsorbents, and the derivative thermo-­gravimetric (DTG) analysis can be used to study the physical and chemical properties of substances and their changing process.

5.3.1.  XRD XRD is primarily used to analyze the formation, structure, and crystalline properties of nanoscale adsorbents. The Bragg equation is used to calculate the basal spacing of clay minerals:



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Figure 5.3.   XRD patterns of the Na+-MMT and as-prepared MMT/C.25

where d(001) denotes the basal spacing of clay minerals and θ is the incident angle. As presented in Fig. 5.3, the basal spacing of Na+-MMT is ∼12.3.34 For the synthesized MMT/C composite, the basal spacing increases to ∼15.06, suggesting that the clay interlayer distance is around 5.1 Å after subtracting 9.6 Å of one montmorillonite layer thickness. The results indicate that some carbon clusters may be inserted into the clay interlayers with size ∼5Å.35

5.3.2.  FTIR FTIR is the main method to characterize the chemical structure of nano-adsorbents. It is primarily used to analyze the formation of functional groups and materials. Figure 5.4 shows the results of infrared spectroscopic analysis for the structural characterization of MMT and MMT/C composites. The characteristic bands of MMT at 1,029 and 530 cm−1 correspond to Si−O stretching and Al−O− Si deformations, respectively.36 The band at 3,635 cm−1 is due to the vibrational stretching of the structural −OH groups. The broad bands centered at 3,422 cm−1 and 1,654 cm−1 correspond to hydrogen-bonded OH stretching, and can be attributed to the hydrated water on the clay surface.37 After the HTC process, some new peaks appeared. The peak at 2,941 cm−1 can be clearly observed, which is attributed to the C−H vibrations. The peaks at 1,707 and 1,623 cm−1 are attributed to the stretching vibrations of C=O and C=C, respectively.38 Thus, these results suggest that MMT clay is partially modified by functional carbonaceous species.

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(a)

(b)

Figure 5.4.    FT-IR spectra of Na+-MMT and MMT/C.25

Figure 5.5.    FT-IR spectra of the lignin, chitosan, and PEI-CS-L sponge.32

The FT-IR spectral study by Zhang et al.32 on the polyethyleneimine-­ functionalized chitosan-lignin composite sponge confirmed the occurrence of the Schiff base reaction. Figure 5.5 compares the infrared spectra of synthetic sponge with chitosan and lignin precursors. The sponge shows strong N-H stretching vibration peaks at 1,392 cm−1 and 1,560 cm−1,39 which are attributed to primary and secondary amines, respectively. In addition, the sponge has a characteristic peak caused by C=N stretching at about 1,647 cm−1, indicating that glutaraldehyde is an efficient crosslinker for chitosan and polyethyleneimine.40

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Figure 5.6.    XPS spectra of the lignin, chitosan, and PEI-CS-L sponge.32

5.3.3.  XPS XPS can be used to quantitatively and qualitatively characterize the chemical composition, including the electronic states the elements of nanocomposites. For example, the peak of bonding energy about 400 eV (Fig. 5.6) represents the N peak in the nanocomposites, indicating the successful introduction of polyethylenimine.41

5.3.4.  SEM and TEM SEM and TEM can be employed to characterize the size and surface morphology of nanocomposites, which are key factors affecting the adsorption performance of nanocomposites. The effect of attapulgite on the microstructure of nanocomposite hydrogels was studied by SEM and TEM.33 The hydrogel without attapulgite presents a smooth surface and dense structure (Fig. 5.7(a)), whereas the hydrogel with attapulgite presents a loose surface and porous 3D structure (Fig. 5.7(d)). Furthermore, the hydrogel alone shows a flat structure (Fig. 5.7(b)), but the hydrogel with attapulgite shows a fibrous structure (Fig. 5.7(e)), and some complete layered structures of the attapulgite (110) planes (1.23 nm) are discovered (Fig. 5.7(f)).42 Significantly, this finding demonstrates the role of attapulgite in facilitating the formation of hydrogel porous structure. For the amino-functionalized sponge adsorbent, the digital photo (Fig. 5.8(a)) shows a non-uniform and dense porous structure. The SEM image in Fig. 5.8(b) further supports the non-uniformity and interconnectivity of the porous sponge structure, where nanotubes (Fig. 5.8(c)) with a thickness of about 75 nm are

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(b)

(d)

(e)

(c)

(f)

(g)

Figure 5.7.    (a) SEM, (b) TEM, and (c) Schematic networks of hydrogels. (d) SEM, (e, f) TEMs, and (g) Schematic networks of hydrogel with 30% attapulgite. Note that loose surfaces and porous 3D structures are produced after introducing attapulgite.33

(a)

(b)

(c)

Figure 5.8.  Digital photo and the SEM image of the as-prepared PEI-CS-L sponge adsorbent. (a) The dry sample, (b) SEM micrograph of the dry sample, and (c) Normal distribution of nanowall thickness.32

connected to form wall-like structures.43,44 These structures increase the distribution of functional groups and expose more active sites for the transportation of solutes, which resulting in complexation of heavy-metal ions with the ­surface functional groups and further accelerating the adsorption process. The SEM images of the Na+-MMT clay and the MMT/C composite are shown in Fig. 5.9.25 The original MMT presents a well-developed stacked-layer structure with a smooth surface and sharp edges (Fig. 5.9(a)), which is consistent with the previous study.45 For MMT/C composite, the clay layers become slightly disordered and less stacked compared with MMT clay, and the surface appears to be

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(a)

(b)

Figure 5.9.    SEM images of (a) Na+-MMT, and (b) MMT/C.25

rough and the edge becomes blunt. This visual morphological change can be attributed to the presence of flaky and granular carbon particles (in the red circles of Fig. 5.9(b)), which is in line with the observation of the lower and broader peak in the XRD spectrum due to the deficiency of structural constancy (Fig. 5.3). The spherical carbonaceous particles with a smooth surface adhere firmly to the MMT flakes. The results based on XRD results and SEM images confirm that the carbon is not only intercalated into the clay interlayer, but also decorated on the external surface of the MMT clay.

5.3.5.  BET The BET method is also known as the low-temperature nitrogen adsorption– desorption isotherm method. The hypothesis of this theory is that adsorption relies on van der Waals forces.46 The adsorption on most surface is multilayer adsorption. When the relative pressure is 0.05–0.35, the linear graph of adsorption data can be made according to the BET equation as follows: 1



( ) − 1

X 

P0 P

=

C −1 P  1 + ,   X m C  P0  X m C

(5.2)

where P is the pressure of the adsorbent, and P0 is the saturated equilibrium vapor pressure of the adsorbent at the temperature of the measuring disk. P/P0 is the relative pressure, C is the physical constant related to the difference between the adsorption heat and condensation heat of the first layer, X is the corresponding adsorption amount of the adsorbent at a certain relative pressure, and Xm is the saturation adsorption capacity of the monolayer. The BET measurement can provide information about the pore structure and specific surface area of nanomaterials by adsorption–desorption isotherms.47 If the

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(a)

(b)

Figure 5.10.    (a) N2 adsorption−desorption isotherm, and (b) Pore size distribution of Na+-MMT and MMT/C.25 Table 5.1.    Surface and pore properties of MMT and MMT/C.25 Sample

BET surf. area (m2/g)

Total pore vol. (cm3/g)

Av. pore diam. (nm)

MMT

41.796

0.512

3.623

MMT/C

69.447

0.928

3.948

prepared material conform to the H3 or H4 isotherms, the porosity and specific surface area of the nanomaterials are relatively large. Figure 5.10 shows the N2 adsorption–desorption isotherms and pore size distribution of MMT and MMT/C composites.25 The BET specific surface areas of Na+-MMT and MMT/C are 41.796 and 69.447 m2/g, respectively, suggesting that the prepared carbonaceous nanoparticles contribute to the surface area (Table 5.1). The increase in adsorption volume at lower relative pressure indicates the presence of a small number of micropores in the MMT/C composite, while the remarkable hysteresis loops corresponding to capillary condensation at a relative pressure ranging from 0.4 to 0.8 signify the presence of mesoporous structure.48 Meanwhile, the peak pore size of Na+-MMT is 3.9 nm, while the MMT/C composite partially inherits the pore distribution around 4 nm of MMT (Fig. 5.10(b)). Notably, a new peak with pore size of 7.8 nm appears in MMT/C, which is associated with the prepared carbon particles. The results indicate that the carbonaceous nanomaterials are an integral part of the MMT/C composite materials and have a significant influence on their porosity and specific surface area.

5.3.6.  TGA TGA is one of the main methods used for evaluating the thermal stability of various materials. TGA was performed on the porous structure sponge adsorbent of

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Figure 5.11.    DTG curves of the lignin, chitosan (CS), and PEI-CS-L sponge.32

the nanowalls designed by Zhang et al.,32 and the temperature range used as the adsorbent was analyzed (Fig. 5.11). For lignin, a significant endothermic peak appears at 364°C, which is caused by the cleavage of the high content hydroxyl bonds in the lignin structure. For chitosan, the decomposition temperature is about 301°C, which is attributed to the breakage of the main chain and the release of some small molecular fragments.49 For the PEI-CS-L sponge, the thermal decomposition temperature of 420°C is higher than that of chitosan and lignin, further demonstrating that the cross-linked composite sponge was successfully obtained. The higher decomposition temperature indicates that the adsorbent has better thermal stability and can be used at room temperature and higher temperature.

5.4. Adsorption of Heavy-Metal Ions The interaction mechanism between nanomaterials and heavy-metal ions can be proved by batch adsorption experiments. Because environmental changes could change the physical and chemical properties of materials and affect the migration efficiency of heavy-metal ions, it is very important to evaluate the effects of environmental conditions on the adsorption of heavy metals by nanomaterials in practical applications.

5.4.1.  Adsorption kinetic studies In general, as the contact time increases, the removal efficiency of heavy-metal ions by adsorbent increases until adsorption equilibrium is reached. The kinetics of adsorption of heavy-metal ions by adsorbents follows two stages.50 At the

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beginning of the adsorption process, a large number of active sites are used for adsorption. The process occurs very quickly and is therefore the rapid adsorption stage, which is considered to be controlled by the external surface adsorption. The second is the relatively slow adsorption stage, which is mainly affected by diffusion within the particles. The adsorption rate depends on the initial concentration of pollutants, the amount of adsorbent and reaction conditions. In the entire adsorption process, the reaction rate of heavy-metal ions on nanomaterials is evaluated by adsorption kinetics. The commonly used kinetic models include Pseudo-first-order, Pseudo-second-order, and Intra-particle diffusion model kinetics, whose expressions are Eqs. (5.3)–(5.5):

Qt = Q f (1 − e − k1t ) ,



t 1 1 = + t, Qt k2Q f 2 Q f



Qt = ki t1/2 + c.



