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Nano-Optics: Fundamentals, Experimental Methods, and Applications (Micro and Nano Technologies) [1 ed.]
 0128183926, 9780128183922

Table of contents :
Cover
NANO-OPTICS
Fundamentals, Experimental
Methods, and Applications
Copyright
Contributors
About the Editors
From nature: Optics, nanotechnology, and nano-optics
Introduction
Nature and optics
Nanotechnology in nature
Presence of nano-optics in nature
Light manipulation
Antireflection
Light focusing
Chirality
Summary
References
Nano-optics: Challenges, trends, and future
An outlook
A historical perspective
Photonics
Speed of light
Focal length of thin spherical lens and refractive index
Brewster's angle
Optical properties of nanoparticles
Challenges: Nano-optics bottleneck
Trends: Current scenario in nano-optics
The future: A world of possibilities
Conclusion
References
Nano-optics for healthcare applications
Introduction
Nano-optics for bio imaging
Nano-optics for biosensing
Nano-optics for cancer therapy
Conclusion
References
Laser, nanoparticles, and optics
Laser-Introduction
Laser principle and properties
Applications of laser in nanotechnology
Applications of nanotechnology in laser devices
Laser-produced nanoparticles
Synthesis approach
Random lasing
Coherent and incoherent random lasers
Fabrication of the random media: Importance of nanostructured materials
Plasmonically enhanced random laser to spaser
Directionality in random lasers
Applications of random lasers
References
Introduction to quantum plasmonic sensing
Introduction
Plasmonic sensing
Surface plasmon resonance sensing
Spectral interrogation
Localized surface plasmon resonance sensing
Other plasmonic sensors
Intensity- and phase-sensitive sensing
Quantum sensing
Shot-noise limit
Subshot-noise sensing
Single-mode schemes
Two-mode schemes
Quantum plasmonic sensing
Quantum sensing with metallic nanoparticles
Refractive index sensing with two-mode squeezed vacuum states
Ultrasound sensing with two-mode squeezed displaced states
Quantum sensing with metallic film-prism setups
Refractive index sensing with two-mode squeezed displaced states
Comparison among different state inputs
Refractive index sensing with photon number states
Quantum sensing with metallic nanowires
Conclusion
References
Nanobiophotonics and fluorescence nanoscopy in 2020
Introduction
Electrons, photons, and plasmons
Nanoparticles
Optical microscopy to nanoscopy
Optical resolution: A historical perspective
Optical nanoscopy
Nanoscale spectroscopy
FRET
FCS
FRAP
FLIM
Plasmonics
Biomolecular spectroscopy
Nanomanipulation and correlative microscopy
Nanomedicine
Conclusions
References
Nanotechnology-based fiber-optic chemical and biosensors
Introduction
Evanescent wave and optical fiber
Surface plasmon resonance
Localized surface plasmon resonance
Optical fiber-based SPR/LSPR probes
Probe designs
Experimental setup of SPR-/LSPR-based sensors
Nanotechnology and nanomaterials
Synthesis of nanomaterials
Top-down technique
Bottom-up technique
Characterization of nanomaterials
Scanning electron microscope
Transmission electron microscopy
Energy dispersive X-ray analysis
X-Ray diffraction
UV-Vis spectroscopy
FTIR (Fourier Transform Infra-Red) spectroscopy
Raman spectroscopy
Types of nanomaterials
Carbon nanosystems
Metal nanosystems
Dendrimers
Composites
Some examples of SPR- and LSPR-based fiber-optic sensors
SPR-based sensors
LSPR-based sensors
Comparison of fiber-optic plasmonic sensors with the sensors based on other techniques
Summary
References
Light transport in three-dimensional photonic crystals
Introduction
Energy bands in photonic crystals
Spontaneous emission in photonic crystals
Applications of photonic crystals
Fabrication and characterization of 3D photonic crystals
Structural characterization
Optical characterization
Multiple Bragg diffraction in opal photonic crystals
Along the ΓL direction
Along the ΓK direction
Polarization anisotropy in photonic crystals
Conclusions
References
Application of two-dimensional materials in fiber laser systems
Introduction
Types of 2D materials
Graphene
Transition metal dichalcogenides
Topological insulator
Black phosphorus
Fabrication of 2D materials
Top-down techniques
Bottom-up techniques
Material characterizations
Scanning electron microscopy
Transmission electron microscopy (TEM)
Atomic force microscopy
Raman spectroscopy
Energy dispersive X-ray analysis (EDX)
Ultraviolet-visible spectroscopy (UV-vis)
X-ray diffraction spectroscopy (XRD)
Application of 2D materials in fiber laser system
Q-switched generation
Mode-locked generation
Photodetectors
Optical modulation using 2D materials
Wavelength modulation
Amplitude modulation
Phase modulation
Conclusion
References
Carbon nanotube-based sensors and their application
Nanoscience and nanotechnology
History of carbon-based materials
Types of carbon nanotubes and related structures
Single-walled carbon nanotubes (SWCNTs)
Double-walled carbon nanotubes (DWCNTs)
Multi-walled carbon nanotubes (MWCNTs)
Synthesis methods for carbon nanotubes
Arc discharge method
Laser-aided vaporization method
Chemical vapor deposition (CVD)
Classification of carbon nanotubes
Optical properties of CNTs
Electrical properties of carbon nanotubes
Mechanical properties of carbon nanotubes
Thermal properties of carbon nanotubes
Applications of carbon nanotubes
Applications as thin-films
Sensor applications
Classification of sensors
Chemical vapor sensors
References
Optical fiber coated with zinc oxide nanorods toward light side coupling for sensing application
Introduction
Light scattering and light side coupling
Improvement of light side coupling
Hydrothermal growth of zinc oxide on plastic optical fiber
Physical characterization
Light side coupling toward sensing application
Conclusion
References
Synthesis, characterization, and applications of opals
Introduction to photonic crystals and opals
Photonic crystals
Opals
Fabrication of opals
Characterization of opals
Embedded structures in opals
Manipulation of the environment of light emitters
Opals using flexible media
Conclusions
References
Theoretical methods, simulation and modeling in nano-optics
Introduction
Electromagnetic theory at nano levels
Analytical methods
Semi-analytical methods
Multiple multipole method
Volume integral methods
Method of moments
The coupled dipole method
Numerical methods
Finite element methods
Finite-difference time-domain methods
Conclusion
References
Further reading
Photonic crystals-based light-trapping approach in solar cells
Introduction
Theory of light-trapping
Ray-optic theory
Wave-optic theory
Absorption enhancement factor in PCs
Absorption enhancement factor in 2D PCs
Absorption enhancement factor in 3D PCs
Summary
References
Index
A
B
C
D
E
F
G
H
I
K
L
M
N
O
P
Q
R
S
T
U
V
W
X
Y
Z
Back Cover

Citation preview

NANO-OPTICS

NANO-OPTICS Fundamentals, Experimental Methods, and Applications Edited by

SABU THOMAS YVES GROHENS GUILLAUME VIGNAUD NANDAKUMAR KALARIKKAL JEMY JAMES

Elsevier Radarweg 29, PO Box 211, 1000 AE Amsterdam, Netherlands The Boulevard, Langford Lane, Kidlington, Oxford OX5 1GB, United Kingdom 50 Hampshire Street, 5th Floor, Cambridge, MA 02139, United States © 2020 Elsevier Inc. All rights reserved. No part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopying, recording, or any information storage and retrieval system, without permission in writing from the publisher. Details on how to seek permission, further information about the Publisher’s permissions policies and our arrangements with organizations such as the Copyright Clearance Center and the Copyright Licensing Agency, can be found at our website: www.elsevier.com/permissions. This book and the individual contributions contained in it are protected under copyright by the Publisher (other than as may be noted herein). Notices Knowledge and best practice in this field are constantly changing. As new research and experience broaden our understanding, changes in research methods, professional practices, or medical treatment may become necessary. Practitioners and researchers must always rely on their own experience and knowledge in evaluating and using any information, methods, compounds, or experiments described herein. In using such information or methods they should be mindful of their own safety and the safety of others, including parties for whom they have a professional responsibility. To the fullest extent of the law, neither the Publisher nor the authors, contributors, or editors, assume any liability for any injury and/or damage to persons or property as a matter of products liability, negligence or otherwise, or from any use or operation of any methods, products, instructions, or ideas contained in the material herein. Library of Congress Cataloging-in-Publication Data A catalog record for this book is available from the Library of Congress British Library Cataloguing-in-Publication Data A catalogue record for this book is available from the British Library ISBN: 978-0-12-818392-2 For information on all Elsevier publications visit our website at https://www.elsevier.com/books-and-journals

Publisher: Matthew Deans Acquisitions Editor: Simon Holt Editorial Project Manager: Ana Claudia Garcia Production Project Manager: Kamesh Ramajogi Cover Designer: Christian J. Bilbow Typeset by SPi Global, India

Contributors

Harith Ahmad Photonics Research Center, University of Malaya, Kuala Lumpur, Malaysia Stuart Bowden Quantum Energy for Sustainable Solar Technology (QESST) Engineering Research Center, Arizona State University, Tempe, AZ, United States Dermot Brabazon I-Form, Advanced Manufacturing Research Centre, & Advanced Processing Technology Research Centre, School of Mechanical and Manufacturing Engineering, Dublin City University, Dublin, Ireland Jenu V. Chacko Laboratory for Optical and Computational Instrumentation (LOCI), University of Wisconsin at Madison, Madison, WI, United States Balu Chandra International School of Photonics, Cochin University of Science and Technology, Cochin, Kerala, India Judith M. Dawes MQ Photonics Research Center, Department of Physics and Astronomy, Macquarie University, Sydney, NSW, Australia Joydeep Dutta Functional Materials division, Materials and Nano-Physics Department, ICT School, KTH Royal Institute of Technology, Stockholm, Sweden Nitin Eapen International and Inter University Centre for Nanoscience and Nanotechnology, Mahatma Gandhi University, Kottayam, Kerala, India Karsten Fleischer I-Form, Advanced Manufacturing Research Centre, & Advanced Processing Technology Research Centre, School of Mechanical and Manufacturing Engineering, Dublin City University, Dublin, Ireland Stephen Goodnick Quantum Energy for Sustainable Solar Technology (QESST) Engineering Research Center, Arizona State University, Tempe, AZ, United States Yves Grohens FRE CNRS 3744, IRDL, University of Southern Brittany, Lorient, France Banshi D. Gupta Physics Department, Indian Institute of Technology Delhi, New Delhi, India

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Contributors

Sulaiman Wadi Harun Department of Electrical Engineering, Faculty of Engineering, University of Malaya, Kuala Lumpur, Malaysia Christiana Honsberg Quantum Energy for Sustainable Solar Technology (QESST) Engineering Research Center, Arizona State University, Tempe, AZ, United States Jemy James FRE CNRS 3744, IRDL, University of Southern Brittany, Lorient, France; International and Inter University Centre for Nanoscience and Nanotechnology, Mahatma Gandhi University, Kottayam, Kerala, India Jerry Jose International and Inter University Centre for Nanoscience and Nanotechnology, Mahatma Gandhi University, Kottayam, Kerala, India Blessy Joseph FRE CNRS 3744, IRDL, University of Southern Brittany, Lorient, France; International and Inter University Centre for Nanoscience and Nanotechnology, Mahatma Gandhi University, Kottayam, Kerala, India Nandakumar Kalarikkal School of Pure and Applied Physics; International and Inter University Centre for Nanoscience and Nanotechnology, Mahatma Gandhi University, Kottayam, Kerala, India Changhyoup Lee Institute of Theoretical Solid State Physics, Karlsruhe Institute of Technology, Karlsruhe, Germany Kwang-Geol Lee Department of Physics, Hanyang University, Seoul, Korea Juby Alphonsa Mathew International and Inter University Centre for Nanoscience and Nanotechnology, Mahatma Gandhi University, Kottayam, Kerala, India  anna McCarthy E I-Form, Advanced Manufacturing Research Centre, & Advanced Processing Technology Research Centre, School of Mechanical and Manufacturing Engineering, Dublin City University, Dublin, Ireland Waleed Soliman Mohammed Center of Research in Optoelectronics, Communication and Control Systems (BU-CROCCS), School of Engineering, Bangkok University, Pathum Thani, Thailand Rajesh V. Nair Department of Physics, Indian Institute of Technology Ropar, Rupnagar, Punjab, India Parvathy Nancy School of Pure and Applied Physics; International and Inter University Centre for Nanoscience and Nanotechnology, Mahatma Gandhi University, Kottayam, Kerala, India

Contributors

Anisha Pathak Physics Department, Indian Institute of Technology Delhi, New Delhi, India Hazli Rafis Abdul Rahim Department of Electrical Engineering, Faculty of Engineering, University of Malaya, Kuala Lumpur; Universiti Teknikal Malaysia Melaka, Melaka, Malaysia Siti Aisyah Reduan Photonics Research Center, University of Malaya, Kuala Lumpur, Malaysia Carsten Rockstuhl Institute of Theoretical Solid State Physics; Institute of Nanotechnology, Karlsruhe Institute of Technology, Karlsruhe, Germany Swasti Saxena Department of Applied Physics, Sardar Valla Bhai National Institute of Technology, Surat, Gujarat, India Vivek Semwal Physics Department, Indian Institute of Technology Delhi, New Delhi, India Ashin Shaji Institute of Physics, Slovak Academy of Sciences, Bratislava, Slovakia Sithara P. Sreenilayam I-Form, Advanced Manufacturing Research Centre, & Advanced Processing Technology Research Centre, School of Mechanical and Manufacturing Engineering, Dublin City University, Dublin, Ireland Ankit Kumar Srivastava School of Applied Natural Science, Adama Science and Technology University, Adama, Ethiopia Mark Tame Department of Physics, Stellenbosch University, Stellenbosch, South Africa Kavintheran Thambiratnam Photonics Research Center, University of Malaya, Kuala Lumpur, Malaysia Siddharth Thokchom National Institute of Technology Manipur, Imphal, India Sabu Thomas School of Chemical Sciences; International and Inter University Centre for Nanoscience and Nanotechnology, Mahatma Gandhi University, Kottayam, Kerala, India Zian Cheak Tiu Photonics Research Center, University of Malaya, Kuala Lumpur, Malaysia Jijo P. Ulahannan Department of Physics, Government College, Kasaragod, Kerala, India Guillaume Vignaud FRE CNRS 3744, IRDL, University of Southern Brittany, Lorient, France

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About the Editors

Prof. Sabu Thomas is Vice Chancellor of Mahatma Gandhi University, Kottayam, Kerala, India. He is also Director of the School of Energy Materials, Professor at the School of Chemical Sciences and the founding Director of the International and Inter-University Centre for Nanoscience and Nanotechnology, at Mahatma Gandhi University, Kottayam, Kerala. Prof. Thomas is an outstanding leader with sustained international acclaim for his work in polymer science, polymer nanocomposites, elastomers, polymer blends, interpenetrating polymer networks, polymer membranes, nanoscience, nanomedicine, and green nanotechnology.

Prof. Yves Grohens is Director of ComposiTIC Laboratory at the University of South Brittany, France. His research interests include interface science in nano and bio-composites. He is also involved in research on confinement in model thin films and its applications, (bio)polymers and their blends, and bio-composites. Interfaces and adhesion of polymers with natural reinforcing agents is one of the hot topics for applications in transportations and others. He is involved in many French and European networks focusing on these topics. He works with many French and some international companies including Arkema, PSA, Cooper Santard, CSP, and Airbus.

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About the Editors

Prof. Nandakumar Kalarikkal is Director of the International and Inter-University Centre for Nanoscience and Nanotechnology, and the School of Pure and Applied Physics, Mahatma Gandhi University, Kottayam, Kerala, India. The research works of his group include the syntheses, characterization, and applications of various nanomaterials, LASER-matter interactions, ion irradiation effects on various novel materials, and phase transitions.

Dr. Guillaume Vignaud is Assistant Professor of Physics at the University of South Brittany, France. His areas of expertise include polymer thin films, ultrathin films and interfaces, thin film deposition, material characterization, X-ray diffraction, and nanomaterials synthesis.

Dr. Jemy James obtained his Ph.D. from the University of South Brittany, France, and is presently working at WITec GmbH, India. He was previously a junior research fellow at Mahatma Gandhi University, Kottayam, Kerala, India.

CHAPTER 1

From nature: Optics, nanotechnology, and nano-optics Ashin Shajia, Jemy Jamesb,c, Parvathy Nancyc,d a

Institute of Physics, Slovak Academy of Sciences, Bratislava, Slovakia FRE CNRS 3744, IRDL, University of Southern Brittany, Lorient, France c International and Inter University Centre for Nanoscience and Nanotechnology, Mahatma Gandhi University, Kottayam, Kerala, India d School of Pure and Applied Physics, Mahatma Gandhi University, Kottayam, Kerala, India b

1. Introduction Nanomaterials are abundant in nature, since everything in our world is composed of very small particles. As a result, nanotechnology is always inspired by nature and natural phenomena. The properties of the materials created by nature through evolutionary processes are highly efficient or optimal, hence the use of natural materials directly in the development of nanotechnology is of great importance. Now scientists have a clear idea of how to create nanoscale materials with unique properties that never existed before. Products using nanomaterials are now available in the market, such as nanoscale silver as an antibacterial [1], sunscreen with nanoscale titanium dioxide that prevents sunburn [2], application in the field of electronics as in batteries, targeted drug delivery, nanofilms for coatings, water filtration, etc. [3] Molecular-level manipulation is the ultimate base of nanotechnology, but that doesn’t mean that this field of science always deals only with artificial materials. In nature, molecules organize themselves into complex structures that could support life, similar to the present nanotechnology that we are used to. Nature constructs everything atom by atom, and understanding the basic principle of natural systems will help nanoscientists to design artificial nanomaterials. For example, oncologists are looking into nanotechnology as a potential way to treat cancer with targeted drug delivery by the use of nanomedicine [4]. The inspiration for this is from the viruses that seek out a specific type of cell to attack in a living organism. Similarly, optically transparent materials have been improved by imitating the nanostructures found in the wings of insects. Finding inspiration from nature’s nanotech is becoming big business nowadays. Nano-optics or nanophotonics has become a serious topic of research over the past decades. The interaction of light with nanometer-scale particles has developed into a new and separate branch from conventional photonics research topics due to its massive presence in the natural world and also from an application point of view. On the nanometer Nano-Optics https://doi.org/10.1016/B978-0-12-818392-2.00001-9

© 2020 Elsevier Inc. All rights reserved.

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scale, materials including metals, semiconductors, dielectrics, and polymers exhibit interesting properties, especially optical properties [5]. Particles that come under a size range of nanometers show special phenomena that are not predictable as in their bulk counterparts. Making use of these properties of the nanoparticles in the field of optics and photonics is the core of nanophotonics [6]. The major aim of this chapter is to give a brief introduction to the presence of nanotechnology and nanophotonics in the natural world rather than the artificially created nano universe. Without going into deeper theoretical aspects, this chapter presents an overall picture of the influence and existence of nanotechnology in nature.

2. Nature and optics In nature, optical phenomena are observable as a result of the interaction of matter and light; interactions of light from the sun and moon with particles in the atmosphere, clouds, water, dust, etc. are the reason for some of the common natural optical phenomenon like mirages and rainbows (Fig. 1). Many of these natural phenomena in nature arise from the optical properties of the atmosphere and due to the presence of other objects in nature or sometimes even due to the visual illusion created by the human eye, such as entoptic phenomena [7]. The particle and wave nature of the light also influences this kind of phenomenon. Some are quite delicate and noticeable only by precise scientific measuring instruments. One of the notable observations is the bending of light from a star by the sun, observed during the time of the solar eclipse. This demonstrates that space is curved, as predicted by Einstein in his theory of relativity. Most optical phenomena can be explained on the basis of the classical electromagnetic explanation of light. But in practical applications, a completely electromagnetic description of light is often difficult to apply in practice. So for practical applications, optics is usually demonstrated using simplified models, like geometric optics, that treat light as a collection of rays that travel in a straight line and bend from a surface when they pass through or reflect from it. Wave optics or physical optics is a more inclusive model of light, which explains the wave nature of such phenomena as diffraction and interference, which cannot be explained using geometric optics. Based on the history of light in nature, the first accepted model to explain the nature of light is the ray-based model of light, and later on, the wave model of light. The introduction of the electromagnetic theory in the 19th century led to the rediscovery of light waves as electromagnetic radiation. Even so, there are some phenomena in nature that can be explained only by considering the fact that light has both wavelike and particlelike nature (dual nature of light), effects that require quantum mechanical explanations. Quantum optics is the field of science that deals with the application of quantum mechanics to optical systems. When considering the particle-like nature of light, light is

From nature: Optics, nanotechnology, and nano-optics

Fig. 1 Some of the common optical phenomena happening in nature: (A) double rainbow and supernumerary rainbows on the inside of the primary arc; (B) very bright sun dogs in Fargo, North Dakota; (C) the reflection of Mount Hood in Mirror Lake; (D) a 22° halo around the sun, as seen in the sky over Annapurna Base Camp, Annapurna, Nepal. ((A) Eric Rolph at English Wikipedia (https:// commons.wikimedia.org/wiki/File:Double-alaskan-rainbow.jpg), “Double-alaskan-rainbow,” size and shape of the image by Ashin Shaji, https://creativecommons.org/licenses/by-sa/2.5/legalcode; (D) Anton Yankovyi (https://commons.wikimedia.org/wiki/File:Halo_in_the_Himalayas.jpg), size and shape of the image by Ashin Shaji, https://creativecommons.org/licenses/by-sa/4.0/legalcode.)

considered as a collection of particles called photons. Optical science is an important and applicable field of science in many related disciplines like astronomy, photography, various engineering fields, and especially in medical fields like optometry and ophthalmology. Practical implementation of optics is found in everyday life and in a variety of technologies like telescopes, mirrors, lenses, microscopes, lasers, optical fibers, etc. Most colors in nature originate due to selective adsorption resulting from the pigmentation embedded in the body or surface of an object. However, a certain range of intense and bright contrast colors result from the interaction of light with nano- and microstructures, which leads to the appearance of color by coherent scattering, interference, and diffraction without any absorption. These colors are commonly known as “structural colors” [8]. The structures that help to modulate light leading to structural colors are part of the family of photonic structures in nature.

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Photonic structures can be defined as regular structures with periodicities matching with the order of the wavelength of the light [9]. Structural colors have been a hot topic of research for centuries, and the involvement of micro- and nanostructures in them was introduced by Hooke (1665), Newton (1704), and Lord Rayleigh (1917) [8]. The first ever imaging and a detailed study of structural elements that induce structural colors were suggested by Anderson and Richards [10] after the introduction of the electron microscope. The interest in natural structural colors was found to increase due to the fast growth in the field of optical spectroscopy and scanning/transmission microscopy. These spectroscopic techniques help to investigate the details of the complex nano and microstructures with unique optical characteristics that evolved and existed solely in nature for millions of years [11]. Optical issues like high reflectivity or transmission, strong polarization of light, dichroism, spectral filtering, etc., can be controlled with the help of the natural world since nature provides solutions for all these in the form of nanostructures of different morphological varieties. Thus, nature offers an abundant number of road maps for multifunctional micro- and nanostructures that show outstanding dynamic and distinctive coloration. This kind of structured material originates as a result of evolution over millions of years and invites the interest of scientists to carry out deeper research that may build the basis of future optical devices. It can find applications in medical diagnostics, communication, information processing, and devices with functionalities that can go beyond the current stage. Therefore, the biomimetic approach is currently a hot field of science. For the purpose of solving complex human problems, imitation or copying the models, systems, or solutions from nature is known as biomimetic or biomimicry [12].

3. Nanotechnology in nature Nanoscience and nanotechnology always find inspiration from nature. Some common nanostructures that are visible in nature include inorganic materials such as carbonaceous soot, clay, organic natural thin films, and a variety of organic nanostructures such as proteins, insects, and crustacean shells. These structures cause a range of behaviors in nature together with the wettability of surfaces, the brightness of butterfly wings, and also the adhesive properties of the lizard’s foot. The coloration of many varieties of beetles and butterflies is created by sets of rigorously spaced nanoscopic pillars. Fabricated from sugars like chitosan, or proteins like keratin, the widths of slits between the pillars are designed to control light to attain certain colors or effects like iridescence. One advantage of this strategy is resilience. Pigments tend to bleach with exposure to light; however, structural colors are stable for remarkably long periods.

From nature: Optics, nanotechnology, and nano-optics

A study of structural coloration in metallic-blue marble berries [13] where the specimens collected in 1974, that had maintained their color despite being long dead. Similarly, a lotus leaf is an example of an engineered surface because of its physical and chemical conditions at the micro- and nanometer scale, able to provide a self-cleaning effect. Wilhelm Barthlott, a German botanist, is considered to be the discoverer of the Lotus effect [14] as he applied for its patent in 1994. He found out that the combination of the chemical makeup of the surface and also the micro-and nano-projections on the surface were the reason behind the effect. The protrusions [15] of the lotus leaf are 10 μm high, with every protrusion covered in bumps of a hydrophobic, waxy material that is roughly 100 nm in height. The chitin polymer and epicuticular wax projections allow the leaf to trap air. Water droplets ride on the tips of the projections and result in a bed of air to make a super-hydrophobic surface (Fig. 2). Scientists designed this behavior

Fig. 2 Examples of self-cleaning surfaces in nature and their SEM images [16]. (Permission has been granted through the Copyright Clearance Center’s Rights Link service.)

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into the product Lotusan®, a self-cleaning paint. This paint mimics the microstructure of the surface of a lotus leaf once it dries and cures within the environment. Small peaks and valleys on the surface minimize the contact area for water and dirt, keeping the surface clean. Various merchandise is currently on the market that mimics this hydrophobic property, including consumer goods, spray coatings, plungers, toilet fixtures, automotive components, etc. Researchers at several universities are synthesizing biomimetic nanocomposites to form robust materials to be used in lightweight armor systems, structures in transportation systems, sturdy electronics, aerospace applications, etc. Nature has evolved an advanced bottom-up approach for fabricating nanostructured materials that have great mechanical strength and toughness. One of nature’s toughest materials is nacre, which is best known as the iridescent mother-of-pearl made by mollusks. Mollusks produce nacre by depositing amorphous calcium carbonate (CaCO3) onto porous layers of polysaccharide chitin. The mineral then crystallizes, producing stacks of CaCO3 that are separated by layers of organic material. Its strength comes from the brick-like assembly (interlocked) of the molecules [17]. A lizard’s feet will bind firmly to any solid surface in a short time, and detach with no apparent effort (Fig. 3). This adhesion is purely physical, with no chemical interaction between the feet and the surface. The active adhesive layer of the gecko’s foot is a branched nanoscopic layer of bristles known as “spatula” that measure about 200 nm in length. Several thousand of those spatulae are connected to micron-sized “seta.” Both spatulae and seta are fabricated from very flexible keratin. Although research into the finer details of the spatulae’s attachment and detachment mechanism is in progress, the actual fact is that they operate with no sticky chemicals. It is an impressive piece of design by Mother Nature. That they are self-cleaning, immune to self-matting (the seta don’t stick to each other), and detached by default (including from each other) are other interesting features of geckos’ feet [18, 20]. These options have prompted ideas and suggestions that in the future, glues, screws, and rivets may all be made by a single method, casting keratin or similar material into completely different molds. Magnetotactic bacteria possess the extraordinary ability to sense minute magnetic fields, together with the Earth’s own magnetic field, using tiny chains of nanocrystals known as magnetosomes (Fig. 4). These are grains sized between 30 and 50 nm, made from either magnetite (a type of iron oxide) or, less commonly, greghite (an iron-sulfur combo). Several types of magnetosomes work together to provide a foldable “compass needle” that is many times more sensitive than its artificial counterparts. Magnetotactic bacteria are pond-dwelling and only need to navigate short distances. However, their precision is incredible. By varying the grain size, these bacteria can store information since the growth is controlled by the most magnetically sensitive atomic arrangements [22]. However, oxygen and sulfur combine rapidly with iron to provide magnetite, greghite, or more than 50 other compounds, only a couple of which are magnetic. Hence

Fig. 3 Nanoengineered structures from nature: (A) microstructure and schematic illustration of gecko feet [18]; (B) micro/nanoarchitecture in the wings of a butterfly [19]. (Permission has been granted through the Copyright Clearance Center’s Rights Link service.)

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Fig. 4 Different morphology of magnetotactic bacteria: (A) vibrios; (B) rods (Bar ¼ 1.0 μm) and (D); (C) coccoid (Bar ¼ 200 nm); (E) spirilla: (F) multicellular organism (Bar ¼ 1.0 μm) [21]. (Permission has been granted through the Copyright Clearance Center’s Rights Link service.)

advanced technologies are needed to produce selectively the proper kind, and build the magnetosome chains. Such manual skill is presently beyond our reach; however, in future, scientists may learn a way to mimic these structures.

4. Presence of nano-optics in nature The increasing research in nanotechnology makes it necessary to deal with the optical phenomenon on the nanometer scale. Since the diffraction limit doesn’t enable us to focus light to dimensions smaller than roughly one-half of the wavelength (200 nm), traditionally it was not practical to optically interact selectively with nanoscale structures. However, in recent years, many new approaches have become available to reduce the diffraction limit or even overcome it. A central goal of nano-optics is to extend the utilization of optical techniques to length scales beyond the diffraction limit. The most

From nature: Optics, nanotechnology, and nano-optics

obvious potential technological applications that arise from breaking the diffraction barrier are super-resolution microscopy and ultra-high-density information storage. However, the field of nano-optics is by no means restricted to technological applications and instrumental design. Nano-optics additionally opens new doors to basic analysis on nanometer-sized structures [23, 24]. Nature has developed numerous nanoscale structures to achieve distinctive optical effects. An outstanding example is photosynthetic membranes that use light-harvesting proteins to absorb daylight and then channel the excitation energy to different neighboring proteins [25]. The energy is guided to a so-called reaction center where it initiates a charge transfer across the cell membrane. Other examples of nanophotonics in nature include sophisticated nano diffractive structures commonly found in insects like butterflies and other animals to produce attractive colors and effects like those on a peacock’s feather. Insect wings have ordered hexagonal close-packed array structures made of chitin. The difference in spacing (from 200 nm to 1 μm) between these small structures allows the wings to serve as self-cleaning and antireflective coatings, along with providing improved mechanical strength and aerodynamics. It also functions as a diffraction gritting, which produces iridescence. Iridescence originates as a result of the interaction of light with the physical structure of the surface. In the Morpho butterfly, the spaces between the ribs of the wing form natural photonic crystals, leading to a brilliant blue color (Fig. 5). No pigments or chemicals are involved in this process. Researchers are exploring these nanostructures as a way for controlling and manipulating the flow of light, which is vital in optical communication. Additionally, researchers have found that when they coat the Morpho wings with a layer of heat-absorbing carbon nanotubes, the shift in reflected wavelength of light will indicate very small temperature changes (Fig. 6). Hence, these sensors could someday be used to discover inflamed or burned areas in people or points of wear and tear due to friction in machines. Another example is a moth’s eye, which has very small bumps on its surface. These bumps are hexagonal-shaped structures that are a few hundred nanometers tall and separated (Fig. 7). Since these patterns are smaller than the wavelength of visible light (350–800 nm), the surface of the moth’s eye can absorb more light, since it has only very low reflectance for visible light. Therefore, the visibility of a moth in dark conditions is much better than a normal human eye because these nanostructures can absorb light very efficiently. Scientists have been inspired to use similar artificial nanostructures to improve the absorption of infrared light in a particular type of thermo-voltaic cell to make it more efficient [29]. A new aim within the study of animal optical structures is to decipher and emulate the animal’s genetic manufacturing process. Animals contain the ultimate factories within them, which means they engineer via nanomachinery and molecular self-assembly, and also the results are excellent, based on the previously reported results [30]. Maybe,

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Fig. 5 The Morpho didius butterfly: (A) image of the Morpho wings; (B) separated images of ground scale and cover scale [26]. (Permission has been granted through the Copyright Clearance Center’s Rights Link service.)

in the near future, living cells will be cultured and photonic crystals can be grown and harvested on an industrial basis. This successively would supply a chance for novel evolutionary studies in the field of nanophotonics. According to the results obtained in previous research carried out by nanoscientists, it was found that the ability to manufacture this kind of natural photonic crystal may be inherent among insect cells. Normally, there is a single (minimal) mutation within the genes required to manage the developmental process. The limited range of photonic crystals found in animals, compared with the potential range in physics (where lattices may also contain sharp edges and corners, etc.), further supports this concept. So, as a consequence of their production by single cells, photonic crystals make ideal phenotypes for evolutionary study in the future. Some of the applications of nature-inspired optical materials are listed below.

4.1 Light manipulation Nature makes use of a large number of optical phenomena to produce attractive colors. The most attractive and striking colors or optical effects are often created through the manipulation of light with the help of intricate microstructures. These are usually known

From nature: Optics, nanotechnology, and nano-optics

Fig. 6 Wing scales of the painted lady, Vanessa cardui butterfly. The wing is covered in overlapping layers of scale cells, as seen in the reflected light image in the region of one of the ventral hindwing eyespots and high magnification SEM images showing the scale cell surfaces [27]. (A) Image of a V. cardui butterfly. Scale bar ¼ 1 cm. (B) Overlapping layers of scale cells in the wing of a V. cardui butterfly. The scales themselves are made of chitin. Scale bar ¼ 2 mm. (C) Higher magnification image of wing scales. The arrangement of scales are like overlapping tiles. Scale bar ¼ 150 μm. (D) SEM image showing the attachment of scale cells to the wing of wing epithelium. Scale bar ¼ 65 μm. (E) SEM image of base of scale cell where scale shaft is attached to the socket cell. Scale bar ¼ 20 μm. (F) High magnification SEM showing the ornately patterned scale cell surfaces. Scale bar ¼ 2 μm. (G) 6-day pupal wing scale stained with Wheat Germ Agglutinin (WGA), a type of fluorescent. Scale bar ¼ 20 μm. (Permission has been granted through the Copyright Clearance Center’s Rights Link service.)

as structural colors. In particular, some types of three-dimensional natural photonic crystals can create a photonic bandgap. This is a frequency band in dielectric structures in which electromagnetic waves are prohibited, irrespective of their direction of propagation in space. Theoretically, the three-dimensional periodicity of three-dimensional photonic crystals can give rise to photonic band gaps in all directions and produce omnidirectional reflection, which can produce bright color over a broad viewing angle [31, 32]. Compared with pigment color, structural color offers ultrahigh saturation brightness and iridescence. Moreover, as a result of not utilizing chemical dyes, structural color is environmentally friendly and exhibits unlimited lightfastness (Fig. 8). Due to these distinctive properties, tunable structural colors have opened new avenues to applications in areas like cosmetics, textiles, printing and painting, displays, and security labeling.

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Fig. 7 The nano-nipple arrays of a Mourning Cloak butterfly eye, as revealed by SEM at different magnifications. The complete eye and its enlarged regional topography are shown. The distribution of nano-nipple diameters for the Mourning Cloak butterfly and for the Question Mark butterfly are shown in the graphs [28]. (A) Overview of butterfly eye. (B) Hexagonal facet lenses. (C) Junction in between three hexagonal facets. (D) Nano-nipples present on a facet surface. (E) Arrangement of nano-nipple diameters for the Mourning Cloak butterfly. (F) Arrangement of nano-nipple diameters for the Question Mark butterfly. (Permission has been granted through the Copyright Clearance Center’s Rights Link service.)

From nature: Optics, nanotechnology, and nano-optics

Fig. 8 (A) Structure-based green color of butterfly wings; (B, C) different magnification times of the optical ultrastructure in its wing scale surfaces [33]. (Permission has been granted through the Copyright Clearance Center’s Rights Link service.)

L’Oreal has manufactured a photonic cosmetic product [34] with a periodic nanoscale structure that produces striking blue color without the help of any chemical pigments. The light manipulation mechanism of butterfly Morpho scales is the inspiration behind this. The structural color is feasibly tuned by modifying the periodicity of the layers. The structural nature of the color also means it is more stable and sturdy than traditional pigment color. Such structurally colored fabric thus has great potential for application in the fashion industry and domestic furnishing textiles.

4.2 Antireflection Antireflection films are widely used to reduce interface reflectance and to enhance the performance of various kinds of optical devices, like LED displays and optic sensors. Conventionally, interference coatings shaped by the deposition of single-layer and multilayer stacks are widely adopted to obtain antireflection performance. Nevertheless, as well as the lack of mechanical stability and the difficulty in fabrication, these antireflection films are only effective over a narrow region of wavelengths at narrow incidence angles. Thicker films are needed to suppress the Fresnel reflection of longer-wavelength light. As an alternative, modern antireflection coatings with intricate microstructures within the subwavelength range inspired by many natural species have helped us regarding how to attain this. The presence of antireflection structures was first reported to exist on the corneas of the eyes of many nocturnal insects [35] (Fig. 9). Thousands of ommatidia with size ranging from 10 to 30 μm are tightly packed on the spherical eye of an insect. They are uniformly arranged with a fixed spatial periodicity of 170 nm. Similar hierarchical subwavelength nanostructures are also observable on the transparent wings of some cicadas and hawk moths [37]. This kind of antireflection structure consists of arrays of nanocones and nanopillars arranged hexagonally. Due to their structured morphology, the Fresnel reflection can be effectively reduced by the gradient refractive index profile. In addition, the spatial periodicity is generally below the wavelength of the visible spectrum that can efficiently

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Fig. 9 (A) Photograph of the moth Cacostatia ossa—the translucent and matte parts show the presence of an antireflective structure; (B, C) SEM images of the cross-section and the up-side of the translucent part of the wings—this shows the non-close-packed nano conical arrays on both sides of the wing membrane [36]. (Permission has been granted through the SciPris Scientific Publishing and Remittance Integration Services.)

prevent the light scattering. Inspired by the excellent antireflection subwavelength nanophotonics structures in the compound eyes of insects and cicada wings, many kinds of artificial nanostructured antireflective coatings have been produced on an industrial basis including gratings, nanorods or nanocones, frustums of cones, nanotips, etc. [38]

4.3 Light focusing Some nocturnal animals have the ability to focus the light from a wide range of angles of incidence using biological microlenses as a solution to trap the light. The brittlestar O. wendtii is a good example of a light-sensitive species in aquatic systems [39]. The surface of each arm contains regular arrays of inorganic photonic structures, similar to a characteristic double-lens design. Each of these microlenses is composed of single anisotropic calcite crystals capable of focusing light toward the nerve photoreceptors, which are 4–7 μm below them. These near-perfect calcite microlenses show a unique focusing effect with significant signal enhancement and intensity adjustment. In addition, the surface design of each microlens and constituent calcite orientation decreases the spherical aberration and birefringence that could degrade the optical function. Thus O. wendtii can detect dark spots extremely sensitively, and quickly escape from predators into a dark area. Inspired by the unique microlens design and the outstanding light-focusing properties in brittlestars, several biomimetic analogs were fabricated by three-beam interference lithography. For the compound eye of several insects, the close-packed micro-ommatidium arrays arranged on the spherical macro basis also act as an effective light focusing tissue. Inspired by this multi-lens focusing mechanism, an artificial compound eye was fabricated by a laser direct writing method [40]. The artificial compound eye exhibits high uniformity, imaging, and focusing capabilities. Moreover, it has the ability for distortion-free wide field of view imaging and possesses high potential for applications in imaging devices and integrated

From nature: Optics, nanotechnology, and nano-optics

Fig. 10 Examples of 3D natural photonic crystals. On the left, the weevil Entimus imperialis; in the center, the longhorn Prosopocera lactator; and on the right, the longhorn Pseudomyagrus waterhousei [41]. Permission has been granted through the Copyright Clearance Center’s Rights Link service.

optical microsystems. The artificial compound eye structure can be combined with photoelectrical micro receivers for wide-angle communication applications [38].

4.4 Chirality As a result of the changes caused by evolution over millions of years, many natural species such as beetles, shrimps, and butterflies have developed different types of threedimensional chiral photonic crystals sensitive to circularly polarized light (Fig. 10). The eyes of insects have polarization-sensitive ommatidia, by which these chiral photonic crystals probably contribute to their camouflage nature and communication systems. The shrimp Gonodactylus smithii can communicate selectively to distinctly circularly polarized light. The beetle species Chrysina gloriosa is significantly more brilliant when it is exposed to left-handed circularly polarized (LCP) than right-handed circularly polarized (RCP) light [42]. Mate-choice experiments performed by scientists proved that the polarized light in the butterfly Heliconius cydno is used for sexual selection and speciation [43]. These chiral biological organisms serve as novel motivations in the search for miniature chiral optical devices.

5. Summary Nature is capable of producing several complicated nanoscale structures. Currently, researchers are exploring the natural world to find out its nanoscale secrets and using nature as a model for producing these same complicated structures. Nanotechnology can and will be used to enhance many products, several of which we interact with in

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our daily lives. As this research using nature continues, there will be more breakthroughs that may result in new devices and materials that can impact several aspects of the current and future societies. Nature has always been efficient in developing numerous photonic structures to fulfill specific biological functions and offers us plenty of technologically unattainable photonic designs. To import them into materials systems, we need a comprehensive understanding of those structures. Although numerous efforts have been dedicated to adding comprehension to these biological structures, the physical difficulty of the many natural systems makes it complex to create an accurate picture. Thus, the structural mechanisms with underlying optical functions should be completely investigated, which is also favorable to the extraction and simplification of the photonic structures for nonnatural fabrication. Apart from the structural properties, the physical properties of the material parts are also crucial for achieving specific optical functions. Thus, we should get enough information concerning the materials utilized in these natural systems, notably the dispersion properties of their real and imaginary refractive index parts across relevant frequency ranges. Fortunately, theoretical modeling and calculation methods have matured to handle these issues. The fabrication of these structures in addition to targeting functional materials could be a key step toward applying them. The biotemplating technique has been established as efficient to maintain the complexness of the natural templates; however, it is not suitable for mass production with high reproducibility. In recent years, biomimetic methods utilizing modern engineering fabrication methods, like 3D lithography, nano-imprinting, and direct laser writing, have made great progress with great improvements in resolution. Nature has also shown various examples of practical integrity, like the iridescent butterfly wings, which not only demonstrate vivid structural color, but additionally possess super-hydrophobicity, directional adhesion, self-cleaning, and fluorescence emission functions. These enhanced biological solutions provide us with design principles for the manufacturing of multifunctional artificial materials with multiscale structures. Moreover, by taking advantage of the practical integrations of structural properties from two or more biological organisms, we are probably able to fabricate a new composite with multiple unique functions. Nature-inspired research is now at a growing stage and the present achievements will function as proofs-of-principle and guides for future investigations.

References [1] G.A. Sotiriou, S.E. Pratsinis, Antibacterial activity of Nanosilver ions and particles, Environ. Sci. Technol. 44 (2010) 5649–5654. [2] J.F. Jacobs, I. van de Poel, P. Osseweijer, Sunscreens with titanium dioxide (TiO2) Nano-particles: A societal experiment, NanoEthics 4 (2010) 103–113. [3] M.J. Asim Kumar, Nanotechnology: A review of applications and issues, Int. J. Innov. Technol. Explor. Eng. 3 (2013) 2278–3075. [4] R. Singh, J.W. Lillard, Nanoparticle-based targeted drug delivery, Exp. Mol. Pathol. 86 (2009) 215–223. [5] J. Hulla, S. Sahu, A. Hayes, Nanotechnology, Hum. Exp. Toxicol. 34 (2015) 1318–1321.

From nature: Optics, nanotechnology, and nano-optics

[6] Anon, ‘Plenty of room’ revisited, Nat. Nanotechnol. 4 (2009) 781. [7] T.D. Rossing, C.J. Chiaverina, Light Science: Physics and the Visual Arts, Google eBook, Springer, 1999. [8] M. Kolle, Photonic Structures Inspired by Nature. Photonic Structures Inspired by Nature, Springer, Berlin, Heidelberg, 2011. https://doi.org/10.1007/978-3-642-15169-9. [9] M.V. Berry, Nature’s optics and our understanding of light, Contemp. Phys. 56 (2014) 1–15. [10] T.F. Anderson, A.G. Richards, An electron microscope study of some structural colors of insects, J. Appl. Phys. 13 (1942) 748–758. [11] A.R. Parker, 515 million years of structural colour, J. Opt. A Pure Appl. Opt. 2 (2000) R15–R28. [12] J.F.V. Vincent, O.A. Bogatyreva, N.R. Bogatyrev, A. Bowyer, A.-K. Pahl, Biomimetics: Its practice and theory, J. R. Soc. Interface 3 (2006) 471–482. [13] S. Vignolini, et al., Pointillist structural color in Pollia fruit, Proc. Natl. Acad. Sci. 109 (2012) 15712–15715. [14] W. Barthlott, C. Neinhuis, Purity of the sacred lotus, or escape from contamination in biological surfaces, Planta 202 (1997) 1–8. [15] K. Balani, R.G. Batista, D. Lahiri, A. Agarwal, The hydrophobicity of a lotus leaf: a nanomechanical and computational approach, Nanotechnology 20 (2009) 305707. [16] M. Zhang, S. Feng, L. Wang, Y. Zheng, Lotus effect in wetting and self-cleaning, Biotribology 5 (2016) 31–43. [17] E.M. Gerhard, et al., Design strategies and applications of nacre-based biomaterials, Acta Biomater. 54 (2017) 21–34. [18] K. Takahashi, J.O.L. Berengueres, K.J. Obata, S. Saito, Geckos’ foot hair structure and their ability to hang from rough surfaces and move quickly, Int. J. Adhes. Adhes. 26 (2006) 639–643. [19] Y. Fang, G. Sun, Y. Bi, H. Zhi, Multiple-dimensional micro/nano structural models for hydrophobicity of butterfly wing surfaces and coupling mechanism, Sci. Bull. 60 (2015) 256–263. [20] K. Autumn, Mechanisms of adhesion in geckos, Integr. Comp. Biol. 42 (2002) 1081–1090. [21] L. Yan, et al., Magnetotactic bacteria, magnetosomes and their application, Microbiol. Res. 167 (2012) 507–519. [22] D. Faivre, D. Sch€ uler, Magnetotactic bacteria and magnetosomes, Chem. Rev. 108 (2008) 4875–4898. [23] L. Novotny, B. Hecht, Principles of Nano-optics. Cambridge University Press, Cambridge, UK, 2012, https://doi.org/10.1017/CBO9780511813535. [24] L. Wu, et al., Optical functional materials inspired by biology, Adv. Opt. Mater. 4 (2016) 195–224. [25] D. Siefermann-Harms, The light-harvesting and protective functions of carotenoids in photosynthetic membranes, Physiol. Plant. 69 (1987) 561–568. [26] X. Yang, Z. Peng, H. Zuo, T. Shi, G. Liao, Using hierarchy architecture of Morpho butterfly scales for chemical sensing: Experiment and modeling, Sensors Actuators A Phys. 167 (2011) 367–373. [27] A. Dinwiddie, et al., Dynamics of F-actin prefigure the structure of butterfly wing scales, Dev. Biol. 392 (2014) 404–418. [28] K.C. Lee, U. Erb, Remarkable crystal and defect structures in butterfly eye nano-nipple arrays, Arthropod Struct. Dev. 44 (2015) 587–594. [29] V.P. Khvostikov, et al., Photovoltaic cells based on GaSb and Ge for solar and thermophotovoltaic applications, J. Sol. Energy Eng. 129 (2007) 291. [30] A.R. Parker, Natural photonics for industrial inspiration, Philos. Trans. R. Soc. A Math. Phys. Eng. Sci. 367 (2009) 1759–1782. [31] J.W. Galusha, M.R. Jorgensen, M.H. Bartl, Diamond-structured Titania photonic-bandgap crystals from biological templates, Adv. Mater. 22 (2010) 107–110. [32] A. Blanco, et al., Large-scale synthesis of a silicon photonic crystal with a complete three-dimensional bandgap near 1.5 micrometres, Nature 405 (2000) 437–440. [33] Y. Liu, et al., Bio-inspired micro-nano structured surface with structural color and anisotropic wettability on cu substrate, Appl. Surf. Sci. 379 (2016) 230–237. [34] S.M. Luke, P. Vukusic, An introduction to biomimetic photonic design, Europhys. News 42 (2011) 20–23. [35] X. Gao, et al., The dry-style antifogging properties of mosquito compound eyes and artificial analogues prepared by soft lithography, Adv. Mater. 19 (2007) 2213–2217.

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[36] O. Deparis, N. Khuzayim, A. Parker, J.P. Vigneron, Assessment of the antireflection property of moth wings by three-dimensional transfer-matrix optical simulations, Phys. Rev. E 79 (2009) 041910. [37] P.R. Stoddart, P.J. Cadusch, T.M. Boyce, R.M. Erasmus, J.D. Comins, Optical properties of chitin: surface-enhanced Raman scattering substrates based on antireflection structures on cicada wings, Nanotechnology 17 (2006) 680–686. [38] Y.-F. Huang, et al., Improved broadband and quasi-omnidirectional anti-reflection properties with biomimetic silicon nanostructures, Nat. Nanotechnol. 2 (2007) 770–774. [39] J. Aizenberg, A. Tkachenko, S. Weiner, L. Addadi, G. Hendler, Calcitic microlenses as part of the photoreceptor system in brittlestars, Nature 412 (2001) 819–822. [40] D. Wu, et al., Bioinspired fabrication of high-quality 3D artificial compound eyes by voxel-modulation femtosecond laser writing for distortion-free wide-field-of-view imaging, Adv. Opt. Mater. 2 (2014) 751–758. [41] J.P. Vigneron, P. Simonis, Natural photonic crystals, Phys. B Condens. Matter 407 (2012) 4032–4036. [42] V. Sharma, M. Crne, J.O. Park, M. Srinivasarao, Structural origin of circularly polarized iridescence in jeweled beetles, Science 325 (2009) 449–451. [43] A. Sweeney, C. Jiggins, S. Johnsen, Polarized light as a butterfly mating signal, Nature 423 (2003) 31–32.

CHAPTER 2

Nano-optics: Challenges, trends, and future Jemy Jamesa,c, Balu Chandrab, Blessy Josephc, Parvathy Nancyc,e, Ashin Shajid, Jerry Josec, Nandakumar Kalarikkalc,e, Yves Grohensa, Guillaume Vignauda, Sabu Thomasc,f a

FRE CNRS 3744, IRDL, University of Southern Brittany, Lorient, France International School of Photonics, Cochin University of Science and Technology, Cochin, Kerala, India c International and Inter University Centre for Nanoscience and Nanotechnology, Mahatma Gandhi University, Kottayam, Kerala, India d Institute of Physics, Slovak Academy of Sciences, Bratislava, Slovakia e School of Pure and Applied Physics, Mahatma Gandhi University, Kottayam, Kerala, India f School of Chemical Sciences, Mahatma Gandhi University, Kottayam, Kerala, India b

1. An outlook If technological advancements and their impact on the general public are considered, the last 200 years can clearly be described as the most progressive period of humankind. The advent of electricity and the subsequent emergence of electronic devices initiated a unique revolution. As technology advanced, the sizes of electronic devices became smaller and smaller. In 1960, when Theodore Maiman built the first laser, the world witnessed the metamorphosis of light as a counterpart to electricity. Devices that harness the nature of light are termed photonic devices, and just as electronic devices evolved over time, photonic devices are now evolving at a faster rate. As stated earlier, devices are getting smaller and smaller and right now, the minimum size parameters of electronic devices have reduced to a few tens of nanometers nm (1 nm ¼ 1  109 m). This was a fundamental problem when photonic devices started getting smaller. And as we found out more and more about the problem, the problem itself opened a doorway to completely unprecedented physical phenomena. It gave birth to a new branch of science, nano-optics, which deal with understanding and tailoring the complex behavior of light in nanometer dimensions. Global communication, and in particular internet and long-distance telephony, is now based primarily on optical fiber technology. The main advantage of optical waves compared to radio waves is the high frequency, which allows high data transmission rates. Nowadays, several terabits per second can be transmitted in a single fiber, which represents an increase by a factor of 1 million to what could be achieved 50 years ago with radio signal transmission. The number of optical fiber cables being installed globally is increasing rapidly. Fiber optics is also important for a huge number of other applications, such as Nano-Optics https://doi.org/10.1016/B978-0-12-818392-2.00002-0

© 2020 Elsevier Inc. All rights reserved.

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in medicine, laser technology, and sensors. An interesting example of the use of fiberoptic communication in science is the advanced fiber optics network developed at the Large Hadron Collider at CERN in Geneva, which will transfer large amounts of information obtained by particle detectors to computer centers all over the world.

