Surface and Interface Science, Volumes 7 and 8: Volume 7 - Solid-Liquid and Biological Interfaces; Volume 8 - Applications of Surface (Wandelt Hdbk Surface and Interface Science V1 - V6) [7-8, 1 ed.] 3527411593, 9783527411597

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Surface and Interface Science, Volume 7: Liquid and Biological Interfaces
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Surface and Interface Science, Volume 8: Interfacial Electrochemistry
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Edited by Klaus Wandelt Surface and Interface Science

Surface and Interface Science Edited by Klaus Wandelt Volume 1: Concepts and Methods Volume 2: Properties of Elemental Surfaces Print ISBN 978-3-527-41156-6 oBook ISBN 978-3-527-68053-5 (Volume 1) oBook ISBN 978-3-527-68054-2 (Volume 2) Volume 3: Properties of Composite Surfaces: Alloys, Compounds, Semiconductors Volume 4: Solid-Solid Interfaces and Thin Films Print ISBN 978-3-527-41157-3 oBook ISBN 978-3-527-68055-9 (Volume 3) oBook ISBN 978-3-527-68056-6 (Volume 4) Volume 5: Solid-Gas Interfaces I Volume 6: Solid-Gas Interfaces II Print ISBN 978-3-527-41158-0 oBook ISBN 978-3-527-68057-3 (Volume 5) oBook ISBN 978-3-527-68058-0 (Volume 6) Volume 7: Liquid and Biological Interfaces Volume 8: Interfacial Electrochemistry Print ISBN 978-3-527-41159-7 oBook ISBN 978-3-527-68059-7 (Volume 7) oBook ISBN 978-3-527-68060-3 (Volume 8) Volume 9: Applications of Surface Science I Volume 10: Applications of Surface Science II Print ISBN 978-3-527-41381-2 oBook ISBN 978-3-527-82249-2 (Volume 9) oBook ISBN 978-3-527-82250-8 (Volume 10)

Edited by Klaus Wandelt

Surface and Interface Science Volume 7: Liquid and Biological Interfaces

The Editor Prof. Dr. Klaus Wandelt University of Bonn Institute of Physical and Theoretical Chemistry Germany and University of Wroclaw Institute of Experimental Physics Poland Cover Pictures: Left: Kindly provided by Dr. Paul Mulheran, University of Strathclyde, Glasgow, UK. Middle: Schulz Grafik-Design, Fußgönheim, Germany. Right: Kindly provided by Prof. Klaus Wandelt, University of Bonn, Germany. Cover Design: Klaus Wandelt and Grafik-Design Schulz

All books published by Wiley-VCH are carefully produced. Nevertheless, authors, editors, and publisher do not warrant the information contained in these books, including this book, to be free of errors. Readers are advised to keep in mind that statements, data, illustrations, procedural details or other items may inadvertently be inaccurate. Library of Congress Card No.: applied for British Library Cataloguing-in-Publication Data A catalogue record for this book is available from the British Library. Bibliographic information published by the Deutsche Nationalbibliothek The Deutsche Nationalbibliothek lists this publication in the Deutsche Nationalbibliografie; detailed bibliographic data are available on the Internet at . © 2020 Wiley-VCH Verlag GmbH & Co. KGaA, Boschstr. 12, 69469 Weinheim, Germany All rights reserved (including those of translation into other languages). No part of this book may be reproduced in any form – by photoprinting, microfilm, or any other means – nor transmitted or translated into a machine language without written permission from the publishers. Registered names, trademarks, etc. used in this book, even when not specifically marked as such, are not to be considered unprotected by law. Print ISBN: 978-3-527-41159-7 Typesetting SPi Global, Chennai, India Printing and Binding

Printed on acid-free paper 10 9 8 7 6 5 4 3 2 1

V

Contents Volume 7 About the Editor XIII Preface XV List of Abbreviations XIX

49 49.1 49.2 49.2.1 49.2.2 49.2.3 49.3 49.3.1 49.3.2 49.4 49.4.1 49.4.2 49.4.3 49.4.4 49.4.5 49.4.6 49.5 49.5.1 49.5.2 49.5.3 49.5.4 49.6 49.6.1 49.6.2

Probing Liquid/Solid Interfaces at the Molecular Level 1 Francisco Zaera Introduction 1 Infrared Absorption Spectroscopy 3 Attenuated Total Reflectance Spectroscopy (ATR) 4 Reflection–Absorption Infrared Spectroscopy (RAIRS) 10 Transmission 14 Other Vibrational Spectroscopies 15 Raman Scattering Spectroscopy 17 Sum Frequency Generation 23 Other UV–Vis and Acoustic Techniques 29 UV–Vis Absorption Spectroscopy 29 Fluorescence Emission Spectroscopy 31 Second Harmonic Generation (SHG) 35 Surface Plasmon Resonance (SPR) 40 Ellipsometry 45 Quartz Crystal Microbalance (QCM) 48 X-ray- and Neutron-based Techniques 52 X-ray Absorption (XAS) and Emission (XES) Spectroscopies 53 X-ray Reflectivity and Scattering 58 X-ray Diffraction 62 Neutron Scattering and Diffraction 67 Other Spectroscopies 70 X-ray Photoelectron Spectroscopy (XPS) 70 Nuclear Magnetic Resonance (NMR) 74

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Contents

49.6.3 49.7 49.7.1 49.7.2 49.7.3 49.7.4 49.8 49.8.1 49.8.2 49.8.3 49.9 49.10 49.11

Electron Spin Resonance (ESR and EPR) 80 Optical Microscopies 83 Fluorescence Microscopy 84 Raman Microscopy 90 Other Nonlinear Optical Microscopies 93 Infrared and X-ray Microscopies 94 Scanning Microscopies 96 Scanning Tunneling Microscopy (STM) 98 Scanning Electrochemical Microscopy (SECM) Atomic Force Microscopy (AFM) 106 Optical Scanning Microscopy 110 Electron Microscopies (SEM and TEM) 112 Concluding Remarks 114 Acknowledgment 117 References 118

50

Structure and Dynamics of Solid/Liquid Interfaces 143 Marie-Pierre Gaigeot and Marialore Sulpizi Introduction 143 Brief Review on Methods 145 Ab Initio Molecular Dynamics and Density Functional Theory (DFT) 145 Calculation of Acidity Constants at Solid–Liquid Interfaces from Reversible Proton Insertion/Deletion 149 Theory for VSFG Vibrational Spectroscopy at Solid–Liquid Interfaces 151 VSFG Signal from Velocity–Velocity Correlation Functions 154 Organization of Water at the (0001) α-quartz/Water Neat Interface 156 How Surface Acidities Dictate the Interfacial Water Structural Arrangement 161 Nonlinear VSFG Vibrational Spectroscopy at the Quartz–Water Interface 165 Electrolytes at the Quartz/Water Interface 170 Acidity at Quartz/Water/Electrolyte Interfaces 176 Fluorite/Water Interface, Structures, and VSFG Intertwined 182 Some Perspectives for Future Works 188 Acknowledgements 190 References 190

50.1 50.2 50.2.1 50.2.2 50.2.3 50.2.4 50.3 50.4 50.5 50.6 50.7 50.8 50.9

51 51.1 51.2

102

Adsorption of Proteins and Anti-biofilm Strategies 197 Vincent Humblot and Claire-Marie Pradier Introduction to Biofilms 197 Protein Adsorption, Key Parameters, and Protein Film Description 199

Contents

51.2.1 51.2.2 51.2.3 51.2.4 51.2.5 51.2.6 51.2.7 51.3 51.3.1 51.3.2 51.3.2.1 51.3.2.2 51.3.3 51.4

52 52.1 52.2 52.2.1 52.2.2 52.2.3 52.2.3.1 52.2.3.2 52.2.3.3 52.2.3.4 52.2.3.5 52.2.3.6 52.2.3.7 52.2.3.8 52.2.3.9 52.2.3.10 52.2.3.11 52.2.4 52.2.5 52.2.6 52.3 52.3.1 52.3.1.1 52.3.1.2 52.3.2 52.3.2.1 52.3.2.2

Some General Considerations 199 Possible Protein Surface Types of Interactions 200 Effects of Surface Hydrophobicity/Hydrophilicity 201 Influence of Surface Topography 204 Effect of Surface Charge on Protein Binding 206 Effect of Concentration and pH of the Protein Solution 209 Protein Unfolding/Denaturation on Surfaces 210 Biofilm Prevention, Some Well-Settled or Innovative Strategies 212 Some General Considerations 212 Antimicrobial Action 213 Antimicrobial Peptides from Animals or Microorganisms 213 Enzymes 218 Surface Structuration 220 Conclusion 223 References 225 Liquid Surfaces 229 Gunther Andersson and Harald Morgner Introduction 229 Methods 230 Metastable-Induced Electron Spectroscopy 230 Angle Resolved X-ray Photoelectron Spectroscopy 233 Neutral Impact Collision Ion Scattering Spectroscopy 236 General 236 Elastic Energy Loss 239 Cross Section 240 Neutralization 243 Inelastic Energy Loss 244 Stopping Power and Energy Loss Straggling 245 Thermal Broadening 253 Concentration Depth Profiles 253 Simulations of NICIS Spectra 260 Experimental Equipment 261 Sputtering and Damaging of the Surface 263 Electron Energy Loss Spectroscopy (EELS) 264 Preparing a Liquid Surface Compatible with High Vacuum 265 Methods Not Covered in This Contribution 267 Concentration Depth Profiles 268 Composition of Top Surface Layer 269 From MIES, a Technique with Perfect Surface Sensitivity 269 Extrapolation from NICISS and ARXPS 279 Depth Dependence of Composition at Liquid Surfaces 280 Surfactant Solutions 280 Thin Foam Films 289

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Contents

52.3.2.3 52.3.2.4 52.4 52.4.1 52.4.2 52.4.2.1 52.4.2.2 52.4.2.3 52.4.3 52.4.3.1 52.4.3.2 52.5 52.6

53 53.1 53.2 53.3 53.4 53.4.1 53.4.2

53.4.3 53.5 53.5.1 53.5.2 53.5.3 53.5.4 53.6

54

54.1

Solutions with Inorganic Salts 303 Ionic Liquids 305 Surface Spectroscopy vs. Thermodynamic Concepts 312 Gibbs Equation 312 Evaluation of the Chemical Potential 316 Solutions with Ionic Surfactants 316 Solutions with Nonionic Surfactants 319 POPC/TBABr/HPN: Solution with a Mixture of Nonionic and Ionic Surfactants 322 Bimodal Distribution of Free Energy 324 Solution of Nonionic Surfactant Near CMC: A Challenge to Common Understanding of Micelle Formation 324 Binary Liquid Mixture with Large Difference in Surface Tension 326 3D Surface Topography of Liquid Surfaces 334 Outlook 342 References 343 Surfaces of Ionic Liquids 351 Kaoru Nakajima, Martin Lísal and Kenji Kimura Introduction 351 Principle of Rutherford Backscattering Spectroscopy 354 Experimental Details 356 Surface Structures of Pure Ionic Liquids 358 1-Ethyl-3-Methylimidazolium Bis(trifluoromethanesulfonyl)imide 358 1-Alkyl-3-Methylimidazolium Bis(trifluoromethanesulfonyl)imide ([Cn C1 Im][Tf2 N]): Effect of Alkyl Chain Length on the Surface Structure 364 Effect of Anion Size 371 Surface Structures of Binary Mixtures of Ionic Liquids 373 Equimolar Mixture of [C2 C1 Im][Tf2 N] and [C2 C1 Im][BF4 ]: Comparison Between HR-RBS and MD Simulation 374 Surface Structure of [C4 C1 Im]0.5 [C12 C1 Im]0.5 [Tf2 N]: An Example of Mixtures with Common Anions 379 Systematic Study on the Surface Structures of Binary Mixtures of Ionic Liquids 382 Comparison with Other Techniques 385 Conclusion 386 References 387 Toward Superhydrophobic Surfaces: Controlling Wetting Characteristics 391 Muhammad A. Raza and E. Stefan Kooij Introduction 391

Contents

54.1.1 54.1.2 54.1.3 54.1.4 54.1.4.1 54.1.4.2 54.1.5 54.1.5.1 54.1.5.2 54.1.5.3 54.1.6 54.2 54.2.1 54.2.1.1 54.2.1.2 54.2.1.3 54.2.2 54.2.3 54.2.4 54.2.5 54.2.5.1 54.2.5.2 54.3 54.3.1 54.3.2 54.3.3 54.3.4 54.3.5 54.4 54.4.1 54.4.2 54.4.3 54.5 54.5.1 54.5.1.1 54.5.1.2 54.5.1.3 54.5.2 54.5.3 54.5.4 54.5.5 54.5.5.1 54.5.5.2

Surface Wettability 391 Natural (Super)Hydrophobicity 391 Biomimetic Surfaces 395 Fabrication Methods 395 Top-down 395 Bottom-up 396 Application in Technology 397 Self-cleaning Surfaces 397 Anti-icing/Antifogging Coatings 398 Microdroplet Manipulation 398 Outline of This Chapter 398 Wetting of Isotropic Surfaces 399 Surface Tension 399 Drops of Water 401 Walking on Water 401 Floating Solid Objects 402 Wetting Regimes 402 Static Contact Angle 403 Dynamic Contact Angles 405 Characterization Techniques 406 Static Contact Angle Techniques 407 Dynamic Contact Angles 408 Chemical Patterning and Morphological Structuring 409 Cassie–Baxter Model 409 Wenzel Model 411 Superhydrophobicity 411 Metastable Wetting States 413 Hierarchical Roughness 415 Dynamic Wetting Behavior 418 Slip Length 419 Contact Angle Hysteresis 420 Impinging Droplets 421 Novel Applications and New Horizons 424 Novel Applications of Superhydrophobic Surfaces 425 Anticorrosion Coatings 425 Drag Reduction 425 Oil–Water Separation 425 Omniphobic Surfaces 426 Slippery Liquid-Infused Porous Surfaces (SLIPS) 429 Recrystallization of Natural Epicuticular Waxes 431 Directional Wetting 432 Anisotropic Wettability 434 Wettability Gradients 436 References 438

IX

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Contents

55

55.1 55.2 55.3 55.4 55.5 55.5.1 55.5.2 55.5.2.1 55.5.2.2 55.5.2.3 55.5.2.4 55.6 55.6.1 55.6.2 55.6.3 55.6.4 55.7 55.8 55.8.1 55.8.2 55.8.2.1 55.8.2.2 55.8.3 55.9

Cell-Penetrating Peptides Targeting and Distorting Biological Membranes 441 Corina Ciobanasu and Ulrich Kubitscheck Introduction 441 Definition 442 Discovery of CPPs 442 Classification of CPPs 443 Modes of Action 444 Endocytosis 445 Membrane Translocation 446 Types of Mechanisms 446 Cationic Peptides 447 Amphipathic Peptides 447 Role of Membrane Composition and Lipid Topology 448 Application Aspects 449 Clinical Application of CPPs 449 Targeting CPPs 450 Cell-Penetrating Homing Peptides 451 Toxicity 452 Focus on TAT 453 Internalization of TAT Peptides 453 Experimental Results on Cellular Systems 453 TAT Peptide and Its Interaction with Model Membranes 455 Membrane Binding 455 Membrane Translocation 457 TAT Peptides for the Delivery of Therapeutic Agents 461 Summary and Conclusions 463 References 463

XI

About the Editor

Klaus Wandelt is currently Professor Emeritus at the University of Bonn, Germany, where he was also Director of the Institute of Physical and Theoretical Chemistry until 2010. He received his PhD on electron spectroscopy of alloy surfaces in 1975 in München; spent a postdoctoral period at the IBM Research Laboratory in San Jose, California, in 1976/1977; and qualified as a professor in 1981 in München. Since then his research focuses on fundamental aspects of the physics and chemistry of metal surfaces under ultrahigh vacuum conditions and in electrolytes, on the atomic structure of amorphous materials, and more recently on processes at surfaces of plants. Professor Wandelt was visiting scientist at the University of Caracas, Venezuela; the University of Hefei, China; the University of Newcastle, Australia; and the University of California, Berkeley, and he was guest professor at the University of Messina, the University of Padua, and the University of Rome Tor Vergata, Italy; the University of Linz and the Technical University of Vienna, Austria; and the University of Wroclaw, Poland. He chaired the surface physics divisions of the German and European Physical Society as well as of the International Union of Vacuum Science Techniques and Applications, has organized numerous workshops and conferences, and was editor of journals, conference proceedings, and books.

XIII

Preface Surfaces and Interfaces: A “Divine Gift”

For decades books, book chapters, theses of generations of PhD students, and, more recently, also presentations on the Internet about subjects of surface and interface science, i.e. the research of physical and chemical properties and processes at solid surfaces, often start with the quotation God made the bulk, surfaces were invented by the devil attributed to Wolfgang Pauli, Nobel Prize Laureate in Physics 1945 [1]. Of course, quotes like this are to be understood from the respective era; a systematic experimental “surface science” did not exist at that time. A description of the field ion microscope (FIM), which for the first time enabled the visualization of individual surface atoms, was published only a few years later by Erwin W. Müller [2]. Now, nearly seventy years later, our profound scientific understanding of the fascinating peculiarities of solid surfaces presented in Volumes 1–8 of this series of books and their fundamental importance for so many vital technological areas emphasized below, and in part addressed in Volumes 9 and 10, make the “invention of surfaces” truly a gift from God. Surfaces and interfaces enrich our world in a double sense. On the one hand, they structure our world and make it so diverse and beautiful. On the other hand, surfaces and interfaces are locations of gradients, which drive spontaneous and mancontrolled processes. These processes change our world and, therefore, our all living conditions in a fundamental way. On the one hand, heterogeneous catalysis of chemical reactions at solid surfaces has enabled the large-scale production of (i) fertilizers and pesticides for agriculture, (ii) a vast variety of plastic commodities, and (iii) pharmaceuticals for medicine and the “health industry.” These products (i) have contributed to a better food supply of the world population and thereby its rapid growth, (ii) appear no longer indispensible in our daily life, and (iii) help to fight diseases and save lives, if produced and applied responsibly and sustainably. On the other hand, besides the growing world population itself, the profit-driven excess production of these products and the accompanying ruthless exploitation of our natural resources are

XIV

Preface

an increasing thread for humanity’s survival. The excess production and thoughtless use and uncontrolled disposal of these products disturbs natural equilibria and leads to an increasing contamination of soil and groundwater, pollution of the atmosphere and oceans, and a weakening or failure of the natural immune systems. Insufficient or neglected air pollution control is most likely a reason for the obvious “global warming.” The concomitant rise of the sea level will cause dramatic erosion processes at ocean shores and dikes, the largest-scale solid/liquid interfaces. The consequent shrinkage of man’s living space will, at best, cause a process of mass migration of people. The physics of interfaces and low-dimensional systems has opened the door to modern electronic devices that are revolutionizing the collection, processing, and availability of information, which not only changes our own communication behavior but has also created the vision of the “Internet of things (IoT)” in which people and mobile and immobile physical objects including buildings communicate within a single and common network with each other, which in the opinion of some people will change the world for the better, while others fear that man may lose control. Biological processes function via processes at and through interfaces of membranes, which in turn can be influenced by traces of drugs. It is, thus, not only a great scientific challenge to investigate the properties and processes at surfaces and interfaces, but also of vital importance for mankind’s future, provided we make wise use of this knowledge. Although theoretical predictions about properties of surfaces as well as intuitive models of surface processes existed much earlier, modern experimental surface science started by now about 50 years ago with the commercial availability of ultrahigh vacuum (UHV) technology. Under UHV conditions, it became possible to prepare clean surfaces and to develop and apply a growing number of “surface-sensitive” methods based on particle beams. Unlike photon beams, for instance, used in X-ray crystallography, electron, ion, and atom beams interact only with the outermost layers of a solid and therefore provide information pertaining only to the surface. While in the beginning, practical surface investigations were concentrated on the changes of surface properties due to exposure to gases or vapors, it soon turned out that the properties of the bare surfaces themselves pose a lot of scientific surprises. Now 50 years later, the so-called reductionist “surface science approach,” that is, the use of well-defined, clean single-crystal surfaces under UHV conditions, enables a microscopic and spectroscopic characterization of these bare surfaces atom by atom. The overwhelming achievements of this research may ultimately be summarized by the general statement: Surfaces are a different state of matter! Moreover, nowadays, it is possible not only to study the interaction of individual atoms and molecules with a surface but also to manipulate them on the surface according to our will. The present series of books aims not only at giving a broad overview of the present state of understanding of the basic physics and chemistry at surface and interfaces but also at highlighting a number of technological applications that rely on the established knowledge about surfaces, like thin film and nanotechnology, highly integrated electronics, heterogeneous catalysis in gaseous and liquid phases,

Preface

electrochemical energy conversion and storage, and bio-functionalization of inorganic materials, to name a few. The intention of this series of books is, thus, not only to give an introduction for those who enter the field of surface research but also to provide an overview for those whose work needs conceptual and analytical input from surface science. According to the original concept, this book series should comprise six volumes. The first volume was planned to describe “bare surfaces and methods,” that is, all the physical properties of clean surfaces of elemental and composite solids as well as the most relevant surface analytical methods. However, it turned out immediately that an adequate treatment of just these topics exceeded by far the reasonable size of a single volume and instead filled three volumes, extending the number of intended volumes to eight. But also the material for Volumes 7 and 8 went beyond the limits of one book each, so, after all, the series comprises 10 volumes now: Volume 1: Concepts and Methods Volume 2: Properties of Elemental Surfaces Volume 3: Properties of Composite Surfaces: Alloys, Compounds, Semiconductors Volume 4: Solid/Solid Interfaces and Thin Films Volume 5: Solid/Gas Interfaces I Volume 6: Solid/Gas Interfaces II Volume 7: Liquid and Biological Interfaces Volume 8: Interfacial Electrochemistry Volume 9: Applications of Surface Science I Volume 10: Applications of Surface Science II. The first eight volumes emphasize the basic insights into the physics and chemistry at surfaces and interfaces and the most important experimental and theoretical methods, which led to these results. The methods are grouped according to the applied probe, namely, electrons, ions, photons, and proximity probes, and are described to an extent to give the reader enough confidence in “what surface scientists are able to do nowadays”; more detailed descriptions of these methods can be found in the existing specialized literature. The last two volumes present a selection of some daily phenomena and technological applications, which depend on and arise from surface-specific properties and processes. The vast material is laid out in 80 chapters and is structured according to increasing complexity of the subject in question. Each chapter is written by experts in the respective field and is supposed to start with an introduction of the basic phenomenon, to develop the problem from simple to more specific examples, and to end, if possible, with the identification of open questions and challenges for future research. This intended strategy “from simple to complex” is graphically expressed by the veil rising from left to right on all book covers. One person alone could hardly ever have written such an extensive and divers oeuvre. I am extraordinarily thankful to all authors who have contributed to this series of books. I am also very grateful to the publisher, namely, Ulrike Werner, Nina Stadthaus, Dr. Frank Weinreich, and Dr. Martin Preuss at Wiley, for their continuous

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support and their understanding and flexibility to adapt the original concept of the whole project to “new circumstances” and to agree with the expansion from 6 to 10 volumes. Altogether it took 12 years to realize this project, and obviously a great deal of patience and persistence was necessary to complete it, patience of the authors and the publisher with the editor, but also persistence of the editor and his patience with some authors. The result of this joint effort of all three parties is now in the hands of the critical readers. After all, surfaces and interfaces are a “divine gift” and as such by no means fully fathomed. Bonn, Wroclaw January 11, 2019

References 1. Quoted in: Jamtveit, B. and Meakin, P.

(eds.) (1999). Growth, Dissolution and Pattern Formation in Geosystems, 291. Kluwer Academic Publishers. 2. Müller, E.W. (1951). Z. Phys. 131: 136.

Klaus Wandelt

XIX

List of Abbreviations 1D-PSD AA AFM AMP APTMS AR-XPS ATR Bac7 BSA CARS CCD CCS CDI CFS cfu CHO CK2 CLSM CPMAS CPP DMPO DMSO DOPC DOPE DOPS DPPC DPPS DRS EDX EELS EGFP EPR EPR

one-dimensional position-sensitive detector amino acid atomic force microscopy antimicrobial peptides (3-aminopropyl)trimethoxysilane angle-resolved X-ray photoelectron spectroscopy attenuated total reflectance bactenecin-7 bovine serum albumin coherent anti-Stokes Raman scattering charge-coupled device cell-containing saliva coherent diffraction imaging cell-free saliva colonies forming unit chinese hamster ovary casein kinase 2 confocal laser scanning microscopy cross-polarization magic angle spinning cell-penetrating peptides 5,5-dimethyl-1-pyrroline-N-oxide dimethyl sulfoxide 1,2-dioleoyl-sn-glycero-3-phosphocholine 1,2-dioleoyl-sn-glycero-3-phosphoethanolamine 1,2-dioleoyl-sn-glycero-3-phospho-L-serine 1,2-dipalmitoyl-sn-glycero-3-phosphocholine 1,2-dipalmitoyl-sn-glycero-3-phospho-L-serine direct recoil spectroscopy energy-dispersive X-ray spectroscopy electron energy loss spectroscopy enhanced green fluorescent protein electron paramagnetic spectroscopy enhanced permeability and retention

XX

List of Abbreviations

EPS ER ESR EXAFS FDA FLIM FMOC FRET FT FTIR GA GdH GFP GISANS GISAXS GIXD GSH GUV HEWL HIV HR-ERDA HR-RBS HSA IFN-β IL-24 IPE IR IRRAS ITC Ld LEIS L-NPU-Si Lo LUV MAP MAS MBC MCAO MD MIC MIES MMP2 MOF MRI NAG

extra polymeric substances endoplasmic reticulum electron spin resonance extended X-ray absorption fine structure food and drug administration fluorescence lifetime imaging fluorenylmethyloxycarbonyl Förster (or fluorescence) resonance energy transfer Fourier transformation Fourier-transform infrared glutaraldehyde glutamate dehydrogenase enzyme green fluorescent protein grazing-incidence small-angle neutron scattering grazing-incidence small-angle X-ray scattering grazing-incidence X-ray diffraction glutathione-SH giant unilamellar vesicles lysozyme from hen egg white human immunodeficiency virus high-resolution elastic recoil detection analysis high-resolution Rutherford backscattering spectroscopy human serum albumin interferon beta interleukin-24 internal photoemission infrared infrared reflection–absorption spectroscopy isothermal calorimetry liquid-disordered low-energy ion scattering N-L-phenylalaninoyl, 11-undecyl-silicon liquid-ordered large unilamellar vesicles model amphipathic peptide magic angle spinning minimal bactericidal concentration middle cerebral artery occlusion molecular dynamics minimal inhibitory concentration metastable impact electron spectroscopy metalloprotease 2 metal–organic frameworks magnetic resonance imaging N-acetylglucosamine

List of Abbreviations

NAM Nc–Lu–OEP NEA NEXAFS NHS NICISS NIR NLP NMR NOE NR OER PBS PC PCA PDMS PE PEG PEI PEM PG PKC PM-IRRAS PMO POPC PPII PS PTD QCM RAIRS RBS RIXS R-TAT SAM SANS SAXS SE-CARS SECM SEM SERS SFG SHG siRNA

N-acetylmuramic acid naphtalocyanine–lutetium–octaethylporphyrin 1-(1-naphthyl)ethylamine near-edge X-ray absorption fine structure N-hydroxysuccinimide neutral impact collision ion scattering spectroscopy near-infrared N-lauroyl phenylalanine nuclear magnetic resonance nuclear Overhauser effect neutron reflectivity oxygen evolution reaction phosphate buffer salt phosphatidylcholine protein-fragment complementation assay polydimethylsiloxane phosphatidylethanolamine polyethylene glycol polyethyleneimine photoelastic modulator phosphatidylglycerol protein kinase C polarization-modulation infrared reflection–absorption spectroscopy phosphorodiamidate morpholino oligomers 1-oleoyl, 2-palmitoyl-sn-glycero-3-phosphocholine polyproline II phosphatidylserine protein transduction domains quartz crystal microbalance reflection–absorption infrared spectroscopy Rutherford backscattering spectroscopy resonant inelastic X-ray scattering rhodamine-TAT self-assembled monolayers small-angle neutron scattering small-angle X-ray scattering surface-enhanced coherent anti-Stokes Raman scattering scanning electrochemical microscopy scanning electron microscopy surface-enhanced Raman spectroscopy sum frequency generation second harmonic generation small interfering RNA

XXI

XXII

List of Abbreviations

SNIFTIRS SNOM SPR SS SSBD STM SUV SXRD TAT TCP TEM TERS THF TIFR TNF-α TNT ToF-SIMS TP10 TXM UHV XAFS XANES XAS XES XPS XR XRD XRR

subtractively normalized interfacial Fourier transform infrared spectroscopy scanning near-field optical microscopy surface plasmon resonance stainless steel silicon surface barrier detector scanning tunneling microscopy small unilamellar vesicles surface X-ray diffraction trans-activator of transcription trichloro-2-pyridinol transmission electron microscopy tip-enhanced Raman spectroscopy tetrahydrofuran total internal reflection tumor necrosis factor-α trinitrotoluene time-of-flight secondary ion mass spectrometry transportan 10 transmission X-ray microscopy ultra-high-vacuum X-ray absorption fine structure X-ray absorption near-edge spectroscopy X-ray absorption spectroscopy X-ray emission spectroscopy X-ray photoelectron spectroscopy X-ray reflectivity X-ray diffraction X-ray reflectivity

1

49 Probing Liquid/Solid Interfaces at the Molecular Level Francisco Zaera

49.1 Introduction

The ability to obtain a molecular-level understanding of the chemistry that takes place at liquid/solid interfaces is the key to the development and improvement of many chemical and biological systems and is arguably the next big challenge for the surface science community [1]. Liquid/solid interfaces are certainly ubiquitous in nature. The biology of life, for instance, relies heavily on the uptake of molecules from aqueous phases onto surfaces, either onto “soft” bilayers or other membranes or onto “hard” surfaces such as bones, cartilages, or teeth. The phenomena related to self-assembly, including the formation of micelles and the use of surfactants, are also based on chemistry at liquid/solid interfaces. Environmental issues involving liquid/solid interfaces go from the chemistry of aerosols to the purification of ground water. The evolution of minerals is greatly affected by their interactions with liquid solutions. The corrosion of many metals is a common problem, and other electrochemical processes are relevant to the development of batteries, fuel cells, catalysts, and many other industrial applications. Lubrication and other tribological problems rely on the use of liquids to improve the performance of solid moving parts. Multiple synthetic processes in industry involve liquid phases and require the use of solid catalysts. A variety of sensors, including a large number of bioassays used nowadays for the analysis of biological samples in medicine, center on chemistry at liquid/solid interfaces. It is not easy to investigate the chemistry of liquid/solid interfaces at a molecular level. Already, the study of the chemistry of any interface is hampered by the need to discriminate between the few atoms at that interface and the much larger number of atoms that exist in the two bulk phases involved. A number of modern surfacesensitive techniques were developed in the late twentieth century to overcome this obstacle, and with those great advances were made on the understanding of the chemistry that occurs on solid surfaces [2–4]. However, many of those techniques Adapted with permission from Chem. Rev. 2912, 112, 5, 2920–2986, https://doi.org/10.1021/ cr2002068; Copyright (2012) American Chemical Society. Surface and Interface Science: Liquid and Biological Interfaces, First Edition. Edited by Klaus Wandelt. © 2020 Wiley-VCH Verlag GmbH & Co. KGaA. Published 2020 by Wiley-VCH Verlag GmbH & Co. KGaA.

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49 Probing Liquid/Solid Interfaces at the Molecular Level

rely on the use of particles such as electrons, ions, or atoms, which work best under a vacuum environment. A second generation of setups has been developed more recently to extend the use of surface probes to cases where the solid is immersed in a gaseous environment [5, 6], and many areas of research have benefited from these advances, heterogeneous catalysis in particular, but systems with interfaces buried between two condensed phases, between a liquid and a solid in particular, can still not be easily probed with the standard surface science approach developed for operation in vacuum or a gas phase. New approaches are needed to study liquid/solid interfaces at a molecular level. Some electron-based surface science techniques are being adapted to probe liquid/solid interfaces by minimizing the paths that the probing particles need to travel through the liquid phase. More promising perhaps is the boon seen in the use of techniques based on light or other electromagnetic radiation for surface analysis, as those are less affected by condensed matter. Optical analytical techniques are typically not surface sensitive, hence their sparse use in surface science problems in the past, but can be made so by using specific setups or by taking advantage of the uniqueness of the surface chemistry to be investigated. Clearly, the study of liquid/solid interfaces is difficult, but a variety of tools are being developed to rise to the challenge. In this review, which is an updated version of previous articles [1, 7] and complements other recent reviews on this subject [8, 9], we take a broad interpretation of what constitutes a solid surface and include examples on the study of liquid/solid interfaces involving not only conventional solids but also nanoparticles and membranes such as lipid bilayers and other self-assembled layers used to emulate biological systems. The liquid phases in our discussions include regular solutions, neat liquids, melts, ionic liquids, and liquid crystals. We start with a review of the use of infrared (IR) absorption spectroscopy to the interrogation of liquid/solid interfaces, perhaps the technique most used for this purpose, and continue with an overview of other vibrational spectroscopies, in particular, Raman scattering spectroscopy and sum frequency generation (SFG). Next, we introduce the use of UV–vis spectroscopies, which are employed mainly to obtain electronic information of adsorbates at the interface but can also be employed to quantify coverages. Acoustic-based techniques such as quartz crystal microbalances (QCMs), which are also used for the latter application, are also mentioned. The Section 49.5 focuses on the use of X-rays and neutrons, both in spectroscopic studies, to extract electronic information about the liquid/solid interface and, in scattering and diffraction modes, to acquire structural details of the interface. The potential use of techniques such as X-ray photoelectron spectroscopy (XPS) and nuclear magnetic resonance (NMR) and electron spin resonance (ESR) spectroscopies for the characterization of liquid/solid interfaces is briefly surveyed. The last chapters are dedicated to the discussion of the approaches available for the acquisition of spatially resolved information on liquid/solid interfaces, including optical and scanning microscopies. We finish our review with some concluding remarks where we provide a brief guideline on the criteria to select the most appropriate techniques for the study of a specific system and give our own

49.2 Infrared Absorption Spectroscopy

assessment of the status and future of the field of surface science as it pertains to liquid/solid interfaces. 49.2 Infrared Absorption Spectroscopy

Perhaps the technique most commonly used to date for the molecular-level characterization of liquid/solid interfaces is IR absorption spectroscopy (see also Chapter 3.4.1 in Volume 1). In IR absorption spectroscopy, the sample being studied is exposed to a broadband IR beam, typically covering the so-called midrange that encompasses wave numbers between approximately 400 and 4000 cm−1 , and the absorption of that light is analyzed to identify the molecular vibrations that the light excites [10]. Modern IR spectrometers are based on the use of a Michelson interferometer, where the full IR beam is split, each half is made to travel a separate path, and the two are recombined again before steering the full reconstituted beam into the sample. By varying the difference in path length traveled by the two half-beams with time, by scanning the mirror used in the path of one of them, an oscillating interference pattern is developed for each wavelength; a Fourier transformation (FT) of the intensity of the beam vs. time provides a plot of light intensity vs. wavelength. The introduction of this FT-IR approach greatly advanced the use of IR absorption spectroscopy because the new technique enhances performance in two ways: (i) the information on the intensity of all wavelengths of light is collected at once; and (ii) the throughput of light is not limited by entrance and exit slits, as is the case with the old monochromator-based spectrometers. Infrared absorption spectroscopy is quite versatile, and with the advent of FT instruments, it has become quite easy and cheap to implement; nowadays, FT-IR instruments are found in almost all analytical chemistry laboratories worldwide. IR absorption spectroscopy is also ideal for the study of chemical problems, including those involving surfaces [11], because it provides information on the vibrational details of molecular structures, which are quite sensitive to local chemical environments [12]. On the negative side, IR absorption spectroscopy is a nonzero background technique; that is, full signal is detected when there is no light absorption at all, a fact that sets a limit on its dynamic range and with that its sensitivity. This is particularly critical in the study of interfacial systems, where the size of the sample is often severely limited (there are only ∼1013 –1015 molecules/cm2 in a typical saturated monolayer of adsorbates). Thankfully, the recent development of highly sensitive, low-noise detectors has minimized this problem. In addition, IR absorption spectroscopy, like most optical analytical techniques, is not intrinsically surface sensitive, although it can be made so in certain circumstances, as discussed in more detail below. Infrared radiation is absorbed by almost all solids and liquids, which means that it is not always easy to reach the interface of interest, especially when dealing with liquid/solid boundaries. Fortunately, great flexibility is provided to address that problem because of the availability of several arrangements for the performance of IR absorption spectroscopy, which

3

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49 Probing Liquid/Solid Interfaces at the Molecular Level

Liquid Adsorbates Solid film

(a) Attenuated total reflectance (ATR)

Prism

Prism (b) Reflection absorption (RAIRS, IRRAS)

Liquid Adsorbates Solid film

(c) Transmission

Prism

Liquid

Adsorbates

Solid nanoparticles

Mirror Figure 49.1 Schematic representation of the different modes available for infrared (IR) absorption spectroscopy studies of liquid/solid interfaces. (a) In attenuated total reflectance (ATR) mode, the IR beam travels through a prism while the evanescence wave that extends toward the outside is used to probe the chemical system of interest, typically molecules adsorbed on the prism itself or on a thin film grown on top. (b) In absorption–reflection (RAIRS) mode, the beam is bounced directly off the solid

substrate where adsorption takes place, and on metals, the surface selection rule that establishes that only p-polarized light can be absorbed at the surface is used to discriminate between molecules on the surface vs. in solution. (c) Transmission IR absorption spectroscopy can also be performed with the RAIRS setup if the metal is used as a mirror and the solid, typically a powder or another type of nanoparticle, is suspended in solution.

include attenuated total reflectance (ATR), reflection–absorption infrared spectroscopy (RAIRS), and transmission (Figure 49.1). In the next few subsections, an overview of the use of these arrangements for the study of liquid/solid interfaces is reviewed. 49.2.1 Attenuated Total Reflectance Spectroscopy (ATR)

Perhaps the most established IR absorption spectroscopy setup for the study of liquid/solid interfaces is ATR (Figure 49.1a) [13–15]. ATR takes advantage of the total internal reflection (TIFR) of light that occurs when the beam impinges on an interface at a certain angle; the angle at which total reflection occurs is determined by the difference in refractive index between the two materials forming that interface. Such reflection results in the generation of an evanescent wave that penetrates

49.2 Infrared Absorption Spectroscopy

into the other side of the interface, to a depth of between 0.5 and 2 μm depending on the wavelength of the light, the angle of incidence, and the relevant indices of refraction. Thanks to this property, an IR beam can be made to travel through the inside of an optical element, a prism typically made out of silicon, germanium, or another high-refractive-index element, while using its outer surface to carry out the chemistry of interest. The absorption of light from the evanescence wave by the chemical system being probed can be then detected on the beam collected at the exit of the prism. Many research projects based on this approach have involved chemistry on the surface of the prism itself, but others have being directed at the study of other systems such as metal thin films, which may be deposited on the surface of the prism, or powders or nanoparticles, which may be placed in a suspension within the liquid right above the ATR optical element. ATR IR absorption spectroscopy has been extensively used for the study of problems related to general adsorption, mineral chemistry, environmental chemistry, biology, surfactants, sensors, electrochemistry, catalysis, and materials science. Recent applications in catalysis include the detection of oxalate intermediates during the photocatalytic decomposition of amino acids over Au/TiO2 catalysts [16], the determination of photoelectrochemical water oxidation intermediates on hematite (α-Fe2 O3 ) electrode surfaces [17], and the study of aqueous-phase hydroxyacetone reforming catalytic reactions at high temperatures (500 K) and pressures (30 bar) [18]. Time resolution was added to the ATR to obtain compelling evidence for the surface hydroperoxide reaction intermediate, by recording the O–O vibrational mode at 830 cm−1 , during water photooxidation at IrO2 nanoclusters in aqueous solutions [19], and two more surface intermediates, a three-electron surface superoxide, and an oxo-Co(IV) species, on Co3 O4 catalysts [20]. The simplest studies of liquid/solid interfaces with ATR-IR have been those on the characterization of adsorption processes. This application already has a relatively long history, particularly in terms of the quantitation of the uptake of adsorbates to estimate adsorption isotherms [21, 22]. As with most optical spectroscopies, IR absorption can be linearly related to concentration, and chemical specificity can be obtained by following a particular vibrational feature in the spectra associated with the compound of interest. One example illustrating this type of application is the ATR-IR study of the adsorption of ethyl acetate and 2-propanol on silica sol–gel films in contact with n-heptane solutions, which was focused on extracting information related to solute retention and elution in normal-phase chromatography [23]. The nonlinear isothermal behavior observed in those systems was explained by differences in the adsorption behavior on silanol groups free on the surface vs. covered with water. ATR-IR has also been recently implemented to monitor the concentrations of solutes in solutions flowing through the microfluidic channels of lab-in-a-chip systems [24]. Spectra could be acquired in those cases for the chemical characterization of the system at the same time as concentration measurements were carried out, affording independent measurements of concentrations of solutes with distinct spectral features in mixed solutions.

5

49 Probing Liquid/Solid Interfaces at the Molecular Level 5.0 × 10–3 4.5

D

Absorbance

Quasi-racemate in solution

L D L D

L-NLP

4.0

+ D-d23-NLP

3.5 3.0 D-d23-NLP

2.5 2.0

L

D

1.5

Evanescent L N-Lauroyl phenylalanine (NLP) wave

L-NLP

1.0

3.0 × 10–3 L L

L L

D L

L

D L

L

L L

L L

L-d23-NLP

L

L L

N-L-Phenylalaninoyl, 11-undecyl (L-NPU)

2.5

L

Covalently attached chiral film

IR beam

Absorbance

6

L-d23-NLP

+ D-NLP

2.0 1.5 1.0 0.5

D-NLP

0.0 3200 3000 2800 2600 2400 2200 2000 1800

Internal reflection element Figure 49.2 Example of the use of infrared absorption spectroscopy in ATR mode to probe liquid/solid interfaces [25]. In this study, the enantioselective uptake of Nlauroyl-phenylalanine (NLP), a surfactant, on a silicon surface modified with a chiral N-L-phenylalaninoyl-11-undecyl (L-NPU) layer is tested. Panel (a) schematically shows the experimental setup, in which an ATR prism derivatized with an L-NPU monolayer is exposed to a quasi-racemate solution of NLP in carbon tetrachloride in which one NLP enantiomer (the D isomer in the right top panel, the L form in the right bottom

Wavenumber (cm–1)

panel) was fully deuterated to differentiate it from its normal hydrogen-labeled NLP counterpart (L in the top, D in the bottom). Preferential adsorption of the L-NLP enantiomer is evidenced by the larger signals for the C–H (C–D) stretching modes in L-NLP (D-NLP) seen around 3000 (2200) cm−1 in the IR spectra compared to those for the C–D (C–H) peaks around 2200 (3000) cm−1 because of D-d23 -NLP (L-d23 -NLP) in the data on the right top (right bottom) panel. (Source: Häbich et al. 2010 [25]. Copyright 2010. Reprinted with permission of American Chemical Society.)

More sophisticated adsorption uptake ATR-IR experiments can be designed by taking advantage of unique characteristics of the surface chemistry to be investigated. Figure 49.2 shows key results from an example where the enantioselectivity of adsorption was tested on chirally modified surfaces [25]. Specifically, the enantioselective adsorption of the chiral N-lauroyl phenylalanine (NLP) surfactant onto a chiral monolayer consisting of N-L-phenylalaninoyl, 11-undecyl-silicon (L-NPU-Si) was demonstrated by using deuterium labeling in one of the enantiomers of the pseudoracemic NLP mixture used in the solution. Another very different recent application where the uniqueness of the chemistry involved was used in the experimental design was for the characterization of the electronic properties of a photocatalyst [26]. In that case, the photogeneration of charge carries in a Pt/GaN photocatalyst developed for hydrogen evolution under light irradiation was investigated by following the vibrational frequency of adsorbed carbon monoxide,

49.2 Infrared Absorption Spectroscopy

used here as a probe: it was determined that after irradiation, the C–O stretching frequency first shifts to higher values, indicating that the Fermi level of the metal particles is positively shifted by the photogenerated holes, but then reverts toward lower frequencies as hydrogen is produced. This is a clever way of using local bonding information as a proxy for changes in local electronic properties. ATR-IR can also be used to determine adsorption geometries [15]. This has often been reported in connection with the characterization of adsorbed surfactants and self-assembled monolayers (SAMs), but the same approach can be easily extended to other systems. In fact, when thin metal films are added to the ATR surface, the electric field distribution of the evanescent wave at the substrate–sample interface is modified, so only the p-polarized light, which is oriented perpendicular to the surface, penetrates into the adsorbed phase [27]. This effectively gives rise to a surface selection rule similar to that more usually associated with reflection–absorption experiments (see Section 49.2.2) by which only IR vibrations with dynamic dipoles with a component perpendicular to the plane of the surface are detected [28–31]. An example on how this selection rule can be used to determine adsorption geometry is provided by the in situ ATR-IR report on the average orientation of the methylene tail of cetyltrimethylammonium bromide adsorbed from solution onto a silica surface as a function of coverage and pH, where it was found that the equilibrium orientation of the surfactant was with a larger angle away from the plane of the surface at higher pH values [32]. This was interpreted as due to an increase in packing density with increasing surface excess. No preferred orientation of the surfactant was observed during the initial stages of adsorption, but a rapid reorientation to a direction normal to the surface was observed after higher surface excesses, as time evolved. In another example, the adsorption of a related surfactant, dodecyltrimethylammonium bromide, was followed as a function of uptake to correlate structural transitions with changes in the properties of the interface determined from contact angle, zeta potential, and force measurements [33]. The data indicated that initially the molecules adsorb in random orientations, but that they then form hemi-micelles (two-dimensional aggregates) above a certain critical concentration, presumably because of hydrophobic association between the surfactant tails, and rearrange again into randomly oriented spherical aggregates at even higher coverages. In terms of mineral and environmental chemistry, Cwiertny et al. provided an example where the adsorption of oxalate on α-FeOOH was used to represent the way the surfaces of mineral aerosol dust dissolve by complexation with organics in solution [34]. Particularly interesting in that study was the identification of a possible dependence of the dissolution on particle size and/or surface orientation. In another report, Strongin’s group characterized the photodissolution of ferrihydrite in the presence of oxalic acid [35]. They found that the oxalate displaces the original carbonate endings on the surface and slowly produces a new type of carbonate. In a third example involving ionic species, Lefèvre et al. followed the IR absorption frequencies of sulfates, phosphates, and carbonates adsorbed on metal oxy-hydroxides to determine bonding modes and to distinguish between outer sphere and inner sphere complexes [36]. These examples illustrate the great chemical specificity of IR

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49 Probing Liquid/Solid Interfaces at the Molecular Level

absorption spectroscopy, which can be used to obtain information about the chemical nature of new adsorbed species. Another interesting application of ATR-IR is in the study of systems of biological interest. In one example related to the biocompatibility of proteins and amino acids with materials used in prostheses, an in situ ATR-IR study of the adsorption of glutamic acid on titania led to the identification of fairly complex chemistry, with several spectroscopically distinct structures and maximized adsorption at pHs where electrostatic interactions between the surface and adsorbate are unfavorable [37]. In another case, the selection rules described above were used to contrast the adsorption geometry of bovine serum albumin on hydrophilic naked silicon oxide/silicon surfaces vs. hydrophobic lipid-covered substrates [38]. On the original silicon surfaces, the protein was found to adsorb in a side-on geometry but with some flattening because of either unfolding or denaturation, whereas on the hydrophobic surface, the adsorption was shown to lead to a film about half as thick but with the same contact angle, indicating more protein unfolding. A third, more recent example of the use of ATR-IR in biological chemistry has shown how the choice of buffer solution can affect protein uptake on solid surfaces in terms of both adsorption kinetics and the evolution of the secondary structure [39]. The adsorption of most proteins exhibits a short period of rapid adsorption involving large secondary structural changes that is followed by a long period of quasi-linear adsorption, but competitive adsorption between the buffer and the protein sometimes depresses the adsorption in the latter kinetic region. One last biological example is that of the study of the setting reaction of calcium phosphate cements, used as bone substitutes, in aqueous citric acid solutions. Citric acid is typically used as a retardant, to increase setting times and mechanical strength [40]. The ATR-IR data in this case provided evidence for the formation of an intermediate dicalcium phosphate–citrate complex, the concentration of which increases with the concentration of citric acid in solution. It was proposed that the reduction in strength of the final material may be related to the formation of that intermediate at the early stages of setting of the cement. All together, these examples show how ATR-IR absorption spectroscopy can be used to identify new surface species and surface chemistry, establish adsorbate structural information, and collect adsorption kinetics at the liquid/solid interfaces of biologically relevant systems. ATR-IR can also provide information on the chemistry that occurs at the surface of electrodes. For this, the electrodes are typically deposited as thin films or pressed in close contact against the ATR prism. In a recent example, the adsorption of adenine on gold electrodes was proposed, based on such IR studies, to involve two nitrogen atoms, a sp3 -hybridized amino nitrogen and the N7 atom of the five-member ring, and to require a tilted geometry [41]. In another study, it was concluded that during the oxidation of methanol on Pt–Ru electrodes, the platinum sites are responsible for the dehydrogenation of adsorbed methanol to CO, whereas the ruthenium sites adsorb water preferentially and promote oxidation between the CO and H2 O adsorbed species [42]. Yet, another example of ATR-IR studies of electrochemical systems is that of the electrooxidation of ethanol on Pt electrodes, where the IR spectra indicated the formation of acetaldehyde and/or acetyl reaction intermediates on

49.2 Infrared Absorption Spectroscopy

the surface and provided a correlation between the rate of acetate formation and the current seen in voltammetry [43]. Again, these examples illustrate the power of ATR-IR as a way to identify surface species and their adsorption sites and geometries and as a means to isolate elementary steps in the mechanism and measure the kinetics of surface electrochemical reactions. ATR-IR can also be set up to study adsorption on powders and nanoparticles. Typically, this is done by placing a suspension of the particles directly above the surface of the prism used as the optical element. Commercial devices are available for the characterization of such samples under flowing liquids and/or while heating to moderate temperatures. Being able to study powders with ATR-IR is quite useful in catalysis because heterogeneous catalysts are typically composed of metal particles or other solid-active phases finely dispersed on high-surface-area supports. An illustration of this type of application is that from the group of Williams, who have reported on the adsorption of species such as CO and formaldehyde on Pt/Al2 O3 catalysts from aqueous and ethanolic solutions [44]. In their investigation on the hydrogenation of butyronitrile in hexane in particular, they were able to detect the presence of a new adsorbed imine species with the CN group in a tilted configuration that, once formed, can be converted into amine products [45]. ATR-IR studies of catalytic systems have been particularly useful for the determination in situ of reaction mechanisms [15, 46–48]. For example, several adsorbed intermediates have been identified during the hydrogenation of nitrites over Pt/Al2 O3 in water, including NO, HNO, and HNO−2 , the conversion of which was determined to result mainly in the production of NH+4 (although traces of N2 O, a potential intermediate for the formation of N2 , was also observed) [49]. In another study, by Ferri and Baiker, on the oxidation of benzyl alcohol over a Pd/Al2 O3 catalyst, selective blocking of adsorption sites was carried out using bismuth and probed with CO. [50] It was determined that open terraces favor product decomposition. The group of Baiker has also carried out extensive in situ studies of chiral catalysis in liquid phase [46]. For instance, regarding the enantioselective hydrogenation of 4-methoxy-6-methyl2-pyrone promoted by Pd/TiO2 powder catalysts modified by cinchonidine, they were able to establish that carboxylate species are formed on the titania surface via alcoholysis of the lactone, which is obtained by a second hydrogenation step, and that the adsorption of those carboxylates follow different kinetics than adsorption of the primary hydrogenation product [51]. In one last example, the catalytic esterification of 1-octanol and hexanoic acid in either cumene or n-decane solvents was tested in situ using a Nafion/silica catalyst in an open reflux configuration at atmospheric pressure [52]. One interesting result in that case was the detection of Si–O–R bonds during reaction, presumably associated with the covalent bonding of octanol to the silica surface. To conclude this subsection, it is worth mentioning the early extensive ATR-IR characterization work performed on surfaces of interest to the microelectronics industry. Those have taken advantage of the fact that the most common materials used for the optical prism in ATR-IR are silicon, germanium, and other semiconductor elements, the same components used in microelectronics fabrication, so the investigation in those cases can be performed directly on the naked optical element.