(5.3) (5.4)

(5.5)

The Pseudo-first-order model is based on the assumption that the adsorption process is controlled by the diffusion steps; in contrast, the Pseudo-second-order dynamic model assumes that the adsorption rate is mainly determined by the chemical adsorption mechanism. Here, qe (mg/g) and qt (mg/g) are the adsorption capacity at equilibrium and time t (min), respectively. k1 (min−1) and k2 (g/(mg min)) represent the adsorption rate constants of Eqs. (5.3) and (5.4), respectively. ki (mg/g min−0.5) is the intra-particle diffusion rate constant, and c is the intercept, which provides insight into the thickness of the boundary layer. Normally, the adsorption rate of heavy-metal ions in nanomaterials decreases from fast to slow, and finally reaches the adsorption equilibrium. The adsorption of heavy-metal ions in nanomaterials mostly conforms to the Pseudo-second-order kinetic model. Mao et al.33 studied the adsorption kinetics of Pb2+ using attapulgite-induced porous nanocomposite hydrogel. The fitting results (Fig. 5.12) showed that the adsorption of Pb2+ on hydrogel was more consistent with the Pseudosecond-order kinetic model, indicating that adsorption was a chemical adsorption behavior influenced by surface adsorption sites and diffusion.51 The addition of attapulgite reduces the number of adsorption sites on the surface of the hydrogel, but significantly enhances its internal diffusion capacity. Zhang et al.32 used a Pseudo-second-order kinetic model to study the adsorption kinetics of Hg(II) ions adsorption on a polyethyleneimine-functionalized chitosan-lignin composite sponge with nanowalls-network structures (Fig. 5.13). The results showed that the removal capacity increases sharply within 1 min and reaches 83.5% of the final adsorption capacity. With the extension of contact time, the adsorption efficiency slows down and finally reaches the equilibrium state

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t/Qt (min·g/mg)

Adsorption capacity (mg/g)

0.5

300

200

100

Hydrogel without attapulgite Hydrogel with 10% attapulgite Hydrogel with 20% attapulgite Hydrogel with 30% attapulgite

30

0

(c) 400

Pseudo-second-order model 0.6

0

Adsorption capacity (mg/g)

(b)

Pseudo-first-order model

400

90 120 150 Time (min)

60

180

0.4 0.3 0.2 Hydrogel without attapulgite Hydrogel with 10% attapulgite Hydrogel with 20% attapulgite Hydrogel with 30% attapulgite

0.1 0.0

210 240

90

120 150 180 210 240 Time (min)

Surface adsorption

Pore diffusion adsorption

0

30

60

(d)

Weber-Morris model

350 300

Dense hydrogel

250 200

Pb2+

150 100

Hydrogel without attapulgite Hydrogel with 10% attapulgite Hydrogel with 10% attapulgite Hydrogel with 10% attapulgite

50 0 0

2

4

6

8

10

12

14

16

Porous composite hydrogel

t1/2 (min1/2)

Figure 5.12.    (a) Pseudo-first, (b) Pseudo-second, and (c) Weber−Morris adsorption kinetic models for the adsorption of Pb2+ by hydrogels. (d) Diagram of hydrogel controlled by surface adsorption sites and in-diffusion.33

Figure 5.13.   Effect of contact time on Hg(II) adsorption by PEI-CS-L, pseudo-first-model and pseudo-second-order model.32

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within 6 h, and the ultimate adsorption capacity is 663.5 mg/g. The adsorption process is controlled by chemical adsorption, which involves electrostatic attraction and chemical adhesion between adsorbate and adsorbent. The rapid adsorption equilibrium and superior adsorption capacity make the polyethyleneimine-­ functionalized chitosan-lignin composite sponge a good material for the adsorption of Hg(II) ions.

5.4.2.  Effect of pH pH is considered to be the most important parameter for the adsorption and treatment of heavy-metal ions due to its important influence on the surface charge, metal ions form, complexation and binding sites of the adsorbent.52 The point of zero charge (PZC) is the pH value at which the net charge on the surface of the adsorbent is zero. Generally, the adsorbent is positively charged when pH < pHpzc, and a negative charge when pH > pHpzc.53 The removal efficiency of heavy-metal ions on adsorbents decreases sharply under extreme pH conditions, which is primarily due to the strong electrostatic repulsion between adsorbates and adsorbents. Under neutral pH conditions, the strong surface coordination, electrostatic attraction, and co-precipitation can lead to high removal rates. As the pH increases and H+ decreases, metal ions can occupy more adsorption sites. According to the report by Zhu et al.,25 the adsorption capacity of the montmorillonite/carbon composite (MMT/C) adsorbent for Pb(II) ions (Fig. 5.14) increased in the range of 2−5 with increasing pH. At low pH, the adsorption capacity of Pb(II) ions is small, which may be due to the protonation of surface active sites and the increase of H3O+ ions in aqueous solution. On one hand, there is

(a)

(b)

Figure 5.14.    (a) Effect of solution pH on the Pb(II) adsorption by MMT, MMT/C, and functionalized MMT/C−COOH, C0 = 100 mg L−1, T = 308 K, and t = 24 h. (b) Effect of solution pH on the zeta potential of MMT, MMT/C, and functionalized MMT/C.25

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electrostatic repulsion between positively charged surface active sites and Pb(II) ions; on the other hand, H3O+ ions compete with active sites and thus reduce the competitiveness of Pb(II) ions that can be used to bind the surface active sites. With the increase of pH, the concentration of H3O+ ions decreases and the concentration of OH− increases, leading to the deprotonation of the adsorbent surface, which means that the surface of the MMT/C composites tends to have a negative charge. As a result, the attraction between the adsorbent and Pb(II) ions in the solution is enhanced.

5.4.3.  Adsorption isotherm studies The effect of temperature on the adsorption of heavy metal ions depends on whether the adsorption process is endothermic or exothermic. In the process of adsorbing heavy-metal ions, the adsorption capacity usually increases with the increase of temperature, indicating that the adsorption has spontaneous and endothermic. Increasing the temperature will increase the diffusion rate of metal ions on the surface and outside of the adsorbent. To further clarify the adsorption mechanism, thermodynamic parameters such as Gibbs free-energy (ΔG, kJ·mol−1), adsorption enthalpy (ΔH, kJ·mol−1), and adsorption entropy (ΔS, kJ·mol−1·K−1) are introduced. The thermodynamic parameters are obtained using the formulas shown in Eqs. (5.6)–(5.8):

∆G = − RT ln K a ,

(5.6)



∆G = ∆H − T∆S,

(5.7)



lnK a =

∆S ∆H − , R RT

(5.8)

where ka, R, and T are the partition coefficient, gas constant (8.314 J·mol−1·K−1), and temperature (K), respectively. The adsorption isotherm is a curve describing the effect of temperature on adsorption behavior. By analyzing the adsorption isotherms, relevant information such as surface properties, pore distribution, interaction between the adsorbate and adsorbent can be obtained. Furthermore, the maximum adsorption capacity (qm) of heavy-metal ions can be obtained by calculating the adsorption isotherm.54 Common isotherms used to describe the adsorption equilibrium of adsorbate on adsorbent include the Langmuir (Eq. (5.9)), Freundlich (Eq. (5.10)), Sips and Dubinin–Radushkevich (D–R) models.55



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ce 1 c = + e , qe bqm qm



(5.9)

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1 log qe = log k + log ce , n

(5.10)

where qe (mg/g) and ce (mg/g) are the adsorption capacity and equilibrium ­concentration of Hg(II) ions in the solution (mg/L), respectively; qm (mg/g) and b (L/mg) are the maximum adsorption capacity and Langmuir constant related to adsorption heat, respectively; and n and k are Freundlich constant, respectively. It is worth noting that the adsorption of heavy-metal ions on nanomaterials mainly conforms to the Langmuir model, indicating that the adsorption is primarily a single-molecular-layer uniform covering process. The Langmuir model assumes that the surface of the adsorbent is uniform and has exactly the same adsorption sites. In contrast, the Freundlich model assumes that the surface of the adsorbent is uneven and the surface energy is different at different surface locations, which can be applied to both single-layer adsorption and multilayer adsorption. Zhu et al.25 used the Langmuir and Freundlich models under different temperature conditions to fit the adsorption isotherms of modified montmorillonite/ carbon composite for Pb(II) ions (Fig. 5.15). The results showed that the Langmuir model has the best fitting value for the isotherm, indicating that the adsorption site energy is constant and the maximum monolayer surface coverage exists. The thermodynamic parameters at different temperatures were further studied, and the ΔG° value of Pb(II) ions on MMT/C-COOH was negative, indicating that the

Figure 5.15.    Adsorption isotherm models for Pb(II) onto MMT/C−COOH.25

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adsorption process had proceeded spontaneously. In addition, the value of ΔG decreased continuously when the temperature increased from 25°C to 45°C, indicating that the adsorption process was easier to proceed at higher temperatures. A positive value of ΔH indicates the entire reaction is an endothermic process. Generally, the water molecules around the target ions form a hydration sheath with the target ions. There is no doubt that the process of the adsorbate passing through the diffusion layer into the adsorption layer requires energy to remove the hydrated sheath, and then the exposed ions are complexed with the surface functional groups. Therefore, the endothermic adsorption is due to the high temperature, which promotes the dehydration process. The positive value of ΔS reflects the increasing disorder of the solid/liquid interface during the adsorption process.56 In general, temperature has a positive effect on the adsorption capacity of the modified montmorillonite/carbon composite material on Pb(II) ions, which is beneficial for environmental in-situ restoration.

5.4.4.  Selective adsorption behaviors Various types of heavy-metal ions often exist simultaneously in the wastewater produced by different industries, and these heavy-metal ions may interfere with each other during the adsorption process. Therefore, it is necessary to investigate the selectivity and comprehensive adsorption effect of nano-adsorbents in the case of mixed solutions of heavy-metal ions.57 In order to evaluate the selectivity of the PEI-CS-L sponge developed by Zhang et al.,32 the adsorption capacities of PEI-CS-L sponge for Hg(II), Pb(II), Cu(II), and Cd(II) ions were compared in a single- and mixed-component solution, respectively. In the single-component solution (Fig. 5.16(a)), the unordinary adsorption capacity of the PEI-CS-L sponge for Hg(II) ions is 663.5 mg/g, which is much higher than other adsorbents, indicating that the PEI-CS-L sponge has adsorption selectivity for Hg(II) ions. The PEI-CS-L sponge also shows superior adsorption capacity to Hg(II) ions even in the multi-component solutions (Fig. 5.16(b)). The distribution coefficients (Kd) for each metal ion and the degree of selectivity for Hg(II) ions in the presence of competing metal ions are calculated using the separation factor (α Hg), according to Eqs. (5.11) and (5.12).17



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Kd =

( C0 − Ce ) × V , Ce

α MHg =

m

Hg d M d

K K

,





(5.11) (5.12)

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(a)

(b)

Figure 5.16.    Selectivity tests for the adsorption of Hg(II) ions by PEI-CS-L sponge. (a) Singlecomponent solution, and (b) Mixed-component solution.32 Table 5.2.    Summary of the competitive adsorption data of PEI-CS-L sponge for different heavymetal ions.32 Ions

C0 (mg/L)

Ce (mg/L)

Qe (mg/g)

R (%)

KdM (mL/g)

α Hg (KdHg/KdM)

Hg (II)

505.5

0.8

504.7

99.84

630875

Cu (II)

479.5

443

36.4

0.08

82.1

7684.2

1

Pb (II)

407.2

311.6

95.6

0.24

306.8

2056.3

Cd (II)

489.5

328.2

161.3

0.33

491.1

1284.6

where C0 is the initial concentration of metal ions, Ce is the equilibrium concentration of metal ions, m is the mass of the PEI-CS-L sponge (g), and V is the volume of solution (mL). KdHg and KdM represent the distribution coefficients of Hg(II) ions and interfering metal ions, respectively. Generally, the higher Kd value indicates the stronger adsorption capacity realized by the adsorbent. The Kd of PEI-CS-L sponge for Hg(II) ions is 630875 mL/g, which is much higher than the other three metal ions. This result evinces that the high selectivity of the PEI-CS-L sponge for Hg(II) ions. The initial and final concentrations of competing ions are listed in Table 5.2. The separation factor is very high for each competing metal ion, which also indicates the PEI-CS-L sponge has high selectivity for Hg(II) ions, even in the presence of interfering metal cations.