1.1 A historical perspective Let there be light and there was light Genesis 1:3

Historically, light was a center of interest for numerous inquisitive people: philosophers were interested in its nature and scientists wanted to interpret its associated phenomena. In antiquity, the Egyptians attempted to discover the mystery of light and to know its structure. From a philosophical point of view, their attempts were fruitless. However, in practice, they implemented impressive mechanisms based mainly on reflection. The Greeks also attempted to decode the enigma of light and considered it a continuous phenomenon propagating in the form of a substance current called the “visual ray.” Nevertheless, based on the work of the Egyptians, they established rules for light deflection. One of the most impressive legacies of the Greeks in optics is the mirror of Archimedes. Aristotle, interested in the sensation in general, refused to admit the existence of the visual ray and believed in the analogy between light and sound, whose vibratory nature was already known. In the 11th century, the thesis of the visual ray was definitively abandoned by the Iraqi Ibn Al-Haytham, whose work revolutionized optics. He detached optics from its philosophical envelope and embedded it in the framework of physics and mathematical sciences. He dealt at length with the theory of various physical phenomena like shadows, eclipses, and rainbows, and speculated on the physical nature of light. Al-Haytham’s optics entered Spain in the 12th century and was adopted by Grossteste, who affirmed the analogy between light and sound, and thoroughly investigated the matter of geometrical optics. After the contributions of the geometro-opticians, Snell and Descartes (see Fig. 1) studied the refraction phenomenon and stated that the speed of light is as high as the covered medium is dense. This hypothesis was contested by Fermat, who attributed indices to the media. Foucault in the 19th century came out in favor of Fermat. This more modern progress still dealt only with geometrical optics, which considered that the behavior of light with respect to obstacles is expressed uniquely in terms of absorption, reflection, or refraction. However, in the 17th century, Grimaldi, using a simple experiment, observed the progressive transition between light and shadow and regarded the corpuscular theory, supposing the rectilinear propagation of light, as insufficient to explain such an effect. Despite Newton’s support of the corpuscular theory (he believed that the light

Nano-optics: challenges, trends, and future

Fig. 1 Some pioneers associated with the refraction. (Photo credit https://twitter.com/sahl_ibn; https:// en.wikipedia.org/wiki/Willebrord_Snellius; https://en.wikipedia.org/wiki/Rene_Descartes.)

propagation is a movement of corpuscles that respects the rules of mechanics and notably that of the universal gravitation), Huygens advanced the undulatory theory based on Grimaldi’s observations. He explained Grimaldi’s observation by a purely intuitive postulation, in which he regarded light propagation as an incessant creation of elementary spherical light sources. At the beginning of the 19th century, after some experiments on the colors of thin plates, T. Young came to the conclusion that the interaction between light rays may produce darkness, thereby discovering a wonderful phenomenon which he called “interference.” Like Huygens, Young supported the undulatory theory. He also developed an elegant technique to handle refraction. His belief in the analogy between light and sound led him to state that light vibration is longitudinal. The famous A. Fresnel was of the same opinion. However, he considered that Huygens’ postulation did not explain the nonexistence of waves that have the same specifications propagating backwards. He combined Huygens’ principle of “envelope” building with the interference principle of Young and, for the purposes of putting forward a coherent theory, he made some supplementary hypotheses on the amplitude and phase of the new elementary waves. At the end of the 19th century, G. Kirchhoff gave a deeper mathematical basis to the diffraction theory introduced by Huygens and Fresnel, and considered Fresnel’s hypothesis as a logical consequence of the undulatory nature of light. Kirchhoff’s work was subjected a few years later to criticisms made by Sommerfeld, who considered the Kirchhoff formulation as a first approximation. He advanced with Rayleigh what was later called the “Rayleigh-Sommerfeld diffraction theory.” Hence a supplementary phenomenon called “diffraction” is added to those concerning the behavior of light

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when coming across obstacles, namely absorption, reflection, refraction, diffusion, and dispersion. Sommerfeld defined this phenomenon conveniently as follows: Diffraction is any deviation of light rays from the initial path which can be explained neither by reflection nor by refraction.

The optical region of the electromagnetic spectrum, corresponding to wavelengths in the visible, near-infrared, or ultraviolet spectrum, has long been considered attractive for communications. The frequency of the light allows for high signal modulation frequency and consequently high transmission speed. In 1880, G.A. Bell patented an air-based optical telephone called the “Photophone,” consisting of focusing sunlight on the surface of a flat mirror vibrating with sound. The light was then sent to a detector of selenium coupled to a telephone receiver. A few later ideas were also patented, one of them in Japan even suggesting quartz as a transmission medium. In the 1950s, however, very few communication scientists considered optical communication as a viable concept. One hundred and twenty years ago, G. Marconi and K.F. Braun were awarded a Nobel Prize “in recognition of their contributions to the development of wireless telegraphy.” Sixty years ago, electronic and radio communications were in rapid expansion. The first transatlantic cable was installed in 1956 and satellites would soon allow even better coverage. The first communication satellite was launched in 1958. Research in telecommunication concentrated mainly on shorter radio waves, in the millimeter range, with the aim to reach higher transmission speeds. These waves could not travel as easily in air as longer waves, and the research focused on designing practical waveguides. N.S. Kapany and H.H. Hopkins at Imperial College London constructed bundles of thousands of fibers of length 75 cm and showed appreciable image transmission [1]. By having a cladding to the fiber bundles, some applications, especially the gastroscope, went into industrial production. The refractive index of the core is slightly higher than that of the cladding. Typical dimensions are 10 or 50 μm for the core and 125 μm for the fiber. In addition, a protecting plastic buffer is placed around the fiber went all the way to industrial production. The theory of light propagation into fibers was described by N.S. Kapany. His article on fiber optics in Scientific American in 1960 established the term “fiber optics.” The invention of the laser in the early 1960s (Nobel Prize in 1964 to C.H. Townes, N.G. Basov, and A.M. Prokhorov) gave a new boost to the research in optical communication. Shortly after the pulsed laser demonstrated in ruby by T.H. Maiman, A. Javan built the first continuous-wave laser using a mixture of He and Ne gas. Semiconductor lasers appeared almost at the same time, but were at first not so practical, since they required high currents and could not work at room temperature. A few years later, the introduction of heterostructures (Nobel Prize in 2000 to Z.I. Alferov and H. Kroemer)

Nano-optics: challenges, trends, and future

enabled operation at room temperature, making them ideal light sources for optical communication. Optical communications today have reached their present status thanks to a number of breakthroughs.

1.2 Photonics Light is primarily used for illumination and some common optical devices/elements are spectacles, mirrors, microscopes, magnifiers, telescopes, etc. Light is built out of photons, which are quantum mechanical and relativistic particles; light shows both a wave and particle nature in the sense of our understanding on a macroscopic level. Light moves at the maximum possible speed. The electromagnetic field of photons oscillates much faster than that is possible for electrons, as electrons are massive when compared to the mass-less photons. Some applications of the photonics are provided in Fig. 2. Optical switches enhance the speed of communication. Laser machining has a great impact on many technological applications in daily life. Micro-machining enables completely new approaches in biology and medicine. LIDAR (light detection and ranging) and laser spectroscopic techniques are being used for pollution estimation. Laser medicine is a developing area which enables treatment of most diseases and lasers are

Laser spectroscopy

Laser chemistry

Laser medicine

Applicaon of photonics

Communicaon

Fig. 2 Some everyday applications of photonics.

Material processing

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highly popular in endoscopy and corrective eye surgery. The advantage is that these techniques are minimally invasive. Some of the advancements include: • rapid developments of optical switches; • optical fibers; • wavelength division multiplexers; • photodynamic therapy; • optical coherence tomography; • detection of single molecules; • detection of gravitational waves; and • optical sequencing of DNA. Light is usually described as a collection of photons or as electromagnetic waves, propagating with speed “c,” with its maximum in a vacuum and a lower speed in materials.

1.3 Speed of light

Cvacuum ¼ 2:998  108 ms1 Cmaterial ¼ Cvacuum= nmaterial

where nmaterial is the conventional refractive index of the materials, Cvacuum is the speed of light in a vacuum, and Cmaterial is the speed of light in the material. curl E ¼ μo curl H ¼

dH δt

∂E ∂P + ∂t ∂t

where H is the magnetic field vector, E is the electric field vector, and P is the magnetic polarization frequency (1013–1015 Hz). The wave equation is described as: 2 1 ∂2 E o ∂ P ðE Þ  grad div E ¼ μ c 2 ∂t2 ∂t 2  2  ∂ ∂2 ∂2 Δ ¼ Laplace operator ¼ + + ∂x2 ∂y2 ∂z2

ΔE 

All material properties can be summarized by the refractive index n: ΔE 

1  grad div E ¼ 0 c2E

Nano-optics: challenges, trends, and future

c2 ¼

c02 c2 1 ¼ 02 ¼ εr μr n μo εo μr εr

where εo is the vacuum permittivity. εo ¼ 8:854  1012 AS εo ¼ 8:854  1012

AS 1 ¼ Vm μo Co2

Vacuum permeability: μo ¼ 4π 

107 VS Am

where εr is electric permittivity, μr is magnetic permeability, μr is 1 for optical materials, and εr is 1 for a vacuum.

1.4 Focal length of thin spherical lens and refractive index Bioconvex lens with radius of curvature R at both sides have focal length as follows: f¼

1 R 2ðn  1Þ

For a plano convex lens, the focal length is: f¼

1 R ðn  1Þ

The influence of magnetic component of light can usually be neglected: μr  1 pffiffiffiffi nreal ¼ εr as μr is neglected. Another way of representing the refractive index is: qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi n ¼ 1 + χ phase  iχ absorption χ ¼ electrical susceptibility The imaginary part represents the absorption: χ absorption. The real part represents the phase change: χ phase. Common optical elements like lenses, fibers, prisms, etc. are based on the refraction and dispersion of light as a result of the fact that the refractive index is greater than 1 in the material. The speed of light in a vacuum is different when compared to that in material. If the frequency of light is different than the resonance of the material, then the nonresonant interaction is dominated by phase changes of the light wave. This interaction is based on the forced oscillation of electric dipoles in the matter with the light frequency.

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Phase velocity of light in a medium is given as: Cp ¼

Cvacuum 1 ¼ pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ¼ ϑlight λinmatter o μ 0 r μ r nmatter

where nmatter is usually the real refractive index: λinmatter ¼

λvacuum nmatter

The refractive index of some of the common materials is given in Table 1. If the light is a mixture of frequency, then the speed of each component will be different due to the varying refractive index. Refractive index variation as a function of the light frequency n(ν) or n(λ) is called dispersion. This can be understood by analyzing how the refraction at air glass interface results in spreading of white light (Fig. 3). dn For normal dispersion, dλ < 0, refraction at shorter wavelength is more, as it can be demonstrated from Huygens’ principle. In anomalous dispersion, dn dλ > 0, the refraction at higher wavelength will be more. In the range of absorption, the conventional refractive index increases with the wavelength of light and this is called anomalous dispersion. Velocity of a mixture of light is called group velocity, which is given as: Cg ¼ Cp  λ

dC p dλ

Group refractive index: ngroup ¼ nðλÞ  λ

dnðλÞ dλ

Law of refraction: n1 sin θ1 ¼ n2 sin θ2 Table 1 Refractive index of common materials. Material

Refractive index @ 546 nm

Air CO2 Water Ethanol Benzene Quartz Plexiglass Diamond

1.00029 1.00045 1.33 1.36 1.51 1.46 1.49 2.42

Nano-optics: challenges, trends, and future

Fig. 3 Normal dispersion of white light into constituent colors.

This is the very important law of refraction, the physical consequences of which have been studied, at least on record, for more than 1800 years. On the basis of some observations, Claudius Ptolemy of Alexandria attempted unsuccessfully to derive the expression. Kepler nearly succeeded in deriving the law of refraction in his book Supplements to Vitello in 1604. Unfortunately he was misled by some erroneous data compiled by Vitello. The correct relationship seems to have been identified first by Snell at the University of Leyden and then by the French mathematician Descartes. In English-speaking countries, this law is referred to as Snell’s law. Though unnoticed, in Baghdad, in the 10th century, an unknown scientist, Abu Sad Al Alla Ibn Sahl, excelled in optics and in his book Burning Mirrors and Lenses, in AD 984, he set out the present-day laws of refraction and how lenses and curved mirrors bend and focus light. However, this has not been much credited, and the law is still known by the name of Snell or Descartes. It is worth noting that Snell’s law (Fig. 4), discovered in Holland in the year 1621, was not well-known until Descartes published it in 1638. The wavelength dependence of the refractive indices is different for different materials, and the dispersion compensation is possible by combining different glasses. If the total refraction is the same for two wavelengths, the system is called achromatic, and if the dispersion compensation works for three wavelengths, it is called apochromatic. Hartmann has given an explanation for the wavelength related dispersion as follows: Cdisp nðλÞ ¼ n0 + 0:5 < α < 2 ðλ  λ0 Þα where Cdisp and λ0 are constant.

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Fig. 4 Snell’s law.

Sellmeir has described the refractive index in the wavelength range of ultra violet to infrared as follows: nðλÞ ¼ 1 + εm

λ2 Adisp, m λ2  λ2m

where Adisp,m is the coefficient and λm is the resonance wavelength.

1.5 Brewster’s angle At a certain angle of incidence θB, the reflected light is perfectly polarized with the polarization direction parallel to the surface (Fig. 5) n2 tanθB ¼ n1 If the first medium is air, then n1 ¼ 1, and thus n2 ¼ tan θB. As Brewster’s angle can be determined precisely, the refractive index can be measured using this method, and this forms the basis of the ellipsometry technique, which is being used to measure the refractive index of thin films. We have up until now discussed optics and photonics, and can now briefly consider the role played by nanoparticles in optics.

Nano-optics: challenges, trends, and future

Fig. 5 Schematic representing Brewster’s angle.

1.6 Optical properties of nanoparticles Focusing attention on interaction of light on particles on the nanometer scale, the field of nano-optics has flourished greatly in recent times. On the nanometer scale, materials including metals, semiconductors, dielectrics, and polymers exhibit interesting properties, especially optical properties. Among the various applications, innovative methods to develop thin film coatings have garnered much attention. Nanocomposites have been designed to achieve materials with tunable refractive indices and enhanced optical properties. Deeper and specific focus should be laid on the interaction of light with a material. When light interacts with a material, there are three possible main effects: absorption, transmission, and reflection of light. The nanoparticles exhibit higher specific surface area, surface energy, and density compared to bulk materials. Hence introducing nanoparticles at even lower filler loadings will have tremendous effect on the physical, thermal, and mechanical properties of the matrix [2]. Nanomaterials when combined with polymers have enhanced mechanical and optical properties (e.g., refractive index and coefficient of absorption) and find applications in light-emitting diodes, solar cells, polarizers, light-stable color filters, optical sensors, etc. They also give rise to new characteristics like light emission [3]. The optical properties of nanoparticles have been investigated for various applications like UV-filters, bio-imaging, photo thermal therapies, oxygen sensors, etc. [4]. Ceria nanoparticles have attracted much attention as luminescent material and material with a high refractive index [5]. Ceria nanoparticles are highly biocompatible and hence several studies have focused on the use of cerium oxide nanoparticles as

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contrast agents for MRI [6]. Cerium is the most abundant element in the rare earth family. Ce has electronic configuration [Xe] 4 f26s2 and has two common oxidation states, Ce3+ and Ce4+ [7]. Cerium has shielded 4f-electrons, which are responsible for the fascinating properties of the rare earth element. PS/PMMA-grafted CeO2 nanocomposite films were prepared by Parlak et al. using spin coating. It was observed that blending of PS and CeO2 nanoparticles resulted in opaque films, whereas grafting of PMMA chains onto CeO2 enhanced the transparency of the composite. This is because there is a strong refractive index mismatch between PS and CeO2 particles. However, when PMMA with a lower refractive index than PS and CeO2 is incorporated, the refractive index mismatch is nullified [8].

2. Challenges: Nano-optics bottleneck Electronic devices in principle work by the manipulation of the flow of electrons. Since the size of an atom is around 0.1–0.5 nm, we can safely assume that the size profile of an electron (if we can speak about the “size” of an electron at all) must be infinitesimally smaller than nanometer dimensions. This effectively means that the electrons are capable of working in dimensions smaller than the size of atoms. Photonic devices, on the other hand, manipulate light, or more specifically electromagnetic radiation near or around visible light. Since the wavelength of light in the region around visible light is in terms of hundreds of nanometers, no photonic devices can therefore be smaller than the wavelength profile of light. This is a major obstacle that limits the plethora of benefits offered by photonic technology while downscaling the size. Though the size parameter is a major stumbling block, it has not limited the curiosity and enthusiasm of physicists to investigate the interaction of light in subwavelength dimensions, and what they found out was enthralling. Light in fact changed its behavior in subwavelength dimensions. Nano-optics emerged as the branch of science dealing with the study of the interaction of light in nanometer dimensions. The branch requires a vivid and avid idea of the complex and rigorous theoretical knowhow as nanoscale interaction of light can barely be understood with the basic knowledge of the interaction of light in the daily life.

3. Trends: Current scenario in nano-optics As stated above, it is impossible to downscale light beyond a limit, more precisely beyond half the wavelength. If that is so, why should we bother about interaction of light on a subwavelength scale? Nano-optics researchers are exploiting this possibility of confining light in a single spatial direction. The field has already branched out into many specific areas of interest.

Nano-optics: challenges, trends, and future

Nano-optics is an emerging area which deals with the manipulation of light at a scale which is much less than the wavelength of light itself. Some innovations in the areas of 3D optical lithography, microscopy beyond the diffraction limit, optical computing at the chip level, and energy efficient light to energy and energy to light conversion are some of the contributions in this research area of nano-optics. These innovations are giving rise to new fields of their own, like superresolution lithography and microscopy. The enhancement of interaction of light with nanoparticles is much sought after. Enhancement of solar energy conversion is being studied very rigorously these days and the improvements in the light guides or concentrators and innovation in the materials used for the light conversion are being sought out, such as making materials on the nanoscale, and making necessary morphological and structural changes to the materials. As a whole, the interaction of light with optical elements, the interaction of light with nanoparticles, and the manipulation of light emerging from nanoparticles are the broad areas of nano-optics. Some recent research has been carried out on the following areas. Further reading on these will be very useful, as detailed explanations on each topic are beyond the scope of this chapter. • Quantum control of atoms using ultra short pulses of laser and optical trapping and cooling of single nanoparticles, which is useful for precise and sensitive detection. • Magneto plasmonics where magnetic fields are used to influence the plasmonics. The magneto-refractive (MR) effect is also being researched, where the optical properties of a system can be controlled using magnetic field-controlled electrical resistivity. • Thermophotovoltaics (TPV), where a solar absorber or emitter is inducted in front of a photovoltaic cell in order to enhance the efficiency. • Nonlinear plasmonics, where nonlinear effects are explored when plasmonic nanostructures are used. • Spatio-temporal control of light where ultrafast optical sources are combined with scattering nearfield microscopes in the presence of 2D materials. • Optical antennas, which are used to enhance local light matter interaction, related to single quantum sources. • Near field optics and near field quantum optics, where evanescent fields are being exploited for sensing and analyzing the change in the properties of materials and light when the near field measurements are being carried out, leading to better control of the materials and photons at that level. • Nanocarbon photonics where the interaction of light with graphene, carbon nanotubes, graphene quantum dots, etc. are being studied along with the effects of dopants and defects in carbon nanostructures. • Optofluidics in nano regimes where optical interferometery and nanofluidics are combined for ultra-precise measurements and detections.

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• Spasers (surface plasmon amplification by stimulated emission of radiation), where the oscillations are maintained by the stimulated emission of surface plasmons. Spasers are nanoscale lasers.

4. The future: A world of possibilities Nano-optics is still a very new topic, and experimental realization of the possibilities of nanoscale interactions in the optical realm started only in 1984 when the optical counterpart of scanning tunneling microscope (STM) was developed. Still not even 50 years old, the science of nano-optics is now a multimillion dollar industry. The possibilities are bigger when we go nanoscale, from immense medical advantages to military applications and beyond. If we can control a nanometer-sized device perfectly, it may even drive humanity to the doorstep of immortality. Nanoscale optical devices can bring the vibrant colors of nature to our living rooms, and the manipulation of light might overpower the electronics industry as we know it. Better and more efficient power generation is another result worth mentioning.

5. Conclusion Wonders of nano-optics are not at all new in nature. The vibrant hues we see in peacock feathers, the magnificent colors on a butterfly’s wing, all make use of nano-optic properties of light. The technology is used in photosynthesis by plants. Understanding the science behind nano-optics essentially bestows us with a better understanding of nature.

References [1] H.H. Hopkins, N.S. Kapany, A flexible fibrescope, using static scanning, Nature 173 (1954) 39–41. [2] A.M. Dı´ez-Pascual, Nanoparticle reinforced polymers, Polymers (Basel) 11 (2019) 625. [3] I. Roppolo, M. Sangermano, A. Chiolerio, Optical properties of polymer nanocomposites, In: Functional and Physical Properties of Polymer Nanocomposites (2016) 139–157. [4] A. Gupta, S. Das, C.J. Neal, S. Seal, Controlling the surface chemistry of cerium oxide nanoparticles for biological applications, J. Mater. Chem. B 4 (2016) 3195–3202. [5] H. Gu, M.D. Soucek, Preparation and characterization of monodisperse cerium oxide nanoparticles in hydrocarbon solvents, Chem. Mater. 19 (2007) 1103–1110. [6] P. Eriksson, A.A. Tal, A. Skallberg, C. Brommesson, Z. Hu, R.D. Boyd, W. Olovsson, N. Fairley, I.A. Abrikosov, X. Zhang, Cerium oxide nanoparticles with antioxidant capabilities and gadolinium integration for MRI contrast enhancement, Sci. Rep. 8 (2018) 6999. [7] C. Sun, H. Li, L. Chen, Nanostructured ceria-based materials: synthesis, properties, and applications, Energy Environ. Sci. 5 (2012) 8475–8505. [8] O. Parlak, M.M. Demir, Toward transparent nanocomposites based on polystyrene matrix and PMMA-grafted CeO2 nanoparticles, ACS Appl. Mater. Interfaces 3 (2011) 4306–4314.

CHAPTER 3

Nano-optics for healthcare applications Blessy Josepha, Jemy Jamesa,b, Nitin Eapena, Nandakumar Kalarikkala, Sabu Thomasa a

International and Inter University Centre for Nanoscience and Nanotechnology, Mahatma Gandhi University, Kottayam, Kerala, India b FRE CNRS 3744, IRDL, University of Southern Brittany, Lorient, France

1. Introduction Nanotechnology is the art of making things smaller. The field of nanotechnology is growing quickly and steadily. Nanoparticles have been used in the design of highly functional materials due to their unique physical and chemical properties. Different types of nanoparticles like metallic, non-metallic, and magnetic, carbon nanotubes, etc. are widely used for various applications. However, inorganic nanoparticles (metallic and metallic oxide nanoparticles) are of great interest due to their excellent physical and chemical properties. They have been particularly effective in the strategic design of optical devices. The need for miniature-sized and lightweight optical components is increasing day by day. This is possible by tailoring the size and shape of nanoparticles. Metallic nanoparticles like gold nanoparticles show tunable radiation and absorption wavelength depending on their aspect ratio, arising due to localized surface plasmon resonance (LPSR) [1, 2]. When the incident light of certain frequency falls on metallic nanostructures, the free electrons in the material resonate at a frequency that is dependent on the size and shape of the material (Fig. 1). The particles will strongly absorb or scatter light when the wavelength of the incident light matches the oscillating frequency. This phenomenon, described as LSPR, involving light matter interaction, a characteristic of plasmonic nanostructures, is exploited for several applications in biomedical sector. The field that studies this light matter interaction at nanometer scale—known as nanophotonics or nano-optics—has been successful in developing new imaging modalities, especially for medical imaging and diagnosis. The optical properties of nanoparticles were initially described in 1857 by Michael Faraday. Studies on plasmonics started in 1899, and experimental observations of plasmonic effects in light spectra were investigated by Robert Wood in 1902 [3]. Inorganic nanoparticles exhibit superior material properties with functional versatility. They are being explored as potential tools for diagnostics as well as for treating diseases due to their size features and advantages over conventional chemical imaging techniques. They have been widely used for cellular delivery due to their versatile

Nano-Optics https://doi.org/10.1016/B978-0-12-818392-2.00003-2

© 2020 Elsevier Inc. All rights reserved.

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Electric field

34

+



+ +





+





+





+ +



+

Light wave

Nanoparticle

Fig. 1 Schematic diagram illustrating the localized surface plasmon on a nanoparticle surface. (Reproduced with permission from S. Unser, I. Bruzas, J. He, L. Sagle, Localized surface plasmon resonance biosensing: Current challenges and approaches, Sensors (Switzerland). 15 (2015) 15684–15716. https://doi.org/10.3390/s150715684.)

features, such as wide availability, rich functionality, good biocompatibility, capability of targeted drug delivery, and controlled release of drugs. For example, gold nanoparticles have been extensively used in imaging, as drug carriers, and in thermotherapy of biological targets. The interesting colors of metallic nanoparticle solutions are due to the red shift of the plasmon band to visible frequencies, unlike that for bulk metals, where the plasmon absorption is in the ultraviolet region. In fact, the optical properties of nanoparticles depend significantly on their size and shape as well as on the dielectric constant of the surrounding medium. Nanoparticles such as 20 nm sized gold (Au), platinum (Pt), silver (Ag), and palladium (Pd) have characteristic wine red, yellowish gray, black, and dark black colors, respectively [4]. Thus LSPR acts as a powerful tool to manipulate light on the nanoscale. This chapter concentrates on the fundamental principles behind nano-optics and the use of optically active nanomaterials for biomedical applications.

2. Nano-optics for bio imaging The ultimate goal of bio imaging is to monitor and record structural and functional information related to biological materials. Over the decades, several imaging systems have been evolved to visualize biological specimens noninvasively, such as X-ray computed tomography (CT), magnetic resonance imaging (MRI), optical coherence tomography (OCT), etc. Optical techniques are highly desirable for bio imaging due to the fact that they are safe, less expensive, and rapid when compared to other conventional techniques. Nanoparticles present new opportunities for bio imaging, providing increased sensitivity in detection through amplification of signal changes. They exhibit specific cellular uptake

Nano-optics for healthcare applications

and possess properties that make them easily visible, such as quantum dots. Nanoparticles act as tracers for improved visualization. Since light absorption from biologic tissues is at its minimum at the near infrared (NIR) wavelengths, most nanoparticles like metal and magnetic nanoparticles are designed to absorb strongly in the NIR region. Thus they have proven to possess remarkable advantages for in vivo imaging [5]. Magnetic resonance imaging (MRI) is a noninvasive imaging modality that offers both anatomical and functional information. Iron oxide nanoparticles are of considerable interest as contrast agents for MRI due to their unique superparamagnetic properties [6, 7]. MRI has been extensively studied for the successful tracking of stem cells. Lin et al. reported the use of superparamagnetic iron oxide nanoparticles (SPIONs) to label mesenchymal stem cells (MSCs). The distribution and migration of MSCs were evaluated using MRI for up to 6 weeks [8]. Nickel ferrite nanoparticles also have been developed for MRI contrast enhancement [9]. In order to alleviate the possible toxicity by nickel, ferrite nanoparticles were coated with oleic acid and tetramethyl ammonium hydroxide (TMAH), where TMAH also acted as a stabilizer. There are two particular types of contrast agents: T1 MRI and T2 MRI [10]. T1 MRI is also known as a positive contrast agent (CA) since it gives brighter images. Gadolinium-based chelates belong to this class. Toxicity is a major concern with this group of contrast agents [11]. The other class, T2 MRI CA, includes dextran- or siloxane-coated super paramagnetic iron oxide nanoparticles [12]. This is also known as negative CA due to the darker images obtained. T1 CA is preferred due to the highly brightened images that are obtained. Several studies have focused on the alternatives for T1 MRI contrast agents. Li et al. reported the advantages of pH-sensitive cross-linked iron oxide nanoparticle assemblies (IONAs) as T1 MRI contrast agents for imaging tumors [13]. The IONAs were extremely stable at neutral pH and had a blood circulation half-time of nearly 2.2 h. Moreover the in vivo studies showed that the IONAs didn’t induce any renal toxicity or hepatotoxicity. The conversion of near-infrared radiations into visible light via nonlinear optical processes, termed upconversion, has received considerable attention in biomedical applications. Among the nanoparticles that exhibit upconversion, rare-earth-doped nanoparticles are considered as an alternative to traditional bio labels. The main advantages include good chemical and physical stability, better spatial resolution, and low toxicity [14]. It is particularly advantageous for background free biological sensing [15]. The main advantages of this technique include improved sensitivity due to the absence of autofluorescence and deeper penetration into tissue, leading to less damage to biological tissues [14]. Upconversion nanoparticles doped with lanthanide ions received considerable attention in this aspect [16]. Europium complex loaded polymer nanoparticles (ECP-NPs) were studied for live cell imaging [17]. PMMA-bearing carboxylate and sulfonate groups were loaded with 1%–40% Europium complexes. The ECP-NPs were incubated with HeLa cells for 3 h and TG (time-gated) imaging was done at a very low excitation power density of 0.24 W cm 2. It could be clearly seen that strong photoluminescence signals arise from the endocytosed ECP-NPs (Fig. 2).

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Fig. 2 Live-cell images of HeLa cells incubated with ECP-NPs. (A) TG PL images with PMMA-COOH 10% (A1), PMMA-SO3H 3% (A2), and PMMA-SO3H 1% (A3) NPs at 40% [Eu(tta)3phen] loading. For a better comparison between the different images, the intensity scale was fixed at 0 to 2  104 counts. (B) Differential interference contrast (DIC) images. (C) Overlay of images from A and B (ECP-NP PL is shown in red, PL intensities were normalized to the highest values in A1, A2, and A3). Scale bars in all images: 10 μm. (Reproduced with permission from M. Cardoso Dos Santos, A. Runser, H. Bartenlian, A.M. Nonat, L.J. Charbonnière, A.S. Klymchenko, N. Hildebrandt, A. Reisch, Lanthanide-complex-loaded polymer nanoparticles for background-free single-particle and live-cell imaging, Chem. Mater. 31 (2019) 4034–4041. https://doi.org/10.1021/acs.chemmater.9b00576.)

3. Nano-optics for biosensing Nanotechnology is becoming a crucial driving force behind innovation in medicine and healthcare, with a range of advances including nanoscale therapeutics, biosensors, implantable devices, drug delivery systems, and imaging technologies. Nanophotonics are particularly attractive in that they provide minimally invasive diagnostics for early detection of diseases and help in real-time monitoring of drug intake. Early detection of cancer can save more lives than any form of treatment at advanced stages. Circulating tumor cells (CTCs) are viable cancer cells derived from tumors. The origin of the metastatic disease is represented by these CTCs. Using nanotechnology, we can develop devices that indicate when these CTCs appear in the body, and hence deliver agents to reverse premalignant changes or kill those cells that have the potential to become malignant. Researchers have demonstrated a carbon nanotube chip that captures and analyses circulating tumor cells in blood, rather than using magnetic and microfluidic methods for the isolation of CTCs [18]. The schematic of the sensing technique is given in Fig. 3. Some interesting works have been reported by Tseng et al., in which a

Nano-optics for healthcare applications

Fig. 3 Schematic of the device for capture of cells spiked in blood. (Reproduced with permission from F. Khosravi, P.J. Trainor, C. Lambert, G. Kloecker, E. Wickstrom, S.N. Rai, B. Panchapakesan, Static micro-array isolation, dynamic time series classification, capture and enumeration of spiked breast cancer cells in blood: the nanotube-CTC chip, Nanotechnology. 27 (2016). https://doi.org/10.1088/09574484/27/44/44LT03.)

nano-silicon platform was developed to capture and release circulating tumor cells [19, 20, 20a]. The 3D substrate was a silicon nanowire substrate (SiNS), which was coated with an epithelial cell adhesion molecule antibody (anti-EpCAM). EpCAM is a transmembrane glycoprotein usually overexpressed in tumor cells. AntiEpCAM acted as the capturing agent due to its specificity for CTCs. The anti-EpCAM capture agent is grafted to the substrate by biotin-streptavidin conjugation (Fig. 4). Due to the characteristic Velcro-like interactions between SiNs and nanoscale cell-surface components, the assay is termed “NanoVelcro assay.” Similarly, the 2nd-gen NanoVelcro Chip developed using PLGA NanoVelcro substrates could interact with nanoscale cellular structures in a similar way to the SiNS, and the SEM images of CNCs on SiNS and PLGA NanoVelcro substrates are depicted in Fig. 4D and E. Another example of nano-biosensors is black phosphorus (BP). This is a fiber optic biosensor for ultrasensitive diagnosis of human neuron-specific enolase cancer biomarkers. Integrating BP nano sheets with largely tilted fiber grating (BP-TFG) exploits its optical-nano configuration, where the BP is bio-functionalized by the poly-L-lysine acting as a critical cross-linker to facilitate bio-nano-photonic interface with extremely enhanced light-matter interaction. The enhanced sensitivity of BP-TFG is 100-fold higher than graphene oxide or AuNPs based biosensors. Black phosphorous-fiber optic configuration opens a new bio-nano-photonic platform for applications in healthcare, biomedical, food safety, and environmental monitoring [21].

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Fig. 4 Velcro-like working mechanism of NanoVelcro cell-affinity substrates. (A) An anti-EpCAM-coated SiNS was employed to achieve significantly enhanced capture of CTCs in contrast to (B) an anti-EpCAMcoated flat silicon substrate. (C) Anti-EpCAM is grafted onto SiNS to confer specificity for recognizing CTCs. (D, E) SEM images of a SiNS and PLGA NanoVelcro substrate, on which MCF7 cells and prostate cancer CTCs were captured, respectively. Reproduced with permission from M. Lin, J.F. Chen, Y.T. Lu, Y. Zhang, J. Song, S. Hou, et al., Nanostructure embedded microchips for detection, isolation, and characterization of circulating tumor cells, Acc. Chem. Res. 47 (10) (2014) 2941–2950.

Optical engineers at Harvard developed fiber-optic catheters using metalenses; this was termed a nano-optic endoscope [22]. Interestingly, they could image deep into tissues at higher resolutions. Plasmonic-based sensors are becoming the method of choice in label-free detection of biomolecules. Because surface plasmon resonance (SPR) is inherently sensitive to a small change in the refractive index (RI) of the dielectric environment, its potential in sensing applications has drawn enormous attention. SPR- and localized SPR- (LSPR-) based sensors have been widely used in several applications, such as power harvesting, biological sensing, medical treatment, and sub wavelength imaging. Simpler instruments are required for LSPR-based sensing technology using metallic nanoparticles when compared with SPR sensors [23]. Detection of small traces of bacteria or virus is

Nano-optics for healthcare applications

Fig. 5 Biotransferable graphene wireless nanosensor. (A) Graphene is printed onto bioresorbable silk and contacts are formed containing a wireless coil. (B) Biotransfer of the nanosensing architecture onto the surface of a tooth. (C) Magnified schematic of the sensing element, illustrating wireless readout. (D) Binding of pathogenic bacteria by peptides self-assembled on the graphene nanotransducer. (Reproduced with permission from M.S. Mannoor, H. Tao, J.D. Clayton, A. Sengupta, D.L. Kaplan, R.R. Naik, N. Verma, F.G. Omenetto, M.C. McAlpine, Graphene-based wireless bacteria detection on tooth enamel, Nat. Commun. (2012). https://doi.org/10.1038/ncomms1767.)

one of the potential fields of research in bio nanotechnology. Due to the growing resistance to antibiotics by many bacteria, early stage diagnosis is becoming crucial to alleviate microbial infections. A highly sensitive sensor based on graphene was developed by Mannor et al. [24]. The sensors were printed onto water soluble silk substrate, which is flexible and biocompatible, and finally the whole structure was transferred to the surface of infection, like tissue or teeth (Fig. 5).

4. Nano-optics for cancer therapy Nearly 85% of human cancers are solid tumors and can be effectively removed through surgery [25]. After surgery, the patients are treated with immunotherapy, radiotherapy, chemotherapy, etc. The main drawback with chemotherapy is its high dose and associated side effects [26]. There has been continuous demand for targeted therapies that clearly differentiate between healthy cells and diseased cells [27, 28]. Newer approaches to cancer treatment involve the use of laser light-induced hyperthermia/thermal ablation, i.e., photo thermal therapy. Metallic nanostructures get heated up when irradiated with NIR light [Fig. 6]. This phenomenon, known as photothermal ablation, can be used to kill cancer cells effectively. Nanomaterials have gained tremendous attention in the recent past owing to their efficacy as photosensitizers or photothermal transducers [29]. Gold-based nanostructures have received great impetus in research owing to their biocompatibility and flexibility in shape, size, and surface chemistry. The fast thermalization of gold nanoparticles (AuNPs) when selective absorption of light occurs, combined with the NIR plasmon resonance, makes them suitable as contrast agents for photothermal therapy [30]. Gold nanoparticles, due to their small size, tend to accumulate in tumor tissues due to the characteristic

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Heat hw0

Fig. 6 Photo-induced thermalization of gold nanoparticles. (Reproduced with permission from I.K. Alexey Povolotskiy, Marina Evdokimova, Alexander Konev, A.P., A. Kalinichev, Molecular-plasmon nanostructures for biomedical application, in: Makarov V., Yamanouchi K., Tunik S. (Ed.), Progress in Photon Science, Springer Series in Chemical Physics, Springer, Cham, 2019: pp. 173–193. https://doi. org/10.1007/978-3-030-05974-3_9.)

enhanced permeation and retention effect [31]. This is because the tumor cells show leaky vasculature and minimal lymphatic drainage. Moreover, the surface of gold is rich in thiols, which makes functionalization with tumor targeting moieties easier [32]. The abovementioned properties of gold nanoparticles make them the preferred candidate for photothermal therapy and photodynamic therapy (Fig. 7). The history of phototherapy greatly acknowledges the contributions by Niels Ryberg Finsen. He was well-known for his phototherapeutic approaches for treatment of various diseases [34, 35]. He was awarded the Nobel Prize in medicine in 1903, for his work on the treatment of diseases, particularly the treatment of lupus vulgaris by means of concentrated light rays [34]. PDT is another promising strategy that can be used against cancer. It involves the irradiation of the drug known as a photosensitizing agent using light of suitable wavelength [33]. As a result, molecular oxygen is generated, which kills the cancer cells.

Fig. 7 Schematic illustration of the physiological and biological effects of gold nanoparticle-mediated photothermal therapy (PTT) and photodynamic therapy (PDT). A large amount of gold nanoparticles accumulate due to the leaky vasculature of the tumor, resulting in a photothermal effect in response to near-infrared (NIR) light and reactive oxygen species (ROS) generated by secondary delivered photosensitizer (PS), ultimately inducing apoptosis and necrosis of tumor tissue [33].

Nano-optics for healthcare applications

PTT has been used in the clinical treatment of breast cancer, liver cancer, melanoma, and many other malignant tumors [36, 37]. The effect of gold nanorods (AuNRs) on human oral squamous cell carcinoma was investigated by Ali et al. They showed that AuNRtreated cells underwent apoptosis [38]. The mechanism of PPT therapy was better understood using metabolomics and proteomics. The authors showed that apoptosis was attributed to the altered phenylalanine metabolism. This study gives great insight into the molecular mechanisms underlying PTT-mediated apoptosis. Many studies have demonstrated the successful utilization of gold nanoparticles of various shapes like nanorods, nanostars, nanospheres, etc. for photothermal applications. Some recent approaches in this direction include gold nanostars in combination with multiwalled carbon nanotubes (MWCNTs) [39]. The hybrid material exhibited enhanced photothermal efficiency compared to the control (gold nanostars alone). They highlighted that the increased light absorption conferred by the MWCNTs in combination with the plasmonic effects from the gold nanostars could be responsible for this improved photothermal effect. The hybrid materials were biocompatible, as evident from the in vitro studies using melanoma cell line B16F10. In another study, the photothermal activity of gold nanostars functionalized with aptamer was evaluated [40]. The authors reported that upon NIR laser irradiation (808 nm), the solution containing aptamer conjugated gold nanostars, showed a rapid increase in temperature as a function of time. The solution temperature of the aptamer-conjugated gold nanostars also increased with an increase in photodensity (Fig. 8). The control used was ultrapure water, which displayed no increase in temperature. Organic nanoparticles like graphene oxide nanoparticles also have unique optical and structural properties that make them effective for photothermal therapy [41]. 55

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Quantum dots (QDs) semiconducting, light-emitting nanocrystals have emerged as powerful fluorescent marker and drug delivery vehicles [42]. Savla et al. developed a quantum dot-mucin1 aptamer-doxorubicin conjugate against ovarian cancer [43]. Here, the anticancer drug doxorubicin (DOX) was loaded into QD conjugated with aptamer specific for mucin (MUC1), which is overexpressed in many cancers. In the acidic environment, successful release of doxorubicin was observed with nearly 35% of drug released after 5 h. Cell viability studies confirmed no notable cytotoxicity of unmodified QD and MUC1 aptamer-QD conjugate. DOX delivered by tumor-targeted QD-MUC1-DOX conjugates accumulated predominately in the tumor and there was an increase in toxicity and a decrease in IC50 value (Fig. 9). 120

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Fig. 9 Cytotoxicity of different formulations of quantum dots (QDs). Multidrug resistant A2780/AD human ovarian carcinoma cell were incubated with the indicated formulations. Upper panel: cellular viability of formulations with and without DOX. Bottom panel: IC50 doses of formulations that contain DOX. (Reproduced with permission from R. Savla, O. Taratula, O. Garbuzenko, T. Minko, Tumor targeted quantum dot-mucin 1 aptamer-doxorubicin conjugate for imaging and treatment of cancer, J. Control. Release, 2011. https://doi.org/10.1016/j.jconrel.2011.02.015.)

Nano-optics for healthcare applications

Fig. 10 Sensitivity and multicolor capability of QD imaging in live animals. (A, B) Sensitivity and spectral comparison between QD-tagged and GFP transfected cancer cells (A), and simultaneous in vivo imaging of multicolor QD-encoded microbeads (B). The right-hand images show QD-tagged cancer cells (orange, upper) and GFP-labeled cells (green, lower). (Reproduced with permission from X. Gao, Y. Cui, R.M. Levenson, L.W.K. Chung, S. Nie, In vivo cancer targeting and imaging with semiconductor quantum dots, Nat. Biotechnol. (2004). https://doi.org/10.1038/nbt994.)

Polymer encapsulated QD probes were developed by Gao et al. [44] The detection sensitivity of QDs was compared with green fluorescent protein (GFP), which has usually been used for tagging cancer cells. Both the QD-tagged and GFP-transfected cells were equally bright in cell cultures, but the QD signals were only visible in vivo (Fig. 10). This shows that the emission spectra of the QDs can be shifted away from the autofluorescence, allowing spectroscopic detection at low signal intensities.

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5. Conclusion The field of medicine has gained extensively from the rapid development in the field of optics. The diagnosis and treatment modalities used from earlier times are usually timeconsuming and impose financial burdens on patients. Nano-optics provides versatile tools for the scientific community and medical field for imaging and diagnostics. Noninvasive technologies using nano-optics provide better resolution compared to conventional methods. Nanotechnology has opened possibilities for engineering various nanoparticles for biomedical applications. Increased sensitivity, specificity, and cost-effectiveness have made nanostructures highly appealing. Moreover, the use of nano-optics has helped to improve the quality of medical diagnosis. However, toxicity is still a major concern with nanoparticles, hence it is important to analyze the interactions of the nanomaterials with biological systems.

References [1] M.M. Bellah, S.M. Christensen, S.M. Iqbal, Nanostructures for medical diagnostics, J. Nanomater. 2012 (2012), https://doi.org/10.1155/2012/486301. [2] S. Unser, I. Bruzas, J. He, L. Sagle, Localized surface plasmon resonance biosensing: Current challenges and approaches, Sensors (Switzerland). 15 (2015) 15684–15716, https://doi.org/10.3390/s150715684. [3] E.A. Coronado, E.R. Encina, F.D. Stefani, Optical properties of metallic nanoparticles: manipulating light, heat and forces at the nanoscale, Nanoscale 3 (2011) 4042–4059, https://doi.org/10.1039/ c1nr10788g. [4] I. Khan, K. Saeed, I. Khan, Nanoparticles: properties, applications and toxicities, Arab. J. Chem. (2017), https://doi.org/10.1016/j.arabjc.2017.05.011. [5] J. Conde, J. Rosa, J.C. Lima, P.V. Baptista, Nanophotonics for molecular diagnostics and therapy applications, Int. J. Photoenergy (2012), https://doi.org/10.1155/2012/619530. [6] J.S. Weinstein, C.G. Varallyay, E. Dosa, S. Gahramanov, B. Hamilton, W.D. Rooney, L.L. Muldoon, E.A. Neuwelt, Superparamagnetic iron oxide nanoparticles: Diagnostic magnetic resonance imaging and potential therapeutic applications in neurooncology and central nervous system inflammatory pathologies, a review, J. Cereb. Blood Flow Metab. (2010), https://doi.org/10.1038/ jcbfm.2009.192. [7] A. Neuwelt, N. Sidhu, C.A.A. Hu, G. Mlady, S.C. Eberhardt, L.O. Sillerud, Iron-based superparamagnetic nanoparticle contrast agents for MRI of infection and inflammation, Am. J. Roentgenol. (2015), https://doi.org/10.2214/AJR.14.12733. [8] B.L. Lin, J.Z. Zhang, L.J. Lu, J.J. Mao, M.H. Cao, X.H. Mao, F. Zhang, X.H. Duan, C.S. Zheng, L.M. Zhang, J. Shen, Superparamagnetic iron oxide nanoparticles-complexed cationic amylose for in vivo magnetic resonance imaging tracking of transplanted stem cells in stroke, Nanomaterials 7 (2017), https://doi.org/10.3390/nano7050107. [9] E. Umut, M. Cos¸kun, F. Pineider, D. Berti, H. G€ ung€ unes¸, Nickel ferrite nanoparticles for simultaneous use in magnetic resonance imaging and magnetic fluid hyperthermia, J. Colloid Interface Sci. 550 (2019) 199–209, https://doi.org/10.1016/j.jcis.2019.04.092. [10] W. Xu, K. Kattel, J.Y. Park, Y. Chang, T.J. Kim, G.H. Lee, Paramagnetic nanoparticle T 1 and T 2 MRI contrast agents, Phys. Chem. Chem. Phys. 14 (2012) 12687–12700, https://doi.org/10.1039/ c2cp41357d. [11] L. Pasquini, A. Napolitano, E. Visconti, D. Longo, A. Romano, P. Toma`, M.C.R. Espagnet, Gadolinium-based contrast agent-related toxicities, CNS Drugs (2018), https://doi.org/10.1007/s40263018-0500-1.

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[12] H. Unterweger, L. Dezsi, J. Matuszak, C. Janko, M. Poettler, J. Jordan, T. B€auerle, J. Szebeni, T. Fey, A.R. Boccaccini, C. Alexiou, I. Cicha, Dextran-coated superparamagnetic iron oxide nanoparticles for magnetic resonance imaging: Evaluation of size-dependent imaging properties, storage stability and safety, Int. J. Nanomedicine 13 (2018) 1899–1915, https://doi.org/10.2147/IJN.S156528. [13] F. Li, Z. Liang, J. Liu, J. Sun, X. Hu, M. Zhao, J. Liu, R. Bai, D. Kim, X. Sun, T. Hyeon, D. Ling, Dynamically reversible iron oxide nanoparticle assemblies for targeted amplification of T1-weighted magnetic resonance imaging of tumors, Nano Lett. (2019), https://doi.org/10.1021/ acs.nanolett.8b04411. [14] M. Wang, G. Abbineni, A. Clevenger, C. Mao, S. Xu, Upconversion nanoparticles: synthesis, surface modification and biological applications, nanomedicine nanotechnology, Biol. Med. 7 (2011) 710–729, https://doi.org/10.1016/j.nano.2011.02.013. [15] S. Wen, J. Zhou, K. Zheng, A. Bednarkiewicz, X. Liu, D. Jin, Advances in highly doped upconversion nanoparticles, Nat. Commun. 9 (2018), https://doi.org/10.1038/s41467-018-04813-5. [16] C. Cao, Q. Liu, M. Shi, W. Feng, F. Li, Lanthanide-doped nanoparticles with upconversion and downshifting near-infrared luminescence for bioimaging, Inorg. Chem. 58 (2019) 9351–9357, https://doi.org/10.1021/acs.inorgchem.9b01071. [17] M. Cardoso Dos Santos, A. Runser, H. Bartenlian, A.M. Nonat, L.J. Charbonnie`re, A.S. Klymchenko, N. Hildebrandt, A. Reisch, Lanthanide-complex-loaded polymer nanoparticles for background-free single-particle and live-cell imaging, Chem. Mater. 31 (2019) 4034–4041, https://doi.org/10.1021/acs.chemmater.9b00576. [18] F. Khosravi, P.J. Trainor, C. Lambert, G. Kloecker, E. Wickstrom, S.N. Rai, B. Panchapakesan, Static micro-array isolation, dynamic time series classification, capture and enumeration of spiked breast cancer cells in blood: the nanotube-CTC chip, Nanotechnology 27 (2016), https://doi.org/ 10.1088/0957-4484/27/44/44LT03. [19] Y.J. Jan, J.F. Chen, Y. Zhu, Y.T. Lu, S.H. Chen, H. Chung, M. Smalley, Y.W. Huang, J. Dong, L.C. Chen, H.H. Yu, J.S. Tomlinson, S. Hou, V.G. Agopian, E.M. Posadas, H.R. Tseng, NanoVelcro rare-cell assays for detection and characterization of circulating tumor cells, Adv. Drug Deliv. Rev. 125 (2018) 78–93, https://doi.org/10.1016/j.addr.2018.03.006. [20] S. Wang, H. Wang, J. Jiao, K.J. Chen, G.E. Owens, K.I. Kamei, J. Sun, D.J. Sherman, C.P. Behrenbruch, H. Wu, H.R. Tseng, Three-dimensional nanostructured substrates toward efficient capture of circulating tumor cells, Angew. Chem. Int. Ed. 48 (2009) 8970–8973, https://doi.org/ 10.1002/anie.200901668. [20a] M. Lin, J.F. Chen, Y.T. Lu, Y. Zhang, J. Song, S. Hou, Nanostructure embedded microchips for detection, isolation, and characterization of circulating tumor cells, Acc. Chem. Res. 47 (10) (2014) 2941–2950. [21] L. Zhou, C. Liu, Z. Sun, H. Mao, L. Zhang, X. Yu, J. Zhao, X. Chen, Black phosphorus based fiber optic biosensor for ultrasensitive cancer diagnosis, Biosens. Bioelectron. (2019), https://doi.org/ 10.1016/j.bios.2019.04.044. [22] H. Pahlevaninezhad, M. Khorasaninejad, Y.W. Huang, Z. Shi, L.P. Hariri, D.C. Adams, V. Ding, A. Zhu, C.W. Qiu, F. Capasso, M.J. Suter, Nano-optic endoscope for high-resolution optical coherence tomography in vivo, Nat. Photonics (2018), https://doi.org/10.1038/s41566-018-0224-2. [23] J. Jiang, X. Wang, S. Li, F. Ding, N. Li, S. Meng, R. Li, J. Qi, Q. Liu, G.L. Liu, Plasmonic nano-arrays for ultrasensitive bio-sensing, Nano 7 (2018) 1517–1531, https://doi.org/10.1515/nanoph-2018-0023. [24] M.S. Mannoor, H. Tao, J.D. Clayton, A. Sengupta, D.L. Kaplan, R.R. Naik, N. Verma, F.G. Omenetto, M.C. McAlpine, Graphene-based wireless bacteria detection on tooth enamel, Nat. Commun. (2012), https://doi.org/10.1038/ncomms1767. [25] A. Shemi, E.Z. Khvalevsky, R.M. Gabai, A. Domb, Y. Barenholz, Multistep, effective drug distribution within solid tumors, Oncotarget 6 (2015) 39564–39577, https://doi.org/10.18632/ oncotarget.5051. [26] J.K. Vasir, V. Labhasetwar, Targeted drug delivery in cancer therapy, Technol. Cancer Res. Treat. 4 (2005) 363–374, https://doi.org/10.1177/153303460500400405. [27] I. Brigger, C. Dubernet, P. Couvreur, Nanoparticles in cancer therapy and diagnosis, Adv. Drug Deliv. Rev. (2012), https://doi.org/10.1016/j.addr.2012.09.006.

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CHAPTER 4

Laser, nanoparticles, and optics Parvathy Nancya, Juby Alphonsa Mathewb, Jerry Joseb, Jemy Jamesd, Blessy Josephd, Ashin Shajie, Sabu Thomasb,c, Nandakumar Kalarikkala,b a

School of Pure and Applied Physics, Mahatma Gandhi University, Kottayam, Kerala, India International and Inter University Centre for Nanoscience and Nanotechnology, Mahatma Gandhi University, Kottayam, Kerala, India c School of Chemical Sciences, Mahatma Gandhi University, Kottayam, Kerala, India d FRE CNRS 3744, IRDL, University of Southern Brittany, Lorient, France e Institute of Physics, Slovak Academy of Sciences, Bratislava, Slovakia b

1. Laser—Introduction In the quantum theory of radiation put forward in 1917, Albert Einstein predicted that the light-matter interaction consists of absorption of light by electrons, spontaneous emission of thus excited electrons through photon emission, and also the stimulated emission of radiation aided by a photon itself [1]. An electron at the excited state, when stimulated by a photon of suitable frequency, de-excites through the emission of another photon of that same frequency. The concept of stimulated emission led to the development of laser-light amplification by stimulated emission of radiation devices. A laser device produces highly directional, monochromatic, coherent, and intense light radiation through population inversion achieved by suitable pumping, a stimulated emission process, and a cavity feedback mechanism. The first laser system was invented and made commercially available by Theodore Maiman in 1960 [2, 3]; it was a pulsed ruby laser with emission at 6943 A˚. In the decades since, the technology of lasers has seen huge advancements and laser devices are indispensable in our day-to-day life with their various medical, military, communication, industrial, and technological applications.

1.1 Laser principle and properties The basic principle of the laser is stimulated emission and oscillation of light inside a resonator cavity. The device contains mainly three components (Fig. 1): • an amplifying medium or gain medium, which amplifies a weak light signal through stimulated emission of radiation; • a pumping source, either electrical or optical, to provide the weak signal for amplification and to achieve population inversion; and • a resonator cavity, for selecting the particular frequency of oscillation or modes of oscillation by providing optical feedback.

Nano-Optics https://doi.org/10.1016/B978-0-12-818392-2.00004-4

© 2020 Elsevier Inc. All rights reserved.

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Pump source

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Fig. 1 Schematic of laser system.