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49 Probing Liquid/Solid Interfaces at the Molecular Level

Much has been learned this way about the surface chemistry of etching of the native silicon dioxide layer that grows on silicon wafers, which is typically carried out by using aqueous HF or NH4 F solutions [53, 54]. IR spectra has been used to follow and characterize the disappearance of the oxide, the formation of new single and geminal Si–H bonds, and the formation of silicic acid associated with hydrogen-bonded water molecules. More recent studies by Chabal and coworkers have established that the SiO2 /Si(100) interface that is revealed as the overlying oxide is stripped away is structurally distinct from the rest of the SiO2 film [55]. 49.2.2 Reflection–Absorption Infrared Spectroscopy (RAIRS)

Another common arrangement for the performance of IR absorption spectroscopy studies on liquid/solid interfaces is in single reflection mode. In that type of setup, the IR beam is directed through the liquid onto a mirror-finished surface and collected after reflection (see also Chapter 3.4.1 in Volume 1). The catch is that, because of the high density and strong absorption in the IR region of most liquids, this approach requires the use of thin films, down to a few micrometers in thickness, in order to minimize the path length of the beam through that media. This is commonly achieved by sandwiching the liquid in between the solid surface being studied and the prism used to guide the light in and out of the liquid/solid interface (Figure 49.1b). The advantage of using this setup is that it affords the characterization of small (∼1 cm2 ) flat surfaces, although the technique requires that those are polished and highly reflective. This single-reflection IR absorption spectroscopy approach is typically referred to as either RAIRS or infrared reflection–absorption spectroscopy (IRRAS). Because it requires a reflecting surface, it has been most commonly used for the study of systems involving metals. In fact, the use of metals brings the added advantage that a surface selection rule can be applied according to which only p-polarized light can be absorbed by the adsorbed species [28, 30, 31, 56]. By contrast, light absorption by species in solution is isotropic, which means that the contribution to the IR spectra from adsorbed species can be separated via the subtraction or ratioing of the traces obtained with p- vs. s-polarized light. Several optical arrangements have been developed to obtain such p/s ratio directly, the most common of which is the use of a photoelastic modulator (PEM) to modulate the polarization of the light [10, 57–60]. The surface selection rule can also be used to extract adsorption geometries, as already mentioned in connection with ATR and as discussed in more detail below. By and large, the most common application of RAIRS has been in the study of electrochemical systems involving metal electrodes. Perhaps disappointedly, those have so far focused mainly on the characterization of only a handful of simple adsorbates [61–64]. A quite extensive RAIRS work has been published on the adsorption of carbon monoxide [65, 66], from which the pioneering work by Weaver’s group aimed at the identification of the effects of electrode potential on the C–O stretching frequencies and local adsorption geometries deserves special

49.2 Infrared Absorption Spectroscopy

mention [67, 68]. Those were interpreted in terms of alterations in both the local electrostatic field and the coordination of the adsorbate on the surface, which are influenced in great part by the solvent (via electrostatic interactions and because of competition for adsorption sites). RAIRS characterization studies are also available on other small molecules, including NO [69], and even on small organics such as indols [70]. In all these, the adsorbates have usually been chosen because they exhibit at least one vibrational mode with a large absorption cross section and a frequency in a relatively clean region of the IR spectrum, ideally between approximately 1500 and 2200 cm−1 . That makes their detection easier, given that the sensitivity of RAIRS in liquid/solid interfaces is limited and the elimination of contributions from the solvent in practice is incomplete. RAIRS has also been used extensively to follow the evolution of the intermediates that may form on electrode surfaces during electrocatalysis. Much of that work in recent years has been directed to the study of fuel cell reactions, which are ideally suited for this technique because they usually involve the simple, high dynamic dipole molecules (CO, carboxyl-containing organics, and alcohols) that best behave for RAIRS detection; many reports have centered on the characterization of hydrogen oxidation/evolution and oxygen reduction reactions and on the electrooxidation of carbon monoxide, formic acid, and methanol [71, 72]. In terms of the electrooxidation of alcohols in particular, a number of surface intermediates have been repeatedly identified with RAIRS, including CO, COH, HCOH, and H2 COH, and two reaction pathways have been confirmed, one involving the oxidation of adsorbed CO and a second involving the formation of an organic intermediate such as an aldehyde or an organic acid [73, 74]. RAIRS studies of oxidation and reduction reactions under electrochemical conditions can, in principle, be extended to other types of systems. In polymer electrochemistry, for instance, the redox processes of aminophenols on a platinum electrode in aqueous acid solutions were shown to differ markedly depending on the isomer used as the reactant [75]. It was established that while p-aminophenol undergoes hydrolysis to hydroquinone/p-benzoquinone, m-aminophenol grows a blocking polymeric film; phenoxazine units are produced during the oxidation/reduction of poly(o-aminophenol). Moreover, with poly(o-aminophenol), the redox chemistry was determined to occur via two consecutive reactions involving a charged intermediate [76]. In a different example, with 2,5-dihydroxybenzyl mercaptan, electrochemical oxidation on a gold electrode was determined to involve a quinone-type moiety [77]. One interesting side conclusion from this latter work is the fact that the performance of RAIRS was found to be quite similar to that of ATR. RAIRS studies with these types of polymers are helped by the strong signals associated with the breathing modes of their aromatic rings and the fact that the corresponding vibrational frequencies appear in a relatively clean spectral range (>1500 cm−1 ). The identification of adsorbed species in electrochemical systems can be enhanced by light polarization modulation, as indicated above, but also by potential modulation, a technique known as subtractively normalized interfacial Fourier transform infrared spectroscopy (SNIFTIRS). For instance, SNIFTIRS (and other

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49 Probing Liquid/Solid Interfaces at the Molecular Level

potential-dependent IR techniques) has been used to investigate the reaction mechanism of the electrooxidation of ethanol on PtSn electrodes [78]. These studies have helped to determine that the presence of tin on the surface allows ethanol to adsorb dissociatively and to be activated via scission of its C–C bond at lower potentials and with a higher selectivity than on pure Pt, to form acetic acid. A potential modulation approach has also been used to explain that the anomalous peaks seen in the cyclic voltammetry of Pt(111) in sulfuric acid are associated with the adsorption of bisulfate anions [79]. In connection with the electrooxidation reactions seen in lithium batteries, SNIFTIRS has been used to establish that, in general, the Li insertion processes include the migration of Li ions through surface films but with surface chemistry that is highly dependent on the solution used: in LiAsF6 , for instance, the chemistry is dominated by solvent reduction, whereas in either 1,3-dioxolane or tetrahydrofuran (THF), the products from salt anion reduction are the major constituents in the surface films formed [80, 81]. In these battery-related studies, the most characteristic species, the easiest to identify by RAIRS, are carbonates, although signals from other moieties such as trifluoromethyls are also observable. By using single crystals, RAIRS can also afford the identification of structural factors in the chemistry of electrodes. For example, in a study on the reduction of nitrate anions on a series of single-crystal platinum electrodes in sulfuric and perchloric acid solutions, it was found that there are noticeable differences in reactivity among different exposed facets but that those are essentially controlled by other species (hydrogen and sulfate) interacting strongly with the electrode surface, not by a structure-sensitive nitrate adsorption, dissociation, or reduction [82]. This type of information may be lost if polycrystalline surfaces are used instead, but, in exchange, an enhancement in IR signal may be gained. Indeed, surface enhancement has been reported on rough metal electrodes, mainly on coinage metals (Au, Ag, and Cu), and may occur on other late transition metals as well [83, 84]. This phenomenon, which also applies to ATR setups, has been explained by an electric field enhancement because of collective electron resonances associated with the island nature of the thin metal films [85, 86]. In a recent example involving a Pt electrode/Nafion interface in HClO4 aqueous solutions (a relevant system in polymer electrolyte fuel cells), SO−3 groups were identified in the ionomer membrane, with their geometrical orientation driven by the electric field [87]. It was inferred that the SO−3 groups act like counterions at the Pt/ionomer interface to form the electric double layer. However, full acceptance and utilization of this IR surface enhancement is yet to be reached. More recently, the use of RAIRS has been extended to the study of the adsorption of species involved in catalysis. For instance, in a study by Baiker’s group, the selectivity of the liquid-phase oxidation of benzyl alcohol to benzaldehyde on a Pd film was contrasted between anaerobic conditions, where toluene was the major side product, and aerobic conditions, where large quantities of CO2 and benzoic acid were also observed [88]. CO formation by decarbonylation of benzaldehyde was also observed on a Pd(111) surface under anaerobic conditions, delayed with respect to the formation of benzaldehyde. We, in our laboratory, have worked

49.2 Infrared Absorption Spectroscopy

Cd in CCI4/Pt RAIRS Polarization dependence

Pt sample

60° CaF2 prism

To MCT detector

5%

p Polarization s Polarization

Micrometer Transmittance

IR beam from FTIR

Pt counter electrode

p-Polarized IR (adsorbed + dissolved) s-Polarized IR (dissolved)

Oscilloscope Syringe Ar

O2

p/s ratio (adsorbed)

Constant-current oscillator

H2 Gas purifier

(a) Figure 49.3 Illustration of the use of RAIRS to characterize the adsorption of molecules from solution onto metal surfaces [89]. (a) Schematic representation of the RAIRS setup used in this study. The cell consists of a calcium fluoride prism and a manipulator for holding, moving, and applying voltages to the platinum solid surface; the liquid samples are pressed in the small volume between those two elements. (b) Spectra obtained by using this instrument for the characterization of the adsorption of cinchonidine, a chiral modifier, from a carbon tetrachloride solution onto the platinum surface. The data provided here illustrate

1200

(b)

1300

1400

1500

Wavenumber (cm–1)

the use of a surface selection rule to discriminate between signals from adsorbates on metals and species dissolved in solution: as the species in solution absorb light isotropically, similar spectra are obtained with s- and p-polarized light, whereas for the adsorbed molecules, only the p component is absorbed. Therefore, a ratio of the traces obtained with p- vs. s-polarized light (the two top traces) yields a spectrum originating exclusively from cinchonidine adsorbed on the platinum surface (bottom trace). (Source: Kubota et al. 2003 [89]. Copyright 2003. Reprinted with permission of American Chemical Society.)

extensively on the use of RAIRS to characterize the adsorption of chiral molecules on metal surfaces, by using a system derivative from those developed for the electrochemical studies (Figure 49.3a) [89, 90]. In catalysis-relevant RAIRS studies, as in the electrochemical work, discrimination between adsorbed species and species dissolved in solution can be achieved by using polarized light (Figure 49.3b) [89], and geometrical information can be extracted from polarization-dependent measurements by using the surface selection rule mentioned before [91–94]. Particularly noteworthy from the example illustrated in Figure 49.3 is the correlation that was identified between the adsorption of cinchona alkaloids with their aromatic ring flat on the surface, which occurs at intermediate coverages and disappears at higher solution concentrations, and their ability to promote enantioselective hydrogenations [91]. In addition, by comparing IR absorption spectra for a family

13

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49 Probing Liquid/Solid Interfaces at the Molecular Level

of related compounds with specific substitutions, we have also determined that it is the amine group and not the aromatic ring that binds to the surface, at least in the case of 1-(1-naphthyl)ethylamine (NEA) [95]. It was also possible to establish in that work the role that adsorbed gases [96] and the nature of the solvent [97] play in the uptake of the cinchona and to characterize the adsorption under competitive conditions [98] and measure adsorption equilibrium constants [93]. More recently, we have used in situ RAIRS to identified differences in adsorption geometry of chiral compounds as a function of enantiocomposition: a significant difference in RAIRS was seen for cases of enantiopure vs. racemic mixtures [99]. Unfortunately, examples such as these where RAIRS has been used to study catalytic reactions at liquid/solid interfaces are quite scarce. 49.2.3 Transmission

Much less common in the IR absorption spectroscopy study of liquid/solid interfaces is the use of transmission modes. First, many solids are opaque to the IR radiation and that renders them nonviable for the use of this setup. In addition, IR signal intensity is easily lost in transmission because of absorption by the solvent. In spite of those difficulties, however, there are a few examples of the use of transmission IR absorption spectroscopy for the characterization of the adsorption of molecules from a liquid phase onto the surface of catalysts or nanoparticles. For instance, if the solid samples are transparent, as in the case of some thin layer electrochemical cells, a simple transmission cell can be easily devised [100]. Baiker’s group has published some reports on the feasibility of an alternative design involving thin liquid films, which they have used for the study of the heterogeneously catalyzed hydrogenation of ethyl pyruvate over Pt/Al2 O3 under high pressures [101]. In their arrangement, however, the surface chemistry is still followed by ATR; transmission IR absorption spectroscopy is used for the simultaneous recording of changes in the liquid phase. Another reported example of the use of transmission IR in liquid/solid systems involves the extraction of chemicals (i.e. CpMn(CO)) by a polymer (polyethylene) in a CO2 supercritical fluid [102]. In our laboratory, we have adapted our RAIRS setup (Figure 49.3) to be used in transmission mode by employing the back metal surface as a mirror and by suspending the solid sample in a liquid thin film trapped between the prism and the mirror. This arrangement is shown schematically in Figure 49.1c. That transmission IR setup has been used to investigate the adsorption of carbon monoxide on dendrimer-encapsulated platinum nanoparticles (Pt-DENs) immersed in different solvents (Figure 49.4) [103]. It was found that although only limited, weak, and reversible adsorption is possible in the gas phase, extensive and stronger adsorption occurs in the liquid phase. It was speculated that the dendrimer structure may collapse in the gas phase, blocking access to the Pt surface, but may expand and open up in the presence of a proper solvent. The same approach was also used to determine the accessibility of metal nanoparticles in yolk–shell structures, where the metal nanoparticles are embedded inside shells

49.3 Other Vibrational Spectroscopies

CO on Pt-DEN IR spectra in ethanol

CO on Pt-DEN/SiO2 IR spectra vs. solvent Tads = 300 K

Tads = 300 K

CCl4

Transmittance

Pt-DEN alone (a)

(a)

Ethanol Pt-DEN/SiO2 (b) as is

(b)

2-Propanol Pt-DEN/SiO2 calcined (c)

1900 (A)

2100

2300 –1

Wavenumber (cm )

(c)

1800 (B)

Figure 49.4 IR absorption spectra for CO adsorbed on Pt dendrimer-encapsulated nanoparticles (Pt-DENs) dispersed on a sol–gel silica support [103]. This provides an example on how the setup in Figure 49.3 can be used to characterize liquid/solid interfaces in transmission mode. (A) Data for CO adsorption on three samples suspended in ethanol: the Pt-DENs by themselves (a); the Pt-DENs dispersed onto a high-surface-area silica support, as prepared (b); and the same supported Pt-DENs after calcination of the

2000

2200 –1

Wavenumber (cm )

organic matter (c). Access of CO to the Pt surface is virtually unhindered even before any pretreatment of the catalyst on Pt-DENs where the dendrimer structure is still intact. (A) Spectra for the silica-supported Pt-DENs suspended in three different solvents (a, carbon tetrachloride; b, ethanol; and c, 2propanol). CO adsorption is greatly affected by the nature of the solvent. (Source: Albiter et al. 2010 [103]. Copyright 2010. Reprinted with permission of American Chemical Society.)

made out of porous oxide materials, to adsorbates diluted in liquid solvents, to carbon monoxide [104], as well as large molecules such as cinchona alkaloids and porphyrins [105]. 49.3 Other Vibrational Spectroscopies

Vibrational information can also be obtained by using visible or ultraviolet light, via the detection of Raman scattering or by using nonlinear laser-based optical spectroscopies such as SFG (Figure 49.5). Often, light in the visible and ultraviolet ranges is absorbed less efficiently by condensed matter than IR radiation and can therefore travel better through thin liquid films. In addition, the high energy of visible and ultraviolet photons affords their individual detection, a fact that makes

15

49 Probing Liquid/Solid Interfaces at the Molecular Level

1

=ω 3

SFG ω3 = ω 2 + ω1

ω

Vis ω2

IR ω 1

Surface-enhanced raman scattering (SERS)

2

Vis ω2

SF G +ω

Sum frequency generation (SFG)

Raman (anti-stokes) ω2 = ω1 + ω3

IR, ω1 (b)

1

=ω 2

ω1

SH G

±ω

3

Second harmonic generation (SHG)

Vis ω1

ω

2

Vis ω 1

1

s Vi

SE RS

ω3

×ω

Raman (stokes) Vis ω2 = ω1 – ω3 ω1

=2

Vis ω1

ω

16

Vis ω1 (a)

SHG ω2 = 2 × ω1

(c)

Figure 49.5 Schematic representation of the laser-based spectroscopies discussed in this review for the characterization of liquid/solid interfaces using UV–vis light. (a) Surface-enhanced Raman scattering (SERS), a technique where the scattered light from a laser illuminating the liquid/solid interface is analyzed for gains and/or losses in energy because of molecular vibrations. (b) Sum frequency generation (SFG), where a tunable infrared laser beam, used as the probe, is combined with a fixed-energy

visible laser at the liquid/solid interface in order to up-convert and individually count the reflected IR photons. A vibrational spectrum is obtained by recording the intensity of those vs. the energy of the IR beam. (c) In second harmonic generation (SHG), two photons from the same laser are added at the liquid/solid interface and detected. The intensity of the outgoing beam is recorded as a function of the energy of the incident photons to follow changes in electronic properties at the interface.

the associated spectroscopy quite sensitive. A third advantage to the use of visible or UV radiation for vibrational studies is that most of the spectroscopies in this category detect signals on top of a zero (or negligible) background. On the negative side, vibrational information can only be extracted from visible or UV excitation indirectly, by relying on low-probability secondary or multiphoton processes. This means that intense beams may be required to increase the signal intensity, leading to a high risk of sample damaging. The latter problem is exacerbated by the possible promotion of decomposition photoreactions by the visible or UV radiation. Also, high-beam intensity can, in many instances, only be obtained with specialized high-power lasers, adding to the cost of the experiment, although laser technology has progressed much in recent years, so cheap lasers are now available for many photon energies. In spite of their limitations, vibrational spectroscopies based on the use of visible or UV light have become quite popular and have, in some instances, been adapted to address issues associated with liquid/solid interfaces.

49.3 Other Vibrational Spectroscopies

49.3.1 Raman Scattering Spectroscopy

In Raman spectroscopy, the light scattered from the sample is energy-analyzed to detect quantum gains or losses because of energy excitations within the molecules being probed (Figure 49.5a). This technique is widely used to obtain vibrational information on many types of samples, not only liquid/solid interfaces. The main shortcoming is the fact that the Raman scattering signals are weak and difficult to separate from the much more intense signal due to the elastically (Rayleigh) scattered light and also from any possible fluorescence that may emanate from the sample upon laser excitation. This makes the general use of Raman spectroscopy for the analysis of surfaces difficult. Some signal augmentation can be achieved by resonantly exciting a particular electronic transition of the sample being probed, but that requires the use of tunable lasers, limits the range of accessible vibrational modes, and increases the probability of sample damage by the laser radiation. Instead, for the study of interfaces, Raman spectroscopy is most commonly set to take advantage of the signal enhancement that comes from excitation of surface plasmons in rough surfaces. Surface-enhanced Raman spectroscopy (SERS), as the technique is known, has long been employed to characterize a variety of molecules, in particular organic species, on gold and silver surfaces [106–110]. Those studies have afforded the determination of chemical identities, structures, and adsorption orientation and also to follow chemical and electrochemical reactions of anions, surfactants, environmental pollutants, biomolecules, and dye molecules. Raman surface enhancement occurs mainly on silver and gold substrates, a fact that makes its application somewhat limited, but by electrodepositing other metals on top of gold nanoparticles, it has been possible to extend the use of SERS to other solid surfaces [111, 112]. To date, the main use of SERS has been for the characterization of bulk samples, which are deposited on rough metal surfaces or on metal nanoparticles only to take advantage of the signal enhancement afforded by those [113]. Many particularly interesting developments have been reported on the analytical use of SERS for the study of biomedical samples [114–119], including live cells [120–122]. It has been possible, for instance, to detect self-assembled Au–imidazole structures formed in vivo in tumor-bearing mice [123]. Sensors have also been developed in which targetspecific SERS probes are prepared by derivatizing appropriate metal surfaces or nanoparticles with labels displaying a characteristic vibrational signature (mercaptobenzoic acid and Rhodamine 6G) and specific binding sites (biomolecules such as antibodies) for the corresponding target molecule (proteins or a nucleic acid) [124]. Analytical applications of SERS have also been advanced for environmental uses such as for the detection of aqueous or airborne contaminants [119]. One example in this category is the SERS-based detection of perchlorate ions in groundwater [125]. Several studies have also focused on the detection and identification of trace elements and compounds [126], in some instances in combination with electrochemistry to tune the cell potential for maximum adsorption [127]. More recently,

17

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49 Probing Liquid/Solid Interfaces at the Molecular Level

SERS has been implemented as a detection tool in chromatography and in capillary electrophoresis schemes [128]. In one example, silica sol–gels were used for the dual purpose of acting as the stationary phase in liquid chromatography and of immobilizing the metal particles needed for SERS detection [129]. Another detection scheme combined SERS with thin layer chromatography, using silica gel coupled with citrate-reduced Ag colloids, to analyze the composition of natural dyes on works of art [130]. In a third case, SERS was used as the detection method in a microflow, lab-on-a-chip cell for the detection of trinitrotoluene (TNT) [131]. In all these examples, however, the interest was to selectively detect and quantify specific compounds in solution; no attention was directed in any of those cases to the study of the adsorption of the molecules probed on the surface of the solid. Although less common, there are also reports on the use of SERS for the characterization of liquid/solid interfacial chemistry [132], many of them related to electrochemistry [111, 133]. One of the early examples of this use of SERS is that of Pemberton and Buck, who combined the advantages of the surface enhancement obtained by using silver rough surface with the resonant effect afforded by tuning the excitation source to obtain adsorption isotherms and determine adsorption geometries during the uptake of diphenylthiocarbazon anions from an alkaline aqueous solution onto a silver electrode at the potential of maximum uptake [134]. Another early example of the use of SERS in electrochemistry is that of Shi et al., who followed the time evolution of the oxidation of p-nitrobenzoic acid on a Ag electrode [135]. In addition to three stable intermediates, p-nitrosobenzoate, hydroxylamine, and azoxy compounds, a transient p-nitrosobenzoate free-radical anion intermediate was detected. The voltage required to decompose methanol on platinum electrodes could be followed by SERS as well; a strong dependence on surface roughness was identified [109]. In all those cases, unique information has been extracted from SERS data about the kinetics and thermodynamics of the adsorption processes and about the chemical and structural details of the adsorbates. Time-resolved SERS has also afforded the measurement of the kinetics of electrochemical processes upon either step changes in potential [136] or fast voltage ramping, as done in cyclic voltammetry [137]. Applications of SERS to other fields involving liquid/solid interfaces are sparser. In those, SERS has typically used to provide molecular information on the interaction of adsorbates with the solid surface and also some indications on adsorption geometry. For instance, SERS was recently used to aid in the identification of the mechanism by which iodine ions enhance the effect of benzotriazole as an inhibitor in iron corrosion [109]. It was concluded that the electrostatic interaction induced by adsorbed I− facilitates the adsorption of a protonated version of benzotriazole and that the resulting mixed layer can protect the iron substrate from corroding agents. In an earlier study, SERS was used to evaluate the effectiveness of selfassembled monolayers of alkanethiols adsorbed on the surface of a polycrystalline bulk copper against corrosion in an aerated Na2 SO4 solution [138]. It was determined that the alkanethiols chemisorb via the formation of strong bonds between Cu and S atoms following cleavage of the S–H bond and form densely packed, water-repellent, monolayers on the surface. Other SAMs, based on benzenethiol,

49.3 Other Vibrational Spectroscopies

benzenemethanethiol, p-cyanobenzenemethanethiol, diphenyl disulfide, and dibenzyl disulfide, were also determined by SERS to adsorb dissociatively as thiolates on rough gold electrodes [139]. The aromatic rings in those cases were found to adopt a tilted geometry, but a reduction in surface coverage was seen upon switching of the applied potential to positive or negative extremes. In terms of the formation of colloidal particles, pyrazinamide and 2-mercaptopyridine were shown to form Ag colloid–adsorbate films at the interface between the silver colloid aqueous and dichloromethane adsorbate solutions, in contrast to 4-mercaptopyridine, which forms Ag organosol aggregates via the transfer of adsorbate-covered Ag colloidal particles from the aqueous to the organic phase [140]. The differences between the two latter systems confirmed that the formation of Ag colloid–adsorbate films is an adsorbate-specific process. In a more complex study of biological relevance, an SERS characterization of the uptake of cytochrome c (Cyt c) on SAMs of mercaptoalkanoic acids on colloidal silver indicated that the adsorption is selective on negatively charged surfaces, on a second layer on top of an SAM directly attached to the Ag [141]. Previous SERS studies had indicated that adsorption of heme proteins on aqueous silver sols occurs with formation of surface-bound hemin μ-oxo dimers, implying that the heme prosthetic groups are extracted from their binding pockets in at least some of the protein molecules and that heme extraction is facilitated under oxidizing conditions, perhaps via increased surface charge on the Ag surface [142]. SERS has also been used to look into the adsorption of amino acids in silver colloidal solutions to determine the geometry and orientation of the adsorbates and to identify their specific interactions with the surface [143]. In most instances, with L-methionine, L-glycine, L-leucine, L-phenylalanine, and L-proline as well as with their homodipeptides, it was found that the majority of the C–C bonds adopt an almost parallel orientation with respect to the surface. In the case of L-cysteine, however, the SERS spectra indicated a potential-induced reorientation of the molecules adsorbed from a KCl solution onto a polycrystalline silver electrode, from bonding with the protonated amino group pointing toward the surface at positive potentials to the carboxylate group becoming closer to the surface at negative potentials [144]. Again, in these examples, key information was extracted from SERS experiments about adsorption affinity and adsorption geometry. In materials chemistry, SERS can also be used to investigate adsorption processes. A SERS study of the adsorption and acidity behavior of the highly fluorescent anthraquinone-based pigment alizarin on Ag colloids, for instance, indicated that the order of deprotonation of the two OH groups reverses on the metal in comparison with the way it happens in the aqueous solution [145]. Other SERS studies have been directed at the characterization of interfaces between solids and nonaqueous liquids [146]. In some instances, SERS can be used to characterize photochemical reactions, as in the case of methylviologen adsorbed on a roughened silver electrode, which was determined to undergo a reduction step at its cation radical position upon irradiation with the blue spectral region of laser excitation light even at liquid nitrogen temperature [147]. It was also established in that study that the dication form of methylviologen interacts more strongly with the

19

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49 Probing Liquid/Solid Interfaces at the Molecular Level

surface than the monocation. SERS can provide information about the collective vibrational characteristics of solids as well. For instance, SERS was used to map the phonon characteristics of mixed CdS/CdSe layers on gold, a system with potential applications in electronics [148]. A phonon band associated with a CdS phonon was found to soften with increasing thickness because of the crystallographic strain caused by expansion to match more closely the adjacent CdSe layer. It has even been possible to record the SERS data from nanomaterials without the aid of any signal-enhancing metals, as in the case of the study of the adsorption of pyridine on InAs/GaAs quantum dots [149]. It was suggested, based on the SERS data, that adsorption occurs via coordination of the lone pair electrons of the N atom to the semiconductor surface. Only a few examples are available to date on SERS applications to materials problems, but those illustrate the range of both molecular and solid-state information that can be extracted from such studies. Next, a couple of examples are provided of applications of SERS to problems of heterogeneous catalysis involving liquid/solid interfaces. In one, Heck et al. looked into the time dependence of the hydrodechlorination of 1,1-dichloroethene in water catalyzed by Pd–Au nanoshells [150]. They identified a sequence of dechlorination and hydrogenation steps involving several intermediates, including π and di-σ bonded species, vinylidene, and other oligomeric moieties (Figure 49.6). In another report, from our laboratory, the adsorption of cinchonidine was tested by SERS on a thin film of platinum deposited on a rough gold surface [92]. Clear features could be identified in the SERS spectra because of the adsorbed molecule, including the ring-breathing mode at close to 1600 cm−1 , even if some interference from the solvent could not be completely avoided (Figure 49.7), and those could be used to establish a change in adsorption geometry with the concentration of cinchonidine in solution. One important conclusion from our work was that the information obtained with SERS is complementary to that extracted from RAIRS experiments because of the different selection rules that apply to each technique, the different cross sections of the different vibrational modes to both types of excitation, and the different experimental setups needed. More recently, SERS work has been reported on the aqueous-phase conversion of glycerol oxidation catalyzed by supported Au nanoshells (on Si wafer) in which glycerate surface species, identified by sharp peaks at 0.990 ± 0.022 Å(KCl). Moreover, although the in-plane silanols accept one H-bond from one water molecule at the neat interface, such silanol–water H-bond is lost at the interfaces with NaCl and NaI electrolytes, and it is only intermittently present (≈50% of the time) and displays rather larger H-bond lengths in the case of the KCl electrolyte. Stabilization of the conjugated SiO− base by both the solvent and the neighboring silanols can also play a key role in shaping the surface silanols pKa , as already demonstrated at the amorphous silica/water interface [33, 37]. In the presence of interfacial cations, these must also play a significant role, given the additional electrostatic interaction between the positively charged cation and the negatively charged silanolate. Representative snapshots showing the local environment of the deprotonated in-plane silanols are depicted in Figure 50.13a–d. At all interfaces but the NaCl interface, the silanolate is stabilized by three hydrogen bonds: either SiO− receives two H-bonds from nearby silanols and one from one water (neat and NaI electrolyte interfaces, Figure 50.13a,c), or SiO− receives three H-bonds (two

(a)

(b)

(c)

(d)

Figure 50.13 Snapshots of the Si-O− silanolate-conjugated base (dark blue) with their direct hydrogen bond environments (dark blue lines) at the hydroxylated (0001) α-quartz/water interface. Sodium appears in orange, potassium in pink, chloride in blue, and iodide in green. From left to right:

(a) the neat interface, (b) the NaCl electrolyte interface, (c) the NaI electrolyte interface, and (d) the KCl electrolyte interface. (Source: Pfeiffer-Laplaud et al. 2016 [100]. reproduced with the permission of American Chemical Society.)

50.7 Acidity at Quartz/Water/Electrolyte Interfaces

H-bonds in the case of NaCl interface) from water molecules only (NaCl and KCl interfaces, Figure 50.13b,d). Analyzing the average H-bond distances one immediate conclusion is that the KCl electrolyte interface is an outlier, the average distances being 1.82 Å while shorter H-bonds about 1.60–1.70 Å are observed for the interfaces with other electrolytes. The SiO− -conjugated base is therefore far less stabilized at the KCl electrolyte interface than at the two others, consistently with the highest pKa indeed calculated at the interface with the KCl electrolyte (most basic silanol among the interfaces with the three electrolytes). Furthermore, the average distance between the silanolate oxygen and the cation (inner-sphere at all interfaces) is substantially larger than the one observed when the surface site is protonated (SiOH), except for the NaCl electrolyte. This increase in the results from the increase in ionic radius in going from the OH form to the O− one. In summary, for NaCl, the cation remains very close to the SiO− surface site, stabilizing the deprotonated form and favoring proton dissociation. This is not the case for NaI. Interestingly, Na+ is also slightly closer to SiOH at the NaCl interface than at the two other interfaces. However, this is not affecting the intrasurface H-bond to the nearby silanol, which remains close to the one at the neat interface. Together, the strong H-bond to the nearby silanol and the conjugated base stabilization explain why the pKa for NaCl is the closest to the one of the neat interface. Finally, the intermediate pKa value at the NaI interface arises from the stabilization of the SiO− -conjugated base: the three H-bonds formed around the silanolate are comparable in distance to the values observed at the NaCl interface, but the Na+ cation is pushed much farther away from the base than it is at the NaCl interface (presumably because the more polarizable I− attracts and displaces the Na+ at the interface). This possibly explains the difference between the two NaCl and NaI interfaces, leading to less stabilization of SiO− at the NaI interface, and therefore turning the silanols at the NaI electrolyte interface into more basic sites than they are at the NaCl interface. Although the specific anion effect on the pKa seems to act mostly on the deprotonated form (comparing NaCl and NaI), specific cation effects, comparing NaCl and KCl, affect both protonated and deprotonated forms. We recall that the surface silanol out-of-plane population is less reduced in the presence of Na+ than in the presence of K+ [80] because of the smaller ionic radius of Na+ . Thus, in the case of the interface with NaCl electrolyte, it is more probable for an in-plane silanol, for which the pKa is here discussed, to find a more acidic (out-of-plane) neighbor silanol with whom to share its proton. On the contrary, in the presence of KCl, the more probable neighbors are the more basic (in-plane) SiOH and the O–H bond is stabilized (higher pKa ). Besides, regarding the conjugated base, the Na+ /SiO− affinity is larger than the K+ /SiO− . In a nutshell, the trend in the pKa ’s for quartz/water/electrolyte interfaces can primarily be understood as the result of the weaker H-bond network around silanol/silanolate in the electrolyte solutions, in comparison to the neat quartz/water interface. At the neutral surface/water interface, the ions weaken the H-bond network around the in-plane Si-OH surface site, therefore stabilizing the O–H covalent bond and preventing deprotonation of the chemical entity. The presence of ions

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50 Structure and Dynamics of Solid/Liquid Interfaces

also reduces the stabilization of the conjugated base, making the proton insertion more favorable, or in other words making the protonated form again more stable, thus rendering these sites more basic compared to the case without interfacial electrolytes. The details in the Hofmeister series of the pKa can be quantitatively understood in terms of silanols properties as they take part in the cation direct solvation shell.

50.8 Fluorite/Water Interface, Structures, and VSFG Intertwined

Water–mineral interactions are of general importance for a wide range of environmental, chemical, metallurgical, and ceramic processes [122, 123]. The interaction of fluorite (CaF2 ) with water is of specific relevance for industrial, environmental, and medical applications, e.g. for understanding fluorine dissolution in drinking water [124]. Recently, there has been a proposal to use CaF2 as an analogue of UO2 in dissolution experiments in order to understand the long-term dissolution behavior of spent nuclear fuel. This has accordingly raised the interest in the interaction of CaF2 with water [125]. Despite the apparent importance of the fluorite/water interface, it has been challenging to obtain detailed insights into this interface at the molecular scale. Recently, frequency modulation atomic force microscopy (FM-AFM) [126] has provided important new information on molecular length scales by analyzing the fluorite/water interface, not only as a function of the pH but also as a function of the concentration of ions in the solution and addressing fluorite/water interfaces with saturated and supersaturated solutions. At high pH, the presence of surface adsorbates is detected and attributed to calcium hydroxo complexes [126]. At low pH, atomic scale disorder was observed, which could be attributed to either partial dissolution of the topmost layer by the creation of F− vacancies or to proton adsorption at the interface. Still experiments seem not to be able to distinguish between the two possible scenarios [126]. As another surface-sensitive technique, VSFG spectroscopy has the ability to selectively address the nanometric interfacial water layer and indeed has contributed substantially to our understanding of the physical and chemical properties of the CaF2 /water interface [127, 128]. VSFG is rather unique in its ability to provide the vibrational spectrum of water molecules specifically at the interface, as the selection rule of VSFG requires symmetry to be broken, i.e. no VSFG signal can be generated from the adjacent centrosymmetric bulk. Previous VSFG investigations of water at the CaF2 /water interface by the Richmond group [127, 128] have revealed dramatic changes in the interfacial hydrogen bonding structure upon changing the pH of the aqueous phase. In particular at low pH, the VSFG experiments have suggested that positive charge develops on the surface, causing orientation of water molecules into highly ordered, tetrahedrally coordinated states. At near-neutral pH, the VSFG signal vanishes and this has been interpreted as the result of a more random orientation of the interfacial water molecules at a near-neutral surface. Finally, in the basic pH regime, dissociative

50.8 Fluorite/Water Interface, Structures, and VSFG Intertwined

adsorption was hypothesized to take place on the solid surface resulting in the formation of Ca–OH species. There are still some open questions: how do these OH groups contribute to the VSFG spectrum? What type of order is established in the interfacial water region? Here, we review a recent simulation study aimed at answering these questions and to provide a new microscopic understanding of the CaF2 /water interface as a function of pH [51]. We explore the effect of surface termination on the interfacial water arrangement and we show the importance of the local electrical field due to ions in solution in the near-surface region on water orientation. Such a detailed analysis is now possible thanks to recent advances in the computational techniques. In particular, we use density functional theory DFT-based molecular dynamics (MD) simulations, which allow an accurate description of the structure and dynamics of hydrogen bonding in highly heterogeneous environments, also including electronic polarization. A newly developed approach is used for the calculation of the VSFG spectra [51, 52] as described in Section 50.2.4, which only requires the atomic positions and velocities without the cost of the additional calculation of molecular dipoles and polarizabilities. At the same time, appropriate selection rules for the VSFG are also taken into account. We describe here atomistic models for the fluorite/water interfaces at different pH conditions. The models are used to calculate the interface vibrational spectra and to provide their molecular interpretation. At low pH, positive charge is expected to accumulate at the fluorite/water interface. In particular, the following reaction is expected to take place: (CaF2 )surf + H+aq ⇄ (CaF+ )surf + HFaq

(50.20)

Fluoride ions dissolving into the water solution leave positive vacancies on the surfaces, which are responsible for “aligning” the water molecules. As the VSFG signal increases with increasing interfacial order in the system, a large VSFG signal is detected [127, 128]. For low pH, model systems that resemble the final equilibrium state can be built with various concentrations of fluorite vacancies on the surface, which correspond to different extents of positive charge on the surface (Figure 50.14). In particular, our model consists of a CaF2 slab in contact with water where two equivalent interfaces are present. Fluoride counterions are added to the solution to compensate the positive surface charge, i.e. to get an overall neutral system. We find that the F− ions tend to prefer to be solvated by water and form a diffuse layer in the near-surface region. Overall, the surface-localized positive charge and the near-surface negative counterions generate a double layer, giving rise to a rather strong electrical field at the solid/liquid interface. We have considered more extreme conditions with 2.58 vacancies/nm (4 vacancies on each surface) and milder conditions with 1.29 vacancies/nm (2 vacancies) or with 0.64 vacancies/nm (1 vacancy), respectively. At high pH, the hydroxide ions in excess are expected to react with the CaF2 surface leading to the following substitution: (CaF2 )surf + HO−aq ⇄ (CaFOH)surf + F−aq

(50.21)

183

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50 Structure and Dynamics of Solid/Liquid Interfaces

(a)

(b)

(c)

Figure 50.14 (a) Random snapshot of the system used to describe the CaF2 /H2 O interface for the neutral pH. Miniatures highlighting the differences between the neutral pH and the (b) low pH with an excess of proton in the form of dissociated HCl, (c) low pH system with partial dissolution of fluoride

(d) ions, (d) high pH with six substitutions of fluorides by hydroxides per surface. For (b)–(d), the water molecules are transparent in order to highlight the position of the ions. The hydrogens are colored in white, the oxygens in red, the fluorines in pink, the chlorines in green, and the calciums in turquoise.

The Ca–OH groups on the surface have been suggested responsible for the narrow band signal at 3645 cm−1 [127, 128]. For high pH, we have constructed a model where a surface modification of the CaF2 has taken place in response to the increased concentration of OH groups in the solution. In the topmost fluorite layer, F− were partially or totally replaced by HO− (Figure 50.14). Different concentrations of OH have been considered in order to establish a relation between the VSFG signal intensity and the pH: 1, 6, and 12 substitution over the 12 available sites per surface. Using the described models, we have calculated the spectral responses from the surface-sensitive vibrational density of states using surface-specific VVCF (see Section 50.2.4) for the XXZ polarization (the indexes of 𝜒 (2) will be omitted hereafter). In the case of low pH, the spectra for the different vacancy densities are reported in Figure 50.15a. The common feature for all the different concentrations of surface vacancies is the presence of a broad negative band in the Im𝜒 (2) spectrum, which, for the 1 and 2 vacancies systems, is located around 3300 cm−1 . As the charge concentration increases to four positive charges, the intensity of the band increases and the band position moves toward lower frequencies, with a maximum located at

185

1

2 0

0

–2

1

2

0

0

3600

2800

Re(X) and Im(X) (arb. units)

0

0

3600

2800

1

2 0

0

–2 –4 3200

3600

2800

3200

3600

Frequency (cm–1)

(a)

(b)

1

2

–4 3200

2 4 2HCl

–2

–4 3200

2 4 1+

–2

–4 2800

2 4 2+

|X|2 (arb. units)

2 4 4+

2 4 12 OH 1

2 0

0

–2

1

2

0

0

3600

2800

4 Neutral 1

2

0

0

–4 3200

3600

2800

2

Int Im Re

0

2 1 0

–2

–2

–4 3200

2 4 1 OH

–2

–4 2800

2 4 6 OH

–4 3200

3600

2800

3200

3600

Frequency (cm–1)

Figure 50.15 Comparison of the Im𝜒 (2) , Re𝜒 (2) , and |𝜒 (2) |2 for different values of the surface defect concentration (plain lines). (a) Low pH. (b) High pH. In order to facilitate

the comparison, the spectra with two HCl per surface have been plotted in dotted lines on the spectra with two vacancies per surface.

3100 cm−1 . If we compare the calculated spectra to the experimental ones [51], we can see that such strong red shift for the four vacancy system is not consistent with the experiment. Better agreement is found for the one and two vacancy systems. Additional information can also be extracted from a comparison between the calculated and experimentally measured Re𝜒 (2) . The computed Re𝜒 (2) (Figure 50.15, blue lines) shows two main peaks, a positive peak at higher frequencies and a negative one at lower frequencies. In the case of one or two positive charges on the surface, the peak position and the crossing from positive to negative values are in good agreement with the experimental spectra (Figure 50.16). However, as the defect number increases to 4, we notice a very strong shift of the negative band to lower frequencies that also shifts the zero crossing toward 3200 cm−1 . Moreover, also for the intensity spectrum, the best match between theory and experiment is found for one or two vacancies per site. Overall, these considerations suggest that the vacancy density is around 0.65 per nm2 for the experimental condition of pH = 2. What is the molecular origin of the strong negative band in Im𝜒 (2) ? A detailed molecular analysis unveils that such a band is due to an ordered layer of water that builds up at the interface, with water dipoles oriented toward the bulk. The water order extends over 4–5 Å, as it can be deduced from the convergence of the Im𝜒 (2) spectrum with increasing probing thickness (Figure 50.17a). Including water molecules more than 5 Å from the surface does not change the shape or intensity of the calculated VSFG spectrum. It is interesting to notice that even for a strongly charged interface, the aqueous order only extends over 4–5 Å, which corresponds to roughly two to three layers of water. However, we should note here that the high computational cost of electronic structure-based methods imposes

|X|2 (arb. units)

Re(X) and Im(X) (arb. units)

50.8 Fluorite/Water Interface, Structures, and VSFG Intertwined

50 Structure and Dynamics of Solid/Liquid Interfaces

2

2

1

1

0

0

0

–1

–1

–1

–2 2800

3200

3600

–2 2800

(111)

3200

3600

6

2

Int Im 1 Re

1+

–2 2800

6 OH

4 2 0

3200

|X|2 (arb. units)

Re(X) and Im(X) (arb. units)

186

3600

Frequency (cm–1) Figure 50.16 Re𝜒 (2) , Im𝜒 (2) , and |𝜒 (2) |2 obtained from simulations (blue, red, and black respectively). Low pH (1+), neutral (111), and high pH (6OH) systems are considered.

severe limitations on the size of the accessible models. In this respect, our model is expected to capture the contribution to the spectra of the Stern layer (possibly the major contribution here) but cannot account for the full diffuse layer, which is expected to extend over a few nanometers thickness. In the case of CaF2 , the experimentally estimated Debye length is around 30 Å [129]. Alternative models have been proposed for the low pH fluorite/water interface. In particular, as also mentioned in the introduction, one of the suggested interpretation for the atomic scale disorder observed at low pH in the FM-AFM experiments, is proton adsorption at the interface [126]. In order to investigate the spectral response of such a system and to compare it to the experimental one, we build an additional model without fluoride vacancy, but instead with an excess of protons in the form of dissociated HCl is present (4HCl, 2.5 M solution). Such a system is reported in Figure 50.14b and would eventually corresponds to two excess protons per surface. The calculated VSFG spectra for this system are shown in Figure 50.15 in the last panel of (a). The first striking result is that overall the signal is much weaker than that obtained for the model with two fluorine vacancies per surface, which exhibit the same overall positive charge at the interface. Moreover, the main peak in the Im𝜒 (2) is located at ≈3500–3600 cm−1 , which is quite far from the peak location in the experimental spectra. This analysis would suggest that the excess proton alone cannot be responsible for the measured spectra, which instead originates from the water aligned by the positive fluorine vacancies. Let us now move to the analysis of the high pH conditions. The imaginary and real part of the VSFG spectrum together with the intensity spectrum calculated from the surface-selective VVCF analysis are presented in Figure 50.15b for the three different values of OH concentration on the surface. For the one and six substitutions, two main features can be observed in the imaginary part: the first is a positive band between 3280 and 3400 cm−1 , the second is a negative feature between 3400 and 3700 cm−1 . In the case of 12OH substitutions, the overall profile of Im𝜒 (2) is very different, with a broad negative band extending up to 3200 cm−1 where a crossing to positive values is finally observed. The real part and the intensity spectrum have a very high intensity below 3600 cm−1 (Figure 50.16), which is not present in the

50.8 Fluorite/Water Interface, Structures, and VSFG Intertwined

10

6

(a)

9 (Bulk)

(b)

3 0 0

–6 –20 2800 10 (c)

–9 3200

3600

2800 6 (d) 3

3200

3600

5

0 0

Slab thickness (Å)

Im(X) (arb. units)

7

–3

–10

3

–3

–10

–6 –20 2800 (A)

–9 3200

3600

Frequency (cm–1)

2800 (B)

3200

3600

1 (Interface)

Frequency (cm–1)

Figure 50.17 Im𝜒 (2) (top) and Re𝜒 (2) (bottom) as a function of the layer thickness included in the calculation. (a) Low pH (1 defect per surface); (b) High pH (6 substitutions per surface).

experiment [51]. The best agreement between calculated and experimental spectra is found for the models with one or six OH substitutions. From this, we can set, for the experimental pH = 13, an upper limit of six OH substitutions per surface corresponding to 3.87 substitutions/nm2 . As done for the low pH, also for the high pH conditions, we can decompose the overall signal in molecular contributions, thus providing a microscopic interpretation of the experimental spectra. In particular, the peak between 3600 and 3700 cm−1 is only associated with the OH groups on the surface, namely those OH groups that replace F− in the topmost layer, which is clear from the purple spectrum in the bottom panel of Figure 50.17. This frequency is very close to that of “free OH” [130, 131]; indeed, such an OH group does not form any hydrogen bond with water. This is clearly shown in the radial distribution function of the Ca–OH hydrogen with water oxygens: the distance between the proton of the Ca–OH and the oxygen from water (red curve, Figure 50.18) is much larger than the distance between the proton from one water molecule and the oxygen from the next water molecule (black curve Figure 50.18). The presence of a “free OH” signal at the solid/liquid interfaces is not so uncommon. A similar high-frequency peak has also been observed for the alumina/water interface [132], where no hydrogen bond is formed between the surface OH groups and the water molecules. In addition to the “free OH” peak, the high pH spectra also present a band between 3280 and 3400 cm−1 , which is instead associated with hydrogen-bonded water molecules at the interface. These hydrogen-bonded waters have an opposite

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50 Structure and Dynamics of Solid/Liquid Interfaces

1.5 Intensity (arb. units)

188

HW –OW HOH –OW HOH –HW

1.0 CaF2–ε 0.5

H H

O

O H

0.0

2

3

4

5

Distance (Å) Figure 50.18 HO and HH radial distribution functions. The subscript “W” stands for water, whereas the subscript “OH” stands for the grafted hydroxide.

orientation with respect to that of the OH groups, as evident from the opposite sign of Im𝜒 (2) for the two different peaks. The water ordering is not very pronounced and saturates with a distance of 2 Å (Figure 50.17). Finally, let us briefly comment on the neutral pH conditions. The neutral pH model is given by a fluorine-terminated surface in contact with neutral water (no excess of hydronium or hydroxide). The calculated Im𝜒 (2) is reported in Figure 50.16 (blue line), along with the overall signal intensity (Figure 50.16, black line). The signal for the Im𝜒 (2) is very weak and presents a negative sign in the higher frequency region (3400–3500 cm−1 ) and a positive band in the lower frequency range (3000–3200 cm−1 ). A molecular analysis shows that there is a strongly adsorbed layer of water at the interface with little or no preferential orientation at the interface. As a summary, the calculation of VSFG spectra from the DFT-based MD simulations and their comparison to experiments allowed us to pin down the atomistic details of the CaF2 interface with water and to provide a first molecular interpretation of the spectra. The very good agreement between theory and experiments in both the Re𝜒 (2) and Im𝜒 (2) signals give us confidence in our interpretations, at an unprecedented level of accuracy. We find that at low pH, the strong band in the hydrogen bond region is due to the highly ordered water as the surface is positively charged because of the F− dissolution. We also show that an eventual excess proton at the interface can only have a minor impact on the spectra. At high pH, the “free OH” signal is due to the surface Ca-OH groups, which do not hydrogen bond strongly to water.

50.9 Some Perspectives for Future Works

In this chapter, we have presented a few selected examples from our current research activity, which show how AIMD simulations can provide a microscopic

50.9 Some Perspectives for Future Works

understanding of properties at oxide/water interfaces, effectively complementing the experiments. We have discussed how ab initio MD simulations can be successfully employed to describe the water structure and dynamics at interfaces, and to understand the microscopic molecular origin of the vibrational properties measured in the experiments. Additionally, we have also shown how it is possible to use AIMD to calculate the local acidity of mineral surfaces, namely the dissociation constants of different surface sites. In addition to the more general challenges to make AIMD techniques more accurate and efficient, which are of course common to other fields, such as solid-state physics or computational biochemistry, we would like to highlight here a few points, which, we believe, are specific to solid/liquid interfaces and therefore of interest for the reader of this book. Several challenges remain ahead in order to move toward more realistic and predictive models of interfaces of interest for geochemistry but also for industrial purposes. The first challenge we foresee is certainly to move from crystalline, welldefined planar surfaces to more realistic amorphous mineral surfaces. Our work on amorphous silica is a first step in this direction. We have shown how the local environment has a strong impact on the silanols acidity and the geminals sites on the silica surface are a good example of how different environments, namely the local topology (nanoroughness) and hydrophobicity/philicity, can lead to very different local reactivity. A second challenge is the microscopic characterization of charged solid/water interfaces. In the last few years, attention has been shifting from uncharged interfaces (e.g. simpler water–vapor interfaces) to charged interfaces. Here, the use of accurate methods that address the heterogeneity of the interface and treat solid and liquid at the same level of theory is even more crucial. Indeed, the presence of localized/delocalized charges on the surface of ions with very different properties, as well as of external fields, is expected to strongly modify the water properties. One has to unravel how the ions are structurally organized at these interfaces, and how these microscopic charged systems compare to the debated EDL or Langmuir models at mineral/water interfaces. Directly or indirectly related is the investigation of inhomogeneous catalysis at solid/water interfaces. This is especially of interest for industrial purposes where rational design of the surface and rational optimization of chemical reactions in a controlled local environment is highly desirable. Our investigation of silica–water interfaces represent a pioneering work on addressing the role of ions from an AIMD point of view. In particular, we have shown how the ions influence the water organization at interfaces and how they can strongly modify the local reactivity, e.g. shifting the local acidity constants by a few units. A rationalization of pKa changes with respect to the types of electrolytes has shown that the stabilization of the conjugated base has to be accounted for. We have also started to address the role of ions in modifying the interfacial vibrational spectra and further investigations in this direction are in progress in order to accurately include the effect of local electric fields on VSFG spectra. Another challenge is to extend the use of ab initio methods to larger systems and longer time scales, which would improve the prediction of macroscopic properties. Addressing realistic interfaces call for multiscale methods where the accuracy

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50 Structure and Dynamics of Solid/Liquid Interfaces

of AIMD techniques may be combined with the efficiency of more approximate methods. In particular, accurate force fields designed from AIMD calculations can help in the equilibration of complex systems, such as those containing ions, where the presence of charges can slow down equilibration processes, e.g. water dynamics. Progresses are also needed in making the calculation of vibrational interface spectra computationally more affordable. In our recent work, we have shown how it is possible to use suitable velocity–velocity correlation functions, also including the appropriate selection rules, to calculate the one-dimensional VSFG spectra. Such progresses have made possible to investigate multiple model systems, still retaining a full electronic structure description of interfaces. Further progress is required in order to tackle more complex spectroscopic response functions, as for example those measured in time-resolved, pump-probe vibrational experiments. Acknowledgements

We are indebted to Dr Morgane Pfeiffer-Laplaud, Dr Rémi Khatib, Dr. Alvaro Cimas, and Simone Pezzotti (PhD student) for their essential works that have been presented here. Marialore Sulpizi thanks Deutsche Forschungsgemeinschaft (DFG) for financial support through TRR146, project A4.