5.5.  Adsorption Mechanism The adsorption mechanism of nanomaterials to heavy-metal ions is the key to understanding the efficiency in treating heavy-metal pollution, and helps to select the best removal conditions. Common adsorption mechanisms include adsorption, oxidation, co-precipitation and other special surface binding. Nano-adsorbents,

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especially their complexes, have abundant active sites or functional groups, which can quickly and efficiently adsorb pollutants onto the surface of the material. In order to determine the main mechanism of adsorption, advanced spectral analysis, surface complexation models and theoretical calculations were used to study adsorption behavior. Adsorption mechanism usually includes ion exchange, electrostatic adsorption, hydrogen bonding, and surface bonding. Generally, spectral analysis techniques such as FTIR and XPS are used to detect the chemical state and structure of surface complexation. Zhu et al.25 used montmorillonite/carbon material (MMT/C-COOH) to remove Pb(II) ions. After the adsorption of Pb(II) ions, the FTIR diagram (Fig. 5.17) of MMT/C-COOH showed a slight redshift of the carboxylate COO−, and the intensity of the absorption peaks at 3,400 cm−1 and 1,714 cm−1 decreased. This ligand exchange phenomenon suggested that the inner-sphere complex may be formed on the MMT/C−COOH surface.58 The main mechanisms proposed for Pb(II) removal can be illustrated as follows: MMT/C-COOH + Pb2+ → MMT/C-COO− − Pb2+ + H+, (5.13) (MMT/C-COOH)2 + Pb2+ → (MMT/C-COO−)2 − Pb2+ + 2H+, (5.14) MMT/C-COOH + Pb(OH)+ → MMT/C-COO− − Pb2+ + H2O. (5.15) The aforementioned reaction between Pb(II) and −COOH might induce the release of H+ in the solution, which is also supported by the decreased pH value during the adsorption of Pb(II) by MMT/C−COOH.

Figure 5.17.  ATR-FTIR spectra of MMT/C−COOH, MMT/C−COOH-Pb(II), and Pb(II) solution. Experimental conditions: sample dose is 20 mg/50 mL, pH=5, initial Pb(II) concentration is 60 mg L−1, temperature is 298 K.25

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Intensity (a. u.)

Survey scan

O 1s

Before absorption of Pb2+

After absorption of Pb2+

Survey scan

0

200

C 1s

284.8 eV

284.8 eV

C 1s

Pb 4d

400

O 1s

531.1 eV

600

800

1000

1200

280

282

Before absorption of Pb2–

(d) N 1s

After absorption of Pb2+ 532.0 eV 532.7 eV

528

292

Before absorption of Pb2+

After absorption of Pb2+

N 1s

Pb 4d 400.2 eV

vanished

526

290

399.9 eV

Intensity (a. u.)

Intensity (a. u.)

535.7 eV

531.1 eV

284 286 288 Binding Energy (eV)

532.0 eV 532.7 eV

O 1s

After absorption of Pb2+ 288.3 eV

Binding Energy (eV)

(c)

Before absorption of Pb2+ 288.1 eV

C 1s AI 2p S1 2p N 1s

Pb 4f

(b)

Intensity (a. u.)

(a)

530 532 534 536 Binding Energy (eV)

538

394

396

398 400 402 404 406 Binding Energy (eV)

408

410

Figure 5.18.    XPS of hydrogel with 10% attapulgite and after adsorption of Pb2+: (a) survey scan, (b) C 1s, (c) O 1s, and (d) N 1s. 33

Mao et al.33 studied the mechanism of Pb(II) ions adsorption using attapulgiteinduced porous nanocomposite hydrogels via XPS, and the results showed that Pb(II) ions were successfully adsorbed to the surface. Apparently, new peaks attributed to Pb 4f and Pb 4d were observed in the survey scan spectrum of XPS after the adsorption of Pb2+ (Fig. 5.18(a)). Other changes observed as follows: (i) The peak of C 1s at 288.1 eV (Fig. 5.18(b)), which is attributed to the amide bond (−C=O), shifts to 288.3 eV after Pb2+ adsorption. (ii) The vanished peak of O 1s (Fig. 5.18(c)) after adsorption at 535.7 eV is assigned to the carboxyl bond (−OH). (iii) The amino (−NH) peak at 399.9 eV shifts to 400.2 eV after Pb2+ adsorption (Fig. 5.18(d)). All data show the stronger interaction between Pb2+ and these groups (−OH, −NHR, and –COO−).59 Based on the above observations, a plausible mechanism for the adsorption of Pb2+ by the hydrogel is proposed, as schematically described in Fig. 5.19. The adsorption of Pb2+ by hydrogels is of monolayer chemisorption that is mainly dependent on the functional groups (−OH, −NHR, and −COO−) and controlled by the surface adsorption sites and in-diffusion of the hydrogel. The effects of attapulgite on the adsorption behaviors of Pb2+ by the composite hydrogel are

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Figure 5.19.    Plausible mechanism for the adsorption of Pb2+ by hydrogel alone and hydrogel with attapulgite.33

summarized as follows: (i) Decrease in the adsorption capacity due to the reduction in quantities of surface adsorption sites. (ii) The adsorption rate is slower because of the diffusion of heavy metals into the adsorbent. (iii) The ability to internally diffuse is enhanced owing to the porous structure.

5.6. Conclusion and Perspective The rapid development and wide application of heavy metal ions and the materials inevitably lead to the release of heavy-metal ions into the environment. As water pollution has become increasingly more severe worldwide, the rapid adsorption and removal of heavy-metal pollution is particularly important and urgent. Because of the absorbability and environmental friendliness, nanomaterials have great potential in pollutant removal and in situ remediation. Nanocomposites are an ideal adsorption material because they have the advantages of a simple synthesis process, low cost, high adsorption efficiency, good stability, easy separation from heavy metals, and reusability. However, more work is still needed for the preparation and application of nanoscale adsorption materials. Future works should focus on the following methods: (1) Select appropriate inorganic groups, organic groups and biological materials to modify the matrix material to obtain a composite nano-adsorbent with higher adsorption efficiency and adsorption capacity. (2) More advanced characterization methods (e.g., in situ Raman spectroscopy) and theoretical chemical calculation simulation methods (e.g., density functional theory and molecular dynamics simulation) are needed to characterize

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adsorbents before and after adsorption in order to study the adsorption ­mechanism at a deeper levels. (3) More efforts are needed to focus on the separation of adsorbents from heavymetal ions and the reuse of adsorbents. For example, electric field stress desorption can be used to accelerate the desorption process. (4) Although nano-adsorbents have potential advantages in the treatment of heavy-metal ions pollution, their industrial applications are still limited. This is partly due to insufficient knowledge on the toxicity and mechanism of composite materials and the fact that wastewater composition is complex. Heavymetal ions and organic pollutants exist simultaneously, so preparing nano-adsorbents that simultaneously remove heavy-metal ions and organic pollutants through a variety of methods is the direction for future development.

References   1. W. Y. Liu, L. Z. Yang, M. Yu, and M. Liu. Preparation of Poly (Acrylate-Acrylamide) Hydrogel and Its Adsorption Performance to Heavy Metal Ions, Chinese J. Anal. Chem., 2016, 44, 707–715.   2. M. Zhang, X. C. Wang, L. Yang, and Y. Y. Chu. Research on Progress in Combined Remediation Technologies of Heavy Metal Polluted Sediment, Int. J. Env. Res. Pub. He., 2019, 16, 5098.   3. G. Pandey and S. Madhuri. Heavy Metals Causing Toxicity in Animals and Fishes, Res. J. Animal, Veter. Fish. Sci., 2014, 2, 17–23.   4. P. K. Rai, A. Mishra, and B. D. Tripathi. Heavy Metal and Microbial Pollution of the River Ganga: A Case Study of Water Quality at Varanasi, Aquat. Ecosyst. Health., 2010, 13, 352–361.  5. A. A. Al-Othman, S. H. Abd-Alrahman, and N. M. Al-Daghri. DDT and Its Metabolites are Linked to Increased Risk of Type 2 Diabetes among Saudi Adults: A Cross-Sectional Study, Environ. Sci. Pollut. R., 2015, 22, 379–386.   6. B. Chen, M. Wang, M. X. Duan, X. T. Ma, J. L. Hong, F. Xie, R. R. Zhang, and X. Z. Li. In Search of Key: Protecting Human Health and the Ecosystem from Water Pollution in China, J. Clean. Prod., 2019, 228, 101–111.   7. M. Gavrilescu. Removal of Heavy Metals from the Environment by Biosorption, Eng. Life Sci., 2004, 4, 219–232.   8. S. M. Kim, I. H. Yoon, I. Kim, J. H. Kim, and S. J. Park. Hydrothermal Desorption of Cs with Oxalic Acid from Hydrobiotite and Wastewater Treatment by Chemical Precipitation, Energies, 2020, 13, 3284.  9. J. Perendija, Z. S. Velikovi, I. Cvijeti, J. D. Rusmirovi, and A. Onjia. Batch and Column Adsorption of Cations, Oxyanions and Dyes on a Magnetite Modified Cellulose-Based Membrane, Cellulose, 2020, 27, 1–21.

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Chapter 6

Low-Dimensional Nanomaterials for Next-Generation Capacitive Deionization Systems Zhi Yi Leong* and Hui Ying Yang† Pillar of Engineering Product Development, Singapore University of Technology and Design, 8 Somapah Road, 487372, Singapore [email protected]

*

[email protected]

†

6.1. Introduction Capacitive deionization (CDI) was first conceived in the early 1960s as a method to demineralize water. Two electrodes comprising porous carbon materials were placed across a channel of salt (NaCl) solution and a potential was imposed across them. The positive electrode was the anode and the negative electrode was the cathode. Under the influence of the electric field, anions and cations migrated toward the anode and cathode, respectively. The ions were then stored in electrical double layers (EDLs) formed within intraparticle pores, and a desalinated/­ demineralized stream was produced. Since this was a purely capacitive process, electrodes could be discharged using a short circuit to release ions and recover adsorption capacity. A typical CDI system performs multiple adsorption–­ desorption cycles to reach the target salinity level. Figure 6.1 depicts one such cycle. CDI is a robust and versatile system capable of handling most feed solutions. Its simple and cost-effective infrastructure has caught the attention of researchers and industrialists alike hoping to develop a sustainable method for desalination. Salt adsorption is directly dependent upon the properties of the electrode material, 141

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(b)

Figure 6.1.    Adsorption–desorption cycle of CDI and corresponding conductivity curves of output solution.

and the system size could possibly be made smaller if the adsorption capacity was larger. This is in stark contrast to classical desalination methods using membranes (reverse osmosis, electrodialysis) or thermal energy (multi-stage flash distillation, vapor compression), which require energy-intensive equipment such as pumps or compressors. Analogous to a supercapacitor, part of the energy invested during adsorption can be recovered when the electrodes are discharged and multiple CDI devices operating in asymmetrical half-cycles can reduce the total energy required. In more advanced CDI architectures, energy can be sufficiently recovered to the extent of generating power.