For the lasing action to occur, the lasing device must satisfy certain conditions: • The presence of a metastable state and the state of population inversion in the lasing material: Inside the gain medium, the number of photons in the excited state (Ne) must be more than half the number of photons in the ground state (Ng). This condition is known as population inversion (Ne ≫ Ng). It can be easily achieved inside a gain medium through pumping, provided it contains the metastable state—an excited quantum state in which the lifetime of an excited photon is longer compared to other excited states. • Threshold pump energy: the state of population inversion can be achieved inside the active medium only if the pumping source provides energy greater than a threshold value—the energy at which the gain inside the medium is equal to the losses suffered by the cavity. This is known as the lasing threshold or pump threshold. Above this threshold, the emission intensity increases rapidly and there is a significant decrease in the linewidth of the output spectrum. • Cavity feedback: Optical feedback is sufficient and necessary for the oscillation to occur in the lasing device. It is provided by an optical resonator cavity consisting of two reflectors, a rear mirror, and an output coupler (a partially reflecting mirror). A resonator cavity of length L sustains only that light oscillation which satisfies the condition nλ/2 ¼ L, where λ is the wavelength of emission, and “n” is an integer. Thus it is the resonator cavity that determines the monochromatic characteristic of laser emission, through the constructive interference of the supported modes. Laser emission characteristics include: • the laser beam being highly coherent and being able to propagate to long distances without much divergence; i.e., it has a directional output; and • monochromatic emission output—it has a very narrow emission linewidth.

Laser, nanoparticles, and optics

1.2 Applications of laser in nanotechnology With the exceptional need for miniaturized materials to be used as therapeutic medicines, optoelectronic devices, sensors, and pollution detectors and controllers, development of nanostructured multifunctional materials smaller than 100 nm has been a recent research trend. A highly energetic, monochromatic, directional, and pulsed/continuous laser output finds many applications in the nanostructuring and architecturing of materials at nanoscale (Fig. 2) [4]. It has the potential for modifying and assembling nanomaterials in different geometries with very high surface area to volume ratio and clean surfaces, and in a very short time. Laser ablation of materials is an efficient application of laser devices in which short pulsed, high peak power laser output beams facilitate the dissociation of atoms, molecules, ions, clusters, and particles from the ablated target surface and/or formation of shock waves for plasma initiation and expansion [5]. Laser ablation of suitable target materials in liquid, gas, or vacuum medium is an efficient method for the synthesis of nanomaterials, nanowires, nanocubes, thin films, etc. [6–10]. The properties of the laser-generated nanoparticles such as shape, size, size distribution, and structural composition depend on the laser parameters—frequency, pulse duration, pulse energy, repetition rate—and also on the surrounding conditions (vacuum, nature of gas or liquid) [11]. Laser nanolithography and laser nanostructuring or surface structuring of metal, semiconductor, and insulator materials including polymers are some other applications of laser devices [12–14]. Laser beams are also used for optically characterizing nanomaterials with the objective of realizing their potential applications [15]. Raman spectroscopy for nondestructive chemical analysis and microscopic imaging of molecules, the dynamic light scattering technique to determine particle size distribution and zeta potential of materials,

Laser ablationwater/gas/liquid phase synthesis

Optical characterization of nanomaterials Laser in nanotechnology Laser nanolithography

Laser nonostructuring of polymers

Fig. 2 Laser applications in nanotechnology.

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photoluminescence and fluorescence characterization of materials for optical and sensing applications, time-resolved photoluminescence spectroscopy, and fluorescence correlation spectroscopy and characterization of nonlinear optical parameters of materials are some characterization techniques that employ different laser systems [16–18].

1.3 Applications of nanotechnology in laser devices Nanotechnology deals with the fabrication of nanomaterials for industrial applications, with a view to the development and sustaining of mankind. The nanoscale light matter interaction leads to the quantum confinement effect and refractive index contrast in nanomaterials. These exceptional properties of nanomaterials find applications in laser device fabrication (Fig. 3). Quantum confined structures find applications in laser devices to function as active gain medium in solid state quantum well, quantum dot, and quantum cascade lasers [19–21]. The quantum confinement effect manifests discrete energy levels in solid state nanomaterials. Under proper application of electric potential electron-hole generation, population inversion and then recombination takes place in these materials, which leads to multiple photon generation. Furthermore, the directional confinement in nanostructured materials imparts directionality to the light emission. A recent advancement in the field of laser technology is the fabrication of disordered laser systems, in which incorporated gain medium scatterers cause multiple scattering, which plays the role of a feedback mechanism, causing amplification and stimulated emission in the medium to produce light emission [22]. Random laser and spaser (surface plasmon amplification by stimulated emission of radiation) devices work on this principle. Suitably structured nanomaterials are essential in these devices to act as scatterers and sometimes to take the place of an optical cavity. The principle and applications of random lasers and spasers are discussed in detail in the following sections.

Applications of nanotechnology in laser

Quantum confined laser devices

Quantum confined semiconductor laser

Quantum cascade laser

Fig. 3 Nanotechnology applications in laser devices.

Random laser

SPASER

Laser, nanoparticles, and optics

1.4 Laser-produced nanoparticles In order to tailor the laser ablation process according to one’s specific processing needs, it is important to understand the underlying physics. In simple terms, laser ablation involves exposure of a solid target to highly concentrated energy for the already calibrated time of material removal. When the bulk of the material is irradiated by electromagnetic radiation, the energy is absorbed by target electrons and transferred to the material’s vibrational lattice. This results in formation of a plasma plume, along with formation of nanoparticles from expulsion of material from the incident surface [23, 24]. The temperature gradient between the plume and surrounding liquid and pressure exerted by the medium are carefully designed to confine the plasma plume spatially. This plasma can set up a cavitation bubble, while transferring energy to surrounding fluid on its decay. The expansion and shrinkage of this cavitation bubble releases the nanoparticles to the ambient liquid. Formation of these nanoparticles depends upon the substrate material, laser parameters, and ablation medium. Laser parameters such as wavelength, fluence, pulse duration, and repetition rate determine the ablation rate. The laser parameters can be used to tune nanoparticle features such as size, shape, surface, aggregation, solubility, structure, and chemical composition (Fig. 4). Typically, wavelengths range from UV-Vis to near IR, pulse duration from nanosecond to femtosecond, and fluence

Laser beam Focusing lens

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Ablated material plume

(C)

(E)

Outlet gas

Inlet gas

Produced Plasma nanoparticles

Fig. 4 Experimental configuration of irradiation by focused laser beam for nanoparticle synthesis: (A) solid target in pure solution; (B) colloidal solution; (C) solution of nanoparticles; (D) solid target with nearby substrate; (E) reactor filled with gaseous precursor.

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between 0.1 and 100 J/cm2 are considered among laser parameters [25, 26]. Before going into the details of laser variables, let us consider the influence of environment on ablation. 1.4.1 Synthesis approach The selection of synthesis protocol can greatly influence the structure and properties of the nanoparticles. Physical and chemical properties needed for various applications are dependent on structure and particle parameters such as particle size, geometry, dropping ratio by different elements, degree of agglomeration, etc. Routes for nanoparticle synthesis make it possible to have control of them, with two major approaches: bottomup and top-down. In the bottom-up approach, the precursor is typically ionized liquid or gas that is dissociated, sublimated, or evaporated, and then condensed to form amorphous or crystalline nanoparticles. Produced nanoparticles tend to have fewer defects, homogeneous chemical composition, less contamination, and a narrow size distribution. In pulsed laser synthesis of nanoparticles, infrared pulsed laser pyrolysis (IPLP), infrared pulsed laser induced breakdown (IR-PLIB), and laser-induced dissociative stitching (LIDS) are prominent bottom-up methodologies. In a top-down approach, bulk material is broken down into smaller fragments by application of a source of energy, which can be mechanical, chemical, thermal, or laser irradiation [27, 28]. Pulsed laser ablation (PLA) and pulsed laser deposition (PLD) are prominent top-down approaches in laser synthesis (Fig. 5).

Fig. 5 Bottom-up and top-down approaches in the synthesis of carbon-based nanomaterials.

Laser, nanoparticles, and optics

This method usually results in formation of smaller flakes with wider distribution, and precise size generation using focused beam or lithography demands expensive equipment [29]. The bottom-up approach is considered simpler and more precise in the synthesis of small nanoparticles less than 100 nm, and the top-down approach is preferred for the synthesis of thin films and nanoparticles larger than 100 nm. To understand the effect of material composition on nanomaterial synthesis, it is helpful to review various inorganic and organic nanostructures by pulsed laser methods. Inorganic nanomaterials are of interest due to their high surface area and surface plasmon resonance, which make them good candidates in magnetic, superconductor, and semiconductor applications [30, 31]. The green method of liquid phase pulsed laser ablation (LP-PLA) is widely used in the synthesis of metal colloidal suspension of nanoparticles of metals. As discussed, by tuning chemical properties of ablation medium, the size and stability of inorganic nanoparticles can be controlled [32–34]. In the case of gold [35], metal oxides [36], and quantum dots [37, 38], changes in geometrical shape on irradiation from a second laser in colloidal suspension have been reported.

2. Random lasing Random lasers are disordered lasing systems, in which multiple scattering of light is the source of feedback to the amplifying medium [39, 40]. This operates under excitation from an optical or electrical pump source. Random lasing can be achieved in any highly disordered dielectric material, with the incorporation of a suitable optical gain mechanism. Random lasers can be produced at low cost, since they do not require complex fabrication of an external optical cavity. The optical feedback provided through multiple scattering increases the dwell time or path length of light inside the gain medium. This multiple scattering causes no detriment to the coherence of light, but results in a complex interference pattern known as speckle [41]. Since strong light scattering leads to the randomization of the light path in highly inhomogeneous media, lasers that operate on the basis of these phenomena are known as random lasers (Fig. 6). As in conventional lasers, random lasers also possess a threshold pump energy at which there is a rapid increase in the emission intensity and a corresponding decrease in full width at half maximum of the emission spectrum. For random lasers, the threshold value of pump energy depends on the scattering mean free path of photons in the random media and the luminescence efficiency of the gain medium [42, 43]. The lasing inside the random medium can be explained as follows. The light entering the active medium, embedded with scattering centers, undergoes multiple scattering and gets amplified before leaving the medium. There are mainly two different length scales that explain the role of multiple scattering in providing feedback [44]. The scattering mean free path, ls, is defined as the average distance the light travels between two consecutive scattering events, and is given by:

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Fig. 6 Schematic representation of random laser system in which the feedback to the optical gain medium is provided through multiple light scattering.

ls ¼

1 ρσ s

(1)

where ρ and σ s are the number density and scattering cross section of scattering particles, respectively. The transport mean free path, lt, is defined as the length at which the light has lost its initial direction completely—i.e., the average distance over which the light has randomized completely—and is given by: ls (2) 1  cos θ where cos θ is the average cosine of the scattering angle, which can be found from the differential scattering cross section. Furthermore, light amplification by stimulated emission is described by the gain length, lg, the distance over which the intensity is amplified by a factor of e. Then, the necessary condition for random lasing to occur is that the light must be amplified enough to balance the losses in the medium, before its escape from the gain medium. This requirement can be described as the condition: ls lg. lt ¼

2.1 Coherent and incoherent random lasers Incoherent random lasers are nonresonant lasers in which light in the cavity undergoes multiple scattering and does not return back to its initial position after one round trip in the gain medium. Thus any spatial resonance for electromagnetic fields is absent in such a cavity. Further, the dwell time of the photons is not sensitive to its frequency. The mean frequency of emission of such a laser system is sensitive to its central frequency of the

(A)

Intensity

Intensity

Laser, nanoparticles, and optics

Wavelength

(B)

Wavelength

Fig. 7 Schematic of laser output from incoherent (A) and coherent (B) random lasers.

emission band at which the gain is maximal (Fig. 7). This kind of feedback is called intensity or energy feedback [45, 46]. In a random medium, where the optical scattering is very strong, light may return to the position from where it was scattered and hence forms a closed loop [47]. Light behaves as if it is trapped within the medium and stimulates lasing through constructive interference. These kinds of random lasers are called coherent random lasers. Here the closed loop acts as a resonator cavity, giving a feedback with phase difference of integral multiple of 2π. The first coherent random laser was realized by the team of H. Cao, who obtained coherent laser emissions from semiconductor nanostructures [48]. The main characteristic is spikes in laser emission. All the backscattered waves interfere and only lights of certain frequency undergo constructive interference in a coherent random laser system. Lasing occurs at these particular frequencies, which contributes to the spikes in the emission spectrum. The discovery of coherent random lasers is still in progress. In 2018, Liang et al. reported the achievement of transition from incoherent to coherent random lasing in aggregates of N,N-di [3-(isobutyl polyhedral oligomeric silsesquioxanes) propyl] perylenediimide (DPP) in CS2 (carbon disulfide) solution, due to the addition of polystyrene to the DPP solution [49]. Here, Liang et al. imparted a control over the Brownian motion of the active scatterers-DPP through the increased viscous nature of solution with different polystyrene concentrations. The work demonstrated a transition from coherent to incoherent random lasing due to the increase in system viscosity. They also demonstrated the role of fine control of Brownian motion in tuning the stability time of the scattering loop in a coherent random laser system.

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2.2 Fabrication of the random media: Importance of nanostructured materials A random lasing system can be constructed with the proper selection of suitable materials as gain medium and scatterers along with an appropriate pumping mechanism. Many nanostructured materials have been used as scatterers since the first demonstration of random laser systems, to provide elastic scattering for the essential amplification through stimulated emission to take place. Elastic scattering falls into different categories based on the size of the nanoparticle. Rayleigh scattering deals with scattering by particles of size less than the wavelength of light. However, Mie scattering approximations are more suitable if the shape of the nanoparticles is spherical or cylindrical with a size of the order of wavelength of light. Dyes combined with dielectric nanoparticles are capable of random lasing. The broad fluorescence spectrum and high quantum yield of dyes makes them suitable gain media. Dielectric nanoparticles provide efficient scattering and necessary feedback for the system. Random lasers with a suitable laser dye as gain medium utilized nano-sized biological tissues, rare earth doped materials, semiconductor powders, etc. as scatterers [50–55]. Later, the quantum confinement effect and refractive index contrast in nanomaterials were utilized to develop random lasers with the same nanostructured material serving as both a gain medium and scattering center [56]. Fig. 8 depicts the schematic of a

Nanorod ZnO MgO Sapphire

Effective refractive index 2.1 1.99

Cross section Deposited ZnO

355 nm pump light

ZnO layer ~200 nm thick Excitation are

MgO layer ~700nm thick

Deposited MgO

TE ZnO nanorods

Lasing TM

Sapphire

Transverse confinement of light inside the slab waveguide

Fig. 8 Schematic diagram of the ZnO nanorods embedded in the ZnO epilayers. The arrangement of optical excitation of the system is shown at the bottom. The top left shows the cross section of the sample, and the corresponding effective refractive index of the slab waveguide and ZnO nanorods is shown at the top right corner.

Laser, nanoparticles, and optics

random laser system realized by Yu et al. by embedding ZnO nanorod arrays in ZnO epilayers [57]. The system was fabricated by depositing an MgO buffer layer onto vertically well-aligned ZnO nanorod arrays grown on a sapphire substrate and followed by a layer of ZnO thin film. This formed a slab waveguide with the advantages of extra gain length and reduced scattering mean free path. The refractive index contrast between MgO and ZnO yielded a discontinuity in refractive index between the slab waveguide and ZnO nanorods, and hence sustained the light scattering formed by ZnO nanorods. Some of the random lasing systems realized so far include semiconductor powders such as ZnO [58, 59], CdS [60], ZnSe [61], etc., rare earth metal doped dielectric nanospheres [46], alumina nanoparticles doped polymer film [62, 63], films of CdSe/CdS core/thickshell colloidal quantum dots [64], biological tissues [65], photonic crystals [66], polymer membrane with silver nanoflowers [67], amino-mediated perovskite quantum dots [68], fluorescent dye-diamond nanoneedles hybrid system [69], etc. In fact, some quantum confined structures also serve as optical cavities for the lasing action to occur.

2.3 Plasmonically enhanced random laser to spaser High gain volume and maximized scattering strength are crucial for the proficient working of random lasers. That is why metallic nanoparticles have found a major role in random lasers as randomizing nanostructures. Metallic nanoparticles rich with plasmons—collective oscillation of free electrons under resonance optical excitation along their interface—exhibit the peculiar surface plasmon resonance (SPR) property [70, 71]. The colossal optical polarization then enhances the local electric field at the nanoparticle surface and augments strong optical absorption and scattering by the nanoparticles, at SPR frequency. The local electric field enhancement and larger scattering cross section of metal nanoparticles manifests them as efficient active scatterers in a random laser compared to the dielectric nanoparticles. They are said to be active scatterers since they confine the light near the surface to enable local enhancement in optical gain for lasing. On these grounds, a metallic nanoparticle incorporated random laser system shows a narrow linewidth and low pump threshold compared to dielectric systems [72]. The first SPR enhanced random laser was proposed by G.D. Dice in 2005 [73]. It is based on a silver nanoparticle suspension in the methanol solution of Rhodamine 6G (Rh6G). Furthermore, the lasing performance can be optimized and improvised when the SPR wavelength overlaps with the random laser emission wavelength. This was exploited by O. Popov et al. with the implementation of gold nanoparticles as the plasmonic scatterers in Rhodamine 6G/polymer gain medium [74]. The overlap of SPR wavelength of gold nanoparticles with emission wavelength significantly increased output intensities and decreased pump thresholds in their system. Another enthralling work, on the fabrication of plasmonic random laser with gold nanostars as efficient broad band scatterers, was reported by Ziegler et al. in 2016 [75]. They concocted random lasing with

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a huge spectral coverage ranging from yellow (560 nm) to infrared (970 nm) with starshaped gold nanoparticles suspended in various laser dyes, at a fixed excitation wavelength of 532 nm. The nanostars, with multiple plasmon resonances, provided broadband scattering spectra and hence sufficient spectral overlap with the emission of different laser dyes (Fig. 9). They randomly formed resonant cavities in the gain medium and yielded coherent lasing emission with a narrow linewidth of 0.13 nm. Another development in plasmonic nanolaser device fabrication is the discovery of the spaser (surface plasmon amplification by stimulated emission of radiation) device. The concept of the spaser was put forward by Stockman and Bergman in 2003 [76]. A spaser, unlike a plasmonically enhanced random laser, is a single metallic nanoparticle embedded in gain medium with core-shell geometry, where the feedback provided by surface plasmon resonance overcomes the losses and contributes to lasing [77]. It should be remembered that plasmonically enhanced random lasers consist of an ensemble of metallic nanoparticles randomly located in an optical gain medium. Fig. 10 schematically distinguishes a spaser from a plasmonically enhanced random laser. Spasers provide a plasmonic coherent feedback cavity with a cavity size beyond the diffraction limit of light in a vacuum [78]. The first spaser, demonstrated by Noginov et al. in 2009, was a system of core-shell silica nanoparticles of size 14 nm with gold at the core for providing plasmon enhancement and confinement [79]. The field of spasers is still advancing with many theoretical and experimental investigations into understanding and demonstrating spaser mechanisms.

2.4 Directionality in random lasers A random laser provides broad angular emission covering the entire solid angle, 4π. It is usually non-directional due to the random scattering in all directions. Directionality can be achieved in random lasers with the introduction of unique components such as optical waveguides, planar medium, etc. to confine the direction along one or two directions. Song et al. fabricated a planar random microcavity laser with unidirectional output [80]. The number of resonant modes was decreased due to the optical confinement provided by two-dimensional random cavity and one-dimensional planar microcavity. They obtained a highly directional random laser beam, at a distance 13 cm away from the microcavity, with a divergence of 1.68° (Fig. 11). Directional emission can also be achieved with the implementation of fiber as waveguide in a random laser system. A hybrid polymer random laser at an optical fiber facet was constructed by dipping an optical fiber end face into the solution of Rhodamine 6G and silver nanowires doped polydimethylsiloxane (PDMS) solution. This work, by Songtao Li et al., accomplished the aim of obtaining a directional coherent random laser output [81]. The PDMS film doped with Rhodamine 6G formed an active waveguide layer in which the silver nanowires provided a three-dimensional plasmonic feedback.

2.25

2.07

1.91

1.77 Pyridine 2 in ethanol

Energy (eV) Styryl 8 in DMSO

1.55

1.46

Rhod. 800 Styryl 9m in DMSO in DMSO

1.38 Styryl 15 IR 140 in DMSO in DMSO

1.31 Styryl 14 in DMSO

Emission

Rhod. 6G Rhod. B in Rhod. 101 DCM DCM Pyridine 1 in ethanol methanol in ethanol in ethanol in DMSO in ethanol

1.65

550

600

650

700

750 800 Wavelength (nm)

850

900

950

Fig. 9 Emission from a series of dyes in random lasers containing gold nanostars. The tunability spans from visible to infrared wavelengths for a single-pulsed excitation at 532 nm wavelength. The narrow linewidth peaks emerging from all studied dyes indicate coherent random lasing.

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Fig. 10 (A) Spaser based on an individual metallic nanoparticle with the red lines indicating local electric field and (B) plasmonically enhanced random laser with ensemble of metallic nanoparticles dispersed randomly in dye-based medium. Such an amplifying random medium may also exhibit spasing when the gain is sufficient to compensate for the losses of individual plasmonic nanoparticles.

Fig. 11 (A) Schematic illustration of the random micro laser cavity and (B) the photograph of the far-field laser beam detected 13 cm away from the planar random microcavity laser constructed by Song et al.

The output emission energy was found to become concentrated in a small angle range, along the axis of polymer dipped optical fiber (Fig. 12). Optically pumped dye doped nematic liquid crystals exhibiting directional random lasing was reported by the group of Gaetano Assanto. When suitably pumped near resonance, dye doped nematic liquid crystals support self-guided optical spatial solitons; the light induced waveguides, through molecular reorientation, and hence directional confinement in output beam was obtained from the system [82, 83].

3. Applications of random lasers The lucrative development of random lasers having sample specific wavelength of operation, flexibility, and durability, along with minimal device size, finds many relevant

Laser, nanoparticles, and optics

Fig. 12 (A) Schematic of a plasmonic random laser on the optical fiber facet. (B) Optical micrograph of the front view of the random laser on the optical fiber facet. (C) Optical micrograph of the side view of the random laser on the optical fiber facet. The scale bars represent 200 nm.

applications in the world today. In the medical and biological fields, random lasers can be used as a diagnostic tool. Polson and Vardeni have demonstrated cancerous tissue mapping from random laser emission spectra [84]. In 2017, Wang et al. reported random lasing from cancerous human tissues embedded with nano-textured organic dye. They conducted the study on both healthy and cancerous breast tissues, where their obtained random lasing thresholds were found to be highly related to the tumor malignancy rate [85]. In addition, Song et al. developed a bone tissue based random laser where they utilized the unique mechanical and structural characteristics of bone random laser to detect nanostructural changes in bone [86, 87]. These results indicate the biomedical imaging and diagnostics applications of random lasers. Random lasers have a broad angular emission covering the entire solid angle, 4π. This makes random lasers suitable for display applications. Random lasers based on organic polymer layers have been made, which are very useful in display and illumination applications due to their stretchability, flexibility, durability, and possibility of electrical pumping. The discovery of a white random laser, in 2018, by Chang et al. certainly points to applications of random lasers as next-generation illuminants [88]. Random laser systems, with their broad wavelength tunability, find application in the generation of lasing devices at different wavelengths, especially in spectral regimes less explored with usual lasers. Document encoding and material labeling with optical barcodes are also realizable using this sample-specific wavelength of random laser systems. Nano- and microstructured random lasers also enable monitoring the flow of liquid over large distances. Optomicrofluidic random lasers with this application were demonstrated in 2012 [89]. The application of random lasers also extends to the field of sensing and tuning. Temperature-tuned random laser were demonstrated by the team of D.S. Wiersma, where the emission spectrum depended on the environmental temperature [90]. They proposed that such systems find applications in temperature-sensitive displays and screens, as well as in remote temperature sensing.

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[50] M. Siddique, L. Yang, Q.Z. Wang, R.R. Alfano, Mirrorless laser action from optically pumped dye-treated animal tissues, Opt. Commun. 117 (5–6) (1995) 475–479. [51] M. Siddique, R.R. Alfano, G.A. Berger, M. Kempe, A.Z. Genack, Time-resolved studies of stimulated emission from colloidal dye solutions, Opt. Lett. 21 (7) (1996) 450–452. [52] E. Jimenez-Villar, I.F. da Silva, V. Mestre, N.U. Wetter, C. Lopez, P.C. de Oliveira, G.F. de Sa´, Random lasing at localization transition in a colloidal suspension (TiO2@ silica), ACS Omega 2 (6) (2017) 2415–2421. [53] J. Azkargorta, M. Bettinelli, I. Iparraguirre, S. Garcia-Revilla, R. Balda, J. Ferna´ndez, Random lasing in Nd: LuVO4 crystal powder, Opt. Express 19 (20) (2011) 19591–19599. [54] J. Azkargorta, I. Iparraguirre, M. Barredo-Zuriarrain, S. Garcı´a-Revilla, R. Balda, J. Ferna´ndez, Random laser action in Nd: YAG crystal powder, Materials 9 (5) (2016) 369. [55] W.C. Liao, Y.M. Liao, C.T. Su, P. Perumal, S.Y. Lin, W.J. Lin, S.W. Chang, Plasmonic carbon-dotdecorated nanostructured semiconductors for efficient and tunable random laser action, ACS Appl. Nano Mater. 1 (1) (2017) 152–159. [56] S.F. Yu, C. Yuen, S.P. Lau, W.I. Park, G.C. Yi, Random laser action in ZnO nanorod arrays embedded in ZnO epilayers, Appl. Phys. Lett. 84 (17) (2004) 3241–3243. [57] H.Y. Yang, S.P. Lau, S.F. Yu, A.P. Abiyasa, M. Tanemura, T. Okita, H. Hatano, High-temperature random lasing in ZnO nanoneedles, Appl. Phys. Lett. 89 (1) (2006) 011103. [58] J. Fallert, R.J.B. Dietz, M. Hauser, F. Stelzl, C. Klingshirn, H. Kalt, Random lasing in ZnO nanocrystals, JOL 129 (12) (2009) 1685–1688. [59] L.W. Li, Random lasing characteristics in dye-doped semiconductor CdS nanoparticles, Laser Phys. Lett. 13 (1) (2015) 015206. [60] T. Takahashi, T. Nakamura, S. Adachi, Blue-light-emitting ZnSe random laser, Opt. Lett. 34 (24) (2009) 3923–3925. [61] G.R. Williams, S.B. Bayram, S.C. Rand, T. Hinklin, R.M. Laine, Laser action in strongly scattering rare-earth-metal-doped dielectric nanophosphors, Phys. Rev. A 65 (1) (2001) 013807. [62] D. Cao, D. Huang, X. Zhang, S. Zeng, J. Parbey, S. Liu, T. Li, Alumina particles doped in a polymer film act as scatterers for random laser generation, Laser Phys. 28 (2) (2018) 025801. [63] S. Xiao, T. Li, D. Huang, M. Xu, H. Hu, S. Liu, T. Yi, Random laser action from ceramic-doped polymer films, J. Mod. Opt. 64 (13) (2017) 1289–1297. [64] C. Gollner, J. Ziegler, L. Protesescu, D.N. Dirin, R.T. Lechner, G. Fritz-Popovski, C. Vidal, Random lasing with systematic threshold behavior in films of CdSe/CdS core/thick-shell colloidal quantum dots, ACS Nano 9 (10) (2015) 9792–9801. [65] R.C. Polson, Z.V. Vardeny, Random lasing in human tissues, Appl. Phys. Lett. 85 (7) (2004) 1289–1291. [66] R.C. Polson, A. Chipouline, Z.V. Vardeny, Random lasing in π-conjugated films and infiltrated opals, Adv. Mater. 13 (10) (2001) 760–764. [67] J. Tong, S. Li, C. Chen, Y. Fu, F. Cao, L. Niu, X. Zhang, Flexible random laser using silver nanoflowers, Polymers 11 (4) (2019) 619. [68] X. Li, Y. Wang, H. Sun, H. Zeng, Amino-mediated anchoring perovskite quantum dots for stable and low-threshold random lasing, Adv. Mater. 29 (36) (2017) 1701185. [69] N.M.H. Duong, B. Regan, M. Toth, I. Aharonovich, J. Dawes, A random laser based on hybrid fluorescent dye and diamond nanoneedles, Phys. Status Solidi (RRL)–Rapid Res. Lett. 13 (2) (2019) 1800513. [70] P.N. Prasad, Nanophotonics, John Wiley & Sons, Hoboken, NJ, 2004. [71] P.K. Jain, X. Huang, I.H. El-Sayed, M.A. El-Sayed, Review of some interesting surface plasmon resonance-enhanced properties of noble metal nanoparticles and their applications to biosystems, Plasmonics 2 (3) (2007) 107–118. [72] G.D. Dice, A.Y. Elezzabi, Random lasing from a nanoparticle-based metal-dielectric-dye medium, J. Opt. A Pure Appl. Opt. 9 (2) (2007) 186. [73] G.D. Dice, S. Mujumdar, A.Y. Elezzabi, Plasmonically enhanced diffusive and subdiffusive metal nanoparticle-dye random laser, Appl. Phys. Lett. 86 (13) (2005) 131105.

Laser, nanoparticles, and optics

[74] O. Popov, A. Zilbershtein, D. Davidov, Random lasing from dye-gold nanoparticles in polymer films: enhanced gain at the surface-plasmon-resonance wavelength, Appl. Phys. Lett. 89 (19) (2006) 191116. [75] Ziegler, J., W€ orister, C., Vidal, C., Hrelescu, C., &Klar, T. A. (2016). Plasmonicnanostars as efficient broadband scatterers for random lasing. ACS Photon., 3(6), 919–923. [76] D.J. Bergman, M.I. Stockman, Surface plasmon amplification by stimulated emission of radiation: quantum generation of coherent surface plasmons in nanosystems, Phys. Rev. Lett. 90 (2) (2003) 027402. [77] Z. Wang, X. Meng, A.V. Kildishev, A. Boltasseva, V.M. Shalaev, Nanolasers enabled by metallic nanoparticles: from spasers to random lasers, Laser Photon. Rev. 11 (6) (2017) 1700212. [78] H. Wu, Y. Gao, P. Xu, X. Guo, P. Wang, D. Dai, L. Tong, Plasmonic nanolasers: pursuing extreme lasing conditions on nanoscale, Adv. Opt. Mater. (2019) 1900334. [79] M.A. Noginov, G. Zhu, A.M. Belgrave, R. Bakker, V.M. Shalaev, E.E. Narimanov, U. Wiesner, Demonstration of a spaser-based nanolaser, Nature 460 (7259) (2009) 1110. [80] Q. Song, L. Liu, S. Xiao, X. Zhou, W. Wang, L. Xu, Unidirectional high intensity narrow-linewidth lasing from a planar random microcavity laser, Phys. Rev. Lett. 96 (3) (2006) 033902. [81] S. Li, L. Wang, T. Zhai, Z. Xu, Y. Wang, J. Wang, X. Zhang, Plasmonic random laser on the fiber facet, Opt. Express 23 (18) (2015) 23985–23991. [82] S. Perumbilavil, A. Piccardi, O. Buchnev, M. Kauranen, G. Strangi, G. Assanto, All-optical guidedwave random laser in nematic liquid crystals, Opt. Express 25 (5) (2017) 4672–4679. [83] S. Perumbilavil, A. Piccardi, R. Barboza, O. Buchnev, M. Kauranen, G. Strangi, G. Assanto, Beaming random lasers with soliton control, Nat. Commun. 9 (1) (2018) 3863. [84] R.C. Polson, Z.V. Vardeny, Cancerous tissue mapping from random lasing emission spectra, J. Opt. 12 (2) (2010) 024010. [85] Y. Wang, Z. Duan, Z. Qiu, P. Zhang, J. Wu, D. Zhang, T. Xiang, Random lasing in human tissues embedded with organic dyes for cancer diagnosis, Sci. Rep. 7 (1) (2017) 8385. [86] Q. Song, S. Xiao, Z. Xu, J. Liu, X. Sun, V. Drachev, et al., Random lasing in bone tissue, Opt. Lett. 35 (9) (2010) 1425–1427. [87] Q. Song, Z. Xu, S.H. Choi, X. Sun, S. Xiao, O. Akkus, Y.L. Kim, Detection of nanoscale structural changes in bone using random lasers, Biomed. Opt. Express 1 (5) (2010) 1401–1407. [88] S.W. Chang, W.C. Liao, Y.M. Liao, H.I. Lin, H.Y. Lin, W.J. Lin, et al., A white random laser, Sci. Rep. 8 (1) (2018) 2720. [89] B.N. ShivakiranBhaktha, N. Bachelard, X. Noblin, P. Sebbah, Optofluidic random laser, Appl. Phys. Lett. 101 (15) (2012) 151101. [90] D.S. Wiersma, S. Cavalieri, Light emission: a temperature-tunable random laser, Nature 414 (6865) (2001) 708.

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CHAPTER 5

Introduction to quantum plasmonic sensing Changhyoup Leea, Mark Tameb, Carsten Rockstuhla,c, Kwang-Geol Leed a Institute of Theoretical Solid State Physics, Karlsruhe Institute of Technology, Karlsruhe, Germany Department of Physics, Stellenbosch University, Stellenbosch, South Africa c Institute of Nanotechnology, Karlsruhe Institute of Technology, Karlsruhe, Germany d Department of Physics, Hanyang University, Seoul, Korea b

1. Introduction In this chapter, we aim to introduce the latest quantum plasmonic sensing techniques that have been developed and studied in the last few years. We therefore begin with a brief review of the individual fields of plasmonic sensing and quantum sensing to understand what the key features or mechanisms are by which one can achieve an enhancement in the sensing performance compared with conventional photonic sensing techniques. Such reviews, although brief due to the limited scope of a book chapter, are helpful for readers who are familiar with only one of the fields, or neither of them. This chapter consists of four sections. Section 2 introduces basic plasmonic sensors with their intriguing properties and working principles. Various behaviors in sensing are also discussed for different plasmonic structures. In Section 3, quantum sensing schemes are elaborated, showing a quantum enhancement and discussing its origin in comparison with the corresponding classical schemes. For comparison, we focus on intensity- and phase-sensitive sensing in both single-mode and two-mode schemes. In Section 4, we explain how the two sensing techniques are merged to enhance further the sensing performance. To the best of our knowledge, we cover all the relevant works for quantum plasmonic sensing that have been published so far. In Section 5, future issues related to the further development and investigation of quantum plasmonic sensing techniques are discussed.

2. Plasmonic sensing Surface plasmon polaritons (SPPs) are hybrid light-matter excitations that consist of an electromagnetic field coupled to the collective electron oscillations at a metal surface [1]. SPPs are highly confined around the metal and consequently the local field can be largely enhanced. The enhancement enables strong coupling with optical structures or environments in the proximity of a metallic surface supporting SPPs. The strong Nano-Optics https://doi.org/10.1016/B978-0-12-818392-2.00005-6

© 2020 Elsevier Inc. All rights reserved.

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coupling implies that the optical properties of SPPs are sensitively affected by the surrounding media, promising great potential for sensing with high sensitivity. In a biochemical setting, the sensitivity S X is generally defined as the derivative of a quantity X being monitored, allowing the estimation of some analyte parameter x with respect to X [2], that is, S X ¼ jdX =dxj. Plasmonic sensing technologies aim to exploit plasmonic structures that enhance the sensitivity S X , that is, the amount of change in the response of the parameter X for a given change in the parameter x. The enhancement is provided by the confined field of SPPs that can be localized below the diffraction limit, which is impossible with conventional photonic sensors [3–5]. In what follows, we discuss the physical basis of surface plasmon resonance (SPR) sensing with typical primitive structures, focusing on showing how the sensitivity is enhanced while exploiting plasmonic features. We also provide a thorough summary, although not fully comprehensive, of plasmonic sensors that have attracted intensive interest from various scientific communities. Interested readers can find a multitude of review articles on SPR sensors, as they are a mature sensing technology that has been well-established over the last few decades [2, 6–16].

2.1 Surface plasmon resonance sensing Consider a bulk dielectric interfaced with a bulk metal as shown in Fig. 1A. Here, “bulk” means a material with a size much larger than the wavelength of light in all directions; theoretically we consider them as semiinfinite half spaces. Apart from freely propagating modes in individual bulk media, one can find bound surface electromagnetic waves at the interface between a metal and dielectric medium. These are self-consistent solutions to Maxwell’s equations for the above interface geometry in the absence of sources. They are

Fig. 1 (A) Electromagnetic surface waves, called SPPs, at the interface between a bulk dielectric and a bulk metal. (B) Dispersion relation for an SPP at the interface between the metal and analyte (dielectric) pffiffiffiffi in panel (C)—see Eq. (1), a freely propagating radiation mode in the analyte [kjj, max ¼ Ea ðω=cÞ], and an evanescent mode that is excited below the prism when total internal reflection occurs for an incident pffiffiffiffiffi field at an angle θin [kjj ¼ Ep ðω=cÞsin θin ]. ωsp denotes the surface plasma frequency, at which surface electrons collectively oscillate. (C) The Kretschmann configuration, composed of three layers, where a thin metallic film is sandwiched by a prism with permittivity Ep and an analyte with permittivity Ea.

Introduction to quantum plasmonic sensing

solved with an ansatz that assumes exponentially decaying fields on either side of the interface, while a plane wave-like propagating solution is assumed along the interface. The dependency of the propagation constant on the frequency is called a dispersion relation and only waves obeying that relation indeed are the solution to Maxwell’s equations. For the bound surface waves, the dispersion relation for the component of their wave vector parallel to the interface is written as follows [1]: sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ω Ed Em ðωÞ (1) kk ðωÞ ¼ , c Ed + Em ðωÞ where c is the speed of light in a vacuum, and Ed(m) is the electric permittivity of the dielectric (metal). Although both permittivities are generally dependent on the frequency, the permittivity of the dielectric Ed is often assumed to be constant in the typical range of frequency of interest in optics, that is, a nondispersive dielectric. The frequency dependence only appears in the complex permittivity of the metal, that is, Em(ω) ¼ Em0 (ω) + iEm00 (ω). The bound surface electromagnetic waves are called SPPs, since they involve light interacting with the collective oscillation of electrons at the metal surface, known as surface plasmons (SPs), which were first predicted by Ritchie [17]. The electrons cause the SPP electromagnetic fields to be highly confined at the surface, resulting in a large improvement in the electromagnetic density of states near the surface. SPPs cannot be excited by incident light from a bulk dielectric due to the mismatch between the modes in terms of frequency and wave vector (see Fig. 1B). The excitation of SPPs thus requires elaborate schemes that rely on particular structures, for example, a prism [18, 19], a grating [20], a randomly rough surface [21, 22], or a scanning near-field optical microscope fiber tip [23]. Among these, prisms have widely been used due to their simplicity. Two typical configurations exist: the Otto configuration [18] and the Kretschmann configuration [19]. We will focus on the latter configuration, since the former requires more dedicated techniques, whereas the latter is simpler and has led to a wide range of successful applications in plasmonic sensing [24]. The Kretschmann configuration consists of three layers: a prism, a metal, and an analyte medium (assumed to be dielectric), as shown in Fig. 1C. When light is incident from the prism region toward the metal interface with an incident angle, θin, greater than the critical angle, total internal reflection occurs and the excitation of an evanescent field in the region below the prism takes place, that is, in the metal and analyte. At a fixed frequency ω, for a specific incidence angle past the critical angle, denoted as θres, the evanescent field satisfies the mode matching condition with the SPP that exists at the metalanalyte interface—shown in Fig. 1B as a crossing point in the dispersion relations. Thus, the evanescent field is able to excite the SPP. The spatial modes of the evanescent field and the SPP also have a significant overlap, which is another important mode-matching condition. The incident light is thus converted into an SPP mode. A more rigorous model, which considers a finite beam width for the incoming light (as in an experiment),

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and the radiative and damped nature of the SPP mode, provides a more comprehensive picture of the physics involved in the conversion process [25]. The conversion of light into an SPP can be verified by measuring the reflectance on the prism side as the angle θin is varied, as shown in the inset of Fig. 1C. The reflection coefficient in the Kretschmann configuration can be written as follows [20]: rspp ¼

ei2k2 d r23 + r12 , ei2k2 d r23 r12 + 1

(2)

    pffiffiffiffi where ruv ¼ kEuu  kEvv =: kEuu + kEvv for u, v {1, 2, 3}, ku ¼ Eu ðω=cÞ½1  ðE1 =Eu Þ sin 2 θin 1=2 denotes the normal-to-surface component of the wave vector in the uth layer, Eu is the respective permittivity, and d is the thickness of the second layer. Here, the first layer is the prism (E1 ¼ Ep), the second layer is the gold film [E2 ¼ Em(ω)], and the third layer is a medium of an analyte to be investigated (E3 ¼ Ea). The resonant excitation of SPPs can be seen as a dip in the reflectance Rspp ¼ jrsppj2 measured in terms of the incident angle θin or the wavelength λ (or frequency ω). The inset of Fig. 1C shows an example of angular interrogation, where a dip is observed at the resonance angle θres. Likewise, the resonance wavelength λres, or frequency ωres, can be defined in the case of spectral interrogation. In either angular or spectral interrogation, around the resonance the reflectance Rspp is significantly sensitive to the analyte permittivity Ea that characterizes optical features of the analyte medium. This indicates that the permittivity Ea, or even other optical properties of the analyte, can be estimated by analyzing the reflected light in the Kretschmann configuration. Here we focus on refractive index sensing that measures the refractive index pffiffiffiffi of the analyte, na ¼ Ea . Angular interrogation One typical sensing scenario exploiting a prism setup such as the Kretschmann configuration is to illuminate the system with a fixed wavelength and monitor the change of the resonance angle θres with respect to changes in the refractive index na of the analyte. The resonant incident angle θres is obtained by matching the wave vector components of the evanescent field and SPP parallel to the surface (see Fig. 1B): sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi n2a E0m ðωÞ (3) k0 np sin θin ¼ k0 , n2a + E0m ðωÞ pffiffiffiffiffiffiffiffi where k0 ¼ ω/c, and naðpÞ ¼ EaðpÞ denotes the refractive index of the analyte (prism). At θin ¼ θres, obeying Eq. (3), the reflectance Rspp takes on its minimum value (see the inset of Fig. 1C). The sensitivity for the angular interrogation is thus written as follows [26]: pffiffiffiffiffiffiffiffiffi   0 dθres  E ðωÞ E0m m ¼ ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi r S θ ¼  , h i (4)  0 dna   0 2 2 2 2 2 Em ðωÞ + na Em ðωÞ na  np  np na

Introduction to quantum plasmonic sensing 250

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Fig. 2 The angular sensitivity in (A) and the spectral sensitivity in (B) with wavelength are presented. A prism coupler that consists of glass (SF14 or BK7), metal (gold or silver), and analyte (na ¼ 1.32) is considered. The sensitivities of Eqs. (4), (5) are evaluated (see solid, dashed, dot-dashed lines) in (A) and (B), respectively, together with an approximated line eligible for long wavelengths (dotted line) and an exact sensitivity calculated numerically via Fresnel equations (crosses) for a structure with SF14-prism and 50 nm thick gold film. (Reprinted from J. Homola, I. Koudela, S.S. Yee, Surface plasmon resonance sensors based on diffraction gratings and prism couplers: sensitivity comparison, Sens. Actuators B 54 (1999) 16, Copyright (1999), with permission from Elsevier.)

h i Interestingly, the sensitivity S θ exhibits a singularity when E0m ðωÞ ¼ n2p n2a = n2a  n2p , occurring in the limit of short wavelengths. This explains why the sensitivity of setups using gold is higher than those using silver in the typical range of wavelengths of interest in optics, as shown in Fig. 2A. The slightly smaller plasma frequency is certainly beneficial here. In addition, decreasing the contrast between na and np is helpful, that is, BK7-glass performs better than SF14-glass. Spectral interrogation Another typical scenario is to monitor the change of the resonance wavelength, λres, obeying Eq. (3), for illumination of the prism system with a fixed incidence angle. In this case, the sensitivity can be obtained, in the same way as earlier, as follows [26]:   dλres  ½E0m ðωÞ2 ¼  0  Sλ ¼  :  i (5) dna  n3a dEm ðωÞ h 0  + E ðωÞ + n2 E0 ðωÞ dnp na  a m m   2 dλres dλres np Here, the resonant wavelength causes a minimum in the reflectance, which occurs when Eq. (3) is satisfied for a fixed angle θin. Like the sensitivity S θ , there exists a singularity in the sensitivity S λ , occurring in the limit of long wavelengths—opposite to S θ , where the singularity occurs in the limit of short wavelengths. This also predicts the behaviors 0 shown in Fig. 2B, where the spectral sensitivity increases with larger jEm (ω)j, that is, silver performs better than gold, and with smaller contrast of the refractive indices of the prism coupler (np) and the analyte (na), that is, BK7-glass performs better than SF14-glass.

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Phase and intensity sensitivities Simpler schemes can be considered that monitor the change in the intensity Rspp or the phase ϕspp ¼ arg ðrspp Þ of the reflected light with respect to the change of the analyte refractive index for a fixed incident angle and wavelength. The intensity and phase sensitivity are accordingly defined as follows [2]:   dRspp  ,  (6) SR ¼  dna    dϕspp  : (7) S ϕ ¼  dna  A particular incident angle θin and the wavelength λ need to be chosen such that the intensity Rspp, or the phase ϕspp, exhibits the steepest behavior with respect to θin and λ for a given na. Such a choice of parameters enables the associated sensitivity to changes in na to be maximized [13]. Limit of detection The noise in the measurement that could nullify sensing can also be used to examine an overall sensing quality of the setup by defining a “limit of detection” (LOD) as follows [2, 13]: LOD ¼

Δχ min : Sχ

(8)

Here, S χ is the sensitivity of the parameter χ, for example, angle, wavelength, intensity, or phase, to changes in the refractive index of the analyte and Δχ min is the minimum detectable change in the measurement of the parameter—or equivalently the value of the noise level. The sensitivity S X can be enhanced by a transduction mechanism, while Δχ min can be enhanced by reducing random or systematic errors in all the involved devices in the setup. These two aspects are accommodated together in the LOD.

2.2 Localized surface plasmon resonance sensing While the SPPs discussed earlier are propagating surface fields, there also exist nonpropagating SPs, called localized surface plasmons (LSPs) [27, 28]. LSPs can be found in and around spatially confined metallic structures with a size comparable to, or smaller than, the wavelength of light, such as metallic nanoparticles [29, 30], which exhibit sharp spectral absorption and scattering profiles at discrete resonance frequencies, in addition to strong electromagnetic near-field enhancement. As the simplest example, consider a single spherical nanoparticle illuminated by a single-mode electromagnetic field under the electrostatic approximation, valid when the characteristic size, that is, the radius ζ of the nanoparticle, is much smaller than the wavelength of the light. One can excite LSPs at the nanoparticle at the discrete resonance wavelengths obeying the following equation:

Introduction to quantum plasmonic sensing

E0m ðωÞ l + 1 ¼ 0, + Ed l

(9)

where l denotes the mode index of angular momentum of the LSP [31]. For small spheres, dipolar excitation, that is, l ¼ 1 is dominant, whereas higher multipoles with l  2 become more important for larger spheres. One can also see that for a very large sphere (large ζ), that is, in the limit of long wavelengths with the electrostatic approximation (ζ ≪ λ) holding, Eq. (9) leads to the relation E0m(ω) ¼ Ed for an SP at a flat surface, corresponding to the dashed line in Fig. 1B. For small spheres, where the dipolar excitation is dominant and so only the term with l ¼ 1 can be taken, Eq. (9) leads to the resonant condition for the dipolar LSP excitation, written as follows: E0m ðωÞ ¼ 2Ed ,

(10)

which is the so-called Fr€ ohlich condition. Eq. (10) leads to the resonance frequency ωres (or wavelength λres) at which the scattering and absorption cross-sections are resonantly enhanced, as can be seen via the expressions given by the following [32]: 00

2ω4 E2d V 2 ½E0m ðωÞ  Ed 2 + ½Em ðωÞ2 σ sca ðωÞ ¼ , 00 c4 ½E0m ðωÞ + 2Ed 2 + ½Em ðωÞ2 3=2

(11)

00

9ωEd V Em ðωÞ σ abs ðωÞ ¼ , 00 0 c ½Em ðωÞ + 2Ed 2 + ½Em ðωÞ2

(12)

where V is the particle volume. Contrary to the case of SPP modes, which can be excited only when both the frequency and wave vector of the incident light equal the SPP frequency and wave vector, LSPs can be resonantly excited when the frequencies match regardless of their wave vector. This can be well understood by considering the full spherical symmetry of the scenario [32]. The cross-sections involving multipolar excitations for larger spheres can also be calculated using Mie theory [30], which provides an analytical solution that characterizes scattering and absorption by spherical particles. LSPs at elliptical particles can be investigated using Gans theory [33], where an analytical solution based on the aspect ratio of the ellipsoid is obtained. An interesting class of ellipsoids are spheroids, which exhibit two spectral resonance peaks, corresponding to electron oscillations along the major or minor axis [32]. Multilayered spheres or ellipsoids with different materials, or nonspherical/nonellipsoidal nanoparticles, may be considered, leading to intriguing features and the potential for interesting optical applications [34–41]. As an individual structure or group of structures gets more complex, numerical electromagnetic methods

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Fig. 3 The spectrum shift of scattered light with the refractive index change Δna, leading to the shift of the resonance wavelength Δλres. A high sensitivity of Sλ, LSP enables more precise detection of such a shift. The FOM is defined as the ratio of Sλ, LSP to the full width at half maximum Γ λ, whereas FOM* is defined as the ratio of the relative intensity change jdI/Ij to the refractive index change dna of the analyte.

such as finite-difference time domain and finite-element methods should be used to obtain solutions that cannot be found analytically [42–45]. Sensitivity and figure of merit As in SPP sensors, the sensitivity S λ, LSP ¼ jdλres =dna j for LSP sensing can be investigated [46]. However, a particular figure of merit (FOM) has been more widely used in LSP sensors in order to accommodate the effect of the width of the resonance peak—an important aspect in sensing [47]. The FOM is thus defined as follows [48]: FOM ¼

S λ, LSP , Γλ

(13)

where Γ λ is the resonance linewidth (full width at half maximum, see Fig. 3). In the case that the line width Γ λ is ill-defined, for example, in complex plasmonic structures such as metamaterials, where the plasmon resonance spectrum does not attain the form of a simple Lorentzian shape [49], an alternative figure of merit FOM* has been suggested as follows [50]:  1 0 1 0 dI  dI   S λ, LSP      B dna C B dλ C ∗ C ¼ max B C: (14) FOM ¼ max B A A @ @ λ λ I I The FOM* quantifies the relative intensity change jdI/Ij with respect to the index change dna at a fixed wavelength λ chosen to maximize FOM*, as illustrated in Fig. 3.

2.3 Other plasmonic sensors Plasmonic sensors have shown to be useful not only for refractive index sensing as discussed earlier, but also for various other purposes, for example, measuring food quality

Introduction to quantum plasmonic sensing

and safety [51–53], or analyzing medical diagnostics [54, 55]. Furthermore, a number of different types of plasmonic sensor have been suggested over the last few decades. For example, plasmonic metamaterials have been extensively studied for biosensing [49, 56, 57]. Chiral plasmonic structures have recently emerged as a useful sensing platform for detection of handedness of electromagnetic fields [58], for example, circular dichroism spectroscopy [59], or detecting amyloid fibrils in Parkinson’s disease at nanomolar concentrations [60]. Magneto-plasmonic structures are another platform where advances have been made for biosensing and bioimaging applications [61, 62]. The latest progress in plasmonic biosensing technology has recently been reviewed in Ref. [16].

2.4 Intensity- and phase-sensitive sensing Plasmonic sensors aim not only to enhance the sensitivity, LOD, FOM, FOM*, and so on by specially designing plasmonic structures, but also to attain sensing capabilities beyond the diffraction limit from which conventional photonic sensors suffer [3–5]. In general, photonic sensors (both plasmonic and conventional), such as the ones covered in the previous sections, can largely be classified depending on the quantity whose change is being monitored [12]. One can see that there exist two fundamental conjugate quantities that describe electromagnetic fields: intensity and phase. The change in these quantities occurs with a change in the optical properties of the analyte. Depending on the sensing scheme and the material, the change in the analyte leads to a change in both quantities, or only one of them. The sensing type is called “intensity-sensitive” sensing if a sensor is based on monitoring the change in intensity, whereas it is called “phase-sensitive” sensing if a sensor monitors the change in phase. Such a classification may or may not be necessary in classical sensing, but it is vital in quantum sensing, since an input state of light should be engineered appropriately. In other words, the two sensing types require completely different input states of light as an optimal signal, which is not usually the case in classical sensing. This aspect is discussed in more detail in the next section.

3. Quantum sensing As discussed in the previous section, in plasmonics the appropriate engineering of electromagnetic structures made from metals allows the confinement of light into spatial domains much smaller than the diffraction limit, while simultaneously enhancing the near-field amplitudes. These features enable sensing or imaging beyond the diffraction limit with high sensitivity. However, despite its great success in photonic sensing applications, a particular type of noise continues to exist that prevents plasmonic sensors from achieving extreme sensitivity or precision [63, 64]. This particular noise is called

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shot noise [65], which never vanishes as long as classical light is employed, even when all technical noises are removed. To understand and to see where it originates from, we discuss a few of the simplest examples here.