References 1. Sy, K., Kumar, N., Persson, P. et al.

2.

3.

4.

5.

(2013). Development of a ReaxFF reactive force field for titanium dioxide/water systems. Langmuir 29: 7838. Sung-Yup, K., van Duin, A., and Kubicki, J. (2013). Molecular dynamics simulations of the interactions between TiO2 nanoparticles and water with Na+ and Cl− , methanol, and formic acid using a reactive force field. J. Mater. Res. 28: 513. Machesky, M., Predota, M., Wesolowski, D. et al. (2008). Surface protonation at the rutile (110) interface: explicit incorporation of solvation structure within the refined MUSIC model framework. Langmuir 24: 12331. Predota, M., Machesky, M., and Wesolowski, D. (2016). Molecular origins of the zeta potential. Langmuir 32: 10189. Parez, S., Predota, M., and Machesky, M. (2014). Dielectric properties of water at rutile and graphite surfaces: effect of molecular structure. J. Phys. Chem. C 118: 4818.

6. Tesson, S., Salanne, M., Rotenberg, B.

7.

8.

9.

10.

11.

et al. (2016). Classical polarizable force field for clays: pyrophyllite and talc. J. Phys. Chem. C 120: 3749. Kerisit, S., Okumura, M., Rosso, K., and Machida, M. (2016). Molecular simulation of cesium adsorption at the basal surface of phyllosilicate minerals. Clays Clay Miner. 64: 389. Meena, S.K. and Sulpizi, M. (2013). Understanding the microscopic origin of gold nanoparticle anisotropic growth from molecular dynamics simulations. Langmuir 29 (48): 14954–14961. Meena, S.K., Celiksoy, S., Schaefer, P. et al. (2016). The role of halide ions in the anisotropic growth of gold nanoparticles: a microscopic, atomistic perspective. Phys. Chem. Chem. Phys. 18: 13246–13254. Hartkamp, R., Siboulet, B., Dufreche, J., and Coasne, B. (2015). Ion-specific adsorption and electroosmosis in charged amorphous porous silica. Phys. Chem. Chem. Phys. 17: 24683. Bacle, P., Dufreche, J., Rotenberg, B. et al. (2016). Modeling the transport

References

12.

13.

14.

15.

16.

17.

18.

19.

20.

21.

22.

23.

of water and ionic tracers in a micrometric clay sample. Appl. Clay Sci. 123: 18. Pean, C., Merlet, C., Rotenberg, B. et al. (2014). On the dynamics of charging in nanoporous carbon-based supercapacitors. ACS Nano 8: 1576. Carof, A., Marry, V., Salanne, M. et al. (2014). Coarse graining the dynamics of nano-confined solutes: the case of ions in clays. Mol. Simul. 40: 237. Born, M. and Oppenheimer, J. (1927). Zur quantentheorie der molekeln. Ann. Phys. 84: 457. Hohenberg, P. and Kohn, W. (1964). Inhomogeneous electron gas. Phys. Rev. 136 (3B): 864–871. Kohn, W. and Sham, L. (1965). Selfconsistent equations including exchange and correlation effects. Phys. Rev. 140 (4A): 1133–1138. Ceperley, D.M. and Alder, B.J. (1980). Ground state of the electron gas by a stochastic method. Phys. Rev. Lett. 45: 566. Perdew, J., Burke, K., and Ernzerhof, M. (1996). Generalized gradient approximation made simple. Phys. Rev. Lett. 77: 3865. Becke, A. (1988). Density-functional exchange-energy approximation with correct asymptotic behavior. Phys. Rev. A 38 (6): 3098–3100. Lee, C., Yang, W., and Parr, R. (1988). Development of the Colle-Salvetti correlation-energy formula into a functional of the electron density. Phys. Rev. B 37 (2): 785–789. Grimme, S., Antony, J., Ehrlich, S., and Krieg, H. (2010). A consistent and accurate ab initio parametrization of density functional dispersion correction (DFT-D) for the 94 elements H-Pu. J. Chem. Phys. 132: 154104. von Lilienfeld, O.A., Tavernelli, I., Röthlisberger, U., and Sebastiani, D. (2004). Optimization of effective atom centered potentials for London dispersion forces in density functional theory. Phys. Rev. Lett. 93: 153004. von Lilienfeld, O.A., Tavernelli, I., Rothlisberger, U., and Sebastiani, D. (2005). Variational optimization of effective atom centered potentials for

24.

25.

26.

27.

28.

29.

30.

31.

32.

33.

34.

molecular properties. J. Chem. Phys. 122: 014113. Burow, A., Bates, J., Furche, F., and Eshuis, H. (2014). Analytical firstorder molecular properties and forces within the adiabatic connection random phase approximation. J. Chem. Theory Comput. 10: 180. Hutter, J., Iannuzzi, M., Schiffmann, F., and VandeVondele, J.; CP2K developers group (2014). Wiley Interdiscip. Rev.: Comput. Mol. Sci. 4: 15–25. http:// cp2k.org. Goedecker, S., Teter, M., and Hutter, J. (1996). Separable dual-space gaussian pseudopotentials. Phys. Rev. B 54: 1703. Hartwigsen, C., Goedecker, S., and Hutter, J. (1998). Relativistic separable dual-space Gaussian pseudopotentials from H to Rn. Phys. Rev. B 58: 3641–3662. Sulpizi, M. and Sprik, M. (2008). Acidity constants from vertical energy gaps: density functional theory based molecular dynamics implementation. Phys. Chem. Chem. Phys. 10: 5238–5249. Sulpizi, M. and Sprik, M. (2010). Acidity constants from DFT-based molecular dynamics simulations. J. Phys. Condens. Matter 22: 284116–284123. Costanzo, F., Sulpizi, M., della Valle, R., and Sprik, M. (2011). The oxidation of tyrosine and tryptophan studied by a molecular dynamics normal hydrogen electrode. J. Chem. Phys. 134: 244508. Cheng, J., Sulpizi, M., and Sprik, M. (2009). Redox potentials and pKa for benzoquinone from density functional theory based molecular dynamics. J. Chem. Phys. 131: 154504–154523. Mangold, M., Rolland, L., Costanzo, F. et al. (2011). Absolute pK(a) values and solvation structure of amino acids from density functional based molecular dynamics simulation. J. Chem. Theory Comput. 7: 1951. Sulpizi, M., Gaigeot, M.P., and Sprik, M. (2012). The silica–water interface: how the silanols determine the surface acidity and modulate the water properties. J. Chem. Theory Comput. 8 (3): 1037–1047. Tazi, S., Rotenberg, B., Salanne, M. et al. (2012). Absolute acidity of clay

191

192

50 Structure and Dynamics of Solid/Liquid Interfaces

35.

36.

37.

38.

39.

40.

41.

42.

43.

44.

45.

edge sites from ab-initio simulations. Geochim. Cosmochim. Acta 94: 1. Churakov, S., Labbez, C., Pegado, L., and Sulpizi, M. (2014). Intrinsic acidity of surface sites in calcium silicate hydrates and its implication to their electrokinetic properties. J. Phys. Chem. C 118: 11752–11762. Cheng, J., Liu, X., VandeVondele, J. et al. (2014). Redox potentials and acidity constants from density functional theory based molecular dynamics. Acc. Chem. Res. 47 (12): 3522–3529. Pfeiffer-Laplaud, M., Costa, D., Tielens, F. et al. (2015). Bimodal acidity at the amorphous silica/water interface. J. Phys. Chem. C 119 (49): 27354–27362. Hummer, G. and Szabo, A. (1996). Calculation of free energy differences from computer simulations of initial and final states. J. Chem. Phys. 105: 2004–2010. McQuarrie, D. (1976). Statistical Mechanics. New York: Harper-Collins Publishers. Kubo, R., Toda, M., and Hashitsume, N. (1985). Statistical PHYSICS IINonequilibrium Statistical Mechanics, 2e. Heidelberg: Springer-Verlag. Gaigeot, M.P. (2010). Theoretical spectroscopy of floppy peptides at room temperature. A DFTMD perspective: gas and aqueous phase. Phys. Chem. Chem. Phys. 12: 3336. Thomas, M., Brehm, M., Fligg, R. et al. (2013). Computing vibrational spectra from ab initio molecular dynamics. Phys. Chem. Chem. Phys. 15: 6608. Marinica, C., Grégoire, G., Desfranˇcois, C. et al. (2006). Ab initio molecular dynamics of protonated dialanine and comparison to infrared multiphoton dissociation experiments. J. Phys. Chem. A 110: 8802. Cimas, A., Vaden, T.D., de Boer, T.S.J.A. et al. (2009). Vibrational spectra of small protonated peptides from finite temperature MD simulations and IRMPD Spectroscopy. J. Chem. Theory Comput. 5: 1068. Sediki, A., Snoek, L.C., and Gaigeot, M.P. (2011). N–H+ vibrational anharmonicities directly revealed from

46.

47.

48.

49.

50.

51.

52.

53.

54.

55.

DFT-based molecular dynamics simulations on the Ala7 H+ protonated peptide. Int. J. Mass Spectrom. 308: 281. Beck, J.P., Gaigeot, M.P., and Lisy, J.M. (2013). Anharmonic vibrations of N–H in Cl– (Nmethylacetamide)1 (H2 O)0–2 Ar2 cluster ions. Combined IRPD experiments and BOMD simulations. Phys. Chem. Chem. Phys. 15: 16736. Gaigeot, M.P. and Sprik, M. (2003). Ab initio molecular dynamics computation of the infrared spectrum of aqueous Uracil. J. Phys. Chem. B 107: 10344. Bovi, D., Mezzetti, A., Vuilleumier, R. et al. (2011). Environmental effects on vibrational properties of carotenoids: experiments and calculations on peridinin. Phys. Chem. Chem. Phys. 13: 20954. Gaigeot, M.P., Sprik, M., and Sulpizi, M. (2012). Oxide/water interfaces: how the surface chemistry modifies interfacial water properties. J. Phys. Condens. Matter 24: 124106. Sulpizi, M., Salanne, M., Sprik, M., and Gaigeot, M.P. (2013). Vibrational sum frequency generation spectroscopy of the water liquid-vapor interface from density functional theory-based molecular dynamics simulations. J. Phys. Chem. Lett. 4 (1): 83–87. Khatib, R., Backus, E.H.G., Bonn, M. et al. (2016). Water orientation and hydrogen-bond structure at the fluorite/water interface. Sci. Rep. 6: 24287. Khatib, R. and Sulpizi, M. (2017). Sum frequency generation spectra from velocity–velocity correlation functions. J. Phys. Chem. Lett. 8 (6): 1310–1314. Shen, Y.R. (1989). Surface properties probed by second-harmonic and sumfrequency generation. Nature 337: 519. Du, Q., Freysz, E., and Shen, Y.R. (1994). Surface vibrational spectroscopic studies of hydrogen bonding and hydrophobicity. Science 264: 826. Tian, C. and Shen, Y.R. (2014). Recent progress on sum-frequency spectroscopy. Surf. Sci. Rep. 69: 105.

References 56. Jubb, A., Hua, W., and Allen, H.

57.

58.

59.

60.

61.

62.

63.

64.

65.

66.

(2012). Environmental chemistry at vapor/water interfaces: insights from vibrational sum frequency generation spectroscopy. Annu. Rev. Phys. Chem. 63: 107. Arnolds, H. and Bonn, M. (2010). Ultrafast surface vibrational dynamics. Surf. Sci. Rep. 65: 45. Dewand, S., Yeganeh, M., and Borguet, E. (2013). Experimental correlation between interfacial water structure and mineral reactivity. J. Phys. Chem. Lett. 4: 1977. Hopkins, A., McFearin, C., and Richmond, G. (2005). Investigations of the solid-aqueous interface with vibrational sum-frequency spectroscopy. Curr. Opin. Solid State Mater. Sci. 9: 19. Geiger, F. (2009). Second harmonic generation, sum frequency generation, and chi(3): dissecting environmental interfaces with a nonlinear optical Swiss Army knife. Annu. Rev. Phys. Chem. 60: 61. Nihonyanagi, S., Kusaka, R., Inoue, K. et al. (2015). Accurate determination of complex X(2) spectrum of the air/water interface. J. Chem. Phys. 143: 124707. Morita, A. (2004). Toward computation of bulk quadrupolar signals in vibrational sum frequency generation spectroscopy. Chem. Phys. Lett. 398: 361. Shen, Y. (2012). Basic theory of surface sum-frequency generation. J. Phys. Chem. C 116: 15505. Morita, A. and Hynes, J.T. (2002). A theoretical analysis of the SFG spectrum of the water surface. II. Time dependent approach. J. Phys. Chem. B 106: 673. Morita, A. and Ishiyama, T. (2008). Recent progress in theoretical analysis of vibrational sum frequency generation spectroscopy. Phys. Chem. Chem. Phys. 10: 5801. Nihonyanagi, S., Ishiyama, T., Lee, T. et al. (2011). Unified molecular view of the air/water interface based on experimental and theoretical spectra of an isotopically diluted water surface. J. Am. Chem. Soc. 133: 16875.

67. Ishiyama, T., Hideaki, T., and Morita,

68.

69.

70.

71.

72.

73.

74.

75.

76.

A. (2012). Vibrational spectrum at a water surface: a hybrid quantum mechanics/molecular mechanics molecular dynamics approach. J. Phys. Condens. Matter 24: 124107. Salanne, M., Vuilleumier, R., Madden, P. et al. (2008). Polarizabilities of individual molecules and ions in liquids from first principles. J. Phys. Condens. Matter 20: 494207. Gaigeot, M.P. and Spezia, R. (2015). Theoretical methods for vibrational spectroscopy and collision induced dissociation in the gas phase. Top. Curr. Chem. 364: 99. Nagata, Y. and Mukamel, S. (2010). Vibrational sum-frequency generation spectroscopy at the water/lipid interface: molecular dynamics simulation study. J. Am. Chem. Soc. 132: 6434. Ostroverkhov, V., Waychunas, G.A., and Shen, Y.R. (2005). New information on water interfacial structure revealed by phase-sensitive surface spectroscopy. Phys. Rev. Lett. 94: 046102. Ostroverkhov, N., Tian, C., and Shen, Y. (2008). Characterization of vibrational resonances of water-vapor interfaces by phase-sensitive sum-frequency spectroscopy. Phys. Rev. Lett. 100: 096102. Gaigeot, M.P., Martinez, M., and Vuilleumier, R. (2007). Infrared spectroscopy in the gas and liquid phase from first principle molecular dynamics simulations: application to small peptides. Mol. Phys. 105: 2857. Martinez, M., Gaigeot, M.P., Borgis, D., and Vuilleumier, R. (2006). Extracting effective normal modes from equilibrium dynamics at finite temperature. J. Chem. Phys. 125: 144106. Nonella, M., Mathias, G., and Tavan, P. (2003). Infrared spectrum of pbenzoquinone in water obtained from a QM/MM hybrid molecular dynamics simulation. J. Phys. Chem. A 107: 8638. Mathias, G., Ivanov, S.D., Witt, A. et al. (2012). Infrared spectroscopy of fluxional molecules from (ab initio) molecular dynamics: resolving large-amplitude motion, multiple

193

194

50 Structure and Dynamics of Solid/Liquid Interfaces

77.

78.

79.

80.

81.

82.

83.

84.

85.

86.

conformations, and permutational symmetries. J. Chem. Theory Comput. 8: 224. Corcelli, S.A. and Skinner, J.L. (2005). Infrared and Raman line shapes of dilute hod in liquid H2 O and D2 O from 10 to 90 degree. J. Phys. Chem. A 109 (28): 6154–6165. Marzari, N. and Vanderbilt, D. (1997). Maximally localized generalized wannier functions for composite energy bands. Phys. Rev. B 56: 12847–12865. Cimas, A., Tielens, F., Sulpizi, M. et al. (2014). The amorphous silicaliquid water interface studied by ab initio molecular dynamics (AIMD): local organization in global disorder. J. Phys. Condens. Matter 26 (24): 244106–244115. Pfeiffer-Laplaud, M. and Gaigeot, M. (2016). Electrolytes at the hydroxylated (0001) α-quartz/water interface: location and structural effects on interfacial silanols by DFT-based MD. J. Phys. Chem. C 120: 14034. Pfeiffer-Laplaud, M. and Gaigeot, M.P. (2016). Adsorption of singly charged ions at the hydroxylated (0001) alphaquartz/water interface. J. Phys. Chem. C 120 (9): 4866–4880. Kosmulski, M. (2002). The pHdependent surface charging and the points of zero charge. J. Colloid Interface Sci. 253: 77. VandeVondele, J., Krack, M., Mohamed, F. et al. (2005). Quickstep: fast and accurate density functional calculations using a mixed gaussian and plane waves approach. Comput. Phys. Commun. 167 (2): 103–128. Grimme, S. (2006). Semiempirical GGA-type density functional constructed with a long-range dispersion correction. J. Comput. Chem. 27 (15): 1787–1799. VandeVondele, J. and Hutter, J. (2007). Gaussian basis sets for accurate calculations on molecular systems in gas and condensed phases. J. Chem. Phys. 127 (11): 114105–114113. Bellucci, F., Lee, S.S., Kubicki, J.D. et al. (2015). Rb+ adsorption at the quartz(101)-aqueous interface: comparison of resonant anomalous X-ray

87.

88.

89.

90.

91.

92.

93.

94.

95.

96.

reflectivity with ab initio calculations. J. Phys. Chem. C 119 (9): 4778–4788. Kroutil, O., Chval, Z., Skelton, A.A., and Predota, M. (2015). Computer simulations of quartz (101)-water interface over a range of pH values. J. Phys. Chem. C 119 (17): 9274–9286. Lopes, P.E.M., Murashov, V., Tazi, M. et al. (2006). Development of an empirical force field for silica. Application to the quartz-water interface. J. Phys. Chem. B 110 (6): 2782–2792. Notman, R. and Walsh, T.R. (2009). Molecular dynamics studies of the interactions of water and amino acid analogues with quartz surfaces. Langmuir 25 (3): 1638–1644. Wander, M.C.F. and Clark, A.E. (2008). Structural and dielectric properties of quartz-water interfaces. J. Phys. Chem. C 112 (50): 19986–19994. Skelton, A.A., Fenter, P., Kubicki, J.D. et al. (2011). Simulations of the quartz(10(1)over-bar1)/water interface: a comparison of classical force fields, ab initio molecular dynamics, and Xray reflectivity experiments. J. Phys. Chem. C 115 (5): 2076–2088. Fenter, P. and Sturchio, N.C. (2004). Mineral-water interfacial structures revealed by synchrotron X-ray scattering. Prog. Surf. Sci. 77 (5–8): 171–258. de Leeuw, N.H., Higgins, F.M., and Parker, S.C. (1999). Modeling the surface structure and stability of α-quartz. J. Phys. Chem. B 103 (8): 1270–1277. Adeagbo, W.A., Doltsinis, N.L., Klevakina, K., and Renner, J. (2008). Transport processes at α-quartz-water interfaces: insights from first-principles molecular dynamics simulations. Chem. Phys. Chem. 9 (7): 994–1002. Musso, F., Mignon, P., Ugliengo, P., and Sodupe, M. (2012). Cooperative effects at water-crystalline silica interfaces strengthen surface silanol hydrogen bonding. An ab initio molecular dynamics study. Phys. Chem. Chem. Phys. 14 (30): 10507–10514. Leung, K., Nielsen, I.M.B., and Criscenti, L.J. (2009). Elucidating the bimodal acid-base behavior of the water-silica interface from first

References

97.

98.

99.

100.

101.

102.

103.

104.

105.

106.

107.

principles. J. Am. Chem. Soc. 131: 18358. Hiemstra, T., Vanema, P., and Riemsdijk, W.V. (1996). Intrinsic proton affinity of reactive surface groups of metal (hydr)oxides: the bond valence principle. J. Colloid Interface Sci. 184: 680. Ong, S., Zhao, X., and Eisenthal, K.B. (1992). Polarization of water molecules at a charged interface: second harmonic studies of the silica/water interface. Chem. Phys. Lett. 191 (3–4): 327–335. Rimola, A., Costa, D., Sodupe, M. et al. (2013). Silica surface features and their role in the adsorption of biomolecules: computational modeling and experiments. Chem. Rev. 113 (6): 4216–4313. Pfeiffer-Laplaud, M., Gaigeot, M., and Sulpizi, M. (2016). pKa at quartz/electrolyte interfaces. J. Phys. Chem. Lett. 7: 3229. Du, Q., Freysz, E., and Shen, Y. (1994). Vibrational spectra of water molecules at quartz/water interfaces.. Phys. Rev. Lett. 72: 238. Ostroverkhov, V., Waychunas, G.A., and Shen, Y.R. (2004). Vibrational spectra of water at water/α-quartz (0 0 0 1) interface. Chem. Phys. Lett. 386: 144. Pezzotti, S., Galimberti, D., Shen, Y.R., and Gaigeot, M.-.P (2018). Structural definition of BIL and DL: a new universal methodology to rationalize non-linear ?(2)(?) SFG signals at charged interfaces, including ?(3)(?) contributions. Phys. Chem. Chem. Phys. 20: 5190–5199. Willard, A. and Chandler, D. (2010). Instantaneous liquid interfaces. J. Phys. Chem. B 114: 1954. Kessler, J., Elgabarty, H., Spura, T. et al. (2015). Structure and dynamics of the instantaneous water/vapor interface revisited by path-integral and ab initio molecular dynamics simulations. J. Phys. Chem. B 119 (31): 10079. Sun, S., Liang, R., Xu, X. et al. (2016). Phase reference in phase-sensitive sumfrequency vibrational spectroscopy. J. Chem. Phys. 144: 244711. Blöchl, P.E. (1995). Electrostatic decoupling of periodic images of plane-wave

108.

109.

110.

111.

112.

113.

114.

115.

116.

expanded densities and derived atomic point charges. J. Chem. Phys. 103: 7422–7428. Azam, M.S., Darlington, A., and Gibbs-Davis, J.M. (2014). The influence of concentration on specific ion effects at the silica/water interface. J. Phys. Condens. Matter 26 (24): 244107–244116. Karlsson, M., Craven, C., Dove, P.M., and Casey, W.H. (2001). Surface charge concentrations on silica in different 1.0 M metal-chloride background electrolytes and implications for dissolution rates. Aquat. Geochem. 7 (1): 13–32. Dove, P. and Craven, C. (2005). Surface charge density on silica in alkali and alkaline earth chloride electrolyte solutions. Geochim. Cosmochim. Acta 69 (21): 4963–4970. Tadros, T. and Lyklema, J. (1968). Adsorption of potential-determining ions at the silica-aqueous electrolyte interface and the role of some cations. J. Electroanal. Chem. 17 (3–4): 267–275. Franks, G.V. (2002). Zeta potentials and yield stresses of silica suspensions in concentrated monovalent electrolytes: isoelectric point shift and additional attraction. J. Colloid Interface Sci. 249 (1): 44–51. Azam, M.S., Weeraman, C.N., and Gibbs-Davis, J.M. (2012). Specific cation effects on the bimodal acid-base behavior of the silica/water interface. J. Phys. Chem. Lett. 3 (10): 1269–1274. Azam, M.S., Weeraman, C.N., and Gibbs-Davis, J.M. (2013). Halideinduced cooperative acid-base behavior at a negatively charged interface. J. Phys. Chem. C 117 (17): 8840–8850. Dewan, S., Carnevale, V., Bankura, A. et al. (2014). Structure of water at charged interfaces: a molecular dynamics study. Langmuir 30 (27): 8056–8065. DelloStritto, M.J., Kubicki, J., and Sofo, J.O. (2014). Density functional theory simulation of hydrogen-bonding structure and vibrational densities of states at the quartz (101)-water interface and its relation to dissolution as a function of solution ph and ionic

195

196

50 Structure and Dynamics of Solid/Liquid Interfaces

117.

118.

119.

120.

121.

122.

123. 124.

125.

strength. J. Phys. Condens. Matter 26 (24): 244101–244112. Cheng, J. and Sprik, M. (2014). The electric double layer at a rutile TiO2 water interface modelled using density functional theory based molecular dynamics simulation. J. Phys. Condens. Matter 26 (24): 244108–244118. Hassanali, A.A., Zhang, H., Knight, C. et al. (2010). The dissociated amorphous silica surface: model development and evaluation. J. Chem. Theory Comput. 6 (11): 3456–3471. Emami, F.S., Puddu, V., Berry, R.J. et al. (2014). Force field and a surface model database for silica to simulate interfacial properties in atomic resolution. Chem. Mater. 26 (8): 2647–2658. Butenuth, A., Moras, G., Schneider, J. et al. (2012). Ab initio derived force-field parameters for molecular dynamics simulations of deprotonated amorphous-SiO2 /water interfaces. Phys. Status Solidi B 249 (2): 292–305. Duval, Y., Mielczarski, J.A., Pokrovsky, O.S. et al. (2002). Evidence of the existence of three types of species at the quartz-aqueous solution interface at pH 0-10: XPS surface group quantification and surface complexation modeling. J. Phys. Chem. B 106 (11): 2937–2945. Putnis, C.V. and Ruiz-Agud, E. (2013). The mineral-water interface: where minerals react with the environment. Elements 9 (3): 177–182. Putnis, A. (2014). Why mineral interfaces matter. Science 343: 1441–1442. Saxena, V. and Ahmed, S. (2001). Dissolution of fluoride in groundwater: a water-rock interaction study. Environ. Geol. 40 (9): 1084–1087. Godinho, J., Piazolo, S., and Evins, L. (2012). Effect of surface orientation on

126.

127.

128.

129.

130.

131.

132.

dissolution rates and topography of CaF2 . Geochim. Cosmochim. Acta 86: 392–403. Kobayashi, N., Itakura, S., Asakawa, H., and Fukuma, T. (2013). Atomic-scale processes at the fluorite-water interface visualized by frequency modulation atomic force microscopy. J. Phys. Chem. C 117 (46): 24388–24396. Becraft, K.A. and Richmond, G.L. (2001). In situ vibrational spectroscopic studies of the CaF2 /H2 O interface. Langmuir 17 (25): 7721–7724. Becraft, K.A., Moore, F.G., and Richmond, G.L. (2003). Charge reversal behavior at the CaF2 /H2 O/SDS interface as studied by vibrational sum frequency spectroscopy. J. Phys. Chem. B 107 (16): 3675–3678. Jena, K.C., Covert, P.A., and Hore, D.K. (2011). The effect of salt on the water structure at a charged solid surface: differentiating second- and third-order nonlinear contributions. J. Phys. Chem. Lett. 2 (9): 1056–1061. Walrafen, G.E. and Douglas, R.T.W. (2006). Raman spectra from very concentrated aqueous NaOH and from wet and dry, solid, and anhydrous molten, LIOH, NaOH, and KOH. J. Chem. Phys. 124 (11): 114504. Roberts, S.T., Petersen, P.B., Ramasesha, K. et al. (2009). Observation of a zundel-like transition state during proton transfer in aqueous hydroxide solutions. Proc. Natl. Acad. Sci. U.S.A. 106 (36): 15154–15159. Tong, Y., Wirth, J., Kirsch, H. et al. (2015). Optically probing Al-O and O-H vibrations to characterize water adsorption and surface reconstruction on alpha-alumina: an experimental and theoretical study. J. Chem. Phys. 142 (5): 054704.

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51 Adsorption of Proteins and Anti-biofilm Strategies Vincent Humblot and Claire-Marie Pradier

51.1 Introduction to Biofilms

More than 70 years ago, the scientific community described for the first time the presence of bacterial contaminations at surfaces and named that effect on a solid surface a biofilm [1]. A biofilm is a multicellular community that is more or less complex, often composed of symbiotic microorganisms (bacteria, fungi, algae, etc.) adhering to a surface or together as aggregates, and characterized by the secretion of a protective and adhesive matrix. Consequently, bacteria can be found in two different states when encountered in natural environment: planktonic, i.e. free in media, and sessile, anchored or attached to a surface [2]. Biofilms generally form in water or in an aqueous medium [3]. The biofilm matrix usually encapsulates and protects the embedded bacteria, thus conferring high resistance to surrounding stresses [4]. The formation of a biofilm is a potentially normal step of the life cycle of most bacteria, displaying cooperative behavior and producing differentiated phenotypes that lead to specific functions, sometimes in response to stress [5]. Nowadays, biofilms are still raising accurate problems in a broad range of fields from biomedical to environmental and food industries [6, 7]; two routes are commonly investigated in order to face the issue of biofilm development, trying to get a comprehensive understanding of the initial step of biofilm formation, and, as a direct consequence, design and elaborate strategies to prevent adsorption/adhesion of biomolecules. Figure 51.1 displays a very complete and complex description of the formation of a biofilm [8]. However, one can simplify this complex mechanism to four major steps to form a functional and mature biofilm: 1)

Initial attachment of bacterial cells (before that, adsorption of proteins and extracellular macromolecules), 2) Aggregation and accumulation of cell layers, 3) Biofilm maturation by secretion of an exopolymeric matrix, and 4) Detachment of cells from the biofilm into the planktonic state [9]. This chapter aims at making a point of some recent advances in these two fields, namely the adsorption of proteins on solid surfaces, the numerous parameters that Surface and Interface Science: Liquid and Biological Interfaces, First Edition. Edited by Klaus Wandelt. © 2020 Wiley-VCH Verlag GmbH & Co. KGaA. Published 2020 by Wiley-VCH Verlag GmbH & Co. KGaA.

1. Substratum pre-conditioning by ambient molecules

2. Cell deposition

6. Convective and diffusive transport of O2 and nutrients

4. Desorption

3. Cell adsorption

5. Cell-to-cell signaling and onset of exopolymer production

9. Detachment, erosion and sloughing 8. Secretion of polysaccharide matrix 7. Replication and growth

Substratum Figure 51.1 Processes governing biofilm formation. (Source: Breyers and Ratner 2004 [8]. Reproduced with permission of the American Society for Microbiology.)

51.2 Protein Adsorption, Key Parameters, and Protein Film Description

can influence it, and the consequence on the biofilm formation; in Section 51.3, we propose an overview of the various strategies, some being at a mature state of development and some still in their infancy, to develop antibacterial surfaces. A brief overview of the mechanism of biofilm formation is given here below; for extensive details, readers should consult comprehensive reviews [10–12]. During the first stage of biofilm formation (steps 1–3 of Figure 51.1), the only reversible step, the interactions that are taking place between proteins and microorganisms and the surface are nonspecific, although they are driven by a multitude of very specific forces such as electrostatic, hydrophobic, and/or van der Waals and also the texture or roughness of the surface or the surface charge and chemistry. One should also consider parameters within the media that could influence cell attachment, such as pH, temperature, or flow velocity, to cite a few. These parameters are of high importance when considering the adsorption of proteins, before the cell adhesion step. Section 51.2 will, thus, specifically look into protein adsorption and the parameters influencing it, while Section 51.3 will deal with the following steps when bacteria are ready to colonize a substratum. During the maturation phase of a biofilm, i.e. once the substratum is almost fully covered by microorganisms, cell-to-cell communication and interactions take place, usually accompanied with the secretion of extra polymeric substances (EPSs) that protect the biofilm against external aggressions. At that point, the only way to get rid of the biofilm is, most of the time, by mechanical methods, such as scratching or polishing of the surface. The key step is thus indeed how to reduce, even suppress, these interorganism interactions, in order to reduce the attachment of microorganisms.

51.2 Protein Adsorption, Key Parameters, and Protein Film Description 51.2.1 Some General Considerations

Protein interaction is a determining step in the formation of biofilms on solid materials, at stake in various fields, going from the integration of materials in the human body [13] to biorecognition, and even biocorrosion and ruin of materials in the building industry or marine environment. Overall, it is now widely admitted that the initial step of biofilm formation is the adsorption of proteins, often surrounded par extracellular polymers and carbohydrates. Proteins are the species that ensure an irreversible attachment of biofilms, providing both a source of nutriments for coming bacterial cells and strong anchoring points to the surface. The biorecognition phenomena indeed greatly influence cell adhesion, implant osseointegration, blood coagulation or biofouling, etc. It is now commonly admitted that all these biointerface phenomena start with protein and exopolymer adsorption, justifying the focus of this chapter. Biosensing is another field of research where adsorption of proteins (specific vs. nonspecific) has to be controlled the best possible way.

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Indeed, after water interaction, which may lead to various situations depending on the material surface properties, proteins arrive at the surface and may undergo simple and nondegrading adsorption or full denaturation; this also depends on the type of interactions (electrostatic, van der Waals, chemical, etc.) between the protein and the surface. One sees the importance of studying theses interactions at the molecular scale. Proteins are the elementary functional molecules of the living world; they are constituted of peptide chains, organized in 2D and 3D structures (conformation), which determine their bioactivity. It is thus essential to address both the nature of the protein–solid surface interaction and the kinetics, energetics, as well as the resulting changes in the protein 3D structure. All these parameters obviously depend on the material chemistry and structure but also on the conditions of interactions, pH, protein concentration, ionic force, etc. Last but not least, talking about the issue of protein–surface interactions cannot be done without citing the extensive work by Latour [14]. The author already established that the understanding and control of the initial protein films is a key issue in the controlled growth of a biofilm on a surface. He then stressed on the importance of understanding the parameters involved in protein–surface interactions, in particular the surface hydrophobicity; last but not least, he then recalled that, by adsorbing proteins from multicomponent solutions, a small adsorbed protein on a surface may be displaced by another one, which is larger and strongly interacting (Vroman effect [15]). Of course, it is not possible to write an exhaustive description of all criteria that regulate protein adsorption; our goal in this chapter is to mention the most important ones and exemplify them with significant results from the recent literature. We encourage the readers to learn more from other well-documented reviews, some of them being cited in Ref. [16]. 51.2.2 Possible Protein Surface Types of Interactions

Protein molecules may interact with materials via at least four types of bonding, ionic, hydrophobic, van der Waals interaction, and charge transfer. Hydrogen bonds are rarely cited as playing an important role in the interaction of proteins with materials; however, they do ensure the 3D structure of the macromolecules and may be altered or modified upon adsorption of the latter. It is now commonly admitted that proteins denature at a solid–liquid or air–liquid interface while they retain their original conformation more easily on neutral hydrophilic surfaces than on hydrophobic or charged ones [17]. Various parameters should be taken into account when studying protein adsorption, going from surface properties, roughness, hydrophobicity, charge, etc., to protein and solution characteristics, size, stiffness, and acidic properties, to mention some that will be exemplified here below. The type of interactions involved in protein binding is also very much dependent on the protein flexibility. As an example, Kubiak-Ossowska et al. used molecular

51.2 Protein Adsorption, Key Parameters, and Protein Film Description

z x

Figure 51.2 Results of a molecular dynamics calculation of lysozyme on a charged silica surface. The protein adsorbs at its N/C terminal as observed experimentally. Only residues close to the surface are shown;

y

for the silica surface, yellow and red balls represent the Si and O atoms, respectively. (Source: Kubiak-Ossowska and Mulheran 2010 [18]. Reproduced with permission of the American Chemical Society.)

dynamics simulations to describe the adsorption of lysozyme on a charged silica surface; they came to the conclusion that thanks to its flexibility, lysozyme can strongly bind to the surface via its N/C terminals (electrostatic interaction between the positively charged Arg128 fragment and the negatively charged surface). By the way, such geometry leaves the active site intact and available for interactions in solution (see Figure 51.2). In other cases, most frequently, the protein flexibility permits multisite interactions, which is also synonymous of protein denaturation (see examples below). Figure 51.3 illustrates the possible interactions between protein and substrate, making clear the role of both actors and the likely unfolding of proteins to maximize their interactions with the surface [19]. 51.2.3 Effects of Surface Hydrophobicity/Hydrophilicity

Proteins are generally organized in such a way that their hydrophobic residues are buried inside, whereas their hydrophilic functional groups are oriented outside, in contact with the aqueous/biological solvent. This naturally implies that contacts with a hydrophobic or hydrophilic surface will cause protein reorientation and possible change of its conformation. Effects of surface hydrophobicity on protein adsorption were very clear for human serum albumin (HSA) IgG, fibrinogen, and lysozyme on silica (oxidized silicon) and on methylated silica. Surprisingly, results were not the same for these four proteins. HSA, IgG, and fibrinogen adsorb in

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51 Adsorption of Proteins and Anti-biofilm Strategies

–

– + + + +

– –

+

Protein –

+

–

– – – – – –

+

+

+

+ +

+

+

+

– – –

+ + + + + + + +

Biomaterial

–

Negative charge

Polar region

+

Positive charge

Hydrophobic region

Figure 51.3 Schematic illustration of molecular surface possible types of interactions. (Source: Dee et al. 2003 [19]. Reproduced with permission of John Wiley & Sons.)

much lower amounts on hydrophilic surface, whereas the reverse was observed for lysozyme [20]. The larger amount of lysozyme on silica, compared to methylated silica was attributed to the electrostatic attractions between oppositely charged surface and enzyme, which overcome hydrophobic interactions; let us recall that such an effect was observed in a 0.01 M phosphate buffer, which represents a rather high ionic strength. Moreover, differences in the layer structure and homogeneity were observed, suggesting the importance of protein chemistry: fibrinogen layers on silica and CH3 –silica have very different thicknesses but rather similar refractive indexes; the authors concluded that fibrinogen exists on the surface under several different orientations and always show quite low density. Lysozyme behaves differently; it adsorbs in a much higher amount on SiO2 and forms a much denser layer (several equivalent monolayers), accompanied by a much higher refractive index on the hydrophilic surface (see Figure 51.4). Interestingly, fibronectin (Fn), a compact and stiffer protein, does not behave like bovine serum albumin (BSA) on polypyrrole surfaces, whatever their roughness. Fn undergoes a considerable unfolding on the hydrophilic surface, even greater than that on the hydrophobic one, conversely to what is commonly observed for most proteins, in particular BSA, the most studied one [21]. Note that this conformation change Fn on a hydrophilic surface, leading to a more “open” and hydrated protein layers, also leads to an enhanced bioactivity (enhanced cell adhesion). Energy loss, upon protein adsorption, is often said to be greater on hydrophilic than on hydrophobic surfaces [22]. Note eventually that changing the hydrophilic/ hydrophobic properties of a surface may have a consequence not only on the amount of adsorbed proteins but also on their orientation and thus bioactivity; this was clearly demonstrated in Lebec et al. work [23, 24] which describes the

5

1.4

4

1.39 1.38

3 nr

Γ (mg/m2)

51.2 Protein Adsorption, Key Parameters, and Protein Film Description

2

1.36

1 0

1.37

1.35 0

2000

4000

6000

1.34

8000

0

Time (s)

6000

8000

1.7

4

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3 nr

Γ (mg/m2)

4000 Time (s)

5

1.5

2 1.4

1 0 0 (a)

2000

1000 2000 3000 4000 5000 6000 Time (s)

1.3 (b)

0

1000 2000 3000 4000 5000 6000 Time (s)

Figure 51.4 (a) Adsorbed layer thickness and (b) mean adsorbed layer refractive index for fibrinogen (upper curves) and lysozyme (lower curves) on silica (full circles) and methylated silica (empty circles). (Source: Malmsten 1995 [20]. Reproduced with permission of Elsevier.)

adsorption of an antibody against the glutamate dehydrogenase enzyme (anti-GdH) adsorb on a COOH- or CH3 -terminated surface. From either in situ quartz crystal microbalance (QCM) or ex-situ polarization-modulation infrared reflectionabsorption spectroscopy (PM-IRRAS), both described in Chapter 49 in this Volume (see Figure 51.5), antibodies tend to adsorb preferentially on the latter surface because of strong hydrophobic interactions. The surfaces were also characterized by time-of-flight secondary ion mass spectrometry (ToF-SIMS), followed by a protein-fragment complementation assay (PCA) data analysis, which revealed a net difference in the detected fragments, demonstrating a change in the antibody orientation (see Figure 51.6). This has a direct consequence on GdH recognition, which was only efficient on the COOH-terminated surface, despite the lower amount of immobilized probes [23]. Last but not least, the folding/unfolding process that proteins undergo at a surface, in particular the exposure of their hydrophobic segments toward a hydrophobic surface, is a dynamic one; it is time dependent and sometimes reversible when, for instance, proteins are adsorbed after one another. Proteins may first interact via a limited number of sites; they then unfold to expose additional functional groups that bind to the surface (see scheme of Figure 51.7a). Note that, at high protein concentration in solution, more proteins adsorb and undergo lower unfolding (Figure 51.7b).

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51 Adsorption of Proteins and Anti-biofilm Strategies

5.00 Amide I + II bands area (arb. units)

204

+ Milk

Anti-GDH

+ GDH

4.00

3.00

2.00

1.00

0.00 –COOH

–COOH, NHS-EDC

–CH3

Figure 51.5 PM-IRRAS amide band intensities, measured on the COOH− , COOH+ activation, and CH3 -terminated surfaces, bare (dark line), after anti-GdH binding (gray line),

after saturation by milk proteins (dark gray line), and after GdH recognition (light gray line). (Source: Lebec et al. 2014 [24]. Taken with permission of Elsevier.)

Fab Anti-GDH

GDH

Fc

GDH/anti-GDH = 0

GDH/anti-GDH ≈ 0.5

–CH3 Gold

–COOH Gold

Figure 51.6 Schematic representation of the adsorbed antibodies on the –CH3 , –COOH, and –COOH activated by N-hydroxysuccinimide

GDH/anti-GDH ≈ 1.3

–COOH, NHS-EDC Gold

(NHS)-1-(3-(dimethylamino)propyl)-N-ethylcarbodiimide hydrochloride (EDC)-terminated surfaces. (Source: Dolatshahi-Pirouz et al. 2008 [24]. Taken with permission of Elsevier.)

51.2.4 Influence of Surface Topography

Another important, although rarely mentioned, parameter is the roughness of the surface, which may greatly affect protein interactions, as well as the growth of protein films in a biological environment; its influence is complex and sometimes controversial. Moreover, how surface roughness changes protein films also depends on the nature of the proteins. Let us take the example of BSA, a so-called “soft” protein; the influence of surface roughness at various scales on its adsorption has been recently performed by several authors. The amount of adsorbed BSA was shown to increase with the surface

51.2 Protein Adsorption, Key Parameters, and Protein Film Description

Low bulk concentration

High bulk concentration

(a)

(b)

Figure 51.7 (a) Schematic representation of the time-dependent unfolding of proteins. (b) Schematic representation of the concentration-dependent unfolding of proteins. (Source: Dee et al. 2003 [19]. Taken with permission of Wiley.)

roughness, in a proportion higher than the variation in the surface geometric areas [25, 26]. In some cases, this was explained by the role of nanobumps on a surface as nucleation sites and ordering promoters for BSA (in the case of nanopyramids on platinum). As an example, fibronectin and BSA were adsorbed on polypyrrole doped with dextran sulfate on a flat gold-coated quartz [27]. On polypyrrole, the authors suggest that a rough surface facilitates the formation of BSA multilayers, driven by a reorientation of the macromolecules and a decrease of the adlayer viscoelasticity. BSA can adsorb in a more compact way on rough surfaces; this goes together with a higher protein denaturation to comply with the surface structure; this is why the viscoelasticity of a protein film is lower on a rough surface than on a flat surface. Linked to the surface topography, adsorption of proteins on nanoparticles, a crucial question for nanomedicine applications, very much depends on particle size and porosity. Clemments et al. characterized the adsorption of proteins from fetal bovine serum on spherical and mesoporous silica nanoparticles whose diameters were in the range of 70–900 nm [28]. They observed that smallest particles adsorb the highest amount of proteins per surface area. Interestingly, a lower amount of proteins adsorb on large and porous nanoparticles than on small porous ones; this has been explained by a blocking of the available sites by proteins, which adsorb first and gather close to the pore entrance. Of course, these considerations have to be modulated by protein size, the smallest ones adsorbing in higher amounts on smallest nanoparticles. This paper is a basic one for designing efficient “particlebased strategies.” In the same vein, Schlipf et al. demonstrated that tuning the size of pores may permit a selective adsorption and loading of proteins into mesoporous silica [29] and reference therein. This result opens the way toward a new type of

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51 Adsorption of Proteins and Anti-biofilm Strategies

biosensors or bioseparation devices. Noticeable is the influence of pH upon protein unfolding (vide infra), which of course may render insertion of unfolded proteins possible while it is not at certain pH values. The control of surface topography is now well known to be essential for the biocompatibility of implants; talking about the possible growth of an osteoblast on a new hydroxyapatite material, the prerequisite of osteointegration is the ability to adsorb bioactive proteins such as immunoglobulins, fibrinogen, and fibronectin, to cite a few of them [30]. It is thus obvious that improving the biocompatibility of a material also means improving protein adsorption. This motivated Lin et al. who fabricated Hap bioceramics of various shapes and sizes and investigated the adsorption of Fg, Fn HSA, and Vn [31]. Compared to flat surfaces, 3D micro- or nanotextured surfaces enhance adsorption of proteins, in particular Fn and Vn in plasma, and further promote cell proliferation. BSA (66 kDa) and Fg, a much larger protein (340 kDa), were adsorbed on silica particles whose sizes were in the range of 15–165 nm and covered either by OH or CH3 groups; protein adsorption isotherms were then determined. Interestingly, the affinity constant and the total amount of proteins at a saturated surface are determined in opposite ways, regardless of the surface hydrophilicity, when varying the size of the particles. Higher affinity constants and lower saturation values were observed on the smaller particles, with net different behaviors of the two proteins (see Figure 51.8). By studying the conformation changes of the adsorbed proteins, the authors correlated the influence of the nanoparticle sizes on the amount and affinity of proteins with their softness or rigidity: BSA, a globular protein, is less distorted on high surface curvature nanoparticles, whereas fibrinogen, a rodlike protein, tends to spread onto the nanoparticle surface [32]. Protein orientation and/or unfolding is also a key issue for chromatographic surfaces where protein binding is at the basis of a purification process. An overview of the complex behavior of proteins on chromatographic substrates, making clear the various parameters influencing protein orientation and unfolding, can be found in Ref. [33]. 51.2.5 Effect of Surface Charge on Protein Binding

An example has been extensively described, namely the case of titanium surfaces. Titanium is a material of choice for bone or dental implant for which protein and other biomolecule adhesion is a clue to biocompatibility or osseointegration. In this context, Guo et al. reported that negatively charged surfaces of titanium have a higher ability to promote bone implant integration; this was explained by the attraction of Ca2+ cations, which in turn attract negatively charged phosphate ions, precursors of apatite, and most importantly cell adhesion proteins (integrins or fibronectin as examples) [34]. On the contrary, positively charged surfaces are commonly said to be antiadhesive [35]. Gluthatione-SH (GSH) (Glu–Cys–Gly), a ubiquitous tripeptide bearing two COOH and one NH2 groups, was adsorbed from solutions at pH 1, 6, or 12

4 0 7.5

10

30

40

82.5

Protein per unit

12 8 4 0 7.5

10

30 15 Substrate radius (nm)

Saturation amount 3 2 1 0 7.5

10

40

82.5 (d)

15

30

40

82.5

40

82.5

Substrate radius (nm)

(c)

Substrate radius (nm)

Affinity constant

Hydrophilic

15

Protein per unit

8

(a)

(b)

Fg

surface area (mg/m2)

BSA

surface area (mg/m2)

Hydrophilic

Affinity constant

Affinity constant 12

3 2 1 0 7.5

10

15

30

Substrate radius (nm)

Figure 51.8 Affinity constants of BSA and Fg on hydrophilic and hydrophobic nanoparticles of various sizes. (Source: Roach et al. 2006 [32]. Taken with permission of the American Chemical Society.)

51 Adsorption of Proteins and Anti-biofilm Strategies

BSA in PBS 20 mg/l 9 8

PM-IRRAS 0.05 XPS

7

0.04

6 0.03 5

3

0.01

2 Cationic Zwitterionic GSH GSH

(a)

Anionic GSH

Au

BSA in water 20 mg/l 9 PM-IRRAS XPS

8 7

0.05 0.04

6 0.03 5 0.02

4 3

XPS : area(N1s)/Au4f

(b)

0.02

4

XPS : area(N1s)/Au4f

PM-IRRAS : amides I and II bands area

on a gold surface; it was first shown that after soft rinsing conditions, the peptides retain on the surface the charges it bears in solution. Tuning the solution pH thus enables to obtain a positively charged surface by adsorption of the cationic (NH3 + /COOH/COOH), neutral (NH2 /COOH/COOH), or anionic (NH2 /COOH/COO− ) form of the peptide. The BSA protein was then adsorbed, either from pure Milli-Q water (pH 5) or from a buffer solution (pH 7); the main result from these experiments is the evidence of a direct influence of the surface charge on BSA adsorption. The latter was significant only in water and when electrostatic interactions were favored by opposite charges of the surface and protein (see Figure 51.9); adsorption from a saline buffered solution was very weak because of the screening of electrostatic interactions by ions in solution [36, 37].

PM-IRRAS : amides I and II bands area

208

0.01

2 Cationic Zwitterionic GSH GSH

Anionic GSH

Figure 51.9 Adsorption of BSA, measured from PM-IRRAS and XPS surface analysis on a gold surface modified by preadsorption of GSH at various pH. (a) Adsorption of BSA in

Au

a PBS saline buffer and (b) adsorption of BSA from water. (Source: Taken with permission of Valle et al. 2011 [36].)

51.2 Protein Adsorption, Key Parameters, and Protein Film Description

51.2.6 Effect of Concentration and pH of the Protein Solution

As biocompatibility is a major concern for polymer-based biomaterials, adsorption of proteins on these materials has also been the subject of detailed investigations [38]. As for inorganic materials, topography and chemistry of the material surface may influence protein adsorption and either favor protein repellence (anti-biofilm surfaces) or, on the contrary, favor selective interaction of proteins. It is now well established that some chemical groups may be used to modify surfaces and increase their resistance to protein adsorption. Polyethylene glycol (PEG) chains are among the most commonly mentioned as efficient protein repellants [39]. We saw several examples where the surface charge and/or hydrophilicity may significantly change protein affinity to the material. Such properties obviously change as a function of the solution pH, which can, in turn, be a way to tune protein adsorption. Let us take the example of titanium surfaces; in solution concentration ranging from 1 to 10 g/l, at physiological pH, BSA adsorption obeys a Langmuir law. Moreover, the Langmuir constant strongly varies with the pH, with a behavior out of the Langmuir law at low pH (5.2), obviously because of the absence of repelling charges at a pH close to the isoelectric points of both the BSA and the titanium surfaces (see Figure 51.10) [40]. Adsorption of various forms of lactoglobulin was also investigated at various pH, on a Pt electrode, and measured by cyclic voltammetry [41]. The amount of adsorbed proteins was the highest at pH 2 and the lowest at pH 11. It is now admitted that the adsorption of β-lactoglobulin on a metal involves a chemical binding of its carboxyl groups [42]. Of course, the adsorption behavior, dictated by such interactions,

1.0 0.8

θ

0.6 0.4 0.2

pH 5.2 pH 7.0 pH 8.5

0.0 0.0000

1E–3

0.01 0.1 c(BSA) (g/l)

Figure 51.10 Surface coverage by BSA, measured by XPS and ToF-SIMS on pure titanium, from solutions at various pH. A Langmuir fit was correct for pH 7 or 8.5. At low

1

10

pH, the surface is saturated at all concentrations. (Source: Wilhelmi et al. 2011 [40]. Taken with permission of Springer.)