6.2. Early Studies of CDI Despite sharing many similarities with supercapacitors, interest in CDI paled in comparison, with only a few studies published each year until the early 2010s.1,2 In the years leading up to 2010, there were a number of sporadic yet influential developments in CDI theory, materials, and operation. While some researchers focused on the theory and operation of CDI systems,3–6 others turned their attention to material research, an area much needed for CDI to compete reasonably with commercial desalination techniques. In this respect, researchers welcomed porous carbon materials due to its low cost, inertness, availability, and tunability. Generally speaking, capacitance scales with surface area. Thus, an initial approach to developing CDI systems used extremely porous carbon for CDI. Activated carbon (AC) was first used for proof-of-concept7,8 but it was soon

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neglected in favor of the more attractive carbon aerogel. Carbon aerogels are monolithic structures typically synthesized from the pyrolyzation of resorcinol– formaldehyde polymeric gels, and they can function as standalone electrodes without the polymeric binders or conductive additives required for AC electrodes. These carbon materials are characterized by an interconnected network of carbon particles a few hundred nanometers in size, with the voids between them serving as a continuous pathway for transporting ions (see Fig. 6.2). The surface area ranges between 100 and 2,000 m2g−1,9–14 and mainly contributed by micropores (50 nm). Unfortunately. enhancements to salt adsorption capacity, if any, were negligible. A seminal work that laid the foundation for more advanced CDI systems was that of Biesheuvel et al. on membrane capacitive deionization (MCDI).30 As the name suggests, MCDI is CDI with ion-exchange membranes. Commercial ionexchange membranes are polymeric membranes, approximately 100 µm-thick, which possess covalently bound charged groups that allow preferential transport of ions.31,32 An anion-exchange membrane (AEM) placed in front of the anode only allows anions to pass through, whereas a cation-exchange membrane (CEM) placed in front of the cathode only allows cations to pass through. While Biesheuvel et al. was not the first to report on MCDI,33,34 the group elucidated the mechanisms responsible for the success of MCDI over CDI. During adsorption,

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Figure 6.3.  TEM image of multiwalled carbon nanotubes (MWCNTs) showing intraparticle ­porosity. Reprinted from Ref. [28]. Copyright @ 2005, with permission from Elsevier.

counter-ions are attracted to their respective electrodes (anions to anode and cations to cathode) and are adsorbed into intraparticle pores. At the same time, coions (cations at anode and anions at cathode) which previously occupied the pores are expelled and enter the feedwater channel. This reduces the overall charge efficiency, since 1 mole of electrical charge transferred is not equivalently exchanged for 1 mole of salt removed. The inclusion of AEM and CEM reduces the effect of co-ion expulsion and confines co-ions to the interparticle pores. This also enhances salt removal, as more counter-ions move in to maintain charge neutrality within interparticle regions. A comparison of performance metrics between CDI and MCDI33,35–37 showed at least a two-fold increase in salt adsorption capacity, near unity charge efficiency, and drastically reduced energy consumption. When compared with conventional reverse osmosis processes, MCDI was energetically more competitive if the influent salt concentration was below 2,000 ppm37,38 (brackish water salinity). The main downside to MCDI is that ion-exchange membranes are comparatively much more expensive than AC, and thus the use of this architecture inherently limits the scalability of the system. While the membranes only enhance the system, and the bulk of the system’s performance still lies with the electrode materials. This chapter discusses four primary forms of CDI: CDI, MCDI, HCDI, and desalination battery, as shown in Fig. 6.4.

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Figure 6.4.    Types of CDI.

6.3. Nanostructured Carbon and Composites for CDI and MCDI Nanostructured carbon materials were initially designed to overcome the shortcomings of bulk carbon materials, which have disordered pore size distributions and inefficient charge or ion transfer. However, nanostructured carbon materials have taken a backseat in recent years due the rise of faradaic materials. Faradaic or redox-active materials employ charge transfer reactions to store ions, and such materials are derived primarily from aqueous battery systems. Many faradaic materials show instability under high flow rates or prolonged cycling times, thus a carbon structure is usually formed to protect them. To avoid repeating the nanostructured carbon materials already reviewed,39–42 this section focuses on the salient works that have propelled CDI beyond the traditional EDL-based system.

6.3.1.  Metal-organic framework-derived carbon Metal-organic frameworks (MOFs) are a series of crystalline, microporous structures assembled from the building blocks of metal cations coordinated to organic ligand groups. MOFs are characterized by extreme porosities (up to 90% free volume)43,44 and have ultrahigh surface areas up to 10,000 m2g−1.45,46 For comparison, the surface area of AC is around 1,000–2,000 m2g−1.47,48 While microporosity in AC is usually disordered and non-uniform, the pores of MOFs can be fine-tuned

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at the molecular level by optimizing the coordination chemistry between the metal cation and the organic ligands. A rational design of MOF using judiciously selected inorganic and organic components can create highly customizable pore structures for specific adsorbates. In terms of its applications, MOFs have been adopted for gas- and liquid-phase separation, biosensing, drug delivery, energy storage, catalysis, and optoelectronics, among many others.49,50 The inherent nanostructured microporosity naturally makes MOFs attractive for CDI. However, this scheme is largely limited due to the low electrical conductivity of MOFs. To overcome this problem, researchers have synthesized MOFs on conductive substrates. For example, Yamauchi’s group experimented with conductive polypyrrole (PPy) nanotubes51 and CNTs52 for this purpose, yielding a 3D network of nanotubes intertwined with a zeolitic imidazolate framework (ZIF) class of MOFs. The conductive nanotubes bridged ZIF-67 polyhedra particles together and functioned as electron highways for reduced electrical impedance. This proved effective, as the resulting composites showed a reduction in charge transfer resistance by more than 50%. However, the specific surface area (SSA) of pure ZIF-67 particles decreased from 1719.6 to 1176.8 m2g−1 after forming a composite with PPy,51 as opposed to the CNT composite, which showed a minor loss from 1687.7 to 1585.8 m2g−1.52 The highest adsorption capacities obtained from ZIF-67/PPy and ZIF-67/CNT were 11.3 and 16.9 mg g−1, respectively, about 3–4 times higher than their pure ZIF-67 counterparts. Instead of using conductive substrates, another method is to pyrolyse MOFs and directly convert its organic components to amorphous and/or graphitized carbon. Additionally, ligands containing nitrogen groups (e.g., bipyridines, imidazoles, azoles)46 could be converted to nitrogen-doped carbon, which further enhances electrical conductivity and electrochemical activity.53 This method essentially uses MOFs as functional templates by first synthesizing MOFs with the desired physicochemical properties and then converting them to nanostructured carbons. A majority of works reviewed, including our own, have found MOFderived carbons to be reasonably suitable for CDI and MCDI. ZIF-67 and its isoreticular cousin, ZIF-8 are the most studied MOFs in CDI. These MOFs share similar lattice parameters, and they are commonly synthesized by mixing a metal precursor (Zn(NO3)2·6H2O for ZIF-8; Co(NO3)2·6H2O for ZIF-67) with the ligand, 2-methylimidazole at room temperature. In a preliminary study, Liu et al.54 investigated the effects of pyrolysis temperature on porous carbons derived from ZIF-8 by varying the temperature between 800°C and 1200°C. The morphology of the ZIF-8-derived carbon was a uniformly shaped polyhedron similar to its parent structure, and selected area diffraction (SAED) results showed that the carbon was amorphous. Raman mapping of the carbon materials showed an increase in the number of defects with an increase in the pyrolysis temperature. This implies the formation of porosity, which corroborated well with the increase in SSA from 606.4 to 1187.8 m2g−1. Porous carbon derived at 1200°C exhibited

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the best salt adsorption performance at 13.9 mg g−1, which was arguably acceptable but not impressive. Salt removal was likely impeded due to poor ion accessibility from the inherited micropores. A later work resolved this issue by “expanding” some of the micropores. Wang et al. used cetyltrimethylammonium bromide as a surfactant to direct the formation of ZIF-8 crystals and thus created a hierarchical porous structure consisting of meso- and micropores.55 They reasoned that the larger mesopores were more ion-accessible and facilitated faster ion transport between separate micropores. That said, the maximum salt adsorption capacity was reported to be 20 mg g−1. The surface area of MOF-derived carbons is usually lower than their parent structures.56–58 Our experiments showed this could be due to a partial collapse of micropores during pyrolysis which leads to a broadening of the pore size distribution. Taking this issue into consideration, a bimetallic ZIF, which combines the advantages of ZIF-8 and ZIF-67, was synthesized.59 First, ZIF-8 derived carbon showed better preservation of the surface area. Second, Co cations in ZIF-67 were able to catalyze the formation of graphitized carbon, which improved conductivity. An optimal ratio of Zn:Co = 3 was determined to possess the highest specific capacity, at 227 F g−1, and the best salt adsorption performance, at 45.6 mg g−1, in an MCDI cell. Most importantly, this work exemplified how desirable properties can be achieved through the modular synthesis of MOF templates. In another work, a pseudocapacitive TiO2/carbon composite was synthesized by pyrolysing a Ti-based MOF, MIF-125.60 By maintaining the pyrolysis temperature between 600°C and 1000°C, TiO2 crystals of anatase and rutile phases could form, as shown in Fig. 6.5. (a)

(b)

(c)

(d)

(e)

(f)

Figure 6.5.    SEM images of TiO2/carbon at (a) 600°C, (b) 800°C, and (c) 1000°C (inserts are low magnification images). TEM images of TiO2/carbon at (d) 600°C, (e) 800°C, and (f) 1000°C. Reprinted with permission from Ref. [60]. Copyright @ 2019, American Chemical Society.

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A TiO2/carbon composite would be advantageous, since TiO2 provides additional ion storage through pseudocapacitance. A pseudocapacitive material stores ions via reversible charge transfer mechanisms such as an intercalation or redox reaction, and it is generally not dependent on surface area. This is in contrast with an EDL capacitor, which relies on the electrostatic adsorption of ions onto porous surfaces. Our results showed that anatase TiO2/carbon offered the highest electrosorption performance (46.7 mg g−1) at the lowest energy consumed, even though SSA was the lowest. This implied that most of the salt was removed through the pseudocapacitive TiO2 instead of carbon. On the other hand, Rutile TiO2/carbon showed a poorer performance of 34.4 mg g−1. This was attributed to a larger TiO2 particle size, which reduced active sites and increased diffusion length.

6.3.2.  3D CNT- and graphene-based frameworks Judging by the vast literature covering 1D CNTs and 2D graphene-type materials, these are the quintessential nanostructured carbon materials. It should be noted that graphene strictly refers to a monolayer of carbon atoms arranged within a honeycomb-shaped lattice.2 Although some CDI research have adopted “graphene” in their descriptions, it usually refers one of its oxidized varieties, graphene oxide (GO) or reduced-graphene oxide (rGO). These carbon nanomaterials are attractive due to their high surface areas (SSACNT = 1315 m2g−1,24,25 SSAGO: 2418m2g−1),61 mechanical strength, and electrical conductivity. However, experiments have shown that CNTs and graphene varieties tend to aggregate, which severely limits the total ion-accessible surface area. Furthermore, the graphitic regions of CNTs and rGO are hydrophobic,62 which can deter the infiltration of solvated ions. When used in their pure forms, these materials perform no better than standard AC, achieving meager adsorption capacities of less than 10 mg g−1.23,28,63,64 To prevent aggregation, a novel strategy was devised based on bottom-up approaches to material design. Individual nanomaterials are treated as building blocks and, under the right conditions, are coerced to self-assemble into a 3D open framework structure. The exact architecture of this structure depends upon the physiochemical properties of each building block and the experimental conditions imposed. By organizing 1D or 2D nanomaterials into a 3D format we can construct hierarchically porous microstructures comprising macropores between adjacent nanomaterials and meso-/micropores on nanomaterial surfaces. Macropores can serve as ion-buffering reservoirs to shorten diffusion lengths and improve ion accessibility.65–67 Today 3D frameworks are no longer limited to bottom-up approaches and broadly include templated methods, much like MOF-derived carbon. An example is carbon obtained from the pyrolysis of Luffa sponge.68 Luffa was chosen for its well-defined mesoporous channels, which was thought to enhance ion transport. A templated method is convenient only if a suitable template can be synthesized