3.1 Shot-noise limit Shot noise in intensity-sensitive sensing Consider a beam splitter whose transmittance T is unknown (see Fig. 4A). The true value T of the transmittance can be estimated by measuring the intensity ratio of the transmitted light to the incident light, that is, Test ¼ It/Iin, where It and Iin are the intensity of the transmitted and incident light, respectively. When such an estimation is made by measuring the intensities with a finite repetition (to make a sample of ν measurements), the mean estimated value T est will fluctuate from sample to sample, resulting in a distribution of T est with a mean value equal to the true value T and a finite standard deviation ΔTest. The latter is exploited to assess the “estimation uncertainty,” or the estimation precision of a sensor, as we will now show. By reducing the estimation uncertainty, the sensor becomes more precise. The quantitative evaluation of the standard deviation ΔTest can be made for the scenario when a typical laser is employed as an input state jψ (a) in i. The state of light emitted from the laser is described by a single-mode coherent state jαicoh, defined as follows: X αn ðaÞ ^ pffiffiffiffi jni, jψ in i ¼ jαicoh ¼ DðαÞj0i ¼ ejαj=2 (15) n! n ^ where jni is the photon number state, DðαÞ ¼ exp ðα^a{  α ^aÞ is the displacement oper{ ator, ^að^a Þ is the annihilation (creation) operator for the photons in mode a, and jαj2 is the average photon number. Note that the photon number statistics of a coherent state follow ∗

(A)

f

(B) Fig. 4 (A) Single-mode intensity-sensitive sensing. (B) Two-mode phase-sensitive sensing.

Introduction to quantum plasmonic sensing 2

the Poisson distribution pn ¼ jhnjαicoh j2 ¼ ejαj jαj2n =n!, where the average photon number, n ¼ jαj2 , equals the variance of the photon number distribution, (Δn)2 ¼ jαj2. In the Heisenberg picture, the beam splitter operator with a transmittance of T transforms the operator ^a{ as follows [66]: pffiffiffiffiffiffiffiffiffiffiffi pffiffiffiffi (16) ^a{ ! T ^a{ + eiθ 1  T b^{ , where b^{ denotes the operator of another mode that is reflected from the beam splitter, and θ is the relative phase between transmission and reflection through the beam splitter. By applying the transformation in Eq. (16) to the displacement operator in Eq. (15), one finds thatpthe ffiffiffiffi transmitted state through the beam splitter is also pffiffiffiffi a coherent state ðaÞ jψ out i ¼ j T αicoh , where the amplitude is decreased by a factor of T . Hence, an intensity measurement, with an observable represented by the operatora I^ ¼ ^a{ ^a, for the transmitted light jψ (a) outi at detector D leads to statistical quantities that follow a Poisson distribution, that is, the distribution for the measured intensity follows the Poisson distribution, where both the mean value, μ ¼ hI^i, and the variance,b 2 σ 2I ¼ hðΔI^Þ2 i ¼ hI^ i  hI^i2 , are equal to Tjαj2. When the sampling of the intensity I is done with a size of ν (number of measurements per sample), the standard deviation ΔTest of the distribution of the mean from many samples is given as follows [67]: rffiffiffiffiffiffi rffiffiffiffi σ 2T T 1 (17) pffiffiffiffiffi , ¼ ΔTest ¼ ν N ν wherepNffiffiffiffiffiffiffiffiffiffi is ffithe average photon number of the input light, that is, N ¼ jαj2 and σ T ¼ T =N , which can be obtained analytically, or by the linear error propagation method (σ T ¼ σ I =jdhI^i=dTj), whereby the standard deviation in one variable can be found from the standard deviation of another on which it is based. The standard deviation ΔTest applies to the case of taking many samples (each with ν measurements per sample), as would be the case when characterizing the sensor. On the other hand, when the sensor is used for sensing with a single sample of ν measurements, the standard deviation ΔTest represents the standard error of the mean of the sample, that is, the standard deviation of the error in the sample mean with respect to the true mean (assuming an ideal/perfect measurement of the intensity). This quantity is the estimation uncertainty (or estimation precision) of the sensor. Such an uncertainty has nothing to do with technical noises, which we assumed to be completely absent in the ideal description, but arises from the fact that light consists of discretized quantum particles, that is, photons. The uncertainty a

Here, the geometric factors associated with the mode have been removed for simplicity. Such a simplification also applies to the multimode case, with the assumption that the same geometric factors appear in all the modes. b ^ the operator ΔO ^ ¼O ^  hOi and variance σ 2 ¼ hðΔOÞ ^ 2 i ¼ hO ^ 2 i  hOi ^ 2 . Note that For an observable O, 2 1=2 ^ i 6¼ hΔOi. ^ the standard deviation σ ¼ ΔO ¼ hðΔOÞ

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that originates from the Poisson nature of light is called Poisson noise or “shot noise,” originally introduced in electronics [68]. The shot noise limits the precision of an intensity-sensitive sensor that uses a coherent state of light as an input signal, and so this limit in sensing is called the shot-noise limit (SNL) [65]. Shot noise in phase-sensitive sensing A second example of shot noise is in another type of sensing called phase-sensitive sensing, or phase estimation. It is complementary to the intensity-sensitive sensing considered earlier as phase and intensity are conjugate variables for light. A typical setup to measure an optical phase ϕ is a Mach-Zehnder interferometer (MZI) (see Fig. 4B). Here, an effective relative phase shift occurs in between the two beam splitters that each have a transmittance of 50%. The first beam splitter transforms the operators ^a{ and b^{ as follows [66]: 1 i ^a{ ! pffiffiffi ^a{ + pffiffiffi b^{ , 2 2 i 1 b^{ ! pffiffiffi ^a{ + pffiffiffi b^{ , 2 2

(18) (19)

where the imaginary unit i represents π/2-phase shiftc that occurs in reflection. Provided (b) that the input states are prepared in jΨ ini ¼ jψ (a) in ijψ in i ¼ jαicohj0i and the phase shifter { { { iϕ^ a ^ a iϕ ^ ¼e operator R transforms the operator ^a to e ^a in the Heisenberg picture, the output state of the MZI is given as follows [71]: jΨ out i ¼ jαa ðϕÞicoh jαb ðϕÞicoh , αa ðϕÞ ¼ 12 ðeiϕ  1Þα

(20)

αb ðϕÞ ¼ 12 ðeiϕ

where and + 1Þα. A common detection scheme to measure the interference pattern generated by the interferometer is an intensitydifference measurement. Consider that we measure the intensity at each output of the MZI (see Da and Db in Fig. 4B). The intensity measurement is described by the photon number operator, I^ ¼ n^, as in the previous section, so that the average value of the measurement outcomes are h^ n a i ¼ jαa ðϕÞj2 and h^ n b i ¼ jαb ðϕÞj2 , and their standard deviations  2   2 1=2 1=2 are Δna ¼ h^ n a i  h^ n a i2 ¼ jαa ðϕÞj and Δnb ¼ h^ n b i  h^ n b i2 ¼ jαb ðϕÞj, respectively. Note that the signal-to-noise ratio (SNR) is given by h^ n j i=Δnj ¼ jαj ðϕÞj for j ¼ a, b, revealing the uncertainty in the intensity measurement. Again, this is attributed to the fact that light consists of discretized quantum particles, that is, photons. The ^ ¼ n^b  n^a . The standard deviintensity-difference measurement is then described by M ation of the measurement of the phase ϕ (also called its uncertainty) can be quantified by the linear error propagation method, written as follows:

c

Note that for nonabsorbing beam splitters, this π/2 relative phase shift between transmission and reflection is always encountered independent of the particular structural implementation of the beam splitter [69, 70].

Introduction to quantum plasmonic sensing

qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ^ Þ2 i hðΔM 1 1  ¼  pffiffiffiffiffi , Δϕ ¼  ^ i ∂hM jα sin ϕj N    ∂ϕ 

(21)

^ 2 i  hM ^ i2 , N ¼ jαj2 and the lower bound of Δϕ is achieved for ϕ ^ Þ2 i ¼ hM where hðΔM ¼ π/2. Such an optimal choice of operating regime for ϕ, for which Δϕ is minimized, is ^ i=∂ϕj, like the LOD in Eq. (8) defined for classical plasdetermined by maximizing j∂hM monic sensing. As in the intensity sensing case, for a single sample of ν measurements, the qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi estimation uncertainty is given by Δϕest ¼ ðΔϕÞ2 =ν, which is lower bounded by pffiffiffiffiffiffiffi Δϕest  1= νN . Thus, the estimation uncertainty is lower bounded by the square root of the average photon number, that is, the average intensity of the input light. This is the SNL caused by the Poissonian photon number statistics of the input coherent state used in the phase-sensitive scheme. As mentioned, the SNL in both intensity- and phase-sensitive sensing originates from the noise that arises from the fact that the electromagnetic energy is quantized in units of photons. In this sense, the SNL defines the limit of precision when considering coherent states as input resources. A more general concept of the limit in quantum estimation theory is called the standard quantum limit (SQL), which is defined as the best scaling in precision (estimation uncertainty) that can be achieved when employing only “classical” resources [72]. In quantum optics, the SNL and SQL are generally synonymous when considering the scaling of the precision with the average photon number, although a quantum strategy can have a scaling that is an SQL in terms of the mean number of photons, but a prefactor improves its performance beyond the SNL [73]. As we will see, this is particularly important for intensity-sensitive sensing, for which it appears that no known quantum strategy is able to go beyond the SQL scaling in terms of the average photon number, whereas for phase-sensitive sensing, one can go beyond the SQL (with N) and achieve Heisenberg scaling (with N2, as explained further below) in terms of the average photon number. Cramer-Rao bound We now consider a more formal description of estimation, allowing one to determine the ultimate limit to estimation uncertainty in sensing scenarios. The estimation process can be divided into four essential steps: (i) an input state preparation; (ii) an interaction for parameter encoding; (iii) a measurement; and (iv) an estimation based on the measurement outcomes, as depicted in Fig. 5. In the earlier examples, the

Fig. 5 The four steps of parameter estimation.

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input state has been a coherent state of light, and the interactions are made by a beam splitter (for encoding the transmittance) and an MZI with a phase shifter (for encoding the phase), respectively. Various kinds of estimator and measurement exist, so one has to choose a good estimator and a good measurement scheme in order to reduce the estimation uncertainty (or equivalently to improve the sensor’s estimation precision). A question then naturally arises: what is the ultimate estimation precision that would be obtained when the estimator and measurement are all optimized? This can be answered in the context of formal estimation theory [74], and in particular by the Cramer-Rao bound [75]. The estimation precision over ν measurements for a parameter x being estimated by unbiased estimators is bounded by the Cramer-Rao inequality, written as follows: 1 Δx  pffiffiffiffiffiffiffiffiffiffiffiffi , νFðxÞ

(22)

where F(x) is the Fisher information (FI) that quantifies the amount of information about x contained in the measurement results. The FI is defined as follows: X 1 dpðyjxÞ 2 FðxÞ ¼ , (23) pðyjxÞ dx y where p(yjx) is the probability of getting the measurement outcome y conditioned on the parameter being x and the measurement, which in the quantum case is described by a positive-operator valued measure (POVM), is chosen P such that the POVM element ^ y leading to the outcome y obeys fΠ ^ y g  0 and y Π ^ y ¼ . For continuous measureΠ R P ment outcomes, y ! dy, the probability p(yjx) becomes a probability density defined Rin a range between y and y + dy and the associated POVM satisfies the completeness ^ y ¼ . In the Cramer-Rao inequality of Eq. (22), the equality holds when the kind dyΠ of estimator that deduces the value of the parameter x out of the measurement results is optimally chosen, that is, the lower bound of the estimation uncertainty Δx is solely determined by the FI that differs with measurement settings. Finding the kind of optimal estimator to reach the lower bound of the estimation uncertainty would be tricky in general, but one can use the maximum likelihood method that has been known to saturate the equality asymptotically of Eq. (22) in the limit of large ν. Note that the FI depends on the probability distribution p(yjx) of the measurement outcome y, that is, one can imagine that an optimal measurement setup should exist that maximizes the FI. Eq. (22) can thus be written as follows: 1 1 Δx  pffiffiffiffiffiffiffiffiffiffiffiffi  pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi , νFðxÞ νFmax ðxÞ

(24)

where Fmax ðxÞ ¼ max FðxÞ, ^ yg fΠ

(25)

Introduction to quantum plasmonic sensing

where the maximization is over all sets of POVMs [74]. The maximized FI, Fmax(x) ¼ FQ(x), is called the quantum Fisher information (QFI), and the QFI thus gives the ultimate lower bound of Δx for a given input state and an interaction scenario [76, 77]. The second inequality of Eq. (24) is called the quantum Cramer-Rao inequality. For an arbitrary parameter-encoded output state ^ρ out ðxÞ, the QFI can be calculated [76, 77]: FQ ðxÞ ¼ Tr½^ρ out ðxÞL^ 2 ðxÞ,

(26)

^ where LðxÞ is the so-called symmetric logarithmic derivative (SLD) operator, defined as follows: ∂^ ρ out ðxÞ 1 ^ ^ (27) : ¼ LðxÞ^ρ out ðxÞ + ^ρ out ðxÞLðxÞ ∂x 2 P Writing the state ^ ρ out ðxÞ in a diagonal form as ^ρ out ðxÞ ¼ n pn jψ n ihψ n j with hψ njψ mi ¼ δn, m, one can calculate the SLD operator [76, 78, 79]: ^ ¼2 LðxÞ

X hψ j∂x ^ρ ðxÞjψ i m out n jψ m ihψ n j, p + p n m n, m

(28)

where the summation is taken over n, m for which pn + pm6¼0. When the output state is a ^ ^ { ðxÞ, unitarily transformed pure state ^ ρ out ðxÞ ¼ jΨ out ðxÞihΨ out ðxÞj ¼ UðxÞjΨ in ihΨ in jU ^ ^ where UðxÞ ¼ eixG with a Hermitian generator G^ encoding the parameter x, the QFI of Eq. (26) can be simplified to the following:   FQ ðxÞ ¼ 4 hΨ 0out ðxÞjΨ 0out ðxÞi  jhΨ 0out ðxÞjΨ out ðxÞij2 h i (29) 2 ^ in i2 ¼ 4hΨ in jðΔGÞ ^ 2 jΨ in i, ¼ 4 hΨ in jG^ jΨ in i  hΨ in jGjΨ where jΨ ’out(x)i ¼ djΨ out(x)i/dx. It can be easily shown that for the aforementioned examples of the beam splitter transmittance estimation and the phase estimation in the MZI, the QFIs are given as FQ(T) ¼ N/T [80] (obtained from Eq. 26) and FQ(ϕ) ¼ 2N (obtained from Eq. 29), respectively. Through (24), these QFIs determine the pEq. ffiffiffiffiffiffiffiffiffiffiffiffiffi pffiffiffiffiffiffiffiffiffi minimum estimation uncertainties: ½ΔTest min ¼ T =νN and ½Δϕest min ¼ 1= ν2N .d Note that these results are the ultimate estimation performance that are obtained when a coherent state is used as an input resource.

d

While the transmittance estimation result matches that given previously in Eq. (17), in the phase estimation a factor of two appears in comparison with Eq. (21). This reflects the fact that there exists a better measurement setting than using the intensity-difference measurement, which is homodyne detection. From another perspective, the single-mode phase shift considered earlier implicitly assumes a reference beam with a certain phase, by which the single-mode phase can only be defined (see Ref. [81] for relevant discussion).

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According to the Cramer-Rao inequality, one may say that the estimation uncertainty can be further reduced by increasing the optical power of an input state (through N), or increasing the number of measurements in the sample (through ν). This is true, but is not applicable to all cases due to optical damage that would occur and destroy samples whose characteristics are being estimated [82]. Thus, cranking up the optical power or number of measurements would not be a universal solution to reduce the estimation uncertainty (or equivalently improve the precision).

3.2 Subshot-noise sensing The quantum nature of light, that is, the discretized particle behavior present in even the most classical state of light (that is usually treated as a definite energy state in classical physics), leads to some trouble in sensing. However, ironically the quantum properties of light can also provide a remedy to overcome the trouble. Here, we look into a few examples, where the QFI can be further enhanced by employing particular quantum states of light jΨ ini, which become ^ ρ out ðxÞ in Eq. (26) after parameter encoding. The comparison of QFIs between classical (using coherent states of light) and quantum (using quantum states of light) scenarios is made for the same average photon number, N. The value of the QFI for the former scenario determines the classical benchmark, which we denote as F Q in what follows. We concentrate not only on single-mode, but also on two-mode sensing schemes, with the parameter that changes being either the intensity or the phase. More information on these aspects beyond what is covered here can be found in other review articles [65, 73, 83–85].

3.3 Single-mode schemes First, consider a single-mode sensing scenario, where one aims to estimate a parameter encoded into a single-mode state of light. Parameter encoding induces the change of the parameter in a relevant physical quantity of light. Of particular interest is the change in the intensity or the phase. Using a photon number state for single-mode intensity estimation Consider the same scenario depicted in Fig. 4A, but with the input state replaced by the photon number state, that is,  E 1  ðaÞ N ψ in ¼ jNi ¼ pffiffiffiffiffiffi ð^a{ Þ j0i: (30) N! The beam splitter transmittance-encoded state in mode a is obtained by applying the beam splitter transformation in Eq. (16) to the input state ðaÞ

ðaÞ

^ ρ in ¼ jψ in ihψ in j ¼ N1 ! ð^a{ ÞN j0ih0jð^aÞN and tracing out mode b. It is thus written as a mixed state:

Introduction to quantum plasmonic sensing

^ ρ out ¼

N  X N k¼0

k

T k ð1  T ÞN k jkihkj,

(31)

and the QFI can be obtained by using Eq. (26), written as follows: FQ ðT Þ ¼

N : T ð1  T Þ

(32)

This clearly shows a quantum enhancement when compared with the classical scenario, where F Q ðT Þ ¼ N =T is achieved using a coherent state of light.e The quantum enhancement by a factor of (1  T) is achieved, which is due to the initial photon number state exhibiting the least uncertainty, indeed no uncertainty, in intensity among other states of light. The coherent state input and photon number state input show Poisson and binomial photon number statistics in the transmitted light, respectively, as shown in Fig. 6A. It is evident that the mean values of the binomial distribution and Poisson distribution are

Fig. 6 (A) Photon number statistics of the transmitted light through a beam splitter when the input state is a coherent state jαicoh (upper) or the photon number state jNi (lower). They follow the Poisson and binomial distribution, respectively. (B) Phase space representation of a coherent state (or equivalently, a displaced vacuum state) and a squeezed vacuum state jξisq. The latter shows less uncertainty (Δϕsq) than the former (Δϕcoh) in a certain direction, exploited for optimal phase estimation.

e

With the triangular parameterization via T ¼ cos 2 ϕ in the QFI, one obtains F Q ðϕÞ ¼ 4N sin 2 ϕ and FQ(ϕ) ¼ 4N for the coherent state and the photon number state input, respectively [80]. Note that a naive transformation from T to ϕ cannot be applied to the already obtained expressions of F Q ðT Þ ¼ N =T and FQ(T) ¼ N/T(1 T), since the derivative needs to be performed with the right parameter in the calculation of the QFI in Eqs. (26), (29).

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equal, that is, μB ¼ μP ¼ NT, but the variance of the binomial distribution, σ 2B ¼ NT ð1  TÞ, is smaller than that of the Poisson distribution, σ 2P ¼ NT . A smaller variance in the transmitted light is advantageous when estimating the change in intensity and manifests itself through FQ(T), thus reducing the estimation uncertainty. The photon number state, which possesses no uncertainty in intensity, as considered earlier, is known as the optimal state that maximizes the QFI in single-mode intensity estimation [80, 86]. The same QFI can also be achieved by number-diagonal-signal (NDS) states when ancillary modes are available [87]. Using squeezed states for single-mode phase estimation For single-mode phase { ^ estimation, we model this as the phase shift operator, RðϕÞ ¼ eiϕ^a ^a , being applied to a single-mode state jψ (a) in i of light (consider Fig. 4A but replace the beam splitter by a phase shifter). Instead of the number state jNi, consider a squeezed vacuum state of light as an input state, written by the following [88]: ðaÞ ^ jψ in i ¼ jξisq ¼ SðξÞj0i,

(33)

^ ¼ exp ð1 ξ ^a2  1 ξ^a{2 Þ and ξ ¼ reiθs , with r where the squeezing operator is defined as SðξÞ 2 2 representing “squeezing” in phase space and θs defining the angle of the axis along which the squeezing takes place (see Fig. 6B). The action of the phase shift operator is to rotate a state in phase space about the origin. For the phase-encoded squeezed state ðaÞ ^ jψ out i ¼ RðϕÞjξi sq , the QFI is obtained as follows [89, 90]: ∗

FQ ðϕÞ ¼ 2 sinh 2 2r ¼ 8ðN + N 2 Þ

(34)

where N ¼ sinh 2 r represents the average photon number of the initial squeezed vacuum state. A scaling with N2 in the QFI or with N1 in the estimation uncertainty Δϕ is called Heisenberg scaling and such a lower uncertainty bound is called the Heisenberg limit. Eq. (34) clearly shows that the squeezed vacuum input state considered earlier reaches Heisenberg scaling in single-mode phase estimation [89, 90]. One may employ more exotic quantum states to obtain larger QFI [91], but the overall scaling with including the resources for a priori probability distribution and a number of repeated measurements has proven to be still Heisenberg scaling-limited [92, 93]. The QFI of Eq. (34) definitely reveals the quantum enhancement compared to the classical case, where F Q ðϕÞ ¼ 4N is obtained with a coherent state input with N ¼ jαj2. Such a quantum enhancement in phase estimation given by the squeezed vacuum state can easily be understood from the fact that the squeezed vacuum state exhibits small uncertainty in phase, as depicted in Fig. 6B, where the angle of the distribution in phase space is well-defined. The photon number state that is the optimal state in single-mode intensity estimation, on the other hand, exhibits a full uncertainty in phase space, which explains why the photon number state is not useful in enhancing phase estimation. Generally speaking, the state with the least uncertainty in a particular physical quantity whose change is induced by a parameter x being encoded is

Introduction to quantum plasmonic sensing

usually the most beneficial in the estimation of x. This can be understood from the fact that the estimation capability is related to the ability to distinguish two infinitesimally close states ^ ρ ðxÞ and ^ ρ ðx + dxÞ for a given parameter x, that is, the QFI can be written in terms of the quantum fidelity F between those two states: FQ ðxÞ ¼ lim dx!0 8f1  F ½^ ρ ðxÞ, ^ ρ ðx + dxÞg=ðdxÞ2 [94, 95]. The optimality of the QFI is reached only by an optimal measurement setup. Here, it is homodyne detection that measures the quadrature variable of pffiffiffi X^ θHD ¼ ðeiθHD ^a + eiθHD ^a{ Þ= 2 [89, 96, 97]. However, care must be taken in setting the homodyne angle θHD, since the required optimal angle θ(opt) HD depends on the value of ϕ + θs, that is, the sum of the phase being estimated and the squeezing phase [97]. When an inevitable thermal photon contribution is included—a usual issue in state-ofthe-art squeezed state generation [98]—the ultimate precision is obtained by a measurement that consists of projectors over the eigenstate of the operator X^ P^ + P^X^ , followed by Gaussian unitary operations, where X^ ¼ X^ θHD ¼0 and P^ ¼ X^ θHD ¼π=2 are the quadrature variable operators [94, 97].

3.4 Two-mode schemes We now discuss two-mode sensing schemes. These are not as trivial as the single-mode schemes since quantum entanglement starts to play an important role in sensing, although entanglement is not always necessary [72]. For simplicity, we consider only single parameter estimation using two-mode states of light. We now describe intensity and phase estimation in this setting. Using photon-number-diagonal signal states for two-mode intensity estimation Consider a setup that consists of two channels, but an object with transmittance of T is placed in the path of one channel. For the classical scenario, the setup shown in Fig. 4B can be employed, but with the phase shifter part replaced by the object. Suppose a classical input state jΨ ini ¼ jαicohj0i with average photon number of jαj2 ¼ N is injected into the first beam splitter, equally dividing the initial light into the output paths. They then pass through the section with the object with transmittance of T on one path. The output state before the measurement (i.e., including the second beam splitter in the measurement) is as follows: pffiffiffiffi pffiffiffi pffiffiffi (35) jΨ out i ¼ j T α= 2icoh jiα= 2icoh : In this case, the QFI sets the lower bound of the estimation uncertainty ΔTest, that is, the classical benchmark is given as F Q ðTÞ ¼ N =2T, where a factor of 2 appears since only half the energy of the initial state of light passes through the object.f For the quantum scenario, consider a two-mode state of light as an input state, where quantum correlations f

In general, probing using a coherent product state jαaicohjαbicoh results in F Q ðT Þ ¼ Na =T , where Na ¼ jαaj2, that is, the QFI is only dependent on the energy that passes the object.

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between the modes can be exploited. It has been shown that the upper bound to the QFI is given as FQ(T) ¼ N/[T(1  T)] (or equivalently FQ(ϕ) ¼ 4N via the triangular parameterization T ¼ cos 2 ϕ), for which the probe with an average photon number of N is used in the path where the object is placed [87]. Such an ultimate bound to the QFI was shown to be achievable by using the NDS (probe) state [87], defined as the state whose reduced state for the signal mode is diagonalized in the number basis, that is, itPcan be written for the two-mode (one signal and the other idler) case as jΨ ini ¼ ncnjnisjχ nii, where the idler states {jχ nii} are orthonormal and the energy P 2 constraint is imposed only on the signal mode: ∞ n¼0 njcn j ¼ N . Such a state includes various useful quantum states, for example, the two-mode squeezed vacuum states for which jχ nii ¼ jnii [99], the N00N states [100], the entangled Fock states jm :: m0 i [101], and the pair coherent states [102]. It has also been shown that a photon-numberresolving measurement scheme attains optimality of estimation [87]. Using squeezed states for two-mode phase estimation Consider Fig. 4B, but instead of the vacuum that is injected into input mode b in the classical sensing example, let a squeezed vacuum state be fed into mode b of the first beam splitter, that is,  E E  ðaÞ  ðbÞ jΨ in i ¼ ψ in ψ in ¼ jαicoh jξisq , (36) where the total average photon number is N ¼ jαj2 + sinh 2 r. This is the paradigmatic input resource that has originally been considered for gravitational wave detectors [103], with research still underway [104, 105]. The first beam splitter in Fig. 4B mixes these two separable states, producing the nonseparable intermediate state jΨ inti to be { phase-encoded, resulting in jΨ int ðϕÞi ¼ eiϕ^a ^a jΨ int i before the measurement.g The QFI can be obtained as follows [106]: sinh 2 2r (37) : 2 This exhibits a quantum enhancement when compared with the classical case that uses the coherent state input jΨ ini ¼ jαaicohjαbicoh with N ¼ jαaj2 + jαbj2, for which F Q ðϕÞ ¼ 2N . The QFI of Eq. (37) is maximized when α ¼ 0 for a fixed total average photon number N of the input state jΨ ini, giving rise to FQ(ϕ) ¼ 2N2 + 3N. The evaluation for Eq. (37) holds only when a definite reference phase of mode b can be defined without uncertainty, but this may not be the case in realistic scenarios. Therefore, one should take into account a relative phase encoding through FQ ðϕÞ ¼ jαj2 ðe2r + 1Þ + sinh 2 r +

ϕ

{

^{ ^

jΨ int ðϕÞi ¼ ei 2 ð^a ^ab bÞ jΨ int i, resulting in the following [105]: FQ ðϕÞ ¼ jαj2 e2r + sinh 2 r: g

Here, again the second beam splitter can be considered as a part of the measurement.

(38)

Introduction to quantum plasmonic sensing

This has been shown to reach Heisenberg scaling, that is, FQ(ϕ) ’ N2 for N ≫ 1, when jαj2 ’ sinh 2 r ’ N=2 [107]. This also shows a quantum enhancement when compared with the corresponding classical case that uses the same coherent state input as earlier, for which F Q ðϕÞ ¼ N . Optimal sensing reaching Heisenberg scaling has been known to be attainable in the absence of loss by employing photon-number-resolving detectors when probing with the earlier considered state jαicohjξisq [107], or more generally with any path-symmetric pure states [108]. The difference between Eqs. (37) and (38), or between two different classical benchmarks F Q ðϕÞ ¼ 2N and F Q ðϕÞ ¼ N , are discussed in Ref. [81], pointing out that the analysis is meaningful only when taking into account an additional reference beam, with which the relative phases can be well-defined. From a practical perspective, the choice of using a single-mode or two-mode quantum sensing scenario in order to gain an advantage over classical sensing for phase and amplitude estimation depends on the experimental accessibility of the input states and the type of measurements needed. From a more fundamental perspective, the benefits of using a single-mode scenario, also called “block encoding,” or two-mode scenario, also called “block-assisted encoding,” are discussed in more detail in Ref. [73].

4. Quantum plasmonic sensing In the previous two sections, we have seen how plasmonic enhancement and quantum enhancement (obtaining the estimation uncertainty below the classical benchmark) are exploited in sensing scenarios, resulting in subdiffraction and subshot-noise sensing, respectively. We now discuss recent studies that have focused on combining the two enhancements in order to further improve the sensing performance—this new combination is known as “quantum plasmonic sensing.” Before looking into examples of quantum plasmonic sensing, it is worth developing a scope to see how the two independent approaches may be combined. In Fig. 5, we showed the four essential steps for parameter estimation, or sensing. While an estimator invokes a mathematical statistical function that manipulates measurement outcomes, the first three parts must involve physical systems. In this sense, an arbitrary photonic sensor can be seen as being decomposed into the three physical parts: a classical or quantum light source preparation; an employment of dielectric or plasmonic transducer (corresponding to the interaction in Fig. 5) that encodes the parameter into the input state; and a classical or quantum measurement that analyzes the parameterencoded state. The sensing process is called ordinary “classical sensing” if classical states, an ordinary dielectric transducer, and a classical measurement scheme are employed. On the other hand, the sensing is called “quantum sensing” if classical states and measurements are replaced by quantum states and measurements, or “plasmonic sensing” if the transducer exploits plasmonic features but the states and measurements remain

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Fig. 7 A classification of different types of sensing approaches.

classical. When quantum states and measurements are considered with a plasmonic transducer for sensing, it is called “quantum plasmonic sensing.” Such a classification is illustrated in Fig. 7 [109]. In the following, we discuss how either LSPs or SPPs can be used in different quantum sensing scenarios. The latter aspect distinguishes furthermore SPPs at either interfaces or nanowires.

4.1 Quantum sensing with metallic nanoparticles 4.1.1 Refractive index sensing with two-mode squeezed vacuum states The first experimental demonstration of a quantum advantage in plasmonic sensing has been made with a structure that consists of an array of gold nanoparticles [110]. Here, the experimental schemes of quantum spectroscopy and conventional classical transmission spectroscopy were used, as shown in Fig. 8A and B, respectively. The quantum spectroscopy scheme employed the two-mode squeezed vacuum (TMSV) state (also called photon-number correlated state) as an input (i.e., a two-mode scheme), whereas the conventional spectroscopy scheme used light generated from a lamp (i.e., a single-mode scheme). The spectral responses were compared with respect to the same sample made of a hexagonal array of spherical plasmonic nanoparticles with a diameter of 130 nm and lattice period of 1.1 μm, as shown in Fig. 8C. For the quantum spectroscopy, the TMSV state (often called a twin beam) is generated from a spontaneous parametric down conversion (SPDC) process [99], realized by pumping a nonlinear crystal as shown in Fig. 8A, which is written as follows [111]: jTMSVi ¼ S^2 ðξÞj0,0i ¼

∞ X n¼0

cn jn, ni,

(39)

Introduction to quantum plasmonic sensing

Fig. 8 (A) Quantum spectroscopy with the photon pairs generated from an SPDC source (i.e., BBO crystals), where the coincidence photon counter with two APDs (avalanche photodiodes) is used. A monochromator that consists of a mirror (M), a diffraction grating (GR), a pinhole (PH), and an APD is used to select a particular single mode of interest under investigation. (B) Conventional spectroscopy with light generated from a halogen lamp, where a single APD is used. (C) Images of the plasmonic nanoparticle array under investigation: dark-field microscope image (upper) and scanning electron microscope image (lower). (Figures are taken from D.A. Kalashnikov, Z. Pan, A.I. Kuznetsov, L.A. Krivitsky, Quantum spectroscopy of plasmonic nanostructures, Phys. Rev. X 4 (2014) 011049.)

∗ where S^2 ðξÞ ¼ exp ½ξ ^ab^  ξ^a{ b^{  denotes the two-mode squeezing operator with ξ and cn ¼ ðeiθs tanh rÞn = coshr for ξ ¼ reiθs . As seen from Eq. (39), such a state exhibits a strong quantum correlation in the photon number between the two modes, leading ^ 2 i ¼ 0—coincidence detection is employed as a measurement scheme, to h½Δð^a{ ^a  b^{ bÞ as shown in Fig. 8A, in order to take advantage of this quantum feature of the input state of Eq. (39). The method provides a strong robustness of the sensing performance against background noise (or noise photocounts). The total number of coincidence counts between the two APDs is attributed not only to the photons generated from the SPDC source but also to the noise photocount. The number of coincidence counts caused by the source photon pairs can be given as Rsource ¼ ηsηiP, where ηs(i) is the quantum efficiency of the signal (idler) channel and P is the number of photon pairs generated

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by the SPDC process [112]. The number of coincidence counts caused by noise is given as Rnoise ¼ NsNiΔt + SsNiΔt + NsSiΔt, where Ns(i) is the number of noise photocounts in the signal (idler) channel, Ss(i) ¼ ηs(i)P is the number of photocounts induced by photons sent from the SPDC sources, and Δt is the time window of the coincidence circuit. The total number of coincidence counts is thus given by Rtotal ¼ Rsource + Rnoise, and the SNR is defined as the ratio of Rsource to Rnoise [110]. Assuming NsNi ≫ SsNi and SiNs, that is, operating at extremely low photon fluxes, the SNRs for the conventional and quantum transmission spectroscopy are written as SNRC  ηsP/Ns, where P is the number of photons generated by the lamp (which is set to be equal to the number of photon pairs in SPDC), and SNRQ  ηiηsP/NsNiΔt, respectively. The quantum enhancement can be quantified by the ratio of these two SNRs, written as follows: SNRQ =SNRC  ηi =Ni Δt:

(40)

A quantum advantage is observed when SNRQ/SNRC is greater than unity, that is, a high-quantum efficiency, low-noise at the APD in the idler channel, and a narrow coincidence time window are required. The spectral responses of the quantum and conventional spectroscopy are compared under various noise conditions. The latter is emulated by artificially adding photocounts to Ns via a lamp in the monochromator, the contribution of which is measured by an additional APD (not shown). Such a trick is introduced in order to exclude the spectral dependence of the noise photons Ns, and the amount of the noise can be controlled by changing the brightness of the lamp, that is, Ns ¼ 102, 2  104, and 7  104 c/s are considered in the experiment. Fig. 9 shows the results measured with two concentrations (40% [red dots] and 50% [black dots]) of glycerin-water solution on top of the array of nanoparticles. The results obtained with conventional transmission spectroscopy (see Fig. 9A, C, and E) reveal that the resonance curves get shallower and more blurred as the noise level increases, so that the two curves measured for different concentrations become nearly indistinguishable under an extreme noise (see Fig. 9E). In quantum spectroscopy, on the other hand, the two curves are still discernible even when the noise is 70 times larger than the signal (see Fig. 9F). The quantum enhancement of SNRQ/ SNRC  100 is achieved with Δt ¼ 5 ns (limited by the jitter of the APD and the resolution of the coincidence circuit), Ni ¼ 105 c/s, and ηi ¼ 5% (determined by the detection efficiency of the APD of ηD ¼ 50% and the idler channel transmittivity). 4.1.2 Ultrasound sensing with two-mode squeezed displaced states An array of subwavelength nanostructured holes in a thin silver film has recently been used in a two-mode scheme (see Fig. 10A) to detect refractive index changes induced by ultrasound waves [113]. The plasmonic nanostructure consists of an array of isosceles

Introduction to quantum plasmonic sensing

(A)

(B)

(C)

(D)

(E)

(F)

Fig. 9 Transmission spectrum of the conventional classical (left) and quantum (right) spectroscopy with different concentrations (40% [red dots] and 50% [black dots]) of glycerin-water solution, measured at different noise conditions (Ns ¼ 102 [top], 2  104 [middle], and 7  104 c/s [bottom]). The photon flux in the signal channel with the monochromator is kept the same for all the experiments as 103c/s with the error bars being the standard deviations. (Figures are taken from D.A. Kalashnikov, Z. Pan, A.I. Kuznetsov, L.A. Krivitsky, Quantum spectroscopy of plasmonic nanostructures, Phys. Rev. X 4 (2014) 011049.)

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Fig. 10 (A) Scheme of ultrasound sensing with entangled bright twin beams. (B) Scanning electron microscope images of the triangular nanohole array. (C) Transmission spectrum of the structure under investigation, showing a transmission of about 66% at 795 nm. (Reprinted with permission from M. Dowran, A. Kumar, B.J. Lawrie, R.C. Pooser, A.M. Marino, Quantum-enhanced plasmonic sensing, Optica 5 (2018) 628, Optical Society of America.)

Introduction to quantum plasmonic sensing

triangular nanoholes (base of 230 nm, side of 320 nm, and pitch of 400 nm), as shown in Fig. 10B, patterned in an Ag film (thickness of 100 nm) evaporated on a boroaluminosilicate glass substrate with an indium tin oxide coating (thickness of 70 nm), covered by a layer (thickness of 200 nm) of poly(methyl methacrylate) (PMMA) to protect the Ag from oxidizing. It results in the extraordinary optical transmission of about 66% at the probing wavelength of 795 nm (see Fig. 10C) [114, 115], which has been shown to preserve quantum properties of incident light [116–118]. Such a plasmonic structure is placed inside a chamber to sense the change in the refractive index of the air (which changes the transmission), whose modulation is controlled by the driving voltage at a particular frequency, here 199kHz. The sensor under investigation employs the so-called two-mode squeezed displaced (TMSD) state (or bright two-mode squeezed state), also often called an entangled bright twin beam of light. This state has been studied from various perspectives [119–121] and is defined as follows: jTMSDi ¼ S^2 ðξÞjαicoh j0i:

(41)

Like the TMSV state in Eq. (39), the TMSD state exhibits quantum correlations between the two modes. It can be generated via a four-wave mixing (FWM) process in a doublelambda configuration provided by an 85Rb vapor cell [122–124]. An intensity-difference squeezingh of 9 dB between the two modes (probe and conjugate) has been achieved, equivalent to a noise reduction of (1109dB/10)  87% below the SNL. The squeezing of 9 dB in the initial twin beams is reduced to 4 dB squeezing in the transmitted twin beams through the plasmonic sensor, equivalent to a noise reduction of (1104dB/10)  60% below the SNL. The intensity-difference measurement scheme with entangled bright twin beams is compared to the balanced configuration with two coherent states when the refractive index modulation of the air is set to Δn ¼ 1.6  107 RIU (see Fig. 11A) and Δn ¼ 8.2  109 RIU (see Fig. 11B). When the modulation is moderate, the signal peak at the driving frequency of 199 kHz is resolvable in both cases (see Fig. 11A). Here, the height of the peak is proportional to the magnitude of the amplitude modulation of the probe field, which is caused by modulation of the transmission through the plasmonic nanostructure as the refractive index of the air is modulated. A much smaller modulation of the air refractive index is, however, only resolved by the twin beams (see Fig. 11B). The sidebands of the signals (away from the peaks) in all cases correspond to the noise floor (the variance) h

The intensity-difference squeezing is a ratio of the intensity-difference variance for the TMSD states, hTMSDj½Δð^ n a  n^b Þ2 jTMSDi, to the SNL that is defined as the intensity-difference variance for the coherent state input which has the same average photon numbers in each mode. The latter can be written by hαa jðΔ^ n a Þ2 jαa i + hαb jðΔ^ n b Þ2 jαb i, that is, the sum of the intensity variances of individual modes, applicable to the case when the two modes are uncorrelated.

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(A)

(B)

(C)

Fig. 11 Measured power spectra with frequency of the spectrum analyzer for probes of coherent states (blue) and entangled bright twin beams (red) for (A) Δn ¼ 1.6  107 RIU and (B) Δn ¼ 8.2  109 RIU. Here the powers are normalized by the SNL, defined as the sideband noise of the case probing with coherent states. (C) Comparison of the cases using (i) a balanced configuration with two coherent states, (ii) entangled bright twin beams, and (iii) the optimal classical single-mode coherent state configuration, but estimated from case (i). A 99% confidence level is considered to determine the minimum resolution of Δnmin. (Reprinted with permission from M. Dowran, A. Kumar, B.J. Lawrie, R.C. Pooser, A.M. Marino, Quantum-enhanced plasmonic sensing, Optica 5 (2018) 628, Optical Society of America.)

of the intensity-difference measurements between the transmitted probe and conjugate modes, as there is little amplitude modulation of the probe (and thus of the intensity difference) at frequencies away from resonance. The use of a spectrum analyzer to give a power spectrum of the intensity-difference detection signal allows the transmitted probe power (proportional to the peak at resonance) and the intensity-difference noise (the sidebands) to be represented on the same plot. An added benefit of this technique is that technical noise at low frequencies (such as laser amplitude noise or vibration noise) can be eliminated and thus bring the sensor’s precision close to the theoretical limit. Furthermore, the SNRs for the intensity-difference measurement with the bright twin beams (see case (ii) in Fig. 11C) is compared with two classical scenarios: the balanced configuration with two coherent states (see case (i) in Fig. 11C), and the optimal single-beam classical configuration that is estimated from the latter (see case (iii) in Fig. 11C). The minimum resolution of the air refractive index modulation can be determined at a 99% confidence level in the SNR, exhibiting Δnmin  5.5  109 RIU with the entangled bright twin beams, 6.8  109 RIU with a single-mode classical configuration, and 9.6  109 RIU with a balanced two-mode classical configuration, respectively, as shown in Fig. 11C. The comparison of Δnmin demonstrates a quantum enhancement of 56% (consistent with the theoretically expected 58% with the measured squeezing of 4 dB) and 24% as compared with case (i)—two mode and (iii)—single mode, respectively. The plasmonic structure shown in Fig. 10 has also been investigated in transferring quantum entangled images [125]. An arbitrary binary image is encoded by a digital light

Introduction to quantum plasmonic sensing

processor on the input probe to the FWM process, and the image is copied to the conjugate field via the FWM process, consequently generating entangled images between the probe and conjugate field. The entangled images are sent to two independent and spatially separated individual structures of Fig. 10, realizing the photon-plasmon-photon conversion. The transmitted light through the plasmonic structure is analyzed by a double balanced homodyne detection in order to measure the noise in the amplitude difference X^  ¼ X^ a  X^ b and the phase sum P^ + ¼ P^a + P^b between the probe (mode a) and the conjugate (mode b) beams. Both of the measured noises hðΔX^  Þ2 i and hðΔP^ + Þ2 i are 1.1  0.2dB below the SNL, which leads to I ¼ hðΔX^  Þ2 i + hðΔP^ + Þ2 i  1:55, satisfying the inseparability condition I < 2 between two continuous variable systems [126]. Therefore, the entangled images are shown to be entangled even after the photon-plasmon-photon conversion through the plasmonic structures shown in Fig. 10.

4.2 Quantum sensing with metallic film-prism setups As introduced in Section 2, the refractive index of an optical sample under characterization can be estimated by measuring the change in the intensity of the reflected light from a prism setup (or one may call it the transmitted light through a prism setup). Here we discuss quantum plasmonic sensors that exploit different types of input states, such as TMSD states, twin Fock (TF) states, and single-mode Fock states. 4.2.1 Refractive index sensing with two-mode squeezed displaced states The first experimental demonstration of quantum noise reduction in a prism setup was made with the TMSD states defined in Eq. (41) [127]. The TMSD states are generated via a FWM process in an 85Rb vapor cell, as considered in Section 4.1.2, revealing a 5 dB intensity-difference noise below the SNL in the absence of the prism sensor. The probe beam of the TMSD states is injected into the prism, whereas the conjugate beam is kept as a reference, as shown in Fig. 12A. The intensity difference is measured at the two output ^  ¼ ^a{out ^aout  b^{out b^out with mode a(b) being the channels, described by the observable N ^  i and the variance of hðΔN ^  Þ2 i. The probe (conjugate) beam, yielding the signal of hN measurement results are analyzed for index matching oils with refractive indices of n ¼ 1.300, n ¼ 1.301, and n ¼ 1.305 deposited on the opposite side of the gold film in the prism setup. A quantum description of the prism setup has been developed in Refs. [128, 129], and is associated with a classical complex reflection coefficient rspp from a multilayer system. Let T ¼ jrsppj2 (with rspp from Eq. 2) be the transmittivity through the prism setup, G a parametric amplifier gain, and ηa(ηb) an effective total transmission on the probe

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Spectrum analyzer

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Fig. 12 (A) Schematic of the prism setup with the FWM process generating the TMSD states of Eq. (41). ^  to the classical case in the intensity difference measurement, as Squeezing, a ratio of the variance in N a function of (B) the reflectivity of the prism (or equivalently the transmission T through the prism setup), and (C) the incident angle for the refractive indices of n ¼ 1.300 (black), n ¼ 1.301 (blue), and n ¼ 1.305 (red). The main difference between (B) and (C) is that in (B) the transmission T is varied linearly, whereas in (C) the transmission T ¼ jrsppj2 is used, which is angle and refractive index dependent (see inset of Fig. 1C). (Reprinted figures with permission from W. Fan, B.J. Lawrie, R.C. Pooser, Quantum plasmonic sensing, Phys. Rev. A 92 (2015) 053812, Copyright (2015) by the American Physical Society.)

(conjugate) beam. One can then write the final probe and conjugate field operators, ^aout and b^out , as follows: pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi { pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi pffiffiffiffiffiffiffiffiffiffiffiffi (42) ^aout ¼ Gηa T ^ain + ðG  1Þηa T b^in + ð1  ηa ÞT ^abath + ð1  T Þ^aspp , pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi pffiffiffiffiffiffiffiffi (43) b^out ¼ Gηb b^in + ðG  1Þηb ^a{in + ð1  ηb Þb^bath , where ^abath ðb^bath Þ is the input operator of a fictitious beam splitter describing a lossy channel of the probe (conjugate) mode (i.e., bath modes), ^aspp denotes the SPP-mode input operator, and G ¼ cosh 2 r with respect to the squeezing given in Eq. (41). Here, θs ¼ π is assumed. ^  Þ2 iQ obtained The measured noise normalized by the SNL, that is, the ratio of hðΔN ^  Þ2 iC obtained with an unbalanced coherent state, is plotted with TMSD states to hðΔN in Fig. 12B and C, as a function of the reflectivity of the prism (equivalently the

Introduction to quantum plasmonic sensing

transmission T through the prism setup) and incident angle, respectively. The reduction of quantum noise is manifested by negative values in a logarithmic scale, as highlighted in gray. The measured SNRs show a good agreement with the theoretical calculation obtained with G ¼ 4.5 and ηa ¼ ηb ¼ 0.84 (see solid lines in Fig. 12B with details of the theoretical model further explained below). The effect of excess noise caused by unwanted processes in the vapor cell is inevitably present in the high-reflectivity region (i.e., when the transmission T is near unity or the incident angle is far from the resonant angle), but becomes negligible as the total transmittance approaches zero. The theoretical prediction of the quantum enhancement can be obtained by the ratio ^  Þ2 iQ to the SNL, hðΔN ^  Þ2 iC ¼ hN ^ a i + hN ^ b i from double channel coherent of hðΔN ^ a i ¼ T ηa ðGjαj2 + G  1Þ and light. For the current configuration, hN ^ b i ¼ ðG  1Þηb ðjαj2 + 1Þ. The use of Eqs. (42), (43) leads the ratio to be written in hN the limit jαj2 ≫ 1 as follows [130]:   ^  Þ2 iQ hðΔN 2ðG  1Þ GðT ηa  ηb Þ2  η2b 2 ^ (44) hðΔN  Þ i TMSD ¼ ¼1+ , ^  Þ2 iC GT ηa + ðG  1Þηb hðΔN which is used to plot the theoretical curve in Fig. 12B with G ¼ 4.5 and ηa ¼ ηb ¼ 0.84, where the noise of 8.4 dB above the SNL (when T ¼ 0) is reduced to 5.8 dB below the SNL (when T ¼ 1). Interestingly, one can always obtain a quantum enhancement when Tηa ¼ ηb ¼ η, with which the ratio becomes: ^  Þ2 i TMSD∗ ¼ hðΔN

^  Þ2 iQ hðΔN 2ηðG  1Þ ¼1 , 2 ^  Þ iC 2G  1 hðΔN

(45)

which is always less than unity as long as G > 1 and η > 0 [123, 131, 132]. The condition of equal transmittivities can be implemented by a neutral density (ND) filter in the conjugate beam, achieving the maximum noise cancellation between the two modes. The ND filter is controlled and set to be equal to the extent of the total transmission Tηa of the probe beam [131]. This ensures that the quantum noise reduction is obtained at any value of total transmittance or an arbitrary incident angle in the prism setup. Using this technique, for the same samples considered in Fig. 12, the amount (i.e., the absolute value) of squeezing, normalized by the SNL, is shown in Fig. 13A, showing that quantum enhancement is achieved at all angles except for the resonance angle (bottom of the dip), at which the measured noise is almost the same as the SNL since the total transmittance is nearly zero. The points in Fig. 13A are obtained from a spectrum analyzer (see Fig. 12A), whose signals are shown in Fig. 13B and C for the two points shown in Fig. 13A, where black and red lines correspond to sensing with entangled bright twin beams and coherent states, respectively, for the same optical power. Here, an acousto-optic modulator set at 1.5 MHz places a modulation on the probe field, similar to the modulation present in the ultrasound sensing scheme in the previous section. As already

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Fig. 13 (A) Squeezing amount normalized by the SNL for the refractive indices of n ¼ 1.300 (red), n ¼ ^  to the classical 1.301 (blue), and n ¼ 1.305 (green). Squeezing is defined as a ratio of the variance in N case. (B) and (C) present raw data for two points along the blue curve in (A). The red and black lines indicate the cases probing with unbalanced coherent states and the entangled bright twin beams, respectively. The peak is related to the transmitted probe power, whereas the sidebands represent the noise floor. The ratio of black to red sidebands provides the normalized squeezing, whose absolute values, that is, a squeezing amount, are plotted in (A). (Reprinted with permission from R.C. Pooser, B. Lawrie, Plasmonic trace sensing below the photon shot noise limit, ACS Photonics 3 (2016) 8, Copyright (2016) American Chemical Society.)

mentioned, this is for convenience to allow for the signal and intensity-difference noise floor to be shown at the same time. As before, the peak height is proportional to the transmitted probe intensity and the sidebands correspond to the noise floor. Thus, Eq. (45) (shown in Fig. 13A) can be compared with Eq. (44) (shown in Fig. 12C) and it can be seen that setting Tηa ¼ ηb ¼ η is a better strategy to achieve noise reduction. Comparison among different state inputs The quantum plasmonic sensing experiments introduced earlier rely on transmission spectroscopy that measures the intensity modulation caused by optical samples. The studies reviewed have taken into account particular quantum state inputs such as TMSV states and TMSD states, but as mentioned in Section 3, in transmission (or absorption) spectroscopy, probing with photon number states and NDS states has been known as the optimal strategy in a single-mode scheme [80, 86] and a two-mode scheme [87], respectively. Although these states with N ≫ 1 are not experimentally realizable, it is informative to compare their performance with those already experimentally studied.

Introduction to quantum plasmonic sensing

Fig. 14 Noise in the intensity difference measurement, normalized by the SNL having the same output power at individual modes, in two-mode scheme with the TMSV state (Eq. 46), TMSD state (Eq. 44), and TF state (Eq. 47). Specific parameter values are chosen: ηa ¼ 0.2, ηb ¼ 0.2, G ¼ 4.5 as an example. Asterisks in TMSV* (Eq. 48), TMSD* (Eq. 45), and TF* (Eq. 48) denote the cases that take into account the balanced transmittivities between the two modes, which would be controlled in an experiment by a neutral density filter.