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51 Adsorption of Proteins and Anti-biofilm Strategies

is logically affected by pH, the enhanced chemisorption at acidic pH, where the protein has a net positive charge and gets depleted in calcium ions, being attributed to an unfolding of the macromolecule that maximizes the number of interactions. At elevated pH, carboxyl groups are deprotonated, they strongly interact with water, and this increases the energy necessary to their binding to the surface. Similar to pH, buffer may also have an influence on protein adsorption; phosphate-buffered saline is the most commonly used solvent at physiological pH, and the presence of phosphate ions compete for adsorption on a surface. It was, for instance, shown that adsorption of negatively charged proteins, such as BSA, IgG, or fibrinogen, is lower in phosphate buffer salt (PBS), compared to tris-HCl on a Ge surface, and this is because of the adsorption of negative phosphate ions from the solvent, in disfavor of protein attachment. Lysozyme, a positively charged protein, was similarly tested; a little effect of solvent was observed confirming the above-cited interpretation [43]. 51.2.7 Protein Unfolding/Denaturation on Surfaces

Proteins mediate the interactions between cells and solid materials in a biological environment. As example, some cell-adhesive proteins, such as fibrinogen, fibronectin, or laminins, have been used to coat substrates and favor cell adhesion and growth [44]. Moreover, once adsorbed, the structure conformation of proteins may either promote or disfavor cell adhesion and/or tissue repairing. In that sense, understanding and controlling protein interactions are vital for designing functional biomaterial surfaces. This was particularly well exemplified in Roach et al.’s paper; they characterized and compared the adsorption of BSA and fibrinogen (Fg) on CH3 - and OH-terminated gold surfaces and paid special attention to the modifications in the infrared (IR) amide bands to obtain a very accurate witness of protein conformational changes. As expected, BSA has a higher affinity toward the CH3 than the OH-terminated surfaces. Grazing angle infrared analysis revealed changes in the shape and positions of the amide I band, as well as of the amide I/II intensity ratio. It is particularly clear that on the CH3 -terminated surface, the contribution at c. 1655 cm−1 , characteristic of a helices, significantly decreases, much more than on COOH-terminated ones; this was true for both proteins, nevertheless less pronounced for fibrinogen, and attributed to a strong interaction with hydrophobic surfaces [45]. Eventually, observing the adsorption isotherms of BSA and Fg on the two surfaces indicates that an elongated protein, such as Fg, adsorbs in a two-step process while a globular one, such as BSA, adsorbs in a continuous way; the authors interpreted these different behaviors in the following way: Fg tends to maximize the number of interactions (i) first with the surface and (ii) then in-between molecules, thus leading to a more compact adsorbed layer. Comparatively, BSA, which has a more “symmetrical” shape, is likely slightly distorted on the surface but does not undergo further rearrangement upon further adsorption (see model on Figure 51.11).

51.2 Protein Adsorption, Key Parameters, and Protein Film Description

BSA

Fg

(i)

Figure 51.11 Schematic representation of conformational changes of globular or elongated proteins (like BSA and Fn, respectively), caused by interaction with a solid surface;

(ii)

the first one is slightly distorted, while the second one adsorbs and rearranges on the surface in two steps. (Source: Taken with permission of Roach et al. [45].)

Interestingly, some authors demonstrated that coadsorption of several proteins may induce a switch in the conformation of one of them, which would not occur if adsorbed alone. As an example, fibronectin can be coadsorbed with HSA on a hydrophobic polymer [46]. Indeed, understanding the complex interplay of several constituents of a biointerface is a necessary step toward controlling differentiation or biofilm construction. Note that the simultaneous presence of several proteins is an obvious case in all biofilms. Giamblanco et al. investigated the change in the conformation, and thus the bioactivity of fibronectin (Fn) when coadsorbed with HSA on a polydimethylsiloxane (PDMS) surface. This analysis was done by examining the availability of the 4 F1 and 5 F1 Fn subunits, supposed to be recognized by the anti-Fn antibodies. To that goal, spectroscopic ellipsometry was combined with atomic force microscopy (AFM) (see Chapter 3.5 in Volume 1 and Chapter 49 in this Volume) and QCM to determine the amount of adsorbed proteins as well as the layer thickness and morphology. Importantly, the accessibility of the Fn binding fragments (4 F1 and 5 F ) was evaluated by submitting the protein films to anti-Fn antibodies and compar1 ing the amount of bound antibodies on a pure Fn and on a mixed Fn + HSA layer. The main results are the following: (i) Fn and Fn + HSA adsorption yield layers of similar thicknesses while a pure layer of HSA is less than twice smaller, suggesting that the layer resulting from a HSA + Fn mixture is mainly composed by Fn proteins. (ii) However, a net lower recognition anti-Fn antibody was observed after adsorption of a HSA + Fn binary mixture. The conclusion is that a low amount of HSA is present and sufficient to considerably reduce the accessibility of the 4 F1 and 5 F1 Fn domains; these results indicate a different conformation of the Fn proteins (less denatured) when coadsorbed with HSA as schematized in Figure 51.12. The first part of this chapter tells us that protein–surface interactions are governed by a combination of numerous factors, some of them being possibly modulated “à façon,” like the pH or concentration of the protein solution and, to a certain extent, the surface topography/roughness. Chemical modifications of surfaces may of course change their hydrophobicity as well as their chemical reactivity; it open new routes toward the control of protein adsorption and subsequent biofilm formation.

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51 Adsorption of Proteins and Anti-biofilm Strategies

RGD

4

Fn

F15F1

Water

Fn on PHMS (a) 4

F15F1

RGD

Fn

Water

HSA

Fn + HSA on PHMS (b) Figure 51.12 Schematic illustration of the change in the HSA conformation when (a) adsorbed alone or (b) coadsorbed with Fn on a PDMS surface; the accessibility of the

4F 1

and 5 F1 Fn subunits, targeted by the anti-Fn antibodies is obviously affected. (Source: Giamblanco et al. 2011 [46]. Taken with permission of ACS Publications.)

51.3 Biofilm Prevention, Some Well-Settled or Innovative Strategies 51.3.1 Some General Considerations

It is now established that the development of a biofilm is done in several steps; it is initiated by the formation of a primary film, which results from the ad-, or the absorption of organic macromolecules (proteins, polysaccharides, lipids, etc.) and the liquid present in the inorganic phase. This phenomenon leads to the change of physical and chemical properties of the surfaces and promotes bacterial adhesion. One of the proposed solutions is to act directly on the first step of forming a biofilm: by inhibiting the adhesion of the primary film. Recent work has been done in this direction with the use of hydrophobic polymers (e.g. PEG) or covalent binding of antibacterial peptides or enzymes [47–49]. These chemical treatments significantly reduce the adhesion of microorganisms but only for limited periods of time. Obtaining bacteria-resistant materials for long-term (several months or even years) is yet another challenge faced by one major difficulty to solve: surface chemistry; if one can slow or reduce protein adsorption, the first layer can hide the chemical functionality of a protective layer underneath. In contrast, the effects of surface topography on bacterial adhesion and subsequent biofilm formation have been identified but and are still not fully exploited because their mechanism of action is still poorly understood [50]. The “structure” of surface (roughness, adding

51.3 Biofilm Prevention, Some Well-Settled or Innovative Strategies

nano or microparticles, etc.) may provide more permanent effects that modify the interactions between bacteria and surfaces. We will show below, through a few chosen examples, how the first steps of biofilm formation can be inhibited or reduced by applying surface protections as above mentioned. 51.3.2 Antimicrobial Action

In this section, examples of antimicrobial functionalization will be presented, which highlight differences in the possible modes of action of surface agents, either bactericidal/biocidal or bacteriostatic (i.e. unable bacterial growth). One of the strategies for preventing the proliferation of microorganisms on surfaces consists in coating the surface with a layer of biocidal molecules, which are able to kill the microorganisms either by contact or by release of active substances. According to their chemical nature, these products may target various physiological or metabolic functions of microorganisms. For instance, some interact with the cell wall, resulting in structural or functional changes that often lead to the lysis of the cell wall; others interact with the cytoplasmic membrane, disrupting its structural organization or increasing its permeability to ions, thereby causing effusion of the intracellular material; others target the cytoplasmic constituents (enzymes and metabolites), inhibiting the cell wall synthesis, proteins, or nucleic acids. Some reviews give details on the various antimicrobial molecules, ranging from antibiotics, polymers, and enzymes to natural products, their grafting strategies, and antifouling efficiency [51, 52]. Depending on the mechanism of action involved, microorganisms may develop a natural resistance to the active substance. Microorganisms can also become resistant to a given product by mutation or gene transfer. This resistance results from several physiological and molecular events: inactivation of biocidal products by enzymatic degradation or modification, alteration of the molecular target, etc. To counter these resistance phenomena, it seems nowadays necessary to reduce the use of biocidal substances and develop new molecular strategies. The efforts focus on finding biocidal agents capable of inducing cell death by acting on several targets (i.e. broad spectrum) or developing original mechanisms of action. Therefore, biocidal molecules that will target microorganism membranes with no specificity, for instance, a molecule attacking any kind of peptidoglycans regardless of the bacterial strain, appear particularly interesting. In this context, an increasing attention has been paid to the strategies developed by Nature and biocides secreted by plants, animals, and microorganisms themselves. These biomimetic approaches have led to put great effort to find, among others, peptides, antimicrobial enzymes, and essential oils. 51.3.2.1 Antimicrobial Peptides from Animals or Microorganisms

Antimicrobial peptides (AMPs) are the key actors of the innate immune system of various organisms (animals, plants, fungi, bacteria, and viruses); they

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51 Adsorption of Proteins and Anti-biofilm Strategies

protect against a broad spectrum of adverse microorganisms through their antimicrobial and immunoregulatory activities [53]. There are numerous natural peptides displaying various structural and functional properties but are all characterized by their cationic and amphiphilic character resulting from the presence of hydrophobic portions in their composition [54]. Among the 2500 AMPs quoted in the literature, we will present here below one example of AMP surface coating from a bio-derived molecule: nisin. However, reviews or articles concerning other AMP studies and mode of action can interest readers, for instance, the use of gramicidin [55, 56], magainin [47, 49], and temporin [57, 58], to cite a few. The Nisin A peptide, a 34 amino acid (AA) AMP produced by Lactococcus lactis [59], is commonly used as a food preservative; it is recognized as a safe preservative by the US Food and Drug Administration (FDA) and known to be effective against Gram-positive bacteria [60, 61]. This peptide is amphiphilic, as most of the antimicrobial bio-derived peptides; it is composed of a hydrophilic part, in the inner part of the molecule, and a hydrophobic part that is located at the C-ter end (right-hand side of Figure 51.13). Nisin immobilized on the surface has been mainly applied in food industry [59, 62, 63], as well as in the biomedical and veterinary fields [64, 65] and, eventually, recent examples report the use of nisin incorporated within polyester fabrics [66]. We will present here few examples of the use of nisin as an antibiofilm agent, either in vitro to attest to the efficiency of the peptides or adsorbed on surfaces as a food antibiofilm coating. Kapila et al. have carried out in vitro saliva-derived biofilm assays to attest to the efficiency of nisin toward Gram-positive and Gram-negative bacteria that could be present in a human oral biofilm [64]. In solution, i.e. toward planktonic bacteria, nisin has very low minimum inhibition and bactericidal concentrations, minimal inhibitory concentration (MIC) and minimal bactericidal concentration (MBC), respectively. The nisin MIC values are in the range of 2–40 μg/ml for Gram negative- and Gram-positive oral biofilm bacteria, respectively, which are only higher by 1 order of magnitude compared to common Ciprofloxacin antibiotic’s MIC, measured at 0.25 μg/ml. Moreover, the MIC/MBC ratio is for several tested Hydrophobic part

Hydrophilic part

Figure 51.13 Nisin structure showing the hydrophobic C-ter side at the right-hand side of the molecule.

51.3 Biofilm Prevention, Some Well-Settled or Innovative Strategies

A

Nisin (μg/ml) Control

40 μm

0.5

40 μm

1

40 μm

Figure 51.14 Nisin inhibits the formation of multispecies biofilms in a static model system. Cell-containing saliva (CCS) was inoculated in filter-sterilized cell-free saliva (CFS) for 20–22 hours at 37 ∘ C with or without nisin. Confocal microscopy images are represented in the x–y plane. A green signal indicates viable/live cells (Syto9), and a red

4

40 μm

8

40 μm

signal indicates damaged/dead cells (propidium iodide). (Source: Reproduced with permission of Shin et al. 2015 [64]. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). Copyright 2015, Creative Commons.)

bacterial strains equal to, or higher than, 4, which evidences a bacteriostatic affect rather than a bactericidal one [67]. The next step in this reported study was to monitor the potential biofilm inhibition in the presence of nisin. Saliva-derived multispecies biofilms were grown with or without nisin. Under static growth conditions, early signs of biofilm membrane damage were observed at nisin concentrations ≥0.5 μg/ml (Figure 51.14). Confocal microscopy gives other information about the biofilm health. Using fluorescent markers (Syto 9 and PI), one can discriminate alive (appearing in green) from damaged (appearing in red) bacteria (see Figure 51.14). The conclusion that can be drawn from the experiments presented in Figure 51.14 is twofold: first the biofilm growth is inhibited from a very low concentration of nisin, c. 0.5 μg/ml, to be almost completely annihilated at 8 μg/ml; second, when looking at the fraction of green vs. red bacteria in the remaining biofilm, it appears that most of the bacteria are colored in red, thus dead. This clearly demonstrates the bacteriostatic and bactericidal effects of nisin in vitro toward a complex multispecies biofilm. Similar studies were carried out using nisin immobilized on surfaces. Among them, one can cite the functionalization of stainless steel (SS) [62, 63] or polyethylene (PE) [59] surfaces, both to solve problems linked to food industry, mainly concerning food-borne diseases due to the packaging or in tools used for food preparation. In all cases, the surfaces need to be pretreated with an anchoring primer before the nisin molecules could be linked. Based on the precedent example showing in vitro (in solution) biofilm growth assays, the primers used in these studies play, most of the time, a twofold role: first as an anchoring platform for strong binding of nisin and second as a possible antiadhesive effect. Thus, in the case of PE, the primer layer was acrylic acid, which renders the surface more hydrophilic with the consequence to increase by a factor of 3 the quantity of adsorbed nisin [59]. Assessment of the antimicrobial activity of these modified PE films was then achieved against food pathogens, namely Listeria innocua or Listeria

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51 Adsorption of Proteins and Anti-biofilm Strategies

monocytogenes. Again, the results of these experiments brought answers to multiple issues, showing various aspects of biofouling. For instance, Chihib and coworkers show that the efficiency of the films was directly correlated with both the nature of the film and the amount of nisin adsorbed on the surfaces [59]. First, by modifying the PE films with acrylic acid, the surface becomes more hydrophilic, and this impact the adsorption of nisin in two ways: nisin has a predominant hydrophobic part, see Figure 51.13, and the molecule will experience less conformational changes when adsorbed on a hydrophilic surface than on a hydrophobic one; hence, its bactericidal activity will be maintained, similar to the one observed for free nisin molecules in solution. The second point lies in the efficiency of the nisin-coated surface, its antimicrobial activity increases by 1 order of magnitude (compared to bare PE), when the PE film was precoated with AA. Turning now to metallic surfaces, and more precisely to SS surfaces, the grafting strategy is different. SS is a material of choice for use when sterile surface are absolutely needed, in food or medical industries for instance; in addition, the coating needs to be resistant to possible leaching of the active compounds. Therefore, the choice of a covalent binding through the use of primers is often made. General grafting strategy is presented in Figure 51.15, where SS is first hydroxylated before being functionalized by spacers that will then anchor the active product. In this example, the functionalization of the SS surface is carried in a three-step process, with the use of a primer, chitosan; a glutaraldehyde (GA) spacer is added in-between the primer and the nisin molecule in order to drive the antibacterial molecule further away from the surface. The second example shows a slightly different two-step process [63]; after activation of the surface by atmospheric plasma, the nisin was covalently bound to the SS surface via an aminosilane, (3-aminopropyl)trimethoxysilane (APTMS). In these two examples, the modified surface was characterized by means of IR spectroscopy and X-ray photoemission spectroscopy (XPS) in order to attest to the grafting and calculate the amount of nisin grafted on the surface. Conditions were optimized to obtain the highest possible coverage in nisin at the surface. The nisin-modified surfaces were then assayed against Gram-positive bacteria, Listeria HO OH H H 2C

HO

H O HO H

OH H H2C H

H H3C

CHI step1

O

NH H C O

SS-SC

HO

OH

H

OH

NH H C O

OH

H H3C

OH

GA-Tere step2

NIS-MAG step3

Figure 51.15 Scheme of the three-step adsorption process leading to the covalent grafting of nisin. (Source: From Héquet et al. 2011 [62]. Copyright 2011, Elsevier.)

51.3 Biofilm Prevention, Some Well-Settled or Innovative Strategies

ivanovii in the first example and Bacillus subtilis in the second one. In both cases, the efficiency of the immobilized nisin was made clear by two different biological methods. Héquet et al. carried out killing assays, i.e. the bacteria are in contact with the nisin-modified surface (SS-nisin) for a given time (typically three hours) and then all bacteria are recovered and grown on an agar plate. The killing rate is obtained by comparing the amount of bacteria that grows on the nonmodified control surface (SS) and on the nisin-modified surface (SS-nisin); it rose to more than 95% of killed bacteria in the latter case [62]. In the second example by Choquet and coworkers [63], the antibacterial test was slightly different: the growth inhibition was tested in order to evidence, not directly the killing efficiency of the modified surface but its capacity to reduce the development of a biofilm. Thus, it was evidenced that the nisin-modified surface inhibits by 3.6 log (i.e. more than 99.9% efficiency) the bacterial growth compared to the SS control surface; in other words, when 1000 bacteria colonies forming unit (cfu) develop from a bacterial solution deposited on the SS surface, only one does on the modified SS–nisin surface. In addition, after 24 hours of contact, the bacteria that are still present in the surface are almost all dead (see on Figure 51.16, the dead bacteria in red). These examples demonstrate that nisin is a very efficient antibiofilm molecules and that its mode of action can be very different and complementary by either killing bacteria or inhibiting their growth. It was first shown that biofilms could be almost annihilated by a low concentration of nisin in solution. In the case of nisin immobilized on SS surface, the chosen examples illustrate two different modes of action for these modified surface: on the one hand, reduce the possible development of a biofilm by 99.9% (bacteriostatic effect); on the other hand, for the remaining bacteria that can be adhered to the surface, a 95% killing efficiency was observed

(a)

(b)

Figure 51.16 Fluorescence microscopic analyses of B. subtilis bacteria incubated for 24 hours on APTMS (a) and on nisin peptide-grafted APTMS (b). Bacteria were stained by using a LIVE/DEAD BacLight viability kit. Live cells are indicated by green

™

fluorescence, whereas cells with damaged membranes are indicated by red fluorescence. (Source: Reproduced with permission of Duday et al. 2013 [63]. Copyright 2013, Elsevier.)

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51 Adsorption of Proteins and Anti-biofilm Strategies

(bactericidal effect), demonstrating the important applications that the use of nisin may have for the engineering of antibacterial surfaces. 51.3.2.2 Enzymes

Another class of antibiofilm molecules, frequently used in coatings in the biomedical field, is the family of enzymes; in fact, the natural antimicrobial strategies of many living organisms involve enzymes. These proteins are capable of lowering the energy of activation of chemical reactions that are involved to limit the growth of microorganisms [68–71]. The antimicrobial activity of proteases (or peptidases) is targeted against the peptides contained in the cell wall. Indeed, these enzymes, such as trypsin, subtilisin, or papain, are capable of breaking the peptide bonds of proteins, in the presence of water. The biocidal mechanism of lysozymes consists in the destruction of microbial walls by catalyzing the hydrolysis of the peptidoglycan component. Enzymes can also be used to produce biocidal compounds; as an example, glucose oxidase produces hydrogen peroxide and haloperoxidases generate halogen acids, such as HOBr or HOCl. However, the fragility and cost of enzymes restrict their wide-scale use. In addition, for surface applications, where enzymes must be immobilized, one needs to make sure to preserve the accessibility of their active site after immobilization. Among the hundreds of antimicrobial enzymes listed in the literature, we will present one example of surface coating by an enzyme, the lysozyme, but examples of surfaces coating with other enzymes such as trypsin [48], subtilisin [72], papain [73], and lysozyme [74] can also interest the readers. Lysozyme, found in tears, mucous, saliva, plasma, tissue fluid, etc., breaks peptidoglycan in bacteria causing osmotic lysis. Specifically, it breaks the bond between the N-acetylglucosamine (NAG) and N-acetylmuramic acid (NAM), the two sugars that constitute the backbone of peptidoglycan. Over the multiple lysozyme enzymes, one has proven to be particularly potent against Gram-positive bacteria, the lysozyme from hen egg white, HEWL, Figure 51.17, especially when immobilized at the surface of polymeric packaging films led to materials displaying bacterial growth inhibiting properties, for instance [75, 76]. Several examples also reported the use of lysozyme, grafted on SS surfaces, often used in the food industry [48, 74, 77]. Most of the grafting strategies on hydroxylated SS surfaces rely on the creation of a first anchoring layer (catechol, polyethyleneimine [PEI], chitosan, etc.), followed by the covalent grafting of a spacer (GA, PEG-derivated, etc.) before the creation of an amide bond between the spacer and the enzyme. Caro et al. have proposed a multiple step process to carry out the grafting of HEWL via the use of two spacers, PEI and GA, Figure 51.18. In this work, the authors have studied different parameters that may influence the enzymatic activity of bounded HEWL such as the quantity of grafted enzymes, the distance from the surface via short or long spacers, and compared the activity of HEWL with another enzyme, trypsin. The authors concluded that the covalent immobilization processes led to firmly bound and active enzyme layers and that their hydrolytic activity originated exclusively from surface-bound

51.3 Biofilm Prevention, Some Well-Settled or Innovative Strategies

Figure 51.17 Structure of lysozyme from hen egg white (HEWL). (Source: Reproduced with permission of Dreamstime.com.)

ENZ CHO CHO

CHO NH2 NH2

O

NH2

NH2 NH2 NH2 OH OH O OH OH OH

SS-SC-PEI

N N

O

ENZ N

N

N

N N

N

N

NH2 NH2 NH2 OH OH O OH OH OH

NH2 NH2 NH2 OH OH O OH OH OH

O

SS-SC-PEI-GA

SS-SC-PEI-GA-ENZ

Figure 51.18 Schematic representation of functionalization steps of stainless steel surface by HEWL. (Source: Reproduced with permission of Caro et al. 2010 [74].)

molecules. The thickening of the attachment layer contributed significantly to the increase of surface enzymatic activity by moving away the proteins from the surface. Another interesting study using lysozyme was reported by Muszanska et al. where they coupled a pluronic copolymer with lysozyme [78]. Their aim was to reduce adhesion of bacteria and kill nonetheless the adhered bacteria onto a hydrophobic surface. They studied the influence of the conformation of an active molecule in the adlayer by varying the fraction of lysozyme into the pluronic copolymer. Surprisingly, the layer exhibiting the lowest amount of pluronic–lysozyme copolymers (1 : 99) was more effective in killing B. subtilis bacteria than the 100% pluronic–lysozyme (100 : 0) adlayer, see green and red pancakes in Figure 51.19. However, the less pluronic–lysozyme conjugate, the better antiadhesive effect was observed toward B. subtilis. A 100% pluronic–lysozyme conjugate layer reduces the bacterial adhesion by 70%, while the 1% conjugate reduces the adhesion of

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51 Adsorption of Proteins and Anti-biofilm Strategies

(a)

(b)

(c)

(d)

Dead Live 7 CFUs cm–2 × 107

220

6 5 4 3 2 1 0

(a)

(b)

Figure 51.19 Adhesion of B. subtilis 168 after 20 hours of growth on (a) unmodified pluronic coating, (b) uncoated silicone rubber, (c) 100% pluronic–lysozyme conjugate, and (d) 1% pluronic–lysozyme-coated silicone rubber together with the percentage

(c)

(d)

of viability of adhered bacteria represented by the pancakes. Error bars indicate standard deviation over four separate experiments. (Source: Muszanska et al. 2011 [78]. Reproduced with permission of Elsevier.)

B. subtilis by 85%. This last example clearly demonstrates that the conformation and quantity of bactericidal agents are key parameters in the anti-biofouling strategies. 51.3.3 Surface Structuration

Nature provides few clues concerning the efficiency of surface roughness to prevent microbial colonization. For example, while the hulls of ships are constantly accumulating layers of algae and crustaceans, materials with topographical features mimicking shark skin, for example, showed a remarkable resistance to marine biofouling [79]. The lotus leaf, on which water and dirt slide are ultrahydrophobic, also prevent microorganism colonization, hence biofilms formation: this is all related to their roughness. This natural micro- or nanostructuration is expressed and act in different ways depending on the animals or plants. For instance, the shark skin, constituted of microstructure ribblets, prevents biofouling by increasing the water flow rate at the solid–liquid interface, thus preventing the building up of biofouling, in addition to some self-cleaning properties. Turning now to lotus leaves, composed of hierarchical micro- or nanopapillae (i.e. micro- or nanobumps superimposed in wax nanostructures), they exhibit superhydrophobic properties and thus causes low adhesion surfaces. Even more impressive are rice leaves (Figure 51.20a) and butterfly wings (Figure 51.20b), which combine the effects observed on both sharks and lotus. One can observe in Figure 51.20a the two different hierarchical structuration of a rice leaf, one at the microlevel showing ribbons, inside which the nanostructuration is visible by the presence of papillae.

51.3 Biofilm Prevention, Some Well-Settled or Innovative Strategies

(a)

(b)

Figure 51.20 SEM and TEM of (a) rice leaf. (Source: Copyright 1996, Museum of Science, Boston.) (b) Butterfly wings. (Source: Copyright 2011, University of Sidney.)

Thus, many research groups tried to mimic Nature, engineering surfaces, and structured adlayers to bestow antiadhesive and/or self-cleaning properties to these surfaces. One can cite some examples where the biofouling control is achieved by plasma polymerization to render surface hydrophobic by smoothing the polypropylene surface [80]; another example shows that bioinspired superhydrophobic surfaces can be created by soft lithography nanostructuring, tuning the size, spacing, and stiffness of the resulting surfaces; promising superhydrophobic and antiadhesive surfaces were obtained [81]. Hochbaum and Aizenberg reported the creation of periodic nanostructure arrays on epoxy surfaces to target and orientate specific bacteria, thus lowering the bacterial attachment, compared to the flat, non-nanostructured surface [50]. We will describe in the following section a study carried out by Lee’s team, who developed bioinspired microstructured surfaces, mimicking the properties of rice leaves and butterfly wings [82]. The authors used photolithography and soft lithography methods to PDMS materials and evaluated their antifouling efficiency (see Figure 51.21a). They tested various geometries in order to unravel which parameters such as height of ribbons, size of pillars, or next-neighboring pillar array are of highest importance in biofouling and antiadhesive properties, as presented in Figure 51.21a. Rice leaf bioinspired surfaces with microsized features were fabricated following a three-step soft lithography procedure [82]. The material PDMS was chosen because of its low surface energy, which leads to high contact angle, and highly hydrophobicity, and therefore promising for biofouling properties. Figure 51.21b presents scanning electron microscopy (SEM) images for each samples 1–4 with top views tilted, by 45∘ or not. The features are accurately produced for each sample with geometric dimensions as indicated in Figure 51.21a, thus validating the procedure of lithography. Moreover, the difference in height and spacing will be of high importance when carrying out antifouling assays. Dual-height pillars are evident in images of sample 3 along with the dual-height pillars/ribs of sample 4. Samples were subjected to bioassay experimentation to determine their antifouling efficiency toward relevant bacterial strain, Escherichia coli HB2151. Antifouling

221

Rice leaf inspired geometries for antifouling Pillar

Rib

Top views

Flow direction

5 μm

D

P

Top view

2 μm high

Sample 2

P

Sample 1

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Top view, tilted at 45°

5 μm

4 μm high

W

5 μm

5 μm

5 μm

5 μm

Side views Sample 1

Sample 4

Sample 2 H2

Sample 3

2 μm high

H1

H2

H2 Sample 4

Sample 3

H1

(a)

4 μm high

H1 2 μm high

4 μm high

5 μm

5 μm

(b)

Figure 51.21 (a) Rice leaf and butterfly wings bioinspired geometries for antifouling assays on PDMS surfaces. (b) Montage of SEM images of the actual PDMS-fabricated samples. Arrows indicate direction of droplet movement for selfcleaning experiments. Each sample is shown with the top view tilted at 45∘ (left side column) and with no tilt (right side column) in order to highlight the feature heights. (a) Sample 3 images show the dual-height pillars and sample 4 images show the combination of pillars and ribs. (Source: Bixler and Bhushan 2014 [82]. Reproduced with permission of Royal Society of Chemistry.)

51.4 Conclusion

assays were then conducted on all four microstructured samples for two bacteria dilution (1/10th and 1/100th from overnight bacterial culture growth) and for two incubation times (two and four hours). Optical microscopy results are presented in Figure 51.22. On a flat PDMS sample, the quantity of adhered bacteria increases with contact time. Moreover, the initial concentration of the bacterial strain is an essential point, as for the highest dilution; only a few bacteria are visible on the surface, while at 1/10 dilution, the amount of bacteria present at the surface after two hours is almost equivalent to the one after four hours at dilution 1/100th. More interesting is the comparison between the flat sample and the various microstructured sampled, for a given dilution and time of incubation. No obvious difference can be noticed between samples 1 and 2, suggesting that the height difference between pillars (2 and 4 μm) has no real influence on E. coli colonization. On the other hand, when the height of the pillars varies on the same surface, sample 3, only a small reduction of the bacteria adhesion was observed. Finally, the simultaneous presence of ribbons and pillars, Sample 4, drastically reduces the bacterial colonization at the highest concentration and incubation time. The authors concluded that the greatest antibiofouling benefit is attained with sample 4, with a reduction of bacterial colonization of 33%; interestingly, the other tested surfaces, samples (1–3), also show a reduction of the bacterial colonization but with values in the range of 20%. Finally, anti-inorganic fouling experimentations have also been carried out in order to assess the self-cleaning properties of these samples; these experiments are carried out by exposing the various surfaces to microorganisms and follow by optical microscopy the evolution of the surface upon rinsing. Results show that the sample being the closest to a rice leaf replica, i.e. sample 4, possesses the better self-cleaning properties, with more than twice as much contaminant being removed, compared to the flat sample.

51.4 Conclusion

After having recalled the successive steps leading to the appearance and growth of a biofilm, this chapter reports on two main issues: first, the adsorption of proteins, the very first, and key step, toward an irreversible surface contamination; second, some strategies, relying on the immobilization of some natural antibiofilm molecules or on the surface nanostructuring, which already proved their efficiency against biofilms. Although far from being exhaustive, this review points out some key features. Proteins interact via numerous and diverse types of interactions; their identification today leads to develop strategies to reduce this adsorption, either by tuning surface chemistry and structure; progresses still need to be done by, for instance, developing in situ characterization of the solid–liquid interfaces or by coupling experimental results and modeling to fully determine the main parameters influencing protein adsorption or nonadsorption.

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E. coli at 1/100 concentration 2 h incubation

E. coli at 1/10 concentration

4 h incubation

2 h incubation

4 h incubation

Flat (PDMS)

E. coli

Sample 1 (PDMS)

Pillar

Sample 2 (PDMS)

Sample 3 (PDMS)

Sample 4 (PDMS)

Rib

Figure 51.22 Light microscopy images using oil immersion techniques at 1000× magnification of samples after bioassay experiments using E. coli. Presented are images of flat PDMS, samples 1–4. Long and

narrow cylindrically shaped objects are the E. coli. (Source: Bixler and Bhushan 2014 [82]. Reproduced with permission of The Royal Society of Chemistry.)

References

Great effort has also to be paid to study cell adhesion and viability, which are the main causes of biofilm building up. Although far from being fully understood, various strategies have proven to be efficient against these steps. We review a few of them; interestingly, they rely either on the chemical modification of surfaces, followed by the binding of antiadhesive or bactericidal species, or on surface structuring in a bio-inspired way. Note eventually that changing the surface chemical properties, or building up 3D hierarchical nanostructures, has the potential of promoting cell adhesion when improving surface biocompatibility is at stake. This demonstrates the complexity of the parameters to be controlled and the overall necessity of a multiscale understanding of the mechanisms of protein– and cell–surface interactions. Finally, recent technical developments associate several of the above-cited strategies; more and more hybrid substrates are developed by combining specific structuration (nano or micro), antiadhesive coatings, and grafting of antimicrobial agents.

References 1. Zobell, C.E. (1943). J. Bacteriol. 46: 2.

3.

4. 5.

6.

7. 8. 9.

10.

11.

12.

39–56. Costerton, J.W., Stewart, P.S., and Greenberg, E.P. (1999). Science 284: 1318–1322. Costerton, J.W., Lewandowski, Z., De Beer, D. et al. (1994). J. Bacteriol. 176: 2137–2142. Drenkard, E. (2003). Microb. Infect. 5: 1213–1219. Bridier, A., Le Coq, D., Dubois-Brissonnet, F. et al. (2011). PLoS One 6 (1): e16177. Hall-Stoodley, L., Costerton, J.W., and Stoodley, P. (2004). Nat. Rev. Microbiol. 2: 95–108. Beech, I.B. (2004). Int. Biodeterior. Biodegrad. 53: 177–183. Breyers, J.D. and Ratner, J.P. (2004). ASM News 70: 232–237. Mack, D., Becker, P., Chatterjee, I. et al. (2004). Int. J. Med. Microbiol. 294 (2-3): 203–212. Verstraeten, N., Braeken, K., Debkumari, B. et al. (2008). Trends Microbiol. 16: 496–506. Manuel Simoes, M., Simoes, L.C., and Vieira, M. (2010). J. Food Sci. Technol. 43: 573–583. Arciola, C.R., Campoccia, D., Speziale, P. et al. (2012). Biomaterials 33: 5967–5982.

13. Horbett, T.A. (2003). Surfactant Sci. Ser.

110: 393–413. 14. Latour, R.A. Jr., (2005). Biomaterials:

15. 16.

17. 18. 19.

20. 21.

22. 23.

protein-surface interactions. In: Encyclopedia of Biomaterials and Biomedical Engineering, 1–15. Taylor & Francis https://doi.org/10.1081/EEBBE-120041856. Vroman, L. and Adams, A.L. (1969). Surf. Sci. 16: 438–446. Bhakta, S.A., Evans, E., Benavidez, T.E., and Garcia, C.D. (2015). Anal. Chim. Acta 872: 7–25. Gray, J.J. (2004). Curr. Opin. Struct. Biol. 14: 110–115. Kubiak-Ossowska, K. and Mulheran, P.A. (2010). Langmuir 26: 7690–7694. Dee, K.C., Puleo, D.A., and Bizios, R. (2003). Protein-surface interactions. In: An Introduction to Tissue-Biomaterial Interactions, 37–52. Wiley. Malmsten, M. (1995). Colloids Surf., B 3: 297–308. Renner, L., Pomper, T., Salchert, K., and Werner, C. (2002). Cell Biol. Int. 27: 101–114. Yang, M. and Thomson, M. (1993). Langmuir 9: 802–811. Lebec, V., Landoulsi, J., Boujday, S. et al. (2013). J. Phys. Chem. C 117: 11569–11577.

225

226

51 Adsorption of Proteins and Anti-biofilm Strategies 24. Lebec, V., Boujday, S., Poleunis, C.

25.

26. 27. 28.

29.

30. 31. 32.

33. 34.

35.

36.

37.

38. 39. 40.

41. 42.

43. 44.

et al. (2014). J. Phys. Chem. C 118 (4): 2085–2092. Dolatshahi-Pirouz, A., Rechendorff, K., Hovgaard, M.B. et al. (2008). Colloids Surf., B 66: 53–59. Riedel, M. and Muller, B. (2001). Biomaterials 22: 2307–2316. Molino, P.J., Higgins, M.J., Innis, P.C. et al. (2012). Langmuir 28: 8433–8445. Clemments, A.M., Botella, P., and Landry, C.C. (2015). ACS Appl. Mater. Interfaces 7: 21682–21689. Schlipf, D.M., Rankin, S.E., and Knutson, B.L. (2013). ACS Appl. Mater. Interfaces 5: 10111–10117. Grinnell, F., Feld, M., and Minter, D. (1980). Cell 19: 517–525. Lin, K., Xia, L., Gan, J. et al. (2013). ACS Appl. Mater. Interfaces 5: 8008–8017. Roach, P., Farrar, D., and Perry, C. (2006). J. Am. Chem. Soc. 128: 3939–3945. Yu, L., Zhang, L., and Sun, Y. (2015). J. Chromatogr. A 1382: 118–134. Guo, C.Y., Matinlinna, J.P., and Tang, A.T.H. (2012). Int. J. Biomater. 2012: 1–5. Ohgaki, M., Kizuki, T., Katsura, M., and Yamashita, K. (2001). J. Biomed. Mater. Res. 57: 366–373. Vallee, A., Humblot, V., Methivier, C., and Pradier, C.-M. (2011). J. Phys. Condens. Matter 23: 484002-1–484002-8. Vallée, A., Humblot, V., Méthivier, C., and Pradier, C.-M. (2008). Surf. Interface Anal. 40: 395–399. Chen, H., Yuan, L., Song, W. et al. (2008). Prog. Polym. Sci. 33: 1059–1087. Norde, W. and Gage, D. (2004). Langmuir 20: 4162–4167. Wilhelmi, M., Müller, C., Ziegler, C., and Kopnarski, M. (2011). Anal. Bioanal.Chem. 400: 697–701. Cabilio, N.R., Omanovic, S., and Roscoe, S.G. (2000). Langmuir 16: 8480–8488. Liedberg, B., Invarsson, B., Hegg, P.O., and Lundström, I. (1986). J. Colloid Interface Sci. 114: 386–397. Wei, T., Kaewtathip, S., and Shing, K. (2009). J. Phys. Chem. C 113: 2053–2062. Qu, X.H., Wu, Q., Liang, J. et al. (2005). Biomaterials 26: 6991–7001.

45. Roach, P., Farrar, D., and Perry,

46.

47. 48.

49.

50. 51.

52.

53. 54. 55. 56.

57. 58. 59.

60.

61.

62.

63.

64. 65.

C.C. (2005). J. Am. Chem. Soc. 127: 8168–8173. Giamblanco, N., Yaseen, M., Zhavnerko, G. et al. (2011). Langmuir 27 (1): 312–319. Humblot, V., Yala, J.-F., Thébault, P. et al. (2009). Biomaterials 30: 3503–3512. Caro, A., Humblot, V., Méthivier, C. et al. (2009). J. Phys. Chem. B 113: 2101–2109. Peyre, J., Humblot, V., Méthivier, C. et al. (2012). J. Phys. Chem. B 116: 13839–13847. Hochbaum, A.I. and Aizenberg, J. (2010). Nano Lett. 10: 3717–3721. Glinel, K., Thébault, P., Humblot, V. et al. (2012). Acta Biomater. 8 (5): 1670–1684. Tan, S.Y.-E., Chew, S.C., Tan, S.Y.-Y. et al. (2014). Curr. Opin. Biotechnol. 26: 1–6. Hancock, R.E. and Sahl, H.G. (2006). Nat. Biotechnol. 24 (12): 1551–1557. Wang, G., Li, X., and Wang, Z. (2009). Nucleic Acids Res. 37: D933–D937. Zuo, R.J. and Wood, T.K. (2004). Appl. Microbiol. Biotechnol. 65: 747–753. Yala, J.-F., Thébault, P., Héquet, A. et al. (2011). Appl. Microbiol. Biotechnol. 89: 623–634. Lombana, A., Raja, Z., Casale, S. et al. (2014). J. Pept. Sci. 20: 563–569. Piras, A.M., Maisetta, G., Sandreschi, S. et al. (2015). Front. Microbiol. 6: e372. Karam, L., Jama, C., Mamede, A.-S. et al. (2013). Appl. Microbiol. Biotechnol. 97: 10321–10328. Bower, C.K., McGuire, J., and Daeschel, M.A. (1995). Appl. Environ. Microbiol. 61: 992–997. Harris, J., Fleming, H.P., and Klaenhammer, T.R. (1991). J. Food Prot. 54: 836–840. Héquet, A., Humblot, V., Berjeaud, J.-M., and Pradier, C.-M. (2011). Colloids Surf., B 84 (2): 301–309. Duday, D., Vreuls, C., Moreno, M. et al. (2013). Surf. Coat. Technol. 218: 152–161. Shin, J.M., Ateia, I., Paulus, J.R. et al. (2015). Front. Microbiol. 6: a617. Field, D., Gaudin, N., Lyons, F. et al. (2015). PLoS One 10 (3): e0119684.

References 66. Kerkeni, A., Behary, N., Dhulster, P.

67. 68. 69.

70.

71.

72.

73.

et al. (2013). J. Appl. Polym. Sci. 129 (2): 866–873. Pankey, G.A. and Sabath, L.D. (2004). Clin. Infect. Dis. 38: 864–870. Oppenheim, A.B. and Chet, I. (1992). Trends Biotechnol. 10: 392–394. Sahai, A.S. and Mannocha, M.S. (1993). FEMS Microbiol. Rev. 11: 317–338. Johansen, C., Christgau, S., and Adler-Nissen, J. (1995). Trends Food Sci. Technol. 6: 390–396. Thallinger, B., Prasetyo, E.N., Nyanhongo, G.S., and Guebitz, G.M. (2013). Biotechnol. J. 8 (1): 97–109. Leroy, C., Delbarre, C., Ghillebaert, F. et al. (2008). Biofouling 24 (1): 11–22. Oliveira, H.L.C.D., Fleming, M.E.C.K., Silva, P.V. et al. (2014). Braz. J. Pharm. Sci. 50 (2): 261–267.

74. Caro, A., Humblot, V., Méthivier, C.

75. 76.

77. 78.

79. 80. 81.

82.

et al. (2010). J. Colloid Interface Sci. 349 (1): 13–18. Appendini, P. and Hotchkiss, J.H. (1997). Packag. Technol. Sci. 10: 271–279. Conte, A., Buonocore, G.G., Bevilacqua, A. et al. (2006). J. Food Prot. 69: 866–870. Yuan, S., Wan, D., Liang, B. et al. (2011). Langmuir 27: 2761–2774. Muszanska, A.K., Busscher, H.J., Herrmann, A. et al. (2011). Biomaterials 32: 6333–6341. Schumacher, J.F., Carman, M.L., Estes, T.G. et al. (2007). Biofouling 23: 55–62. Reid, K., Dixon, M., Pelekani, C. et al. (2014). Desalination 335: 108–118. Pokroy, B., Epstein, A., Persson-Gulda, M.C., and Aizenberg, J. (2009). Adv. Mater. 21: 463–469. Bixler, G.D. and Bhushan, B. (2014). Nanoscale 6 (1): 76–96.

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52 Liquid Surfaces Gunther Andersson and Harald Morgner

52.1 Introduction

In 1927, Davisson and Germer reported the experimental observation of diffraction of electrons off crystalline nickel. At the time, the significance of the experiment was considered in demonstrating that particles with rest mass can behave as waves, thus verifying de Broglie’s concept. However, the practical usefulness of the lowenergy electron diffraction (LEED) technique to study the surface as opposed to bulk properties was recognized as well, e.g. by demonstrating the sensitivity of the signal to the presence of adsorbed gas [1]. Another technique, which was introduced by Siegbahn and coworkers under the name of electron spectroscopy for chemical analysis (ESCA), emerged as a natural complement to X-ray spectroscopy. Instead of observing the attenuation of an X-ray beam by a solid sample or the X-ray fluorescence from it, he and his coworkers analyzed the photoelectrons and Auger electrons [2]. Again, inspection of the emitted electrons restricts the information gained to a thin surface layer because of their limited mean free path. The habit of applying these techniques to solid samples led to the notion that solid surfaces were the natural domain of these surface spectroscopies. This widespread notion can be retrieved in the renowned German encyclopedia Brockhaus in the year 1991. The entry on surface physics starts with the explanation: “special subject of solid state physics that is occupied with the physical properties of the interfaces of solids with respect to gas phase or vacuum” [3]. It is noteworthy that this statement was written about two decades after the pioneering work of H. Siegbahn characterizing liquid surfaces by X-ray photoelectron spectroscopy (XPS) [4]. However, even today, the field of liquid surfaces is not generally accepted as a genuine scientific subject in surface science. Instead, it is often assigned to colloid science. The most recent online edition of Brockhaus starts with the same sentence but adds later on that studying the interface between solids and liquids is now incorporated into the concept of surface physics [5]. Thus, one may conclude that some scientists are still reluctant to accept liquid surfaces (or else the liquid/vapor interfaces) as an established field of scientific activity. Other presentday encyclopedias explicitly list the liquid/gas interface as a subject of surface science Surface and Interface Science: Liquid and Biological Interfaces, First Edition. Edited by Klaus Wandelt. © 2020 Wiley-VCH Verlag GmbH & Co. KGaA. Published 2020 by Wiley-VCH Verlag GmbH & Co. KGaA.

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[6]. We hope that the present contribution helps to convince readers that studying the surfaces of liquids unrelated to solid substrates is a field in its own merit that is interesting and useful. The motivation in investigating liquid surfaces with spectroscopic techniques arises from the interest to understand the structure of liquid surfaces itself and from the interest in the relationship between the structure and the properties of liquid surfaces as well as the relationship between the structure of liquid surfaces and processes involving liquid surfaces. Traditionally, the properties of liquid surfaces of main interest are surface tension (another term for surface energy) and the electric properties such as the surface potential and the dielectric constant. The processes of main interest are the adsorption kinetics and uptake of gases across the gas/liquid interface. The latter finds its counterpart in the transport over the liquid/liquid interface, which is of interest in extraction processes. The structural features of interest at liquid surfaces are the composition of the outermost layer (or the surface excess), orientation of molecules, and density and concentration depth profiles. A number of techniques have been developed for analyzing the molecular structure of liquid surfaces. Each of the techniques has its own strength. A comprehensive understanding is often only possible by considering the results of a range of techniques. Electron spectroscopy can be used as XPS [4], UV photoelectron spectroscopy (UPS) [7], and metastable-induced electron spectroscopy (MIES) [8] revealing both composition and electronic structure. Rutherford backscattering (RBS) [9] and neutral impact collision ion scattering spectroscopy (NICISS) [10] are used to determine the concentration depth profiles. Density and concentration depth profiles can be determined with neutron reflectivity (NR) [11] and X-ray reflectivity (XR) [12]. Both techniques are complementary to RBS and NICISS. Nonlinear optical methods such as second harmonic generation (SHG) [13] and sum-frequency generation (SFG) [14] have their strength in determining the orientation of molecules at surfaces. All these spectroscopic techniques analyzing the structure of the surface are complemented by measuring the forces at surfaces and interfaces. The methods to be named are the surface force apparatus (SFA) [15] and colloid probe atomic force microscope (AFM) [16]. Detailed descriptions of several of these methods are given in Volume 1 and in Chapter 49 in this Volume. The present contribution is focused on electron spectroscopies and ion spectroscopies as this is the field of expertise of the authors.

52.2 Methods 52.2.1 Metastable-Induced Electron Spectroscopy

Any version of electron spectroscopy is suited to characterize surface properties, as the limited mean free path of electrons discriminates strongly against signal from the bulk. The observation depth of, say, photoelectron spectroscopy or electron energy

52.2 Methods

loss spectroscopy (EELS) is governed by the energy-dependent mean free path of electrons in condensed matter that ranges from a few tenths of a nanometer to several nanometers. There is, however, one particular electron spectroscopy whose surface sensitivity is perfect and which does not rely on a small mean free path. This is MIES. This method consists in colliding a beam of electronically excited metastable particles with the investigated surface. In most cases, the projectiles are helium atoms He* (23 S,21 S) in a metastable state, which carries about 20 eV excitation energy. The transfer of this energy to the surface is very efficient and causes electron emission with high probability. After the reaction with the surface, the helium atoms have returned to their ground state. Thus, there is no danger that they contaminate the surface. Further, the kinetic energy being in the thermal energy range, the helium projectiles do not penetrate the surface nor can they modify the surface by means of their impact energy. This is important as it is a desirable property of an analytical tool that it does not induce changes in the surface investigated. In conclusion, we state that the electron spectroscopy based on the excitation energy of metastable helium atoms has an excellent potential for surface analysis. Indeed, the small number of research groups employing the technique has slowly, but steadily, grown in the past years. Those who have applied MIES for the characterization of liquid surfaces have overwhelmingly focused on ionic liquids (ILs). This is easily understood, as this class of liquids is distinguished by a negligible vapor pressure. Accordingly, the typical ultrahigh vacuum (UHV) equipment designed for analysis of solid surfaces can be used to investigate ionic liquids [17–19]. Liquids with higher but moderate saturation pressure up to 10 Pa can nonetheless be investigated by MIES, the first study being published in 1986 [20]. The application of MIES to more volatile liquids such as water has been made possible in 1999 [21]. Further experimental details are summarized in Section 52.2.6. In the following, we will focus on the mechanism of energy transfer in the case of MIES that causes the mentioned perfect surface sensitivity. The transfer of energy from the approaching metastable atoms to the surface may occur along two different mechanisms [22]. If allowed with respect to energy and symmetry, the dominant process is the resonant transfer of the excited electron of the metastable atoms into unoccupied states at surfaces. As metals do have unoccupied states in resonance with the excited orbital of the metastable atoms, the occurrence of this process can be taken as an indication of the metallic character of a surface, e.g. if metal atoms are deposited on an insulating surface, MIES can provide an unambiguous indication for metallization to set in. The accepted term for this process is resonant ionization (RI) that has been introduced by Hagstrum 1954 [23]. The ensuing process of electron emission called Auger neutralization (AN) involves two electrons from the surface, one electron tunneling into the 1s-hole of the helium ion while the other is emitted. The requirement of total energy conservation couples the initial and final states of both electrons. Auger neutralization leads to electron energy spectra, which are not related in a simple manner to the target density of states, and, thus, look very different from spectra obtained by UPS.