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or chosen. Bottom-up approaches, on the other hand, are operationally more diverse, with a wider range of functionalization, material selection, and choice of synthesis method. An example of a bottom-up approach is the synthesis of rGO sponge by Xu et al.69 They simply freeze-dried a vial of GO solution to obtain an aerogel-like sponge which was subsequently reduced to rGO by annealing. The final structure was a 3D network of interconnected rGO sheets that possessed macroporous channels. Nitrogen sorption experiments revealed a low broad macroporous peak and a high mesoporous peak at approximately 1.8 nm. The SSA was determined to be 356 m2 g−1, and an electrosorption capacity of 14.9 mg g−1 was achieved using a standard CDI cell charged at 1.2 V. Much like Galelich’s work,18 the relatively low adsorption capacity could be attributed to a lack of ion-accessible surface area. Following Xu’s work, a nanoporous rGO framework using an H2O2-mediated hydrothermal process66 was developed. Under typical hydrothermal conditions, GO sheets were reduced and driven to self-assemble due to competing interactions between π–π and hydrophobic forces.70,71 However, the addition of H2O2 induced in situ chemical etching of carbon atoms located along the basal planes of GO sheets, leading to the formation of a nanoporous 3D rGO framework (NP-3DG). NP-3DG showed a sharp mesoporous peak at about 4 nm, which was large enough to minimize EDL overlap. EDL overlapping is a phenomenon prominent in micropores, in which diffusion layers from opposing pore walls overlap and decrease the surface available for ion adsorption.72–74 An improvement of more than 80% adsorption capacity was obtained for NP-3DG (15 mg g−1) over 3DG (8.3 mg g−1) at 1.2 V. Although 15 mg g−1 is not considered high, other sponge-like or aerogellike CNT and rGO 3D framework materials also showed adsorption capacities between 10 and 20 mg g−1,75–78 under similar testing conditions. Thus, even 3D nanostructured carbon frameworks are insufficient for CDI. One reason why NP-3DG did not live up to expectations is because the creation of pores on rGO sheets directly contributed to defective zones, which impaired electron transport and reduced electrode stability. To avoid a trade-off between surface area and conductivity, composites can be formed between 3D rGO and functional materials such as pseudocapacitive metal oxides or porous carbons. In one example, Leong et al.79 developed a 3D AC composite framework based on the hydrothermal assembly of GO sheets and polyvinyl alcohol (PVA) polymer chains. During hydrothermal synthesis, PVA and GO were covalently cross-linked together using glutaraldehyde to form a composite hydrogel. Not only did the bonds that formed during cross-linking provide a tether between PVA and GO but they also served as conductive junctions for electron transport after the composite was pyrolyzed. The hydrogel was freeze-dried to obtain an aerogel, which consequently was pyrolyzed and activated with KOH. Pyrolysis converts GO to rGO and PVA to carbon. Finally, a composite carbon bearing the features of a 3D rGO framework and AC was formed (schematic shown in Fig. 6.6).

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(b)

(c)

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(e)

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(g)

(h)

Figure 6.6.    (a)–(d) SEM images of unactivated composites at increasing GO: PVA ratios. (e)–(h) SEM images of activated composites at increasing GO: PVA ratios. Reprinted from Ref. [79].

The ratio of GO to PVA was varied and its effects on a composite microstructure was investigated. Understandably, a more stable 3D framework was formed with a higher GO percentage. The increased structural integrity also allowed for greater cross-linking of PVA with GO, resulting in a highly porous composite. An optimal 3D AC composite was achieved at GO:PVA = 10: 1, with a surface area of 1,067 m2g−1 and a low series resistance. When tested in a symmetrical MCDI cell, the 3D AC achieved a favorable electrosorption performance of 36.1 mg g−1 in a 750 ppm NaCl solution at 1.2 V. So far we have discussed multiple strategies to synthesize and optimize nanostructured carbon materials, but we have not considered them within the context of electrode fabrication. The properties of carbon nanomaterials, however excellent, can only be fully realized through an appropriate electrode fabrication technique. Presently, electrodes are fabricated using a slurry method common in battery or supercapacitor research. A slurry is first prepared by mixing the electrode material together with a binder (e.g., PTFE, PVDF), additive (e.g., carbon black, CNTs), and solvent. This mixture is then coated on current collectors (e.g., graphite, stainless steel) and used as electrodes. While this method is convenient for experimental characterization, it is not ideal for industrial application. The insulating binder dampens conductivity and causes the nanomaterials to aggregate. To that end, 3D printing was proposed as a viable method to fabricate free-standing CDI electrodes. In Vafakhah’s work,80 a modified technique combining 3D printing and hydrothermal synthesis was developed to create a nitrogen-doped rGO/CNT composite macrostructure. A 3D printing ink was first prepared using GO and CNT precursors in a ratio optimized for shear-thinning and electrochemical behavior. GO functions as the active material whereas CNT acts as a conductive and viscosity agent. Cubic-like lattices of GO/CNT composite were printed and subjected to

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an ammonia-mediated hydrothermal process for self-assembly and nitrogen doping. The end-product was a macrostructure of nitrogen-doped rGO/CNT clusters bound by physiochemical interactions and assembled into a defined lattice. Magnified SEM images of the 3D-printed macrostructure in Fig. 6.7(b) and (c) revealed a dense network comprising self-assembled GO and CNT. Energydispersive X-ray spectroscopy (EDS) (Fig. 6.7(d)) of the structure showed carbon, nitrogen, and oxygen elements. A 3D-printed macrostructure does not possess the same degree of “openness” as one might expect from a 3D framework. Instead, the self-assembled clusters tend to coalesce and result in a smaller surface area (40–83.2 m2g−1). Despite this, the electrode still maintained an excellent adsorption performance of 75 mg g−1 at high salt concentrations of over 2500 ppm. This seemingly anomalous result could be directly attributed to the unique lattice macrostructure, which enabled the rapid redistribution of ion concentration throughout the inner space. The “macro-holes” served a function similar to that of macropores, acting as highly concentrated sources of ions and directing diffusion toward inner surfaces. The finite element simulation in Fig. 6.7(e)–(h) demonstrates how a non-linear gradient flow was achieved for the 3D-printed composite. A substantial amount of research has also been dedicated to other rGO frameworks comprising conductive polymers, metal oxides, mesoporous carbons, and functionalized and doped materials. Unfortunately, these materials failed to provide a much-needed breakthrough in electrosorption performance. The reasons are speculated to include wettability, localized aggregation, defects and/or the fabrication technique. Apart from these reasons, we believe that a narrow focus on adsorption based on EDL principles alone has limited researchers’ perspectives. To overcome the limitations of EDL adsorption, faradaic materials have been applied instead of carbon. Faradaic materials store ions within the bulk of the material via intercalation or conversion reactions, and they can remove more ions than porous materials. The use of faradaic materials also marked a turning point in CDI, as the focus shifted toward material crystallography and the development of new CDI architectures.

6.4. Faradaic Materials for HCDI Hybrid capacitive deionization (HCDI) is a variant of standard CDI. For the purposes of this chapter, HCDI is defined as any cell that uses an asymmetric pair of electrodes that is one part faradaic and one part carbon. Since the ion storage capacity of the faradaic electrode is usually higher than that of carbon, the carbon electrode is fabricated in excess such that kinetic limitations are minimized. An approximate mass ratio between the two electrodes can be obtained by matching their gravimetric capacitances. All works discussed in the following subsections used sodium (Na, Na+)-selective cathodes due to the heavy influence from sodium

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(a)

(b)

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Figure 6.7.    (a)–(c) SEM images of 3D printed nitrogen-doped rGO/CNT at different resolutions. (d) EDS mapping. (e)–(h) Simulation results for 2D constant concentration gradient between inlet and outlet for time = 0 and time = tfinal. (e) and (f) belong to 3D printed electrodes; (g) and (h) belong to bulk electrodes. Color-coding varies from blue (2,460 ppm) to red (2,500 ppm). Reprinted from Ref. [80].

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ion battery materials. Compared with batteries, HCDI prioritizes salt removal over energy storage, and operating voltages are limited to the electrolysis potential of water. Therefore, these differences should be considered when adopting faradaic materials for HCDI. Examples of materials already employed in HCDI include various MnO2 polymorphs, MXenes, transition-metal dichalcogenides (TMDs), titanates, and Prussian blue analogues (PBAs). In the future, salt removal capacity should be used in lieu of salt adsorption capacity to reflect the change in the dominant ion storage mechanism.

6.4.1.  Manganese oxides Manganese oxide (MnO2) materials belong to a large class of polymorphic crystal structures comprising Mn-based structural units. Depending on how the structural units are organized, a number of tunnel-like vacancies can be obtained. These tunnel-like vacancies exist in multi-dimensional forms and can serve as host sites for the reversible insertion of ions. For example, Na0.44MnO2 (NMO) is an orthorhombic crystal constructed from MnO6 octahedra and MnO5 square-pyramidal units.81,82 One sodium site is located in a distorted hexagonal tunnel whereas two other sites are located within the S-shaped tunnels. In a highly cited paper, Lee et al.83 investigated the use of NMO as a sodium selective cathode, pairing it with an AC anode in an HCDI cell. An AEM was also placed in front of the AC anode to prevent re-adsorption of sodium ions during electrode regeneration. During salt removal, a positive voltage was applied to the anode and the chloride (Cl−) ions were adsorbed. The cathode side, however, told a different story. Unlike AC, Na+ ions did not undergo EDL adsorption but were intercalated into the crystallographic lattice sites of the NMO. It should be noted that prior to the first salt removal cycle, a positive voltage was applied to NMO to vacate the Na+ ions originally occupying the sites. Overall, salt removal was achieved by the simultaneous adsorption of Cl− ions and intercalation of Na+ ions. To regenerate the electrodes, a negative voltage was applied to the anode. This system achieved a salt removal capacity of 31.2 mg g−1 in 10 mM NaCl solution using a cycling voltage of 1.2 and −1.2V, which was approximately two times higher than that of a typical CDI cell. While the salt removal performance was considered impressive at that time, the authors discounted the contributions of the AEM to overall desalination efficiency. Apart from the bulk intercalation of Na+ ions, it is also possible for MnO2 to store ions through a single-electron surface redox process (Eq. 6.1). It should be noted that MnO2 produces an electrochemical signature similar to EDL capacitors, which are also known as pseudocapacitors. Na+ ion storage occurs through the two reactions below:

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MnO 2 + Na + + e −  MnOONa



( MnO2 )surface + Na + + e −



 ( MnO 2− Na + )surface

(6.1) (6.2)



Although both reactions occur concurrently, one process usually dominates the other. Dominance is directly due to factors such as crystallography, crystal defect, and phase. To shed light on the ion storage mechanism, we investigated the electrochemical and desalination performances of various polymorphic MnO2 crystals.84 Crystalline forms of 1D tunneled α-MnO2, 2D layered δ-MnO2 and 3D tunneled λ-MnO2 were selected as the cathode materials in an HCDI cell. Additionally, we synthesized poorly crystalline versions of α-MnO2 and δ-MnO2 to investigate the effects of crystallinity. Schematic representations of α-MnO2, δ-MnO2 and λ-MnO2 are shown in Fig. 6.8. A plain AC without an AEM was used as the anode to obtain unadulterated values of salt removal capacity. The ion storage mechanism was investigated by ex situ X-ray photoelectron spectroscopy (XPS) on MnO2 electrodes cycled between 0 and 1 V to determine the change in mean Mn oxidation state. Crystalline materials tend to show an obvious 3+/4+ redox couple during cycling, indicating the stabilization of sodium ions within the crystal. In contrast, the poorly crystalline MnO2 showed minor deviations in the oxidation state, since redox processes only occurred at the surface. Any change in the oxidation state would be rapidly normalized by the large chemical potential between bulk and surface MnO2 [85]. Thus, there was a dominance of intercalation in crystalline MnO2 and a dominance of surface reaction in poorly crystalline MnO2. The SSA of poorly crystalline MnO2 ranged between 200 and 400 m2 g−1 whereas the crystalline MnO2 was between 20 and 120 m2 g−1. Desalination performance was further evaluated by polarizing the cathode from −0.6 to −1.0 V in a 20 mM NaCl solution. The poorly crystalline α-MnO2 and δ-MnO2 showed salt removal capacities of 9.93 and

(a)

(b)

(c)

Figure 6.8.    Crystal structures of (a) α-MnO2, (b) δ-MnO2, and (c) λ-MnO2. Green spheres: Mn. Red spheres: O. Pink spheres: interstitial cations (K+, Na+, or Li+). Blue spheres: interstitial water. Reprinted with permission from Ref. [84]. Copyright @ 2019, American Chemical Society.