In the two-mode case, similar relations to Eqs. (44), (45) can be calculated, for example, for photon-number diagonal TMSV states and twin-Fock (TF) states, we have ^  Þ2 i TMSV ¼ 1 + hðΔN ^  Þ2 i TF ¼ hðΔN

GðTηa  ηb Þ2  ðT 2 η2a + η2b Þ , Tηa + ηb

Tηa ð1  T ηa Þ + ηb ð1  ηb Þ : T ηa + ηb

(46)

(47)

We compare the noise in intensity-difference measurements, normalized by the SNL having the same output power at individual modes, in the two-mode scheme for different state inputs: TMSD state, TMSV state, and TF state (see solid lines in Fig. 14). As an example, the channel transmissions are set to ηa ¼ ηb ¼ 0.2 and the gain is chosen as G ¼ 4.5, the noise is compared as a function of the object transmittivity T. The figure shows that the TF state outperforms the other two states at any value of T, whereas the other two states are comparable. Furthermore, TF states always outperform the SNL, but the other two states are advantageous only when T is not low. By setting Tηa ¼ ηb ¼ η, that is, the overall transmittances are balanced between the two modes, one can make all the three states outperform the SNL at any T (see dashed lines in Fig. 14). Interestingly, when the transmittivities are balanced between the two modes, the use of a TF state or a TMSV state lead to the same results, written as follows: ^  Þ2 i TF∗ ¼ hðΔN ^  Þ2 i TMSV∗ ¼ 1  η: hðΔN

(48)

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This is in good agreement with the proof in Ref. [87] that the NDS states (such as TMSV and TF states) give rise to the same optimal result. One can also find a similar analysis in Ref. [133]. Recently the use of Fock states and TF states have been studied for plasmonic sensing using a prism setup [134, 135]. This is discussed in detail in the next section. 4.2.2 Refractive index sensing with photon number states Consider a similar two-mode scheme as mentioned in SectionP4.2.1, but with the particular type of two-mode state, called twin-mode states jψ twin i ¼ Cn, m jn,mi, with jCn, mj ¼ jCm, nj [134]. The twin-mode states also include the so-called path-symmetric states, for which Cn, m ¼ Cm∗ , n e2iγ with a constant phase γ [108, 136]. The considered sensing scheme assumes that one mode (signal) is sent to the prism sensor, but the other mode (idler) is used as a reference. The intensity of the transmitted light (reflected from the prism) is compared ^ . with the intensity of the reference mode by an intensity-difference measurement of N Additionally assuming the same channel transmittivities ηa ¼ ηb ¼ η, the ratio R of the intensity-difference noise for twin-mode states to that for a balanced configuration with the product coherent state can be given by the following [134]: !1=2 1 + jrspp j2 (49) R¼

, 2 1  jrspp j2 η2 QM + 2jrspp j2 η2 σ c + 1 + jrspp j2 ð1  2η2 Þ where rspp is given in Eq. (2), QM represents the Mandel-Q parameter [137] of a single mode of the twin-mode state, and σ c is the degree of correlation between the two modes, ^ a{ ^aÞ2 i=ðh^a{ ^ai + hb^{ biÞ ^ [138]. defined as σ c ¼ hΔðb^{ b^ One can immediately see that the quantum enhancement in noise reduction is maximized by lowering the two features of the input state QM and σ c below the values for the product coherent states (QM ¼ 0 and σ c ¼ 1). Among all kinds of twin-mode states, the state that takes on the minimum is the TF state jN, Ni, for which QM ¼ 1 and σ c ¼ 0. The TMSV state of Eq. (39), on the other hand, exhibits the same degree of correlation as the TF state (σ c ¼ 0), but QM ¼ N, which implies that the quantum enhancement diminishes with increasing the input power N. There also exist other useful states with σ c ¼ 0 and QM < 0, for example, pair coherent states [102] or finite-dimensional photonnumber entangled states [139]. In terms of experimental accessibility, consider a simpler scenario, a single-mode intensity-sensitive scheme with the use of the photon number state jNi that has been known as the optimal state for this scheme [80, 87], as described in Section 3. The generation of arbitrarily high photon number states is extremely challenging in practice; however, one can instead use N consecutive single photons, yielding the same performance, for example, in ΔTest of Eq. (17), as the photon number state jNi, since an individual single photon in jNi is subject to an independent Bernoulli sampling.

Introduction to quantum plasmonic sensing

The experimental single-mode scheme considered in Ref. [135] is shown in Fig. 15, where heralded single photons are injected into the prism setup to measure changes in the concentration (or the refractive index) of the blood protein of bovine serum albumin (BSA) in aqueous solution deposited on the opposite side of a gold film, as shown in the inset. The injection of a single photon state into the prism is certified, or “heralded,” by the detection of the idler photon, and sampling with a size of N ¼ 104 is repeated μ ¼ 103 times, yielding a distribution of the estimated transmittance through the prism setup, Tprism ¼ Nt/N, where Nt is the number of transmitted signal photons out of N injected heralded single photons. From the measured transmittance Tprism and the theory model using Tprism ¼ Rspp with Eq. (2), one can estimate the refractive index of BSA for a given concentration.

Fig. 15 Schematic of the prism setup probing the concentration of BSA with heralded single photons. (Reprinted with permission from J.-S. Lee, S.-J. Yoon, H. Rah, M. Tame, C. Rockstuhl, S.H. Song, C. Lee, K.-G. Lee, Quantum plasmonic sensing using single photons, Opt. Express 26 (2018) 29272, Optical Society of America.)

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To remove any effect caused by an incident angle-dependent misalignment, the transmittance Tprism can be defined as a normalized effective transmittance at an individual incident angle θin, that is, Tprism(θin) ¼ Ttotal(θin)/hTair(θin)i, where Ttotal(θin) is the overall raw transmission including all losses and hTair(θin)i is the averaged transmittance measured with an air analyte. We now suppress the θin dependence for clarity in what follows. The average of the transmittance hTprismi is shown in Fig. 16A as a function of the incident angle for deionized (DI) water and BSA with concentration of 2%. The error bar at each point is determined by the standard deviation of the histogram obtained by μ samples with a size N (see the inset of Fig. 16A and Eq. 17 with ν ¼ N and N ¼ 1 used). The error

Fig. 16 (A) The effective transmittance normalized by the data measured with air is shown as a function of the incident angle θin for DI water and BSA with 2%. (B) The variance of the (unnormalized) total transmittance is measured as a function of the total transmittance, and compared with the theoretical expectation. (C) At a particular incident angle θin ¼ 67.5degrees, the refractive index of BSA is estimated as its concentration varies from 0% to 2% in 0.25% steps. (D) The estimation uncertainty of the refractive index, given as the error bar at each point in (C), is compared with the theoretical expectation for the classical and quantum schemes. (Reprinted with permission from J.-S. Lee, S.-J. Yoon, H. Rah, M. Tame, C. Rockstuhl, S.H. Song, C. Lee, K.-G. Lee, Quantum plasmonic sensing using single photons, Opt. Express 26 (2018) 29272, Optical Society of America.)

Introduction to quantum plasmonic sensing

bars thus constitute the estimation undertainty for a single sample and they are compared with the uncertainty that would be obtained when probing with a coherent state having the same input power, as shown in Fig. 16B where the uncertainties are given as a function of the total transmittance hTtotali including loss. The latter exhibits a quantum enhancement in the estimation uncertainty of the transmittance compared to the classical benchmark. For a particular incidence angle, θin ¼ 67.5degrees (see Fig. 16A), the refractive indices are estimated using Eq. (2) (i.e., the reflectance) and data obtained for Ttotal while changing the concentration of BSA from 0% to 2% in 0.25% steps, as shown in Fig. 16C. The sensitivity can be quantified by the slope of the linear fitting function, giving rise to dhnBSAi/dC ¼ (1.933  0.107)  103, which is in good agreement with the previously reported value of 1.82  103 [140]. Through linear error propagation, the estimation uncertainty of the refractive index coming from the statistical fluctuation of Ttotal due to the state can be quantified as a function of nBSA, shown in Fig. 16D, demonstrating a 10%20% quantum enhancement in comparison with the classical benchmark. Since the quantum enhancement in the estimation uncertainty depends on the total transmittance of the sensing setup, the quantum enhancement would be further enhanced by improving the total transmittivity of the setup, including the detection efficiency.

4.3 Quantum sensing with metallic nanowires The refractive index of an optical sample can be measured by analyzing the phase accumulated in the probe beam in two-mode sensing, as mentioned in Section 2. One promising plasmonic platform for phase-sensitive sensing is a metallic nanowire, as it has led to many applications in plasmonic circuitry [5, 141], including miniaturization for classical sensing [6]. A typical setup for a plasmonic-based phase measurement is an interferometer that consists of metallic nanowires, as shown in Fig. 17A [109]. Considering a biological sample as an analyte enclosing a waveguide in one arm of the interferometer, it is easy to see that the use of a metallic waveguide supporting a propagating plasmonic field is beneficial in improving sensitivity, defined as the derivative of a propagation constant β with respect to the refractive index nbio of an analyte. The reason for this is that as nbio varies, β changes more sensitively with plasmonic waveguides than with conventional dielectric waveguides due to the field confinement stemming from the dispersion relation of the plasmonic mode, which is dictated by the waveguide geometry and material response, as shown in Fig. 17B. Theoretical schemes using the coherent state with an intensity^  and the N00N state with a particular measurement M ^¼ difference measurement of N jN ih0j + j0ihN j are considered as example classical and quantum scenarios, respectively. The linear error propagation method enables a comparison among the four cases as shown in Fig. 17C, clearly showing that the plasmonic case using quantum resources leads to the smallest estimation uncertainty in nbio in the absence of loss. When losses

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(B)

(A) (C) Fig. 17 (A) Two-mode interferometric phase-sensitive sensing scheme with a quantum source, plasmonic waveguide and measurement. (B) The sensitivity of the propagation constant of the propagating plasmonic mode of the waveguide with respect to changes in the refractive index of a biochemical sample surrounding the plasmonic waveguide in (A). (C) The estimation uncertainty (or precision) of the refractive index δnbio sensing for classical (C) and quantum (Q) states with dielectric and plasmonic waveguides. Here, the total photon number on average is N ¼ 4 and the propagation length is 4 μm, chosen as examples. (Reprinted with permission from C. Lee, F. Dieleman, J. Lee, C. Rockstuhl, S.A. Maier, M. Tame, Quantum plasmonic sensing: beyond the shotnoise and diffraction limit, ACS Photonics 3 (2016) 992, Copyright (2016) American Chemical Society.)

are taken into account for a realistic consideration, the N00N state is very fragile, so that one has to employ specialized quantum states beyond simple N00N states, for example, photon number definite states with the optimized photon number distribution between the two modes, or chopped N00N states [142]. Relevant experiments have recently been carried out with the excitation of a twophoton plasmonic N00N state in a silver nanowire [143]. A two-photon polarization N00N state generated via Hong-Ou-Mandel (HOM) interference (see Fig. 18A) is transferred to a plasmonic entangled state in the silver nanowire via a tapered-fiber nanowire coupled structure, as shown in Fig. 18B and C. It is shown in the inset in region V of Fig. 18 that the two-photon coincidence counts of the transmitted plasmonic N00N state through a silver nanowire plasmonic system exhibit not only “super resolution” in their response to changes in the phase, but also a visibility of V ¼ 0:88  0:013. The density matrix in the two-photon subspace is also constructed via quantum state tomography with an implementation illustrated in region VI of Fig. 18, leading to a fidelity of F ¼ 0:879 in comparison with the ideally expected two-photon N00N state. Despite the super-resolution behavior observed with the two-photon entangled state, a rigorous

Introduction to quantum plasmonic sensing

Fig. 18 (A) Experimental setup for generating a two-photon polarization entangled state via the HOM effect (realized in region II) for two single photons (initially generated in region I). (B) The generated entangled state is transferred to plasmonic entangled states through a tapered-fiber silver nanowire structure. An additional half-wave-plate (HWP) and quarter-wave plate (QWP) are inserted before region III to enhance the signal-to-noise ratio. The HOM interference and super-resolution measurements are implemented by twofold coincidence measurement in regions IV and V, respectively, while quantum state tomography is performed in region VI. (C) SEM image of the tapered-fiber silver nanowire structure converting photons into plasmons. IF denotes interference filters. (Reprinted with permission from Y. Chen, C. Lee, L. Lu, D. Liu, Y.-K. Wu, L.-T. Feng, M. Li, C. Rockstuhl, G.-P. Guo, G.-C. Guo, M. Tame, X.-F. Ren, Quantum plasmonic N00N state in a silver nanowire and its use for quantum sensing, Optica 5 (2018) 1229, Optical Society of America.)

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assessment of quantum enhancement in sensing requires a “super-sensitivity” inequality to be satisfied [144, 145], defined as follows [143]: 11 GS/s) can also measure the photon distribution at a nanosecond timescale and directly record the decay. Current frequency domain-based methods [156] use a digital gating window to find the correlated cross phase of heterodyned signals between two signal streams (periodic excitation and modulated emission). TCSPC-TAC (timecorrelated single photon counting-time to analog amplitude converter), TDC (time to digital converter), and FD-FLIM (frequency domain FLIM) are compared in the FLIM, in Fig. 6D. Time-resolved fluorescence measured from “n” species is written n P as a sum of exponentials as F ðtÞ ¼ ai eðt=τi Þ , where τ is the lifetime of species i and ai i

is the fractional component of that species in the sample volume. FLIM is closely related to the quantum yield and anisotropy of the molecule. FLIM is also sensitive to the local environment of the molecule, described by the Strickler Berg [157] relation connecting the emission of a photon to the physical parameters of viscosity, temperature, and other lifetime can be written as R physical   parameters.   The  radiative R R 1=τ ¼ 2:88  109 n2 F νf dνf = νf 3 F νf dνf fEðνa Þdνa =νa g ; the first integral

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Fig. 6 Fluorescent techniques that transcend into molecular dynamics. Panels show different fluorescent probing methods. (A) Model function for FRAP (fluorescence recovery after photobleaching). (B) Model function for FCS (fluorescence correlation spectroscopy) autocorrelation €rster resonance energy transfer). The donor molecule “D” curve. (C) Model function for FRET (Fo transfer energy to the acceptor molecule “A” as shown in the panel. (D) Model functions for FLIM (fluorescence lifetime imaging microscopy). The instrument response function (IRF) from the excitation and two example decay functions are presented. (E) Different FLIM/single-photon techniques. Three methods are compared from three commercial vendors: Becker-Hickl, Picoquant, and ISS. FLIM imaging modules are compared with their respective coloring schemes. The first two systems color the image based on the lifetime measured in the time-domain imaging; the third system uses a frequency domain method which colors the photon count and displays phase delay simultaneously in a phasor plot. (F) Principle of N&B (number and brightness) of single-molecule measurements. The intensity traces of a single molecule and trimer are compared.

Nanobiophotonics and fluorescence nanoscopy in 2020

R   constitutes the quantum yield of the moelecule ϕ ¼ F νf dνf , where F(v) is the amount of fluorescence intensity per unit wave number. ε is the molar absorption coefficient, n is the refractive index, and v is the wavenumber. The quantum yield can be written also as ϕf ¼ τ/τr, the ratio rate constants for fluorescence and total decay from the excited state. These environment-sensitive features let FLIM be used in predicting changes in a biological specimen and interestingly, autofluorescence-based metabolism imaging. Autofluorescence imaging (AFI) FLIM has shown promise in the field of oncology and cellular redox estimation [158]. This field has emerged from optical redox imaging (1964), which finds the change in the redox rate of a biological specimen to metabolic enzyme binding estimation and robust imaging system for tumors and other metabolic disorders (2018). The techniques in Fig. 6 comprise of four major fluorescence-based sensing methods. FRAP shows the recovery of intensity after photobleaching. The recovery rate, modeled to find the t-half (time required to recover half of the recoverable population), shows the single-molecule diffusion parameters such as size, strength, etc. FCS shows the correlative analysis of fluorescence fluctuation from a small focal volume. The autocorrelation of the fluctuating fluorescence intensity from the focal volume at different time scales denote different physical fluorescence principles. Autocorrelation-signal measured at time-lags shorter than the lifetime of the molecule will show antibunching characteristics. Correlation timescales longer than the fluorescence lifetime of the molecule (yet shorter than triplet state transition time of the molecule) characterize the rotational dynamics of the molecule. Further slower time scales show the triplet-level quenching and blinking of the molecule during its transit in the focal volume. Autocorrelation in the order of several microseconds measures the translational diffusion of the molecule through the focal volume. The spatial scale of this correlation is usually a tight, focused “femtoliter” volume using a high magnification lens, typically coupled to a single molecule detection system. FRET, commonly accepted as the “nanoscale measurement scale/ruler” of fluorescence, works by the principle of energy transfer between two molecules that has a spectral overlap and preferential orientation. When the molecules are aligned preferentially at distances smaller than a few nanometers, the donor molecule “D” will lose its excited state energy to the acceptor molecule “A” without emitting a fluorescent photon. However, if the acceptor molecule is fluorescent, the energy can still be emitted as a fluorescent photon with acceptors emission properties. The efficiency of FRET is denoted by the term R0 (F€ orster’s radius), which is a function of the donor-acceptor pair and can be calculated as the distance at which the efficiency of energy-transfer becomes half. FLIM is a fast-temporal measurement system that calculates the average time spent by a molecule in its excited state. The decay of fluorescence from a molecule can be measured and modeled to an exponential curve. Molecular species present a characteristic lifetime of its own, making them distinguishable in an ensemble. Apart from the four primary methods listed earlier, general intensity traces can also be analyzed to interpret single-

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molecule dynamics. The difference in the signal fluctuation of fluorescence intensity over time can measure the “mean:standard deviation” ratio, which helps us to identify the aggregation state of the molecule, its size, and other similar sterical parameters.

3. Plasmonics Nanobiophotonics applications focus on recording the plasmonic enhancement of the signal and the use of a substrate to detect changes in refractive indices and corresponding changes in the signal. Typical applications include the use of localized light-matter interactions, applied to specific cells or tissues to measure a biological activity like metabolism or binding affinity of a protein. Some are discussed later in the nanomedicine section. Electrons on metal surfaces are modeled to a sea of particles following Fermi-Dirac statistics. The highest occupied state or Fermi energy for metals is in the order of a few electron volts. This low binding force makes the particle move at speeds near the speed of light. The collective oscillation of these particles are quantized and called plasmons. Color of metal and reflectivity of metals are explained as absorption and emission of photons, but for nanoparticles (smaller than the size of 400 nm wavelength of light), only the resonant wavelength to the surface plasmons is absorbed. The shape and aspect ratio of the nanoparticles can affect nanoparticles colors based on supported standing waves supported by the surface plasmons of the shape. Gold, silver, and copper particles have a different color response because they have different electron density. SPR requires momentum conservation of the photon and plasmon generated, and this is achieved by adaptive optics and optical prism. Localized SPR happens on metal nanoparticles instead of a thin metal layer, and hence removes the bulk effect of nearby plasmon. Localized interaction of plasmons can be achieved without complex optical coupling. Localized plasmon resonance is sensitive to the surface dielectric, and changes on the surface will translate into resonance shift. Anisotropic nanoparticles are more sensitive to these changes, and surface modeling and spectral detection are used to design LSPR sensors. Surface plasmon resonance is sensitive to the changes on the thin metal surface and refractive index changes. Conjugated ligand on metals and their interactions with binding proteins change the SPR resonance. This phenomenon is used to detect binding, affinity, kinetics, and concentration for much of label-free analytics. Propagating surface plasmons are called polaritons, which allow photon transmission and broadly fall under the plasmonic waveguides. The extinction spectrum of a metal particle is a function of wavelength in SPR and can be modeled for a known dielectric function, geometry, and an interaction function of material (number of polarizable elements) [159]. This model also describes a shift in the maximum wavelength in an LSPR sensor as Δλmax ¼ m Δn [1  exp (2d/ld)], where m is the bulk refractive-index response of the nanoparticles, Δn is the change in the refractive index induced by the antigen, d is the effective adsorbate layer thickness, and ld is the characteristic electromagnetic field decay length

Nanobiophotonics and fluorescence nanoscopy in 2020

Fig. 7 Surface plasmon resonance. The principle of surface plasmon resonance-based sensors are compared with their novel counterpart, localized surface plasmon resonance. SPR is sensitive toward the change in refractive index and can be visualized by observing the change in reflected power from a metal coating of the sensor. LSPR uses nanoparticles instead of film, and hence the sensitivity is tuned toward its spectral response. This refractive index sensitivity can be used to identify changes in antibody-antigen binding by attaching an antibody to the nanoparticle and monitoring the change in the sensitive physical parameter (angle/wavelength).

(following exponential decay like TIRFM). The working principles of SPR and LSPR imaging/biosensing are shown in Fig. 7. Surface enhanced Raman scattering (SERS) is an enhanced Raman effect (10 [6] times) achieved by using the scattering enhancement from a metal nanoparticle. With new nanoparticle designs, SERS has grown to become a prominent single-molecule analysis. SPR can be extended into fluorescence as well, but the enhancement (10 [1] times) is not as powerful as SERS. This surface enhanced fluorescence (SEF) is distancedependent enhancement because metals can also quench fluorescence at very close distances. Two-photon luminescence (2PL) is sensitive to polarized excitation and shape of the nanoparticles. This property makes gold nanorods useful for in vivo imaging and can even use the thermal activation of nanoparticles in the 2PL mode of imaging.

4. Biomolecular spectroscopy Three Nobel Prize-winning techniques are as dynamic as the optical imaging field and share the mantle for most-applied techniques for understanding biological structures of proteins and enzymes—EM, NMR, and XRD (X-ray diffraction) received recognition for their outstanding impact in their fields: 2017 (Chemistry), 2003 (Medicine), and 1914 (Physics), respectively. Modern crystallography methods based on X-rays have indirectly accumulated more than 25 Nobel prizes in the 20th century [160]. Three analytical

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techniques are used in structural studies: IR spectroscopy (measures the transitions in the vibrational states of covalent bonds), UV–Vis spectroscopy (measures the transitions in the energy levels of electrons in the pi bonds), and mass spectrometry (finds the mass/ charge ratio of the sample after ionizing it and studying the deflection through a magnetic field) [161]. It is essential to mention the roles of mass spectrometry and Raman spectroscopy in this context. In MALDI-TOF-MS (matrix-assisted laser desorption/ionization time-of-flight mass spectrometry), protein samples are bombarded with a laser pulse causing thermal desorption, and the sample gets embedded in the matrix material. An electric field assists these matrix ions to be accelerated according to their mass and the time of flight of these analytes can be used to calculate the mass of the polypeptides ionized in the matrix. A review on mass spectrometry-imaging can be found elsewhere [162]. IR/FTIR spectroscopy [163] is sensitive to hetero-nuclear functional group vibrations and polar bonds, while Raman spectroscopy [164] visualizes homo-nuclear molecular bonds (CdC). Raman and IR methods look into vibrational bands, but these techniques are far from each other in terms of applied research. Raman microscopy methods [165] are popular and consists of a large set of techniques that include surface-enhanced Raman scattering, tip-enhanced Raman scattering, stimulated Raman scattering, FT-Raman, resonance Raman, coherent anti-Stokes Raman imaging, and others. A brief overview of these Raman methodologies and their applications can be found in Chrimes et al.’s review [166]. Mass spectrometry and vibrational spectroscopy methods were limited to study the atomic levels in the last century, without any spatial information about the molecule. However, this scenario changed in the 21st century. The imaging mass spectrometry implementation of MALDI and Raman imaging microscopy methods changed the older limitations of spectroscopy, and by 2019, most spectroscopic methods had gained spatial dimension or imaging capacity. However, these methods are limited in resolution and diffraction limitations. In addition to these methods, combustion analysis and measuring the index of hydrogen deficiency in an organic compound can get the chemical formula of a molecule, but structural information is still missing. In 2019, NMR spectroscopy, X-ray crystallography, and Cryo-EM studies provide invaluable structural information with high resolution. Nuclear magnetic resonance (NMR) spectroscopy, in combination with prior knowledge of molecular formulae, can reconstruct the atomic structure of a molecule. Hydrogen (1H), carbon (13C), nitrogen (15N), phosphorus (31P) nuclei, and other nuclei are studied to model structure and ligand binding. NMR microscopy or magnetic resonance imaging (MRI) works similarly to NMR spectroscopy measuring nuclear spins, but uses a different geometry for illumination and collection. The unpaired protons in individual atoms present a specific oscillating frequency (photon) under a strong magnetic field, which can be detected and used to create an image. Three contrast mechanisms are commonly used in MRI imaging: T1/longitudinal relaxation time (the rate at which excited protons return to equilibrium, 0.5 s), T2/transverse relaxation time (the rate at which excited protons reach equilibrium, 4 s), and FLAIR (fluid-attenuated inversion recovery, 9 s). MRI

Nanobiophotonics and fluorescence nanoscopy in 2020

scanners in medical imaging detect contrast in a sample through these alignable hydrogen atoms in water/fat or using a more robust paramagnetic tracer. Two clinical additions to MRI are PET (positron emission tomography), which uses a radioactive tracer, and fMRI, which calculates the oxygen level in the blood. Magnetic resonance microscopy is still limited in its resolution scale to hundreds of micrometers, and the limited amount of free water in a cell also restricts its single-cell imaging applications, as does sensitivity in the millimolar range. However, for larger samples, NMR microscopy is also a handy tool because of its advantage in working on intact 3D samples. The noninvasive nature of NMR imaging enabled the extensive use of NMR in metabolic studies in animals using Carbon-13 labeled glucose, fed to animals. The different metabolic fates of the C-13 glucose can be followed over time in an animal or a model system. Substrate level identification of metabolism is possible using this method [167]. X-ray crystallography has contributed greatly to the current knowledge of proteins and understanding of biochemical pathways. The molecular structure DNA (1962), vitamin B12 (1964), fullerenes (1996), ATP-synthase (1997), ribosomes (2009), graphene (2010), and G-protein coupled receptors (2012) are a few of the scientific breakthroughs made possible with the help of X-ray crystallography [160]. The periodic structure of molecules in a 3D lattice forms a diffraction pattern when illuminated with X-rays. A wide range of illumination angles can reconstruct the atomic-level structure of the material. Hence, X-ray imaging is implemented with a transmission X-ray microscope. The image reconstruction step of many pixels separates X-ray diffraction imaging from X-ray crystallography. Extensive processing algorithms and computation can create a real highresolution image from the diffracted phase information. A discussion on X-ray diffraction microscopy and its workings can be found in the review by Thibault and Elser [168]. The development of the X-ray-free electron laser (XFELS, 1971) enabled the probing of matter at very small dimensions [169]. New typography and Fourier holography methods can solve the diffraction pattern to find the relative phase and reconstruct the image. These developments collectively make X-ray diffraction microscopy a promising tool, but still require more development to catch up with the resolution and information density of X-ray crystallography. Small-angle X-ray scattering (SAXS) has been a successful tool for protein structure analysis since the 1960s [170]. Development in stable nanoscale X-ray sources [171] made possible advancements in X-ray optics and instrumentation enabled “nanobeam diffraction” for material sciences [172]. A scanning nano-diffraction/ scanning SAXS setup is implemented in biology and cellular imaging [173]. Cells and tissue scattering maps (dark-field X-ray images) have also been extended into live cells [174]. The distinct interaction with X-ray is elastic for most of these methods, but inelastic interaction is also used to study quasiparticle interactions and atomic structure [175]. Electron microscopy (EM) has the edge in terms of imaging resolution when compared to optical methods, and Cryo-EM can work in non-crystalline structures, unlike X-ray crystallography. Using electrons (shorter wavelength than visible light) allows a higher resolution, and using cryogenic conditions (rapid freezing using liquid ethane

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at 196 °C allows the vitrification of the protein, freezing in its conformation) enables us to image biological samples without vacuum/dry conditions. Electron beam geometry designs and sophisticated image processing algorithms using contrast transfer functions are recognized for the rise of Cryo-EM as a parallel for X-ray crystallography. In comparison to X-ray diffraction imaging, Cryo-EM images preserve the phase information to produce a more precise map of the structure and atomic models [176]. Electrons are more accessible to control than X-rays, because the charge, mass, and magnetic properties of electrons help us to focus and control the electron beam. Using high-energy electrons instead of photons to image thin samples in a transmission geometry limits the methods to topographic imaging, but it is a versatile tool that spans macromolecular levels to a variety of specimen types. As observed earlier, biomolecules of interest can be frozen at any time to study the molecular machinery and biochemical maps using electron microscopes. The first atomic-level protein structure was shown in 1990, and recent advances in Cryo-EM confirm the technique as the mainstream biological tool of investigation in nanoscale [177]. EM, NMR, and XRD are the main sources for structural studies below a nanometer. XRD and NMR generated higher-resolution data than EM in 2019, but EM presents a better biologically friendly sample preparation in comparison. XRD has contributed more toward our knowledge of protein modeling; NMR contributed more toward dynamic studies and 3D conformational changes. XRD is limited to crystallizable samples, while NMR is limited to concentrated samples and is less developed than XRD methods.

5. Nanomanipulation and correlative microscopy Force microscopy (FM) methods belong to a different class of techniques when compared to the electron- or photon-based methods discussed before. Force microscopy uses material vibrations and changes in the material resonance frequency to study a surface of interest in atomic and molecular scales. Two domains exist in this manipulation field: (1) optical and magnetic methods such as optical/magnetic tweezers; and (2) material methods like AFM and microneedle manipulation. The scanning tunneling microscope, the parent to AFM, led to a Nobel Prize in 1986 (Physics), and optical tweezers a Nobel Prize in 2018 (Physics). Single-molecule force spectroscopy techniques can derive information in both contact and noncontact modes [178]. A comparison of the physical limits of optical tweezers and AFM can be found in a review of methods by Neuman and Nagy [178]. Optical traps [179] and optical tweezers (OT) [180] are sensitive optical manipulation methods [181] used to study single-molecule interaction in three dimensions in the 100 fN–100 pN range. OT works by monitoring the changes in trapped dielectric particles in a strong gradient force generated by a focused laser beam. Molecules attached to the trapped-particle and the fluctuations of the dielectric particle can be monitored with high precision to study spatial changes below 1 nm. Surface-tethered molecules can be stretched

Nanobiophotonics and fluorescence nanoscopy in 2020

by the OT, and bumps in force felt by the particle can be studied. A dumbbell trap can be used to study the folding energy landscape using two traps. Optical manipulation methods are seen in micro-robotic surgery [182], applied using plasmonic tweezers [183] and multiple complex optically engineered beams, holographic OT, dark optical traps, counterpropagating beams, and others. Optical manipulation for nanoparticles [184] has opened countless opportunities in nano drug delivery research, optical injecting of a gold nanoparticle into the cell, and so on [185]. Nanoparticle embedded in lipids would respond to heat [186] and be controlled using optical tweezers for lipid vesicle fabrication [187], attractive for drug delivery approaches. Nanoscale methods made a significant leap when the STM (scanning tunneling microscope) [188] was introduced in 1981. It was soon followed by a more biologically friendly version, the atomic force microscope (AFM) [189], which revolutionized atomic-level imaging, atomic manipulation, and so on in the 1990s. In this path of imaging samples using a scanning probe, near-field imaging methods like near-field scanning optical microscopy (NSOM) were developed to target single molecules [190]. Optical microscopy techniques at that time were lagging behind these near-field imaging techniqies because of the diffraction limit, which allowed only very low wavelength electromagnetic waves such as X-rays. A trend in nanobiophotonics in the use of “light robotics” gained attention in 2018, which combines nanofabrication methods with nanomanipulation to achieve control systems [191]. Techniques of optically driven micromachines [192] and optically localized light sources that can be generated in a natural setting promise a bright nano-control system for the future. AFM-TIRF correlational microscopy [193] and correlative light electron microscopy (CLEM) [194] have been integral in identifying biological landscapes to molecular size scales from the 1980s. Super-resolution imaging was integrated into the correlative methodology scheme in the 2010s, forming cryo-fluorescence microscopy using STED, SIM, and localization methods [195]. Super-resolution CLEM has been demonstrated in identifying single particles and protein organization in actin-rich cellular structures [196]. Correlative imaging using a tangible (contact-mode AFM/OT and others) system and an optical method has a practical side of nanomanipulation and cross-validation of two or more techniques. This scheme of nanomanipulation can be exemplified in correlated SR and AFM implementations using SR methods: STED [197] and STORM [52]. This trend of connecting multiple techniques has proven an effective tool in nanoscale imaging, spectroscopy, and computer-vision. For example, a cost-efficient fast FLIM algorithm that was adapted in a frequency domain system was reported in recent correlative approaches connecting time-of-flight and fluorescence lifetime imaging techniques [198]. Optical microscopy, electron microscopy, and atomic force microscopy have a correlative balance that was discussed in detail in a review [199]. These methods, discussed in this section, give an overview of many fluorescence-based methods used in nanobiophotonics in 2020 with an impetus toward nanoscopic imaging. The Nature Publishing Group lists a technique every year as the method of the year. Fig. 8 lists these techniques up to 2018.

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Fig. 8 Methods that are trending in 2019. The top panel shows the named method of the year by the Nature Publishing Group. The bottom panel shows the correlative scheme of microscopy that generates complementary data from their corresponding techniques. The super-resolution field has generated many new methods that add to the available set of techniques that could be used to probe nanoscale biology.

Nanobiophotonics and fluorescence nanoscopy in 2020

Methods in nanobiophotonics have grown in number in the 21st century, and a beginner would find it hard to track all the progress occurring in multiple aspects of one technique. Techniques such as ESEM, FIB, OPT, terahertz imaging, and many others are not discussed in this chapter to constrain the scope of light microscopy methods. A more exhaustive roadmap of these techniques can be found here [4, 200–202].

6. Nanomedicine Nanomedicine is often considered as an application layer built on nanotechnology-based science and materials. However, practically, nanomedicine also presented a robust clinical front in 2019 with tumor-targeting nanoparticles and drug-delivering nanoparticles, which are being established as clinical techniques [203]. In recent years, there has been a surge in the research of nanophotonics applications in cancer, especially for molecular diagnostics and therapy. Nanoparticles selectively accumulated in tumors aid in precise diagnosis and therapy. A roadmap of the nanomedicine development from 1964 to 2015 can be found in Shi’s review [204]. Optical imaging of nanoparticles has the advantage over other modalities due to its high sensitivity, excellent spatial and temporal resolution, and, most importantly, no associated damaging radiation. Imaging techniques such as fluorescence, Raman, optical coherence tomography, two-photon luminescence, and photoacoustic imaging of such nanoparticles have been widely employed. Commercial vendors like Nanospectra Biosciences, Oxonica, and Nanosphere launched colorimetric sensors and photo-activatable drugs that became contrast agents with the mentioned complementary detection methods such as optical coherence tomography, photoacoustics, and two-photon fluorescence [205]. A thorough review of nanomedicine applications was recently published by the American Chemical Society [206]. This section of the chapter addresses cutting-edge nanoparticle applications in nanomedicine. Fluorescent nanoparticles have the advantage of unique optical properties and photostability, which provides an advantage over fluorescent proteins or other probes. These include (1) fluorescent-dye loaded nanoparticles, (2) QD, and (3) upconversion nanoparticles (UCNPs) and others. UCNPs are NPs that can absorb low-energy, near-infrared light and emit light in the higher energy visible spectrum by a nonlinear conversion process. Fluorescent nanoparticle-based cancer biomarker detection is highly successful due to its sensitivity, which allows identification of early-stage biomarkers even at low numbers. The surface of the fluorescent nanoparticles is typically modified to attach to ligands like antibodies and peptides, which are specific to a cancer biomarker. Kantamneni et al. developed fluorescent nanoprobes, which are capable of tracking cancer metastasis [207]. These are rare-earth-doped albumin-encapsulated nanoparticles emitting short-wave infrared light, which were injected intravenously in a human breast cancer mouse model and functionalized to target specific metastatic sites. Surface functionalized fluorescent QDs have also been employed for cancer imaging. QD, by definition, are semiconductor-based nanocrystals of size below 10 nm with the exclusive property of controlled emission. The sizes and compositions are often manipulated to obtain the

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desired fluorescence/luminescence emission wavelength. As mentioned before, QDs are often used with surface modification to make them compatible for use in a biological sample. One example is ZnS-capped CdSe QDs, synthesized by Akerman et al., which were coated with three peptides that were able to target preferentially tumor vasculature, endothelial cells, and specific tumor cells in vivo [208]. Recently, Pons et al. loaded red blood cells and lymphoma cells with ZnCuInSe/ZnS quantum dots with infrared emission and characteristic long fluorescence lifetimes [209]. They were able to detect the QD fluorescence of a single red blood cell or tumor cells in a high-velocity bloodstream by detecting time-gated fluorescence (a method that eliminates the fast lifetime component of background fluorescence and hence increases the signal-to-noise ratio of the detected signal). Other fluorescent nanoparticles developed for cancer imaging include fluorescent metal nanoclusters [210, 211], fluorescent gold nanoparticles, and nanorods [212–215], conjugated polymer nanoparticles [216], dye-doped silica nanoparticles [217–220], cellulose acetate nanoparticles [221], and carbon-based fluorescent nanoparticle or carbon dots [222–225]. A new and unique class of nanoparticles being developed is plant virus nanoparticles. These are non-infections and have the advantage of being biocompatible and biodegradable. Bioconjugation with fluorescent molecules renders most of these agents an interesting contrast mechanism and biological application for optical imaging [226, 227]. Hauert and Bhatia’s review [228] on the current trend in nanoparticles analyze NP applications in a functional and material aspect, as illustrated in Fig. 9 (reproduced from

Fig. 9 Nanoparticles in the context of nanomedicine. (Figure reproduced from S. Hauert, S.N. Bhatia, Mechanisms of cooperation in cancer nanomedicine: towards systems nanotechnology, Trends Biotechnol. 2014, 32 (9), 448–455. https://doi.org/10.1016/j.tibtech.2014.06.010. Permission has been granted through Copyright Clearance Center’s Rights Link service.)

Nanobiophotonics and fluorescence nanoscopy in 2020

Hauert et al.). The size, shape, charge, material properties of nanoparticles, and functionalizing using charge and loading cargo play key roles in categorizing and applying them in different biological applications. Another mainstream application of nanoparticles in nanomedicine is drug delivery, which includes hydrophilic and hydrophobic drugs in a biological system of interest. This entails the delivery of short-interfering-RNAs (siRNA/inhibits specific mRNA) and micro-RNAs (miRNAs/regulates mRNA) for specific genomic-level control. In the last decade, the drug delivery applications of NPs have gained substantial momentum. In the 21st century, the development of nanoparticles for multimodal and multifunctional applications has emerged as a holistic approach in the field of theranostics (therapeutics+diagnostics). These nanoparticles are employed for simultaneous diagnosis as well as imaging-guided delivery of chemotherapy drugs. These NPs allow the union of multiple complementary imaging modalities and oncological therapeutics as all-in-one nanoplatforms. Image-guided drug delivery to cancer cells has been reported by Guo et al., who developed a fluorescent silicone nanoparticle to deliver siRNA and doxorubicin [229]. Yang et al. studied the potential of doxorubicin-loaded indocyanine-greenmodified hollow mesoporous Prussian blue nanoparticles for a combination of fluorescence imaging and light-induced chemotherapy, photothermal therapy, and photodynamic therapy when injected intravenously into 4T1 tumor-bearing mouse models [230]. Similar nanoparticles have been developed by multiple research groups for cancer theranostics [231–236]. Upconverting nanoparticles have the advantage of deeper penetration due to NIR excitation along with high photostability and low cytotoxicity [237]. These properties make them attractive theranostics agents [238–241]. Combinations of nanoparticle technology with optical imaging modalities are explored for oncological imaging and therapy. Gobin et al. performed in vivo mice tumor imaging with gold nanoshells. Gold nanoshells are resonant in the near-infrared region, causing them to have high contrast for optical coherence tomography (OCT) [242]. Mishra et al. demonstrated the use of zinc oxide nanoparticles as a swept-source OCT contrast agent in chicken breast tissues [243]. Approaches to visualize tumors during surgery was achieved using activatable cell-penetrating peptides in 2010 [244]. Fluorescently labeled, the polycationic cell-penetrating peptide was used to visualize and surgically remove tumors [244]. Tumor-targeting QDs were developed based on tumor response to growth-factor receptor ligands. These were tested to identify tumor boundaries and in aiding the surgical removal of tumors [245]. An incredible amount of work is being done on SERS-based nanoprobes for cancer imaging, especially for intraoperative image-guided resection. These contrast agents have high specificity due to the Raman spectral signatures and surmount obstacles like photobleaching or background tissue autofluorescence. Kircher’s research group has demonstrated the application of SERSnanoprobes in brain tumor margin detection in a glioblastoma mouse model with a hand-held imager, liver cancer imaging in mice, and more recently, DNA-based multimodal fluorescence-Raman nanoparticles [246–248].

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Theranostics that include therapeutic and diagnostic modalities use NP triggers based on either internal (redox, oxidative stress, ROS, temperature, pH) stimuli or external stimuli (light, ultrasound, magnetic, or electric field) [249]. Major developments in this field of nanoparticle-based theranostics can be found in Younan Xia’s review of nanoparticles [250]. Gold nanorods are used as a contrast agent in two-photon luminescence for imaging cells at low excitation powers, which allows better deep tissue imaging than conventional fluorescent agents [251]. Photoacoustic imaging (PAI) is a technique that amplifies the contrast in the biological tissue based on optical absorption of hemoglobin and traces optical contrast agents; PAI has shown the use of nanoshells as cortical vasculature contrast agents [252]. PAI has demonstrated numerous imaging applications using single-walled carbon nanotubes and gold nanorods, and has also been shown recently to sense ROS production using water-soluble semiconducting pi-conjugated nanoparticles [253]. Polymer encapsulated nanoparticles containing conjugated polymers or fluorogens with aggregation-induced emission in the core have shown applications in combination with biophysical characterization methods like FRET and in vivo imaging schemes [254]. The concept of lab-on-a-chip (LOC) integrates any form of spectroscopic method that can be used to screen samples on a chip (small area). Micromanufacturing and complex microliter measurement methods have made this possible and helped in addressing disease screening in a global health scenario. These approaches have many excellent applications in the 21st century, mainly with the development of efficient detection and high throughput screening. A review of LOC methods used in biology was done by Gupta et al. [255]. The introduction of single cell lasers demonstrating the possibility of a high-Q microcavity lasing in single live cells using recombinant GFP [256] was another eye-opening microscale scenario (recombinant proteins: the gene is isolated and cloned into an expression vector conveniently in other organisms such as bacteria or cells in culture). This method uses external cavity mirrors to demonstrate single cell lasing at pump energy above the 0.4 nJ lasing threshold. In this context of single-cell lasers, optofluidic bio-lasers are mainly used for biosensing and LOC [257]. These lasing effects seen in a protein solution or single eukaryotic cell may get extended into intracellular lasing and LOC implementations using nonlinear imaging schemes. FRET in the context of LOC has also been appealing as a bio-spectroscopy tool for the detection of molecular activity and increases the span of use with newly developed FRET-based biosensors [258].

7. Conclusions Numerous methods have emerged in the field of nanobiophotonics in the 21st century, mainly toward identifying the different biological functions and understanding nanomechanical phenomena. Our definitions of physical dimensions and the interest to extend

Nanobiophotonics and fluorescence nanoscopy in 2020

the investigation of size limits in both macro and micro scales have made a profound impact on current trends in science and technology. The complexity of techniques has increased over the last decades, with new, sophisticated methods emerging faster than before, to study interesting and unforeseen phenomena at smaller dimensions. Probing techniques based on single-particle dynamics, imaging, and other biophysical principles dominated in the late 20th century. However, the 21st century has been revolutionary with super-resolution imaging tools and atomic-level probing resolution. The micrometer scale has extended down to the nanometer and picometer scale. This chapter considered some of the significant development in these areas that are significantly based on fluorescence. It discussed several key aspects of nanobiotechnology: (1) background to nanobiophotonics; (2) tools linked to nanoscopy; imaging, and sensing; (3) tools used in nanomanipulation; and (4) nanomedicine applications. Nanobiophotonics is a rapidly evolving field with new methods and development of nanoparticles, changing the landscape of the ongoing research and downstream applications in nanomedicine. Addressing this large area of research in one chapter is an optimistic attempt to give an overview of the subcategories of the field and some relevant techniques that oversee the progress in this field. This chapter also demonstrated how to construct one nanoscopic tool of choice (SIM-TIRF) and discussed the power of emerging trends in correlative nanoscopy. Numerous bifurcating fields under this broad nanobiotechnology umbrella produce a better understanding of the physicochemical consequences of the use of nanoparticles. Developments in nanoscale probing tools that visualize and fabricate matter at the nanoscale address the challenges in controllable and scalable nanotechnology within the light of nanobiotechnology.

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CHAPTER 7

Nanotechnology-based fiber-optic chemical and biosensors Banshi D. Gupta, Vivek Semwal, Anisha Pathak

Physics Department, Indian Institute of Technology Delhi, New Delhi, India

1. Introduction Developments in nanotechnology have been of tremendous importance for every area of science and technology. The phenomenal properties of matter at nanoscale has outperformed in applications related to almost every area of research. A wide variety of applications from various materials have been explored at this scale in the fields of biophotonics, catalysis, optoelectronics, environmental, and sensing. Owing to these advances, the last decade has witnessed a surge in the studies and applications of light-matter interaction on subwavelength scale. The area of science dealing with the light-matter interaction on metallic surfaces at nanoscale is termed nanoplasmonics [1, 2]. Plasmons are the collective oscillations of free electrons in conductors and the light-induced oscillations at the metal-dielectric interface are termed surface plasmons (SPs). The oscillations of free electrons at such interfaces result in the generation of a charge density wave propagating along the interface and are known as propagating surface plasmons (PSPs). In the case of metal nanoparticles, the collective oscillations are localized within the nanoparticles and are known as localized surface plasmons (LSPs) [3–6]. The frequency of such resonant oscillations depends not only on metal composition but also on its shape, size, and the medium surrounding it. The unique optical properties of noble metal nanoparticles were witnessed and noticed around the 4th century CE, when artists used them for vibrant colored glass artifacts and stained church windows [7]. Since then, a flurry of activities have continuously emerged on the fundamental research, devices, and other applications of this burgeoning field in optical communication in subwavelength structures, single molecule biosensing, optical imaging reaching diffraction limit, light manipulation, and guidance in nanosystems. Plasmonics research is augmenting the pioneering field of nanotechnology, with the advent of technologies like nanolithography and high density electronics reaching fundamental physical limits. Numerous technological challenges can be addressed utilizing the unique properties of SPs, with rigorously growing literature. Biodiagnosis and environmental monitoring are two major fields profiting from the developments of nanoplasmonics [8]. Detection of minute biomarkers and pollutants is of prime Nano-Optics https://doi.org/10.1016/B978-0-12-818392-2.00007-X

© 2020 Elsevier Inc. All rights reserved.

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importance in many areas, such as bioscience, environmental monitoring, defense, and food security. Thus, a huge scientific community is focusing on new methodologies for the sensing of various analytes for addressing the needs of high sensitivity, specificity, low cost, miniaturized device, and long shelf life. Surface plasmon-based sensors eliminate the limitations of existing conventional techniques and offer a promising platform for unlabeled and stable sensors. Surface plasmons excited at the metallic thin films and nanostructures resonantly oscillate with the driving field and the resonance condition is susceptible to the permittivity of the dielectric medium around the nanoparticle/nanostructure. This forms the principle of sensing based on SPs. The interaction of analyte molecules with a specially synthesized nanomaterial layer around the metal nanosystem changes the resonance condition of the excitation of SPs and hence the concentration of analyte is calibrated in terms of resonant parameter. The chapter focuses on the diverse and rapidly growing field of nanotechnologybased plasmonics for sensing applications in various areas like bioscience, the environment, and chemical industries. The substantial applications of nanoplasmonics need synthesis of well-defined metal nanostructures, their assembly on various substrates, and their structural and morphological characterization for exploring their full potential. Thus, a brief description of various synthesis and characterization techniques required for nanoplasmonics are reviewed here. The excitation mechanisms of SPs and LSPs are provided in brief with an overview of evanescent field and optical fiber substrate for the surface plasmon-based sensors. Further, various kinds of optical fiber probes utilized in plasmonic sensors are discussed briefly along with their applications for the detection of various biological and environmental analytes. A few examples of sensors utilizing surface plasmon resonance (SPR) and localized surface plasmon resonance (LSPR) phenomena are presented for the detection of ethanol, pH, SMX, ammonia, dopamine, and glucose, based on enzymatic reaction, rGO-PANI nanocomposite, carbon nanotubes, tin-oxide thin films, and molecular imprinting on the surface of CNTs.

2. Evanescent wave and optical fiber In fiber-optic SPR- and LSPR-based sensors, evanescent waves play a significant role; therefore, the understanding of the basic concept of the evanescent wave is very important. To understand it, let us consider an interface of two media of different refractive indices. If a plane wave propagating in the denser medium hits the interface at an angle greater than the critical angle, then, according to the geometrical optics, the plane wave is totally reflected back from the interface. However, this is not the complete reality. Instead, the wave penetrates into the rarer medium for a very short distance and then returns [9]. The incident and reflected plane waves superpose in the denser medium, which results in the generation of standing wave propagating along the interface, as shown in Fig. 1. Further, the associated electric field intensity is maximal at the interface

Nanotechnology-based fiber-optic chemical and biosensors

Fig. 1 Total internal reflection and the generation of evanescent field in the rarer medium.

and decays exponentially in the rarer medium. The decaying field is known as the evanescent field and the wave associated with it, known as the evanescent wave, propagates along the interface. The physical understanding of the existence of the evanescent wave is that the electric and magnetic fields satisfy the boundary conditions and cannot be zero at the boundary. The evanescent wave and its field in the rarer medium play a very important role in optical fiber-based sensors. An optical fiber is a cylindrical waveguide in which the light propagates through the total internal reflections. The schematic of an optical fiber is shown in Fig. 2. It consists of two concentric cylinders; the internal one is called the core while the outer one is the cladding. The outermost part is the jacket to protect the optical fiber. The refractive index of the core is greater than that of the cladding [10]. The light launched from one end of the optical fiber propagates through it by means of total internal reflection (TIR) at the core-cladding interface, as shown in Fig. 3. The TIR at the core-cladding interface gives a decaying electric field in the cladding region with an evanescent wave propagating along the interface. The perpendicular distance from the core-cladding interface at which the evanescent field decays to 1/e of its value at the interface is called the penetration depth. Mathematically, it can be written as: dp ¼

Fig. 2 Schematic of an optical fiber.

λ 2πn1 ð sin 2 θ  sin 2 θc Þ1=2

(1)

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Fig. 3 Propagation of a ray inside an optical fiber.

where λ is the wavelength of the light in free space, n1 is the refractive index (RI) of the core, θ is the angle of the guided ray with the normal to the core-cladding interface, and θc is the critical angle, which is defined as:   1 n2 (2) θc ¼ sin n1 where n2 is the RI of the cladding of the optical fiber. Eq. (1) implies that as the angle of the ray at the core-cladding interface approaches the critical angle, the evanescent field increases. The evanescent field is widely used in the excitation of surface and localized surface plasmons in the case of optical fiber plasmonic sensors [11].

3. Surface plasmon resonance A wide range of fundamentals of science and technology are based on the modulation of light-matter interaction. In the case of metals, the study of interaction is based on a plasma model, where the free electrons are considered as an electron liquid of high density (1023 cm3), which moves freely in a background of positively charged atoms. These longitudinal electron density oscillations in the volume of the metal are known as plasma oscillations and their quanta are known as “volume plasmons.” According to the simple Drude model, these free electrons oscillate 180 degree out of phase with the driving field, imparting them negative dielectric constant and hence high reflectivity at optical frequencies. However, apart from acting as mirrors, a fascinating aspect of light-matter interaction in metals is surface plasmon polaritons (SPPs). These are the electromagnetic modes of interaction of light with mobile charges at the surface of a metal. The study of electromagnetic response of metals is broadly termed “plasmonics.” Thus the trapped light waves, due to their interaction with surface charges of a metal, are known as “surface plasmons” (SPs). The free electrons of the metal at the surface collectively oscillate in resonance with the incident light wave and this phenomenon is known as “surface plasmon resonance” [12]. The SPs have a history dating back more than 100 years. They were first observed in 1902 by Wood as anomalous dark and light patterns in metallic gratings. Maxwell Garnett explained his theory of effective dielectric constant through

Nanotechnology-based fiber-optic chemical and biosensors

different colors of metal-doped glasses in 1904. In 1907, Zenneck suggested the binding of EM waves to a surface due to the imaginary part of the dielectric constant. In 1956, Pines and Fano attributed the characteristic energy losses in metal to the free electron oscillations, termed “plasmons.” Ritchie demonstrated experimentally the losses in thin films and gave the first theoretical description to SPs in 1968, along with an explanation of the anomalous behavior of metallic gratings. A complete description of the phenomenon was only possible after the excitation mechanism of plasmons proposed by Otto, Kretschmann, and Raether in 1968 using attenuated total reflection (ATR) prism coupling. The first evidence of sensing by SPs was given by Nylander and Reidberg in 1982 for gas and biomolecular sensing [13–15]. The rapidly growing field of nanoscience has renewed interest in SPs due to structured metallic surfaces. Thus, plasmonics is now seen as a branch of nanophotonics, which deals with the confinement of the electromagnetic waves at subwavelength level. The mathematical formulation of such confinement at a metal surface lies within the classical frame of solving Maxwell’s equations for a boundary where the dielectric constant changes sign, as in a metal-dielectric interface. The use of Maxwell’s equations under the appropriate boundary conditions yields the propagation of a TM polarized wave along the interface of the two media with a frequency-dependent wave vector (ksp) as: rffiffiffiffiffiffiffiffiffiffiffiffiffiffi εm εd ksp ¼ k0 (3) εm + εd where k0 is the free space wave vector, and εm and εd are the permittivity of the metal and dielectric supporting SPs, respectively [3, 4]. As can be seen from the dispersion relation (Eq. 3), such a light-matter interaction leads to SP modes whose momentum (ℏksp) is greater than the momentum of incident light (ℏk0). As a consequence, SPs have a non-propagating bound nature to the surface, with field amplitudes exponentially decaying in both the media. Thus, for light to generate SPs, the momentum mismatch has to be bridged, which can be broadly done in three ways. The first and most frequently used method involves ATR prism coupling, the second is the scattering from a surface topological defect, and the third is the use of periodic metallic corrugation [12]. In the first method, proposed by KretschmannRaether, the momentum mismatch is fulfilled using the base of a high refractive index prism in contact with metal and the generation of the evanescent field at the prism base-metal interface, when a ray with the angle of incidence greater than the critical angle is incident, as shown in Fig. 4A. The evanescent wave excites the SPs at the metaldielectric interface when the thickness of the metal layer is less than the penetration depth of the evanescent field. The resonance occurs when the propagation vector of evanescent wave matches with the propagation vector of surface plasmon wave resulting in a sharp dip in the reflected light intensity. From Fig. 4B, it can be seen that the direct light in air or the light through the dielectric medium cannot excite the surface plasmon wave

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Fig. 4 (A) Surface plasmons excitation in the Kretschmann-Raether configuration and (B) dispersion relations of various configurations and surface plasmons.

because of the smaller momentum or propagation constant. Instead, the evanescent wave can excite the surface plasmon wave as evident from the intersection of the dispersion curves of evanescent wave and surface plasmon wave [12, 15]. Mathematically, the resonance condition is written as: rffiffiffiffiffiffiffiffiffiffiffiffiffiffi ω0 pffiffiffiffi ω0 εm εd Kev ¼ (4) εp sinθ ¼ c c εm + εd where εp is the permittivity of the prism and θ is the angle of incidence of the ray at the prism-metal interface. As can be seen, the resonance condition is satisfied at a particular angle, which is highly susceptible to change in the value of εd. Change in its value changes the value of resonance parameter—the angle of the ray, in this case. This forms the foundation of SPR-based sensors. As mentioned earlier, the light energy is transferred to SPs for their excitation, which results in the generation of a surface plasmon wave propagating along the metal-dielectric interface. As the wave propagates, it is gradually attenuated due to the metal absorption losses. Thus, the wave has a finite propagation length owing to its complex wave vector, ksp ¼ ksp0 + iksp00 , due to the complex dielectric constant of metal [16]. The propagation length of the surface plasmon wave is given as:    1 c ε0m + εd 3 ε0m 2 2 (5) δsp ¼ 00 ¼ 2ksp ω ε0m εd ε00m Further, the tight confinement of EM field at such interfaces has a short penetration depth of the field in both the media. The penetration depths of the field in dielectric (δd) and metal (δm) are given as: sffiffiffiffiffiffiffiffiffiffiffiffiffiffi 1 ε0m + εd δd ¼ (6) ε2d k0

Nanotechnology-based fiber-optic chemical and biosensors

1 δm ¼ k0

sffiffiffiffiffiffiffiffiffiffiffiffiffiffi ε0m + εd ε0 2m

(7)

Due to the enhanced EM field, SPs are extremely sensitive to the changes at the surface, leading to highly sensitive optical sensors. With advancements in nanotechnology and the possibility of tailoring the shape, size, and properties of metallic nanostructures, the field of SPR now has new paradigms in the field confinement at these nanostructures. This area of SPR technology is termed localized surface plasmons (LSPs), which means SPs confined at the surface of a metal nanostructure.