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If RI + AN does not occur, e.g. because of the lack of unoccupied states at an insulating surface, electron emission occurs via the process of Auger de-excitation (AD). Again, an electron from the surface tunnels into the hole orbital of the metastable atom while the excited electron of the metastable is emitted. The resulting spectra show, in general, the same bands as UPS, even though the relative activities of the different target orbitals are different between MIES and UPS. The electron density of an orbital outside the surface defined via the turning points plays the key role for its activity in the AD process [24]. According to the typical interaction potential between He* and surfaces, the AD process samples the electron density approximately 2–4 Å above the outermost atomic layer of a surface. The perfect surface sensitivity of MIES is now easily explained. The metastable atoms approach the surface with a kinetic energy in the thermal energy range. This prevents them from penetrating into the surface. Their turning point is about 2 Å in front of the topmost surface layer. As in both energy transfer mechanisms, RI and AN and AD, a tunneling process from an orbital at the surface into the hole at the metastable atoms is involved, and as tunneling occurs only over short distances, the technique is sensitive for those orbitals with a high electron density about 2 Å in front of the surface. As a consequence, we understand, in addition, why MIES is able to discover a preferred orientation of a molecule at the surface: the molecular orientation decides which orbital protrudes out of the surface layer. The influence of molecular orientation on the shape of MIE spectra is discussed for several molecules in Ref. [22]. For most liquid surfaces, the energy transfer mechanism that plays a role is AD, which leads to a spectrum that reflects the electron density in front of the surface. However, a liquid surface may well have metallic character and thereby react via the RI and AN mechanism. An example is found in Ref. [25], where the spectra taken by MIES at a surface of liquid mercury are displayed and discussed. They are interpreted in terms of the symmetry of the involved electronic bands of metallic mercury, which are the sp-conduction band and the two d-bands d5/2 and d3/2 . Thus, the interpretation of the spectra obtained from MIES depends on the electronic nature of the surface. In general, the interpretation of MIES data is less straightforward compared to data taken by UPS. Strategies for quantitative data evaluation have been discussed in detail in Ref. [22]. In particular, the evaluation of a series of spectra is discussed where one experimental parameter is varied in a controlled manner. The mathematical tool singular value decomposition (SVD) can be employed to perform a rigorous test on the question whether the series of spectra is to be interpreted by a finite number of reference spectra each representing a well-defined chemical species at the surface. Two different species in the sense of this strategy of data evaluation may refer to different chemical compounds, but as well to one compound found in different orientations [22]. It turns out that the composition of the topmost layer of a liquid surface as well as the distribution of molecular orientations can be recognized with good accuracy, provided that data with good quality have been measured. A few examples will be presented in Section 52.3.1.1.

52.2 Methods

52.2.2 Angle Resolved X-ray Photoelectron Spectroscopy

XPS is known for identifying elements as well as the chemical situation within which the elements are found (see also Chapter 3.2.2 in Volume 1). Thus, two species that can be distinguished by XPS may either be different elements or else the same element in two different chemical environments. The observation depth of XPS is governed by the mean free path 𝜆(Eel ) of the emitted electrons whose size depends on the material studied and on the energy of the electron. A typical value for the mean free path amounts to a few nm. The probability of a photoelectron to travel in a bulk material on a straight trajectory over a distance d without losing a significant amount of kinetic energy is given by ) ( d (52.1) P = exp − ( ) 𝜆 Eel From our own experience, we found this formula to be valid with good accuracy for electron energies above 250 eV, cf. [26]. For electron energies below 250 eV, correction factors had to be applied in order to compensate for slight deviations from the assumption of straight trajectories underlying the above formula. The concept of the mean free path and the expression in Eq. (52.1) can be employed to determine the concentration depth profiles of all species that can be distinguished by XPS. The strategy relies on the relation between the observed signal intensity for every species and the observation depth 𝜆′ (Eel , 𝜃) = 𝜆(Eel ) ⋅ cos 𝜃

(52.2)

that can be controlled either by varying the angle of emission of the photoelectrons (𝜃 = 0∘ referring to the surface normal) or by varying the photon energy h𝜈 = Ebind +Eel and thereby the energy Eel of the photoelectrons. The following expression evaluates the experimental signal intensity for species i as a function of the observation depth 𝜆′ (Eel , 𝜃), provided the depth-dependent concentration ci (z) of the species is known. ) ( ∞ z ′ (52.3) c (z) ⋅ exp − ( Ii (𝜆 ) = ) dz ∫0 i 𝜆′ Eel , cos 𝜃 Here, we have made use of Eq. (52.1). The range of experimental parameters that justify this approximation will be addressed at the end of this section. Of course, hardly ever do we encounter the situation that we know the concentration depth profile ci (z) and wish to predict the signal intensity. In contrast, in general, we measure the signal intensity for as many values of 𝜆′ (Eel , 𝜃) as possible and try to evaluate the depth-dependent concentration ci (z) of the species. Formally, one could interpret I i (𝜆′ ) in Eq. (52.3) as Laplace transform of the concentration profile ci (z) and conclude that ci (z) could be evaluated as inverse Laplace transform of I i (𝜆′ ). This would allow a direct inversion of the measured intensity I i (𝜆′ ) into the concentration profile ci (z). Unfortunately, it is well known that small uncertainties in I i (𝜆′ ) lead

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52 Liquid Surfaces

to unbearable uncertainties in ci (z). Therefore, it has been a common practice from early on to represent ci (z) by an analytical expression with a set of parameters and to carry out a fit of these parameters to the experimental data, cf. [27]. With increasing number and precision of experimental data points, it pays to invest even more effort into the evaluation of the concentration depth profiles ci (z). Based on a large data set with the observation depth 𝜆′ in Eq. (52.2) being varied both by varying angle 𝜃 as well as by varying the electron energy Eel , the concentration depth profile of the system tetrabutyl ammonium iodide/formamide (TBAI/FA) has been evaluated with the aid of the genetic algorithm, cf. [26] and references quoted therein. In this study, the surface layer has been subdivided into 30 equidistant sublayers with each sublayer being assigned a value for the concentration of solute and solvent. Effectively, one can say that this approach is free from an analytical model function. Details of the procedure are found in Ref. [26]. Nonetheless, some key formulae will be given below after introducing a few definitions:

• i, k are indices of the species that can be distinguished by XPS in the liquid with i, k ∈ [1, imax ]

• j numbers the layers with j = 1 indicating the topmost layer and j ∈ [1, jmax ]; beyond layer j = jmax , the system is supposed to have bulk properties

• dj is the thickness of layer j • qj denotes the photoionization cross section of species i, which depends on the orbital ionized, the photon energy, and the light polarization (as in Ref. [26], only signal from C1s orbitals is evaluated, these dependencies are not relevant in that study) • ci (j) denotes the concentration of species i in layer j The experimental contribution for species i in layer j can then be expressed as Ii (j, 𝜃) = qi ci (j) 𝜆′j (Eel , 𝜃) ⋅ (1 − exp(−dj ∕𝜆′j (Eel , 𝜃)) ⋅

j−1 ∏

exp(dj ∕𝜆′j′ (Eel , 𝜃)) (52.4)

j′ =1

The factor on the right side contains the weakening of the signal strength of the electrons from layer j when passing through all layers above. The respective contribution from the bulk, i.e. from the material below the last layer jmax , is given as Ii (bulk, 𝜃) = qi ci (bulk) 𝜆′bulk (Eel , 𝜃) ⋅

jmax ∏

exp(dj ∕𝜆′j′ (Eel , 𝜃))

(52.5)

j′ =1

The total signal from species i at emission angle 𝜃 and electron energy Eel is then Ii (𝜃) =

jmax ∑

Ii (j, 𝜃) + Ii (bulk, 𝜃)

(52.6)

j=1

The number of parameters to be fitted is fairly large. If the surface near range is subdivided into, say, 30 layers and contains two species, then this leads to no less than 60 parameters to be determined. The genetic algorithm, which has been applied in Ref. [26] and other studies later on, is ideally suited to come up with a stable answer. The strategy underlying the genetic algorithm is taken from biological evolution.

52.2 Methods

Any set of parameters is considered as individual. In Ref. [26], a number of 1000 individuals constitute a generation. The members of the first generation are established by means of a random number generator. No attempt is made to create the first generation in a somehow “meaningful” way. In contrast, as the researcher stays away from introducing his expectations into the fitting procedure, the outcome can be considered as particularly reliable. As in biological evolution, all members of a generation are tested for their “ability,” which in the present case is evaluated by the expression ( )2 ∑ Iicalc (𝜃) − Iiexp (𝜃) (52.7) D= exp Ii (𝜃) all data

Equation (52.7) determines the deviation between the calculated and the experimental data. The sum in Eq. (52.7) runs over all combinations of emission angle 𝜃 and electron energy Eel that have been used in the experiment. The individuals that show least agreement with the experiment are disposed of. In Ref. [26], this fraction is chosen to be 50%. The remaining individuals are crossbred (again with the aid of a random number generator) in order to create offsprings until the disposed individuals are replaced. The individuals existing now form the next generation and are processed in the same way as the preceding generation. Experience shows that this procedure begins to converge after a few generations. After a few hundred generations, the quality of the individuals does not improve any more, i.e. with respect to the fitting purpose, one has achieved convergence. It is interesting to note that the quality of the evolution is considerably improved if one allows for mutations, i.e. a random number-controlled arbitrary change of individuals. The rate of mutations itself has successfully been made a subject of evolutionary development, cf. [26]. At the end of the evolution process, one has a few thousand individuals that represent equally good fits to the experimental data. Thus, in contrast to normal fitting routines, one does not come up with the most likely solution, but with a large number of solutions of comparable quality. It turns out that this result is not an disadvantage but, in contrast, can be turned into an advantage: one evaluates over all good solutions the average and the standard deviation, thus ending up with an intrinsic error bar for the evaluated concentrations ci (j). One can show that the error bar determined in this way is very conservative, i.e. it overestimates the real error. Still, in all cases studied so far, we have obtained clear answers as to the behavior of, say, a surfactant at the surface of a solution. For example, the question whether the surfactant forms a monolayer or leads to a thicker layer has been unambiguously answered by the genetic algorithm, cf. [26]. Details of the numerical procedure, e.g. the introduction of reduced quantities as proposed by Baschenko [28] and the conversion from decimal numbers into binary numbers for the purpose of crossbreeding, are found in Ref. [26]. The determination of a concentration depth profile is based on a known relation between the measured intensity of the XPS signal and the experimental parameters Eel and 𝜃. In Eq. (52.3), a simple relation is noted. There are limitations for the applicability of this simple expression.

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52 Liquid Surfaces

For liquids containing overwhelmingly second-row elements, we found 𝜃 ≤ 60∘ a safe regime for electron energies above 250 eV [26]. Beyond this parameter range, correction factors are to be applied, which can be built into the expression in Eq. (52.3) in a straightforward manner, cf. [26]. The first to study correction factors by means of computer simulation was Baschenko more than two decades ago [28]. However, today, the topic is still actively investigated, cf. [29]. Our own experience, Baschenko’s findings [28], and the recent results by S. Oswald and R. Oswald [29] indicate that employing emission angles 𝜃 > 70∘ is accompanied by a large amount of uncertainty. To which extend angles 𝜃 ≥ 80∘ can lead to data that contain useful information on the system investigated rather than artifacts of electron scattering is far from settled. Some authors have developed the habit of taking data only at the two angles 𝜃 = 0∘ and 80∘ in order to qualitatively test whether a compound displays enrichment in the surface or not [30–32]. As quantitative data evaluation for establishing concentration profiles is not attempted by these groups, one could accept this strategy, were it not for the unanswered doubt, whether the measurement at 𝜃 = 80∘ contains artifacts from elastic scattering which in turn depends on the mass of the atomic species that the electrons encounter, cf. [29]. Thus, electrons originating from different species and thereby experiencing different species on their way to and through the surface may show very different deviations from a straight trajectory. To our knowledge, there is no clear proof, that a compound, that shows an enlarged intensity at 𝜃 = 80∘ compared to 𝜃 = 0∘ , necessarily must be considered surface active. On the other hand, taking several data points at angles between 𝜃 = 0 ∘ and 80∘ would allow judging whether the outcome of measurements at several angles appears consistent and, thus, make data at large angles more trustworthy. 52.2.3 Neutral Impact Collision Ion Scattering Spectroscopy 52.2.3.1 General

Ion scattering spectroscopy uses the interaction of ions with matter to gain information on the composition and the structure and surfaces or the near-surface region. The interaction involves charge transfer processes, elastic transfer of kinetic energy, inelastic energy transfer such as electronic excitations, or removing atoms or groups of atoms by sputtering. There is a broad range of methods that differ in the energy, type, and charge state of the projectiles, the scattering angle, and charge state and type of the detected particles. A number of selected methods are listed in Table 52.1 (see also Chapter 3.3 in Volume 1). Here, the focus is on NICISS, which is particularly important for the investigation of soft matter surfaces. The reason why NICISS is so important in this area is that the technique can be used to determine concentration depth profiles delivering very important information for the understanding of the structure of soft matter surfaces. NICISS uses rare gas ions as projectiles with a kinetic energy of a few keV. The projectiles backscattered under an angle close to 180∘ are detected with a time-of-flight (TOF) analyzer. Projectiles are detected irrespective of their charge state with most of them being in the neutral state. NICISS was originally developed to investigate

Table 52.1 Overview over frequently used ion scattering techniques. Method

Projectile and energy

Detection of

Information about

Low-energy ion scattering (LEIS) [33–37]

Ions (rare gas), kinetic energy 1–10 keV

Ions backscattered at ∼135∘ (90∘ –180∘ )

Elemental composition of the outermost layer

Impact collision ion scattering spectroscopy (ICISS) [38–41]

Ions (rare gas), kinetic energy 1–10 keV

Ions backscattered at 180∘

Elemental composition of the outermost layer Crystalline structure of solid surfaces

Neutral impact collision ion scattering spectroscopy (NICISS) [40, 42]

Ions (rare gas), kinetic energy 1–10 keV

Neutrals backscattered at 180∘

Concentration depth profiles of the elements (noncrystalline samples) Crystalline structure of solid surfaces

Direct recoil spectroscopy (DRS) [43, 44]

Ions (rare gas), kinetic energy 1–10 keV

Composition of the outermost layer

Rutherford backscattering (RBS) [45–48]

Single and multiple charged ions, kinetic energy 1–10 MeV

Recoils (fragments removed from the surface by a single energy transfer) Single and multiple charged backscattered ions

Elastic recoil detection analysis (ERDA) [47, 49]

Single and multiple charged ions, kinetic energy 1–10 MeV

Recoils (fragments removed from the surface by a single energy transfer)

Concentration depth profiles of the “heavy” (atomic number ≤ 10) elements of crystalline and noncrystalline samples

Concentration depth profiles of the “heavy” (atomic number ≥ 10) elements of crystalline and noncrystalline samples

52 Liquid Surfaces

3keV He+

W atom d Rs = dsinαc

αc L = dcosαc

1Å

(a)

(b) α0 αc2 αc1

α

Δαc

Δαc

A Intensity

238

d

B

(c) Figure 52.1 Schematic for the shadowing cone. At a given impact direction, backscattering from an atom in the shadowing cone (a) is not possible. Scattering from an atom located on the line indicating the shadowing cone is just possible (b). Thus, in the

(d)

αc1

α0

αc2

α

interval of the polar angle 𝛼 c1 to 𝛼 c2 (variation of the angle of incidence (c), the intensity of backscattered projectiles drops dramatically (d). (Source: Choi et al. 2011 [53]. Reprinted with permission of Elsevier.)

the structure of single-crystal surfaces. Changing the azimuth and the polar angle, the position of the atoms in the outermost layer relative to each other, the crystal structure, and the orientation of the crystal can be determined [50–52]. The effects used here are shadowing and blocking. Shadowing and blocking effects appear when a projectile is scattered off an atom in the sample (target). The scattering leads to a shadowing cone as illustrated in into which the scattered projectile cannot enter and thus cannot be backscattered from an atom in this cone [53] (Figure 52.1). Later, it was shown that the method can also be used to determine concentration depth profiles at surfaces [10, 54]. The prerequisite for determining concentration depth profiles with NICISS is that the structure of the sample does not show a longrange order in the sample, which is especially the case for soft matter but not for crystalline structures. Only in this case, the influence of the effects is excluded that enabled to determine the crystal structure. Before we go into details, we will first describe schematically in an overview which processes the projectiles experience in a NICISS experiment carried out at soft matter surfaces. Subsequently, the processes will be described in detail.

52.2 Methods

The projectiles backscattered from the sample are almost all in a neutral charge state. Only a few tenths of a percent of projectiles leave upon backscattering the sample as ions. The trajectories of the backscattered projectiles consist of a number of small-angle scattering events and one process with a large scattering angle [10]. The energy loss during backscattering depends on the mass of the atom hit. The small-angle scattering events have a scattering angle of a few tenths of a degree while the scattering angle of the large-angle scattering event is close to the total scattering angle. The small-angle scattering together with the electronic excitations along the trajectory of the projectile can be treated as a continuous energy loss. The energy losses on the entire trajectory can be summarized in two different classes of energy losses. The first consists of the large energy losses depending on the mass of the backscattering atom and appearing only once on a single trajectory. The second consists of small energy losses appearing in a large number on a single trajectory and can be treated as a continuous energy loss proportional to the length of the trajectory. Thus, by determining the energy loss of the projectiles, the information is gained first from which type of element the projectile is backscattered and second in which depth this target atom has been during the backscattering process. This is all the information required to determine concentration depth profiles. It is most common to use helium ions as projectiles. Soft matter mainly consists of the elements hydrogen, carbon, nitrogen, and oxygen, and these elements can be detected only when helium is used as a projectile. A second reason is that the projectiles sputtered hydrogen. Using neon instead of helium increases the sputter rate of hydrogen so strong that the sputtered hydrogen particles dominate the spectra. NICISS is used to determine concentration depth profiles in a similar way as in a RBS experiment. The main difference between both techniques is in the energy of the projectiles and the depth that can be investigated. RBS uses ions with kinetic energies of a few MeV. The depth up to which concentration depth profiles can be determined with RBS is about an order of magnitude larger compared to those obtained with NICISS. The depth resolution in a NICISS experiment, however, is about an order of magnitude higher than in a RBS experiment. In the intermediate energy range of a few ten to a few hundred keV, the method is called medium energy ion scattering (MEIS) with a range and depth resolution between NICISS and RBS. In the following sections, several details of the method NICISS will be discussed in more detail. 52.2.3.2 Elastic Energy Loss

The elastic transfer of energy from the projectile to the target atom during the collision can be calculated from energy and momentum conservation (Figure 52.2). A projectile with the mass m1 and the velocity v1i parallel to the x-axis in the laboratory system hits the target atom with the mass m2 being initially at rest. From the momentum conservation, the equations m1 v1,i = m1 v1,f cos 𝜃 + m2 v2,f cos 𝜙

(52.8)

0 = m1 v1,f sin 𝜃 + m2 v2,f sin 𝜙

(52.9)

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52 Liquid Surfaces

θ

y x

1

2

Φ

Figure 52.2 Scattering of a Projectile in the Laboratory System.

are obtained, where v1f is the velocity of the projectile and v2f is the velocity of the target atom after the collision and 𝜃 and 𝜙 the scattering angles. From energy conservation, the equation 1 1 1 m v2 = m v2 + m v2 2 1 1,i 2 1 1,f 2 2 2,f

(52.10)

is obtained with E0 = 1∕2 ⋅ m1 v21,i is the kinetic energy of the projectile before hitting the target atom and Ef = 1∕2 ⋅ m1 v21,f is the kinetic energy of the projectile after the collision. The scattering angle, the initial energy, the final projectile energy, the scattering angle of the projectile 𝜃, and the mass of the projectile are known. The mass of the target atom, the scattering angle of the target atom 𝜙, and the kinetic energy of the target atom after the collision are not known. Inserting the Eqs. (52.8) and (52.9) into (52.10) and solving for Efinal yields Efinal = E0 A=

[cos 𝜃 + (A2 − sin2 𝜃)1∕2 ]2 (1 + A)2

m2 m1

(52.11)

It can be seen from Eq. (52.11) that backscattering (scattering angle >90∘ ) is only possible for A > 1, i.e. for target atoms that have a greater mass than the projectile. It must be noted that Eq. (52.11) considers only elastic energy transfer and can be used to calculate all elastic scattering processes including small-angle scattering. 52.2.3.3 Cross Section

For quantitative data evaluation, it is required to calculate the cross section of the scattering processes. An excellent description for the calculation of the cross section is given with a similar approach as here also in [37]. The cross section d𝜎/dΩ of a scattering process is defined as 2 ⋅ π ⋅ p ⋅ dp p ⋅ dp d𝜎 = = (52.12) dΩ 2 ⋅ π ⋅ d cos 𝜃 sin 𝜃 d𝜃 As shown in Figure 52.3, all projectiles passing through the area d𝜎 will hit the target atom with the same impact parameter p and will be scattered under the same angle 𝜃 into the solid angle dΩ. For calculating the cross section, the scattering

52.2 Methods

dp p

O dσ

dΩ Figure 52.3 Schematic of the definition of the cross section.

angle 𝜃 has to be determined as a function of the impact parameter and is calculated by solving the equations of motion of projectile and target atom. It is convenient to solve the equations of motion in the center of mass system (CMS). The most appropriate coordinate system for this calculation is the CMS in polar coordinates. The velocity of the center of mass vc is related to the initial velocity of the projectile v0 in the laboratory system by vc =

Mc v m2 0

(52.13)

where Mc is the reduced mass given by Mc =

m1 m2 m1 + m2

(52.14)

The velocity of the target atom in the CMS is vc , whereas the velocity of the projectile is v 0 − v c = A ⋅ vc

(52.15)

with A from Eq. (52.11). The scattering angle 𝜑 in the CMS is related to the scattering angles 𝜃 and 𝜙 of the laboratory system by tan 𝜃 =

A sin 𝜑 1 + A cos 𝜑

(52.16)

tan 𝜙 =

sin 𝜑 1 − cos 𝜑

(52.17)

A schematic for the scattering process in the CMS is shown in Figure 52.4. The total energy ECM in the CMS can be written in polar coordinates as [( ) ( )2 ] 2 dr 1 2 d𝜑 + V (r) (52.18) +r ECM = MCM 2 dt dt where V (r) is the interaction potential between target atom and projectile, r the distance between target atom and projectile, and 𝜑 the scattering of the projectile

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52 Liquid Surfaces

φ V0 -VCM p

1 2 VCM

Figure 52.4 Scattering process in the CM system.

as illustrated in Figure 52.4. The angular momentum J CM in the CMS with p as the impact parameter can be written as JCM = MCM vCM p

(52.19)

when the projectile and the target atom are far away from each other. The angular momentum can also be expressed in polar coordinates as JCM = MCM r2

d𝜑 dt

which leads to d𝜙 vCM ⋅ p = dt r2 Inserting Eq. (52.21) into Eq. (52.18) and rearranging yields ( )1∕2 E − V (r) vCM2 ⋅ p2 dr − = 2 CM dt MCM r2

(52.20)

(52.21)

(52.22)

Combining Eqs. (52.21) and (52.22), we obtain p d𝜑 dt d𝜑 = = dr dt dr (1 − V (r)∕ECM − p2 ∕r2 )1∕2

(52.23)

And thus, the scattering angle in the CMS is given by ∞

𝜑=π−2

pdr ∫rmin (1 − V (r)∕ECM − p2 ∕r2 )1∕2

(52.24)

The value of rmin is the distance of closest approach between the projectile and the target atom and is calculated from Eq. (52.22) for the case that dr/dt = 0. The integral in Eq. (52.24) is an improper integral and has to be calculated numerically. A screened Coulomb potential has to be used as an interaction potential V (r) in Eq. (52.24) and is given by ) ( Z1 Z2 e2 Φ(r∕a) (52.25) V (r) = 4π𝜀0 r where Φ(r/a) is the screening function. Different screening functions are used in the literature. The empirical universal screening function (USP) [55] with the screening

52.2 Methods

length aUSP Φ(r∕a) = 0.0281 exp(−0.2016(r∕a)) + 0.2802 exp(−0.4029(r∕a)) + 0.5099 exp(−0.9423(r∕a)) + 0.1818 exp(−3.2(r∕a)) 0.4685 with aUSP = 0.23 Z1 + Z 20.23

(52.26)

and the Moliére potential [56, 57] with the screening length aLin proposed by Lindhard et al. [58] and aFir proposed by Firsov [59] Φ(r∕a) = 0.35 exp(−0.3(r∕a)) + 0.55 exp(−1.2(r∕a)) + 0.1 exp(−6.0(r∕a)) 0.8854 ⋅ a0 with aLin = 2∕3 2∕3 (Z1 + Z2 )1∕2 0.8854 ⋅ a0 or aFir = 1∕2 (52.27) 1∕2 (Z1 + Z2 )2∕3 are frequently used. Experimental data to measure cross sections at low and medium energies are rare [60–62]. Good agreement between measured and calculated cross sections can be achieved only by further corrections of the screening length. One example is multiplying the screening length with the expression [63] a = aFir ⋅ (0.045(Z1 1∕2 + Z2 1∕2 ) + 0.54)

(52.28)

52.2.3.4 Neutralization

In an NICISS experiment, rare gas ions – mostly helium ions – are used as projectiles. Helium ions with low kinetic energies undergo charge exchange as soon as they are in close distance of a few Å to another atom. Charge exchange processes have been studied experimentally [64, 65] and theoretically based on molecular orbital calculations [66–69]. Projectiles moving through matter experience a continuous charge exchange with their environment where the charge state of the rare gas projectiles is most of the time neutral. Rare gas ions have a low probability not to be neutralized because of their large ionization energy. The probability is much higher for alkali ions. The charge exchange processes are very complex and depend on the electronic configuration of the projectile and atoms in the sample, kinetic energy of the projectile and the impact parameter or the scattering angle. This subject is described comprehensively and very clearly by Brongersma et al. in Ref. [34]. The electronic states of a projectile approaching another atom shift and broaden. There are different charge exchange mechanisms leading to the neutralization of the projectiles. (i)

A first channel is Auger neutralization, which is possible for all types of samples. An electron tunnels from an occupied state of the substrate into the 1s ground state of the helium ion (see Figure 52.5a). The energy difference between the occupied state of the substrate and the 1s ground state of the helium is used to emit an Auger electron [70, 71].

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244

52 Liquid Surfaces

Ebin

1: resonant neutralization

Ebin

Auger neutralization

2

Evac

1

2s

EFermi

2s

e–

2s

2 1s

1s (a)

2: Auger de-excitation

He+

He*

1s He+

(b)

Figure 52.5 Schematic for the neutralization processes of helium ions: (a) Auger neutralization and (b) Auger de-excitation subsequent to resonant neutralization.

(ii)

A second channel is Auger de-excitation, which requires the transition of the helium ion into an excited state via resonant neutralization [70, 71] (see Figure 52.5b).

Auger de-excitation and Auger neutralization mechanisms are also used for the transition of excited helium atoms to the ground state that follow the resonant neutralization. The Auger neutralization mechanism requires the ionization of the excited helium into via RI. The process opposite to neutralization, i.e. ionization of the projectile, is possible during the collision. Because of the large excitation energy of helium, the ionization processes are a few orders of magnitude less likely than the neutralization processes. The probability for the charge transfer processes depends on the length of the time interval for which the projectile and the ion are in close contact. Thus, the probability + Psurv for an ion not to be neutralized depends on its velocity parallel to the surface normal and can be described with [34, 71] ( ) vc vc + Psurv = exp − − (52.29) v0,⊥ vf ,⊥ where vc is the characteristic velocity specific for an element, v0,⊥ the velocity of the projectile parallel to the surface normal before the collision, and vf ,⊥ the velocity of the projectile parallel to the surface normal after the collision. Measuring characteristic velocities require the knowledge of the surface number density of the respective element. 52.2.3.5 Inelastic Energy Loss

The charge transfer processes described in the previous Section 52.2.3.4 are inelastic energy loss processes. The energy loss due to excitation processes has been measured experimentally [64, 65] and are also part of the calculation of neutralization processes, which is the subject of the following section. Excitation processes for medium and high energies have been calculated from first principles [72, 73]. The calculated spectra for the energy transfer at high kinetic energies show peaks for

52.2 Methods

the excitation of the electrons to various energy levels including multiple excitation. Energy loss spectra of the scattered ions show a broad distribution and are asymmetric with the broader part toward the low energy side of the spectrum. The different excitation processes could not yet be resolved experimentally in an energy loss spectrum of scattered ions [74] because of several reasons. One reason is the broadening of the spectra because of thermal motion of the target atom. Another reason is the experimental resolution of the analyzer. Experimental spectra could be fitted by convoluting the calculated energy loss spectra with a typical assumed experimental resolution [74]. Inelastic energy losses appear during both forward scattering and backscattering. Nevertheless, the amount of inelastically lost energy depends on the impact parameter of the projectiles and decreases with increasing impact parameter [70, 74]. In general, the inelastic energy loss during a backscattering process is greater than the inelastic energy loss during a forward scattering process because the distance of closest approach is smaller in the first case than in the latter case. In the data analysis of a NICIS spectrum, the inelastic energy loss has to be considered in two different ways. First, inelastic energy loss has to be taken into account for gauging the energy of a projectile backscattered from a target atom. Equation (52.11) considers only the elastic energy loss, and for determining the energy of the projectile after the scattering process, the inelastic energy loss Qin has to be added yielding [10] Efinal = E0

[cos 𝜃 + (A2 − sin2 𝜃)1∕2 ]2 − Qin (1 + A)2

(52.30)

The term Qin is the mean inelastic energy loss during the backscattering process. We will see later that Qin will be considered by measuring NICIS spectra of gas phases. Second, the inelastic energy loss of the small-angle scattering processes is part of the continuous energy loss of the projectiles penetrating through matter, which is subject of the section “stopping power and energy loss straggling.” 52.2.3.6 Stopping Power and Energy Loss Straggling

Particles penetrating through matter lose energy because of low-angle scattering and electronic excitations. Examples for electronic excitations are the Auger neutralization and de-excitation processes as described in the previous Sections 52.2.3.4 but also excitations to bound states with subsequent radiative decay are possible [75]. The total energy loss is a sum of independent processes where each of them could be calculated either as a low-angle scattering process with Eq. (52.11) or by calculating the electronic excitation of the individual process as described in the previous section. In general, this approach is possible but is rather elaborate. A common and very practical way is to treat the energy loss of the independent processes as a continuous energy loss. The mean value of this energy loss is referred to the energy loss after penetration of a projectile through a layer of 1015 particles/cm2 . Another, however, less frequently used unit is energy loss per length of the trajectory. The energy loss due to excitation of electrons is called electronic stopping power and the energy loss due to low-angle scattering is called nuclear stopping power. At low

245

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52 Liquid Surfaces

energies (approximately a few keV), the nuclear part of the stopping power prevails the electronic part while at high kinetic energies (approximately a few hundred keV or more). Because of the fact that there is a distribution in energy loss of the nuclear and electronic energy loss processes and that each projectile experiences usually a number of various energy loss processes, the total energy loss which a projectile experiences has a statistical fluctuation. As a consequence, a beam of monoenergetic particles has a distribution of energies after penetrating through a thin layer, even if this layer has a constant thickness and composition over its entire area. The first moment of the energy loss distribution is the mean energy loss and called stopping power while the second moment is a measure for the width of the energy loss distribution and is called straggling [76]. The stopping power and the energy loss straggling have to be determined experimentally. 52.2.3.6.1 Stopping Power The usual way to determine the stopping power is measuring the energy loss of projectiles passing through a layer of well-defined thickness. Instead of measuring the stopping power for all substances separately, it is possible to calculate the stopping power of a particular substance as a linear combination of the stopping power of the elements constituting the substance according to Braggs rule [77] and taking into account the binding energy between the atoms [78, 79]. Taking into account, the binding energy becomes less important for low energies because the nuclear stopping power is the dominant part in this energy regime. Theories have been developed to calculate the energy loss of particles traveling through matter [80–82]. The stopping power of high energy projectiles is measured with a variety of samples [83, 84]. In most cases, self-supporting foils of the desired material with a thickness of a few 10 nm are used. The minimum thickness of self-supporting foils depends on their material and is around a few tens of nm. Such thickness is appropriate for energy loss measurements at high kinetic energies because the energy loss in the foil is small compared to the kinetic energy of the projectiles and the kinetic energy can be approximated as a constant on the trajectory through the foil. Thin self-supporting foils cannot be used for low-energy projectiles because the thickness of self-supporting foils is comparable to the penetration depth of the projectiles in matter. Thus, the energy loss of low-energy projectiles in the foil would be almost as large as the kinetic energy of the projectile. Hence, the kinetic energy cannot be approximated as being constant when passing through the foil. The stopping power of low-energy projectiles can be extrapolated from measurements at higher energies. However, a more reliable method to obtain data for lower energies is making use of thin layers adsorbed on substrates. The prerequisites for this method are that the adsorbed layer has a homogeneous thickness and composition over a large area and that the thickness of the layer can be determined experimentally with another technique. Based on this procedure, Andersson and Morgner have determined the stopping power of helium projectiles in alkanethiolate monolayers adsorbed on silver and gold [85]. The quality of the

52.2 Methods Theta = 35° Theta = 20°

250

loss of energy [eV]

E0 = 4.5 keV

Theta = –20°

200

Theta = 0°

150 Theta = –20° Theta = 0° Theta = 20° Theta = 35°

100 50 0 0

20

40

60

80

100

effective length of trajectory [Å] Figure 52.6 Energy loss of helium projectiles with a kinetic energy of 4.5 keV in alkanethiolate monolayers of varying alkyl chain length. (Source: Andersson and Morgner 1999 [85]. Reprinted with permission of Elsevier.)

self-assembled alkanethiolate layers was controlled with MIES and the thickness determined with XPS [86]. The energy loss of the projectiles in the alkanethiolate monolayers was determined by comparing NICIS spectra of the uncovered substrate and the substrate covered with alkanethiolate layers of different alkane chain length. In addition to varying the length of the trajectories of the projectiles in the alkanethiolate layer by varying the chain length, spectra were measured at different azimuths. The energy loss as a function of the length of the trajectory did not show a constant slope. However, the data points for a single azimuth could be fitted with a straight line as shown in Figure 52.6 [85]. Simulations of the trajectories showed that those trajectories with a direction almost parallel to the orientation of the alkyl chain have a reduced nuclear energy loss than other trajectories. As the nuclear part of the stopping power prevails over the total stopping power, the conclusion is justified that the alkanethiolate monolayer is more transparent for the projectiles because of the orientation of the alkyl chains. The measurements had been carried out at 1.2 and 4.5 keV. The stopping power between these values can be interpolated by using the proportionality of the stopping power to the square root of the projectile kinetic energy [87]. Energy Loss Straggling The variation of the thickness of thin selfsupporting foils determines the uncertainty in the measured energy loss. In most cases, the contribution of the thickness variation to the variation of the measured energy loss cannot be separated from the energy loss straggling, because the thickness variation is not known. The same holds for thin layers adsorbed on substrates. The measured energy loss variation only gives an upper estimation for the energy loss straggling. However, knowing the energy loss straggling quantitatively becomes important when all the factors are considered, which influence the shape of the spectra. The shape of a measured NICIS spectrum is given firstly by the concentration depth profiles of the sample, secondly by the inelastic energy loss during the backscattering 52.2.3.6.2

247

248

52 Liquid Surfaces

process, and thirdly by the straggling of the energy loss. In some cases, it is of interest to determine the details of the concentration depth profiles as exactly as possible. In such cases, the measured spectra have to be corrected for the distribution of the energy loss, which requires not only knowing the straggling of the energy loss but also measuring the energy loss distribution itself. In general, the straggling of the stopping power could be determined from the NICIS spectra of alkanethiolate layers on gold and silver. However, there are two reasons why these spectra should not be used to determine the energy loss straggling. First, the alkanethiolate layers have a thickness and density variation, which is difficult to be determined. These variations cannot be separated from the straggling. Second, the energy loss distribution can be determined most accurately if both the rising and the falling edge of a signal can be used for the data evaluation. However, the spectra of alkanethiolate layer on gold only show the raising edge of the silver spectrum. A suitable alternative for determining the energy loss straggling is the surface of the surfactant solutions in equilibrium with a gas phase. The gas phase should have a density that is so high that the energy loss in the gas phase is not negligible, i.e. the energy loss in the gas phase should be larger than a few eV. Surfactants have an enhanced concentration at the surface and lead to a peak-like structure in the NICIS spectra. The broadening of such peaks due to the energy loss straggling in the gas phase can readily be determined. Also, the fluctuations of the density of the gas phase are so small that variation in energy loss caused by the fluctuations is small compared to the energy loss straggling. Andersson et al. [88, 89] have investigated the spectra of an aqueous 0.01 m Bu4 NI and 2.5 m LiCl solution at temperatures between −13.2 and −4 ∘ C. Bu4 NI is a surfactant and the iodide spectra have a peak showing the enrichment of this ion at the surface. Thus, in the iodide spectra, a peak both with a rising and a falling edge can be identified. The vapor pressure of these solutions is a few mbar and the projectiles lose energy not only in the bulk of the liquid phase but also in the gas phase as shown schematically in Figure 52.7. In Figure 52.7a, the case is sketched when the energy loss in the gas phase can be neglected: the projectiles backscattered from the outermost layer lose energy only during the backscattering process. In Figure 52.7b, the case is sketched when the energy loss in the gas phase cannot be neglected: the projectile lose energy passing through the gas phase. As the projectiles experience different energy loss processes – both in number and in magnitude – a monoenergetic beam of projectiles turns into a beam of projectiles with an energy distribution. Thus, the part of the spectrum that has to be assigned to projectiles backscattered from the liquid surface will be shifted to a greater energy loss. Also, the width of the spectrum will be increasing with increasing density of the gas phase. Projectiles passing through the bulk experience the same energy loss processes for all temperatures investigated. The energy loss spectra show the broadening and shift with increasing temperature and increasing vapor pressure, respectively [89]. The series of energy loss spectra can be used to determine the energy loss distribution quantitatively. The energy loss in the gas phase can be determined by comparing spectra at different temperatures of the solutions. The vapor pressure of the solution

52.2 Methods

liquid phase E0

E1 E2

(a) liquid phase

gas phase E0

E1* E2*

(b) Figure 52.7 Scheme of the influence of the gas phase on the energy loss of the backscattered projectiles [88]. (a) The projectiles backscattered from the outermost layer lose energy only during the backscattering process and have the energy E 1 and projectiles backscattered from a deeper layer E 2 . (b) The projectiles lose energy also in the

gas phase, and E1∗ < E1 as well as E2∗ < E2 . E1∗ has to be considered as the mean value of an energy distribution as the projectiles experience different energy loss processes depending on whether their trajectory goes through the gas phase or not. (Source: Andersson 2007 [88]. Reprinted with permission of American Physical Society.)

is increasing with the temperature and thus also the densities of the gas phase. The single energy loss processes of the projectiles in the gas phase are independent, which is justified by their small cross section of the energy loss processes. As a consequence, it is appropriate to seek for an energy loss distribution, which depends solely on the mean energy loss. Two different procedures have successfully been used to determine the energy loss distribution. (i)

The first method [88] seeks for a single energy loss distribution f sing at a fixed mean energy loss Emean . The energy loss distribution of a mean energy loss, which is a multiple integer of Emean , is described by a repetitive self-convolution of the single energy loss distribution, where the number of repetitive convolutions is given by the multiple integers. This procedure is solely a consequence of the fact that the energy loss events are independent. The energy loss distribution f sing is determined from the energy loss spectra [88]. A spectrum measured at a lower vapor pressure (lower temperature) is repetitively convoluted with the single energy loss distribution f sing to fit a spectrum measured at a higher vapor pressure (higher temperature). As a first step in the procedure, it has to be determined how often a low vapor pressure spectrum has to be convoluted to fit a high vapor pressure spectrum. Thus, the first step determines the multiple integers. For this purpose, the center of gravity Ei of all spectra are

249

52 Liquid Surfaces

calculated with Ea

Ei =

∫Eb

E ⋅ Ii (E)dE∕

Ea

∫Eb

(52.31)

Ii (E)dE

where E is the energy loss, I i (E) the energy loss spectrum, and Ea and Eb are reasonable boundaries. The multiple integer for a pair of spectra is calculated from the difference of their center of gravity divided by Emean . This means that the number of convolutions nij to fit spectrum j by convoluting spectrum i is given by (52.32)

nij = (Ei − Ej )∕Emean

In practice, the nij are not integers but have to be rounded off to the closest integer. The choice of the value of Emean has no physical meaning. However, Emean has to be chosen for a particular set of spectra such that the values of nij are as close as possible to integers. The shifts of the center of gravity (Ei − Ej ) as a function of the pressure difference of the spectra i and j during their data acquisition are shown in Figure 52.8. The shifts are a linear function of the difference of the pressure during the measurement in the vacuum chamber and can be used to determine the offset of the depth scale because of the presence of the water gas phase as will be discussed below. In the second step, the energy loss distribution f sing at a fixed mean energy loss Emean has to be found. This is achieved by fitting the measured spectra with the energy loss distribution f sing . The fitting parameters are the intensities of the single energy loss distribution f sing with the condition that f sing is the same for all possible combinations of spectra. The single energy loss distribution f sing has been determined using the spectra in Ref. [88] for Emean = 8.3 eV. The measured spectra have been the fitted curves 200 shift center of gravity [eV]

250

55±2 eV/10–4 mbar 150 100 pressure difference 50

fit pressure difference

0 0

0.5

1

1.5

2

2.5

3

3.5

pressure difference [10–4 mbar] Figure 52.8 Shift of the center of gravity as a function of the pressure difference of the respective spectra during data acquisition. The slope of the linear fit is 54 ± 2 eV/10−4 mbar. (Source: Andersson 2007 [88]. Reprinted with permission of American Physical Society.)

52.2 Methods

while the probabilities of the single energy loss distribution have been used as the fitting parameters. Thus, this method does not require a mathematical function to describe the energy loss distribution. The energy loss distributions for a multiple integer of the energy loss of 8.3 eV can then be calculated. The advantage of this procedure is that no assumption has to be made about the shape of the energy loss distribution function. The disadvantage is that energy loss distributions with a mean energy loss other than a multiple integer of Emean are complicated to calculate. (ii) The second procedure is based on the Poisson statistic [89]. The Poisson statistic can be used as the energy loss events are independent as discussed above. As additional simplification, it can be assumed that all collisions lead to the same amount of energy loss ΔEloss . This is less well justified, but it will be seen later that comparison with experiment indicates this approximation being acceptable. ΔEloss is a parameter that has to be determined by comparison with the experiment. It turns out that the value of this parameter is not very critical. The number of scattering centers per area in the layer considered is d. This quantity grows linearly with the layer thickness and thus can be considered as a measure for layer thickness. The cross section for the event leading to energy loss ΔEloss is 𝜎. The Poisson distribution yields the probability that the projectile undergoes a number of k scattering events when passing through the considered layer. PPoisson (k) = (𝜎 ⋅ d)k ⋅

exp(−𝜎 ⋅ d) k!

(52.33)

The values of 𝜎 and d cannot be fitted independently but only their product. The distribution is normalized to give ∞ ∑

PPoisson (k) = 1

(52.34)

k=0

The mean energy loss ⟨Eloss ⟩ described by the Poisson distribution is given by ⟨Eloss ⟩ = 𝜎 ⋅ d ⋅ ΔEloss

(52.35)

The individual energy loss is related to the number of collision events by Eloss = k ⋅ ΔEloss

(52.36)

To any layer thickness d belongs a distribution of k and, thus, a distribution of energy losses. For the application, it would be easier to have the distribution not as a function of k but of the energy loss. For this purpose, we set k=

Eloss ΔEloss

(52.37)

This turns the integer number k into a continuous variable. This can be accounted for by replacing the faculty k! by the Gamma function Γ(k + 1),

251

52 Liquid Surfaces

0.12 0.08

no. of energy loss events

energy loss 16.6 eV energy loss 41.5 eV energy loss 66.4 eV energy loss 91.3 eV energy loss 116.2 eV

0.16 probability

252

0.04 0 0

(a)

100

200

300

400

(b)

Figure 52.9 (a) Energy loss distributions for indicated mean energy losses in water vapor described with Poisson distributions. The energy loss per energy loss event is chosen as 6 eV (b) number of energy loss events as

PPoisson (Eloss ) =

(

(𝜎 ⋅ d)

Eloss ΔEloss

Γ

(

60 40

9.7 events/10–4 mbar

20 6.6 events/10–4 mbar

0 0

500

energy loss

which leads to

–4 loss/event = 3 eV 18.7 events/10 mbar loss/event = 6 eV loss/event = 9 eV

1

2

3

4

pressure difference [10–4 mbar]

a function of the pressure difference in the vacuum chamber during the measurements. (Source: Andersson 2007 [88]. Reprinted with permission of American Physical Society.)

)

⋅ exp(−𝜎 ⋅ d) ) +1

Eloss ΔEloss

(52.38)

The fitting procedure is similar to the previous method. A spectrum measured at a lower temperature is convoluted with a Poisson distribution to fit the spectra measured at a higher temperature. The parameter d is the only fitting parameter while 𝜎 is set equal to unity. Fits were carried out for ΔEloss in the range of 3–9 eV. The justification for this range of ΔEloss is that approximating the water molecules as spheres and taking into account the density of water, the number of water molecules per monolayer in the condensed phase is about 0.9 × 1015 cm−2 . Using the extrapolated stopping power from [90] the average energy loss of a projectile passing by an oxygen atom is about 5.5 eV and by a single water molecule about 8.3 eV. The energy loss passing by a single hydrogen atom is small and can be neglected in reasoning the chosen range of ΔEloss . However, the fit results are almost independent from the value of ΔEloss in this range. The energy loss distributions for ΔEloss = 6 eV are shown in Figure 52.9. The slopes of the mean energy loss as a function of the pressure difference slightly depend on the value of ΔEloss and they vary between 56 and 59 eV eV/10−4 mbar, which is very similar to the value of the slope in Figure 52.8. Andersson et al. found that both descriptions of the energy loss distribution fit the measured spectra almost equally well and the authors concluded that further measurements will be required to decide whether or not one of both procedures should be preferred. Comparing the energy loss distributions of both methods, it can be seen that both descriptions differ in their strength of asymmetry with the Poisson distribution being less asymmetric. The advantage of the first procedure is that no

52.2 Methods

mathematical function is required to describe the slope of the distribution and thus does not restrict the slope. The advantage of the procedure using the Poisson distribution is in its simplicity, which makes it preferable for practical applications. 52.2.3.7 Thermal Broadening

The thermal motion of the atoms in the sample leads through momentum transfer to a broadening of the energy of the scattered projectile. In a solid sample, the thermal motion is described by the phonon energy. In soft matter, the thermal motion has to be expressed by translational, vibrational, and rotational motion of the atoms. The broadening can be estimated with [91] ΔEtherm =

8(A − 1)(A ⋅ E0 Etherm )1∕2 (A + 1)2

(52.39)

where A is the same as in Eq. (52.11), E0 the primary energy of the projectile, and Etherm the thermal energy of the target atom. Etherm can be estimated for typical molecules. A nonlinear molecule consisting of three atoms has three translational, three rotational, and three vibrational degrees of freedom. For the transfer of momentum, only the motion parallel to the direction of the trajectory is important, which is only 3 out of the 9 degrees of freedom of the considered molecule, neglecting whether or not each degree of freedom is thermally activated. Thus, Etherm can be estimated as 1.5 kT or 38 meV. The energy broadening for a 3 keV helium projectile backscattered from carbon yields ΔEtherm = 19 eV and for helium projectiles backscattered from iodide ΔEtherm = 14 eV. Both values are small compared to the total line width of backscattered projectiles of about 50 eV at these conditions as measured in the spectra of gas phases. 52.2.3.8 Concentration Depth Profiles

NICIS spectra are measured as TOF spectra. The ratio between a TOF spectrum and an energy spectrum is given by [10] I(t) = I0 ∕((d𝜎∕dΩ)∕(d𝜎∕dΩ)0 )

(52.40a)

I(E) = I(t)(dt∕dE)∕(dt∕dE)0

(52.40b)

where t is the TOF, E the energy, d𝜎/dΩ(E) is the differential cross section, (d𝜎/dΩ)0 a reference cross section of arbitrary value and det(E) the detector sensitivity. The factor dt/dE has to be taken into account since as equal intervals on the TOF scale are not equal on the energy scale because of the nonlinear relation between the TOF and the energy. The factor (dt/dE)0 is also of arbitrary value to normalise the conversion. dE f (52.41) dz where z is the depth and f a factor relating the concentration to the yield of backscattered projectiles. In most cases, the purpose of the measurement is to determine the concentration depth profiles of the elements. The NICIS spectra have a contribution of helium projectiles backscattered from the different elements constituting the sample and of I(d) = I(E)

253

52 Liquid Surfaces

4000

C

3500 count rate [counts/h/nA]

254

3000

O

2500 2000

photons

recoil hydrogen

1500 1000 step O

500

step C

0 –500 –1.0

0.0

1.0

Figure 52.10 Spectrum of benzyl alcohol with 4.5 keV helium ions used as projectiles. The spectrum consists of the broad distribution of recoil hydrogen and steps for the elements oxygen and carbon.

2.0 3.0 time-of-flight [μs]

4.0

5.0

6.0

The photons are used to gauge the zero mark of the TOF scale. (Source: Andersson 2005 [92]. Reprinted with permission of The Royal Society of Chemistry.)

sputtered hydrogen atoms. Different steps are required to determine the concentration depth profiles: 1. Extracting the NICIS spectra of a single element 2. Converting the NICIS spectra to the depth scale and gauging the depth scale 3. Deconvolution Although the first two steps are required for each NICISS data evaluation, the third will be carried out only if the exact details of the concentration depth profiles are of interest. 52.2.3.8.1 Extracting the NICIS Spectrum of a Single Element In Figure 52.10, a TOF spectrum of benzyl alcohol (BA) is shown as a typical example of a NICIS spectrum of an organic compound. The spectrum consists of backscattered projectiles, which appear as a step for each element constituting the sample: a step for oxygen and a step for carbon. Additionally, there is a broad background consisting of hydrogen atoms, which are hydrogen atoms sputtered by the projectiles. In the evaluation of the spectra, each step has to be separated from the total spectrum. Photons are emitted from the surface upon the impact of the projectiles. The photons appear as a peak in the spectrum. The photon peak is used to determine the zero mark of the TOF scale. The procedure of separating the steps from the total spectrum is based on two assumptions. Firstly, the background of the sputtered hydrogen is a smooth curve.