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9.35 mg g−1, respectively, which outperformed their crystalline counterparts. The higher desalination performance of poorly crystalline MnO2 was partly due to a larger SSA which provided more sites for sodium storage. Overall, salt removal capacities were low due to the poor electrical conductivity of MnO2 materials, and crystalline MnO2 seemed to be more adversely affected. Other works sought to overcome this limitation by forming composites with AC or carbon nanomaterials,86–91 but they did not achieve significantly better performances. In one example, however, Younes et al.91 used an MnFe2O4/rGO composite as the cathode and α-MnO2/rGO composite as the anode to achieve a high salt removal capacity of 38.3 mg g−1. However, they noted that part of the salt removal was due to EDL adsorption.

6.4.2.  Prussian blue analogues PBAs can be considered to be alternative forms of the original Prussian blue compound Fe4[Fe(CN)6]3∙nH2O. In its basic form, Fe4[Fe(CN)6]3∙nH2O comprise Fe2+ and Fe3+ ions coordinated to carbon and nitrogen atoms, respectively. Additionally, interstitial voids exist within [Fe2+(CN)6]4− clusters which may contain water and/ or alkali metal cations, depending upon the synthesis process. By substituting structural Fe2+/3+ ions for other transition metal elements or by modifying the interstitial units, researchers can create a whole new series of open structures with the general formula AxM(I)[M(II)(CN)6]y∙nH2O (A: alkali cations, M: transition metal cations). Theoretically, a suitable substitution for the transition metal can lower diffusion barriers and lead to the reversible insertion/extraction of sodium cations.92,93 Like MnO2 compounds, PBAs show poor electrical conductivity and usually require the aid of a conductive additive. A basic study was conducted using FeFe(CN)6 Prussian blue to understand how PBAs perform in HCDI.94 Cyclic voltammetry (CV) experiments in a three-electrode half-cell arrangement showed reversible redox peaks at 0.05 and 0.15 V, which corresponded to the insertion/extraction of two Na+ ions. The two-Na+ insertion/extraction reactions were further confirmed using a full-cell arrangement with an AC anode. Desalination experiments using an HCDI cell equipped with AEM and CEM showed high salt removal capacities greater than 75 mg g−1. The best performance was achieved using an extremely high flow rate of 650 mL min−1 to minimize concentration polarization effects. The higher flow rates degraded the electrodes and flushed out the active material. To enable longer cycling lifetimes and a more robust performance, ideas were borrowed from previous work on nanoporous rGO frameworks to synthesize a Prussian blue and rGO framework composite (PB/ NPG). The nanoporous rGO framework was employed for three reasons: First, it acts as a substrate for the uniform nucleation and growth of Prussian blue particles. Second, it functions as a conductive framework for rapid electron

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conduction. Third, it anchors the Prussian blue particles and provides mechanical stability to the composite. Thermogravimetric analysis of the composite showed a low percentage of rGO at 12%, which meant a negligible contribution to overall desalination performance. An improved salt removal capacity of 120 mg g−1 was recorded for the PB/NPG composite, with high capacity retention after 100 cycles. Not only did this work showcase the potential of PBAs in advanced CDI systems, it also demonstrated how 3D carbon frameworks can serve as performance-­ supporting agents. A more uniformly dispersed Prussian blue composite was synthesized using an improved synthesis method.95 Prussian blue nanocubes of Fe4[Fe(CN)6]3 were nucleated and grown directly on GO sheets to serve as precursors for the subsequent hydrothermal formation of a 3D framework. The GO sheets possessed numerous oxygen functional groups, which acted as nucleation sites for the Prussian blue. Furthermore, the Prussian blue nanocubes prevented the GO sheets from restacking during hydrothermal assembly. The eventual PB/rGO composite showed a uniform distribution of nanocubes between 500 and 600 nm and an rGO percentage of 28%. An experimental HCDI cell was set up using PB/rGO and rGO as the standalone cathode and anode, respectively. Peak desalination performance was reached at a lower flow rate of 150 mL min−1, and the salt removal capacity could be as high as 130 mg g−1.

6.4.3.  Ternary titanates A promising group of intercalation materials that has been successful in lithium and sodium ion battery applications is the ternary titanate. Depending upon specific synthesis conditions, ternary titanates are usually constructed from titaniumoxygen octahedral units which form layered or tunneled structures.96,97 One prime example is spinel Li4Ti5O12 (LTO), which is a well-known “zero-strain” material used in lithium ion batteries. The moniker “zero-strain” comes from the ability of LTO to retain its volume during multiphasic lithium insertion/extraction reactions. This has allowed LTO to consistently deliver a highly reversible electrochemical performance. When faced with larger ions such as sodium (1.02 Å), it is doubtful whether LTO can perform just as well.97 Therefore, a preliminary study was carried out to evaluate the efficacy of LTO for sodium ion insertion/extraction in HCDI.98 Two types of LTO materials were synthesized: a carbon-cloth-supported LTO nanoflake array (CC/LTO) and a carbon-coated version of CC/LTO (CC/ LTO/C). The reason for using carbon cloth is simple: it serves as a readily available, low-cost substitute for 3D carbon frameworks. Carbon cloth provides conductive pathways and prevents the LTO nanoflakes from aggregating. The carbon coating, on the other hand, helps to accommodate the volume expansion of LTO during sodium insertion. The thickness of the carbon coating was adjusted such

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that it was thick enough to restrict volumetric changes yet porous enough not to obstruct the ion transport. Ex situ X-ray diffraction (XRD) results showed that successful sodium insertion increased the lattice parameter a from 0.836 to 0.846 nm while extraction returned it to 0.837 nm. Even though reversible insertion/extraction of sodium was possible, the highest desalination capacities were only 20 and 25 mg g−1 for CC/LTO and CC/LTO/C, respectively. A ternary titanate that can accommodate sodium ions more effectively is Na2Ti3O7 (NTO). NTO has a stepped-layer structure constructed entirely from TiO6 octahedra connected at the corners and edges, with sodium ions residing between layers.97 This structure is particularly interesting for HCDI, since it only requires a small voltage for sodium ion insertion.99 An NTO/CNT composite was synthesized using a hydrothermal process and then “sandwiched” between the GO layers by filtering between alternate GO layers across a polytetrafluoroethylene (PTFE) membrane. This produced a film, which could be peeled off after heating in vacuum. GO was also converted to rGO during heating. The layer-by-layer structure was designed to increase the electrical contact area throughout the entire composite. The CNTs acted as bridging contacts and stabilized the distribution of NTO within the NTO/CNT layers while rGO compacted each layer for overall mechanical stability. An opposing AC/rGO composite electrode was also prepared using a similar method. An HCDI cell equipped with membranes was used to determine the desalination performance of 3,000 ppm under brackish water conditions, which resulted in a high salt removal capacity of 129 mg g−1. The energy recovery was calculated to be 19%, but it could be up to 23% by increasing the percentage of NTO. These were promising results, considering that experiments with carbon-based nanomaterials have yielded low and inconsistent energy recovery. With the advent of intercalation materials, it might be possible to store and release ions reversibly and efficiently without much loss. This potentially leads to a highly efficient desalination system that doubles as an energy storage system, a concept we will explore in the next section.

6.5.  Desalination Battery In retrospect, the HCDI architecture can be seen as a prelude to the full desalination battery. A desalination battery is a dual-ion electrochemical cell that uses a sodium-ion-selective cathode and a chloride-ion-selective anode. Ion-exchange membranes are usually included as auxiliary components to enhance unidirectional ion transport. As with HCDI, the mass ratio of the two electrodes should be matched to their gravimetric capacitances. The concept of selectively removing sodium and chloride ions is not new, yet there has been little progress in this field due to a lack of anion-selective materials. In fact, almost all desalination batteries use a silver (Ag) or bismuth (Bi)-type chloride ion anode.

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6.5.1.  NMO desalination battery The original desalination battery was developed by Pasta et al.,100 which used a basic two-electrode system with an impure NMO phase and an Ag electrode to achieve 25% salt removal in seawater. Since 2012, the concept of the desalination battery is introduced to the research community, yet we are only seeing a resurgence of interest now. Despite the wealth of knowledge surrounding sodium selective materials, very little is known about chloride-ion-selective materials. In Pasta’s work, Cl− ions were stored by undergoing a conversion reaction to form AgCl,101 and then were released when Ag was reduced. Inspired by Pasta’s work, we developed our own desalination battery using updated synthesis and characterization methods.102 NMO was synthesized by a solid-state reaction under high temperatures and AgCl was prepared by ball-milling commercial AgCl powder with carbon black. Unlike Pasta’s original desalination battery, in which NMO was used as a sodium acceptor against Ag, our system used NMO as a sodium donor against AgCl. The reversal of roles was due to the fact that polarization was more significant when NMO was oxidized as a sodium acceptor. Polarization could lead to a low capacity and/or increased capacity fading overtime. The two reactions are described below:

AgCl + e −  Ag + Cl −

Na 0.44 − x MnO 2 + xNa + + xe −  Na 0.44 MnO 2

(6.3) (6.4)

A desalination battery equipped with ion-exchange membranes was used to determine the desalination performance of the NMO-AgCl pair. A stable salt removal capacity of 57.4 mg g−1 maintained over 100 cycles was obtained when the charging current was 100 mA g−1 and the feed solution was around 890 ppm. A notable high charge efficiency of 0.96 during salt removal was also calculated, indicating a highly efficient electrochemical desalination process. However, a decrease in salt removal capacity was also observed when the current was increased from 100 to 500 mA g−1, a phenomenon that alluded to incomplete redox reactions due to kinetic limitations. Interestingly, this characteristic is parallel to that of battery systems. The Ag/AgCl conversion reaction was investigated using ex situ XRD, and the ratio of the characteristic peak intensities of Ag to AgCl under different charging states was determined. In the as-prepared state and during salt removal (chloride storage), this ratio was shown to be close to 0; however, it increased to 1.25 when Cl− was extracted from AgCl, which confirmed the reversibility of the conversion reaction. Pure AgCl itself is an insulator, which is troubling since it could result in a large overpotential. This problem was averted by pulverizing the AgCl into nanosized particles with conductive carbon black. A second chloride-selective material that partnered well with NMO is BiOCl. A preliminary evaluation of BiOCl was carried out using CV, and characteristic

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oxidation and reduction peaks were identified at 0.31 and −0.97 V,103 respectively. The redox peaks corresponded to the following conversion reaction:

3BiOCl + 3e −  Bi + Bi 2 O3 + 3Cl −

(6.5)

A desalination battery based on NMO-BiOCl managed to achieve a stable salt removal capacity of 68.5 mg g−1 over 50 cycles in a 760 ppm NaCl solution at a high charge efficiency of 0.96. More impressively, a parallel arrangement of three LED bulbs was charged to 4.5 V when three of these devices were connected in series and operated in “salination” mode (electrode discharging). However, significant kinetic limitations were observed for both NMO-AgCl and NMO-BiOCl systems, which resulted in desalination rates of only 0.209 and 0.092 mg g−1 s−1. For reference, the desalination rate of the FeFe(CN)6 Prussian blue HCDI cell was 0.543 mg g−1s−1.94