4. Localized surface plasmon resonance LSPR is the confinement of the plasmons on the metal nanoparticle if the size of the nanoparticle is smaller than the wavelength of the light. When an electromagnetic wave is incident on the metal nanoparticles, at a certain frequency the fractional part of the EM wave is coupled to the coherent oscillations of the free electrons inside the nanoparticles and the strong electric field enhancement is observed near the metal nanoparticles. This frequency is known as the resonance frequency and the phenomenon is known as the localized surface plasmon resonance. A schematic illustration of the LSPR in metal nanoparticles is shown in Fig. 5 [5, 6]. In the LSPR phenomenon, the electric field typically exists 30–50 nm around the nanoparticle. The optical absorption/extinction of a nanoparticle is maximal at the resonance frequency. According to Mie theory, the expression of extinction cross section (Cext) of a spherical nanoparticle can be written as: " # 24π 2 εs 3=2 R3 Imðεm Þ (8) Cext ¼ λ ½ Reðεm Þ + 2εs 2 + ½ Imðεm Þ2

Fig. 5 Schematic of the localized surface plasmons on a metal nanoparticle surface.

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where εm and εs are the dielectric constants of the metal nanoparticle and surrounding medium respectively, and R and λ are the radius of nanoparticle and wavelength of the light, respectively. From this expression, it is clear that the LSPR phenomenon depends strongly on the various parameters such as the shape, size, and the dielectric constant of the nanoparticle, and the refractive index of the medium surrounding the nanoparticle. The position of the peak absorption/extinction wavelength of the metal nanoparticle depends heavily on the refractive index of the surrounding medium, which can be incorporated in the sensing applications. In the case of noble metal nanoparticles like gold, silver, and platinum, the optical absorption occurs in the visible region, while for the semiconductor nanoparticles, the optical absorption occurs in the near-infrared or mid-infrared region. The term “localized” indicates that the field around the nanoparticle is non-propagating, while in the case of surface plasmon resonance, the field is propagating. In the case of LSPR, the coupling between the EM wave and free electron oscillations of the nanoparticle does not require any special arrangement like that in the case of SPR.

5. Optical fiber-based SPR/LSPR probes 5.1 Probe designs The method of excitation of surface plasmons using the Kretschmann configuration, shown in Fig. 4A, can also be applied to optical fiber. As mentioned above, the light guidance of a ray in an optical fiber occurs due to the TIR at the core-cladding interface, resulting in the propagation of an evanescent wave along the interface and the evanescent field in the cladding region. Similar to a prism, this evanescent field can be used for the excitation of surface plasmons if a small portion of the cladding is removed from the fiber and it is coated with a thin film of metal. The use of optical fiber in plasmonic sensors provides advantages like miniaturized probe, easy handling, and capability of remote sensing with continuous online monitoring [15]. The schematics of various probe designs of SPR-based fiber-optic sensors are shown in Fig. 6. Fig. 6A shows a simple refractive index probe where the liquid sample of unknown refractive index is kept around the metal layer. Unlike a prism, it is difficult to launch light of one particular angle in the fiber and, therefore, in the case of fiber-based SPR sensors, all the guided rays from a polychromatic light source are launched into the fiber, and the condition of resonance (Eq. 4) is achieved at a particular wavelength called the resonance wavelength. A change in refractive index of the liquid sample changes the resonance wavelength in the SPR spectrum recorded at the output end of the fiber probe. To sense a chemical/bio analyte, the sensor should be specific/selective to that particular analyte, and hence the probe requires a sensing layer whose refractive index changes on interaction with the analyte. Therefore, depending on the analyte, a sensing layer is coated over the metal layer and the sample of unknown concentration of the analyte is kept around the sensing layer for its

Nanotechnology-based fiber-optic chemical and biosensors

Fig. 6 Fiber-optic SPR-based (A) refractive index sensor, (B) chemical/biosensor, and (C) chemical/ biosensor with enhanced sensitivity.

detection, as shown in Fig. 6B. For the enhancement of sensitivity, a thin layer of high refractive index material is coated between the metal and the sensing layer in the fiberoptic probe, as shown in Fig. 6C. The additional layer of high refractive index, apart from enhancing sensitivity, protects the oxidation of the metal film and, in the case of the enzyme entrapped sensing layer, it protects the enzyme activity from deactivation by

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Fig. 7 Fiber-optic LSPR-based (A) refractive index sensor and (B) chemical/biosensor.

the metal [17]. For the probe fabrication, generally, a plastic clad silica fiber of high numerical aperture and large core radius is used. In the case of an LSPR probe, metal nanoparticles are immobilized over the unclad core of the fiber using the dip coating method. The schematic of the LSPR probe is shown in Fig. 7A. It can be used for the sensing of refractive index of the fluid around the nanoparticles by measuring the absorbance spectrum. The specific biomolecules are attached on the surface of the metal nanoparticles using various functional groups such as APTES, thiol, and EDC-NHS coupling for biosensing applications. The schematic of such a biosensor is shown in Fig. 7B. Generally, gold nanoparticles are preferred in most LSPR-based biosensors due to their biocompatibility and easy functionalization of the thiol group for the immobilization of the biomolecules. In recent years, various gold nanoparticle-based LSPR biosensors have been developed [18].

5.2 Experimental setup of SPR-/LSPR-based sensors The schematic of the experimental set-up of an SPR-/LSPR-based fiber-optic sensor is shown in Fig. 8. The fiber probe is fixed in a glass flow cell with facility of inlet and outlet of the sample. Polychromatic light from a tungsten halogen lamp is launched at one end of the probe and the SPR/LSPR spectrum is recorded at the other end of the fiber probe by a spectrometer interfaced with a computer. The guided ray in the fiber excites the SPs/ LSPs at the metal-dielectric interface and the change in the SPR/LSPR properties can be

Nanotechnology-based fiber-optic chemical and biosensors

Fig. 8 Experimental setup of a fiber-optic SPR-/LSPR-based sensor.

evinced in the spectra recorded by the spectrometer/computer with the change in the refractive index/concentration of the analyte sample in the flow cell [3, 15].

6. Nanotechnology and nanomaterials Nanotechnology is a science that deals with systems the dimensions of which are in the range 1–100 nm. Although the definition of nanotechnology is not very precise, a material or structure is considered as a nanostructure if at least one of its dimensions lies in the range 1–100 nm. In the past few decades, a multidisciplinary area of research has focused on this unit of length, i.e., one-billionth of a meter (109 m), at which fundamental properties of a material can be manipulated. Nano-dimensional materials exhibit novel and significantly improved physical and chemical properties, which can be tailored at nano levels for applications in almost every area of science and technology. It has been found that the material properties change dramatically as dimensions are decreased from macro to micro to nano level, and the same material can be exploited for various applications by controlling its properties and functionalities at various dimensions. The dimensions of various items can be seen in Fig. 9. When any material reaches nano-dimensions, it has a relatively large surface area compared to the bulk material and the quantum effects start to dominate. Due to this, materials show superior chemical, optical, electrical, and magnetic properties and become exemplary for numerous applications like catalysis, sensors, medicines, electronics, and many more [19].

6.1 Synthesis of nanomaterials For the synthesis of nanoparticles, top-down and bottom-up are two major techniques, as shown in Fig. 10. These two techniques have many advantages and disadvantages and,

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Fig. 9 Dimensions of various objects.

Fig. 10 Concepts of top-down and bottom-up methods for the synthesis of nanoparticles.

therefore, according to the required nanostructure, one can use any one of these techniques that are suitable for a particular nanostructure [20]. 6.1.1 Top-down technique In this technique, the bulk materials are converted into nanomaterials via various physical methods like cutting, etching, grinding, crushing, ball milling, laser ablation, and lithography processes. The advantages of these methods are that one can produce a large number of nanomaterials without any chemical purification. These methods also have some limitations like the production of nanostructures of varying sizes and shapes or geometry. Ball milling is one of the simplest techniques for the production of nanostructures. In this method, kinetic energy from a grinding medium is transferred to a material undergoing reduction and, due to this, the bulk material converts into the nanostructures [21]. Laser ablation method, shown in Fig. 11, is also used for the formation of nanoparticles, coreshell nanostructures, quantum dots, etc. In this method, a laser beam is focused on the

Nanotechnology-based fiber-optic chemical and biosensors

Fig. 11 Schematic of the laser ablation technique.

surface of the bulk material suspended in an ambient medium (liquid or gas), the temperature of the irradiated spot increases, and the material starts to vaporize and converts into nanostructures. The size and shape of the nanostructures depend on various laser parameters, solvent, and quantity of the target material [22]. The lithography technique is also very useful for the creation of very precise nanostructures. Although this technique requires very complex and expensive instrumentations, one can fabricate highly accurate nanostructures by this method [23]. 6.1.2 Bottom-up technique The bottom-up technique refers to the building of a nanomaterial from the bottom through self-assembly, atom by atom or molecule by molecule. Atom-by-atom deposition leads to the formation of self-assembly of atoms/molecules and clusters and they come together to form various nanomaterials. Various bottom-up techniques have been used for the synthesis of nanomaterials such as sol-gel, hydrothermal growth, physical vapor deposition, and chemical vapor deposition. Sol-gel is a chemical technique to produce ceramic and glass materials in the form of powders, thin films, or fibers. In this method, sol is a colloidal suspension of solid particles of ions in a solvent, and when the solvent from the sol starts to evaporate, the ions begin to join together and make a continuous network, which is called a gel. Generally, metal alkoxides and metal chlorides are the precursors for this technique, which are broken down by the reaction with water (hydrolysis) and polycondensation. The sol-gel process depends on various parameters, such as pH, temperature, concentration of precursor, water content, and drying conditions [24]. The second technique is hydrothermal, in which the liquid sample is kept in a steel pressure vessel called an autoclave, where the temperature of the liquid

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sample is varied. Due to the temperature, high pressure is created inside the autoclave. At a particular temperature and pressure, synthesis of the nanomaterials occurs. By varying the concentration of the precursor and temperature conditions, the size and shape of the nanomaterials can be changed. The autoclave is maintained typically between 70°C and 200°C for 10–24 h, depending on the process, and then air-cooled to room temperature. The resulting precipitate is collected by filtration, washed, and finally dried. Various metal and metal oxide nanostructures can be synthesized by the hydrothermal technique [25]. In physical vapor deposition method, a thin film is deposited on any substrate in the presence of vacuum via the various techniques such as thermal evaporation method, pulse laser deposition, electron beam physical vapor deposition, and sputtering. In these methods, materials are initially converted into vapor phase from the condensed phase and then to thin film condensed phase over the substrate. It is a very popular method for the thin film deposition of metals, metal oxides, composites, etc. [26].

6.2 Characterization of nanomaterials The measurement of morphological and compositional properties of nano-dimensional material is not an easy task, and needs specialized instruments. The morphological techniques involve microscopy techniques like scanning electron microscopy (SEM), transmission electron microscopy, and atomic force microscopy. The compositional properties are observed by techniques like X-ray diffraction (XRD), energy dispersive X-ray analysis, and spectroscopic methods like Raman spectroscopy, FTIR spectroscopy, and UV-Vis spectroscopy. These techniques are briefly discussed below to provide an overview of material characterization at nano level [27–29]. 6.2.1 Scanning electron microscope A scanning electron microscope creates the image of a sample by exploiting high energy electrons beam to raster scan the sample. Just like conventional light microscopes, SEM utilizes a series of condenser lenses to collimate, direct, and collect light from the sample to create a magnified image providing nano-dimensional information of the sample. Typically, a high energy electron beam (a few eV to 50 keV) from a source is passed through a series of electromagnetic lenses to provide a narrow spot size of 5 nm. The beam is then directed to scanning coils, which provide it with horizontal and vertical deflections for a raster scan on the sample. The amplified signals from the sample are fed to a cathode ray tube (CRT), which provides a distribution map of signal intensity of the scanned sample area as a black and white topographical image. In principle, when the high energy electron beam strikes a sample, it interacts with its atoms and generates secondary electrons, back scattered electrons, and characteristic X-rays containing morphological and compositional information about the sample. SEM produces a highresolution micrograph of a sample with details ranging from hundreds of nm to a few nm [27]. The magnification of such microscope ranges from 20  to 300,000 ,

Nanotechnology-based fiber-optic chemical and biosensors

compared to 10,000 for optical microscopes. Due to the narrow electron beam, the image, with a high depth of field, provides an apparently 3D topography of the sample surface. The advantages of SEM include minimal sample preparation as it operates in reflection mode. 6.2.2 Transmission electron microscopy In transmission electron microscopy (TEM), a beam of electrons is passed through an ultra-thin sample. After interacting with it, an image is formed by collecting the transmitted electrons containing information about the sample. Typical TEM equipment consists of four parts: an electron gun for producing a monochromatic electron beam, condenser lenses for focusing the beam on the sample, an objective lens for collecting the transmitted beam from the sample, and magnifying lenses to create the final image. A beam of monochromatic electrons is focused using condenser lenses into a fine beam by knocking out high angle electrons. The beam transmitted through the sample is collected by an objective lens. A selected area aperture then enables the ordered diffraction of electrons from the atomic planes of the sample. The image is finally projected on a phosphor screen through a series of intermediate lenses. The darker and brighter areas signify smaller and larger numbers of electrons transmitted through the sample. There are broadly three modes of operation in TEM: bright field (BF), dark field (DF), and selected area electron diffraction (SAED). An aperture is placed in the back focal plane of the objective, allowing only a direct beam or one of the diffracted beams to form the image. When a direct beam is allowed, the mode is known as bright field imaging, whereas the image formation by one of the diffracted beams is known as dark field imaging. In BF mode, mass thickness and diffraction contrasts contribute to image formation, whereas the diffracted beam in DF mode interacts strongly with the sample to provide information about crystalline planes, defects, and particle size [29]. In SAED, the elastically scattered electrons produced by the interaction with the sample follow Bragg’s law and are collected by magnetic lenses to form a diffraction pattern on the screen containing information about atomic arrangement of the sample. Crystal spacing, inter-planar distances, and phase information can be confirmed by the SAED pattern in TEM. 6.2.3 Energy dispersive X-ray analysis Energy dispersive X-ray analysis (EDX) is performed in conjunction with SEM or TEM. It provides the elemental details of near surface elements of a sample and the overall positional mapping in it. Here a high energy electron beam 10–20 keV is bombarded on a sample and X-rays emitted from the sample are collected by an energy dispersive spectrometer. The energy of the X-rays generated are characteristics of the atomic structure of the element from which it is emitted, and hence provides the elemental details of the sample. X-rays are generated in approximately 2 μm depth of the sample and, therefore,

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EDX is generally a bulk characterization technique. The electron beam is scanned across the sample to verify the spatial uniformity and homogeneity [28, 29]. 6.2.4 X-Ray diffraction XRD is a nondestructive analytical tool to identify and characterize crystalline samples providing insight into crystal structure, chemical composition, unit cell information, and chemical bonding. X-rays have wavelengths an order smaller than the wavelength of light and can therefore provide the atomic level information of a sample. Typically, X-rays produced in a CRT are incident on a sample and the different lattice planes of the sample reflect these X-rays to produce constructive or destructive interference. For some of the directions, where constructive interference occurs, Bragg’s law provides characteristic information about atomic structure of the sample. Bragg’s law is given as: 2d sin θ ¼ nλ

(9)

where d is the lattice spacing, λ is the wavelength of X-rays, and θ is the diffraction angle, i.e., the angle where constructive interference of scattered X-rays occurs. Due to the random orientation of powdered material, all possible diffraction directions are attained by scanning through the sample. As each crystalline material has a unique crystal structure, its XRD pattern can be treated as its fingerprint for identification of its phase and composition [30]. 6.2.5 UV-Vis spectroscopy Determination of molecular composition of a material is also of prime importance for its characterization. Thus, interaction of light with matter leads to transitions in its molecular energy states. The transitions may lead to movement of electrons, giving rise to electronic energy states or of the constituent atoms leading to motional states (vibration or rotation) of the molecule. Depending on the energy separation, the molecular spectrum is classified as electronic, vibrational, or rotational. Energy emitted or absorbed by a molecular transition is a direct function of radiation frequency, and hence each type of transition can occur in a particular region of the electromagnetic spectrum. Electronic transitions occur in the UV-Vis region. Thus, UV-Vis absorption spectroscopy deals with the transition between electronic energy states of a molecule. A UV-Vis spectrometer consists of a source from which the beam is divided into reference and sample beams and then reunited at the photodetector. The sample beam passes through the sample, and if the beam energy is appropriate to excite an electron to higher energy level, the light is absorbed. The detector records the absorbance according to Beer Lambert’s law by recording the intensities of reference and sample beams [31]. UV-Vis spectroscopy is hence employed to calculate the band gap of a material and determines its chemical composition and particle size and structure.

Nanotechnology-based fiber-optic chemical and biosensors

6.2.6 FTIR (Fourier Transform Infra-Red) spectroscopy Just like UV-Vis spectroscopy, when light of the IR region of the EM spectrum is absorbed by a sample, transitions between its vibrational energy levels occur. Thus, IR spectroscopy reveals information about vibrational stretches of a molecule. Most IR spectrometers are built in the form of a Michelson interferometer. Interference of sample and reference beam is recorded at the detector. Final light intensity is plotted as a function of distance of one of the movable mirrors in the interferometer to produce the Fourier transform. Thus, the final spectrograph consists of light intensity vs. wavenumber corresponding to the chemical bonding structure of the molecule under test. Each dip or peak in the final spectrum corresponds to specific bonds of the molecule relating to their various vibrational motions [32]. 6.2.7 Raman spectroscopy Raman spectroscopy comes under the vibrational spectroscopy of a molecule and rather than absorption of light, scattering of light from the molecule is considered. It is used to analyze vibrational modes of a system and provides information about its chemical bonding structure. Laser light impinged on a sample interacts with its vibrational modes, i.e., phonons, to produce an inelastically scattered Raman signal. The energy of the incident laser photons shifts up or down, providing information about its chemical composition. The Raman effect arises when an electron of a molecule is excited from one of the vibrational states to a virtual state and scatters energy while returning back to a higher or lower vibrational state, generating Stokes or anti-Stokes Raman lines, respectively. The energy lost or gained by a photon in a scattering process is known as Raman shift [33]. Thus the Raman spectrum is shown as Raman scattered intensity as a function of Raman shift. Peaks in the spectrum are characteristics of vibrational modes of a molecule and are regarded as a fingerprint of a chemical bond in a molecule.

6.3 Types of nanomaterials Nanomaterials can exist in various shapes, sizes, and structures in single, fused, or agglomerated forms. According to their dimensional confinement, Seigel classified nanomaterials as 0D, 1D, 2D, or 3D. For example, spheres and nanoclusters are 0D nanomaterials, nanorods, nanotubes, and nanowires are 1D nanomaterials, films and sheets constitute 2D nanomaterials, and interconnected architectures of nanomaterials or bulk nanomaterials are classed as 3D nanomaterials [19, 20]. Similarly, depending on the type of materials, the classification can be made in the following four categories. 6.3.1 Carbon nanosystems The most extensively used nanomaterials today are composed of carbon. They range from fullerenes in 0D to carbon nanotubes in 1D, graphene sheets in 2D, and graphite

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Fig. 12 TEM images of (A) graphene oxide and (B) carbon nanotube.

in 3D. The TEM images of graphene oxide and carbon nanotubes are shown in Fig. 12. They have a wide range of applications in diverse fields, including sensing. 6.3.2 Metal nanosystems Metallic and metal oxide nanostructures like nanoparticles, nanotubes, and nanorods have exceptional properties and applications in every possible scientific area. The whole branch of plasmonics is based on metal nanostructures. Apart from sensing, metal nanoparticles are extensively utilized in drug delivery, reflective coatings, catalysis, solar applications, and magnetic devices. Similarly, metal oxide nanostructures show unique gas sensing characteristics and provide materials for rechargeable batteries and transparent coatings. Fig. 13 shows the TEM/SEM images of gold nanoparticles, SnO2 nanocubes, and a ZnO nanostructure. 6.3.3 Dendrimers The chained or branched units of nanosized polymers are known as dendrimers. Their shapes and branches can be tailored for various functionalities including catalysis and loading of other materials for applications like drug delivery and molecular imprinting.

Fig. 13 (A) TEM image of gold nanoparticles, (B) SEM image of SnO2 nanocubes, and (C) SEM image of ZnO nanostructure.

Nanotechnology-based fiber-optic chemical and biosensors

Fig. 14 (A) SEM image of graphene oxide, (B) SEM image of carbon nanotubes, and (C) SEM image of graphene oxide and carbon nanotubes composite.

6.3.4 Composites Nanocomposites are the augmentation of one or more types of nanomaterials or bulk for the blend of the best properties of each constituent. Tailoring or combination of various properties of different matrices and nanomaterials results in reinforced mechanical, optical, thermal, and electrical properties required in numerous devices and applications. Fig. 14A and B show the SEM images of graphene oxide and carbon nanotubes, respectively, while Fig. 14C shows the SEM image of their nanocomposite.

7. Some examples of SPR- and LSPR-based fiber-optic sensors Fiber-optic SPR/LSPR sensors find applications in detection/monitoring of environmental, biological and medical analytes, gas, food contamination, etc. A sensor consists of a sensing element and a signal transfer element known as a transducer, depending on a suitable technology. The basic idea behind sensor fabrication is to detect a shift in the environment from a normal to a dangerous level. Sensitivity to low concentrations, specificity, accuracy, reproducibility, fast response, miniaturization, and real-time monitoring are some of the basic requirements for a good sensor. In recent years, SPR technology has proved to be an excellent transducing mechanism by gaining focus in almost all areas of sensing due to its highly sensitive response toward change in the surrounding medium. The selectivity of an SPR-based sensor for a specific analyte is attained by designing a proper sensing layer keeping in mind the analyte to be sensed. Further, the use of optical fiber as a substrate for SPR sensors by exploiting the evanescent field from the core provides an edge to these sensors in terms of EM immunity, real-time online monitoring with miniaturized probe, and capability of remote sensing [3, 4, 15]. The choice and designing of sensing layer for SPR-based fiber-optic probes has been a burgeoning research area to augment their sensitivity and selectivity with continuous new developments in the field of nanotechnology. Various nanomaterials significantly improve the sensitivity of SPR-based sensors along with providing specific attachment of the targeted analyte. Some examples of SPR- and LSPR-based fiber-optic sensors are discussed below.

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7.1 SPR-based sensors A fiber-optic SPR sensor based on the gel entrapment of enzyme for the detection of ethanol has been reported [34]. The probe is designed by coating a 40-nm thin film of silver, an 8 nm thin film of silicon for enhancing the sensitivity, and a hydrogel layer for the immobilization of enzyme over a small unclad portion of the fiber. In the study reported, the enzyme alcohol dehydrogenase (ADH) and coenzyme nicotinamide adenine dinucleotide (NAD+) were entrapped inside the hydrogel. The schematic of the probe is shown in Fig. 15A. The hydrogel provides a good environment for enzymes, coenzymes, and other biomolecules, and maintains their activity. It has the ability to swell/shrink in an aqueous medium. Due to the swelling/shrinkage of the hydrogel, its refractive index changes, which is required for an SPR-based sensor. The sensing mechanism is based on the following enzymatic reaction:

Fig. 15 (A) Schematic, (B) SPR spectra, (C) resonance wavelength, and (D) sensitivity variation of a fiberoptic SPR-based ethanol sensor. Reprinted with permission from V. Semwal, A.M. Shrivastav, R. Verma, B.D. Gupta, Surface plasmon resonance based fiber optic ethanol sensor using layers of silver/ silicon/hydrogel entrapped with ADH/NAD. Sensors Actuators B Chem. 230 (2016) 485–492, Elsevier.

Nanotechnology-based fiber-optic chemical and biosensors

ADH

Ethanol + NAD + $ Acetaldehyde + NADH + H + When the ethanol sample is kept around the vicinity of the probe, it comes in contact with ADH and NAD+, and due to the enzymatic reaction, it gets converted into acetaldehyde and NADH. ADH works as a catalyst for the reaction. By proton transfer, NAD+ is used to convert ethanol to acetaldehyde in the presence of ADH, which changes the dielectric constant of the gel. To record the SPR spectra for different concentrations of ethanol, an experimental setup similar to that shown in Fig. 8 was used. To achieve the best performance of the sensor, various parameters such as silicon layer thickness, pH of the sample, and dipping time for the gel coating were optimized. The SPR spectra of the ethanol sensor with optimized parameters for the physiological range 0–10 mM are shown in Fig. 15B. It may be noted from the figure that as the concentration of ethanol increases, the resonance wavelength shifts toward the blue wavelength side. This is due to the enzymatic reaction in the gel layer where the conversion of ethanol to acetaldehyde occurs in the presence of enzyme NAD+ and ADH. This decreases the refractive index of the gel layer. In addition to this, the swelling of hydrogel network occurs due to aqueous ethanol, which also decreases the effective refractive index of the gel layer around the silver layer. This decrease in effective refractive index with the increase in ethanol concentration is seen as the blue shift of resonance wavelength in Fig. 15B. Fig. 15C shows the variation of the resonance wavelength with ethanol concentration. Fig. 15D shows the variation of sensitivity calculated by taking the derivative of the curve in Fig. 15C as a function of ethanol concentration. The sensitivity decreases with the increase in the ethanol concentration. The maximum sensitivity of the sensor has been reported as 21.70 nm/mM. This sensor was tested in the physiological range and was reported to be useful for healthcare applications. In another example of an SPR-based sensor, nanocomposite of reduced graphene oxide (rGO) and polyaniline (Pani) was used as a sensing layer to sense the pH of a sample. The role of pH is very significant for the chemical reaction, clinical diagnosis, biological process (e.g., monitoring of bacteria, enzymes, DNA, and cells), pharmaceuticals, etc. Therefore, the accurate value of pH is very important for the chemical reactions and all biological phenomena. The rGO and Pani are both very sensitive for pH; therefore, for the fabrication of the pH sensor, an rGO-Pani nanocomposite was prepared by an in situ method to exploit their combined effect [35]. The SEM and TEM images of the nanomaterials are shown in Fig. 16. The morphology of the polyaniline network can be seen in the SEM image in Fig. 16A, while in Fig. 16B the transparency in the TEM image of rGO indicates very thin nanosheets. Fig. 16C and D show the SEM and TEM images of nanocomposite of rGO-Pani, in which the wrapping of the polyaniline over the rGO nanosheets can be seen.

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Fig. 16 (A) SEM image of polyaniline, (B) TEM image of rGO nanosheets, (C) SEM image of rGO-Pani nanocomposite, and (D) TEM image of rGO-Pani nanocomposite. Reprinted with permission from V. Semwal, B.D. Gupta, Highly sensitive surface plasmon resonance based fiber optic pH sensor utilizing rGO-Pani nanocomposite prepared by in situ method. Sensors Actuators B Chem. 283 (2019) 632–642, Elsevier.

The formation of rGO-Pani nanocomposite was further confirmed by the Raman and FTIR spectroscopy. The sensor was fabricated by coating a layer of rGO-Pani nanocomposite over the silver-coated unclad core of the optical fiber using dip coating method. The dipping time and the pulling speed were optimized for the best performance of the sensor. Fig. 17A shows the SPR spectra corresponding to the pH samples from pH 7.0 to 2.4. It was observed that the resonance wavelength increases with the decrease in the pH from 7.0 to 2.4. For this range of pH, the emeraldine form of Pani gets converted to pernigraniline base through an oxidation process, due to which the effective RI of Pani changes. Since at low pH, the sample has excess of H+ ions; therefore, when these H+ ions come in contact of rGO sheet they attract electrons from rGO nanosheet and make it n-doped. The optical band gap of rGO nanosheet changes due to n-doping, which alters the refractive index of the rGO nanosheet. Thus, the effective refractive index of the nanocomposite material changes, resulting in the shift in resonance wavelength as pH changes.

Nanotechnology-based fiber-optic chemical and biosensors

Fig. 17 SPR spectra for the pH range (A) 7.0–2.4 and (B) 7.0–11.35. Reprinted with permission from V. Semwal, B.D. Gupta, Highly sensitive surface plasmon resonance based fiber optic pH sensor utilizing rGOPani nanocomposite prepared by in situ method. Sensors Actuators B Chem. 283 (2019) 632–642, Elsevier.

Fig. 17B shows the SPR spectra for the variation of pH from 7.0 to 11.35. It was observed that with the increase in pH, the resonance wavelength increases. The reason for this is again the change in the effective RI of the composite. When Pani interacts with the sample of pH greater than 7.0, it gets converted into a leucoemeraldine base, which alters the RI of the layer. In this case, the rGO nanosheet gets converted into p-doped, due to the excess of OH- ions in the sample, and alters the refractive index. In this range of pH, both the nanomaterials in the nanocomposite change their RI, and hence a shift in resonance wavelength was observed. The sensor has been reported to be repeatable, stable, and highly selective for pH. Advantages of nanomaterials-based sensing layers have been realized in the next example where a multiwalled carbon nanotube (MWCNT) layer has been compared with an enzyme entrapped gel approach for the sensing of sulfamethaxazole (SMX) [36]. SMX is an antibacterial drug of the sulfonamide family extensively used to treat many diseases, infections, and for prophylactic purposes. However, the unnecessary dumping of antibiotics leads to their unwanted residues in rivers, soil, and human food. The redundant amount of antibiotics in the ecological system may have plausible threats due to the development of resistance making them inefficient in curing diseases. Fiberoptic SPR sensors with two kinds of probes, one with tyrosinase enzyme entrapped polyacrylamide gel as a sensing layer and another with functionalized MWCNTs as a sensing layer, have been reported for the sensing of SMX for the concentration range 0–200 μM. Schematics of these two probes are shown in Fig. 18A and B, respectively. The interaction mechanism of the probe based on the enzyme entrapped gel layer is the formation of SMX-enzyme complex through the active sites of the enzyme. In the second probe, dOH functionalized MWCNTs interact with SMX through the oxidation of amine

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Fig. 18 Schematic of (A) enzyme and (B) MWCNTs-based fiber-optic SMX probes; (C) SPR and (D) absorbance spectra of enzyme, and (B) MWCNTs-based fiber-optic SMX probes for different concentrations of SMX. Reprinted with permission from A. Pathak, S. Parveen, B.D. Gupta, Fiber optic SPR sensor using functionalized CNTs for the detection of SMX: comparison with enzymatic approach. Plasmonics 13 (2018) 189–202, Springer Nature.

Nanotechnology-based fiber-optic chemical and biosensors

groups in SMX by hydroxyl groups of MWCNTs and π-π interaction between the two. Hydroxylized MWCNTs act as strong π-donors due to the -OH group, whereas the presence of amino group and N-heteroaromatic ring in SMX makes it a strong π-acceptor. Thus, SMX is easily adsorbed on the surface of MWCNTs during the sensing procedure to observe a shift in resonance wavelength. In both the enzymatic and MWCNTs case, effective RI of the sensing layer increases, as manifested by the red shift in SPR spectra (Fig. 18A and B). The MWCNTs-based probe offers better sensitivity (0.37 nm/μM) and limit of detection (LOD) (0.8918 μM) compared to the enzymatic approach, in addition to advantages such as better shelf life, cost-effectiveness, biocompatibility, and stability. A similar approach utilizing the synergistic catalytic properties of Cu nanoparticles on MWCNTs has been reported for the trace sensing of nitrate in the range 106 to 5 103 M [37]. The next example of a fiber-optic SPR sensor considered here is on the sensing of a hazardous gas like ammonia [38]. Ammonia gas is inevitably released in the environment by numerous industries like fertilizers, automobile, food, and refrigeration. It is a highly flammable and pungent gas, which is harmful even at the lowest concentrations, causing nausea, irritation to eyes and skin, pulmonary edema, and convulsions. A fiber-optic SPR probe designed for its sensing consists of coatings of a thin layer of silver and a layer on top of tin oxide (SnO2), which acts as the sensing layer. The change in physicochemical properties of SnO2 thin film on exposure to ammonia gas forms the basis of its sensing. Adsorbed oxygen on the surface of SnO2 interacts with adsorbed ammonia gas to form NHx species by the oxidation of ammonia gas. These species diffuse through the surface to release back the electrons or increase the surface conductivity. This increase in conductivity is realized as the increase in RI of the sensing layer. A red shift of 72 nm in resonance wavelength was achieved for the ammonia concentration change from 10 to 100 ppm for the probe fabricated with a 40-nm thick layer of silver and 15 nm thickness of SnO2 layer, as shown in Fig. 19A. The optimization of the thickness of the SnO2 layer was also carried out theoretically to achieve the maximum sensitivity of the sensor. The maximum sensitivity of the SPR sensors is related to the maximized electric field intensity at the sensing interface. Thus, the field intensity was calculated for varied thickness of the SnO2 layer and it was found that the optimized thickness was 18 nm, for which the field intensity and hence the sensitivity was maximal. The probe was tested for other interfering gases to ensure its selectivity toward ammonia gas, as shown in Fig. 19B. A state-of-the-art technology in synthesis of materials for sensing applications is molecular imprinting (MIP). In MIP, artificial receptor sites are frozen in a polymeric medium for the required template molecule [39]. The sites are highly specific toward the shape, size, and chemical binding structure of the template. It encompasses the advantage of biological receptors like antibodies and enzymes along with superior lifetime, cost,

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Fig. 19 (A) Variation of resonance wavelength with concentration of ammonia gas and (B) selectivity results of the probe for the change in ammonia gas concentration from 0 to 100 ppm. Reprinted with permission from A. Pathak, S.K. Mishra, B.D. Gupta, Fiber optic ammonia sensor using Ag/SnO2 thin films: optimization of thickness of SnO2 film using electric field distribution and reaction factor. Appl. Opt. 54 (2015) 8712–8721, OSA.

and stability in harsh environments. MIP can be performed in a bulk polymeric medium or with the advantage of nanotechnology, on the surface of a nanomaterial for ensuring better template removal and binding during sensing of complex molecules. A surface imprinting methodology on the MWCNTs matrix has recently been proposed for the sensing of dopamine (DA) in artificial cerebrospinal fluid (aCSF). DA is a prime neurotransmitter and biomarker for many neural functions and disorders. A pyrrole monomer was utilized for MIP with H2O2 as the oxidizing agent. A layer of polypyrrole was formed around MWCNTs with complementary sites for DA. The sensing layer was further coated with nanometer-scale permselective cationic nafion membrane to ensure zero interference from high concentration anionic analytes like ascorbic acid and uric acid. Various probe fabrication parameters and chemical interactions involved are shown in Fig. 20. As DA molecules diffuse through the nafion membrane and interact with the frozen sites in polypyrrole on the surface of MWCNTs, the RI of the sensing layer changes. The increase in RI has been validated from SPR curves, shown in Fig. 21A, for DA samples of 0 to 105 M concentrations in aCSF. The probe parameters were also optimized to achieve the best performance with LOD of 18.9 pM. MIP, non-imprinting (NIP), and nafion-based probes were tested to ensure negligible interference from other constituents in aCSF, as shown in Fig. 21B. The probe was reported to be highly selective due to MIP and the nafion layer. A similar approach was also used for the sensing of neutral bovine serum albumin (BSA) protein. The preparation of the sensing layer consisted of several steps, including vinyl functionalization of MWCNTs and attaching the MIP layer on it, utilizing

n

n O HO

O

OH

O

HO

NH2

OH

O

O HO O

NH HN

HO

O

HO

O

HO

HO

NH

n

O

O

OH

NH

OH

NH

HO

HN

HN

HN

HO

HO

HO

HO O HO O

OH

H N

H N

O

H N

O OH O

O

O

NH2

OH

O

n

HO HO

n

O HO

OH

H N

n

O

Fig. 20 Nanocomposite preparation steps and chemical reactions involved. Reprinted with permission from A. Pathak, B.D. Gupta, Ultra-selective fiber optic SPR platform for the sensing of dopamine in synthetic cerebrospinal fluid incorporating permselective nafion membrane and surface imprinted MWCNTs-PPy matrix. Biosens. Bioelectron. 133 (2019) 205–214, Elsevier.

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Fig. 21 (A) SPR spectra and (B) selectivity test of optimized dopamine sensor. Reprinted with permission from A. Pathak, B.D. Gupta, Ultra-selective fiber optic SPR platform for the sensing of dopamine in synthetic cerebrospinal fluid incorporating permselective nafion membrane and surface imprinted MWCNTs-PPy matrix. Biosens. Bioelectron. 133 (2019) 205–214, Elsevier.

methacrylic acid (MAA) as a monomer, ethylene glycol dimethacrylate (EGDMA) as a cross-linker, and 2-2-azobisisobutyronitrile (AIBN) as an initiator [40].

7.2 LSPR-based sensors LSPR based fiber-optic sensors are an ideal choice for the detection of small biomolecules. In recent years, a number of fiber-optic LSPR-based sensors have been developed for the detection of analytes such as cholesterol, glucose, DNA, bacteria, viruses, etc. [41]. One of the LSPR-based fiber-optic biosensors reported is for the detection of blood glucose. In this study, gold nanoparticles were attached over the U-shaped unclad core of an optical fiber via the aminosilane functional group, and for the selective sensing enzyme, glucose oxidase was immobilized over the gold nanoparticles [42]. The following enzymatic reaction between the glucose and glucose oxidase provides high selectivity to the sensor: Glucose Oxidase

Glucose + O2  ! Gluconic Acid + H2 O2 Fig. 22 shows the experimental setup along with the schematic of the probe for the sensing of glucose. The absorbance spectra for the samples of glucose with concentration varying from 0 to 250 mg/dL in a sample container are shown in Fig. 23. The decrease in absorbance with the increase in the glucose concentration of the sample was observed. The reason behind the change in absorbance is the enzymatic reaction, which changes the refractive index of the sensing medium around the gold nanoparticles. The U-shaped probe was used to enhance the evanescent field, and hence the absorbance, which increases the sensitivity of the sensor.

Microscope objective

Spectrometer Tungeten halogen lamp Sensing probe

Computer Glucose oxidase

Sample container

Gold nanoparticles

Small portion of unclad optical fiber core Extended view of Au NP coated fiber surface

Fig. 22 Experimental setup of LSPR-based fiber-optic U-shaped glucose sensor. Reprinted with permission from S.K. Srivastava, V. Arora, S. Sapra, B.D. Gupta, Localized surface plasmon resonance based fiber optic U-shaped biosensor for the detection of blood glucose. Plasmonics 7 (2012) 261–268, Springer Nature.

Fig. 23 Absorbance spectra of LSPR probe for glucose concentration ranging from 0 to 250 mg/dL. Reprinted with permission from S.K. Srivastava, V. Arora, S. Sapra, B.D. Gupta, Localized surface plasmon resonance based fiber optic U-shaped biosensor for the detection of blood glucose. Plasmonics 7 (2012) 261–268, Springer Nature.

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8. Comparison of fiber-optic plasmonic sensors with the sensors based on other techniques Recently, numerous techniques have been used in the literature for the sensing of various biological, chemical, and environmental entities. The conventional methodologies used for sensing, including high performance chromatography, require bulky, complex, and costly setups, and personalized trained professionals for their operations. Thus, there is a surge in development of new miniaturized, easy to handle, cost-effective techniques for sensor industries. Thus, many methodologies like electrochemical, piezoelectric, mechanical, and optical have been reported for the development of innovative sensors for diversified applications. Of all these methods/techniques, optical techniques offer a plethora of advantages over other methods. Optical techniques offer advantages like passiveness in electrical and explosive environments, freedom from electromagnetic interference, small size and integration ability with various systems, resistance to high temperature and chemically harsh environments, ability to monitor various physical and chemical parameters, high sensitivity, and fast response, along with multiplexing possibilities. Moreover, the use of optical fibers in the design of optical probes for various optical sensors offers additional benefits of their unique geometry, providing miniaturized size of the probe, remote sensing scenarios, and biological in vivo detection. The signals can be transmitted over a large distance from the fiber probes with negligible interference from the neighboring environment. Thus, optical fiber as a substrate for sensors is an ideal candidate for real-time online monitoring sensors required in remote areas, along with the conventional advantages of fast response, high sensitivity, and carefree handling devices. Due to these advantages, the focus, in this chapter, is on optical fiber-based sensors combined with highly sensitive transduction mechanism of SPR and LSPR. The role of various engineered nanomaterials has been emphasized in the context of applications in plasmonic sensors. A comparison of the performance of the optical fiber plasmonic sensors mentioned above with the other techniques reported in the literature is provided in Table 1. Table 1 Comparison of fiber-optic plasmonic sensing techniques with other techniques. Analyte

Technique

Operating range

LOD/Sensitivity

Ref.

Ethanol

Electrocatalytic oxidation MCP electrode SPR Fiber Bragg grating Charge transfer technique SPR

1.5–79 mM 0–60 mM 0–10 mM

32 μM 16.8 μM 15.34 μM

[34, 43, 44]

3–8 (pH) 3–10 (pH) 2.4–11.35 (pH)

0.166 nm/pH 229 mV/pH 75.09 nm/pH

[35, 45, 46]

pH

Nanotechnology-based fiber-optic chemical and biosensors

Table 1 Comparison of fiber-optic plasmonic sensing techniques with other techniques—cont’d Analyte

Technique

Operating range

LOD/Sensitivity

Ref.

SMX

Electrochemical DPV SPR Mach Zehnder interferometry Fiber Bragg grating SPR Voltammetric Fluroimetric SPR Non-invasive RL-BGM Electrochemical LSPR

20–200 μM 50–10000 μM 0–200 μM 0–350 ppm 0–100 ppm 10–100 ppm

22.60 μM 10 μM 0.89 μM 0.3 ppm 0.2 ppm 0.154 ppm

[36, 47, 48]

50 nM–100 μM 0.012–50 μM 1 nM–10 μM 0–450 mg/dL 0–360 mg/dL 0–250 mg/dL

33 nM 8.2 nM 18.9 pM 0.66 mV/ (mg/dL) 1.782 μA/ (cm2 mM) 0.005 au/ (mg/dL)

[39, 51, 52]

Ammonia gas

Dopamine

Glucose

[38, 49, 50]

[42, 53, 54]

9. Summary In summary, principles and examples of propagating and localized surface plasmon resonance-based fiber-optic chemical and biosensors for the detection of different kinds of analytes have been described. The efficient and selective detection of analytes require special kinds of nanostructures. The chapter discussed the methods for the fabrication and characterization of nanomaterials to be used for sensing. Optical fiber as a substrate offers advantages such as low cost, immunity to EM field, miniaturized probe, online monitoring, and capability of remote sensing.

References [1] M.I. Stockman, Nanoplasmonics: past, present, and glimpse into future, Opt. Express 19 (2011) 22029–22106. [2] H. Duan, A.I. Ferna´ndez-Domı´nguez, M. Bosman, S.A. Maier, J.K.W. Yang, Nanoplasmonics: classical down to the nanometer scale, Nano Lett. 12 (2012) 1683–1689. [3] B.D. Gupta, S.K. Srivastava, R. Verma, Fiber Optic Sensors Based on Plasmonics, World Scientific, Singapore, 2015. [4] J. Homola, S.S. Yee, G. Gauglitz, Surface plasmon resonance sensors: review, Sensors Actuators B Chem. 54 (1999) 3–15. [5] K.A. Willets, R.P.V. Duyne, Localized surface plasmon resonance spectroscopy and sensing, Annu. Rev. Phys. Chem. 58 (2007) 267–297. [6] K.M. Mayer, J.H. Hafner, Localized surface plasmon resonance sensors, Chem. Rev. 111 (2011) 3828–3857.

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Nanotechnology-based fiber-optic chemical and biosensors

[36] A. Pathak, S. Parveen, B.D. Gupta, Fiber optic SPR sensor using functionalized CNTs for the detection of SMX: comparison with enzymatic approach, Plasmonics 13 (2018) 189–202. [37] S. Parveen, A. Pathak, B.D. Gupta, Fiber optic SPR nanosensor based on synergistic effects of CNT/ Cu-nanoparticles composite for ultratrace sensing of nitrate, Sensors Actuators B Chem. 246 (2017) 910–919. [38] A. Pathak, S.K. Mishra, B.D. Gupta, Fiber optic ammonia sensor using Ag/SnO2 thin films: optimization of thickness of SnO2 film using electric field distribution and reaction factor, Appl. Opt. 54 (2015) 8712–8721. [39] A. Pathak, B.D. Gupta, Ultra-selective fiber optic SPR platform for the sensing of dopamine in synthetic cerebrospinal fluid incorporating permselective nafion membrane and surface imprinted MWCNTs-PPy matrix, Biosens. Bioelectron. 133 (2019) 205–214. [40] A. Pathak, S. Parveen, B.D. Gupta, Ultrasensitive, highly selective, and real-time detection of protein using functionalized CNTs as MIP platform for FOSPR-based biosensor, Nanotechnology 28 (2017) 355503. [41] V. Semwal, B.D. Gupta, LSPR- and SPR-based fiber-optic cholesterol sensor using immobilization of cholesterol oxidase over silver nanoparticles coated graphene oxide nanosheets, IEEE Sensors J. 18 (2018) 1039–1046. [42] S.K. Srivastava, V. Arora, S. Sapra, B.D. Gupta, Localized surface plasmon resonance based fiber optic U-shaped biosensor for the detection of blood glucose, Plasmonics 7 (2012) 261–268. [43] E.T. Hayes, B.K. Bellingham, H.B. Mark Jr., A. Galal, An amperometric aqueous ethanol sensor based on the electrocatalytic oxidation at a cobalt–nickel oxide electrode, Electrochim. Acta 41 (1996) 337–344. [44] J. Shi, P. Ci, F. Wang, H. Peng, P. Yang, L. Wang, Q. Wang, P.K. Chu, Pd/Ni/Si-microchannelplate-based amperometric sensor for ethanol detection, Electrochim. Acta 56 (2011) 4197–4202. [45] I. Yulianti, A.S.M. Supaat, S.M. Idrus, O. Kurdi, M.R.S. Anwar, Sensitivity improvement of a fibre Bragg grating pH sensor with elastomeric coating, Meas. Sci. Technol. 23 (2012) 015104. [46] T. Hizawa, K. Sawada, H. Takao, M. Ishida, Fabrication of a two-dimensional pH image sensor using a charge transfer technique, Sensors Actuators B Chem. 117 (2006) 509–515. [47] L.T. Roman, M.A. Alonso-Lomillo, O. Dominguez-Renedo, M.J. Arcos Martinez, Tyrosinase based biosensor for the electrochemical determination of sulfamethoxazole, Sensors Actuators B Chem. 227 (2016) 48–53. [48] S. Issac, K.G. Kumar, Voltammetric determination of sulfamethoxazole at a multiwalled carbon nanotube modified glassy carbon sensor and its application studies, Drug Test. Anal. 1 (2009) 350–354. [49] B. Yao, Y. Wu, Y. Cheng, A. Zhang, Y. Gong, Y.J. Rao, Z. Wang, Y. Chen, All-optical Mach– Zehnder interferometric NH3 gas sensor based on graphene/microfiber hybrid waveguide, Sensors Actuators B Chem. 194 (2014) 142–148. [50] Y. Wu, B. Yao, A. Zhang, Y. Rao, Z. Wang, Y. Cheng, Y. Gong, W. Zhang, Y. Chen, K. S. Chiang, Graphene-coated microfiber Bragg grating for high-sensitivity gas sensing, Opt. Lett. 39 (2014) 1235–1237. [51] Y. Ying Teng, F. Fen Liu, X. Xianwen Kan, Voltammetric dopamine sensor based on three-dimensional electrosynthesized molecularly imprinted polymers and polypyrrole nanowires, Microchim. Acta 184 (2017) 2515–2522. [52] J. Tashkhourian, A. Dehbozorgi, Determination of dopamine in the presence of ascorbic and uric acids by fluorometric method using graphene quantum dots, Spectrosc. Lett. 49 (2016) 319–325. [53] H. Ali, F. Bensaali, F. Jaber, Novel approach to non-invasive blood glucose monitoring based on transmittance and refraction of visible laser light, IEEE Access 5 (2017) 9163–9174. [54] A. Mugweru, B.L. Clark, M.V. Pishko, Electrochemical sensor array for glucose monitoring fabricated by rapid immobilization of active glucose oxidase within photochemically polymerized hydrogels, J. Diabetes Sci. Technol. 1 (2007) 366–371.

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CHAPTER 8

Light transport in three-dimensional photonic crystals Rajesh V. Nair

Department of Physics, Indian Institute of Technology Ropar, Rupnagar, Punjab, India

1. Introduction Controlling and manipulating light through different routes has been a topic of active research for more than a century [1]. The concept of photons, introduced in 1930, and the development of lasers made a platform for studying light propagation and its application in unimaginable ways [2]. This resulted in tremendous breakthroughs in spectroscopy, optical communications, imaging, medical physics, and diagnostics. All these possibilities arise due to the reliable control now available on the propagation of light. Long before the research community started to understand how to control propagation of photons, scientists and engineers had developed knowledge and technology to control the motion of electrons. This is evident from the remarkable discoveries and progress made in the field of semiconductor materials and devices. This understanding led to the invention of transistors and integrated devices, and offered us unparalleled miniaturization of devices in the field of computers, cell phones, lighting, and entertainment. Today we all appreciate that these unimaginable applications are possible due to the control of motion of electrons using functional semiconductors. In this context, it is interesting to ask two simple questions: can we mimic the functionalities offered by electrons in semiconductors using photons (the fastest information carrier)? Is a semiconductor for photons possible? The search for answers to these questions has given birth to photonic bandgap materials or photonic crystals [3–5]. Photonic crystals are structures where the dielectric constant (refractive index) is periodically altered; the period is of the order of the wavelength of light. The underlying structure has translational periodicity [6]. This is similar to atomic crystals, where the atoms are arranged periodically with a period of the order of A˚. This periodicity of atoms in crystals provides the necessary potential for electrons, resulting in a series of allowed and forbidden bands for electrons. The same concept is also applicable for photonic crystal structures, where the potential is created using the difference in the refractive index, leading to energy bands that can be described as propagating or forbidden for photons. We thus enter into the new and exciting field of research into nanoscale structured photonic

Nano-Optics https://doi.org/10.1016/B978-0-12-818392-2.00008-1

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materials, wherein the light propagation and emission can be tuned and controlled at nanoscale spatial dimensions. The objective of this chapter is to provide detailed optical studies of self-assembled opal photonic crystals using the reflectivity measurements. The formation of photonic stopgaps and their role in modifying light transport will be discussed along with some important applications. The angle- and polarization-resolved reflectivity measurements suggest nontrivial interaction of polarized light with photonic crystals. We will discuss the bifurcation of reflectivity spectra for TE polarized light, which is absent for TM polarized light until the Brewster angle. The energy exchange, which is responsible for the bifurcation of reflectivity peaks, is also discussed.

2. Energy bands in photonic crystals Light propagation in a non-magnetic, isotropic, linear, homogenous medium with dielectric constant ε is described by the following wave equation: !

!