52.2 Methods

This is evident from the spectra of alkanes, which only show one step and a smooth distribution before and after the onset of the carbon step [10]. Upon adding a solute to a solvent, the distribution of the sputtered hydrogen atoms remains smooth. As long as the concentration of heavy elements does not change strongly upon adding a second component to the sample, the shape of the recoil hydrogen distribution does not change significantly. As an example, the recoil hydrogen distribution of a pure solvent can also be used for the evaluation of the spectra of a solution as long as the concentration of the solute is low. In most cases, concentrations 1 nm. In the angle-resolved mode, NICISS can achieve even higher depth resolution. DRS detects particles that are sputtered from the surface in a single scattering process. Such particles are called recoils. The interpretation of DRS usually requires simulations [122]. A further scattering technique is molecule scattering allowing to investigate the uptake and solvation processes of gases into liquids [123]. Other techniques are the reflectivity techniques, NR and XR. NR probes the profile of index of refraction of neutrons along the surface normal, which is determined by the scattering length density at the gas/liquid interface. The index of refraction is determined by both the local composition of the sample and the local density of the sample. It is difficult to separate change in composition and change in density. The scattering lengths of hydrogen and deuterium are very different [124]. The deuterium:hydrogen ratio of the different species can be adjusted to match the index of refraction of a selected compound to that of the gas phase. Thus, it is possible to choose whether a given species is visible or invisible for the neutrons. The composition of the surface is determined with NR from a set of different measurements where the index of refraction of each compound is changed. In general, it should be possible to determine the concentration depth profiles of the constituents by Fourier transformation of the reflectivity profiles. In practice, this is not done because of the statistical uncertainty of the reflectivity profiles. Instead, the measurements are mostly fitted by assuming that the distribution of

267

268

52 Liquid Surfaces

each substance at the surface can be described with a Gaussian curve. The FWHM of the Gaussian curves and the relative distance of them in the direction parallel to the surface normal are fitting parameters. NR is mostly used to determine the surface excess of surfactant solutions. In the case of mixed surfactant solutions, the relative distance of both surfactant monolayers can also be determined. XR probes the profile of the index of refraction of the X-ray. The index of refraction is determined by the electron density profile across the surface and thus the concentration depth profiles and the density profile in the surface near region [125]. As for NR, it is difficult to separate change in concentration and change in density. XR allows investigating density profiles and distributions of species at liquid interfaces such as concentration depth profiles of ions [125, 126]. In summary, both NR and XR measure the reflectivity close to the angle of total reflection and probe the difference in reflectivity predicted by the Fresnel formula for an ideal, smooth, and flat interface and the reflectivity of a real interface [127]. Nonlinear optical methods such as SFG or SHG make use of the nonvanishing first-order nonlinear susceptibility at interfaces because of the broken inversion symmetry at the interfaces. The interaction of differently polarized light with the molecules is probed and yields conclusions about the orientation [128–130] and the electric field [131] at interfaces. As long as the absorption of light in the upper phase is not too great, it is possible to also probe liquid/liquid interfaces. In some cases, non-linear optics (NLO) methods were used to quantitatively probe the amount of surfactants at an interface [132, 133]. This requires the calibration of the signal with surfaces of a known composition. For the quantitative analysis of the composition of the surface, it has to be assumed that the orientation of the molecules in the reference surface and the probed surface are the same. It is hard to prove this assumption independently by the method itself. 52.3 Concentration Depth Profiles

The quantities of interest at liquid surfaces that can be derived from concentration depth profiles are the amount of substances adsorbed at the surface or desorbed from the surface, orientation of molecules, the change of the overall density, the distribution of charges along the surface normal, and as a consequence also the electric field and the electric potential along the surface normal. In the above list, the density profile is named separately to the concentration depth profiles. Both quantities have to be distinguished. The concentration depth profile usually means the concentration relative to a reference concentration, e.g. the bulk concentration, and is given in units of amount of solute per amount of solvent. The density profile means the change in total density of the substances. The density changes across the gas/liquid interface from that of the gas phase to that of the liquid phase. When we consider a pure substance, the concentration does not change at all in such a case because it is unity across the interface. In multicomponent systems, both the concentration and the density can change and usually do. Some experimental methods are sensitive for either concentration or density changes and some are sensitive for both. Electron

52.3 Concentration Depth Profiles

and ion scattering spectroscopies are usually sensitive only for changes in concentration and allow for determining the absolute concentration. The depth is derived in both electron and ion scattering spectroscopies indirectly. In the case of ion scattering spectroscopy, the depth is measured with reference to the stopping power; in the case of electron spectroscopy, the electron mean free path serves as the reference. Both electron and ion scattering spectroscopies could thus use a reduced length as the depth scale instead of a depth scale in units of meters. Baschenko has used this concept for electron spectroscopy data [28]. The reflectivity methods are sensitive for both changes in density and changes in relative concentration. However, applying reflectivity methods, it can be difficult to disentangle changes in concentration and changes in density. The quantities that can be derived from concentration depth profiles are of interest in many areas. We want to name here explicitly the formation of thin liquid films and adsorption at solid surfaces from solutions as two examples. The stability of foam films impacts on the coalescence of bubbles that has practical consequences for flotation and emulsification. Bubbles are used to separate minerals in the flotation process and their formation and stability impact on the efficiency of the separation [134, 135]. In emulsion, two liquids are dispersed into each other forming a twophase system consisting of small bubbles and thin liquid films. The stability of the bubbles and the thin liquid films determines the overall stability of the emulsion [136]. Adsorption at solid surfaces impacts on coating and again on flotation [135]. Surfactant molecules adsorb onto small mineral particles determining the attachment of the particles to a foam bubble. In all these processes, dynamics plays an important role. Often, the dynamics is difficult to be investigated with the existing analytical techniques because they all rely on the formation of equilibrium or at least a static situation. The only possibility for investigating the dynamic of liquid surfaces would be the isolation of a liquid surface and investigating the concentration depth profiles of the liquid at a given time after formation and isolation. 52.3.1 Composition of Top Surface Layer 52.3.1.1 From MIES, a Technique with Perfect Surface Sensitivity 52.3.1.1.1 Binary Mixture of Two Polar Solvents A few years ago, the surface of several binary mixtures of polar solvents [137] has been studied by the combination of surface tensiometry and surface spectroscopy MIES. As all mixtures investigated are miscible in all proportions, the bulk molar fraction could be varied over the full range from zero to unity. It turned out that the MIE spectra of the mixtures Smix (Eel ) could be reproduced by a linear combination of the spectra taken for the pure liquids SA (Eel ) and SB (Eel )

Smix (Eel ) = 𝛼A ⋅ SA (Eel ) + 𝛼B ⋅ SB (Eel )

(52.46)

The meaning of the coefficients 𝛼 A and 𝛼 B was identified as representing the fraction of the surface covered by either of the components. For all mixtures of the three

269

52 Liquid Surfaces

binary mixture HPN/FA

62

surface tension (mN/m)

270

60 58 56 54

plotted against surface fraction plotted against surface molar fraction

52 50 0

0.2

0.4

0.6

0.8

1

surface fraction HPN Figure 52.17 Binary mixture of HPN and FA. The surface tension when plotted against the surface fraction of either component yields a straight line with great accuracy. The plot against the surface molar fraction clearly

indicates that linearity is less well satisfied if the surface tension is plotted against the molar fraction in the surface layer. (Source: Kirmse and Morgner (2006) [137]. Adapted with permission of ACS Publications.)

liquids, FA, 3-HPN, and PEG, we made the observation that the surface tension when plotted as a function of the surface fraction obtained from MIES via Eq. (52.46) rather than as a function of the bulk molar fraction can be fitted to high precision by a straight line. For the mixture HPN/FA, this outcome is displayed in Figure 52.17. It has been argued in Ref. [137] that this linearity proves that the topmost layer is identical to the entire surface layer. For comparison, we have defined another quantity that characterizes the surface. It is the molar fraction within the top surface layer (or surface molar fraction). It can be computed from the surface fractions 𝛼 via the relations 2

xsurf A

=

2

𝛼A ⋅ nA3 2

2

𝛼A ⋅ nA3 + 𝛼B ⋅ nB3

and

xsurf B

=

𝛼B ⋅ nB3 2

2

(52.47)

𝛼A ⋅ nA3 + 𝛼B ⋅ nB3

where nA and nB are the molar densities of the pure components. Once the top surface layer is identified with the entire surface, one can easily evaluate the surface concentration for both components. Equation (52.48) assumes that the molar areas 2 of the molecules are proportional to n 3 , i.e. that no preferential orientation prevails. It is important to note that any definition of the surface molar fraction depends on a model assumption. Thus, we choose here a definition that is easily computed from the data available. It is interesting to note that the surface tension displays a noticeable deviation from linearity if plotted against the molar fraction in the surface, cf. Figure 52.17. From the observation that Eq. (52.46) reproduces the spectra of the binary mixtures so well, one can conclude that the average orientation of the molecules in the mixture must be very similar to the average orientation of the molecules in the environment of the pure liquids. As is well known [22], the perfect surface

52.3 Concentration Depth Profiles

binary mixture BA/FA

surface tension [mN/m]

60

55

50

45

40 0

0.2

0.4

0.6

0.8

1

surface fraction BA Figure 52.18 Plotted is the surface tension of the mixture BA/FA as a function of the surface fraction 𝛼 BA of benzyl alcohol. Three piecewise linear sections can be discerned. (Source: Kirmse and Morgner (2006) [137]. Adapted with permission of ACS Publications.)

sensitivity of the MIES technique implies an orientation sensitivity. As no change of orientation is observed in the mixture, one could be inclined to assume that those molecules forming the topmost layer have a high probability to be in an environment similar to that in the pure liquid. This is best understood if one assumes that the surface is composed of domains of like molecules. Although the difference in surface tension between the three liquids HPN, FA, and PEG does not exceed 10 mN/m, we will now address a system in which the respective surface tensions differ by almost 20 mN/m. It is the binary mixture of BA and FA. The absence of a miscibility gap allows again measuring the two quantities, surface tension and surface fraction, over the entire range of concentrations. Plotting for this system, the surface tension as a function of the surface fraction leads to a surprise: the curve turns out to be linear, but piecewise linear. No less than three linear parts can be discerned in the plot, cf. Figure 52.18. The intersections occur at 𝛼 BA = 0.79 and 0.96. The interpretation of this phenomenon will be discussed in Section 52.4. 52.3.1.1.2 Sodium Oleate/FA The capability of MIES to identify the orientation of molecules at a liquid surface has been used to study the surfactant sodium oleate (NaOl) dissolved in FA [138]. Even though the evaluation technique SVD has not been employed at that time, a spectroscopic feature could be used in order to assess the orientation of the surfactant: NaOl contains a double bond 𝜋 C = C in the hydrocarbon chain, which has the lowest binding energy among all occupied orbitals in the system. Thus, it can be easily identified in the spectrum.

271

272

52 Liquid Surfaces

(a) t=1.5 ms

πC = C

× 20

(b) t=5.5 ms

× 20

(c) t=11.5 ms × 20

(d) t=17.5 ms × 20

4

6 8 10 kinetic energy [eV]

12

Figure 52.19 Time evolution of the surface of a 8.7 mmolal solution of sodium oleate in formamide. The spectra are taken at increasing age of the surface (a–d). This is experimentally realized by taking the spectrum at varying distance from the outlet of the liquid beam. The first spectrum (a) represents a situation with a just completed NaOl layer.

The peak area of the 𝜋 C = C orbital falls off as (a) 100%, (b) 50%, (c) 44%, and (d) 38% indicating that the center of the hydrocarbon chain gets less accessible with increasing surface age and, thus, with increasing coverage. (Source: Morgner et al. 1993 [138]. Data are taken with permission of Taylor and Francis.)

In MIES, this orbital can only be populated if it protrudes from the surface, which is impossible if the hydrocarbon chain stands upright on the surface. The occurrence of this orbital is therefore an unambiguous indication that sodium oleate molecules are lying flat on the surface. We found that the intensity of the 𝜋 C = C -orbital is largest at low concentration and decreases with increasing coverage. This is demonstrated by the time evolution of the spectrum at constant bulk concentration, displayed in Figure 52.19. With increasing surface age, the relative intensity of the 𝜋 C = C orbital decreases significantly. In view of the perfect surface sensitivity of MIES, this observation clearly indicates that the broad side of the hydrocarbon chains is less and less exposed to the impinging metastable helium atoms. On the other hand,

52.3 Concentration Depth Profiles

increasing surface age leads to an increased amount of surfactant at the surface. The combination of these two facts indicates that increasing coverage causes the surfactant molecules to push each other into a more and more upright orientation. The same observation has been made for other surfactants dissolved in polar solvents, e.g. lecithin and 1,2-dioleoyl-sn-glycero-3-phosphocholine (DOPC) dissolved in HPN. These systems will be discussed in the next Sections 52.3.1.1.3 and 52.3.1.1.4. 52.3.1.1.3 Lecithin and POPC (Two Orientations of Surfactant Molecules) In this section, we summarize the results on lipids dissolved in the polar solvent HPN (Figure 52.20). The systems are distinguished by the property of forming micelles and by the fairly low diffusion coefficient that makes it possible to follow the building up of the surface layer as a function of time on a time scale between a few ms and several seconds, which lends itself to experimental observation. The first experiments have been carried out with egg lecithin, which is a mixture of molecules with phosphatidylcholine as the head group [139, 140]. Later on, chemically pure lipids were employed: 1-palmitoyl-2-oleoyl-sn-glycero-3-phosphocholine (POPC) [141] and DOPC [142]. At first, we will focus on the time evolution of the lipid layer. This aspect has been extensively studied for lecithin dissolved in HPN, the surface age being Lecithin / HPN curves of constant surface composition

5 a=0.6 a=0.15

bulk concentration [mmol/l]

4

F=1

a=0.45 a=0.3

3

F=0.5

2

1

0 1

10

100

1000

age of surface [ms] Figure 52.20 Composition of the top layer of the surface of lecithin/HPN as a function of bulk concentration and surface age. F measures the fraction of the surface covered by lecithin. In the case of full coverage, i.e.

beyond F = 1, the quantity a denotes the fraction of the surface covered by upright standing lecithin. (Source: Knoll et al. 2000 [140]. Adapted with permission of Taylor and Francis.)

273

52 Liquid Surfaces

HPN pur

8000

Lecithin lying 3500

7000

3000

6000

2500

5000

Counts

Counts

4000 3000

2000 1500

2000

1000

1000

500

0

0 0

2

4 6 8 10 12 Kinetic Energy/eV

14

0

2

4 6 8 10 12 14 Kinetic Energy/eV

Lecithin standing

2500 2000 Counts

274

1500 1000 500 0 0

2

4 6 8 10 12 Kinetic Energy/eV

14

Figure 52.21 The three reference spectra the linear combination of which allows reproducing all spectra taken by MIES from the surface of the system lecithin/HPN.

varied in the range from 2 to 650 ms and the bulk concentration between 0.1 and 7 mmol/l. The data were taken with the spectroscopic technique MIES, which identifies not only the species but also its sensitiveness to the orientation of the lipid molecules. The spectra taken by MIES from the surface of lecithin/HPN can, under all experimental conditions, be reproduced by the linear combination Smix (Eel ) = 𝛼 HPN • SHPN (Eel ) + 𝛼 Lec, lying • SLec, lying (Eel ) + 𝛼 Lec, upright • SLec, upright (Eel ). The three reference spectra are plotted in Figure 52.21. It was found that the lecithin molecules are lying flat on the surface as long as the solvent is not entirely covered. After the spectral features of the solvent have vanished from the spectrum, the lecithin molecules start to reorient into an upright position. Evaluating the fractions of the surface covered by lying and by standing lecithin molecules allows computing the coverage ΓLec (t) of the surface by the solute. From these data, it is possible to assess the diffusion coefficient of the molecules via the equation by Ward and Tordaj [143] √ D⋅t ΓLec (t) = 2c0 (52.48) 𝜋

52.3 Concentration Depth Profiles

Cholesterol (full coverage) 3500 3000

Counts

2500 2000 1500 1000 500 0 0

2

4

6

8

10

12

14

Kinetic Energy (eV) Figure 52.22 The spectrum of cholesterol at the surface of HPN at full coverage. It serves as the reference spectrum for cholesterol for all bulk concentrations of the ternary mixture lecithin/cholesterol/HPN. This

proves that cholesterol showing up at the surface has always either the same orientation or the same superposition of different orientations.

As long as experimental conditions leading to back diffusion are omitted, the diffusion coefficient amounts to D = (2 ± 0.5) × 10−7 cm2 /s [139, 144]. 52.3.1.1.4 Lecithin/Cholesterol The interaction of lecithin with cholesterol plays a role in biological transport of cholesterol in the bloodstream. The solubility of cholesterol in blood is so low that effective cholesterol is transported inside lipoproteins that are spherical particles composed of a polar surface of protein, phospholipid, and free cholesterol, plus a nonpolar core containing cholesterol esters and triglycerides [145]. Even though the solvent employed in our experiment is HPN rather than water (or blood), it still appeared interesting to study the interaction between cholesterol and lecithin in the polar solvent HPN [146]. The spectra taken by MIES from the surface of the ternary mixture lecithin/cholesterol/HPN could be reproduced by the linear combination of four reference spectra. In addition to the three spectra displayed in Figure 52.21, a fourth reference spectrum indicating the presence of cholesterol was required, cf. Figure 52.22. An interesting observation is noteworthy: unlike the system lecithin/HPN, the present ternary system does not show the orientation of lecithin into an upright position at full coverage. The data analysis is carried out with all four reference spectra, but the weight of the reference spectrum for standing lecithin (right panel in Figure 52.21) always remains negligibly low. Again, it is possible to study the time dependence of the surface composition. In particular, it was found that the diffusion of lecithin toward the surface is strongly influenced by the concentration of cholesterol. The diffusion coefficient of lecithin is plotted in Figure 52.23 for three different concentrations of cholesterol.

275

52 Liquid Surfaces 2.5E–7

2.5E–7

c(chol) = 0.2mmolal D(Lec) (cm^2/s)

D(Lec) (cm^2/s)

2.0E–7 1.5E–7 1.0E–7 5.0E–8 0.0E+0

c(chol) = 0.4mmolal

2.0E–7 1.5E–7 1.0E–7 5.0E–8 0.0E+0

0

1 2 bulk conc. Lec / mmolal

3

0

1 2 bulk conc. Lec / mmolal

3

c(chol) = 0.8mmolal

2.5E–7 D(Lec) (cm^2/s)

276

2.0E–7 1.5E–7 1.0E–7 5.0E–8 0.0E+0 0

1

2

3

4

bulk conc. Lec / mmolal

Figure 52.23 The diffusion coefficient of lecithin as a function of lecithin concentration for three selected values of the cholesterol concentration. The full line is the best fit with an analytical expression.

One finds in all three cases that the diffusion coefficient of lecithin passes through a pronounced minimum below the concentration of cLec = 1 mmolal and then recovers with increasing lecithin concentration in order to drop again. The behavior has been fitted with an analytical expression flexible enough to model the local minima and maxima. The diffusion coefficient of lecithin DLec is composed of a power function plus a Lorentzian DLec (cLec ) = D0 ⋅ exp(−b ⋅ cLec ) + const

c2 ((cLec − d)2 + c2 )

(52.49)

The diffusion coefficient of pure lecithin D0 and the parameter const are kept constant, whereas the parameters b, c, and d depend on the cholesterol concentration cChol via a second-order polynomial. These parameters vary smoothly with the concentration of cholesterol. By interpolation, it is possible to evaluate the diffusion coefficient for all combinations of lecithin and cholesterol concentrations, cf. Figure 52.24. Cholesterol lowers the diffusion coefficient, the effect being small or very pronounced depending on the ratio of both lipids. It is interesting to note that the maximum value is taken on for low concentration of cholesterol (as expected), but as well for low concentration of lecithin irrespective of the cholesterol concentration. Obviously, the diffusion coefficient of lecithin is significantly lowered, if the concentrations of both lipids are comparable, cf. Figure 52.24. The strong effect of cholesterol onto the diffusion coefficient of lecithin suggests that aggregates form as a consequence of the interaction between both lipids. If one relates the effective diffusion coefficient of lecithin to the size of the aggregate it forms together with cholesterol, it is possible to give an estimate for the size of the

52.3 Concentration Depth Profiles

0.8 0-0.5

0-0.5 0.5-1.0

2-2.5 1.5-2 1-1.5 0.5-1 0-0.5

0.7 0.6 0.5 0.4

1.0-1.5

0.3 0.2

1.5-2.0

0.1

concentration of cholesterol[mmolal]

diffusion coefficient of lecithin in HPN in presence of cholesterol[10–7cm2/s]

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 1.1 1.2 1.3 1.4 1.5 1.6 1.7 1.8 1.9 2.0 2.1 2.2 2.3 2.4 2.5 2.6 2.7 2.8 2.9 3.0

0.0 concentration of lecithin [mmolal]

Figure 52.24 Diffusion coefficient of lecithin concentration. The presence of cholesterol causes a significant variation of the diffusion in HPN in the presence of cholesterol. In coefficient with both concentrations. the absence of cholesterol, the diffusion coefficient of lecithin hardly depends on its

aggregate for any value of the diffusion coefficient. Under the experimental conditions of the study, the maximum radius of the aggregates estimated amounts to 70 nm [146]. So far, we have discussed the influence that cholesterol exerts on the behavior of lecithin. Of course, the spectroscopic technique allows focusing on the behavior of the other component cholesterol as well. At low surface age (225 ms), the presence of lecithin enhances the adsorption of cholesterol compared to pure cholesterol, whereas at larger surface age (900 ms), lecithin appears to suppress cholesterol adsorption noticeably [146]. It is interesting to keep in mind that even though surface analytical techniques were employed in the experiments, the information gained refers to diffusion, i.e. to bulk properties. We will encounter a similar situation for another system with two surfactants in Section 52.4.2.3. There, the experimental data allow evaluating the influence of one surfactant onto the activity of the other surfactant, i.e. on static bulk properties. 52.3.1.1.5 PD/FA (Three Reference Spectra, Unresolved Problem of Upright PD Molecules, Mixture of Liquid with Miscibility Gap) An interesting phenomenon has

been found for a binary liquid mixture composed of the polar component FA and the nonpolar component pentadecane (PD) [147]. The nonpolar component PD has an electric conductivity that is so low that electron spectroscopy of the pure liquid is prohibited by severe charging up. Thus, the primary goal of mixing these two liquids has been to create a thin layer of PD on top of a conducting substrate consisting of FA. As these two liquids have a large miscibility gap and as the surface tension of PD is much below that of FA, it was expected that the formation of the thin PD layer on top of FA would proceed spontaneously. Indeed, thermodynamic principles are driving the system into the desired arrangement. The time scale on which this arrangement was achieved had to be explored experimentally. The two

277

52 Liquid Surfaces

mixture of pentadecane and formamide

reference 1: pure formamide

intensity [a.u.]

278

reference 3: standing pentadecane

reference 2: lying pentadecane

0

2

4 6 8 electron energy [eV]

Figure 52.25 The three reference spectra are identified as the spectrum of pure FA (reference 1), the spectrum of lying PD (reference 2), and the spectrum of alkanes

10

12

standing upright (reference 3). The error bars indicate possible uncertainties in determining the third reference spectrum. (Source: Data are taken from [147].)

parameters that could be varied were the relative amount of PD and the age of the surface at the location of the experiment. The relative amount of PD was set to 0, 2.7, 5.4, and 20% by weight and the age of the surface could be changed between 2 and 23 ms. Spectra were taken for several combinations of these parameters. It was obvious that the characteristic spectral features of FA vanished as larger as the relative amount of PD and as greater as the age of the surface was. Increasing age and/or increasing PD fraction made the spectrum converge to a shape that did not show any features of FA but resembled the spectrum of lying alkanes [148]. Thus, the strategy to build up a thin liquid layer of PD to overcome its low conductivity was successful (Figure 52.25). It turned out, however, that the spectra showing up for intermediate values of surface age and PD amount displayed even more interesting findings. It was found that the intermediate spectra could not be reproduced by the simple formula in Eq. (52.46). The SVD algorithm required three rather than only two reference spectra in order to reproduce all spectra taken for all surface ages and all relative PD amount [22]. Two of the reference spectra could easily be assigned: the spectrum of pure FA and the converging spectrum of PD that had been identified as that of lying alkanes. The third reference spectrum had to be reconstructed and could be reconstructed according to the guidelines developed in a previous publication [22]. The shape of the third reference spectrum could unambiguously be identified as the spectrum of alkane molecules standing upright, i.e. exposing their methyl groups to the impinging metastable helium atoms [22, 148].

52.3 Concentration Depth Profiles

Although this result of the spectroscopy rests on firm grounds, the understanding of the intermediate upright orientation of the PD molecules represents an open question until now. The first attempt to solve the riddle was inspired by the fact that the upright position shows up only as long as the PD layer is incomplete. Thus, we were misled to associate this orientation with the dynamics of the transport process of the PD molecules to the surface. The reptation [149] model for the motion of polymers would suggest that the PD molecules would arrive at the surface with their head group first. Although this concept was put down in the first draft, a referee kindly pointed out that the time scale for transport of the PD molecules and the time scale for carrying out the spectroscopy were too different in order to expect the transport-related orientation of the molecules to be preserved until the spectroscopy experiment is carried out. We removed the remark from the manuscript and are still grateful to the referee for his common sense. Thus, the observation of the standing PD molecules remains an unsolved question. 52.3.1.2 Extrapolation from NICISS and ARXPS

In the previous Section 52.3.1.1, we have discussed the properties of the top surface layer of a liquid system as evaluated by the technique MIES, which is distinguished by perfect surface sensitivity. Here, we address the obvious ability of any surface spectroscopy to yield information about the composition of the top surface layer: if the concentration depth profile is known, the composition of the top layer is necessarily known as well. As MIES is specialized to characterize the top layer, it appears obvious that the precision of MIES in doing so is most reliable. Thus, the information obtained from the techniques NICISS and ARXPS with respect to the top layer can be gauged against MIES. Here, we will inspect data obtained for the system TBAI/FA, which consists of the ionic surfactant dissolved in the polar solvent formamide. The surface of this system has been investigated by the three methods MIES, ARXPS, and NICISS and thereby is a suitable testing ground for comparison. For a large number of concentrations, the composition of the top layer has been studied by MIES. The results are found in Figure 52.26. MIES data have been taken for the range of concentrations 0.02–0.95 mol/kg almost up to the solubility limit. No less than 37 concentrations have been investigated. Any data point stands for one spectrum, which has been evaluated as linear combination Ssolution (Eel ) = 𝛼TBAI ⋅ STBAI (Eel ) + 𝛼FA ⋅ SFA (Eel ) Expressed as a ratio between surface molar fraction over bulk molar fraction, the surface enhancement reaches a factor of about 20 at low concentrations and drops off to about 8 at the largest concentration investigated. The concentration depth profile has been taken at three concentrations by ARXPS. The composition of the top surface extrapolated from these data is indicated in Figure 52.26 as open circles, together with the estimated error bar. The comparison with the technique NICISS has been carried out for the concentration of 0.25 mol/kg, indicated by a full quadrangle. The general agreement between the methods is not perfect, but certainly satisfactory.

279

52 Liquid Surfaces

surface molar fraction / bulk molar fraction

280

25

solution of TBAI in FA composition of top surface layer MIES best fit to MIES upper bound lower bound ARXPS NICISS

20 15 10 5 0 0.0

0.2 0.4 0.6 0.8 bulk concentration [mol TBAI / kg FA]

Figure 52.26 TBAI/FA in the range of concentrations 0.02–0.95 mol/kg. The composition of the top layer is directly measured with MIES. The thick line represents the best fit of a smooth curve to all MIES data. The estimated uncertainties of the data are indicated by the lower and upper bounds (thin

1.0

lines). ARXPS has been carried out for the system at three concentrations (0.1, 0.4, and 0.5 mol/kg). The extrapolated surface composition is inserted (open circle) including the estimated error bar. The result from NICISS for a 0.25 m solution is added for comparison as well.

52.3.2 Depth Dependence of Composition at Liquid Surfaces 52.3.2.1 Surfactant Solutions 52.3.2.1.1 Quantification with XPS and ARXPS The first publication investigating liquid surfaces with electron spectroscopy was published by Siegbahn et al. in 1973 [4]. The first quantitative results were reported by the group of Hans Siegbahn, the son of Kai Siegbahn, who won in 1981 the Nobel Prize for Physics for inventing XPS – or ESCA as it was called at that time. His group was the first to apply angle-resolved electron spectroscopy for analyzing the structure of a surfactant solution [27, 150]. The solutions investigated were tetra-N-alkylammonium halide salts – with one to six C atoms per alkyl chain – in the solvent formamide. The tetra-N-alkylammonium halide salts are known to be surface active in water. With these pioneering measurements, Holmberg et al. aimed for a quantitative measurement of the surface excess and the influence of both the length of the alkyl chain of the cation and the counterion on the overall surface excess. The measurements were recorded at single X-ray excitation energy, three different observation angles, and some variation in surfactant concentration. The investigators recorded the C, O, and halide signals. In the C signal, three different C species could be identified: the C atom not bound to the central N atom of the tetra-N-alkylammonium cation, the C atom bound to the N atom, and the C in the solvent. Each set of three measurements was fitted with a step model and a model describing the concentration

52.3 Concentration Depth Profiles

gradient from the surface to the bulk with an exponential function. The surface excess for both models was largely the same and increasing with increasing bulk concentration. The halide counterions were found to also influence the surface excess with a decreasing order from I− to Cl− . Eschen et al. further developed Holmberg’s ideas and added two qualitative new ideas to the concept [151]. Firstly, they performed angle- and energy-resolved measurements and secondly they strongly improved the data evaluation by applying the genetic algorithm. The first introduces a second way to change the depth sensitivity of the XPS signal, which is independent of the variation of the observation angle. The latter allows quantitative data analysis in cases where the number of variables is very large and fitting procedures have to take variation of the starting values for the fitting procedures into account. In the data analysis, the variation of the concentration was considered not only for the concentration depth profile itself but also for the electron mean free path. Both the local elemental composition and the density influence the electron mean free path. The advancement of the model for fitting the ARXPS data allowed that the concentration in each layer can be considered as a fitting parameter opposed to the simplified model of Holmberg et al. who assumed an exponential relationship between concentration and depth. The concentration depth profiles determined with ARXPS of a 0.5 M solution of tetraN-butylammonium iodide (Bu4 NI) in formamide is shown in Figure 52.27. Because of the excitation energy range available to Eschen et al., only spectra for C could be recorded, and as a consequence, only the concentration depth profile for the alkyl chains could be determined. The profile of the counterion I− is not available for these measurements. 3.5E-07 3E-07

TBAI/(mol/m^2)

2.5E-07 0.5 m TBAI / FORMAMIDE

2E-07 1.5E-07 1E-07 5E-08 0 1

6

11

16 LAYERS

21

26

Figure 52.27 Concentration depth profiles of the cation of Bu4 NI in a 0.5 M solution in formamide. The thickness of the layers can be approximated with ∼1.5 Å. (Source: Reprinted from [151].)

281

52 Liquid Surfaces

3

Concentration/(mol/L)

282

Cation

Anion

2

1

0 0

5

10

20 15 Depth/Å

25

30

35

Figure 52.28 Concentration depth profiles of the cations and anions of Bu4 NI in a 0.5 M solution in formamide. (Source: Reprinted from [153].)

Wang et al. investigated with a lab-based X-ray source for Al-K𝛼 radiation solutions of Bu4 NCl and Bu4 NI in formamide at various concentrations [152–154]. Calibration of the analyzer transmission function allowed for quantitatively determining the concentration depth profiles of anions and cations. However, the lab-based Xray source did not allow to change the excitation energy; thus, the depth resolution in these experiments is more limited than for those of Eschen et al. In Figure 52.28, the profiles of both cations and anions are shown. The position of the cation is closer to the surface than that of the anion. However, such information has to be treated cautiously because the cation is represented by the carbon atoms of the cation butyl groups rather than nitrogen, which is the place where the charge is located on the cation. Hence, it is likely that the distribution of the positive charge is slightly different from that indicated by the cation profile and is most likely shifted for a few Å toward the bulk relative to the curve displayed in Figure 52.28. 52.3.2.1.2 Quantification with NICISS Andersson et al. were the first to apply ion scattering techniques for investigating liquid surfaces. In their first published work, they investigated by means of NICISS solutions of various tetra-N-butylammonium and tetra-N-butylphosohonium halides in formamide [10]. NICISS allows determining the elemental concentration depth profiles. However, different from ARXPS, it is not possible to separate chemical species of the same element. It is described in a previous chapter that the quantification of the stopping power [85], straggling, [88, 155] and the inelastic energy losses through the backscattering process [94] allow for the deconvolution of the measured concentration depth profiles and calibration of the depth scale. In Figure 52.29a, the concentration depth profiles of cations and anions of Bu4 PBr are shown [94]. The cation represented by P is positioned slightly closer to the surface than the anion. Different from the ARXPS study by Wang et al. [153], the position of the positive and negative charge can be determined directly from the concentration depth profiles of the cation and anion. The only remaining limitation

52.3 Concentration Depth Profiles 2.5 Bromide Phosphor

concentration [10–3 mol/cm3]

concentration [10–3 mol/cm3]

2

1.5

1

0.5

0

2 1.5 1 0.5 0

–5

(a)

Bromide Iodide

0

5

10 Depth [Å]

15

20

–5

25

(b)

0

5

10

15

20

25

Depth [Å]

Figure 52.29 (a) Concentration depth profiles of the cations and anions of Bu4 PBr and (b) concentration depth profiles of the anions of Bu4 NBr and of Bu4 NI in 0.25 m solutions in formamide. (Source: Reprinted from [94].)

is that the profiles shown in Figure 52.29a assume that the surface is laterally homogeneous. In a later chapter, a method will be discussed that also allows probing lateral inhomogeneity of a liquid surface. In Figure 52.29b, the concentration depth profiles of iodide and bromide are compared, showing that iodide is located slightly closer to the surface than bromide. Measuring concentration depth profiles at surfaces of surfactant solutions can be used to determine the surface excess. The surface excess is a quantity relevant to the thermodynamic understanding of surfaces of solutions, which will be the subject of a later chapter. The surface excess has to be measured over a range of concentrations and concentration depth profiles of the solute and the solvent need to be determined Figure 52.30). Andersson et al. have studied both solutions with an ionic [93] and a nonionic surfactant [156]. As a side remark, it is worth mentioning that it was shown that NICISS is able to detect the presence of surface-active impurities [93]. The ionic surfactant Bu4 PBr investigated appeared to contain a non-negligible concentration of starting material of the synthesis in case purification of the synthesized surfactant had not been carried out carefully. The impurity appeared as surface-active P containing compound and was detectable with nuclear spin resonance [93]. The unbalance between P and Br disappeared in the NICIS spectra after recrystallization of the surfactant. The focus of the publication, however, was not on the procedure of purifying the surfactant. From Figure 52.29b, it is evident that the nature of the counterion has an influence on the amount of surfactant adsorbed to the surface. Iodide has a stronger presence at the surface than bromide. Even though the profiles of Bu4 N+ could not be measured in this case, it can be concluded based on charge equilibration that the iodide surfactant (Bu4 NI) covers the surface to a larger extent than the bromide surfactant (Bu4 NBr) at the same bulk concentration. This observation is in line with the report by Holmberg et al. described above who concluded from ARXPS data that the surface excess increases in the order Bu4 NCl < Bu4 NBr < Bu4 NI for these

283

52 Liquid Surfaces 30 measurement 25 20

10 5

–20

–10

0

10

20

30

depth [Å] surface excess [10–10 mol/cm2]

(a)

Gibbs dividing plane

15

0 –30 –5

0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0.0 –0.1–30 –20 –10

concentration [10–3mol/cm3]

concentration [10–3mol/cm3]

284

Gibbs dividing plane

0

10

20

30

40

50

60

70

depth [Å]

(b)

2 1.5 1

– Br +

Bu4P

0.5 0 0

(c)

– Br

0.5

1

1.5

2

concentration [molal]

Figure 52.30 (a) Gibbs dividing plane of a Bu4 PBr in formamide solution determined from the concentration depth profile of the solvent, (b) surface excess of the solution using the position of the Gibbs dividing

plane determined from (a), and (c) surface excess of the solutions over a wide range of concentrations. (Source: Reprinted from [93].)

cationic surfactants [150]. Wang et al. found for the anionic surfactants sodium dodecyl sulfate (SDS) and cesium dodecyl sulfate (CDS) in formamide solutions that the surface excess depends on the alkali halide anion [157–159]. Keeping the total surfactant concentration constant, Wang et al. showed that the total surface excess increases (Figure 52.31a). A similar effect was reported by Schelero et al. for aqueous solutions based on the evaluation of surface tension data [160]. As NICISS also reveals the position of the elements relative to the surface, the authors could also demonstrate that Cs+ is located closer to the surface than Na+ (Figure 52.31b). Also, the variation in charge density with depth is stronger for the CDS solutions than for the SDS solutions [157] The same correlation between the position of the counterion and the total surface excess was found for the tetra-N-alkylammonium halide cationic surfactants. The debate is still ongoing in the literature how the ionic radius and the size of the solvation sphere are correlated with the positioning of ions relative to the surface, i.e. whether there is a correlation between adsorption and desorption of the ions and the ionic radius [160–173]. Computer simulations show that the ionic radius in solution increases with the atomic number for both the halide ions [174, 175] and the alkali ions [176] with the larger ions seeming to have a less ordered solvation sphere in aqueous solutions [175, 176]. Although ion-specific adsorption at interfaces has been demonstrated with computer simulations and calculations [165, 168,

52.3 Concentration Depth Profiles

1.2E-06

1.6

Dodecyl sulfate Sodium Cesium

Concentration [mol/L]

Surface excess/(mol/sqm)

1.5E-06

9.0E-07 6.0E-07 3.0E-07 0.0E-00 –10%

(a)

Na S of SDS S of CDS Cs

1.2

0.8

0.4

0 10%

30%

50%

70%

0

90% 110%

CDS-fraction in bulk

Figure 52.31 (a) The surface excess of mixed SDS/CDS formamide solution. The total surfactant concentration was kept constant and the ratio between Na+ and Cs+

(b)

10

20

30

40

Depth [Å]

was changed [159]. (b) The position of the cation and the anion of both the SDS and the CDS solution. (Source: Reprinted from [157].)

171, 173], questions have been raised whether the potentials used for calculations and simulations are sufficiently accurate [170]. Schulze et al. used the information available through the concentration depth profiles to derive information about a material property. The dielectric permittivity as a material property could be different for surface and bulk and depend on the presence of ions in a solution. The reason is that the ability of the solvent molecules to adapt the orientation to the existing electric field might be limited at the surface and might be limited in a solvation shell purely because of the constraint of the possible orientation of the molecules. It can be shown that the charge distribution, the dielectric permittivity, and the electric surface potential are correlated [177]. Measuring two of the three quantities directly allows calculating the third quantity. Schulze et al. determined the concentration depth profiles of the ions at surfaces of Bu4 XY solutions where X stands for either N or P and Y stands for Cl, Br, and I. The authors found that the surface excess is increasing with increasing atomic number of the anion, i.e. from Cl− to I− , and that the cation and anion separate with Cl− being further away from the surface than I− . From the measured concentration depth profiles, the charge distribution was determined. Measuring also the electric surface potential with the Kelvin probe, the dielectric permittivity was determined. Because of the applied procedure, the dielectric permittivity is a lateral average over the region of the surface where enhanced concentration of the surfactant is found. The key quantity determining the dielectric permittivity in the solution turned out to be the ion concentration. The dielectric permittivity decreases with the ion concentration approximately exponentially (Figure 52.32). Schulze et al. modeled the dielectric constant by assuming that the solvent molecules in the solvation shell of the ions are limited in their orientation because of the local electric field generated by the ions. Based on this model, the number of ions in the solvation shell can be estimated. The number determined for bromide and chloride is quite similar (31 for Br− and 25 for Cl− ) but much smaller for I− (12). Even though the model is simplifying the complexity of a solvation shell, this finding is another indication that the size of the solvation sphere increases with the decreasing size of the ion and is

285

52 Liquid Surfaces

dielectric permittivity

120 100 80 60

TBPCI

40

TBPBr TBPI

20 0

0

1

2 3 4 5 6 max. ion concentration / M

7

8

Γ ePOPC [10–10 mol/cm2]

Figure 52.32 Dielectric permittivity of formamide as a function of the ion concentration in the surface layer. (Source: Reprinted from [177].)

CPOPC [10–4 mol.cm–3]

286

8 6 4 2

0 –20 –2 (a)

0

20

40

depth [Å]

60

80 (b)

2.5 2.0 1.5 1.0 0.5 0.0 0.1

1.0

10.0

100.0 1000.0

CPOPC [10–5 mol.kg–1]

Figure 52.33 Concentration depth profile of P of 1-palmitoyl-2-oleoyl-sn-glycero-3phosphocholine in 3-hydroxypropionitrile (a) and surface excess as a function of concentration of the solution (b). (Source: Reprinted from [156].)

in agreement with the above discussion of Migliorati’s and Ikeda’s calculations and simulations [175, 176] The concentration depth profile of P of the nonionic surfactant solution POPC in 3-HPN is shown in Figure 52.33. Different from the Bu4 PBr solutions in formamide, the POPC in HPN solutions have a negligible bulk concentration, thus determining that the Gibbs dividing plane is not required for quantitative determination of the surface excess. The POPC molecule is a large molecule with a length in the order of ∼20 Å depending on the configuration of the molecule. Investigations with MIES of phospholipid solutions had shown indications for a preferential orientation at the surface of the phospholipid molecule depending on the coverage of the surface [139]. Pohl et al. determined with NICISS the orientation of the POPC molecule over a large range of concentrations in HPN and formamide solutions [178]. Measuring of the concentration depth profile of P is demonstrated in Figure 52.33. Determining the C concentration depth profile of the POPC molecule is demonstrated in Figure 52.34a. The profile of nitrogen is a good approximation of the concentration depth profile of the solvent 3-hydroxypropionitrile (HOPC) because

distance between C and P maxima [Å]

52.3 Concentration Depth Profiles

cc [10–2 mol.cm–3]

6 5

0 –5 –10

4 total carbon

3

carbon HPN

2

formamide HPN

0

1

2

3

4

5

surface excess [10–10 mol cm–2]

carbon POPC

1 0 –20

20 15 10 5

vacuum P C

0

20 depth [Å]

40

60

solution

P

C

C P

Figure 52.34 Concentration depth profile of C of the solvent and the surfactant in a POPC molecule in HPN solution (a) and orientation of the POPC molecule in HPN and formamide solutions (b). (Source: Pohl et al. (2009). Reprinted from [178]).

N is almost exclusively found in the solvent. The small amount of N in POPC can be neglected. The C of HPN can be approximated with the N profile. Subtracting the C contribution from the overall C concentration depth profile then results in the profile of C in POPC. This procedure demonstrates that contributions of various compounds to the concentration depth profile of a specific element can also be separated with NICISS in special cases. Pohl et al. found that comparing the P and C profile of POPC allows for analyzing the orientation of the phospholipid molecule. In formamide, the POPC molecule has an orientation with its long axis parallel to the surface at low surface excess, whereas at high surface excess, the alkyl chains point away from the surface as shown in Figure 52.34. For HPN, the situation is different. At the highest surface excess – which is below the highest surface excess of the formamide solutions – the orientation of the molecule is parallel to the surface, thus rather similar to the situation in formamide. However, at low surface excess, it is the polar group that is exposed to the surface and not the alky chains. This finding is counterintuitive to considerations of the surface energy. Coverage with the nonpolar alkyl chains should result in a lower surface energy than coverage with the polar phosphocholine head group. Reconciling the experimental finding with the surface energy considerations did not succeed so far. NICISS and ARXPS are two powerful tools to determine concentration depth profiles. The underlying concepts of the two methods are very different and it is worthwhile to compare the experimental results that can be achieved with the two methods. In Figure 52.35, concentration depth profiles measured with the two methods are shown. The kinetic energy of the He ions used for NICISS was 4.5 keV. For ARXPS, monochromatic Al K𝛼 was applied and the observation angle varied. The authors considered three criteria for the comparison of the concentration depth profiles: (i) the surface excess, (ii) the gradient of concentrations between the surface and the bulk, and (iii) the relative position between the maxima of the cation and anion distribution. The demand of the accuracy in the details of the concentration depth profiles increases from (i) to (iii). Wang et al. found good agreement between the

287

Bu4P+ ARXPS

3

Bu4P+ NICISS

2 1 0

(a)

Bu4N+ Eschen et al.

0

10

20

30

40

concentration [mol/dm3]

52 Liquid Surfaces

concentration [mol/dm3]

288

Figure 52.35 (a) The concentration depth profiles of Bu4 P+ measured with NICISS and ARXPS and (b) the concentration depth profiles of Br− are compared [179]. For

Br– ARXPS

5

Br– NICISS

4 3 2 1 0

50

depth [Å]

6

(b)

0

10

20

30

40

50

depth [Å]

comparison, the Bu4 N+ concentration depth profile measured by Eschen et al. is shown in (a) as well. Source Eschen et al. (1995). Reprinted from [151].)

methods for the surface excess and the length over which the concentration changes from the surface to the bulk, thus for the first and second criteria. Separation of the ions in their concentration depth profiles is not achieved with either method. The last conclusion takes NICISS results into account that were obtained at lower kinetic ion energy; thus at experimental conditions that allow for a higher depth resolution in NICISS. The authors conclude that neither ARXPS nor NICISS at higher kinetic energies is suitable for accurate identifying of the position of maximum concentration of the ions. For meeting the third criterion, NICISS at lower kinetic energy would have to be applied. 52.3.2.1.3 Quantification with NR, XR, and NLO Both NR and XR can be used to quantitatively determine the distribution of solute and solvent at interfaces. In this section, we will discuss a few examples and will not cover the field comprehensively. Both the quantity of surfactant molecules adsorbed at the air/liquid interface and the orientation of the surfactant molecules are of interest. NR offers the possibility to change the reflectivity of various components of the solutions by fully or partially deuterating thus by changing the scattering length of a specific component. In Figure 52.36, NR curves are shown and in Figure 52.37 the model fitted to the NR data. Luo et al. have used XR to determine the distribution of bromide across the interface between a 0.01 M tetrabutylammonium tetraphenylborate (TBATPB) solution in nitrobenzene and aqueous tetrabutylammonium bromide (TBABr). The concentration profile of bromide across the interface can be determined by fitting the reflectivity curves [181]. The authors found that the ion distribution across the interface is substantially different from profiles predicted by the Gouy–Chapman theory. Fitting of the experimental reflectivity curves shown in the left panel in Figure 52.38 cannot be fitted well with profiles calculated on the basis of the Gouy–Chapman theory. However, computer simulation results using a mean force potential in a generalized Poisson–Boltzmann equation taking the ion size and ion–solvent interactions into account lead to the profiles of the bromide ions, which can be used to well fit

52.3 Concentration Depth Profiles

10–3

Reflectivity

10–4

10–5

10–6

10–7 0.00 0.05 0.10 0.15 k/Å–1 (a)

0.20

0.25 0.30 0.05 (b)

Figure 52.36 Neutron reflectivity curves including their fits of hexadecyltrimethylamonium bromide (C16 TABr) in water. (a) Either the chain of the C16 TABr has been

0.10

0.15 0.20 k/Å–1

0.25 0.30

deuterated (Δ) or the head is deuterated (○) or the entire molecule. (b) Null reflecting water has been used. (Source: Lu et al. 2000 [180]. Reprinted with permission of Elsevier.)

the measured reflectivity curves [181]. The profiles calculated on the basis of the Gouy–Chapman theory and on the basis of the computer simulation are shown in the right panel of Figure 52.38. Koelsch et al. have determined with XR the distribution of bromide at a charged interface between water and a mixed layer of the cationic surfactant di-octadecyl-di-methyl-ammonium-bromide (DODAB) and the neutral phospholipid 1,2-dipalmitoyl-sn-glycero-3-phosphocholine (DPPC) attached to a silicon substrate [173]. The variation of the charges at the interface was achieved by changing the ratio between the ionic compound DODAB and the nonionic compound DPPC. The authors could fit the measured XR curves with a model for the bromide distribution, which is based on the Poisson–Boltzmann theory. The authors included in the fitting of the XR curves a broadening of the measured signal reflecting height fluctuations of the mixed DODAB/DPPC layer, the finite size of the ions, and the experimental resolution [173]. Bu et al. used a similar approach for investigating the distribution of ions across a liquid interface by generating a monolayer of the ionic compound dihexadecyl hydrogen phosphate floating on an aqueous CsI solution [182]. 52.3.2.2 Thin Foam Films

Foam films are thin liquid films. They have a very large surface to bulk ratio. Creating surface requires energy; thus, foam films are thermodynamically not in a stable state. However, foam films can exist over minutes, hours, or even much longer time scales and thus can be described as metastable. Thus, it can be assumed that there must be stabilizing forces within foam films. These stabilizing forces are the origin of the structure of foam films but also influence the process of their formation,

289

52 Liquid Surfaces

(I)

0.03 0.02 0.01

(II)

0.002 0.001 0.002

(III)

0.001

(I)

0.03 0.02 Number density/Å–3

290

0.01 (II)

0.002 0.001 0.002

(III)

0.001

(I)

0.03 0.02 0.01

(II)

0.002 0.001 0.002

(III)

0.001

–10

0 10 Distance/Å

Figure 52.37 Distribution of various components (I, water; II, heads; and III, chain) of the C16 TABr/water surface. The interface has been fitted either with a two uniform layer model (top three sections),

20

a single-block model (middle three blocks), or with a Gaussian distribution (bottom three blocks). (Source: Lu et al. 2000 [180]. Reprinted with permission of Elsevier.)

52.3 Concentration Depth Profiles

104 kscat α

x-rays kin α

nitro– benzene

100

10–2

10–4

10–6

10–8 0

0.1

0.2

Qz (Å–1)

Figure 52.38 Investigations of the interface between a 0.01 M tetrabutylammonium tetraphenylborate (TBATPB) solution in nitrobenzene and aqueous tetrabutylammonium bromide (TBABr) with TBABr. On the left side the reflectivity curves including fits and on the right side the bromide across the interface for the 0.08 M TBABr solution is shown [181]. The dashed reflectivity curves

Electron Density xy (e–/Å3)

X-ray Reflectivity

102

water Qz

0.37

0.35

0.33 –40

nitro– benzene

water

–20 0 20 40 Interfacial Depth, z (Å)

in the left panel on the left side and the ion distribution on the right side are calculated on the basis of the Gouy–Chapman theory. The solid curves in both panels are based on a computer simulation taking the ion size and ion-solvent interactions into account. (Source: Luo et al. (2006) [181]. Reprinted with permission of Science.)

drainage, and collapsing. The pressure resulting from the stabilizing forces are called disjoining pressure. In foam films, two surfaces come into such close contact that Coulomb and van der Waals forces – the main forces in colloid materials – can reach across the foam film, and each surface of a foam film experiences a force because of the presence of the other surface. For this reason, foam films offer the unique opportunity to directly investigate the structure of the liquid surface and simultaneously the forces between the liquid surfaces. Other forces that are considered to play a role in foam films are related to structuring of the foam film on the scale of the individual molecules. One example is the steric forces that are considered to play a role in films with a thickness of 0.1 M is of interest but difficult to be described by theory [170]. Secondly, by solving the Poisson–Boltzmann equation, the course of the electric potential is usually assumed to be [185] Φ(x) ∝ log(cos2 Kx)

(52.56)

where K is a constant and depends on the charge density at the surface and the dielectric constant of the liquid phase. Assuming the charge distribution in the foam film allows reducing the electric properties of the foam film to one quantity, which is either the surface charge or the surface potential leading to Eqs. (52.52) and (52.53). In contrast, measuring the charge distribution directly allows both investigating ionspecific effects and makes the need to assume the charge distribution in the foam film redundant. The investigation of the structure of foam films in comparison with the structure of the surface of the bulk solution offers the possibility to gain insight into the forces stabilizing foam films because the size of the liquid structure is changed between much larger than the range of the forces (surface of the bulk liquid) to be in the order of the range of the forces (foam film). Changing the size of the liquid sample should result thus in differences in the structure of the surface and the near-surface region. Concentration depth profiles of the components are the most interesting structural information because changing of the size of the sample in the order of the range of the forces will influence how the forces affect the composition of the nearsurface region. Further concentration depth profiles reveal the charge distribution in the near-surface region and thus the course of the electric potential and also allow insight into ion-specific effects. We first want to consider the concentration depth profiles of a foam film formed by an ionic surfactant. In Figure 52.43, the concentration depth profiles of cation and anion of 4.0 mM hexadecyltrimethylphosphonium bromide (C16 TPB) in glycerol are shown [186]. The cation is represented by the phosphorous (P) and the

4 3.5 3 2.5 2 1.5 1 0.5 0

Film Bulk

0

(a)

Concentration [mol/dm3]

Concentration [mol/dm3]

52.3 Concentration Depth Profiles

40

20 Depth [Å]

Figure 52.43 Concentration depth profile of cation and anion and space charge at the surface of a bulk solution (a) of 4.0 mM hexadecyltrimethylphosphonium bromide (C16 TPB) in glycerol and the surface of a

60

4 3.5 3 2.5 2 1.5 1 0.5 0

Film Bulk

0

(b)

20

40

60

Depth [Å]

foam film (b) [186]. The pressure across the film was 5000 Pa. (Source: Ridings and Andersson (2015) [186]. Reprinted with permission of Wiley.)

anion by bromide (Br). The measurements are performed with NICISS and have been deconvoluted. In Figure 52.43a, the profiles at the surface of the bulk solution and in Figure 52.43b the profiles at the surface of the foam film can be found. The authors had found less anion than cation in the concentration depth profiles, which they ascribe to bromide forming a diffuse layer below the surface. Other reasons for the unbalance of P and Br could be excluded. The difference of the profiles of cation and anion is the space charge distribution and is also shown. At the surface of the bulk solution, there is a small region with some positive charge followed by a broader region with negative and then again a positive charge. As a consequence, the surface potential is found to be negative. The surface of the foam film shows an inverted distribution of charges. A small region with negative charge is followed by a broader region with positive charge and then again negative charge resulting in a positive surface potential. The charge distribution can be determined from the difference of the concentration depth profile of cation and anion and the electric potential by integrating twice the charge distribution [177]. The electric potential at the surface was found to be −0.81 ± 0.2 V for the bulk surface and −0.17 ± 0.2 V for the foam film surface. It must be emphasized that for calculating the electric field and electric potential, the dielectric constant of glycerol at the surface was assumed by Ridings et al. to be the same as of that of the bulk solvent. This assumption is in contrast to the finding of Schulze et al. who determined the dielectric constant at the surface of surfactant solutions of formamide to be about 10% of the dielectric constant of the value in the bulk of formamide. However, the dielectric constant at the surface of glycerol has not yet been determined and thus cannot be used. Any error in the assumption of the dielectric constant at the surface leads to a scaling of the values of the electric field and potential by a factor. Although for the calculation of the electric field and electric potential assumptions about the value of the dielectric constant have been made and the concentration depth profiles have an uncertainty (in particular, the exact shape of the diffuse layer of the anion at larger depth), the overall result is that the absolute value electric potential at the surface of the C16 TPB solution is larger than in the foam film [186]. The surface excess in contrast is higher

299

1 0

0.5 0.0

–1

–0.5

–2

–1.0

–3 0

40

20

(a)

1.0

3 Charge Distr. Electric Potential

2

0

0.0

–1

–0.5

–2 –1.0

–3

60

0

20

(b)

Depth [Å]

Figure 52.44 Charge distribution and electric potential at the surface of a bulk surface (a) and foam film (b) formed by a 4.0 mM hexadecyltrimethylphosphonium bromide (C16 TPB) in glycerol [186]. The pressure across the film is 5000 Pa. For calculating the electric field and electric potential, the

0.5

1

Electr. Potential [V]

Charge dis. [×108 c/m3]

Charge Distr. Electric Potential

Electr. Potential [V]

1.0

3 2

Charge dis. [×108 C/m3]

52 Liquid Surfaces

40 Depth [Å]

60

dielectric constant of glycerol at the surface was assumed to be 10% of that of the bulk solvent. The electric potential of the bulk surface is −0.81 ± 0.2 V for the foam film surface −0.17 ± 0.2 V. (Source: Ridings and Andersson (2015) [186]. Reprinted with permission of Wiley.)

at the surface of the foam film. However, Ridings and Andersson could not yet determine whether the electrochemical potential overall increases or decreases when the foam film forms (Figure 52.44) [186]. As a second case of concentration depth profiles, we want to consider foam films formed by a nonionic surfactant solution. Through Figure 52.45, it can be seen that the head group of the nonionic surfactant dodecyldimethyl phosphine oxide (C12 DMPO) moves slightly away from the surface upon formation of the foam film. It is worth noting that the concentration depth profiles shown in Figure 52.45b are of a film with a very low ion concentration. No ions were present through an added salt and glycerol has an autodissociation constant much lower than that of water; thus, autodisscociation of the solvent cannot contribute significantly to the concentration of cations and anions in the foam film [197]. Figure 52.46 shows that adding the salts NaI and NaCl increases the amount of surfactant at the foam film 3

3 2.5 2

P

1.5 1 0.5 0 –20

(a)

Alkyl Concentration (M)

Concentration (M)

300

Alkyl

2.5

P

2 1.5 1 0.5 0

–10

0

10 20 Depth (Å)

30

40

Figure 52.45 (a) Concentration depth profiles of the alkyl chain represented by C and the head group represented by P are shown for a 2.66 mM solution of dodecyldimethyl phosphine oxide (C12 DMPO) in glycerol [199]. (b) The respective profiles of

–20

(b)

–10

0

10 20 Depth (Å)

30

the foam film are shown. The head group shifts slightly toward the bulk after the formation of the film. (Source: Ridings et al. 2015 [199]. Reprinted with permission of American Chemical Society.)