6.5.2.  NASICON desalination battery The NASICON (sodium (Na) super ionic conductor) desalination battery replaces NMO with NASICON-type materials such as Na2FeP2O7,104 NaTi2(PO4)3,105 and Na3V2(PO4)3.106,107 A characteristic among this family of materials is an openframework crystal structure containing a plurality of interstitial sites along with low-energy barriers favorable for rapid ion hopping.108,109 The general chemical formula for such materials can be written as AxMM′(XO4)3, where A is an alkali metal, M and M′ are transition metals, and X is a non-metal.101,108 For example, NaTi2(PO4)3 (NTP) is a rhombohedral crystal constructed from TiO6 octahedra with corner-sharing PO4 tetrahedra.110 Within the crystal is a network of interconnected channels that lead from one site to the other, allowing fast ion transport throughout the structure.111 NTP is an intercalation material, and Na+ intercalation/ deintercalation can be expressed as:

NaTi 2 ( PO 4 )3 + 2Na + + 2e −  Na 3Ti 2 ( PO 4 )3



(6.6)

Despite its structural advantages, pristine NTP suffers from poor electrical conductivity which ultimately limits its electrochemical performance. To alleviate this issue, researchers have once again invoked the strategy of forming composites with the 3D rGO framework.112 The rGO framework referred here is a macroassembly of rGO sheets whereas the NTP framework is a crystal assembly of atomic constituents. Although both structures are termed “framework”, they differ by structural units and scale. Prior to its debut in a desalination battery, a hydrothermally synthesized NTP/ rGO composite was used in HCDI, which had an excellent desalination

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performance of 120 mg g−1 in a 1,000 ppm NaCl solution.112 The charge efficiency during intercalation and deintercalation was calculated to be greater than 96%, indicating a stable and reversible electrochemical performance. A similarly synthesized NTP/rGO composite was formally used in a desalination battery alongside an Ag/rGO composite.105 Ag nanoparticles were used instead of AgCl due to their faster reaction rates.101 The NTP/rGO composite was first prepared by synthesizing NTP nanoparticles before mixing NTP and GO together for hydrothermal assembly of the framework. The Ag/rGO, on the other hand, was simply prepared using a chemical precipitation method with GO. Since both materials are composites, a reasonable mass ratio could not be estimated without first knowing the mass percentage of NTP and Ag. Thermogravimetric analysis showed the rGO percentage was about 17% in NTP/ rGO and 15% in Ag/rGO. This information was used in conjunction with known gravimetric capacities (NTP: 133 mAh g−1, Ag: 248 mAg g−1) to determine a mass ratio of approximately NTP: Ag = 2:1. During desalination, a current was used to charge the electrodes and a concurrent salt removal occurred through sodium intercalation and chloride conversion, according to Eqs. (6.3) and (6.6), respectively. The operating conditions (e.g., charging current, voltage range, NaCl concentration) were varied, and a high salt removal capacity of 105 mg g−1 was determined when charged at 100 mA g−1 in a 2,500 ppm NaCl solution for more than 50 cycles. The average energy recovery (Echarge/Edischarge) per cycle was around 20%, but it could exceed 30% if the desalination performance considerably decreased (15 mg g−1) by decreasing the voltage range. As such, the operating conditions could be selected in order to minimize energy consumption at the expense of desalination performance. From a feasibly standpoint, the power output of a desalination battery is still significantly lower than that of an actual battery, and the energy savings are usually used to offset the energy costs of the next cycle. Another NASICON-type material investigated was Na3V2(PO4)3 (NVP). NVP shares a crystal structure similar to NTP, except for the arrangement of VO6 octahedra with PO4 tetrahedra. Each unit of V2(PO4)3 can host up to four sodium ions.113 A general equation for Na+ intercalation/deintercalation can be written as:

Na 3 V2 ( PO 4 )3  Na 3− x V2 ( PO 4 )3 + xNa + + xe −



(6.7)

NVP was first investigated by Zhao et al.,106 in which a trio of NVP samples was synthesized using a hydrothermal method. Depending upon the duration of the hydrothermal process, micro-sized NVP particles could be obtained as spheres, flowers, or wires (Fig. 6.9). Like NTP, NVP suffers from poor electrical conductivity and thus a carbon-coating process was performed to alleviate this problem. Polydopamine was used to coat the NVP before a two-stage annealing process converted it to amorphous carbon. Approximately 34% of the final mass was

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(b)

10 µm

(d)

Na+ e-

Carbon shell

Na PO4 VO6

(c)

10 µm

(e)

1 µm

10 µm

(f)

1 µm

1 µm

Figure 6.9.    (Left) Schematic of carbon-coated NVP wires and a crystal structure. (Right) SEM images of NVP samples. (a, d) Spheres. (b, e) Flowers. (c, f) Wires. Reprinted with permission from Ref. [106]. Copyright @ 2019, American Chemical Society.

attributed to the carbon coating. The theoretical capacitances of NVP and AgCl are around 118 and 187 mAh g−1, suggesting a mass ratio of NVP: AgCl = 0.63. However, the authors noted that the kinetic limitations of AgCl could result in a lower capacitance value than expected. Therefore, a series of mass ratios were experimentally verified which determined an optimal mass ratio of 1:1. The highest salt removal capacity for an NVP-AgCl desalination battery was 102 mg g−1, which was obtained using carbon-coated NVP wires in a 1,000 ppm NaCl solution charged at 100 mA g−1. All things considered (i.e., microstructure, crystallinity and surface area), the superior performance of NVP wires was largely attributed to a quasi-1D structure which formed an ion conductor network and thus allowed sodium ions to move across the NVP unhindered. The influence of carbon coating was investigated by comparing the desalination performance with an uncoated NVP wire sample. Initially the salt removal capacity for the uncoated sample was high (94 mg g−1), but it gradually decreased over multiple cycles to a final capacity of 69 mg g−1. The lack of performance stability was likely due to an imperfect ion conductor network. In contrast, the carbon-coated sample was more stable because the carbon helped to preserve the structural integrity during annealing. The carbon coating also acted as parallel electron pathways for higher conductivity.

6.6. Outlook CDI innovation has grown immensely following the introduction of faradaic materials, new architectures, and modes of operation. The promise of extremely high salt removal capacities and energy-efficient performance has researchers scrambling to adopt new and novel materials, especially those in low dimensions. Since the concept of CDI is intimately similar to energy storage systems such as batteries and supercapacitors, most materials are conceived from energy storage

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research. However, this also has led to inconsistent reporting on desalination performance. A basic example is the terminology for anode, which is the negative electrode associated with the release of electrons in batteries. In CDI, the anode is referred to as a positive electrode because it removes the negatively charged anions. Other performance metrics, such as rate capability or rate cycling, are borrowed from battery research and have just entered the CDI field. Although some researchers have previously tried to standardize performance metrics,114 their recommendations have fallen short in view of newer desalination systems. As faradaic systems continue to evolve it will be increasingly important to develop consistent operating parameters and desalination metrics for comparison. It will also be helpful to include metrics common within the general desalination field, such as water recovery (%) or energy consumption (kWh m−3), especially where scalability is discussed. For CDI or any of its variants to be commercialized, materials need to be cheaply sourced and appropriately designed to achieve a balance between energy consumption and performance. Although some small-scale CDI systems exist, none of them have been put to use in mainstream desalination applications. Despite the lack of commercialization, the low-energy requirement for CDI provides an edge over conventional desalination methods. Ideally, CDI should be combined with renewable energy sources such as solar or wind to achieve a fully environmentally friendly and sustainable system, which can enable the creation of off-grid desalination systems for communities not serviced by the main water distribution channel. There is also interest in exploring CDI for applications other than desalination, of which ion-selective removal is particularly interesting. Studies have shown that in addition to salt ions, CDI has been effective for removing contaminants such as sulfates,115 nitrates,116–118 arsenic,119,120 and heavy metals.121–124 Furthermore, ionselective removal can also be used in electronic waste to specifically recover precious metals. These are attractive developments which we anticipate will be concomitant with the discovery of new materials.

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  80. S. Vafakhah, G. J. Sim, M. Saeedikhani, X. Li, Y. Alvarado, and H. Y. Yang. 3D Printed Electrodes for Efficient Membrane Capacitive Deionization, Nanoscale Adv., 2019, 1, 4804–4811.  81. C. Ferrara, C. Tealdi, V. Dall’Asta, D. Buchholz, L. G. Chagas, E. Quartarone, V. Berbenni, and S. Passerini. High-Performance Na0. 44MnO2 Slabs for SodiumIon batteries Obtained through Urea-Based Solution Combustion Synthesis, Batteries, 2018, 4, 8.  82. Y. Wang, J. Liu, B. Lee, R. Qiao, Z. Yang, S. Xu, X. Yu, L. Gu, Y.-S., Hu, and W. Yang. Ti-Substituted Tunnel-Type Na 0.44 MnO 2 Oxide as a Negative Electrode for Aqueous Sodium-Ion Batteries, Nat. Commun., 2015, 6, 1–10.   83. J. Lee, S. Kim, C. Kim, and J. Yoon. Hybrid Capacitive Deionization to Enhance the Desalination Performance of Capacitive Techniques, Energy Environ. Sci., 2014, 7, 3683–3689.   84. Z. Y. Leong and H. Y. Yang. A Study of MnO2 with Different Crystalline Forms for Pseudocapacitive Desalination, ACS Appl. Mater. Interf., 2019, 11, 13176–13184.   85. M. Toupin, T. Brousse, and D. Bélanger. Charge Storage Mechanism of MnO2 Electrode Used in Aqueous Electrochemical Capacitor, Chem. Mater., 2004, 16, 3184–3190.   86. S. Hand, and R. D. Cusick. Characterizing the Impacts of Deposition Techniques on the Performance of MnO2 Cathodes for Sodium Electrosorption in Hybrid Capacitive Deionization, Environ. Sci. Technol., 2017, 51, 12027–12034.   87. J. Yang, L. Zou, H. Song, and Z. Hao. Development of Novel MnO2/Nanoporous Carbon Composite Electrodes in Capacitive Deionization Technology, Desalination, 2011, 276, 199–206.   88. Y.-H. Liu, H.-C. Hsi, K.-C. Li, and C.-H. Hou. Electrodeposited Manganese Dioxide/ Activated Carbon Composite As a High-Performance Electrode Material for Capacitive Deionization, ACS Sustain. Chem. Eng., 2016, 4, 4762–4770.   89. B. Chen, Y. Wang, Z. Chang, X. Wang, M. Li, X. Liu, L. Zhang, and Y. Wu. Enhanced Capacitive Desalination of MnO 2 by Forming Composite with Multi-Walled Carbon Nanotubes, RSC Adv., 2016, 6, 6730–6736.  90. A. G. El-Deen, N. A. M. Barakat, and H. Y. Kim. Graphene Wrapped MnO2Nanostructures as Effective and Stable Electrode Materials for Capacitive Deionization Desalination Technology, Desalination, 2014, 344, 289–298.   91. H. Younes and L. Zou. Asymmetric Configuration of Pseudocapacitive Composite Electrodes for Enhanced Capacitive Deionization, Environ. Sci.: Water Res. Tech., 2020, 6, 392–403.   92. L. Shen, Z. Wang, and L. Chen. Prussian Blues as a Cathode Material for Lithium Ion Batteries, Chem. — A Euro. J., 2014, 20, 12559–12562.   93. A. Paolella, C. Faure, V. Timoshevskii, S. Marras, G. Bertoni, A. Guerfi, A. Vijh, M. Armand, and K. Zaghib. A Review on Hexacyanoferrate-Based Materials for Energy Storage and Smart Windows: Challenges and Perspectives, J. Mater. Chem. A, 2017, 5, 18919–18932.