1 ∂2 E ðz, tÞ 1 ∂2 E ðz, tÞ ¼ 2 (1) ε ∂z2 c ∂t 2 The solution to the above equation can be of the form: E(z, t) ¼ E0ei(kzωt),which is a wave propagating in the z direction, with an electric field oriented in the x or y direction, with wave vector k; ω is the angular frequency and c is the speed of light in a vacuum. After solving Eq. (1), we arrive at the dispersion relation for light propagation in a dielectric homogenous medium with no absorption: nω k¼ (2) c pffiffiffi n ¼ ε is the refractive index of the medium, which is assumed to be constant, irrespective of the frequency of incident light. Propagation of waves is described using the dispersion relation wherein the energy or the frequency is plotted as a function of wave vector or momentum [6, 7]. Generally, the dispersion relation for photons is discussed assuming the medium as a vacuum with n ¼ 1.0, which is also known as free space dispersion relation. Eq. (2) indicates a linear relation between the wave vector and the frequency of light, and all the frequencies are allowed to propagate through the structure for any incident wave vector. Fig. 1A shows that the entire incident light on a transparent slab is transmitted to the other side; this is schematically shown in the dispersion relation depicted in Fig. 1B. However, the light propagation is changed dramatically when it propagates through a medium with spatial structural inhomogeneity in a periodic manner. It is seen in Eq. (2) that the parameter that describes the properties of a medium is embedded in the n value, which can be real or complex depending on the frequency of light in the material. Hence

Light transport in three-dimensional photonic crystals

Fig. 1 (A) Light incident on a transparent medium is passed through it without any changes in the intensity and direction. (B) The dispersion relation for light propagating in a homogenous and transparent medium.

the parameter n is like a potential for the light propagation in space, and by the manner in which the n value changes inside the medium, the optical properties can be engineered. An obvious choice is the case wherein the light propagation occurs in a medium in which the n value is spatially periodic. The wave propagation in periodic structures has a history of more than 100 years [8]. Photonic crystals or photonic bandgap materials are structured photonic materials wherein the dielectric constant (refractive index) is periodically altered with a period of the order of the wavelength of light [9]. It generally consists of two materials with different refractive indices, arranged one after the other. The periodicity can be in one, two, or three orthogonal directions, which results in one-dimensional (1D), two-dimensional (2D), or three-dimensional (3D) photonic crystals [6]. The propagating light experiences the variation in dielectric constant in the structure, resulting in the partial reflection of light at each interface inside the structure. The reflected light from each refractive index discontinuity interferes constructively at certain conditions, leading to a high reflection of light for certain wavelengths of incident light. This indicates that the photonic structure exhibits frequency gaps in the propagation of light, which are known as the photonic stopgaps. This wavelength of maximum reflection is decided by the Bragg diffraction condition for light waves [6, 10]: mλ ¼ 2neff dcosðθÞ

(3)

where m is the order of diffraction, λ is the wavelength of light, neff is the effective refractive index of the structure, d is the distance between the lattice planes in the crystal structure, and θ is the angle of incidence of light on the crystal with respect to normal.

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When the incident wave satisfies the conditions given in Eq. (3), a band of wavelengths centered at λ is reflected strongly, accompanied with zero transmission of light in the same wavelength range. The peak in reflectivity spectra is accompanied by the trough in the transmittance spectra at the wavelength which is the signature of the photonic stopgap. The wavelengths that are absent in the transmission spectra are not absorbed by the crystal, but are reflected back into the incident medium due to the Bragg diffraction of light. When the photonic stopgaps in different directions of the crystal overlap in the same wavelength range for all polarization states of incident light, this leads to the opening of a 3D photonic bandgap. The photonic stopgap wavelength is decided by the structural period and the refractive index contrast between the two materials constituting the photonic crystal at a certain angle of incidence and for a given polarization of light. The period of the structure decides the functional spectral range of photonic crystals and the refractive index contrast decides the width of the photonic stopgap. In the case of 1D photonic crystals, the variation of refractive index is in one direction only, and in the other two directions, the structure is homogenous [11]. This can be achieved by stacking two materials with different refractive indices one over another. In the case of 2D photonic crystals, the refractive index is varied in two orthogonal directions, whereas in the third direction, the structure is homogenous. This is achieved by stacking cylindrical rods in air or by drilling air holes in a highly refractive index material [12]. The refractive index variation occurs in all three orthogonal directions of space for the 3D photonic crystals, and hence achieving 3D photonic crystals is a challenge in reality [13]. To understand the physics of light propagation in photonic crystals, let us consider a 1D photonic crystal wherein the refractive index has a spatial variation with a period of the order of the wavelength of light [11]. The refractive index is varied in the x direction only, and in the other two directions, the structure is homogenous. The wave equation for the electric field inside such a structure is given by: 1 ∂2 E ðx, tÞ 1 ∂2 Eðx, tÞ ¼ 2 EðxÞ ∂x2 c ∂t2

(4)

where ε(x) ¼ ε (x + d) is the dielectric constant with a period d and n2(x) ¼ε(x) in contrast to Eq. (1). Any function that has a translational symmetry can decompose into Fourier coefficients and, therefore, both ε(x) and E(x,t), which are periodic in x, can be expanded in Fourier components. Due to the existence of translational periodicity, the Bloch theorem can also be evoked in photonic crystals, which is of the form: E ðxÞ ¼ Un ðxÞeikx

(5)

where E(x) is the wave function associated with the light inside the structure, Un(x) is a periodic function with the same period as the lattice in the crystal, and k is the wave vector in the first Brillouin zone.

Light transport in three-dimensional photonic crystals

The solution to Eq. (5) is generally constrained to the first Brillouin zone in the reciprocal space with each band labeled with a band index. There are many numerical methods to solve the wave equation (Eq. 4) in photonic crystals. The important ones are the plane wave expansion method [14], scalar wave approximation [15], the Korringa-KohnRostoker (KKR) method [16], and the transfer matrix method [17]. We will discuss some of the interesting physics associated with photonic crystals using the simple picture of 1D photonic crystals. Fig. 2A shows a schematic representation of a 1D photonic crystal with alternating layers of two materials having thicknesses of da and db, respectively. The thickness is generally chosen to be a quarter-wave thick at the stopgap wavelength. The period of the structure in the x direction is d ¼ da +db. The refractive index of the high and low refractive index materials are nH and nL, respectively. Fig. 2B represents the schematic of the dispersion curve for light in a homogenous medium (dashed line) and that for a 1D photonic crystal (solid line). Light propagation through photonic crystals show breaks in the dispersion curve centered at a particular value of k ¼ π=d called the photonic stopgaps. The incident light is Bragg diffracted at the stopgap frequency, which appears as a peak in the reflectivity spectra with an associated dip in the transmission spectra. The periodicity of the photonic crystal must be around half the wavelength of the incident light in order

Fig. 2 (A) A representative picture of 1D photonic crystal with a period d in the x-direction. The dark layer is high-index material nH and grey layer is the low-index material nL. (B) A schematic representation of the dispersion relation. The dashed line is the free space dispersion relation and solid line is the same in a photonic crystal structure. The stopgap is shown as a break in the dispersion curve at k ¼ π/d, which is due to the Bragg diffraction.

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to satisfy the Bragg diffraction condition [18]. Thus at a value of k ¼ π=d, the Bragg diffraction is satisfied for the incident light. The incident light cannot couple to the photonic crystal structure as there is no allowed propagation constant at the stopgap frequencies. Therefore, the incident wave is diffracted, giving rise to a high reflectivity at that wavelength. Another interesting aspect of the dispersion curve occurs near the edge of the stopgap, in which the dispersion curve is nearly flat giving rise to very low value of dω , which is nothing but the group velocity. Thus, near the stopgap edges, the group dk velocity reduces, leading to some unexceptional applications in lasing, nonlinear optics, and slow light generation. As we have seen, when light is incident on the photonic crystal at the stopgap wavelength, the light is reflected at each dielectric discontinuity inside the structure. Therefore, the nature of the wave at the stopgap wavelength is decaying inside the structure. The interference between the incident and Bragg diffracted light creates standing waves inside the structure [18]. These standing waves store energy in either of the dielectric materials, depending upon the frequency of the light. Low frequency states store energy in the high-index materials whereas the high frequency states store in low-index material, leading to a separation in frequency in the dispersion relation. The band below the stopgap is referred to as the dielectric band and the band above the stopgap is assigned as the air band (assuming air as the low index medium) [6]. The photonic strength (S), which is related to the full width at half maximum of the stopgap, is a direct measure of the strength of interaction between the incident light and photonic crystals. The S value is strongly dependent on the refractive index contrast between the two materials constituting the photonic crystals structure for a given symmetry [19]. Hence, a larger index contrast leads to larger values of S. There are no propagating states inside the stopgap and the amplitude of the incident light decays exponentially into the depth of the crystal. The distance at which the incident light amplitude at the stopgap wavelength decays 1/e times of its value at the interface is defined as the Bragg attenuation length (LB) [18, 19]. The value of LB is strongly dependent on the index contrast and the number of crystal planes in the structure. Fig. 3A shows the calculated normal incidence (θ ¼ 0°) reflectivity (R) and transmittance (T) spectra using the transfer matrix method for the light propagation in 1D photonic crystals [20]. The 1D photonic crystal structure consists of TiO2 and SiO2 layers with refractive indices of 2.36 and 1.47, respectively, with 20 periods. The peak in reflectivity spectra is in agreement with the trough in transmission spectra centered at 1500 nm. This is the signature of the stopgap with R + T ¼ 100% with zero absorption of light with S value of 31%. The interference fringes seen on either side of the stopgap are called Fabry-Perot fringes; these originate due to the interference between the light reflection from the top and bottom surfaces of the structure. The thickness of the photonic crystal can be estimated using the fringe separation with a known value of neff. The stopgap

Light transport in three-dimensional photonic crystals

Fig. 3 (A) Reflectivity (solid line) and transmittance (dashed line) spectra for the light propagating in a 1D photonic crystal made of TiO2 and SiO2 layers. The peak in reflectivity is in agreement with the trough in transmission, indicating the signature of the stopgap. (B) The electric field intensity inside the structure at the stopgap wavelength.

wavelength shifts to the blue side with an increase in θ. The stopgaps present for transverse electric (TE) polarization might not appear for transverse magnetic (TM) polarization, which arises due to the vectorial nature of Maxwell’s Eq. [6]. Fig. 3B depicts the calculated field intensity for the light propagating inside the structure at the stopgap wavelength 1500 nm. The field intensity shows exponential decay into the structure as the wavelength of the light incident is within the stopgap. The concepts explained above are valid in general for 2D and 3D photonic crystals. For 2D photonic crystals, one expects a stopgap to be present in the two-orthogonal direction of light propagation for both TE and TM polarization of light [20]. The state

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of polarization is defined with respect to the rods in 2D photonic crystals [6]. When the electric field direction is parallel to the rod direction, it is called TE polarization, and when the electric field direction is perpendicular to the rod direction, it is termed TM polarization [6]. Generally, 2D photonic crystals are fabricated by etching air pores in a high refractive index semiconductor wafer with either square, hexagonal, or centered rectangular symmetry. Dispersion relations are calculated for light propagating in different symmetric directions, and stopgaps appear at the high symmetric point in the Brillouin zone for TE and TM polarizations. The overlapping frequency range of stopgaps in different symmetry directions simultaneously for TE and TM polarization results in a 2D photonic bandgap [21–23]. A 3D photonic crystal is characterized by a 3D photonic bandgap wherein the light propagation is forbidden in the three-orthogonal spatial direction [13]. The two important structural symmetries that have been considered for 3D photonic crystals are facecentered cubic (fcc) and diamond symmetry [20]. Diamond crystal structure is formed by two interpenetrating fcc unit cells with one of them displaced along the body diagonal with respect to the other by a distance of a/4; “a” is the fcc lattice constant. The second requirement is a large index contrast between the two materials constituting the structure. When the stopgap width in different directions is wider, the chance of exhibiting a common overlapping frequency range in different directions of the crystal is higher, which results in a photonic bandgap [20]. Therefore, high refractive index materials are required for the photonic bandgap. The large index contrast leads to a wider stopgap in different directions, which increases the probability of obtaining a common overlapping frequency region of stopgaps in different directions. The photonic band structure calculations have shown that a minimum index contrast of 2.8 is required to open a 3D photonic bandgap for a crystal arranged in fcc, whereas an index contrast of 2.0 is sufficient to obtain the 3D bandgap for a crystal in the diamond lattice structure [18]. For a crystal in the fcc structure, a 3D bandgap occurs in the high energy region, that is, between the eighth and ninth bands; for the diamond crystal structure, the 3D bandgap occurs between the second and third bands [20]. Since the optical response in the high energy region is greatly affected by defects and disorder, the 3D bandgap for the fcc crystal can get smeared out in optical reflectivity measurements. Thus, the diamond crystal structure is ideal for obtaining a 3D photonic bandgap, since the bandgap occurs in the low energy region. On the practical side, experimental realization of photonic crystals with diamond symmetry is a significant challenge. Therefore, it is proposed to search for symmetries that mimic diamond lattice, which has led to the development of rod-connected crystal structures called woodpile photonic crystals. Stacking air rods in a highly refractive semiconductor material results in another class of diamond-like photonic crystals called inverse woodpile photonic crystals [24–26]. The signature of the photonic bandgap is demonstrated by measuring the reflectivity spectra in different symmetry directions for TE and TM polarized incident light [27].

Light transport in three-dimensional photonic crystals

3. Spontaneous emission in photonic crystals Spontaneous emission is a process in which an excited atom removes its energy by emitting a photon in the absence of an external stimulus. It was long believed that spontaneous emission is an immutable property of matter, with no control over it. However, later it was realized that spontaneous emission depends strongly on the electromagnetic environment of the emitter [28, 29]. The Fermi-Golden rule shows that the emission decay rate (Γ) is proportional to the local density of photon states (LDOS), which represents the number of states available for a photon to make a transition within a frequency range ω and ω + dω. This follows as [30]: πω Γ ðω, r Þ ¼ ^j ij2 ρðω, r Þ (6) jf j μ 3ε0 where ω is the transition (emission) frequency, μ ^ is the dipole moment of the emitter, and ρ(ω, r) is the local density of photon states (LDOS) that depends on the emitter position r. The spontaneous emission decay rate depends strongly on the density of available modes or the LDOS [4]. The total density of states is the unit cell averaged LDOS. The transition dipole moment depends on the choice of the emitter. The LDOS is decided by the electromagnetic environment around the emitter, which can be engineered deterministically, and the spontaneous emission can therefore be rigorously modified in terms of emission intensity and rate [4]. Therefore, if the LDOS can be manipulated, light emission can be tailored in an efficient and controlled way [31–38]. In a photonic crystal, light of wavelength within the stopgap is not allowed to propagate through the crystal, resulting in a strong reflection, since there is no allowed mode within the structure to which the incident light can be coupled. This means that the LDOS is less than the free space value (in free space, density of states varies as ω2) at the stopgap frequency as seen in Fig. 4.. If the light source, for example, quantum dots or dye molecules, is present inside the photonic crystal with its emission wavelength the same as the stopgap

Fig. 4 The variation of (local) density of states for light in a homogenous medium (dashed line) and that in a photonic crystal with bandgap (solid line). For light in a homogenous medium, light can couple to any state as there is finite density of states for each frequency. The density of states is zero at the bandgap frequencies, as no electromagnetic mode exists inside the bandgap.

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wavelength, then the spontaneous emission can be suppressed [4]. In a photonic bandgap the radiative decay rate of the emitter is zero, as the value of LDOS is zero through Eq. (6). Hence an excited atom stays in the upper state forever as it cannot decay through radiative mode. However, the atom can make a transition to the lower state through non-radiative decay channels. It is also proposed that the excited state atom tries to emit a photon, but this is forbidden, as the emission frequency is in the photonic bandgap, which leads to an atom-photon bound state [35]. The enhancement of spontaneous emission is also possible using photonic crystals in addition to the suppression. The photonic bands flatten near the band edge frequencies, leading to a reduced group velocity. The LDOS is inversely proportional to the group velocity and hence increases near the photonic band edges. When the emission wavelength matches with the photonic band edge wavelength, the emission can be enhanced, since there are more available states for the emitted light to couple with, which has enormous potential in band edge lasing in photonic crystals [36–38].

4. Applications of photonic crystals Photonic crystal-based devices are proposed that can be used in integrated circuits and optical communications. The reduced group velocity at the band edge frequencies leads to increased light-matter interactions in photonic crystals with a more efficient nonlinear optical process in photonic crystals containing materials with nonlinear susceptibility. The field localization for the band edge frequencies inside the photonic crystal has an impact on Kerr switching and the third-order nonlinear optical process. When a defect is introduced into a photonic crystal structure, a defect mode appears in the photonic bandgap. When active materials such as dyes or quantum dots are doped in the defect layer and when the defect mode wavelength and the emission wavelength match, emission can be enhanced due to the increased density of photon states at the defect mode frequency [39]. If the defect is a line or a channel waveguide, light guidance can be achieved using the photonic bandgap effect rather than the total internal reflection of light [40]. These waveguides allow the use of sharp bends, which is not possible with conventional waveguides [41]. When certain light emitting species embedded in photonic crystals are excited, the spontaneous emission from the species may get suppressed or enhanced. When the dynamics of spontaneous emission are altered, the stimulated emission characteristics also get modified when the photonic stopgap effect is present. When the defect layer consists of Kerr nonlinear material, the defect mode can be switched in an ultrafast timescale using high intensity beam that results in fast photonic switches [42]. The only application of photonic crystals that has been successfully commercialized is photonic crystal fibers [43]. In these fibers, light guidance is due to the bandgap effect instead of total internal reflection. This is done by surrounding a hollow core with a cladding containing a periodically structured set of air holes. In these fibers,

Light transport in three-dimensional photonic crystals

light guidance is within an air core so that propagation losses are very low. Minimization of nonlinear optical effects and single-mode operation over an infinite range of wavelengths make these fibers attractive for applications.

5. Fabrication and characterization of 3D photonic crystals Fabrication of 3D photonic crystals is quite a challenge compared to 1D or 2D photonic crystals. Lithographic techniques are used to make 3D ordered photonic crystals. Both photo-lithography and electron beam lithography followed by reactive ion etching are used for making 3D photonic crystals [44, 45]. The resulting structures are generally called woodpile or layer-by-layer photonic crystal structures. It is also possible to fabricate air cylinders of hundreds of nanometer in diameter stacked over others in a layer-by-layer fashion. Defects and other functionalities can be introduced into the 3D photonic crystals during the fabrication process. Many studies have been carried out to measure and interpret 3D photonic bandgap on these kinds of 3D structures. Recently, a signature of 3D photonic bandgap was shown for inverse woodpile photonic crystals by measuring the stopgaps in different symmetry directions for different polarization states of incident light [27]. Another technique that has been widely used is the holographic technique, which is relatively inexpensive and takes less time. The method is based on the interference of four non-coplanar laser beams within a photoresist (SU8) to create the photonic crystal structure whose period and symmetry can be controlled [46]. The intensity and the polarization of the incident laser beam will determine the final photonic crystal structure. In addition to the aforementioned methods, there exists a simple and inexpensive method known as the self-assembly of micro-particles in a colloidal suspension [47, 48]. Self-assembling methods are quite inexpensive and require only minimal laboratory equipment. The main disadvantage associated with the self-assembly method is the difficulty in getting crystal symmetry other than face-centered cubic (fcc) symmetry. However, by optimizing the synthesis process, self-assembled photonic crystals of superior optical quality with minimal defects and disorder can be achieved. Self-assembly involves the crystalline assembling of spherical colloidal particles having sub-micron diameters, dispersed in a suspension, using different forces such as gravitational and convective forces. The mono-dispersed colloidal spheres in a suspension organize themselves into highly ordered 3D photonic structures under appropriate chemical and environmental conditions. The spheres in suspension all undergo Brownian motion, which ensures that a dispersion of such particles can lower the free energy, and by doing so, they selforganize in a regular fashion. The spheres with larger diameters take a shorter time compared to those with smaller diameters for assembling into an ordered form because of their reduced Brownian motion in the colloidal suspension. Spheres with larger diameters will assemble very fast, and they may therefore assemble in irregular form while spheres with smaller diameters take a longer time to find an equilibrium position, and this can also

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result in a disordered photonic structure. Hence, by varying the sphere diameter, the concentration and volume of the suspension, and the temperature of the sedimentation process, high optical quality of self-assembled photonic crystals can be achieved [49]. The method of self-assembly for the synthesis of photonic structures was initially proposed by Nagayama [50] for the growth of a monolayer array of spheres on a glass substrate. Later it was revisited for 3D photonic crystals by Jiang et al. [48], which resulted in high-quality photonic crystal samples on a glass substrate. The self-assembling process is shown schematically in Fig. 5. A flat clean glass substrate is dipped into the vial containing the colloidal suspension made of organic or inorganic materials of known concentration and volume. The whole assembly is kept inside a temperature-controlled oven. The temperature is chosen according to the diameter of the spheres. A meniscus is formed in which the liquid, air, and substrate meet. The evaporation of the solvent forces the colloidal particles toward the meniscus due to the convective force. Once evaporation starts, these colloids experience capillary force and organize in a close packed form [49]. The complete evaporation of solvent results in a crystal on a glass substrate that can be taken out and further annealed or dried in ambient conditions. Since the crystals are not freestanding but are deposited on a substrate, they are easier to handle for optical studies and applications. The substrate is kept vertically in the colloidal suspension and this method is therefore known as the vertical deposition method [48]. The convective forces are

Fig. 5 Schematic setup used for the fabrication of 3D ordered photonic crystals using the convective self-assembling technique. The microspheres are dispersed in a vial together with a clean substrate, which is kept vertically inside the vial. The whole assembly is kept in a temperature-controlled oven.

Light transport in three-dimensional photonic crystals

involved in the self-assembling process, which is why this method is also known as convective self-assembly. The sphere diameter, concentration, and volume of the colloidal suspension affect strongly the quality of a photonic crystal structure. If the sphere diameter is large, the Brownian motion will be reduced in the suspension and result in the sedimentation of spheres at the bottom of the cuvette instead of moving toward the meniscus. To obtain high optical quality photonic crystals, the velocity of the colloids must compete with the evaporation rate of the solvent. This dependence on the Brownian motion of the colloids (which is dictated by the sphere diameter) on the crystal synthesis can be avoided by applying a temperature gradient across the cuvette, so that the spheres will always be available for crystallization at the meniscus [49]. The sample growth temperature, depending on the sphere diameter, can be adjusted to obtain high-quality photonic crystals. If the temperature is higher than the optimum, the solvent will evaporate very fast and all the colloids will sediment at the bottom of the cuvette. The temperature must be chosen according to the sphere diameter and the solvent. Another important parameter to be considered is the concentration of the colloidal suspension. If the concentration of the colloids is high, then a large number of spheres will be forced toward the meniscus for crystallization by the convective force, resulting in a poor, close-packed structure. The low concentration colloidal suspension leads to a lower number of spheres available at the meniscus for crystallization, which results in disordered samples. Once the photonic crystals are synthesized, the characterization of the samples can be divided into two parts: structural characterization and optical characterization.

5.1 Structural characterization In structural characterization, the ordering of building blocks (spheres), size of the crystal domains, and thickness of the structure can be estimated. Scanning electron microscopy (SEM) is a versatile technique for characterizing nano/microstructured samples such as photonic crystals [10]. The resolution provided by SEM is far better than a standard optical microscope. This technique can also be used for observing the three-dimensional view of the photonic crystal samples. One important constraint with SEM characterization is that the samples should be conductive. Insulating samples get charged due to the bombardment of the electrons and this will block the generation of further secondary electrons. This can be avoided by sputtering a thin layer of metal onto the surface of photonic crystal samples. The SEM characterization results show ordering of building blocks in small regions of the sample surface. However, to gain insights into the ordering in the depth of the crystal and in large areas, optical characterization of the sample is necessary. The self-assembled photonic crystal can be ordered in either face-centered cubic (fcc) close packing (ABCABC…) or hexagonal close packing (hcp) (ABAB…), or even a mixture of both. Due to the small free energy difference between the fcc and hcp ordering, the

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crystal can be assembled in both ways. However, it is believed that if the rate of formation of the crystal is large, the structure of the crystal will be in the fcc close packed form [50, 51]. Furthermore, simulation work shows that the fcc structure is more stable than the hcp structure considering the thermodynamic property [52]. It is not easy to confirm whether the photonic crystals fabricated using the self-assembling routes are in fcc or hcp solely from the structural characterization. By looking at the cross-sectional image of the photonic crystals using SEM, one can interpret whether it is ordered in fcc or hcp. However, stacking faults can occur during the growth and result in samples of randomly close packed structure, whereas an optical characterization combined with fcc photonic band structure calculations can reveal (as discussed later) that these crystals are ordered in the fcc form. Fig. 6 shows a SEM image of photonic crystals grown by self-assembly methods using polystyrene (PS) spheres of diameter 803  26 nm, which are procured commercially (Ms. Microparticles GmbH). Fig. 6A represents a large area image of the crystal wherein the ordered domains separated by cracks (marked regions) are clearly visible. The presence of cracks is inevitable in self-assembled photonic crystals, occurring due to drying tension. By tuning the synthesis conditions, the size of the ordered domains can be increased. The ordering of domains is crucial in the light scattering process. If the ordering of adjacent domains is non-identical, the light scattering by different domains is not coherent and that results in a reduction of scattered intensity. It is also important to know the ordering on either side of the crack as it will affect the angle-resolved reflectivity measurements. Sometimes the identical orientation of the ordering on either side of the domains is observed due to the twinned nature of the fcc domains. Fig. 6B indicates a magnified picture of the top surface of the photonic crystal within a domain, which represents the (111) plane of the fcc photonic crystal [50]. Most of the optical properties of the

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Fig. 6 (A) A large area view of the self-assembled photonic crystal surface indicates a well-ordered region surrounded by cracks (circled region). The presence of cracks separates ordered regions into domains. Scale bar is 10 μm. (B) A crack-free top surface of the photonic crystal indicates the (111) plane of the fcc lattice. The sample growth takes place in the (111) direction. Scale bar is 1 μm.

Light transport in three-dimensional photonic crystals

self-assembled photonic crystals are studied from this plane. The optical response due to other crystal planes in the fcc lattice can be revealed in the angle resolved reflectivity spectra.

5.2 Optical characterization Even though the structural characterization shows very good ordering of the building blocks (spheres) on the surface and in the depth of photonic crystals, optical characterization will provide more information about the quality of the samples. This is due to the fact that structural characterization probes small areas of the sample, but probing with light gives information from large areas within the photonic crystals. Even for a disordered sample, one can see ordering of building blocks in microscopic regions of the sample; therefore, optical characterization is the ultimate measure of the extent of ordering present in photonic crystals. In addition, optical characterization shows whether the photonic crystals have a photonic stopgap or not, and if present, the wavelength region of its occurrence [53–55]. Reflectivity and transmittance of light passing through the photonic crystal are measured to characterize and interpret the photonic stopgap. This stopgap is measured in different directions either by rotating the sample or by changing the angle of incidence. For polarization-dependent measurements, the pass axis of the polarizer can be rotated for different states of polarization of the incident light. Fig. 7 shows the photonic stopgap measured from the (111) plane of the self-assembled photonic crystal with fcc symmetry

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Fig. 7 (Left) Reflectivity (dashed line) and transmittance (solid line) of light propagating through a photonic crystal consists of PS spheres of diameter 803 nm. The stopgap is centered at 1750 nm with a peak reflectance of  57% with a transmission of  18% at the same wavelength. The peak in reflectivity spectra is in agreement with the trough in transmission spectra, which indicates the signature of the stopgap. (Right) The comparison between the photonic stopgap and the dispersion relation for light propagating through a PS self-assembled photonic crystal of sphere diameter of 803 nm at near-normal incidence.

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with lattice constant (a) of 1135 nm. The measurement is done at near-normal incidence (θ ¼ 8°) with non-polarized light using a commercial spectrophotometer. The measured S value is 6.57%, which is in good agreement with the calculated value of 6.50% obtained from the photonic band structure [19]. This close agreement in S-parameter value indicates the fine structural quality of the self-assembled photonic crystals. The value of LB is estimated using the formula LB ¼ 2dhkl/πS, where dhkl is the pffiffi 2D distance between the crystal planes in the [hkl] direction. The value of dhkl ¼pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi , 2 2 2 h +k +l

where D is the sphere diameter. The value of LB is estimated to be 6 μm. The Fabry-Perot (FdP) oscillations (see inset of Fig. 7) observed on either side of the stopgap indicate uniform thickness of the sample, which also indicates the good ordering of each layer in the depth of the photonic crystal. The thickness (t) of the photonic crystal is estimated from the FdP fringes using the formula [56] t ¼ 1239/(2  neff  ΔE), where ΔE is the difference in energy positions in eV of two consecutive troughs in the FdP oscillation. The value of neff is estimated to be 1.44, assuming a refractive index of PS spheres as 1.59 with 74% packing efficiency in the crystal. The estimated thickness from FdP oscillations on the long wavelength side of the stopgap is t  14 μm (number of layers 22) and t  2.4LB. Optical processes are predicted theoretically for an ideal photonic crystal without any structural defects with an infinite size in all the three-orthogonal spatial directions. The question is when the real synthesized photonic crystals behave similar to an infinite size crystal so as to eliminate the finite-size effects in the optical process. In synthesized photonic crystals, finite size effects can be eliminated with samples with t > LB. Hence in our photonic crystals, the finite size effects are eliminated. The reflectivity and transmittance value in the wavelength range below 1000 nm is decreased rapidly due to the reflection of light in the non-specular direction [57]. The measured photonic stopgap can also be compared and interpreted using calculated dispersion relation for ideal fcc photonic crystals. The dispersion relation is calculated in reciprocal space using the plane wave expansion method for an ideal fcc photonic crystal. Fig. 7 (right) shows the comparison between the measured stopgap and the calculated dispersion relation. The measurements are done on the (111) plane which corresponds to the Γ- L direction in reciprocal space. A very good agreement between the measurement and the calculation is evident. The dispersion relation along the Γ-L direction is calculated using the plane wave expansion method. The calculated stopgap frequency is centered at 0.6 in scaled frequency unit of a/λ; λ is the stopgap wavelength. Since the frequency is scaled in a/λ unit due to the scalability of Maxwell’s equations, PS photonic crystals always show the first-order stopgap frequency at 0.6 a/λ irrespective of the sphere diameter. The excellent agreement between the measured stopgap and the calculated dispersion relation indicates the fine structural quality of the self-assembled fcc photonic crystal.

Light transport in three-dimensional photonic crystals

Fig. 8 The calculated photonic band structures showing complex interaction photonic band at high symmetry points in the Brillouin zone of the crystal with fcc symmetry.

6. Multiple Bragg diffraction in opal photonic crystals Angle-resolved stopgaps can provide information about range and extent of tunability of stopgaps [58, 59]. This also facilitates the study of light scattering by different crystals planes in the depth of the photonic crystals. The photonic band structure calculated for the self-assembled photonic crystal with fcc symmetry made using PS spheres is shown in Fig. 8. It can be seen that at high symmetric points like K, U, and W, multiple photonic bands are interacting, which results in a complex spectra in the experimentally measured reflectivity spectra. These symmetric points in the Brillouin zone of the crystal with fcc symmetry is accessed by changing the angle of incidence of light in the optical reflectivity measurements, as discussed in the following. Self-assembled 3D photonic crystals are synthesized employing the convective selfassembly method [48], using the PS colloidal suspension with D ¼ 280  6 nm for the angle-dependent studies. When the wave vector is incident normally on the (111) plane, the photonic stopgap is probed along the ΓL direction in the hexagonal facet of the Brillouin zone (see Fig. 9). The incident wave vector is shifted away from the ΓL direction to access other high-symmetry points (K, U, and W) in the Brillouin zone by illuminating the crystal at different angles of incidence (θ). Since the K and U points are symmetric on either side of the L point, one cannot make sure a priori in which direction the incident wave vector shifts and hence it is difficult to predict the contributing crystal plane for the optical diffraction process [60]. The angle-dependent stopgap dispersion can be calculated using Bragg’s law for optical diffraction. The diffracted

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wavelength (λhkl) from planes with Miller indices (hkl) in photonic crystals is given by the formulae [61]:    1 1 λhkl ¼ 2  neff  dhkl  cos α  sin sinθ (7) neff where dhkl is interplanar spacing, neff is effective refractive index of the crystals, andαis the internal angle between the (hkl) and (111) plane. Fig. 9 shows the calculated stopgap wavelengths for incident wave vectors that are shifted along the lines LK and LU in the Brillouin zone (inset) of photonic crystals with fcc symmetry. The (111) (solid line) stopgap shifts toward the shorter wavelength region whereas the (111) (dashed line) and (200) (dotted line) stopgaps show an opposite dispersion with increase in θ, as seen in Fig. 9. The (111) stopgap crosses the (111) and (200) stopgaps at θ ¼ 56° for wave vector shifting toward the K or U point. This is due to the equal length of the vector LK or LU on the hexagonal facet of the Brillouin zone [60]. This is a subtle issue on assigning the crystal planes responsible for the origin of stopgaps when the wave vector spans the K or U point. High-resolution microscope images can be used as a gauge for identifying the orientation of crystal planes involved in the formation of stopgaps at the K or U point [59, 61]. This is done by imaging the samples across the depth, which reveals either hexagonal or square ordering of spheres comprising the {111} or {200} family of planes, respectively, in crystals with fcc symmetry. However, it can give specious results as the assignment of crystal planes must come from optical spectroscopic methods. Therefore, the optical reflectivity measurements are more consistent since large numbers of domains with many crystal planes deep into the sample are mapped.

Fig. 9 The calculated diffraction wavelengths showing stopgaps dispersion for wave vector shifting along the LK and LU lines on the hexagonal facet of the Brillouin zone of the crystal with fcc symmetry. The calculations are done for different crystal planes using estimated values of neff ¼ 1.436 and D ¼ 266 nm.

Light transport in three-dimensional photonic crystals

6.1 Along the ΓL direction The reflectivity and transmittance spectra are measured to identify the signature of photonic stopgaps. Fig. 10 presents the reflectivity spectra at near-normal incidence (θ ¼ 10°) showing a peak centered at 610 nm with a reflectivity of 55%. The transmittance value at the same wavelength is 2%. This constitutes the signature of (111) photonic stopgap in the ΓL direction, as seen in Fig. 8. The measured photonic strength is 5.75%, which is in agreement with calculated values from the photonic band structure for similar crystals [19]. The value of LB is estimated to be 2.4 μm or 10d111, where d111 is the interplanar spacing in the (111) direction. The thickness (t) obtained from the Fabry-Perot (FdP) fringes, in the long wavelength limit, is 9 μm (35 ordered layers) or t ¼ 3.9LB. This indicates that our crystals are strongly interacting and the finite-size effects are minimized in the direction of propagation. The availability of large number of planes within the depth of the crystal is necessary to perceive the angular dispersion of stopgaps. The θ value on the (111) plane is varied to measure the stopgaps at other high-symmetry points like K, U, or W, while keeping the sample position fixed. This assures that the optical reflectivity is measured from the same spatial location on the sample surface so that the comparison between reflectivity measurements at different θ values is reliable.

6.2 Along the ΓK direction Fig. 11A depicts the reflectivity spectra measured with TE polarized light when the tip of the wave vector shifts toward the K (U) point in the hexagonal facet of the Brillouin zone of a crystal with fcc symmetry. The reflectivity spectra are shown for selected values of θ

Fig. 10 Reflectivity (solid) and transmittance (dotted) spectra measured at near-normal incidence (θ ¼ 10°). The peak in reflectivity at 600 nm is accompanied by the trough in transmittance at the same wavelength.

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Fig. 11 Reflectivity spectra measured for wave vectors shifting toward the K point. (A) The measurements are done using TE polarized light at angles of incidence 45° (dotted), 49° (dashed), 53° (short-dotted), 55° (dash-dot), 56° (solid), 57° (short-dashed), 61° (dash-dot-dot), and 67° (short dash-dotted). (B) The measurements are done for TM polarized light at angles 45° (dotted), 49° (dashed), 53° (dash-dot), 55° (dash-dot-dot), 57° (solid) and 59° (short dash-dot). (Inset) The measurements at θ ¼ 60° (dash-dot-dot), 62° (solid), 64° (short dash-dot), 66° (short dash), and 67° (dotted) also using TM polarized light. The intensity of the peak increases from 62° onwards, in addition to the building up of a weak reflectivity lobe in the long wavelength side (shown with an arrow).

on either side of the expected stopgap crossing (see Fig. 9). The measured spectra show a peak at 539 nm with a reflectivity of 52% at θ ¼ 45° (black dotted), which corresponds to the (111) stopgap. A new peak arises at 490 nm, with low reflectivity near the shortwavelength band edge for θ ¼ 45°. The (111) stopgap is blue shifted to 531 nm with a slightly reduced peak reflectivity of 51% for θ ¼ 49° (purple broken). The new peak becomes more intense at θ ¼ 49° with a red shift to 495 nm. When θ ¼ 53° (orange dotted), the peak reflectivity of the (111) stopgap is reduced to 47% with a further blue shift to 527 nm. The new reflectivity peak is now well-resolved at 495 nm. At θ ¼ 55° (green dash-dotted), the peak reflectivity of the (111) stopgap is reduced marginally, with a further blue shift to 526 nm, whereas the new peak at 495 nm gains intensity. At θ ¼ 56° (red solid), the reflectivity spectra shows a remarkable feature wherein both peaks show equal reflectivity (40%) and line-width (20 nm). Here at θ ¼ 56°, both diffraction peaks appear at the same wavelength, and therefore result in an avoided crossing with exchange in their spectral positions. The (111) stopgap is now centered at 495 nm and the new peak is at 526 nm. The separation between the two peaks is 31 nm, which is higher than the individual peak width of 20 nm in correlation with a strong-coupling regime [62]. With further increases in θ, the (111) stopgap maintains the blue shift, whereas the new peak retains a red shift. The (111) stopgap retains its intensity whereas the new peak diminishes beyond the crossing for θ 56°. It is fascinating to observe the simultaneous diffraction in the form of multiple peaks from different crystal planes for

Light transport in three-dimensional photonic crystals

45°  θ  65°. Such multiple Bragg diffraction is associated with the branching of stopgaps in the reflectivity spectra and extends over an angular range of more than 20°. Fig. 11B depicts the reflectivity spectra at different θ for TM polarization for wave vectors shifting toward the K (U) point. Contrary to TE polarized illumination, the TM polarized reflectivity spectra show an altogether different feature. Fig. 11B indicates the measured reflectivity spectra at θ ¼ 45° (black dotted), 49° (orange dashed), 53° (brown dash dotted), 55° (green double dash), 57° (red solid), and 59° (blue dash dotted). The reflectivity peak at these θ values corresponds to the (111) stopgap. The peak reflectivity of (111) stopgaps constantly decreases from 28% to 4% accompanied with narrowing of line-widths for change in θ from 45° to 59°. The observed closing of gaps with increase in θ is similar to the theoretically calculated photonic bands for PS photonic crystals [60]. However, the photonic strength is nearly the same (4%) for 45°  θ  59°. The decrease in peak reflectivity with constant photonic strength at higher θ promulgates that the deterioration of stopgaps is a polarization-induced process and not due to any structural imperfections in crystals. Fig. 11B inset indicates the reflectivity spectra measured at θ ¼ 60° (blue dash-dot-dot), 62° (red solid), 64° (brown short dash-dot), 66° (orange short dash), and 67° (black dotted). The minimum peak reflectivity of 2.5% is observed at 491 nm for θ ¼ 62°. The monotonous decrease in the peak reflectivity values for θ  62° is attributed to the Brewster angle (θB) effect at the air-crystal boundary. The absence of any extra reflectivity peak for 50°  θ  62° shows that the θB is also satisfied for planes within the crystal other than the top (111) plane. Therefore, planes within the crystal are unable to diffract light, which is ultimately collinearly reflected along with (111) stopgap as seen for TE polarized light in Fig. 11A. or θ > 62°, the (111) stopgap reflectivity increases gradually, albeit this increase is small. A small reflectivity peak is visible at 537 nm, which has a red shift for θ 62° (shown using an arrow in Fig. 11B inset). Unlike the case of TE polarized light, the (111) stopgap for TM polarized light does not show anti-crossing and branching of stopgaps. Thus, there exists a substantial difference in the interaction of TM polarized light with photonic crystal structure compared to TE polarized light. We now compare the measured and calculated stopgap wavelengths at different θ. The calculation of λhkl for different crystal planes using Eq. (7) requires the value of neff and D. The estimation of neff is quite delicate in subwavelength photonic structures like photonic crystals. Many models are used to estimate the value of neff such as those using material refractive indices with aided knowledge of their filling fractions or techniques based on spectroscopic ellipsometry [62]. However, the neff can be estimated in a unique way using the measured optical reflectivity spectra [63]. We have observed that the reflectivity spectra are branched into two equal reflectivity peaks for TE polarized light at θ ¼ 56°. Let us assume that the stopgap branching occurs for wave vector incident along the K point (see Fig. 9, inset). Then the internal angle (θK int) between  the  incident wave vector and the ΓL direction is half the angle between the [111] and 111 directions

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with an external incident angle θK ¼ 56°. Using this geometrical picture and Snell’s law, we can estimate neff ¼ 1.73 * sin θK ¼ 1.436. Also at θK ¼ 56°, the reflectivity spectra show a trough at λK ¼ 510 nm that results in the high transmission of light through the crystal. The photonic crystal structure acts like a homogenous medium with certain neff at λK and the light propagation is well-described using the free-photon dispersion rela! tion. Therefore, the relation between the frequency (ω) and the wave vector ( k ) is written as:    ! ! c ΓK   ck ω¼ ¼ (8) neff neff    ! where ΓK  represents the length of incident  wave vector at θK and c is the speed of light.   ! Rewriting Eq. (2) in terms of λK using ΓK  and ω (¼2πcλ1 K ), we can estimate the value of D as: D ¼ 3λK 4neff (9) The obtained value of D is 266 nm for the light incident along the K point. If we assume that the stopgap branching occurs at the U point, then we estimate neff ¼ 2.18 * sin θK ¼1.805, and the value of D, thus obtained, is 195 nm. Such large variations in the value of neff and D cannot be justified. In order to check the consistency of neff and D along the K point, we have calculated the expected stopgap wavelength in   ! the ΓL direction  (λL). Substituting the length of wave vector  ΓL  ¼ π=d111 in Eq. (8) in   ! place of ΓK , the estimated value of λL is 623 nm; this is in close agreement with the measured value at θ ¼ 10°. Fig. 12A shows the calculated diffraction wavelengths using Eq. (1) for different crystal planes, such as (111) (black solid), (111) (red dashed), and (200) (blue dotted) that can take part in diffraction for wave vectors shifting toward the K point. The value of α used is 70.5° for the (111) plane and 54.7° for the (200) plane. The diffraction wavelengths are also deconvoluted from the measured spectra using multiple Gaussian fittings. The measured (squares) and calculated (111) stopgap wavelengths (solid line) are in good agreement, as seen in Fig. 11A. When the measured (111) stopgap appears near the crossing regime, it deviates from its calculated curve due to the band repulsion forced by the presence of new peaks (circles). The new peaks emanate near the crossing regime and are in good agreement with calculated (111) stopgap wavelengths (dashed line). Our measured band crossing occurs at 0.75 a/λ (a is the fcc lattice constant), in complete agreement with calculated photonic band structure in the Γ-L-K orientation [31]. This also supports the fact that the tip of the incident wave vector shifts along a line connecting the L and K points in the hexagonal facet of the Brillouin zone of the crystal. Beyond the crossing angle, both measured stopgaps show an opposite dispersion in parallel with the calculations.

Light transport in three-dimensional photonic crystals

Fig. 12 The measured (symbols) and the calculated (lines) Bragg wavelengths for crystal planes (111) (solid), (111) (dashed), and (200) (dotted) that intersect at the K (U) point for (A) TE and (B) TM polarized light. (A) When the (111) stopgap approaches the crossing regime, it deviates from the calculated curve due to band repulsion; thereafter, both stopgaps show opposite dispersion in consistent with calculations. (B) The (111) stopgap does not exhibit any repulsion due to the absence of a second peak in the crossing regime, and hence crossing of bands for TM polarized light is not avoided.

Fig. 12B shows the measured (symbols) and calculated (lines) stopgap wavelengths for TM polarized light. The calculations are done using neff ¼ 1.436 and D ¼ 266 nm, similar to the case with TE polarized light. The stopgap wavelengths for TE and TM polarized incident light remain the same for θ  45°, confirming that the value of neff is the same for both TE and TM polarizations in self-assembled 3D photonic crystals. This is comparable to the calculated polarization resolved photonic band structure for PS photonic crystals as reported in the literature [31]. The measured (squares) and calculated (111) stopgap wavelengths (solid line) are in good agreement. The measurements clearly indicate the absence of any other peak in the crossing regime. However, we observe a new peak far off the calculated band crossing for θ 62° and it is in good agreement with the (111) stopgap. The origin of the (111) stopgap beyond θB is in complete agreement with calculated reflectivity spectra for ideal photonic crystals with fcc symmetry [17]. Fig. 13A depicts the peak reflectivity values of (111) (open symbols) and (111) (closed symbols) stopgaps for both TE (squares) and TM (circles) polarized light as a function of θ. The (111) stopgap reflectivity value decreases with increase in θ, whereas that for the (111) stopgap increases until they become exactly equal and cross each other at θ ¼ 56°, for TE polarized light. For θ > 56° the peak reflectivity of (111) stopgap increases slightly, whereas that for the (111) stopgap decreases. There exists a continuous exchange of energy between the two stopgaps. Conversely, for TM polarized light, the peak reflectivity of the (111) stopgap decreases continuously for θ  62°. The decrease in peak reflectivity value is caused by the inhibited photon scattering within the crystal planes because of the Brewster effect. Hence there is no efficient light diffraction by (111) planes within the crystal to supply

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Fig. 13 (A) The variation of peak reflectivity values for the (111) (open symbols) and (111) (closed symbols) stopgaps as a function of θ for TE (squares) and TM (circles) polarized light. (B) The crosssection of the Brillouin zone of crystal with fcc symmetry with relevant high-symmetry points Γ, X, ! U, L, K, and L1. The wave vector k 0 is shifted along the K point (green!dashed line) and satisfies ! the Laue condition simultaneously for two reciprocal lattice vectors G 111 and G 111 , leading to multiple Bragg diffraction at the K point. Similarly, multiple Bragg!diffraction can also occur at the ! U point (red dashed line) for the reciprocal lattice vectors G 111 and G 200 .

energy to (111) planes, which results in the absence of the (111) stopgap for 45°  θ  62°. The reflectivity of the (111) stopgap increases once θB is crossed and it funnels energy into (111) planes within the crystal. The (111) stopgap appears for θ 62° with small reflectivity values. This validates the requirement of energy exchange for the origin of (111) stopgap and manifests the significant interaction of polarized light in the multiple Bragg diffraction process in photonic crystals [64–68]. The branching of photonic stopgaps at high-symmetry points can be well understood through evoking the Laue condition for diffraction in a reciprocal lattice [23]. Fig. 13B shows the cross-section of the Brillouin zone with high-symmetry points relevant to the experiment. At θ ¼ 56°, the incident wave vector passes through the K point. Then the Laue condition is satisfied simultaneously for two !reciprocal lattice vectors corresponding to the (111)!and (111) ! ! ! ! ! ! plane with conditions: k 0 + G 111 ¼ k 1 and k 0 + G 111 ¼ k 2 . Here, k 1 and k 2 are!dif! fracted wave vectors corresponding to the reciprocal lattice vectors G and G , ! ! 111 ! 111 respectively. The interaction of three reciprocal lattice vectors (G 000 ,G 111 , and G 111 ) engenders multiple Bragg diffraction at the K point. A similar optical process can also happen at the U point, as shown in Fig. 13B. The role of light polarization in branching of stopgaps at the K point can be mapped by measuring the reflectivity spectra for different polarizer angles (ϕ). The value of ϕ dictates the orientation of the electric field associated with incident light. The TM polarized light corresponds to ϕ ¼ 0° and TE polarized light corresponds to ϕ ¼ 90°. Fig. 14

Light transport in three-dimensional photonic crystals

Fig. 14 The reflectivity spectra measured at an angle of incidence of 56° for wave vector incident along the K point at different polarizing angles (ϕ). The measurements are shown for ϕ ¼ 0° (dashed line), 20° (dotted line), 40° (short dash-dot line), 60° (dash-dot-dot line), 80° (dash-dot line), and 90° (solid line). The inset shows the normalized reflectivity spectra at θ ¼ 56° using TE (solid line) and TM (dotted line) polarized light. The TM polarized stopgap occurs at the mid-point between two TE polarized stopgaps.

shows the reflectivity measurements at different values of ϕ for θ ¼ 56°. The reflectivity spectra for ϕ ¼ 0° (black dashed) shows a single peak at 510 nm with a peak reflectance of 7.4% reminiscent of the TM polarized stopgap (see Fig. 11B). The reflectivity spectra at ϕ ¼ 20° (dotted) show an abrupt change in the spectral shape with a complex reflectivity profile. This conveys that even a small increase in ϕ can induce appreciable changes in the reflectivity spectra. The reflectivity spectra measured for ϕ ¼ 40° (short dash-dot) shows two well-resolved peaks at 495 and 526 nm. The reflectivity spectra becomes more pronounced for ϕ ¼ 60° (dash-dot-dot) with a very clear trough between the two peaks. Here, the component of TE polarized light is 0.87│E│ and TM polarized is 0.50│E│, where │E│ is the magnitude of the electric field. Hence, the reflectivity spectra resemble more closely TE polarized light. The increase in ϕ from 40° to 90° brings about two well-separated individual peaks with a clear trough between them. Fig. 14 (inset) shows the TE (solid) and TM (dotted) polarized reflectivity spectra for θ ¼ 56°. The (111) stopgap for TM polarization appears at 510 nm, which is exactly midway between the (111) and (111) stopgaps for TE polarized light. This supports our earlier hypothesis that the (111) stopgap is repelled by the presence of the (111) stopgap at θ ¼ 56° for TE polarized light.

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7. Polarization anisotropy in photonic crystals We have estimated the polarization anisotropy factor to comprehend the interaction of polarized light with photonic crystals. The anisotropy factor (Pa) is defined as Pa ¼ RTM/RTE, where RTE (RTM) is the TE (TM) polarized reflectivity value. Fig. 15A illustrates Pa at different θ along LK line for an off-resonance (squares) wavelength at 700 nm, the on-resonance (circles) wavelength corresponding to the (111) stopgap, and the calculated values (triangles) using Fresnel equations [69]. The calculations are done for a film of refractive index of 1.436. At θ ¼ 0° the measured on-and off-resonance and calculated Pa values are the same, as there is no distinction between TE and TM polarizations. The Pa value decreases with increase in θ for both on- and off-resonance wavelengths. The off-resonance Pa value achieves its minimum at θ ¼ 55°, similar to the calculations. The photonic crystal structure shows its properties at the stopgap wavelength (on-resonance wavelength). However for off-resonance wavelengths (wavelengths away from stopgap) photonic crystal response to incident light is similar to normal medium (homogeneous medium). The minimum Pa value corresponds to θB for an air-dielectric boundary with nBeff as 1.428. The minimum Pa value for on-resonance is achieved at θ ¼ 62°, which is higher than that in the case of the off-resonance condition. The shift in θB for on-resonance is in accordance with the calculated deviation of 7° for the ideal fcc crystals [70]. Such closeness in the deviation of θB is demonstrated experimentally here for the first time. The minimum Pa value is far above zero for on-resonance as compared to off-resonance or calculated Pa values due to the unique light scattering from subwavelength meta-surfaces. The variation in θB for on- and off-resonance Pa values is also

Fig. 15 Polarization anisotropy (Pa) factor as a function of angle of incidence for wave vector shifting toward the (A) K and (B) W point. The Pa value is shown for long-wavelength limit of 700 nm (squares), at the (111) stopgap wavelength (circles), and calculated (dotted lines) value for a film with neff ¼ 1.436. (A) The inset shows the Pa value for the (111) stopgap wavelength. (B) The inset indicates the reflectivity spectra at θ ¼ 62° for wave vector shifting toward the K (solid line) and W (dotted line) points.

Light transport in three-dimensional photonic crystals

confirmed for (111) stopgap (diamonds), as seen in Fig. 15A inset. Thus, the polarization anisotropy does indicate an inherent feature of the photonic crystals [71, 72]. Fig. 15B depicts the value of Pa at different θ for wave vectors shifting along the LW line. The off-resonance Pa values (squares) at a wavelength of 700 nm first decreases, reaches a minimum at θ ¼ 55°, and thereafter it increases again with θ. The minimum Pa value is comparable to calculated θB for neff ¼ 1.436. The on-resonance Pa value (circles) at the stopgap wavelength shows minimum at θ ¼ 59°. This shift in on-resonance θB is similar to the observations made the in case of wave vectors shifting toward the K point. The difference in the minimum values of on- and off-resonance Pa does show involuted light scattering at the air-crystal boundary rather than a simple air-dielectric interface. The on-resonance minimum Pa value is lower in the case of the K point compared to that at the W point. This supports the fact that polarization anisotropy is dependent on high-symmetry points in photonic crystals similar to theoretical calculations [70]. Fig. 15B inset shows the TM polarized reflectivity spectra at θ ¼ 62° for the K point (solid line) and W point (dotted line). The (111) stopgap for both symmetry directions is centered at 490 nm with lower reflectivity values for the K point compared to the W point. This verifies the intrusive light reflection at high-symmetry points in accordance with ideal fcc photonic crystals [70].

8. Conclusions In summary, we have discussed the angle- and polarization-dependent photonic stopgaps at high-symmetry points in 3D photonic crystals with fcc symmetry. We have observed the branching of photonic stopgaps into two reflectivity peaks with equal intensity at θ ¼ 56° when the tip of the wave vector spans the K point for TE polarized light. However, the TM polarized light does not show any such stopgap branching. We explained the absence of stopgap branching as a consequence of the Brewster effect for TM polarization, which forbids energy exchange within the crystal. A new reflectivity peak appears beyond the Brewster angle in the case of TM polarized light. The new peak originates in the crossing regime is assigned to the (111) plane through the comparison between the measured and calculated stopgaps. The polarization anisotropy factor corroborates the modification of θB in photonic crystals structure owing to the critical definition of neff. This deviation in θB is mainly due to the air-photonic crystal interface rather than air-dielectric film interface, as is the case in conventional optics. The observed polarization anisotropy is in complete agreement with calculated values for ideal fcc crystals. The reflectivity measurements for wave vectors shifting toward the W point show branching of stopgaps into three peaks for both TE and TM polarized light. The stopgap branching is observed at 65° for TE polarization, whereas that for TM polarization is observed at 59° and 65°, which confirms the complex interaction of light at the W point.