40

52.3 Concentration Depth Profiles

2.5 No salt Concentration (M)

2

NaCl Nal

1.5 1 0.5 0 –20

–10

0

10

20

30

40

50

Depth (Å) Figure 52.46 Concentration depth profiles of phosphorus at the glycerol/C12 DMPO bulk liquid surface without adding salt and with 7.90 mM adding NaCl and NaI, respectively. (Source: Ridings et al. 2015 [199]. Reprinted with permission of American Chemical Society.)

surface. Both anions, i.e. I− and Cl− , appear on the outermost layer. The cation Na+ , however, cannot be detected with NICISS and is assumed to form a diffuse layer in the surface near region. Because of the atomic masses, Na is more difficult to be detected in NICISS compared to I− and Cl− . The cross section for backscattering from Cl− is about twice as large as that for Na+ , which causes the count rate of backscattering from Na being lower than that for backscattering from Cl and I. It is interesting to note that I− is found at the surface of the bulk solution as well as that of the foam film while Cl− only appears at the surface of the foam film. I− has a larger ionic radius than Cl− and the first is more polarizable than the latter. The finding that iodide has a stronger tendency to be found at the surface of the surfactant solution corresponds to the finding at other liquid surfaces that the propensity of ions to adsorb at liquid surfaces is as stronger as the polarizable the ion is [164, 198]. Figure 52.47 demonstrates that iodine moves toward the surface when the foam film is formed. Moving of the anion toward the surface increases the surface potential as long as the counterion does not change its location. As stated above, the authors could not detect the cation in the spectrum; thus, it cannot be determined whether the surface potential in fact changes. It can be seen in the disjoining pressure curves of the respective aqueous system in Figure 52.48 that the films formed by salty solutions are thicker than the films formed without adding salt to the solution with the films formed by the solution containing NaI being slightly thicker than that containing NaF. The concentration depth profiles and the TFPB data complement each other. Firstly, the increase in the thickness of the foam film upon adding salt is related to the increase of the surfactant concentration at the foam film surface. Secondly, cation and anion show separation with I− having a stronger preference for the surface than Cl− causing the foam film surface more negatively charged in the case of adding NaI compared to adding NaCl to the surfactant solution. The separation

301

52 Liquid Surfaces

0.4

Concentration (M)

Bulk 0.3 Film 0.2

0.1

0 –20

–10

0

10

20 30 40 Depth (Å)

50

60

70

Figure 52.47 Concentration depth profiles of iodide for glycerol/C12 DMPO/7.90 mM NaI solutions. The profiles are shown for the bulk surface and the foam film surface [199]. (Source: Ridings et al. 2015 [199]. Reprinted with permission of American Chemical Society.)

10000

No salt 0.1 mM Nal 0.1 mM NaF

Disjoining pressure (Pa)

302

OH- / q0 = 0.5 mC m–2 Nal / q0 = 1.5 mC m–2 NaF / q0 = 1.3 mC m–2 1000

100

0

20

60 40 Thickness (nm)

Figure 52.48 The thickness of the foam film as measured with a TFPB is shown as a function of the disjoining pressure aqueous solutions of C12 DMPO. Data are shown for films formed without added salt and after adding 0.1 mM NaI, and 0.1 mM NaF, respectively

80

100

[199]. The dotted lines are fits to the measured data based on the DLVO theory. The surface charge resulting from the fits is given in the legend. (Source: Ridings et al. 2015 [199]. Reprinted with permission of American Chemical Society.)

52.3 Concentration Depth Profiles

of charges at the surfaces is related to an increase in the thickness of the foam film with the anion showing the stronger propensity for the surface forming the thickest films. 52.3.2.3 Solutions with Inorganic Salts

Binary solutions can show depletion and enrichment of one of the components at the solution surface. Although solutions of inorganic salts are strictly binary systems, separation of the ions in the system, i.e. cation and anion, is possible to a limited extent while obeying overall charge neutrality. Overall depletion or enrichment of one of the components of a solution would influence macroscopic properties such as the surface tension. Even separation of the ions alone would influence macroscopic properties such as the electric surface potential. However, it is not possible to deduce from macroscopic properties how a given ion species is distributed along the surface normal. Instead, the concentration depth profiles and the composition of the outermost layer have to be measured directly and for specific combinations of all species, which could involve the solvent, inorganic anions, and cations or in some cases also surfactants. The positioning of ions relative to a surface is part of the larger research area ion-specific effects. The composition of the surface and interface of solutions with inorganic salts has practical relevance. One example is the uptake of gases into solutions where the presence of ions determines the rate of uptake [200–203]. Another example is the biological membranes where the concentration and position in the membrane is related to their functioning [204, 205]. Bubble coalescence has been found to depend on the combination of anion and cation present in a solution. Anions and cations can be separated in two classes each [167, 172]. The combination of the ions is assumed to influence the drainage of foam films and consequently the coalescence of bubbles [172]. Ion-specific effects at surfaces are a large research area. It is not the aim of this contribution to cover this research area comprehensively. More detailed information can be found in a number of reviews [173, 205–207]. The aim of this chapter is to show how surface and interface analysis can contribute to the understanding of ion-specific effects at surfaces formed by solutions with inorganic ions. Systems involving surfactants also show ion-specific effects but have been covered in the Section 52.3.2.1. Ionic liquids are substances constituting exclusively of ions and are different from ionic solutions. Ionic liquids will be covered below in a separate chapter. The subject of this chapter is concentration depth profiles of solutions with inorganic solutes. NICISS has been applied to investigate solutions of inorganic salts in formamide [208–210] and in water [88, 155]. In Figures 52.49 and 52.50, concentration depth profiles of halides at the surface of 0.85 M alkali halide solutions in formamide are shown. In Figure 52.49, the profile of I− shows an enrichment of iodide in the outermost layer followed by a depletion region between 6 and 12 Å [210]. In Figure 52.51 the chloride profile is almost constant and shows a slight depletion between 6 and 12 Å [210]. The surface excess of the solute in both cases is negative. The iodide and chloride profiles have similarities to those calculated by Jungwirth and Tobias for

303

52 Liquid Surfaces

concentration iodide [mol/cm3]

1.8

measurement

1.6

conc. depth profile

1.4

fit non-monotonic

1.2 1 0.8 0.6 0.4 0.2 0 –20

–10

0

20 10 depth [Å]

Figure 52.49 Concentration depth profile of iodide of a 0.85 M solution of LiI in formamide. The measured NICISS data are deconvoluted and show an enrichment of

1.8

concentration iodide [mol/cm3]

304

30

40

I− at the surface of the solution followed by a depletion at a deeper layer. (Source: Andersson et al. 2007 [210]. Reprinted with permission of American Chemical Society.)

measurement

1.6

conc. depth profile

1.4

fit

1.2 1 0.8 0.6 0.4 0.2 0 –20

–10

0

20 10 depth [Å]

Figure 52.50 Concentration depth profile of chloride of a 0.85 M solution of LiCl in formamide. The measured NICISS data are deconvoluted and show an almost constant level of Cl− concentration at the surface

30

40

and in the near-surface region of the solution with a slight depletion between 6 and 12 Å. (Source: Andersson et al. 2007 [210]. Reprinted with permission of American Chemical Society.)

52.3 Concentration Depth Profiles

concentration [molal]

8

6

4

measurement fit

2

0 –20

deconvoluted

0

20

40

60

80

100

depth [Å]

Figure 52.51 Concentration depth profile of iodide of a 7.2 M solution of LiI in water. The solution was investigated at −13.8 ∘ C. The measured NICISS data are deconvoluted and show a depletion of I− concentration at

the surface and in the near-surface region of the solution. (Source: Andersson et al. 2008 [155]. Reprinted with permission of American Physical Society.)

the same anions at the surface of aqueous sodium halide solutions [164]. In case of water, the calculated and measured surface tension of the sodium halide solution increases which is in agreement with an overall depletion of the solute at the surface and surface near region of the solution. 52.3.2.4 Ionic Liquids

ILs are substances that consist only of ions where typically either anions or cations are organic ions. ILs are liquid at low temperatures with the threshold usually taken at around 373 K [211–213]. The structure of the organic ion is usually the reason that the interaction between the ions is not as strong as in a typical inorganic salt like NaCl [212]. The concentration of ions in ILs is much higher than in a typical salty solution and the interaction between the ions is thus very different from that in a solution. The structure of the organic ion makes the ion often amphiphilic and presents an interesting case how electrostatic and van der Waals forces interact. The chemical structure of the ions can easily be varied and allows tailoring the properties of ILs such as viscosity, density, surface tension, surface composition, and surface potential. Surface properties can be measured macroscopically. However, for understanding the relationship between surface properties, surface structure, and the chemical structure of the ionic liquid, analysis of the surface is required. Structural properties of interest are the arrangements of ions at the surface [214] such as the orientation of molecules [215–218], the preferential adsorption of ions, [217–220] or more specifically the charge distribution along surface normal, the influence of the chain length on the surface structure [221, 222], the presence of water, [223, 224]and other impurities [225]. The literature on analyzing the structure of ionic liquids is extensive and it is beyond the scope of this chapter to cover the literature in a representative way. We

305

52 Liquid Surfaces

0

1

DEPTH (nm) 2

3

4

electron density 4

30 C F

20

P (X5)

2

10 N

0

0

1 2 DEPTH (1016 atoms cm–2)

3

4

ELECTRON DENSITY (1023 cm–3)

40

CONCENTRATION (at.%)

306

0

Figure 52.52 Concentration depth profiles of the elements C, F, P, and N of [BMIM][PF6 ] as well as the electron density. (Source: Ohno et al. 2009 [215]. Reprinted with permission of AIP Publishing.)

will focus here on electron spectroscopy and ion scattering spectroscopy techniques (see Chapter 53 in this Volume). Both methods give complementary information about the surface structure of ILs. Aspects of the structure that are of interest are the composition of the surface, the orientation of the molecules, the position of the charges relative to the surface, but also the influence of the presence of water on the surface structure. Ohno et al. have investigated the surface of 1-butyl-3-methylimidazolium hexafluorophosphate ([BMIM][PF6 ]) with RBS. Figure 52.52 shows the concentration depth profiles of the elements C, P, N, and F of [BMIM][PF6 ] [215]. The C profile in Figure 52.52 indicates an enhanced concentration at the surface while the profiles of N, P, and F show broad maxima just below the surface. The authors conclude that the alkyl chain of the cation is pointing toward the surface and consequently that the cation and anion are depleted at the surface. As a consequence, the cation and anion show an enhanced concentration just below the surface [215]. The finding that the alkyl chains are oriented toward the gas phase is compatible with the concept that a surface is preferentially covered with that component of a system that gives rise to the lowest surface energy. Surfaces covered with alkyl chains have a lower surface energy than those covered with more polar groups such as the immidazolium ring or the hexafluorophosphate anion. Thus, the finding of Ohno et al. that the alkyl chains are oriented toward the surface is compatible with the concept that a system tries to lower the surface energy as much as possible. Lockett et al. investigated a series of 1-alkyl-3-methylimidazolium tetrafluoroborate ILs with ARXPS [218]. The high-resolution spectra can be plotted with a

52.3 Concentration Depth Profiles

2.2

C4/(C1 + C2 + C3)

2.0 1.8 1.6 1.4 1.2 0

30

60

90

Take-off angle (°) Figure 52.53 Ratio of the C 1s peak intensities [OMIM][BF4]. C4 represents the aliphatic carbon chain whereas C1–C3 represent the C atoms in or close to the immidazolium ring. (Source: Lockett et al. 2008 [218]. Reprinted with permission of Royal Society of Chemistry.)

series of four peaks [218]. The change of the ratio of the intensity of the peaks in the C1s spectrum informs about the orientation of the alkyl chain of the cation. In Figure 52.53, the ratio of the intensity of the peak representing the alkyl chain to the intensity of all other carbon atoms representing the immidazolium ring is shown for the IL 1-octyl-3-methylimidazolium tetrafluoroborate ([OMIM][BF4 ]). The intensity ratio increases with the angle of observation, which gives evidence that the alkyl chain is oriented toward the gas phase [218]. In regard to the angles used for the ARXPS measurements, it should be noted that the maximum angle for ARXPS at the excitation energy used by the authors should be about 70∘ . At larger angles, it cannot be excluded that artifacts influence the intensity ratios [28]. However, the conclusions the authors are drawing are also supported by the measurements with an angle of observation angle M1 ) are masses of the incident ion and the target atom, respectively. The energy of the scattered ion depends on the mass of the atom from which the ion is scattered. The heavier the atom, the higher the energy of scattered ion is. The ratio K, which is called “kinematic factor,” is the basis of the identification of elements. The differential cross section for Coulomb scattering (Rutherford cross section) is given by a simple analytical formula (Rutherford formula), ) )2 ( ( Z1 Z2 e2 4[(M22 − M12 sin2 𝜃)1∕2 + M2 cos 𝜃]2 d𝜎 = , (53.2) dΩ R 4E M2 sin4 𝜃(M22 − M12 sin2 𝜃)1∕2 where Z 1 and Z 2 are the atomic numbers of incident ion and target atom, respectively (see also Chapter 3.3 in Volume 1), and E is the ion energy. This simple formula allows to extract quantitative information about the abundance of elements from measured Rutherford backscattering spectroscopy (RBS) spectra. The cross section is proportional to Z2 2 , indicating that the sensitivity of RBS is higher for heavier elements. When the incident ion travels through the specimen, the ion loses its energy mainly through excitation of electrons. The rate of energy loss per unit path length, dE/dx, is called stopping power. There were a number of measurements of stopping powers for various combinations of ions and target elements. The accumulated data were fitted to a semi-empirical formula [58]. Using this formula, the stopping power can be estimated with the accuracy of several percentages. For the estimation of the stopping power of compound materials, it is convenient to introduce the stopping cross section given by ( ) dE 1 , (53.3) 𝜀= Natom dx

53.2 Principle of Rutherford Backscattering Spectroscopy

where N atom is the atomic density. The stopping power of a compound can be calculated using the so-called Bragg’s rule, ( ) ∑ dE i = Natom 𝜀i , (53.4) dx i i and 𝜀i are the atomic density and the stopping cross section of element i, where Natom respectively. The energy of the backscattered ion depends on the depth t from which the ion was scattered, ] ( ) ( ) [ dE t t dE − , (53.5) E(t) = K E0 − dx in sin 𝜃i dx out sin 𝜃e

where the subscript “in” and “out” refer to the inward and outward paths, respectively, and 𝜃 i and 𝜃 e are the incident and exit angles with respect to the surface plane. Using Eq. (53.5), the energy can be converted to the depth. In conventional RBS, 1–4 MeV He ions are used as primary ions and the energy of the scattered ion is measured using a silicon surface barrier detector (SSBD). The depth resolution of the conventional RBS is about 10 nm, which is mainly determined by the energy resolution of SSBD (∼15 keV). Improvement of the depth resolution up to atomic level can be achieved when a high-resolution spectrometer (energy resolution of ∼0.1% can be easily realized using an electrostatic or a magnetic spectrometer) is used instead of SSBD. This is called HR-RBS. An example of HR-RBS spectrum observed for a PbSe(111) single crystal is shown in Figure 53.3. Shown is the energy spectrum of He+ ions scattered at 65∘ when 300 keV He+ ions were incident on PbSe(111). There are several well-defined peaks. These peaks correspond to the Pb and Se in the individual atomic planes. The depth Depth for Pb (nm) 1 0.5

1.5 300 keV He

+

0

PbSe(111)

600

Counts

Se edge

Pb edge

400

200

0

280

285 Energy (keV)

290

295

Figure 53.3 Energy spectrum of He+ ions scattered from PbSe(111) when 300 keV He ions are incident on the surface.

355

356

53 Surfaces of Ionic Liquids

scale shown in the upper abscissa was calculated for Pb using Eq. (53.5). It is shown that the observed peak separation is about 0.3 nm, which is equal to the interplanar distance of PbSe(111). The peak yield directly corresponds to the atomic areal density of each atomic layer. The width of the first peak is about 0.1 nm, showing that the depth resolution of ∼0.1 nm can be achieved at the surface in this case. It is worth noting that that the energies of ions passing through the same path length fluctuate around an average value because of the stochastic nature of the energy loss process [59]. This is called energy loss straggling and causes deterioration of depth resolution. Because the energy loss straggling increases with increasing path length, the depth resolution becomes worse with increasing depth. This can be seen as the broadening of the peak of the individual atomic plane with increasing depth in Figure 53.3. In order to reduce the effect of the energy loss straggling on the depth resolution, grazing geometry (measurement at grazing exit angle) is useful. In the grazing geometry, the path length becomes longer. The energy loss straggling (corresponding to peak width) is proportional to the square root of the path length [59], whereas the energy loss itself (corresponding to peak separation) is proportional to the path length. Accordingly, a smaller ratio of the peak width to the peak separation, i.e. better depth resolution, can be achieved in the grazing geometry [60].

53.3 Experimental Details

Figure 53.4 schematically shows a setup of HR-RBS [55]. An ion beam of 400 keV He+ was produced by a Cockcroft Walton-type accelerator. The ion beam was collimated to 2 × 2 mm2 by two sets of a four-jaw slit system. The beam (typical bam current is 50 nA) was sent to a UHV scattering chamber (base pressure 1 × 10−8 Pa) via a differential pumping system. A sample was placed in the UHV chamber and irradiated with the He+ ions. The He+ ions scattered from the sample were energy

Differential pumping UHV chamber

Accelerator 1D-PSD

1m

Spectrometer

Figure 53.4 Setup of HR-RBS. The same equipment can be used for HR-ERDA. (Source: Reproduced with permission of Kimura et al. 2004 [55]. Copyright 2004, Elsevier.)

53.3 Experimental Details

analyzed by a 90∘ sector-type magnetic spectrometer, which has a one-dimensional position-sensitive detector (1D-PSD) of 100 mm length on the focal plane. The measured position spectrum can be converted to the energy spectrum. The energy resolution and the energy window of the spectrometer is 0.1% and 25% of the central energy, respectively. The acceptance angle of the spectrometer is 0.3 msr, which is 2 orders of magnitude smaller than the typical acceptance angle of SSBD used in the conventional RBS. This small acceptance angle can be compensated by large scattering cross sections of 400 keV He+ (6–100 times larger than 1–4 MeV He+ , which are used in the conventional RBS). There is an additional advantage of medium energy (400 keV) over 1–4 MeV. The stopping powers of He+ have a maximum around 400 keV for various materials. Accordingly, better depth resolution can be obtained using 400 keV He+ compared to 1–4 MeV He+ . It is worth noting that that the same setup can also be used for HR-ERDA measurements. In the HR-ERDA measurements, ions recoiled from the sample by the incident ions are energy analyzed using the magnetic spectrometer. A thin selfsupporting mylar foil (typical thickness 1 μm) is placed in front of the 1D-PSD to reject both scattered primary ions and recoiled heavy elements. Quantitative depth profiling of hydrogen can be performed with a subnanometer depth resolution using this setup [56]. For the measurement of liquid samples, a special preparation technique is required. The most popular method for preparing liquid surfaces for the ion beam analysis is the “rotating disc” method [61]. Figure 53.5 shows a picture of the rotating disc used for HR-RBS measurements. A flat disc made with stainless steel

Rotating disc

Reservoir

Figure 53.5 Rotating disc target used in HR-RBS measurements of ionic liquids. A fresh thin layer of ionic liquid is continuously prepared on the disc surface. (Source: Reproduced with permission of Nakajima et al. 2009 [62]. Copyright 2009, Elsevier.)

357

358

53 Surfaces of Ionic Liquids

(diameter 38 mm) was partially immersed in a reservoir of ionic liquid. The surface of the rotating disc was continuously covered by a fresh thin layer of ionic liquid after every immersion into the reservoir. For the ionic liquids having higher surface tensions, the disc was coated with gold to improve the wettability. The radiation damage caused by incident ions can be avoided by choosing an appropriate rotation rate, typically 5 rpm [62]. This rotating disc system was mounted on a precision goniometer in the UHV scattering chamber. After loading the reservoir with ionic liquids, the scattering chamber was maintained under UHV conditions for more than 1 day before measurements to reduce possible water contamination. In the following sections, unless otherwise stated, HR-RBS (HR-ERDA) spectra were measured at a scattering angle of ∼50∘ (recoil angle ∼25∘ ) with 400 (200) keV He+ ions as primary ions.

53.4 Surface Structures of Pure Ionic Liquids 53.4.1 1-Ethyl-3-Methylimidazolium Bis(trifluoromethanesulfonyl)imide

The most commonly studied ionic liquids are imidazolium-based ionic liquids, such as 1-alkyl-3-methylimidazolium bis(trifluoromethanesulfonyl)imide ([Cn C1 Im][Tf2 N]) [42]. In this section, the result of HR-RBS measurement of [C2 C1 Im][Tf2 N] is presented as an example of HR-RBS analysis. The result is compared with the results of other techniques, including MD simulations. The molecular structures of [C2 C1 Im] cations and [Tf2 N] anions are shown in Figure 53.6. The [Tf2 N] anion has two stable conformers of C 1 and C 2 symmetry and the C2 conformer is more stable than C1 by 3.5 kJ/mol [63]. Figure 53.7 shows the HR-RBS spectra for [C2 C1 Im][Tf2 N] observed at exit angles 𝜃 e = 3∘ and 5∘ with respect to the surface plane. The spectra have five steps at ∼365, ∼343, ∼332, ∼323, and ∼312 keV. These energies correspond to the energies of He ions scattered from S, F, O, N, and C atoms, which can be calculated with Eq. (53.1). The energy scale can be converted to the depth scale using Eq. (53.5). The obtained depth scales for Surface

0.5 nm

[C2C1lm]

[Tf2N] C1 conformer

[Tf2N] C2 conformer

Figure 53.6 Two conformers of [Tf2 N] anions and their possible orientations at the surface together with [C2 C1 Im] cations.

53.4 Surface Structures of Pure Ionic Liquids

20 000

400 keV He+

[C2C1Im] [Tf2N]

Depth for F (nm) 3

Counts (arb.units)

15 000

2

1

0

θe = 3° θe = 5° 10 000

Depth for S (nm) 3

2

1

0

C N O

5 000

Depth for S (nm)

F 3 2 1 0 Depth for F (nm)

0 300

3 2 1 0

S

320 340 360 Energy of scattered He+ (keV)

Figure 53.7 HR-RBS spectra of [C2 C1 Im][Tf2 N] observed at exit angles 𝜃 = 3∘ and 5∘ with respect to the sure

face plane. The incident energy of He ions was 400 keV and the scattering angle was 50∘ . The dashed lines show the calculated spectrum for a uniform and stoichiometric

composition. The solid lines show the spectrum calculated with the composition depth profiles derived by the combination analysis of HR-RBS and HR-ERDA. (Source: Reproduced with permission of Nakajima et al. 2010 [64]. Copyright 2010, AIP Publishing LLC.)

sulfur and fluorine are shown in the figure. If a uniform stoichiometric composition is assumed, the expected spectra can be easily calculated and the results are shown by dashed lines. The calculated spectra are roughly in agreement with the observed spectra. There are, however, discrepancies at the leading edges of elements. For example, there is a sharp peak at ∼342 keV near the leading edge of fluorine at 𝜃 e = 3∘ . This peak is followed by a shallow dip at ∼340 keV. There is another small peak at ∼363 keV near the leading edge of sulfur. Such structures are not seen in the calculated spectrum. These discrepancies suggest that the composition deviates from the stoichiometric one in the surface region of ∼1 nm. The discrepancy is more clearly seen at smaller 𝜃 e , indicating that better depth resolution can be achieved at smaller 𝜃 e . On the other hand, the energy spectrum observed at larger 𝜃 e provides other important information, i.e. the information of the deeper region. The observed spectrum at 𝜃 e = 5∘ agrees with the calculated spectrum (dashed line) in the region deeper than ∼2 nm, indicating that the composition is stoichiometric in this region. This allows to separate the contribution of each element from the observed spectrum [65]. The yield of each element divided by the Rutherford cross section is proportional to the concentration of the element. The depth information can be derived from the scattered ion energy using the stopping power. The Rutherford cross section can be precisely calculated using the simple Rutherford formula (Eq. (53.2)) and the stopping power can be estimated with good precision (error is less than 5% in the present energy region). Thus, precise

359

53 Surfaces of Ionic Liquids

quantitative depth profiling can be performed by HR-RBS. The depth resolution can be roughly estimated from the width of the observed fluorine peak at 342 keV. The observed width is about 1.5 keV, which corresponds to ∼0.3 nm, indicating that the depth resolution is better than 0.3 nm at the surface in the present case. There are, however, some drawbacks in HR-RBS as was discussed in the introduction. The major drawback is its low sensitivity for light elements. In the present case, carbon signal is clearly seen, but hydrogen cannot be detected at all. However, if HR-RBS is combined with HR-ERDA, perfect composition depth profiling, including hydrogen, can be performed. Figure 53.8 shows HR-ERDA spectra for [Cn C1 Im][Tf2 N] (n = 2, 6, 10) observed at 𝜃 e = 5∘ . Shown are energy spectra of H+ ions recoiled at 25∘ when 200 keV He+ ions were incident on these ionic liquids. The depth scale derived from the stopping power of [C6 C1 Im][Tf2 N] is shown in the upper abscissa. The depth scales for other two ionic liquids are not shown but are approximately the same. The expected spectra calculated for the uniform stoichiometric compositions are shown by dashed lines for comparison. Although the calculated spectra show a simple steplike structure, the observed spectra have a broad peak at the surface. The peak becomes larger with increasing length of alkyl chain n, indicating that the hydrogen concentration is enhanced at the surface especially for the ionic liquids having longer alkyl chains. Note that the width of the peak corresponds to ∼0.5 nm. This demonstrates that the depth resolution of Depth for [C6C1Im][Tf2N] (nm) 2 1

3

600

+

200 keV He

[CnC1Im][Tf2N]

0

ERDA θr = 25°

[C10C1Im][Tf2N] 400 Counts

360

[C6C1Im][Tf2N]

200

[C2C1Im][Tf2N]

0 100

102 104 Energy of recoiled H+ (keV)

Figure 53.8 HR-ERDA spectra for [Cn C1 Im][Tf2 N] (n = 2, 6, 10) observed at 𝜃 e = 5∘ with respect to the surface plane. The incident energy of He ions was 200 keV and the recoil angle was 25∘ . The dashed lines show the calculated spectra for uniform and stoichiometric compositions. The solid

106

lines show the spectrum calculated with the composition depth profiles derived by the combination analysis of HR-RBS and HRERDA. (Source: Reproduced with permission of Nakajima et al. 2016 [42]. Copyright 2016, The Japan Society for Analytical Chemistry.)

53.4 Surface Structures of Pure Ionic Liquids

HR-ERDA is better than ∼0.5 nm at the surface, which is comparable to the depth resolution of HR-RBS. The elemental depth profiles of all constituent elements can be derived so that the profiles would reproduce both the HR-RBS and HR-ERDA spectra in parallel. The result of [C2 C1 Im][Tf2 N] is shown by histograms in Figure 53.9. It should be noted that both HR-RBS and HR-ERDA provide the depth scale in units of areal density of atoms (e.g. atoms/cm2 ). This is because the depth information is derived on the basis of the energy loss of the ion. If the areal atomic density is the same, the energy loss is also the same irrespective of the thickness of the specimen (see Eqs. (53.4), (53.5)). The depth scale obtained in units of areal atomic density can be converted to “nanometer” using the stopping power of bulk and the result is shown in the upper abscissa of Figure 53.9. This scale in nanometer is just a rough measure because the atomic density may deviate from the bulk density in the surface region. The derived composition agrees with the bulk composition at the depth deeper than ∼1.5 nm while there is a large deviation in the surface region. The thickness of one molecular layer can be estimated by 1/n1/3 , where n is the density of ion pair (2.34 × 1021 ion pairs/cm3 for [C2 C1 Im][Tf2 N]). The depth regions corresponding to the first and second molecular layers are shown by arrows in Figure 53.9. By integrating the depth profiles in the first molecular layer, the composition of the first molecular layer is estimated to be S2.0 F6.0 O4.0 N2.8 C8.0 H11.2 , which is very close to the stoichiometric composition of [C2 C1 Im][Tf2 N] (S2 F6 O4 N3 C8 H11 ). Depth (nm) 1

0 First layer

Concentration (at.%)

40

2 [C2C1Im][Tf2N]

Second layer

H C ×1.2

20

F O N S

0

0

1 Depth (1016 atoms/cm2)

Figure 53.9 Composition depth profiles for [C2 C1 Im][Tf2 N] derived from the combination analysis of HR-ERDA and HR-RBS (histograms). The depth scale shown in the upper abscissa was calculated using the bulk stopping power. The dashed lines show the

2

result of MD simulation. The solid lines show the result of MD simulation including the effect of capillary waves. (Source: Reproduced with permission of Nakajima et al. 2016 [42]. Copyright 2016, The Japan Society for Analytical Chemistry.)

361

362

53 Surfaces of Ionic Liquids

This indicates that neither the cation nor the anion is enriched in the surface in agreement with the results of XPS [66, 67], NICISS [68], and MIES [46]. On the other hand, the compositions of the top and bottom half of the layer are estimated to be S2.0 F7.2 O3.5 N2.3 C8.0 H11.0 and S2.1 F4.8 O4.4 N3.2 C8.0 H11.3 , respectively, which significantly deviate from the stoichiometric composition. The origin of the observed deviation can be attributed to the preferential orientation of the cations and/or anions in the first molecular layer. Looking at the depth profiles, fluorine has a sharp peak at the surface and sulfur has a broad peak around ∼0.4 nm. This indicates that [Tf2 N] anions are oriented with their CF3 groups pointing toward the vacuum and N(SO2 )2 groups pointing toward the bulk. Such a preferential orientation of [Tf2 N] anions was also observed by MIES [46] and LEIS [39]. Figure 53.6 schematically shows possible surface orientations of [Tf2 N] anions for both C 1 and C 2 conformers. If the C 2 conformer is dominant at the surface, fluorine profile would have two peaks corresponding to the two CF3 groups. The theoretical separation between two fluorine planes in C 2 conformer is ∼0.5 nm [63]. There is, however, no second fluorine peak at the expected depth in the observed depth profile. Instead, there is a large dip at ∼0.5 nm. This indicates that the C 1 conformer is dominant at the surface and has a preferential orientation shown in Figure 53.6. This seems in contradiction with the theoretical energy difference between these conformers (C 2 conformer is energetically more stable than C 1 conformer by 3.5 kJ/mol [63]). The abundance of C 2 conformer is calculated to be ∼80% at room temperature from the energy difference. This discrepancy may be explained by the large reduction of surface energy caused by the fluorine exposure to the vacuum. Concerning the orientation of [C2 C1 Im] cations, the nitrogen peak seen at ∼0.4 nm indicates that the imidazolium rings do not occupy the surface but locate in the subsurface region. A small hydrogen surface peak suggests that the ethyl chains are pointing toward the vacuum because the majority of hydrogen atoms are in the ethyl chain. Although this is consistent with the SFG result [33], AR-XPS and NICISS showed no preferential orientation of [C2 C1 Im] [68]. In order to confirm the preferential orientation of [C2 C1 Im], MD simulations would be useful. Since the pioneering work by Lynden-Bell [50], there have been a number of MD simulations on the surface structures of ionic liquids [51–53]. Sophisticated force fields have been developed specifically for ionic liquids [51]. As a result, MD simulation becomes a reliable method and qualitatively reproduces many experimental findings such as surface-layered structures [54], preferred orientation of molecules [52], and so on. Figure 53.10 shows the atomic number density profiles for [C2 C1 Im][Tf2 N] at 300 K derived by the MD simulation [42, 53]. In order to quantitatively compare this result with the HR-RBS result, the depth scale of the simulation profile was converted to the areal atomic density. The ordinate was also converted from “atomic number density” to “concentration” and the results are shown by dashed curves in Figure 53.9. The agreement with the HR-RBS result (histogram) is roughly good. For example, the fluorine profile has a sharp peak at the surface and a dip at

53.4 Surface Structures of Pure Ionic Liquids

[C2C1Im][Tf2N] MD simulation

Atomic number density (nm–3)

30 H Total (×0.3) 20

C F O N S

10

0

0

1 2 3 Distance from center of simulation box (nm)

Figure 53.10 Atomic number density profiles for [C2 C1 Im][Tf2 N] at 300 K derived from MD simulations. The abscissa shows the distance, perpendicular to the surface, from the center of the simulation box.

∼0.5 nm, which qualitatively reproduces the HR-RBS result. The simulation profile, however, shows more pronounced structures, and there is even the second peak at ∼0.9 nm, which is not seen in the observed profile. For other elements, namely oxygen and nitrogen, the simulation profiles show reduction at the surface and have a broad peak in the subsurface region. These features are qualitatively the same as the observed ones, but again more pronounced compared to the observed profiles. These results suggest that the fine structures seen in the simulation are smeared out in the observed result. A possible origin of the observed discrepancy between the HR-RBS measurement and the MD simulation is surface roughening caused by capillary waves. The surface roughness cause by capillary waves is given by [69], ) ( kmax dqx dqy kmax k T kB T ≈ , (53.6) ln 𝜎𝜌2 = B2 2π𝜎 kg 4π 𝜎 ∫0 (qx2 + qy2 ) + kg2 where k g 2 = 𝜌g/𝜎, k max = 𝜋/a, 𝜌 is the mass density, g is the acceleration of gravity, 𝜎 is the surface tension, a = 1/n1/3 is the size of the molecule, T is temperature, and k B is the Boltzmann constant. Because of the finite size of the simulation box (5.98 × 5.98 × 16 nm3 in the present simulation), the MD simulation does not include capillary waves with wave length longer than 𝜆sim = 2 × 5.98 nm. The effect of these capillary waves should be taken into account to compare the MD simulation with the HR-RBS result [70]. The modified simulation profiles are shown by solid lines in Figure 53.9. The agreement with the HR-RBS profiles is improved dramatically, demonstrating that the MD simulation is a reliable method to study the surface structures of ionic liquids. Knowing that the MD simulation reproduces the observed result almost perfectly, the orientation of [C2 C1 Im] cations can be discussed based on the simulation

363

53 Surfaces of Ionic Liquids

1 [C2C1Im][Tf2N] θ

[C6C1Im][Tf2N] Surface

364

CT

0.5 N1

0

–1

0 1 2 Depth from Gibbs dividing surface (nm)

Figure 53.11 Orientation of the ethyl chain for [C2 C1 Im] (solid line) calculated by the MD simulation. The results for [C6 C1 Im] (dashed line) are displayed for comparison. The origin of z-coordinates coincides with the position of the surface as determined by the Gibbs

3

dividing surface. The inset shows the definition of the tilt angle, 𝜃. (Source: Reproduced with permission of Nakajima et al. 2016 [42]. Copyright 2016, The Japan Society for Analytical Chemistry.)

result. Figure 53.11 shows the orientation of the ethyl chain evaluated by the MD simulation. The orientation of the ethyl chain was characterized by the tilt angle 𝜃 between the surface normal and vector pointing from the ring nitrogen to the terminal C atom of the ethyl chain as shown in the inset of Figure 53.11. It is evident that the alkyl chains are pointing toward the vacuum in the topmost molecular layer (⟨cos 𝜃⟩∼0.7) and that there is no preferential orientation in the deeper layers. Thus, the MD simulation confirms the preferential orientation of the [C2 C1 Im] cations suggested by the HR-RBS and HR-ERDA analysis. 53.4.2 1-Alkyl-3-Methylimidazolium Bis(trifluoromethanesulfonyl)imide ([Cn C1 Im][Tf2 N]): Effect of Alkyl Chain Length on the Surface Structure

Figure 53.12 shows observed HR-RBS spectra of [Cn C1 Im][Tf2 N] (n = 2, 4, 6, 8, 10, 12). For comparison, the calculated spectra for uniform stoichiometric compositions are shown by dashed lines. Looking at [C2 C1 Im][Tf2 N], the agreement with the observed spectra is rather good except for the sharp fluorine peak at ∼341 keV, as was discussed in the Section 4.1. With increasing chain length n, the spectrum systematically changes. For the detailed discussion of these systematical changes, the composition depth profiles were derived from these spectra so that the observed spectra can be reproduced by the spectrum calculated with the derived

53.4 Surface Structures of Pure Ionic Liquids

400 keV He+

[CnC1Im][Tf2N]

n = 12 n = 10

Counts (arb. units)

30 000

n=8 n=6

20 000

n=4 n=2 C N O

10 000

F S 0

300

320

340

360

Energy of scattered He+ (keV) Figure 53.12 HR-RBS spectra of [Cn C1 Im][Tf2 N] (n = 2, 4, 6, 8, 10, 12) observed at exit angles 𝜃 e = 3∘ with respect to the surface plane. The incident energy of He ions was 400 keV and the scattering angle was 50∘ . The dashed lines show the calculated spectrum for uniform and

stoichiometric compositions. The solid lines show the best fit results. (Source: Reproduced with permission of Nakajima et al., 2010, 2016, 2017 [64, 70, 71]. Copyright 2010, 2016, AIP Publishing LLC and Copyright 2017, Elsevier.)

depth profiles. The calculated spectra are shown by solid lines in Figure 53.12. The good agreement with the observed spectra indicates that the derived depth profiles are reliable. Figure 53.13 shows thus derived carbon (a), nitrogen (b), fluorine (c), and sulfur (d) profiles. Except for n = 2, all carbon profiles have a surface peak and the peak becomes larger and broader with increasing chain length n. There is a shallow dip just behind the surface peak for n ≥ 6. On the other hand, nitrogen profiles have a subsurface peak and the peak moves to deeper regions with increasing n. Considering that carbon and nitrogen are representative elements of the alkyl chain and the imidazolium ring, respectively, these behaviors indicate that the alkyl chains of [Cn C1 Im] protrude to the vacuum in the topmost molecular layer. Similar preferential orientation of [Cn C1 Im] was also observed using AR-XPS for n ≥ 4 [66]. In XPS, signals of the carbon atoms that have nitrogen atoms as neighbors (denoted as Chetero ) and the carbon atoms with exclusively carbon (or hydrogen)

365

53 Surfaces of Ionic Liquids

Concentration of C (at.%)

35

[CnC1Im][Tf2N]

30 n = 12 n = 10n = 8 n=6 n=4

25

n=2

20

0

(a)

1 2 Depth (1016 atoms/cm2)

3

Concentration of N (at.%)

[CnC1Im][Tf2N]

10 n=2 n=4 n=6 n=8 n = 10 n = 12

5

0

0

(b)

1 2 Depth (1016 atoms/cm2)

3

30

[CnC1Im][Tf2N] Concentration of F (at.%)

366

20

n=4 n=6 n=8 n =10 n =12

10

0

(c)

n=2

0

1 2 Depth (1016 atoms/cm2)

3

Figure 53.13 Elemental depth profiles of [Cn C1 Im][Tf2 N] derived by HR-RBS measurements. The results of carbon (a), nitrogen (b), fluorine (c), and sulfur (d) are shown. The concentration ratio of fluorine to sulfur is also shown (e).

53.4 Surface Structures of Pure Ionic Liquids

8

Concentration of S (at.%)

[CnC1Im][Tf2N] n=2

6

n=4 n=6 n=8 n = 10 n = 12

4

2

0

0

1 2 Depth (1016 atoms/cm2)

(d) 8

n=2 n=4 n=6 n=8 n = 10 n = 12

[CnC1Im][Tf2N]

6

Ratio of F/S

3

4

2

0

(e)

0

1 Depth (1016 atoms/cm2)

2

Figure 53.13 (Continued)

neighbors (denoted as Calkyl ) can be distinguished. Figure 53.14 shows the intensity ratio of Calkyl /Chetero for [Cn C1 Im][Tf2 N] observed at emission angles of 0∘ , 70∘ , and 80∘ , as a function of chain length n. The nominal ratio is shown by a dashed line [66]. At grazing emission angles, the observed intensity ratio is larger than the nominal ratio for n ≥ 4, indicating that Calkyl atoms are located at shallower depth compared to Chetero . SFG measurements provided detailed information on the orientation of [Cn C1 Im] cations, namely the alkyl chains of [Cn C1 Im] are tilted by 50∘ at the surface [32]. With this tilt angle, the thicknesses of the topmost molecular layer can be calculated using the lengths of [Cn C1 Im] cations. By integrating the elemental depth profiles in the topmost molecular layer, the surface composition can be estimated as was done for [C2 C1 Im][Tf2 N] in the Section 4.1. The results are shown in Table 53.1 together

367

53 Surfaces of Ionic Liquids

7 0° 70° 80° Nominal

6 IC1s(Calkyl)/IC1s(Chetero)

368

5 4 3 2 1 0 0

2

4

6

8

10 12 14 16 18

[CnC1Im][Tf2N] Figure 53.14 XPS intensity ratio of Calkyl /Chetero for [Cn C1 Im][Tf2 N] observed at emission angles of 0∘ , 70∘ , and 80∘ , as a function of chain length n. Calkyl denotes carbon atoms with exclusively carbon (or

hydrogen) neighbors and Chetero denotes carbon atoms, which have nitrogen atoms as neighbors. (Source: Reproduced with permission of Lovelock et al. 2009 [66]. Copyright 2009, American Chemical Society.)

with the stoichiometric compositions. The table also shows the results of some other imidazolium-based ionic liquids. All measured compositions are in good agreement with the stoichiometric compositions, indicating that there is no surface enrichment of either cation or anion. The position and orientation of [Tf2 N] anions can also be discussed based on the observed elemental depth profiles. It is seen that all sulfur profiles have a peak in the subsurface region (Figure 53.13d). The peak position and width are almost the same as the nitrogen peak (Figure 53.13b). Considering that sulfur and nitrogen are representative elements of [Tf2 N] and the imidazolium ring, respectively, this indicates that [Tf2 N] anions (more precisely, N(SO2 )2 groups) are located at the same depth where the imidazolium rings are. This is a reasonable result because the positive charge of the [Cn C1 Im] cation is localized in the imidazolium ring and the negative charge of the [Tf2 N] is localized in the N(SO2 )2 group. Concerning the orientation, the fluorine profile has a peak at the surface and the sulfur profile has a peak in the subsurface region for n ≤ 6. This indicates that the [Tf2 N] has the preferential orientation with their CF3 groups pointing toward the vacuum and the N(SO2 )2 group toward the bulk. With increasing chain length n, the fluorine surface peak becomes less pronounced and disappears for n ≥ 8, suggesting that the preferential orientation of [Tf2 N] disappears. In order to confirm this, the concentration ratio of fluorine to sulfur is calculated from the observed profiles and shown in Figure 53.13e. The ratio is higher than the nominal ratio of 3 in the surface region irrespective of the length of the alkyl chain n. This indicates that the preferential orientation of [Tf2 N] still exists even for [C12 C1 Im][Tf2 N], although the degree of the orientation becomes weaker with increasing chain length [64].

Table 53.1 Summary of ionic liquids observed using HR-RBS. Chemical formula

Name

𝝈: Surface

d: Areal atomic

A: Molar

Bulk

Observed surface

tension

density of

surface area

composition

composition

(mN/m)

surface layer

(1010 cm2 )

(1015 atoms/cm2 )

[C2 C1 Im][Tf2 N]

[C4 C1 Im][Tf2 N]

[C6 C1 Im][Tf2 N]

[C8 C1 Im][Tf2 N]

[C10 C1 Im][Tf2 N]

1-Ethyl-3methylimidazolium bis[(trifluoromethyl) sulfonyl]imide 1-Butyl-3methylimidazolium bis[(trifluoromethyl) sulfonyl]imide 1-Hexyl-3methylimidazolium bis[(trifluoromethyl) sulfonyl]imide 1-Octyl-3methylimidazolium bis[(trifluoromethyl) sulfonyl]imide 1-Decyl-3methylimidazolium bis[(trifluoromethyl) sulfonyl]imide

36.43 [72], 41.6 [73], 35.1 [74]

5.74

12.1

S2 F6 O4 N3 C8 H11

S2.0 F6.1 O3.9 N2.8 C8.0 H11.2

33.09 [72], 33.6 [75]

7.25

13.3

S2 F6 O4 N3 C10 H15

S2.0 F6.0 O3.9 N2.8 C10.0 H15.2

31.76 [72], 31.0 [75]

8.85

14.4

S2 F6 O4 N3 C12 H19

S2.0 F5.7 O3.5 N2.7 C12.4 H19.7

31.30 [72]

10.47

15.6

S2 F6 O4 N3 C14 H23

S2.0 F6.0 O3.8 N2.8 C14.4 H23.0

31.34 [72]

12.12

16.7

S2 F6 O4 N3 C16 H27

S1.7 F5.4 O3.7 N2.9 C16.5 H27.9

(continued overleaf )

Table 53.1 (Continued) Chemical formula

Name

𝝈: Surface

d: Areal atomic

A: Molar

Bulk

Observed surface

tension

density of

surface area

composition

composition

(mN/m)

surface layer

(1010 cm2 )

(1015 atoms/cm2 )

[C12 C1 Im][Tf2 N]

[C2 C1 Im][TfO]

[C2 C1 Im][BF4 ]

[C4 C1 Im][PF6 ]

[C6 C1 Im][PF6 ]

[C6 C1 Im][Cl]

1-Dodecyl-3methylimidazolium bis[(trifluoromethyl) sulfonyl]imide 1-Ethyl-3methylimidazolium trifluoromethylsulfonate 1-Ethyl-3methylimidazolium tetrafluoroborate 1-Butyl-3methylimidazolium hexafluorophosphate 1-Hexyl-3methylimidazolium hexafluorophosphate 1-Hexyl-3methylimidazolium chlorine

29.8 [76]

14.87

16.6

S2 F6 O4 N3 C18 H31

S2.1 F5.8 O3.7 N2.7 C18.2 H31.4

44.4 [73]

6.24

7.0

S1 F3 O3 N2 C7 H11

S1.1 F3.1 O3.3 N2.1 C7.7 H9.8

53.9 [76]

6.74

5.1

F4 N2 C6 B1 H11

F4.1 N2.0 C6.0 B1.0 H10.9

42.8 [75], 47.2 [77]

8.18

7.5

P1 F6 N2 C8 H15

P1.0 F6.2 N2.1 C8.5 H14.1

38.25 [75], 43.0 [77]

9.92

8.8

P1 F6 N2 C10 H19

P0.9 F5.0 N1.7 C10.5 H19.9

41.5 [77]

10.2

6.0

Cl1 N2 C10 H19

Cl1.1 N2.3 C10.1 H18.5

(Source: Reproduced with permission of Nakajima et al. 2017 [71]. Copyright 2017, Elsevier.)

53.4 Surface Structures of Pure Ionic Liquids

53.4.3 Effect of Anion Size

In this section, the effect of the anion size on the surface structure is briefly discussed. Figure 53.15 shows the observed HR-RBS spectrum of [C6 C1 Im][Cl]. The composition depth profiles derived from the observed HR-RBS spectrum are shown in Figure 53.16. The nitrogen profile has a peak at ∼0.9 nm, and the carbon profile has a small surface peak. This indicates that the hexyl chains of [C6 C1 Im] protrude to the vacuum similar to [C6 C1 Im][Tf2 N]. Concerning the position of the anion, the chlorine profile has a peak at ∼0.7 nm. This suggests that chlorine is located at slightly shallower depth compared to the imidazolium ring. In order to confirm this, the concentration ratio of chlorine to nitrogen is shown by a dashed line in Figure 53.16. The ratio is slightly larger than the nominal ratio (0.5) near the surface, confirming that chlorine is located at a shallower depth but only slightly shallower. Thus, the surface structures of [C6 C1 Im][Cl] are very similar to those of [Cn C1 Im][Tf2 N], although the size of Cl− anion is much smaller than that of [Tf2 N]. The surface structures of ionic liquids having various anions (see Table 53.1) were also studied using HR-RBS [65, 71, 78]. The results showed that all these ionic liquids have similar surface structures to [Cn C1 Im][Tf2 N], irrespective of the size of anions, namely (i) there is no surface enrichment of either cation or anion, (ii) alkyl chains protrude to the vacuum, (iii) the polar parts of anions are located at almost the same depth as the imidazolium rings (i.e. the polar part of cations), and (iv) CF3 moieties of [Tf2 N] and [TfO] anions are pointing toward the vacuum. Similar results were also obtained by the MD simulations for various ionic liquids [51–53]. It is worth noting that that such a surface structure reduces the electrostatic energy because

10

4000

Depth for Cl (nm) 5

0

[C6C1Im]Cl, θe = 3°

+

400 keV He

3000

Counts

C 2000

N 1000 3

2

1

0

Cl

Depth for N (nm)

0 300

320 340 360 Energy of scattered He+ (keV)

Figure 53.15 HR-RBS spectrum of [C6 C1 Im]Cl observed at exit angles 𝜃 e = 3. The dashed line shows the calculated spectrum for a uniform and stoichiometric composition. The solid line shows the best fit result.