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  94. L. Guo, R. Mo, W. Shi, Y. Huang, Z. Y. Leong, M. Ding, F. Chen, and H. Y. Yang. A Prussian Blue Anode for High Performance Electrochemical Deionization Promoted by the Faradaic Mechanism, Nanoscale, 2017, 9, 13305–13312.  95. S. Vafakhah, L. Guo, D. Sriramulu, S. Huang, M. Saeedikhani, and H. Y. Yang. Efficient Sodium-Ion Intercalation into the Freestanding Prussian Blue/Graphene Aerogel Anode in a Hybrid Capacitive Deionization System, ACS Appl. Mater. Interf., 2019, 11, 5989–5998.   96. X. Sun, P. V. Radovanovic, and B. Cui. Advances in Spinel Li 4 Ti 5 O 12 Anode Materials for Lithium-Ion Batteries, New J. Chem., 2015, 39, 38–63.   97. M. M. Doeff, J. Cabana, and M. Shirpour. Titanate Anodes for Sodium Ion Batteries, J. Inorg. Org. Polym. Mater., 2014, 24, 5–14.   98. L. Guo, D. Kong, M. E. Pam, S. Huang, M. Ding, Y. Shang, C. Gu, Y. Huang, and H. Y. Yang. The Efficient Faradaic Li 4 Ti 5 O 12@ C Electrode Exceeds the Membrane Capacitive Desalination Performance, J. Mater. Chem. A, 2019, 7, 8912–8921.   99. P. Senguttuvan, G. Rousse, V. Seznec, J.-M. Tarascon, and M. R. Palacin. Na2Ti3O7: Lowest Voltage Ever Reported Oxide Insertion Electrode for Sodium Ion Batteries, Chem. Mater., 2011, 23, 4109–4111. 100. M. Pasta, C. D. Wessells, Y. Cui, and F. La Mantia. A Desalination Battery, Nano Lett., 2012, 12, 839–843. 101. P. Srimuk, X. Su, J. Yoon, D. Aurbach, and V. Presser. Charge-Transfer Materials for Electrochemical Water Desalination, Ion Separation and the Recovery of Elements, Nat. Rev. Mater., 2020, 1–22. 102. F. Chen, Y. Huang, L. Guo, M. Ding, and H. Y. Yang. A Dual-Ion Electrochemistry Deionization System Based on AgCl-Na0. 44MnO2 Electrodes, Nanoscale, 2017, 9, 10101–10108. 103. F. Chen, Y. Huang, L. Guo, L. Sun, Y. Wang, and H. Y. Yang. Dual-Ions Electrochemical Deionization: A Desalination Generator, Energy Environ. Sci., 2017, 10, 2081–2089. 104. S. Kim, J. Lee, C. Kim, and J. Yoon. Na2FeP2O7 as a Novel Material for Hybrid Capacitive Deionization, Electrochim. Acta, 2016, 203, 265–271. 105. Y. Huang, F. Chen, L. Guo, J. Zhang, T. Chen, and H. Y. Yang. Low Energy Consumption Dual-Ion Electrochemical Deionization System Using NaTi2 (PO4) 3-AgNPs Electrodes, Desalination, 2019, 451, 241–247. 106. W. Zhao, L. Guo, M. Ding, Y. Huang, and H. Y. Yang. Ultrahigh-DesalinationCapacity Dual-Ion Electrochemical Deionization Device Based on Na3V2 (PO4) 3@ C–AgCl Electrodes, ACS Appl. Mater. Interf., 2018, 10, 40540–40548. 107. W. Zhao, M. Ding, M., Guo, L., and H. Y. Yang. Dual — Ion Electrochemical Deionization System with Binder — Free Aerogel Electrodes, Small, 2019, 15, 1805505. 108. N. Anantharamulu, K. Koteswara Rao, G. Rambabu, B. Vijaya Kumar, V. Radha, and M. Vithal. A Wide-Ranging Review on Nasicon Type Materials, J. Mater. Sci., 2011, 46, 2821–2837. 109. Wu, W. Sodium Titanium Phosphate as Anode Materials for Aqueous Sodiumion Batteries, Ph.D. diss., Ann Arbor: Carnegie Mellon University, 2014, p. 170.

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110. C. Delmas, F. Cherkaoui, A. Nadiri, and P. Hagenmuller. A Nasicon-Type Phase as Intercalation Electrode: NaTi2 (PO4) 3, Mater. Res. Bull., 1987, 22, 631–639. 111. D. T. Qui, J. Capponi, J. Joubert, and R. Shannon. Crystal Structure and Ionic Conductivity in Na4Zr2Si3O12, J. Solid State Chem., 1981, 39, 219–229. 112. Y. Huang, F. Chen, L. Guo, and H. Y. Yang. Ultrahigh Performance of a Novel Electrochemical Deionization System Based on a NaTi 2 (PO 4) 3/rGO Nanocomposite, J. Mater. Chem. A, 2017, 5, 18157–18165. 113. X. Zhang, X. Rui, D. Chen, H. Tan, D. Yang, S. Huang, and Y. Yu. Na 3 V 2 (PO 4) 3: An Advanced Cathode for Sodium–Ion Batteries, Nanoscale, 2019, 11, 2556–2576. 114. M. E. Suss, S. Porada, X. Sun, P. M. Biesheuvel, J. Yoon, and V. Presser. Water Desalination Via Capacitive Deionization: What is It and What Can We Expect From It? Energy Environ. Sci., 2015, 8, 2296–2319. 115. K. Zuo, J. Kim, A. Jain, T. Wang, R. Verduzco, M. Long, and Q. Li. Novel Composite Electrodes for Selective Removal of Sulfate by the Capacitive Deionization Process, Environ. Sci. Technol., 2018, 52, 9486–9494. 116. J. J. Lado, R. E. Pérez-Roa, J. J. Wouters, M. I. Tejedor-Tejedor, C. Federspill, J. M. Ortiz, and M. A. Anderson. Removal of Nitrate by Asymmetric Capacitive Deionization, Sep. Purif. Tech., 2017, 183, 145–152. 117. M. Zafra, P. Lavela, C. Macías, G. Rasines, and J. Tirado. Electrosorption of Environmental Concerning Anions on a Highly Porous Carbon Aerogel, J. Electroanal. Chem., 2013, 708, 80–86. 118. J.-H. Yeo and J.-H. Choi. Enhancement of Nitrate Removal from a Solution of Mixed Nitrate, Chloride and Sulfate Ions Using a Nitrate-Selective Carbon Electrode, Desalination, 2013, 320, 10–16. 119. L. Peng, Y. Chen, H. Dong, Q. Zeng, H. Song, L. Chai, and J.-D. Gu. Removal of Trace As (V) from Water with the Titanium Dioxide/ACF Composite Electrode, Water Air Soil Poll., 2015, 226, 203. 120. C.-S. Fan, S.-C. Tseng, K.-C. Li, and C.-H. Hou. Electro-Removal of Arsenic (III) and Arsenic (V) from Aqueous Solutions by Capacitive Deionization, J. Hazard. Mater., 2016, 312, 208–215. 121. Y. Chen, L. Peng, Q. Zeng, Y. Yang, M. Lei, H. Song, L. Chai, and J. Gu. Removal of Trace Cd (II) from Water with the Manganese Oxides/ACF Composite Electrode, Clean Tech. Environ. Policy, 2015, 17, 49–57. 122. Z. Huang, L. Lu, Z. Cai, and Z. J. Ren. Individual and Competitive Removal Of Heavy Metals Using Capacitive Deionization, J. Hazard. Mater., 2016, 302, 323–331. 123. X. Gu, Y. Yang, Y. Hu, M. Hu, and C. Wang. Fabrication of Graphene-Based Xerogels for Removal of Heavy Metal Ions and Capacitive Deionization, ACS Sustain. Chem. Eng., 2015, 3, 1056–1065. 124. Y. Wei, L. Xu, K. Yang, Y. Wang, Z. Wang, Y. Kong, and H. Xue. Electrosorption of Toxic Heavy Metal Ions by Mono S-or N-Doped and S, N-Codoped 3D Graphene Aerogels, J. Electrochem. Soc., 2017, 164, E17–E22.

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Chapter 7

Graphene Oxide and Nanocomposite Electrodes for Capacitive Deionization Linda Zou Department of Civil Infrastructure and Environmental Engineering, Khalifa University of Science and Technology, Abu Dhabi, UAE [email protected]

7.1. Introduction As the global demand for freshwater continues to rapidly increase, the development of sustainable alternative technologies for freshwater production is urgently needed. To meet this enormous demand while minimizing impacts on the environment, sustainable water technologies such as forward osmosis, capacitive deionization, and solar-powered desalination plants have emerged and are being developed to attain the next levels of application. As such, electrosorptive deionization, also called as capacitive deionization (CDI), is a promising alternative water technology that is particularly suitable for small-scale inland brackish water desalination due to its lower energy demand for separation and fewer maintenance requirements. In order to make CDI viable to industry, it is imperative that its low energy consumption can be maintained at industrial scale and its limited treatment capacity of salinity can be expanded. Electrodes are the most important components of CDI devices. CDIs primarily use carbon materials such as activated carbons (ACs) in the current electrode, which facilitate ion adsorption by forming an electrical double layer (EDL). However, improvement of these carbon electrodes’ desalination performance is needed for wider application of this technology. Hierarchical nanomaterials such as graphene nanosheets have been employed as electrodes, and more recently it has been reported that using electrochemically active materials, such as metal oxides and the salt of metal oxides as pseudocapacitive electrodes by using the 173

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so-called Faradaic process, can offer superior salt removal capacity compared with the EDL types of electrodes (e.g., sodium manganese oxide (Na4Mn9O18) electrodes). These electrochemical active electrodes store the ions by a faradaic process that can involve under potential deposition or intercalation of ions. This implies that the various materials possessing the reversible intercalation of ions could be considered as electrode materials in the CDI system. This chapter introduces some of the latest developments in graphene oxide (GO) and nanocomposite materials as electrodes for CDI, including the 3D porous graphene sponge (GS) and nanostructured pseudocapacitive electrodes. The design, synthesis, characterization, and performance evaluation for CDI are discussed.

7.2. Effects of the Hierarchical Porosity of 3D rGO on Ion Electrosorption 7.2.1.  Introduction As an electro-driven adsorption process, the electrodes for CDI not only conduct the electrical potential but also adsorb ionic species into their porous structure. The ideal electrode materials exhibit a high surface area, good electrical conductivity, and high EDL capacitance, as well as good wettability to water, narrow pore size distribution, and chemical stability.1,2 Many studies have investigated various porous carbon materials such as graphene,3–5 carbon nanotubes (CNTs),6,7 AC,8,9 ordered mesoporous carbon (OMC),10,11 and other carbonaceous materials. Manipulation of the hierarchical porosity of graphene frameworks has also been reported.12–15 Graphene has distinctive physical and chemical properties, including a theoretical surface area of nearly 2,600 m2/g,16,17 and a 2D planar, which are more open for ion adsorption. It was found that heavily reduced GO easily undergoes strong agglomeration between graphene nanosheets and results in low electrosorption capacity. To overcome this, a good strategy is to use 2D nanosheets to fabricate three-dimensional (3D) graphene materials with a macro-porous structure, which also has a favorable pore size distribution of approximately a few nanometers and maintains a high surface area, both of which contribute to higher electrosorption capacity. Similarly, a GS was synthesized by directly freeze-drying a GO solution and then annealing the material in a nitrogen atmosphere. The specific surface area obtained was 356.0 m2/g with an electrosorption capacity of 14.9 mg/g.18 However, the type of pores (i.e., micropores, mesopore, and macropores) are critical for electrosorption performance. Very small pores (