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The multiple stopgaps in the crossing regime at the W point is due to the diffractions from the family of (111) and (200) crystal planes. The experimental results for the W point also show a notable shift in θB as propounded from the theoretical calculations for ideal photonic crystals with fcc symmetry. The results establish the strong polarization anisotropy, which is dependent on the high-symmetry directions in photonic crystals.

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[50] H. Miguez, F. Meseguer, C. Lopez, A. Mifsud, J.S. Moya, L. Vazquez, Evidence of FCC crystallization of SiO2 nanospheres, Langmuir 13 (1997) 6009. [51] B. Cheng, P. Ni, C. Jin, Z. Li, D. Zhang, P. Dong, X. Guo, More direct evidence of the fcc arrangement for artificial opal, Opt. Comm. 170 (1999) 41. [52] L.V. Woodcock, Entropy difference between the face-centred cubic and hexagonal close-packed crystal structures, Nature 385 (1997) 141. [53] A. Reynolds, F. Lopez-Tejeira, D. Cassagne, F.J. Garcia-Vidal, C. Jouanin, J. SanchezDehesa, Spectral properties of opal-based photonic crystals having a SiO2 matrix, Phys. Rev. B 60 (1999) 11422. [54] A.F. Koenderink, W.L. Vos, Light exiting from real photonic band gap crystals is diffuse and strongly directional, Phys. Rev. Lett. 91 (2003) 213902. [55] J.F. Galisteo-Lo´pez, E. Palacios-Lido´n, E. Castillo-Martı´nez, C. Lopez, Optical study of the pseudogap in thickness and orientation controlled artificial opals, Phys. Rev. B 68 (2003) 115109. [56] S.G. Romanov, M. Bardosova, D.E. Whitehead, I.M. Povey, M. Pemble, C.M. Sotomayor Torres, Erasing diffraction orders: opal versus Langmuir-Blodgett colloidal crystals, Appl. Phys. Lett. 90 (2007) 133101. [57] R.V. Nair, R. Vijaya, Observation of higher-order diffraction features in self-assembled photonic crystals, Phys. Rev. A 76 (2007) 053805. [58] J.F. Lopez, W.L. Vos, Angle-resolved reflectivity of single-domain photonic crystals: effects of disorder, Phys. Rev. E 66 (2002) 036616. [59] R.V. Nair, B.N. Jagatap, Bragg wave coupling in self-assembled opal photonic crystals, Phys. Rev. A 85 (2012) 013829. [60] L.C. Andreani, A. Balestreri, J.F. Galisteo-Lo´pez, M. Galli, M. Patrini, E. Descrovi, A. Chiodoni, F. Giorgis, L. Pallavidino, F. Geobaldo, Optical response with threefold symmetry axis on oriented microdomains of opal photonic crystals, Phys. Rev. B 78 (2008) 205304. [61] A.V. Baryshev, A.B. Khanikaev, R. Fujikawa, H. Uchida, M. Inoue, Polarized light coupling to thin silica-air opal films grown by vertical deposition, Phys. Rev. B 76 (2007). 014305. [62] M. Ahles, T. Ruhl, G.P. Hellmann, H. Winkler, R. Schmechel, H. von Seggern, Spectroscopic ellipsometry on opaline photonic crystals, Opt. Commun. 246 (2005) 1. [63] A. Avoine, P. Hong, H. Frederich, J.-M. Frigerio, L. Coolen, C. Schwob, P.T. Nga, B. Gallas, A. Maitre, Measurement and modelization of silica opal reflection properties: optical determination of the silica index, Phys. Rev. B 86 (2012) 165432. [64] M. Muldarisnur, I. Popa, F. Marlow, Angle-resolved transmission spectroscopy of opal films, Phys. Rev. B 86 (2012). 024105. [65] S.G. Romanov, T. Maka, C.M. Sotomayor Torres, M. M€ uller, R. Zentel, D. Cassagne, J. ManzanaresMartinez, C. Jouanin, Diffraction of light from thin-film polymethylmethacrylate opaline photonic crystals, Phys. Rev. E 63 (2001). 056603. [66] I. Shishkin, M.V. Rybin, K.B. Samusev, V.G. Golubev, M.F. Limonov, Multiple Bragg diffraction in opal-based photonic crystals: spectral and spatial dispersion, Phys. Rev. B 82 (2014). 035124. [67] R.V. Nair, B.N. Jagatap, Multiple Bragg diffraction at W point in the face centered cubic photonic crystals, J. Nanophoton. 9 (2015) 093076. [68] A.V. Baryshev, A.B. Khanikaev, H. Uchida, M. Inoue, M.F. Limonov, Interaction of polarized light with three-dimensional opal-based photonic crystals, Phys. Rev. B 73 (2006). 033103. [69] M. Born, E. Wolf, Principles of Optics, seventh ed., Cambridge University Press, Cambridge, 2002. [70] S.G. Romanov, U. Peschel, M. Bardosova, S. Essig, K. Busch, Suppression of the critical angle of diffraction in thin-film colloidal photonic crystals, Phys. Rev. B 82 (2010) 115403. [71] Priya, R.V. Nair, Polarization-selective branching of stop gaps in three-dimensional photonic crystals, Phys. Rev. A 93 (2016) 063850. [72] Priya, R.V. Nair, Observation of wavelength-dependent shift in Brewster angle in 3D photonic crystals, Aust. J. Optom. 19 (2017) 065001.

CHAPTER 9

Application of two-dimensional materials in fiber laser systems Kavintheran Thambiratnam, Siti Aisyah Reduan, Zian Cheak Tiu, Harith Ahmad Photonics Research Center, University of Malaya, Kuala Lumpur, Malaysia

1. Introduction In recent years, graphene and other two-dimensional (2D) layered materials such as transition metal dichalcogenides (TMDs) and black phosphorus have attracted significant attention in research due to their potential applications in electronics, photonics, and optoelectronics. 2D layered materials can exhibit a rich variety of physical behavior, ranging from that of a wideband insulator to a narrow-gap semiconductor, to a semimetal or metal, as well as unique electrical and optical properties. They provide exciting opportunities for diverse photonic and optoelectronic functions by realizing new conceptual photonic devices that are fundamentally different from those based on traditional bulk materials. A perfect example of this is graphene, the well-known 2D material that has been widely used for numerous photonic and optoelectronic devices. Graphene, unlike its bulk counterpart graphite, is capable of operating at an extremely broad spectral range extending from the ultraviolet, visible, and near-IR regions to the mid-IR, far-IR, and even to the THz and microwave regions due to graphene’s unique linear energymomentum dispersion relation. These devices include transparent electrodes in displays, solar cells, optical modulators and photodetectors, and many more. Aside from graphene, monolayer TMDs such as MoS2 and WS2 as well as other exotic 2D materials such as black phosphorus are direct bandgap semiconductors, offering properties complementary to graphene. In this regard, different atomically thin 2D materials can be readily stacked together by van der Waals forces to make 2D heterostructures without the conventional “lattice mismatch” issues, thus offering a flexible and easy approach toward designing materials with desired physical properties. Furthermore, the surfaces of 2D materials are free from any dangling bonds as well as compatible with different photonic structures, such as well-developed fibers and silicon devices, giving them high potential for large-scale fabrication and low-cost integration into the current optical fiber networks and silicon CMOS devices. Compared to traditional bulk semiconductors, 2D materials also provide additional values, such as mechanical flexibility, easy fabrication and integration, and robustness. Furthermore, previous demonstrations of graphene and other 2D materials suggest that almost all functions required for Nano-Optics https://doi.org/10.1016/B978-0-12-818392-2.00009-3

© 2020 Elsevier Inc. All rights reserved.

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integrated photonic circuits including the generation, modulation, and detection of photons, can be accomplished by 2D materials. Such versatility of operation of combined fiber laser systems have been widely studied, particular in pulse fiber laser, photodetectors, optical modulation, and optical sensors. In this review, we discuss the various types of 2D materials and their fabrication process, as well as the application of 2D materials in fiber laser systems. This review also provides a list—not exhaustive, but as comprehensive as possible—of works done using 2D materials in fiber cavities to serve as a useful reference point for readers.

2. Types of 2D materials 2D materials typically refer to crystalline materials consisting of a single or a few layers of atoms, with thicknesses varying from one atomic layer to more than 10 nm. Until now, a number of 2D materials have been successfully fabricated and studied, and can be generally classified into a number of major groups. These will be the focus of this section of our review.

2.1 Graphene Graphene is a single atomic plane of graphite, which is sufficiently isolated from its environment to be considered freestanding. Atomic planes are, of course, familiar to everyone as constituents of bulk crystals, but until only recently, one-atom-thick materials such as graphene remained unknown. The basic reason for this is that nature strictly forbids the growth of low dimensional (low-D) crystals. Crystal growth requires high temperatures and the associated thermal fluctuations are detrimental for the stability of 1D and 2D materials. However, thanks to the work of Andre Geim and Konstantin Novoselov from the University of Manchester, obtaining 1D and 2D materials became possible through various exfoliation processes. Thus began the interest in 2D materials, beginning with graphene. Graphene is an allotrope of carbon in the form of a 2D, atomic-scale, hexagonal lattice, in which one atom forms each vertex. Graphene has many properties, which are dependent upon its thickness, and is able to conduct heat and electricity efficiently as well as being nearly transparent. More importantly, graphene shows key optical properties including large nonlinearity, fast recovery time, and broadband properties.

2.2 Transition metal dichalcogenides 2D TMDs, first experimentally isolated in 2010, are atomically thin semiconductors of the type MX2, where M is a transition metal atom and X is a chalcogen atom. Typically, TMDs have bandgaps ranging from 1 to 2.5 eV, corresponding to near-infrared to visible frequencies. One of the remarkable properties of the TMD group is the indirect-todirect bandgap transition that occurs when the material thickness decreases from multilayer to monolayer; this evolution of the band structure results from changed

Application of two-dimensional materials in fiber laser systems

confinement effects and the interaction of the neighboring layers. For a bulk or a multilayer TMD, the photoluminescence is negligible due to the slow and inefficient phonon-assisted second order radiative recombination of indirect excitons. When decreasing its thickness to a monolayer, the TMD changes from indirect to direct bandgap, with orders of magnitude stronger enhancement in photoluminescence, although the quantum yield is still not very high. Unlike the universal optical conductance in graphene, the semiconducting TMD may exhibit multiple absorption peaks from ultraviolet to near infrared frequencies due to excitonic and interband transitions. Typically, there are two groups of exciton resonance peaks, attributed to the optical excitation of electrons from the spin-orbit-split valence band hill to the degenerate conduction band valley at the K point and band nesting, respectively.

2.3 Topological insulator A topological insulator is a material with nontrivial symmetry-protected topological order that behaves as an insulator in its interior but whose surface contains conducting states, meaning that electrons can only move along the surface of the material. However, having a conducting surface is not unique to topological insulators, since ordinary band insulators can also support conductive surface states. What is special about topological insulators is that their surface states are symmetry-protected by particle number conservation and time reversal symmetry. In the bulk of a noninteracting topological insulator, the electronic band structure resembles an ordinary band insulator, with the Fermi level falling between the conduction and valence bands. On the surface of a topological insulator, there are special states that fall within the bulk energy gap and allow surface metallic conduction. Carriers in these surface states have their spin locked at a right-angle to their momentum (spin-momentum locking). At a given energy, the only other available electronic states have different spin, so the U-turn scattering is strongly suppressed and conduction on the surface is highly metallic. Noninteracting topological insulators are characterized by an index similar to the genus in topology.

2.4 Black phosphorus Black phosphorus is a recently discovered single-element layered material of the phosphorene family. Monolayer and few-layer phosphorenes, including black phosphorus, are predicted to bridge the bandgap range from 0.3 to 1.5 eV, between the zero bandgap of graphene and bandgaps higher than 1.57 eV in semiconducting TMDs. Inside monolayer phosphorene, each phosphorus atom is covalently bonded with three adjacent phosphorus atoms to form a puckered, honeycomb structure. The three bonds take up all three valence electrons of phosphorus, so unlike graphene, monolayer phosphorene is a semiconductor with a predicted direct optical bandgap of  1.5 eV at the Γ point of the Brillouin zone. The bandgap in few-layer phosphorene can be strongly modified by interlayer interactions, which leads to a bandgap that decreases with phosphorene

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thickness, eventually reaching 0.3 eV in the bulk limit. Unlike TMDs, the bandgap in few-layer phosphorene remains direct for all sample thicknesses. This makes black phosphorus an attractive material for mid-IR and near-IR optoelectronics. The puckered structure of black phosphorus breaks the three-fold rotational symmetry of a flat honeycomb lattice, and leads to anisotropic electronic and optical properties.

3. Fabrication of 2D materials 2D materials can be fabricated through a variety of techniques. While these techniques vary significantly from one to another, they can generally be classified as either top-down techniques, which extract the 2D material from a source bulk material, or bottom-up techniques, which see the desired 2D material grown to specification. Each approach has its pros and cons, and this section will summarize the two general approaches.

3.1 Top-down techniques 2D materials can be fabricated using a number of techniques that are generally classified as top-down or bottom-up. Typical top-down techniques include mechanical exfoliation, solution processing, and electromechanical exfoliation, whereas the bottom-up techniques include chemical vapor deposition (CVD), hydrothermal synthesis, and pulsed laser deposition (PLD). Fig. 1 shows a flowchart of the various fabrication techniques for 2D materials. In general, top-down techniques are relatively simple compared to bottom-up techniques, but with the trade-off of higher difficulty in controlling the thickness of the fabricated 2D material. Mechanical exfoliation is the simplest fabrication technique to exfoliate bulk 2D material into few-layer or monolayer structures, and is accomplished 2D Material fabrication techniques

Top-down

Mechanical exfoliation

Bottom-up

Solution processing

Liquid-phase exfoliation

Chemical vapor deposition (CVD)

Ion intercalation

Fig. 1 Overview of 2D TMDs fabrication techniques.

Hydrothermal synthesis

Pulsed laser deposition (PLD)

Application of two-dimensional materials in fiber laser systems

by repeatedly peeling the bulk material by using an adhesive tape. Solution processing techniques on the other hand are slightly more complex, and can be categorized into two categories: liquid-phase exfoliation and ion intercalation. Liquid-phase exfoliation is the more commonly used technique for the fabrication of 2D materials, and involves several processes such as: (i) dispersion of bulk material into solvent; (ii) sonication; and (iii) centrifugation. To ensure good dispersion of the 2D material, a solvent that offers the lowest enthalpy of mixing is preferable. Lithium ion is the most commonly used intercalant in the intercalation process due to its high efficiency. The intercalation process, on the other hand, is accomplished by immersing the bulk 2D materials into a lithiumcontaining solution such as n-butyllithium for more than 24 h. The lithium ion functions to increase the interlayer separation of the bulk material to ease the subsequent process of hydrothermal exfoliation. In the exfoliation process, the lithium ions between layers react vigorously with the water and produce H2 gas. The formation of H2 gas forms microbubbles that separate the 2D material layers.

3.2 Bottom-up techniques As well as extracting 2D materials from their bulk counterparts or source materials, these nanomaterials can also be grown using bottom-up techniques. The CVD technique requires the heating of a solid precursor at high temperature to promote vaporization. The volatile compound that is formed through the heating process will be deposited as 2D material thin film on a substrate. The final thickness of the 2D material film is controllable by adjusting the concentration of the initial precursor. The growth of 2D material film through CVD process is highly dependent on the nucleation rate of the substrate. The fabrication of 2D material through hydrothermal synthesis requires high temperature and pressure conditions. The 2D materials fabricated through hydrothermal synthesis are often in the lateral size of sub-micrometers to a few micrometers. The fabrication of few-layers 2D material films using PLD results from the ablation of the targeted bulk material. In the fabrication proses, a pulsed laser source is irradiated onto the bulk 2D material, which is placed in a vacuum chamber. The irradiance of a strong intensity laser resulted in extraction from the bulk 2D material, which is then deposited onto a substrate. Fig. 2 summarizes the benefits and disadvantages of the various topdown and bottom-up fabrication techniques.

4. Material characterizations Different approaches can be taken when characterizing 2D TMD materials, depending on particular requirements. This section will touch on the various common techniques used to characterize 2D materials, including scanning electron microscopy, transmission electron microscopy, and more.

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Mechanical exfoliation

Liquid-phase exfoliation

Ion intercelation

Simple,high quality flakes

Simple, high yield

Simple, high yield

Low yield, poor scalability

High defects

Leads to structural alteration

Chemical vapor deposition

Hydrothermal synthesis

Pulsed laser deposition

Ease of layers controlling, high quality flakes

Ease of layers controlling

Ease of layers controlling

Complex parameters control, needs of sacrificial substrate

Needs of high pressure & temperature

No need of sacrificial substrate

Fig. 2 Comparison of various fabrication techniques including (top) top-down and (above) bottom-up.

4.1 Scanning electron microscopy In general, there are two types of electron microscopes that can be used to extract information from the 2D material sample surface. These electron microscopes are known as scanning electron microscopes (SEMs) and field emission scanning electron microscopes (FESEMs). The basic concept of operation is similar in both the SEM and FESEM, with the main difference between the two types being the electron beam generation system. In the case of the SEM, a thermionic emitter is used to generate an electron beam source, whereas in the FESEM, a field emitter is used. The FESEM can generally provide imaging at higher resolutions with less damage to the specimen compared to the SEM. The term SEM is used in this chapter to represent generally both the SEM and FESEM. An SEM generates a variety of information signals from the surface of a specimen through irradiance by high energy electrons [1–5]. The interactions between the incidence electrons—which are also known as primary electrons—and the specimen results in the generation of various information signals such as backscattered electrons, secondary electrons, and X-rays. For surface imaging, only the information carried by secondary electrons is processed, which reveals the morphology and topography of the specimen [1, 3]. One advantage of the SEM is the direct determination of the physical sizes of the particles in the sample.

Application of two-dimensional materials in fiber laser systems

4.2 Transmission electron microscopy (TEM) A transmission electron microscope (TEM) enables the imaging of a specimen by transmitting a beam of electrons through the specimen [6–8]. Depending on the highest resolution that can be offered, the equipment can be further classified into conventional TEM or high-resolution transmission electron microscope (HR-TEM). However, the term TEM is used to represent generally both TEM and HR-TEM. The interaction of the transmitted electrons and the specimen provides various types of information, such as the morphology, crystal structure, and grain boundaries of the specimen. The final image of the spectroscopy is visualized on a phosphor screen or charge coupled device (CCD) camera. The dark region of the image represents the thicker area of the sample where fewer transmitted electrons are detected and vice versa. Higher-resolution imaging can be achieved with the use of TEM compared to SEM, which allows only scanning at the surface of the specimen. However, the sample preparation process for TEM imaging is slightly more tedious, as a very thin specimen with thickness less than 100 nm is often required to create electron transparency.

4.3 Atomic force microscopy An atomic force microscope (AFM) is a type of scanning probe microscope and is commonly used in 2D material studies to measure the height, friction, magnetism, electrical, and thermal properties of the material of interest [9–11]. The height of 2D materials is of particular interest in the study of its saturable absorption properties. The scanning mechanism of an AFM can generally be categorized into three different modes: contact, intermittent contact, and noncontact [12–17]. In the contact mode, an AFM image surface topography of a specimen is performed by scanning a cantilever over the specimen surface in close contact [16, 17]. The rise and fall of the specimen surface causes deflection of the cantilever due to the attractive and repulsive force between the cantilever and the specimen surface. The deflection of the cantilever is monitored by locating the position of a laser spot on a position-sensitive photodetector (PSPD). This scanning mode is suitable only for hard and robust specimens that can handle high loads and torsional forces. In the noncontact mode, the probe is set to oscillate at the resonant frequency [14, 15]. Scanning is done without having direct contact between the surface of the specimen and the probe. In fact, the distance between the specimen and the probe is kept constant. The interactions between the sample and the probe result in changes of the resonance frequency and the consequent oscillation amplitude, and from these changes, the surface topography is deduced. This operating mode, however, has poor sensitivity due to the weak interactive force between the specimen and the probe, which is easily susceptible to interference by the presence of contaminants on the specimen surface. However, this scanning mode is well-suited for soft materials.

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The concept of intermittent contact mode, which is also known as tapping mode, is similar to that of noncontact mode. Initially, the probe is set to oscillate at the resonance frequency with a certain amplitude known as free vibration amplitude. The free vibration amplitude is typically higher than the oscillating amplitude in noncontact mode to avoid the stickiness of the contaminant layer on the specimen surface [12, 13]. As the probe taps along the surface of the specimen, the interactive force causes variation of the oscillation amplitude. The variation of oscillating amplitude is fed back to the control system for readjustment of probe position. Intermittent contact mode is advantageous as compared to the contact mode in reducing the destructive frictional force between the specimen surface and the scan probe. Compared to the noncontact mode, the scanning resolution of intermittent contact mode is greatly enhanced due to the elimination of destructive effect from the contaminant layer on the specimen surface.

4.4 Raman spectroscopy A Raman spectrometer is a device that is commonly used to visualize the Raman scattering effect in a specimen. The Raman scattering effect is an inelastic scattering process that happens when a certain degree of energy absorption or energy transfer occurs in the specimen upon irradiance by a laser source. The scattering effect can generate both Raman Stokes and anti-Stokes depending on whether energy is absorbed from or transferred to the scattered photon during the interaction. In normal room conditions, the probability of generating Raman Stokes is higher than that of Raman anti-Stokes due to the higher density of states (DOS) at the excited vibrational level compared to the ground state [18, 19]. The Raman scattering effect manifests itself with the existence of peaks at certain frequencies, known as Raman shift, with a certain intensity depending on the degree of Raman scattering. The Raman shift is calculated based on the difference in the reciprocal of initial and final wavelength of scattered photons. The frequency of the Raman peak is the intrinsic properties of the material and will experience slight shifting mainly due to the existence of stress and strain in the specimen. On the other hand, the intensity of the Raman peak is dependent on the degree of molecular vibration in the specimen, which is affected by several factors, including: • type of molecular bonding in the specimen; • concentration of molecules in the specimen; • the wavelength of the exciting laser; • adsorption of specimen on structured metal surface; and • degree of change in polarizability due to molecular vibration. The study of the Raman shift enables qualitative analysis, which reveals the identity of the specimen, whereas the study of the intensity of the Raman peak enables qualitative

Application of two-dimensional materials in fiber laser systems

analysis, which reveals information such as changes in molecular bonding and concentration of molecules in the specimen.

4.5 Energy dispersive X-ray analysis (EDX) Energy dispersive X-ray (EDX) analysis is an analytical technique used to characterize the elemental composition and chemical analysis of a specimen with atomic number (Z) > 3 [20]. It is usually used in conjunction with an SEM and a TEM. The analytical process is done by bombarding the specimen with a beam of high energy electrons. The bombardment of electrons results in ejection of X-rays from the atoms on the specimen surface. The X-rays emission resulting from the interaction can be classified into two categories: Bremsstrahlung X-rays, which are also known as continuous X-rays, and characteristic X-rays. The Bremsstrahlung X-rays are generated due to the interaction between incident electron and atomic nuclei of the specimen. This manifests itself as the background spectrum upon which the characteristic X-rays spectrum is superimposed. On the other hand, characteristic X-rays are generated due to the transition of higher state electrons to the lower energy state vacancies upon bombardment by high energy electrons. The energy difference between the higher and lower energy state corresponds to the energy of emitted X-rays and it is dependent on the characteristics of the specimen. The characteristic X-rays manifest as X-ray lines in the X-ray spectrum. Capital Roman letters such as K, L, or M indicate the shell containing the inner vacancy, whereas the Greek letters and the numbers indicate the group to which the line belongs in order of decreasing importance from α to β and the intensity of the line in a decreasing order from 1 to 2, respectively. Both the Bremsstrahlung and characteristic X-ray emissions are captured by an X-ray detector and presented as a spectrum of X-ray energy versus intensity. The energy of the characteristic X-rays enables qualitative analysis to reveal the elements that contribute to the specimen, whereas the corresponding intensity enables quantitative analysis to reveal the concentration of the elements present.

4.6 Ultraviolet-visible spectroscopy (UV–vis) Ultraviolet-visible spectroscopy (UV–Vis) is another technique used to perform both qualitative and quantitative analysis of a specimen using a light source at the ultraviolet wavelength range of 200–400 nm to the visible wavelength range of 400–700 nm. The interaction between the light source and specimen can be analyzed through the absorption, transmission, or reflection in the UV–Vis spectrum [20, 21]. The characteristics peak in the UV–Vis spectrum is a representation of the energy bonding of the specimen. The existence of conjugation and delocalization significantly affects the wavelength of characteristic peaks in the UV–Vis spectrum. From the absorption peak of the UV–Vis

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spectrum, the concentration (c, in mol/L) of the sample can be calculated by using the Beer-Lambert law [22]. The Beer-Lambert equation relates the concentration of the specimen to the absorbance (A) and molar absorptivity (E, L-mol1 cm1), presented below in Eq. (1). The molar absorptivity is a characteristic value for a given specimen. E¼

A c

(1)

In addition, the energy bandgap of the specimen can be derived from the UV–Vis spectrum by applying the Tauc formula, which is shown in Eq. (2) [23, 24]. The energy bandgap extraction using Tauc formula requires the plot of (ahν)(1/n) versus hν, where a ¼ 2.303(A/t) and n ¼ 0.5, 1.5, 2.0 or 3.0 depending on the type of energy transition in the specimen. In the equation, A represents the absorbance of the specimen as indicated from the UV–Vis spectrum whereas t represents the thickness of the specimen along which the light source propagates. On the other hand, the different types of energy transitions in the specimen can generally be classified as: (i) allowed direct (n ¼ 0.5), (ii) forbidden direct (n ¼ 1.5), (iii) allowed indirect (n ¼ 2.0), and (iv) forbidden indirect (n ¼ 3.0). The extrapolation of the Tauc curve along the straight-line portion to the x-intercept at y ¼ 0 reveals the energy bandgap value of the specimen. n αhν ¼ k hν  Egap (2)

4.7 X-ray diffraction spectroscopy (XRD) An X-ray diffractometer is used to perform structural characterization on a wide range of specimens from powder to thin films. The analytical study of crystal lattice is enabled in XRD through the constructive interference of monochromatic X-rays and a crystalline sample [25]. In fact, the detailed mechanism of an XRD follows the Bragg equation, which is shown in Eq. (3). In the equation, n is an integer, λ is the characteristic wavelength of the X-ray source, d is the interplanar spacing between rows of atoms in the specimen, and θ is the angle of X-rays with respect to the plane of atoms. The compliance of diffraction condition to Bragg’s law results in constructive interference and a diffracted ray at a particular direction. The lattice spacing (d-spacing) of the specimen can then be derived from the direction of the diffracted ray to allow the identification of various structural characteristics in the specimen. The characteristics include the chemical composition, crystal structure, crystallite size, strain, and structural orientation. nλ ¼ 2d sin θ

(3)

Fig. 3 summarizes the typical hierarchy of techniques used to characterize 2D materials, while Fig. 4 provides a brief outlook on the form that 2D materials can be obtained and their method of incorporation into a laser cavity.

Application of two-dimensional materials in fiber laser systems

SEM Topography TEM Measurement

AFM Raman spectroscopy

Thickness Typical characterization for 2D TMDs

Estimation

TEM

EDX

SEM

Raman spectroscopy

Composition

UV-Vis XRD Lattice structure TEM

Fig. 3 Typical characterization for 2D materials.

Typical form of 2D TMDs and method of incorporation into laser cavity

Thin film

Sandwiching

Coating

Exfoliated crystal

Solution

Drop casting

Optical deposition

Spray deposition

Sandwiching

Coating

Fig. 4 Typical form of 2D materials and the method of incorporation into laser cavity.

5. Application of 2D materials in fiber laser system 5.1 Q-switched generation Q-switching operation in fiber lasers has attracted attention for use in the applications of various fields such as medicine, sensing, material processing, and imaging [26–29]. This is because of the advantages of these lasers in terms of their simplicity, compactness, and cost-effectiveness compared to bulk lasers [30, 31]. Q-switched fiber lasers can be generated either passively or actively, though active Q-switching operation requires an electro-optic or acoustic-optic modulator to be integrated into the system, making the system complicated and costly. As such, passive Q-switching is typically the preferred option over active Q-switching, particularly for applications requiring a compact and cheap pulsed laser source. Generally, saturable absorbers (SAs) are used as nonlinear optical devices to generate a passively Q-switched laser. Semiconductor saturable absorber mirrors (SESAMs) were typically the common approach to generate Q-switched lasing in fiber laser systems

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[32]. However, SESAMs had several drawbacks such as larger insertion loss, high fabrication cost, and complex implementation into the system. This motivated researchers to investigate other potential SAs. The advent of graphene was the catalyst for the development of a new generation of SAs, and led to the revolution of the use of 2D materials as SAs [33, 34]. From the discovery of graphene and its optical applications, there have been significant breakthroughs in the development of SAs, with new materials such as transition metal dichalcogenides [35, 36], topological insulators [37, 38], and black phosphorus [39] already being successfully demonstrated. There have been many previous works reported on the use of 2D materials as SAs operating in the C-band, L-band, and 2.0 μm regions [36, 37, 40]. There have also been reports on the passive generation of Q-switched lasing in the S-band, O-band, and 1.0 μm region using these 2D material-based SAs [41–43]. In this chapter, however, only the C-band region will be discussed as it covers the critical telecommunication wavelength of 1550–1630 nm. Table 1 shows the characteristics of passively Q-switched erbium doped fiber laser with a material consisting of graphene, TMDs, TIs, and black phosphorus in the C-band region. Table 1 shows the many works reported on the generation of passively Q-switched fiber lasers by using different material as SAs. This table focuses mainly on C-band laser cavities employing erbium-doped fibers (EDFs) as the primary gain medium. As shown in the table, graphene has been successfully used to generate Q-switched pulses passively by Ahmad et al. [45] and L. Wie et al. [44]. The maximum output power and maximum average pulse energy reported by L. Wie et al. [44] is larger than that reported by H. Ahmad et al. [45]. However, H. Ahmad et al. [45] report a longer central wavelength as compared to L. Wie et al. [44], which has a significant important value in telecommunication applications. Three TMD-based SAs are also included in the table: MoS2, WS2, and WSe2. It can be seen that MoS2 SA, reported by Chen et al. [46], has the highest maximum average pulse energy as compared to the other two SAs. Nevertheless, it is reported by Guo et al. [48] that using WSe2 as an SA has the shortest minimum pulse duration and larger repetition rate. All of the reports on TMDs have nearly the same central wavelength. Another potential SA, which are TIs, have also become attractive for the design of pulsed lasers. Bi2Te3 and Bi2Se3 are the common TIs to be used as SAs. From the table, Bi2Te3 based SA reported by Y. Chen et al. [37] generates larger maximum average pulse energy pulses as compared to Bi2Se3-based SA reported by Y. Chen et al. [49]. Both of the experiments observed nearly the same central wavelength. Another unique material that shows potential characteristics as an SA is black phosphorus. There were two reports on generating Q-switched pulsed laser by using black phosphorus SA in Table 1. One, reported by Y. Chen et al. [39], using black phosphorus as an SA observed a larger maximum average pulse energy. However, E. I. Ismail et al. [50] report a shorter pulse duration and larger repetition rate of Q-switched pulses by using black phosphorus. Both of their works report a central wavelength in the

Table 1 Characteristics of passively Q-switched erbium doped fiber laser with different materials.

Type of material

Graphene TMD

Topological insulator Black phosphorus

MoS2 WS2 WSe2 Bi2Te3 Bi2Se3

Center wavelength

Repetition rate

1538.0 nm 1557.66 nm 1560 nm 1558 nm 1560 nm 1566.9 nm 1565.1 1562.87 nm 1561.0 nm

31.7–236.3 kHz 1.387–206.613 kHz 7.758–41.452 kHz 79–97 kHz 92.46–138 kHz 2.154–12.82 kHz 4.508–12.88 kHz 6.983–15.78 kHz 31.53–82.85 kHz

Pulse duration

Maximum output power

Maximum average pulse energy

Ref

2.5–206 ns 94.8–0.412 μs 13.534–9.92 μs 3.4–1.1 μs 1.478–0.754 μs 49–13 μs 36–13.4 μs 10.32–39.84 μs 5.52–9.36 μs

7.8 mW 4.055 mW 0.77 mW 16.4 mW 4 mW 19.56 mW 112 μW 0.3–1.4 mW  0.5–4.2 mW

33.2 nJ 16.26 nJ 184.7 nJ 179.6 nJ 29 nJ 1.525 μJ 13.3 nJ 94.3 nJ 51 nJ

[44] [45] [46] [47] [48] [37] [49] [39] [50]

240

Nano-optic

C-band region. The performance of the output pulsed laser can be improved by optimizing the coupling ratio of the cavity and reducing the cavity length, as well as improving the quality of the SAs. By reducing this limitation, better performance of Q-switched pulses can be generated.

5.2 Mode-locked generation The technique of producing an ultra-short pulse laser (with pulse durations varying from picoseconds (1012 s) to femtoseconds (1015 s)) by locking multiple longitudinal modes in a laser cavity is known as mode-locking [51]. The ultrashort pulse fiber laser has attracted significant attention due to its excellent heat dissipation, freedom from alignment, compact laser cavity design, capability to generate a high-quality pulse with picosecond and subpicosecond duration, and wide wavelength tunable span over several tens of nanometers [52, 53]. Due to these advantages, the ultrashort pulse fiber laser offers promising possibilities in various applications such as biomedical diagnostics, optical fiber communication, supercontinuum generation, and material processing [54, 55]. In general, the ultrashort optical pulses can be achieved by either active mode-locking or passive mode-locking operation. The active mode-locking operation is generated by using an external signal to develop a modulation on the propagating electromagnetic field inside the laser cavity, such as an acousto-optic modulator [56], while the passive mode-locking operation is initiated by a nonlinear element such as bleached SA, which demonstrates negligible loss when high excitation intensity is applied [57]. As a result of this, passive mode-locking is preferable due to its compactness, simplicity, and flexibility, since no external synchronized driving circuits are required to perform loss modulation, as well as low operation cost [58]. In the past few years, 2D materials have attracted intense interest among researchers due to their unique electronic and linear and nonlinear optical properties that bring benefits to various fields of application. This finding has also contributed to the recent development of photonics and optoelectronics applications. At present, the possibility of employing these 2D materials as SAs for mode-locked pulsed laser generation at different wavelength operation is being studied. In order to generate lasing at different wavelength regions, different types of gain medium need to be used. Fig. 5 demonstrates three different cavity designs used to produce passively mode-locked pulsed laser at 1.0 (Fig. 5A), 1.5 (Fig. 5B), and 2.0 μm (Fig. 5C) regions, in which each of the cavities used different types of gain medium and SA. In previous literature by Du et al. [59], they successfully obtained the mode-locking operation at 1064 nm wavelength with the assistance of fewlayer molybdenum disulfide (MoS2)-taper-fiber device as SA and ytterbium-doped fiber (YDF) as the gain medium. Meanwhile, H. Ahmad achieved stable passive mode-locked operation at 1.5 μm using an EDF as the gain medium and in the 2.0 μm regions using thulium-doped fiber (TDFs) as the gain medium and induced by an rGO-TiO2 coated on a side-polished fiber [60, 61]. It is suggested that the evanescent wave interaction between the incident light and rGO-TiO2 composites promotes mode-locking in the EDF and TDF lasers.

Fig. 5 Schematic diagram of the mode-locked laser produced using (A) YDF (Reprinted/adapted with permission from J. Du, Q. Wang, G. Jiang, C. Xu, C. Zhao, Y. Xiang, et al., Ytterbium-doped fiber laser passively mode locked by few-layer molybdenum disulfide (MoS2) saturable absorber functioned with evanescent field interaction. Sci. Rep. 4 (2014) 6346.), (B) EDF (Reprinted/adapted with permission from H. Ahmad, S.A. Reduan, N. Yusoff, M.F. Ismail, S.N. Aidit, Mode-locked pulse generation in erbiumdoped fiber laser by evanescent field interaction with reduced graphene oxide-titanium dioxide nanohybrid, Opt. Laser Technol. 118 (2019) 93–101.), and (C) TDF (Reprinted/adapted with permission from H. Ahmad, R. Ramli, H. Monajemi, S.A. Reduan, N. Yusoff, M.F. Ismail, Soliton modelocking in thulium-doped fibre laser by evanescent field interaction with reduced graphene oxidetitanium dioxide saturable absorber, Laser Phys. Lett. 16 (2019), 075102.) as gain medium.

Nano-optic

One of the 2D materials groups that have been attracting attention for a long time in various fields of application is the graphene family. The few-layers graphene, reduced graphene oxide (rGO), and graphene oxide (GO) that are included in this group have been widely used as SA material in initiating the mode-locked pulsed laser at different wavelengths. This is due to their broadband saturable absorption, ultrafast recovery time, controllable modulation depth, and easy fabrication, thus making graphene the most versatile and attractive material to be employed as SA in mode-locked fiber lasers. For instance, Ahmad et al. [62] fabricated a side-polished fiber and prepared the rGO solution via Hummer’s method followed by the hydrothermal method purposely for the use as SA to achieve stable passive mode-locking operation in the C-band region. The schematic diagram as well as a real image captured for the polishing assembly setup are illustrated in Fig. 6A and B, respectively. The side-polished fiber with rGO has been dropped on the polished side of the fiber and is inserted into the fiber ring, resulting in a stable output of

Fiber holders

Rubber grip contact fibre holder

Polishing wheel SMF-28 fibre

SMF-28 fibre

DC voltage

(A)

(B)

Polisher

Translation stages

1.2

0 CW Mode-locked

–20 –30

λ= 1568.44 nm Δλ= 1.02 nm

1.0

λ= 1544.02 nm Δλ= 4.04 nm

0.8 Intensity (V)

–10

Intensity (dBm)

242

–40 –50 –60

0.6

0.4

0.2

–70 0.0 1510 1520

(C)

1530 1540 1540 1560 1570 Wavelength (nm)

1580 1590

(D)

0

200

400 600 Time (ns)

800

1000

Fig. 6 (A) Schematic illustration and (B) image captured for the polishing assembly setup. (C) Optical spectrum and (D) wideband oscilloscope trace of the generated mode-locked pulses using rGO as SA. (Reprinted/adapted with permission from H. Ahmad, S. Soltani, K. Thambiratnam, M. Yasin, Z.C. Tiu, Mode-locking in Er-doped fiber laser with reduced graphene oxide on a sidepolished fiber as saturable absorber, Opt. Fiber Technol. 50 (2019) 177–182.)

Application of two-dimensional materials in fiber laser systems

mode-locked pulses with the laser optical spectrum centered at 1544.02 nm, as depicted in Fig. 6C. Fig. 6D shows the typical output mode-locked pulse train with uniform pulse generation and pulse repetition rate of 16.79 MHz. After the polishing process, the core of the fiber has been exposed, thus allowing the evanescent wave interaction between the incident light and rGO, resulting in the generation of mode-locking operation. Apart from the reduced version of GO, GO itself has proven its applicability as SA for generating the mode-locked pulses, as reported by Ahmad et al. [63] In this report, they have shown the potential of GO optically deposited on a microfiber to produce short pulse-width mode-locked with a large bandwidth using EDF as gain medium. A systematic flame brushing technique is conducted to fabricate the microfiber originally from single mode fiber (SMF). The as-fabricated optical SA device induces the generation of mode-locking pulses at 1560 nm with pulse duration of 3.46 ps and a repetition rate of 0.92 MHz measured at an input power of 188 mW. Even though the pulse repetition rate obtained in this work is quite low, the fabrication of microfiber-based GO SA might be applicable for future implementation of ultrafast lasers as the probability of thermal damage of SA has been minimized due to the uses of evanescent field effect to generate mode-locker in the proposed system. Other than the graphene family, TMDs have also emerged as potential candidate materials for future SA for mode-locked lasers due to their unique optical, mechanical, and electronic properties [64]. The bandgap tunability of TMD material has been experimentally proven to be achievable by altering the number of layers or introducing defects, hence giving it an advantage for application in optoelectronics [65]. For example, E.J. Aiub et al. [66] reported a mode-locked pulse generation in the wavelength region of 1560 nm using molybdenum disulfide (MoS2). The MoS2 is prepared by using a mechanical exfoliation method (as can be seen in Fig. 7A) before being transferred directly onto a side-polished surface of D-shaped optical fiber and used as SA. The proposed laser generated pulses at pump power of 110 mW with the pulse width of 200 fs, traced using autocorrelator and the SNR value of 84 dB as obtained from RF spectrum, as shown in Fig. 7B and C, respectively. The high SNR value indicates that the generated pulses possess high stability. In the previous studies done by other researchers, the MoS2 demonstrated better saturable absorption response (34.4%) than graphene (16.5%) upon characterizing the ultrafast nonlinear optical using an open-aperture Z-scan technique [67]. It is believed that the existence of edge states created the sub-bandgap within the bandgap, leading to this nonlinear optical absorption possessed by MoS2 [68]. This has resulted in broadband saturable absorption of MoS2 from visible to infrared spectrum regions. Generally, TI are a series of Dirac materials that look like graphene. They have insulating bandgaps in bulk but gapless surface states with a linear dispersion relation between energy and momentum of surficial Dirac electrons originating from strong spin-orbit coupling. This results in a wideband absorption of optical photons, thus it can be applied as SA in pulsed lasers [69]. Xu et al. [70] have successfully demonstrated a passive mode-locking EDF laser operated in 1571 nm operating wavelength using one of the

243

Nano-optic

(1)

PVA layer

PMMA layer

(2)

(3) Preparated substrate

Glass substrate PVA water soluble layer (1.2µm) MosPMMA layer (300nm) Supporting (5) (6) adhesive tape MoS2

Mechanically exfoliation of MoS2 (4) MoS2

MoS2

(A) –40

1.2 Experimental pulse duration Sech2 fitting

1.0 0.8 0.6

RF spectrum

–60

Intensity (dBm)

Normalized intensity (a.u.)

244

–80

–100

Δτactual = 200 fs

0.4

SNR = 84 dB

–120

0.2 –140 0.0 –1.5

(B)

–1.0

–0.5 0.0 0.5 Time delay (ps)

1.0

1.5

(C)

14.5290

14.5295

14.5300

14.5305

Frequency (MHz)

Fig. 7 (A) The process flow of mechanical exfoliation for MoS2; (B) autocorrelation trace of output pulses; and (C) RF spectrum measure at fundamental frequency of the exfoliated MoS2 [66]. (Reprinted/adapted with permission from H.-P. Komsa, A.V.J.P.R.B. Krasheninnikov, Electronic structures and optical properties of realistic transition metal dichalcogenide heterostructures from first principles, Phys. Rev. B 88 (2013) 085318.)

TI materials: Bi2Se3 nanoplatelets with uniform morphology and average thickness down to 3–7 nm as SA. The Bi2Se3 nanoplatelets are deposited onto the end-facet of optical fiber and inserted into an EDF laser cavity for pulse generation. The proposed modelocked EDF laser generated pulses with duration as short as 579 fs with corresponding output power of 1.59 mW at a repetition rate of 14.25 MHz measured at a pump power of 160 mM. The successful production of mode-locking pulses in this work may result from the unique nonlinear optical responses of bilayer Bi2Se3 nanoplatelets where its modulation depth and the saturable intensity are calculated to be about 36% and 3.7 MW/cm2 at 1565 nm, respectively, obtained using the Z-scan technique. Apart from the aforementioned materials, there are various types of materials in the 2D chalcogenides group that have obtained considerable interest in the development of SA to allow passive mode-locking operation. Table 2 compares the passively mode-locked fiber laser performance by different SAs based on 2D materials including the graphene family, TMDs, Tis, and black phosphorus.

Table 2 Performance comparison of passively mode-locked fiber laser using different 2D materials-based SAs.

Group of material

Name of material

Fabrication method

Graphene family

rGO

Hummer’s method and hydrothermal Hummer’s method Mechanical exfoliation –

GO Graphene GraphenePMMA Few-layers graphene Graphene

TMD

MoS2

CVD Hummer’s method and ultrasoniccation Mechanical exfoliation

MoS2

LPE

MoS2



WS2



WS2

Magnetron sputtering technique

Center wavelength (nm)

Threshold pump power (mW)

Operating pump power (mW)

Repetition rate (MHz)

Pulse width

Pulse energy (J)

Output power (mW)

SNR value (dB)

Ref

Dropcasting

1544.02

64.44

64.4–280.5

16.79

1.17 ps



0.32

53.76

[62]

Optical deposition Ferrules sandwiching Ferrules sandwiching Ferrules sandwiching Optical deposition

1560

74

74–188

0.92

3.46 ps



0.597

57.45

[63]

1568

60

60–414

16.34

844 fs



30

27

[71]

1556.57

24.5

24.5–105.2

9.65

1.03 ps

420.8 pJ

3.7



[72]

1568.1

60

60–210

7.29

58.8 ps

0.23 nJ

1.68

48

[73]

1562.26

47.34

47.34–< 61.07

15.71



0.8 μm) wavelengths. Photons exhibits large absorption lengths at this near-IR wavelength band and to increase these path lengths of photons, light reflects at an oblique angle by the diffractive grooves on the DBR. According to geometrical optics, in loss-less strongly textured metallic surfaces, the absorbance increases by a factor of 4n2 (45 for silicon), where n is the absorber materials refractive index (Fig. 2). In order to improve optical energy absorption and solar cell efficiency, PCs can be used in three different approaches. The use of PBGs is the first method that enhances the emission in the desired direction. The second one is the light diffraction which reroutes the incoming light rays into the guided modes. Here, the light diffraction is the resultant of scattering that was generated by the periodic modulation. Slow Bloch modes is the third approach, which leads to a strong light-matter interaction for the absorption enhancement. Due to the multi-parameter influences, (such as lattice constant, lattice symmetry, shape of the scattering object, thickness of the PC layer, filling factor, and intermixing materials), structuring and optimization of PCs are relatively complicated and it is difficult to point out which one provides a highly efficient PC structure for obtaining improved absorption efficiency in solar cell structures. The main advantage of PCs is that they can generate diffraction where the momentum of photons (k) can be scattered away with kk ¼ kki +G from the specular reflection direction, where ki is the incident wave vector and G is a reciprocal lattice vector. Many research groups have been developed novel approaches to enhance trapping of light in solar cells using PC backreflectors combined with diffraction [23] or reflection [12, 13] gratings. Fig. 3 shows the schematic of two-dimensional (2D) PC-enhanced solar cell configuration with an antireflection coating and DBR. In this schematic solar cell configuration, light

339

340

Nano-optics

Fig. 3 Schematic of PC enhanced solar cell configuration with the top anti-reflective indium tin oxide (ITO) coating, square lattice of two-dimensional (2D) PC layer and DBR with its top view of 2D PC grating layer with dielectric cylinders in ITO background.

diffraction occurs at the cell back where the absorption of the light is too low. Within the absorber layer, light diffraction occurs by 2D PC and DBR specularly reflects light with small losses.

2. Theory of light-trapping The fundamental idea of light-trapping is to store maximum optical energy in an absorber layer than the energy absorbed in a single pass of solar light. Light-trapping permits PV cells to collect sunlight by an active absorber layer that is thinner than the intrinsic absorption length of the material. This thinner material layer leads to the usage of reduced amount of materials for the PV cell fabrication and then decreases the costs in general. Furthermore, light-trapping leads to solar cell efficiency progress. Light-trapping structures are necessary for harvesting maximum optical energy. For enhancing the light absorption efficiency, traditional methods integrate anti-reflection coatings, high performance backreflectors, and top textured surfaces that scatter and reflect light very effectively in the absorber layer. Generally, these methods are centered on a geometrical optical approach and the size of the textured surfaces will be larger than the optical wavelength [24, 25]. The main aim of any light-trapping structures is to acquire maximum light absorption beyond the possible absorption at a single pass of light through the absorbing media, while keeping the same amount of absorber material. The absorption enhancement factor for such light-trapping 3D structures can be represented as follows [26]:

Photonic crystals-based light-trapping approach in solar cells

f ðω, θ, φÞ ¼

Aðω, θ, φÞ αdeff

(1)

where A is the absorption coefficient of the light-trapping configuration, ω is the frequency of light, θ is the angle of incidence, φ is the polar angle of light (φ dependency absent in 2D), α is the absorption coefficient of the material (α ¼ 2k0Im{n}, where k0 is the light wave vector in vacuum and Im{n} is the refractive index imaginary part), and deff is the thickness of the uniform absorber layer. The light-trapping mechanism in PC-based solar cells can be described in terms of not only ray-optic but also wave-optic theory.

2.1 Ray-optic theory Initially developed light-trapping theory using the ray optics approach was mainly for the conventional crystalline silicon (c-Si) solar cells having a photon absorber layer many wavelengths thick [27,28]. This ray-optic analysis has become the basis for photon management in bulk silicon solar cells. In general, it has had a huge impact on solar cell optical design. The statistical ray-optic approach shows that the most achievable absorption enhancement for a PV cell having an isotropic emission structure (the top is designed as a Lambertian surface, θ ¼ 90 degree) (Fig. 4A), where the absorption enhancement factor for the sunlight entering from any angle to the absorber layer is the same, is the Yablonovitch limit of 4n2, where n is the absorbing layers’ refractive index [27]. However, for the anisotropic angular response condition where the top surface is randomly textured (Fig. 4B) and receives sunlight only within a cone angle (Fig. 4C) θ, the upper limit of the absorption enhancement factor is 4n2/Sin2θ [14, 15], by an assumption of the same enhancement factor for all the directions of light incidents within that cone angle. The randomly textured absorber-air interface randomizes the directions of sunlight

Fig. 4 Slab structure with a (A) flat top surface (isotropic) and (B) randomly textured top surface with a backside mirror (anisotropic). (C) The schematic of light-trapping in a structure having a randomly textured surface.

341

342

Nano-optics

propagation inside the absorber layer leading to the total internal reflection phenomena between the surrounding medium (air) and absorber material. As a result, the light will propagate through a longer path inside the absorber material and then enhance the light absorption with a limiting factor of 4n2/Sin2θ. Both these anisotropic and isotropic angular response conditions can be explained in a unified manner by defining the angle-integrated absorption enhancement factor as follows: ð π=2 ð 2π F3D ðωÞ ¼ dθ dφf ðω, θ, φÞ cosθ sin θ ¼ 4πn2 (2) 0

0

where f(ω, θ, φ) is defined in Eq. (1). This angle-integration is carrying out for comparing physical light-trapping configurations performance to the conventional limit or Yablonovitch limit, as there are no physical light-trapping configurations exhibiting an ideal isotropic angular response. The ray-optic theory can explain the conventional structure shown in Fig. 4B in 2D. The enhancement factor for the isotropic case is f (ω, θ) ¼ πn and an upper limit of angleintegrated enhancement factor is as follows: ðπ 2 F2D ðωÞ ¼ dθf ðω, θÞ cosθ ¼ 2πn (3) π 2

This applies to both anisotropic and isotropic conditions in 2D. The conventional light–trapping upper limits in 2D structures for a single angle of incidence and after the angle-integration are πn and 2πn; similarly, the conventional light–trapping upper limits in 3D structures for a single angle of incidence and after the angle-integration are 4n2 and 4πn2. This ray-optic theory describes the conventional light-trapping approach; however, it is not appropriate for solar cells having thicknesses that are comparable to the wavelength scale of interest, and hence in such situations the wave-optic theory is mandatory for creating new performance upper limits.

2.2 Wave-optic theory This wave-optic approach is suitable for many complex nanostructures including PCs. In this theory, light-trapping is explained by the coupling of incident sunlight into the guided optical modes that are supported by the configuration. Each incident optical wave couples with each optical guided mode within the structure, generating guided resonance and then strongly enhances the resultant optical absorption by the accumulated contribution of these large number of guided resonances. Here we assume N number of guided modes in the free space and the absorber that supports M number of resonances. According to the temporal coupled mode theory, the coupling between resonance and guided mode described for the nth guided mode and mth resonance to the optical plane wave is as follows:

Photonic crystals-based light-trapping approach in solar cells

0 B B d am ¼ B Bjωm  dt @

N X s¼1

1 γ m, s + γ 0 C C ffi Cam + jpffiffiffiffiffiffiffi γ m, n Sn C 2 A

(4)

where am is the amplitude of resonance, with | am |2 corresponding to the stored electromagnetic energy in the absorber per unit area. Sn is the amplitude of incident optical wave. The normalized | Sn |2 corresponds to its intensity. The ωm is the frequency of mth resonance, γ m,s represents the rate of loss to the Sth mode from the mth resonance, and γ 0 is the rate of loss of resonance as a result of intrinsic absorber material absorption. From Eq. (4), it is possible to calculate the enhancement of wideband absorption by the mth optical resonance when the light is incident from the nth guided mode. By adding all the contributions of M optical resonances from the N guided modes in the ω, ω+Δω frequency range and by the comparison of light absorption to the single pass light absorption (αdeff), it is possible to calculate F, the angle-integrated enhancement factor, as follows: X γ m, n γ 0 X 2π X n 2πcΓ X F¼ fn ¼  ρðωÞ (5) αdeff Δω m γ m, n + γ 0 ndη n n

where fn is the nth guided mode contribution to the enhancement factor, η is the volume fraction of the absorber, and ρ(ω) ¼ M/Δω represents the spectral density of states.

3. Absorption enhancement factor in PCs 3.1 Absorption enhancement factor in 2D PCs The angle integrated light-trapping enhancement factor for a 2D PC system is represented using Eq. (3) as follows [29]: ðπ 2 F2D ¼ dθf ðθÞ cosθ (6) ¼

1 k0

π 2

ð =kx =