371

53 Surfaces of Ionic Liquids

20

Depth (nm) 1

0

2

1

[C6C1Im]Cl C ×0.5 15

Cl/N

10

0.5

Cl/N ratio

Concentration (at.%)

372

N 5

Cl 0

0 Depth

(1016

1 atoms/cm2)

2

0

Figure 53.16 Composition depth profiles for [C6 C1 Im]Cl derived from the HR-RBS spectrum. The depth scale shown in the upper abscissa was calculated using a bulk density.

(i) the polar parts of cations and anions attract each other and (ii) the electrostatic field induced by the polar parts is effectively screened by the nonpolar parts located in the outer region. Effect of the anion size was also studied using AR-XPS [67]. Figure 53.17a shows the intensity ratio of Calkyl /Chetero for [C8 C1 Im][X] observed at emission angles of 0∘ and 80∘ , as a function of molecular volume. At 80∘ , the intensity ratio is larger than the nominal ratio (1.4) for all anions, indicating that the octyl chains protrude to the vacuum irrespective of the anion size. The ratio increases with decreasing size of anion. This suggests that the surface enrichment of alkyl carbon relative to the ring carbon is more pronounced for the smaller anions [67]. The relative position of the anion with respect to the imidazolium ring was also discussed based on AR-XPS measurements. Figure 53.17b shows the intensity ratio of the XPS signals of the representative element of anion to the nitrogen in the imidazolium ring for [C8 C1 Im][X] [67]. The nominal ratio is 0.5 for all ionic liquids studied. The ratio seems to increase with an increasing emission angle, suggesting that the anions are located at slightly shallower depth compared to the imidazolium ring. The signalto-noise level for the measured ratio was, however, quite low because of the low intensity of the anion-related element, especially at grazing emission angles. Consequently, only a tentative conclusion derived that the anions are located nearly at the same depth as the imidazolium rings, irrespective of the nature of the anion [67]. In passing, the surface enhancement of electron density observed by XR [27] can be explained by the surface structure observed by HR-RBS. Figure 53.18 shows the composition depth profiles of [C4 C1 Im][PF6 ] measured by HR-RBS [65]. The electron density profile calculated with the measured composition depth profiles is shown by a thick line. The profile has a peak at ∼0.3 nm with a long tail toward

53.5 Surface Structures of Binary Mixtures of Ionic Liquids

IC1s(Calkyl) / IC1s (Chetero)

3.5 3.0

Cl–

0° 80° Nominal

Br– I– [PF6]–

2.5

[Pf2N]– [BF4]–

2.0

[TfO]–

[Tf2N]–

[FAP]–

1.5

0.4

0.5

0.6

0.7

Ionic liquid molecular volume (nm3)

(a) 0.7

Cl–

Aanion:Ncation ratio

Br– I–

0.6

[PF6]– [BF4]–

0.5

[TfO]– [Tf2N]– 0.4

[Pf2N]– [FAP]– 0

(b)

70 80 Electron emission angle (°)

Figure 53.17 (a) XPS intensity ratio of Calkyl /Chetero for [C8 C1 Im][X] observed at emission angles of 0∘ and 80∘ , as a function of molecular volume, where X denotes anion. (b) XPS intensity ratio of Aanion /Ncation for [C8 C1 Im][X], where Aanion and Ncation denote

Nominal

the element selected from anion and nitrogen in the imidazolium ring, at different electron emission angles. (Source: Reproduced with permission of Kolbeck et al. 2009 [67]. Copyright 2009, American Chemical Society.)

the bulk, which is similar to the profiles of phosphor and fluorine. Thus, the origin of the electron density enhancement is attributed to the enrichment of these heavy elements in the subsurface region. The dotted line shows the result of XR measurement analyzed with a single-box model [79]. Although XR cannot provide detailed profiles, the result is roughly in agreement with the HR-RBS result.

53.5 Surface Structures of Binary Mixtures of Ionic Liquids

The surface properties of ionic liquids can be tuned by choosing an appropriate combination of a wide range of cations and anions. Fine-tuning can be realized by

373

53 Surfaces of Ionic Liquids

0

1

Depth (nm) 2

3

4

Electron density 4

30

C F

20

2

P (×4) 10

N

0

0

1 2 Depth (1016 atoms/cm2)

Figure 53.18 Composition depth profiles for [C4 C1 Im][PF6 ] derived from the HR-RBS spectrum. The depth scale shown in the upper abscissa was calculated using a bulk density. The electron density profile calculated with the measured composition depth

3

4

Electron density (1023 /cm3)

40

Concentration (at.%)

374

0

profiles is shown by a thick line. The dotted line shows the electron density profile measured by XR [71]. (Source: Reproduced with permission of Ohno et al. 2009 [65]. Copyright 2016, AIP Publishing LLC.)

utilizing the mixtures of ionic liquids [38]. There have been several studies on the surface structures of ionic liquid mixtures. In some cases, strong surface segregation of one component was observed [38, 45]. This suggests the possibility that the surface properties can be controlled separately from the bulk properties. In this respect, understanding the mechanism of surface segregation and predicting the degree of surface segregation are of pivotal importance. In the following sections, the results of HR-RBS studies on ionic liquid mixtures are presented. As an example of mixtures, a mixture of [C2 C1 Im][Tf2 N] and [C2 C1 Im][BF4 ] is chosen because large surface segregation of [C2 C1 Im][Tf2 N] is expected because of their large surface tension difference (36.43 and 53.9 mN/m for [C2 C1 Im][Tf2 N] and [C2 C1 Im][BF4 ], respectively [72, 76]). The result is compared with the MD simulation to see if the MD simulation can reproduce the observed surface segregation or not. 53.5.1 Equimolar Mixture of [C2 C1 Im][Tf2 N] and [C2 C1 Im][BF4 ]: Comparison Between HR-RBS and MD Simulation

Figure 53.19 shows the observed HR-RBS spectrum of pure [C2 C1 Im][BF4 ] [80]. The dashed line shows the calculated spectrum for a uniform stoichiometric composition. The agreement with the observed spectrum is reasonably good, although there is small discrepancy at the leading edge of fluorine, i.e. the observed fluorine yield is higher than the expected spectrum at energies higher than 340 keV. It is worth noting

53.5 Surface Structures of Binary Mixtures of Ionic Liquids

8000

6000

400 keV He+

[C2C1Im][BF4]

B

Counts

C 4000

2000

0 300

N

Experimetal Stoichiometric Best fit MD simulation (CHARMM) 310

320

F

330

340

350

Energy of scattered He+ (keV) Figure 53.19 HR-RBS spectrum of [C2 C1 Im][BF4 ] observed at an exit angle 𝜃 e = 3∘ with respect to the surface plane. The incident energy of He ions was 400 keV and the scattering angle was 50∘ . The dashed line shows the calculated spectrum

for a uniform and stoichiometric composition. The solid line shows the best fit result of the spectrum calculation. (Source: Reproduced with permission of Nakajima et al. 2016 [80]. Copyright 2016, AIP Publishing LLC.)

that that boron leading edge is seen at ∼308 keV, but it is not clear because of its small cross section. Figure 53.20 shows the composition depth profiles derived from the observed spectrum (histograms). The profiles slightly deviate from the uniform stoichiometric composition in the surface region. By integrating these profiles in the first molecular layer, the surface composition was estimated to be F4.1 N2.0 C6.0 B1.0 H10.9 . In this estimation, the thickness of the first molecular layer (0.722 nm) was calculated using the length of [C2 C1 Im] cation and its tilt angle (50∘ ) [32]. The good agreement with the stoichiometric composition (F4 N2 C6 B1 H11 ) shows that there is no surface enrichment of either cation or anion. For comparison, composition depth profiles derived by the MD simulation including the effect of capillary waves are shown by dashed lines. Similar to [C2 C1 Im][Tf2 N], the MD simulation reproduces the HR-RBS result very well. Figure 53.21 shows the HR-RBS spectrum of the equimolar mixture of [C2 C1 Im][BF4 ] and [C2 C1 Im][Tf2 N] together with the spectrum calculated with the uniform stoichiometric composition (short dashed line). There are large discrepancies near the leading edges of constituent elements, especially sulfur and fluorine. The elemental depth profiles derived from the observed spectrum are shown by histograms in Figure 53.22. The composition of the first molecular layer was estimated to be S1.4 F5.0 O2.6 N2.6 C7.1 B0.2 H10.1 by integrating the profiles in the first molecular layer (from 0 to 0.6243 atoms/cm2 , which is the average thickness of the first molecular layer of [C2 C1 Im][BF4 ] and [C2 C1 Im][Tf2 N]). Differently from the pure ionic liquids, the obtained surface composition significantly deviates from the stoichiometric composition S1 F5 O2 N2.5 C7 B0.5 H11 . Sulfur

375

53 Surfaces of Ionic Liquids

Depth (nm) 1

0

2

[C2C1Im][BF4] Experimental (histogram) MD simulation C

Concentration (at.%)

30

20

F

10

N B

0

0

1 Depth (1016 atoms/cm2)

Figure 53.20 Composition depth profiles for [C2 C1 Im][BF4 ] derived from the observed HR-RBS spectrum through spectrum modeling (histograms). The depth scale shown in the upper abscissa is calculated using a bulk

400 keV He+

15 000

B

10 000

2

density. The dashed lines show the result of the MD simulation including the effect of capillary waves. (Source: Reproduced with permission of Nakajima et al. 2016 [80]. Copyright 2016, AIP Publishing LLC.)

[C2C1Im][Tf2N]0.5[BF4]0.5 Experimental Stoichiometric Estimation 1 Estimation 2

C

N

Counts

376

O F 5 000

Depth for S (nm) 3 2 1 0

S 355

0 300

360

Energy (keV)

S

365

320 340 Energy of scattered He+ (keV)

Figure 53.21 HR-RBS spectrum of [C2 C1 Im][Tf2 N]0.5 [BF4 ]0.5 observed at an exit angle 𝜃 e = 3∘ with respect to the surface plane. The incident energy of He ions was 400 keV and the scattering angle was 50∘ . The short dashed line shows the spectrum calculated with a uniform stoichiometric composition. The long dashed line shows the spectrum calculated with the first molecular layer

360

composition of [C2 C1 Im][Tf2 N]0.72 [BF4 ]0.28 . The solid line shows the spectrum calculated with the first molecular layer composition of [C2 C1 Im][Tf2 N]0.72 [BF4 ]0.28 and the second molecular layer composition of [C2 C1 Im][Tf2 N]0.55 [BF4 ]0.45 . (Source: Reproduced with permission of Nakajima et al. 2016 [80]. Copyright 2016, AIP Publishing LLC.)

53.5 Surface Structures of Binary Mixtures of Ionic Liquids

Depth (nm) 30

0

1

2

[C2C1Im][TFSI]0.5[BF4]0.5

Concentration (at.%)

C Experimental (histogram) MD simulation

20

F N ×1.4 10

O S B 0

0

1

2

Depth (1016 atoms/cm2) Figure 53.22 Composition depth profiles for [C2 C1 Im][Tf2 N]0.5 [BF4 ]0.5 derived from the observed HR-RBS spectrum through spectrum modeling (histograms). The depth scale shown in the upper abscissa is calculated using the average density. The dashed lines

show the result of the MD simulation including the effect of capillary waves. (Source: Reproduced with permission of Nakajima et al. 2016 [80]. Copyright 2016, AIP Publishing LLC.)

and oxygen concentrations are enhanced by 35–40%. Because these elements are representatives of [Tf2 N], this indicates strong surface enrichment of [Tf2 N] over [BF4 ]. If the first molecular layer of the mixture has a composition [C2 C1 Im][Tf2 N]x [BF4 ]1−x , the surface concentration (concentration in the first molecular layer), Cαmix (x), of element 𝛼 can be estimated from the observed surface concentrations C i,𝛼 of the pure ionic liquids [80], Cαmix (x) =

C1,α N1 x + C2,α N2 (1 − x) N1 x + N2 (1 − x)

,

(53.7)

where N i is the number of atoms in one ion pair of the ith ionic liquid (i = 1 and 2 denote [C2 C1 Im][Tf2 N] and [C2 C1 Im][BF4 ], respectively). The surface mole fractions, x1 , of [C2 C1 Im][Tf2 N] can be determined so that Eq. (53.7) reproduces the observed surface concentrations, Cαobs , of the mixture. Figure 53.23 shows the squared relative deviation from the observed surface concentration (53.8) Δ2α (x) = {(Cαmix (x) − Cαobs )∕Cαobs }2 , ∑ together with the summation α=S,F,O,N,C Δ2α (x). The summation has a minimum at x = 0.72. Accordingly, the surface mole fraction is determined to be ∑ x1 = 0.72 ± 0.05, where the error 𝛿x1 was estimated so that α=S,F,O,N,C Δ2α (x1 ± ∑ 2 𝛿x1 ) = 2 α=S,F,O,N,C Δα (x1 ). In this procedure, boron and hydrogen were excluded because hydrogen cannot be measured by RBS and the accuracy of boron concentration is poor because of its small scattering cross section.

377

53 Surfaces of Ionic Liquids

0.1

First layer 2 Σ Δα(x) Δ2S(x) Δ2F(x) Δ2O(x)

Δ2α (x)

378

Δ2N(x)

0.05

0

x = 0.72

Δ2C(x)

0

0.5 x: [C2C1Im][Tf2N]x[BF4]1–x

Figure 53.23 The squared relative deviation of the estimated surface composition from the observed result, Δ2α (x) = {(Cαmix (x) − Cαobs )∕Cαobs }2 for sulfur, fluorine, oxygen, nitrogen, and carbon, where Cαobs is the observed composition of element 𝛼 in the first molecular layer and Cαmix (x) is the

1

estimated composition when the mole fraction of [C2 C1 Im][Tf2 N] is x. The summation ∑ 2 Δα (x) is also shown by a solid line. The best fit is obtained at x = 0.72. (Source: Reproduced with permission of Nakajima et al. 2016 [80]. Copyright 2016, AIP Publishing LLC.)

The mole fraction of the second molecular layer was also estimated using the same procedure. The obtained mole fraction was x2 = 0.55 ± 0.05. Using these mole fractions (x1 = 0.72 and x2 = 0.55) and mole fractions equal to 0.5 for other layers, the elemental depth profiles of the mixture were estimated by the weighted average of the measured profiles of the pure ionic liquids. The energy spectrum was calculated using the estimated elemental depth profiles and the result is shown by a solid line in Figure 53.21. The agreement with the observed spectrum is very well. It is worth noting that that if x2 = 0.5 is used instead of x2 = 0.55, the agreement is relatively poor (see long dashed line in Figure 53.21). This confirms that [Tf2 N] is enriched not only in the first molecular layer but also in the second molecular layer. The MD simulation for the [C2 C1 Im][Tf2 N]0.5 [BF4 ]0.5 mixture was performed using 512 ion pairs (512 [C2 C1 Im] cations, 256 [Tf2 N] anions, and 256 [BF4 ] anions) in a simulation box of 5.62 × 5.62 × 14 nm3 [80]. The composition depth profiles derived by the MD simulation are shown by dashed lines and compared with the HR-RBS result in Figure 53.22. The MD simulations qualitatively reproduce the HR-RBS result. The agreement is reasonably good, especially for fluorine and carbon profiles. For other elements (sulfur, oxygen, and nitrogen), however, the agreement is less satisfactory. The simulation profiles of these elements have a peak at 0.3 × 1016 atoms/cm2 , indicating the surface enrichment of [Tf2 N] in agreement with the result of HR-RBS. However, these peaks are smaller compared to the peaks observed by HR-RBS. From the result of MD simulations, the surface mole fraction of [C2 C1 Im][Tf2 N] was estimated to be x1 = 0.59, which is much smaller than

53.5 Surface Structures of Binary Mixtures of Ionic Liquids

the HR-RBS result (x1 = 0.72). Such a large discrepancy is somewhat surprising because the MD simulations reproduce the HR-RBS results of the pure ionic liquids very well (see Figures 53.9 and 53.20). This suggests that a refinement of the force field describing the interactions between [Tf2 N] and [BF4 ] is required in addition to a refinement of the partial charges to better mimic the anions to cation charge transfer and many-body effects. It is interesting to see if the surface tension difference between [C2 C1 Im][Tf2 N] and [C2 C1 Im][BF4 ] can explain the observed surface segregation of [C2 C1 Im][Tf2 N]. According to the classical thermodynamics, the surface tension of a mixture of liquids can be given by the Sprow–Prausnitz equation [81] ) ( xi,s 𝛾i,s A RT 𝜎 = i 𝜎i + , (53.9) ln xi,b 𝛾i,b Ai Ai where 𝜎 and 𝜎 i are the surface tensions for the mixture and the component i, Ai is the molar surface area of component i, Ai is the partial molar surface area of component i in the mixture, R and T are the universal gas constant and the temperature, respectively, xi,b and xi,s are the mole fractions in the bulk and surface, respectively, and 𝛾 i,b and 𝛾 i,s are the corresponding activity coefficients. Assuming that the binary mixture is an ideal solution, i.e. 𝛾 i, s = 1, 𝛾 i, b = 1, and Ai = Ai , the surface mole fraction x1,s of component 1 can be calculated using the following equation: ( ( )) (1 − x1,b )n (1 − x1,s )n A1 𝜎1 − 𝜎2 = exp , (53.10) x1,s x1,b RT where n = A1 /A2 . The present assumption of ideal solution may be justified by the recent study, which demonstrated that bulk properties of ionic liquid mixtures adhere closely to ideal mixing laws [82]. Using Eq. (53.10), the surface mole fraction of [C2 C1 Im][Tf2 N] is estimated to be 0.86. In this estimation, the molar surface area was calculated by Ai = N A N i /di , where N A is the Avogadro number and di is the areal atomic density of the first molecular layer. If a simple relation, i.e. Ai = N A ni −2/3 , is used, the surface mole fraction is estimated to be 0.84. The difference may be considered as the error of the estimation, i.e. x1,s = 0.86 ± 0.02. The estimated mole fraction is slightly larger but roughly in agreement with the observed fraction x1 = 0.72 ± 0.05. 53.5.2 Surface Structure of [C4 C1 Im]0.5 [C12 C1 Im]0.5 [Tf2 N]: An Example of Mixtures with Common Anions

In this section, an example of the ionic liquid mixtures with common anions is presented [71]. The chosen mixture is [C4 C1 Im]0.5 [C12 C1 Im]0.5 [Tf2 N]. Figure 53.24 shows the observed HR-RBS spectra of [C4 C1 Im][Tf2 N], [C12 C1 Im][Tf2 N], and [C4 C1 Im]0.5 [C12 C1 Im]0.5 [Tf2 N]. From these spectra, composition depth profiles were derived for the mixture and the pure ionic liquids as well. Using the derived composition depth profiles, the surface mole fraction of [C12 C1 Im][Tf2 N] was determined to be xs = 0.63 ± 0.03 by the same procedure explained in the Section 5.1.

379

53 Surfaces of Ionic Liquids

20 000

Best fit Stoichiometric 15 000

Counts

380

C

10 000

N 5 000

0

O

[C4C1Im][Tf2N] [C12C1Im][Tf2N] [C4C1Im]0.5[C12C1Im]0.5[Tf2N] 300

F S

320 340 Energy of scattered He+ (keV)

Figure 53.24 HR-RBS spectra of [C4 C1 Im][Tf2 N], [C12 C1 Im][Tf2 N], and [C4 C1 Im]0.5 [C12 C1 Im]0.5 [Tf2 N] observed at a scattering angle of 50∘ . The dashed lines show the calculated spectra for uniform

360

stoichiometric compositions. The solid lines show the best fit results. (Source: Reproduced with permission of Nakajima et al. 2016 [80]. Copyright 2016, AIP Publishing LLC.)

This result agrees with the surface mole fraction xs = 0.60 ± 0.04 calculated using Eq. (53.10). With the obtained surface mole fraction, xs = 0.63, for the topmost molecular layer and mole fractions equal to 0.5 for deeper layers, the expected HR-RBS spectrum was calculated using the weighted average of the composition depth profiles of pure ionic liquids. The result is shown by a solid line and compared with the observed spectrum (dots) in Figure 53.25. A very good agreement indicates that the derived surface composition, xs = 0.63, is reliable. It should be noted that the shown HR-RBS spectrum was calculated assuming that the two ionic liquids are randomly mixed with xs = 0.63 in the topmost molecular layer. If the surface has a domain structure, namely the surface is divided into the [C12 C1 Im][Tf2 N] and [C4 C1 Im][Tf2 N] domains, the expected HR-RBS spectrum is slightly different from the random mixture as shown by a dashed line in Figure 53.25. It is clear that the agreement with the observed spectrum is worse in the case of domain structure, indicating that there is no domain structure at the surface. The above two examples, [C2 C1 Im][Tf2 N]0.5 [BF4 ]0.5 and [C4 C1 Im]0.5 [C12 C1 Im]0.5 [Tf2 N], showed that the composition depth profile of the mixture can be approximated by the weighted average of the profiles of pure ionic liquids. However, this is not always the case. As an example of such exceptions, the surface structure of [C6 C1 Im][Tf2 N]0.5 Cl0.5 is discussed below. Figure 53.26 shows the observed HR-RBS spectrum of [C6 C1 Im] [Tf2 N]0.5 Cl0.5 . Because the chlorine mass is very close to sulfur, separation of the chlorine signal from the sulfur signal is difficult. This makes accurate analysis of these elements difficult. Accordingly, the surface mole fraction x ([C6 C1 Im][Tf2 N]x Cl1−x ) was

53.5 Surface Structures of Binary Mixtures of Ionic Liquids

20 000

[C4C1Im]0.5[C12C1Im]0.5[Tf2N] Estimation (xs = 0.63) Domain structure (xs = 0.63)

Counts

15 000

C C

N

10 000

S O F

O 5 000

F 0

300

S

320 340 Energy of scattered He+ (keV)

Figure 53.25 The comparison between the observed HR-RBS spectrum (dots) and the spectrum calculated using the estimated composition depth profiles (solid line) for [C4 C1 Im]0.5 [C12 C1 Im]0.5 [Tf2 N] (see text). The HR-RBS spectrum calculated for the domain

360

structure is also shown by a dashed line for comparison. Parts of the spectrum are magnified for clarity. (Source: Reproduced with permission of Nakajima et al. 2017 [71]. Copyright 2017, Elsevier.)

estimated using Eq. (53.8) but without these elements. Using the obtained surface mole fraction of [C6 C1 Im] [Tf2 N] (xs = 0.58 ± 0.04), the expected HR-RBS spectrum was calculated. The result is shown by a solid line and compared with the observed spectrum in Figure 53.26. The overall agreement is rather good, but there are small Stoichiometric Estimation

[C6C1Im][Tf2N]0.5Cl0.5 10 000

C Counts

F N O

5 000

S

F

S Cl 0

300

Cl

320 340 Energy of scattered He+ (keV)

Figure 53.26 Observed HR-RBS spectra of [C6 C1 Im][Tf2 N]0.5 Cl0.5 . The dashed line shows the calculated spectra for a uniform stoichiometric composition. The solid line shows the spectrum calculated using the estimated

360

surface mole fraction of [C6 C1 Im][Tf2 N], x s = 0.58. Parts of the spectrum are magnified for clarity. (Source: Reproduced with permission of Nakajima et al. 2017 [71]. Copyright 2017, Elsevier.)

381

382

53 Surfaces of Ionic Liquids

deviations near the chlorine and fluorine edges. The observed chlorine edge is shifted to lower energies compared to the calculated one. The observed fluorine surface peak is more pronounced compared to the calculated one. Note that the observed surface fluorine peak is also more pronounced compared to the pure [C6 C1 Im][Tf2 N] (see Figure 53.12). These results indicate that chorine ions move toward the bulk compared to the pure [C6 C1 Im]Cl and fluorine atoms (and so [Tf2 N] anions) move toward the surface compared to the pure [C6 C1 Im][Tf2 N]. Similar shifts of larger (smaller) anions to the surface (bulk) in the mixture were also found in the mixtures [C4 C1 Im][PF6 ]0.5 Cl0.5 and [C4 C1 Im][TfO]0.5 Cl0.5 using the MD simulation [83]. The MD simulation showed that [PF6 ] and [TfO] anions are located at slightly shallower positions than the imidazolium rings, whereas Cl anions are located at deeper positions in the topmost molecular layer. 53.5.3 Systematic Study on the Surface Structures of Binary Mixtures of Ionic Liquids

All three mixtures discussed above show that the larger cation or anion is enriched in the first molecular layer [71]. A systematic study was performed to see if this behavior is universal or not [71]. The chosen ionic liquids are 1-alkyl-3methylimidazolium-based ionic liquids (from [C2 C1 Im] to [C12 C1 Im]) with anions ranging from small anions (Cl and [BF4 ]), medium-sized anions ([TfO] and [PF6 ]), to a large anion ([Tf2 N]), which are listed in Table 53.1. The surface tensions of these ionic liquids found in the literature [72–77] are also shown. Eleven equimolar mixtures of these ionic liquids (Table 53.2) were observed using HR-RBS. The surface mole fractions of constituent ionic liquids were derived from the observed results using the same procedure explained in the Sections 5.1 and 5.2. The derived surface mole fractions are summarized in Table 53.2. All results showed that the larger ionic liquid (the ionic liquid having a larger cation or anion) is enriched at the surface. Table 53.2 also shows the surface mole fractions calculated using Eq. (53.10). Figure 53.27 shows the relation between the measured and calculated surface mole fractions. The abscissa shows the calculated mole fraction of larger ionic liquids and the ordinate shows the measured one. The simple calculation based on the assumption of ideal solution (i.e. Eq. (53.10)) reproduces the measured results reasonably well. The agreement is especially good for the mixtures with common anions (shown by open circles). For the mixtures of common cations (shown by closed circles), however, the ideal solution assumption overestimates the surface enrichment of a larger ionic liquid. Figure 53.28 shows the ratio of the calculated to measured surface mole fractions, xcal ∕xmeas , as a function of the ratio of molar surface areas, A1 /A2 , 1,s 1,s where the subscript “1” denotes the larger ionic liquid. There is a positive correlation between xcal ∕xmeas and A1 /A2 . The ratio xcal ∕xmeas is almost unity when A1 /A2 is 1,s 1,s 1,s 1,s small and increases with increasing A1 /A2 . This means that Eq. (53.10) overestimates the surface mole fraction of the larger ionic liquid when A1 /A2 is large. In the derivation of Eq. (53.10), the partial molar surface area of the mixture is assumed to be equal to the molar surface area of the pure ionic liquid (i.e. Ai = Ai ). In the actual mixture, such an assumption does not hold and the ratio A1 ∕A2 would

Table 53.2 Observed and calculated surface mole fraction of larger ionic liquids. Mixture

Observed surface mole

Calculated surface mole

Surface tensions used

fraction of larger ionic liquids

fraction of larger ionic liquids

in the calculation

[C2 C1 Im]0.5 [C6 C1 Im]0.5 [Tf2 N]

0.66 ± 0.07

0.65 ± 0.01

[C2 C1 Im]0.5 [C8 C1 Im]0.5 [Tf2 N]

0.67 ± 0.08

0.66 ± 0.02

[C2 C1 Im]0.5 [C10 C1 Im]0.5 [Tf2 N]

0.67 ± 0.07

0.66 ± 0.02

[C2 C1 Im]0.5 [C12 C1 Im]0.5 [Tf2 N]

0.66 ± 0.06

0.70 ± 0.04

[C4 C1 Im]0.5 [C12 C1 Im]0.5 [Tf2 N]

0.63 ± 0.03

0.60 ± 0.04

[C2 C1 Im][Tf2 N]0.5 [TfO]0.5 [C2 C1 Im][Tf2 N]0.5 [BF4 ]0.5 [C4 C1 Im][Tf2 N]0.5 [PF6 ]0.5 [C6 C1 Im][PF6 ]0.5 [Cl]0.5 [C6 C1 Im][Tf2 N]0.5 [Cl]0.5 [C6 C1 Im][Tf2 N]0.5 [PF6 ]0.5

0.57 ± 0.33 0.72 ± 0.05 0.61 ± 0.07 0.53 ± 0.05 0.58 ± 0.04 0.71 ± 0.05

0.58 ± 0.02 0.86 ± 0.02 0.73 ± 0.04 0.47 ± 0.02 0.72 ± 0.07 0.68 ± 0.04

36.43 [72], 31.76 [72] 36.43 [72], 31.30 [72] 36.43 [72], 31.34 [72] 36.43 [72], 29.8 [72] 33.09 [72], 29.8 [76] 41.6 [73], 44.4 [73] 35.1 [74], 53.9 [76] 33.6 [75], 42.8 [75] 43.0 [77], 41.5 [77] 31.0 [75], 41.5 [77] 31.0 [75], 38.25 [75]

The surface tensions used in the calculation are shown. (Source: Reproduced with permission of Nakajima et al. 2017 [71]. Copyright 2017, Elsevier.)

Measured surface mole fraction of larger IL

53 Surfaces of Ionic Liquids

1 Common anion Common cation [C2C1Im]0.5[C8C1Im]0.5[Tf2N]

0.8

[C2C1Im]0.5[C10C1Im]0.5[Tf2N]

[C6C1Im][Tf2N]0.5[PF6]0.5 [C C Im][Tf N] [BF ] 2 1 2 0.5 4 0.5

[C2C1Im]0.5[C6C1Im]0.5[Tf2N]

[C2C1Im]0.5[C12C1Im]0.5[Tf2N] [C4C1Im][Tf2N]0.5[PF6]0.5

[C4C1Im]0.5[C12C1Im]0.5[Tf2N]

0.6 [C6C1Im][Tf2N]0.5Cl0.5 [C2C1Im][Tf2N]0.5[TfO]0.5 [C6C1Im][PF6]0.5Cl0.5

0.4 0.4

0.6 0.8 Calculated surface mole fraction of larger ionic liquid

cal

[C2C1Im]0.5[C12C1Im]0.5[Tf2N]

[C4C1Im]0.5[C12C1Im]0.5[Tf2N]

meas

1.2

[C2C1Im]0.5[C8C1Im]0.5[Tf2N]

[C2C1Im]0.5[C6C1Im]0.5[Tf2N]

1.4

[C2C1Im]0.5[C10C1Im]0.5[Tf2N]

Figure 53.27 Comparison between the measured and calculated surface mole fractions of the larger ionic liquid for the mixtures studied. The open (closed) circles show

x1, S/x1, S

384

1

the results of mixtures having a common anion (cation). (Source: Reproduced with permission of Nakajima et al. 2017 [71]. Copyright 2017, Elsevier.)

[C6C1Im][Tf2N]0.5Cl0.5 [C4C1Im][Tf2N]0.5[PF6]0.5

[C2C1Im][Tf2N]0.5[BF4]0.5

1 [C2C1Im][Tf2N]0.5[TfO]0.5 [C6C1Im][Tf2N]0.5[PF6]0.5 [C6C1Im][PF6]0.5Cl0.5

0.8 1

Common anion Common cation

1.2 1.4 1.6 A1/A2: Ratio of molar surface area

Figure 53.28 Ratio of the calculated to the measured surface mole fraction of the larger ionic liquid as a function of the ratio of molar surface area. The open (closed) circles show the results of mixtures having a

1.8

common anion (cation). The dashed line is drawn to guide the eye. (Source: Reproduced with permission of Nakajima et al. 2017 [71]. Copyright 2017, Elsevier.)

be smaller than A1 /A2 because of the interaction between the two ionic liquids [71]. In this case, Eq. (53.10) is modified: ( ( )) (1 − x1,b )𝛼n (1 − x1,s )𝛼n A1 𝜎1 − 𝛼𝜎2 = exp , (53.11) x1,s x1,b RT

53.5 Surface Structures of Binary Mixtures of Ionic Liquids

where 𝛼 = (A1 A2 )∕(A2 A1 ). Considering that 𝛼 = (A1 A2 )∕(A2 A1 ) is smaller than unity (i.e. A1 ∕A2 < A1 ∕A2 ), Eq. (53.11) gives smaller x1, s compared to Eq. (53.10). In the case of [C6 C1 Im][Tf2 N]0.5 [Cl]0.5 , for example, Eq. (53.11), with 𝛼 = 0.87, reproduces the observed result xs = 0.58. The observed results for [C2 C1 Im][Tf2 N]0.5 [BF4 ]0.5 and [C4 C1 Im][Tf2 N]0.5 [PF6 ]0.5 can also be reproduced with 𝛼 = 0.8 and 0.88, respectively. For the mixtures of common anions, the molar surface areas of constituent ionic liquids are similar in size, i.e. A1 ∕A2 ≈ A1 ∕A2 ≈ 1 (see Table 53.1). This is the reason why Eq. (53.10) reproduces well the observed results of the mixtures with common anions. 53.5.4 Comparison with Other Techniques

The HR-RBS measurements show moderate surface enrichment of larger ionic liquids for the mixtures studied. On the other hand, LEIS and ToF-SIMS showed very strong surface enrichment [38, 45]. For example, ToF-SIMS showed strong surface enrichment of [C4 C1 Im][Tf2 N] for [C4 C1 Im][Tf2 N]x [PF6 ]1−x [45]. The present HR-RBS measurement, however, shows only slight surface enrichment. The LEIS measurements also demonstrated that the surface of [C4 C1 Im][Tf2 N]x Cl1−x is predominantly occupied by [C4 C1 Im][Tf2 N] for x ≥ 0.3 [38], whereas the present HR-RBS measurement does not show such strong surface enrichment in the similar mixture ([C6 C1 Im][Tf2 N]0.5 Cl0.5 ). These discrepancies are attributed to the difference in the probing depth between different techniques. Both ToF-SIMS and LEIS exclusively analyze the topmost atomic layer. As was discussed above, in the case of [C6 C1 Im][Tf2 N]0.5 Cl0.5 mixture, [Tf2 N] anions (fluorine atoms) are shifted to the surface and Cl anions are shifted to the bulk. Because LEIS is very surface sensitive, these shifts may result in larger enhancement of the fluorine signal and larger reduction of the chlorine signal compared to the actual composition of the topmost molecular layer. The same thing happens for ToF-SIMS because of its excellent surface sensitivity. There is also discrepancy between the HR-RBS and XPS measurements. Figure 53.29 shows the XPS spectra of C 1s observed at emission angles 0∘ and 80∘ for [C2 C1 Im]0.9 [C12 C1 Im]0.1 [Tf2 N] and pure ionic liquids as well [84]. The peaks seen at 285 and 286.5 eV correspond to Calkyl and Chetero , respectively. For the 80∘ measurement with increasing surface sensitivity, the Calkyl intensities are enhanced compared to the 0∘ measurement. The degree of enhancement for the mixture is similar to that for [C4 C1 Im][Tf2 N], but much smaller than that for [C12 C1 Im][Tf2 N]. From this observation, no preferential enrichment of [C12 C1 Im][Tf2 N] as compared to [C2 C1 Im][Tf2 N] at the surface was concluded [84]. This seems in contradiction with the HR-RBS results, which showed weak but notable surface enrichment of [C12 C1 Im] (xs = 0.66 ± 0.06) for [C2 C1 Im]0.5 [C12 C1 Im]0.5 [Tf2 N]. If the XPS results are carefully examined, however, weak surface enrichment of [C12 C1 Im][Tf2 N] can be concluded as shown below. At 𝜃 = 80∘ , the observed Calkyl intensity of the mixture is approximately equal to that of [C4 C1 Im][Tf2 N] (see Figure 53.29b), namely the number of Calkyl seen at 𝜃 = 80∘ is 3. The number of Calkyl seen at 𝜃 is

385

53 Surfaces of Ionic Liquids

0° Emission

300

(a)

[C2C1Im][Tf2N] 9 : 1 mixture [C4C1Im][Tf2N] [C12C1Im][Tf2N]

Intensity

386

0 80° Emission

300

(b)

[C2C1Im][Tf2N] 9 : 1 mixture [C4C1Im][Tf2N] [C12C1Im][Tf2N]

0 297

294

291

288

285

282

Binding energy (eV)

Figure 53.29 XPS spectra of C 1s region for [C2 C1 Im]0.9 [C12 C1 Im]0.1 [Tf2 N], [C2 C1 Im][Tf2 N], [C4 C1 Im][Tf2 N], and [C12 C1 Im][Tf2 N] observed at emission angle 0∘ (a) and 80∘ (b). (Source: Reproduced with permission of Maier et al. 2010[84]. Copyright 2010, Royal Society of Chemistry.)

given by [ ( N(𝜃) = Ns 1 − exp −

t 𝜆 cos 𝜃

)]

( + Nb exp −

) t , 𝜆 cos 𝜃

(53.12)

where N s and N b are the numbers of Calkyl atoms per one ion pair in the surface and the bulk, respectively, 𝜆 is the inelastic mean free path of photoelectrons (typically 3 nm for organic materials [66]), and t is the thickness of one molecular layer. Using Eq. (53.12) with 𝜆 = 3 nm, t = 1.65 nm, N b = 2 and the abovementioned result N(80∘ ) = 3, N s is estimated to be 3.0. This means that the surface composition is [C2 C1 Im]0.8 [C12 C1 Im]0.2 [Tf2 N], i.e. there is weak surface enrichment of [C12 C1 Im][Tf2 N]. This surface composition is also in good agreement with the surface mole fraction calculated using Eq. (53.10), i.e. xs = 0.19.

53.6 Conclusion

In this contribution, the results of HR-RBS studies on the surface structures of typical ionic liquids, i.e. imidazolium-based ionic liquids, and their binary mixtures are reviewed. Not only the surface composition but also the orientation of molecules can be derived using HR-RBS. For the pure ionic liquids studied so far, the HR-RBS measurements show a universal surface structure: (i) there is no surface enrichment

References

of either cation or anion, (ii) the polar parts of cations and anions are located at almost the same depth in the subsurface region, and (iii) nonpolar parts of cations and anions are pointing toward the vacuum. These results are in good agreement with the observations by other surface analysis techniques and also with the results of MD simulations. For the mixtures of ionic liquids, there is a general tendency that the surface is slightly enriched by larger ionic liquids. The observed surface enrichment can be explained rather well by the simple thermodynamic calculation based on the Sprow–Prausnitz equation with the assumption of ideal solution, although the deviation from the simple calculation becomes larger when the ratio of the molar surface areas of the component ionic liquids becomes larger. This deviation can be qualitatively explained by the nonideality of the mixture. There are apparent discrepancies in the observed surface enrichment between HR-RBS and some other surface analysis techniques, namely ToF-SIMS, LEIS, and XPS. These discrepancies are attributed to the difference in the probing depth between different techniques. Considering such a difference, more detailed information of the surface structure can be obtained by combination analysis with other complemental techniques.

References 1. Wasserscheid, P. and Welton, T. (2008).

2.

3.

4.

5. 6.

7.

8. 9.

10.

Ionic Liquids in Synthesis, 2e. Weinheim: Wiley-VCH. Wilkes, J.S., Levisky, J.A., Wilson, R.A., and Hussey, C.L. (1982). Inorg. Chem. 21: 1263. Tokuda, H., Tsuzuki, S., Susan, M.A.B.H. et al. (2006). J. Phys. Chem. B 110: 19593. Chiappe, C. (2007). Ionic liquids, Chapter 5. In: Synthesis (eds. P. Wasserscheid and T. Welton). Weinheim: Wiley-VCH. Dupont, J. and Scholten, J.D. (2010). Chem. Soc. Rev. 39: 1780. Wender, H., Goncalves, R.V., Feil, A.F. et al. (2011). J. Phys. Chem. C 115: 16362. Torimoto, T., Tsuda, T., Okazaki, K., and Kuwabata, S. (2010). Adv. Mater. 22: 1196. Kuwabata, S., Tsuda, T., and Torimoto, T. (2010). J. Phys. Chem. Lett. 1: 3177. Riisager, A., Fehrmann, R., Haumann, M., and Wasserscheid, P. (2006). Top. Catal. 40: 91. Riisager, A., Fehrmann, R., Haumann, M., and Wasserscheid, P. (2006). Eur. J. Inorg. Chem. 695.

11. Mehnert, C.P., Mozeleski, E.J., and Cook,

R.A. (2002). Chem. Commun. 3010. 12. Lin, Y.-F. and Sun, I.W. (2000). Elec-

trochim. Acta 45: 3163. 13. Endres, F. (2002). Chem. Phys. Chem. 3:

144. 14. Freyland, W., Zell, C.A., Abedin, S. et al.

(2003). Electrochim. Acta 48: 3053. 15. Garcia, B., Lavallé, S., Perron, G. et al.

(2004). Electrochim. Acta 49: 4583. 16. Matsumoto, H., Sakaebe, H., and

17. 18. 19. 20.

21. 22. 23.

Tatsumi, K. (2005). J. Power Sources 45: 45. Ue, M., Takeda, M., Toriumi, A. et al. (2003). J. Electrochem. Soc. 150: A499. Kim, Y., Matsuzawa, Y., Ozaki, S. et al. (2005). J. Electrochem. Soc. 152: A710. Noda, A., Susan, A., Kudo, K. et al. (2003). J. Phys. Chem. B 107: 4024. Hagiwara, R., Nohira, T., Matsumoto, K., and Tamba, Y. (2005). Electrochem. Solid-State Lett. 8: A231. Heintz, A. (2005). J. Chem. Thermodyn. 37: 525. Han, X. and Armstrong, D.W. (2007). Acc. Chem. Res. 40: 1079. Poole, C.F. (2004). J. Chromatogr. A 1037: 49.

387

388

53 Surfaces of Ionic Liquids 24. Poole, C.F. and Poole, S.K. (2011). J. Sep. 25. 26.

27.

28. 29. 30. 31. 32. 33. 34. 35.

36.

37. 38. 39. 40. 41. 42. 43. 44. 45. 46. 47.

48.

Sci. 34: 888. Lovelock, K.R.J. (2012). Phys. Chem. Chem. Phys. 14: 5071. Steinrück, H.-P. (2012). Phys. Chem. Chem. Phys. 14: 5010. and the references therein. Sloutskin, E., Ocko, B.M., Tamam, L. et al. (2005). J. Am. Chem. Soc. 127: 7796. Bowers, J. and Vergara-Gutierrez, M.C. (2004). Langmuir 20: 309. Baldelli, S. (2003). J. Phys. Chem. B 107: 6148. Rivera-Rubero, S. and Baldelli, S. (2004). J. Am. Chem. Soc. 126: 11788. imori, T., Iwahashi, T., Ishii, H. et al. (2004). Chem. Phys. Lett. 389: 321. Iimori, T., Iwahashi, T., Kanai, K. et al. (2007). J. Phys. Chem. B 111: 4860. Santos, C.S. and Baldelli, S. (2007). J. Phys. Chem. B 117: 4715. Gannon, T.J., Law, G., and Watson, P.R. (1999). Langmuir 15: 8429. Law, G., Watson, P.R., Carmichael, A.J., and Seddon, K.R. (2001). Phys. Chem. Chem. Phys. 3: 2879. Caporali, S., Bardi, U., and Lavacchi, A. (2006). J. Electron. Spectrosc. Relat. Phenom. 151: 4. Kauling, A., Ebeling, G., Morais, J. et al. (2013). Langmuir 29: 14301. Villar-Garcia, I.J., Fearn, S., Ismail, N.L. et al. (2015). Chem. Commun. 51: 5367. Villar-Garcia, I.J., Fearn, S., De Gregorio, G.F. et al. (2014). Chem. Sci. 5: 4404. Andersson, G. and Ridings, C. (2014). Chem. Rev. 114: 8361. Nakajima, K., Ohno, A., Suzuki, M., and Kimura, K. (2008). Langmuir 24: 4482. Nakajima, K., Zolboo, E., Ohashi, T. et al. (2016). Anal. Sci. 32: 1089. Smith, E.F., Rutten, F.J.M., Villar-Garcia, I.J. et al. (2006). Langmuir 22: 9386. Günster, J., Höfft, O., Krischok, S., and Souda, R. (2008). Surf. Sci. 602: 3403. Souda, R. (2010). Surf. Sci. 604: 1694. Höfft, O., Bahr, S., Himmerlich, M. et al. (2006). Langmuir 22: 7120. Krischok, S., Eremtchenko, M., Himmerlich, M. et al. (2007). J. Phys. Chem. B 111: 4801. Cumpson, P.J. (1995). J. Electron. Spectrosc. Relat. Phenom. 73: 25.

49. Kimura, K., Nakajima, K., Conard, T.,

50. 51.

52. 53. 54.

55.

56. 57.

58.

59.

60. 61. 62.

63. 64. 65. 66. 67. 68.

and Vandervorst, W. (2007). Appl. Phys. Lett. 91: 104106. Lynden-Bell, R.M. (2003). Mol. Phys. 101: 2625. Pensado, A.S., Malfreyt, P., and Padua, A.A.H. (2009). J. Phys. Chem. B 113: 14708. Lynden-Bell, R.M. and Del Popolo, M.G. (2006). Phys. Chem. Chem. Phys. 8: 949. Lísal, M., Posel, Z., and Izák, P. (2012). Phys. Chem. Chem. Phys. 14: 5164. Sloutkin, E., Lynden-Bell, R.M., Balasubramanian, S., and Deutsch, M. (2006). J. Chem. Phys. 125: 174715. Kimura, K., Joumori, S., Oota, Y. et al. (2004). Nucl. Instrum. Methods Phys. Res., Sect. B 219–220: 351. Kimura, K., Nakajima, K., Yamanaka, S. et al. (2001). Appl. Phys. Lett. 78: 1679. For example,Feldman, L.C. and Mayer, J.W. (1986). Fundamentals of Surface and Thin Film Analysis. Amsterdam: North-Holland. Ziegler, J.F. (1977). Helium stopping powers and ranges in all elemental matter. In: The Stopping and Ranges of Ions in Matter, vol. 4 (ed. J.F. Ziegler), 66–71. New York: Pergamon Press. Bohr, N. (1948). The penetration of atomic particles through matter. Kgl. Danske Videnskab. Selskab. MattFys.Medd. 18 (8). Kimura, K., Ohshima, K., and Mannami, M. (1994). Appl. Phys. Lett. 64: 2232. Lednovich, S.L. and Fenn, J.B. (1977). AlChE J. 23: 454. Nakajima, K., Ohno, A., Suzuki, M., and Kimura, K. (2009). Nucl. Instrum. Methods Phys. Res., Sect. B 267: 605. Fujii, K., Fujimori, T., Takamuku, T. et al. (2006). J. Phys. Chem. B 110: 8179. Nakajima, K., Ohno, A., Hashimoto, H. et al. (2010). J. Chem. Phys. 133: 044702. Ohno, A., Hashimoto, H., Nakajima, K. et al. (2009). J. Chem. Phys. 130: 204705. Lovelock, K.R.J., Kolbeck, C., Cremer, T. et al. (2009). J. Phys. Chem. B 113: 2854. Kolbeck, C., Cremer, T., Lovelock, K.R.J. et al. (2009). J. Phys. Chem. B 113: 8682. Hammer, T., Reichelt, M., and Morgner, H. (2010). Phys. Chem. Chem. Phys. 12: 11070.

References 69. Braslau, A., Pershan, P.S., Swislow, G. 70.

71.

72.

73. 74. 75. 76.

et al. (1988). Phys. Rev. A 38: 2457. Nakajima, K., Nakanishi, S., Lísal, M., and Kimura, K. (2016). J. Chem. Phys. 144: 114702. Nakajima, K., Nakanishi, S., Lísal, M., and Kimura, K. (2017). J. Mol. Liq. 230: 542. Carvalho, P.J., Freire, M.G., Marrucho, I.M. et al. (2008). J. Chem. Eng. Data 53: 1346. Kilaru, P., Baker, G.A., and Scovazzo, P. (2007). J. Chem. Eng. Data 52: 2306. Fröba, A.P., Kremer, H., and Leipertz, A. (2008). J. Phys. Chem. B 112: 12420. Osada, R., Hoshino, T., Okuda, K. et al. (2009). J. Chem. Phys. 130: 184705. Kolbeck, C., Lehmann, J., Lovelock, K.R.J. et al. (2010). J. Phys. Chem. B 114: 17025.

77. Ghatee, M.H. and Zolghadr, A.R. (2008).

Fluid Phase Equilib. 263: 168. 78. Nakajima, K., Oshima, S., Suzuki, M.,

79. 80. 81. 82. 83.

84.

and Kimura, K. (2012). Surf. Sci. 606: 1693. Jeon, Y., Sung, J., Bu, W. et al. (2008). J. Phys. Chem. C 112: 19649. Nakajima, K., Nakanishi, S., Chval, Z. et al. (2016). J. Chem. Phys. 145: 184704. Sprow, F.B. and Prausnitz, J. (1966). Trans. Faraday Soc. 62: 1105. Clough, M.T., Crick, C.R., Gräsvik, J. et al. (2015). Chem. Sci. 6: 1101. Palchowdhury, S. and Bhargava, B.L. (2015). Phys. Chem. Chem. Phys. 17: 19919. Maier, F., Cremer, T., Kolbeck, C. et al. (2010). Phys. Chem. Chem. Phys. 12: 1905.

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54 Toward Superhydrophobic Surfaces: Controlling Wetting Characteristics Muhammad A. Raza and E. Stefan Kooij

54.1 Introduction 54.1.1 Surface Wettability

When a liquid (for example, water, milk, or oil) is brought into contact with a solid surface (for example, paper, wood, or our skin), it either spreads completely or partially wets the surface and forms droplets. In specific cases, the degree of wetting is very low and the liquid adopts a spherical shape. A measure for the preferential tendency of immiscible fluids to spread over a solid surface is referred to as “wettability.” Wettability refers to the interaction between the liquid and solid phases and depends on the competition of cohesive forces of liquid molecules among themselves and the adhesive forces that result from the molecular interactions between the liquid and solid. In Figure 54.1, the cohesion (L/L) and dispersive adhesion (S/L) between liquid and solid molecules are depicted. Cohesive forces within the liquid hinder the droplet to contact with the solid surface, inducing the natural tendency to adopt a spherical bead-like shape. On the other hand, adhesion forces between liquid and solid give rise to spreading of the liquid on the solid surface. Large cohesive forces with respect to the adhesion interaction will lead to a limited contact with the substrate, which results in a “low” surface wettability. On the other hand, if the adhesive forces between solid and liquid molecules are greater than the cohesive forces within the bulk liquid, the liquid tends to contact as much surface as possible, thus leading to a “high” surface wettability. To summarize, wettability is closely linked to the relative strength of cohesive (liquid/liquid) and adhesive (solid/liquid) forces as summarized in Table 54.1. 54.1.2 Natural (Super)Hydrophobicity

Natural evolution over billions of years has resulted in a range of materials with many fascinating characteristics. “Superhydrophobicity” comprises one of such marvelous Surface and Interface Science: Liquid and Biological Interfaces, First Edition. Edited by Klaus Wandelt. © 2020 Wiley-VCH Verlag GmbH & Co. KGaA. Published 2020 by Wiley-VCH Verlag GmbH & Co. KGaA.

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Liquid

Solid Work of cohesion (L/L) Work of adhesion (S/L) Figure 54.1 Schematic representation showing cohesive interaction between liquid molecules (red lines) and adhesive interaction between solid and liquid molecules (yellow lines). Table 54.1 Dependence of surface wettability on the relative strength of cohesion and adhesion forces. Wettability

Cohesion (L/L)

Adhesion (S/L)

Complete (very high) Partial Moderate Low Nonwetting

Very weak Weak Weak Strong Very strong

Very strong Strong Weak Weak Very weak

properties. It refers to the complete nonwetting state of water on a solid surface; a droplet typically adopts a spherical shape on such a substrate. Superhydrophobic surfaces can be categorized into two groups on the basis of liquid–solid adhesion: a. Slippery or nonsticky: Water droplets not only exhibit a pearl-like shape but also roll off upon slightly tilting the substrate; the spherical water droplet as it were “floats” on the surface. b. Sticky: Water droplets still assume a spherical shape but stick to the surface upon tilting the substrate to large angles, or even upside down. In nature, there are many biological materials that possess the ability to resist water from wetting and spreading on their surfaces. Biomaterials employ superhydrophobic characteristics to keep dry and clean or protect from diseases. Naturally occurring superhydrophobic surfaces include plant leaves, bird feathers, and insect wings and legs (Figure 54.2). The most famous example is the lotus leaf (Nelumbo nucifera), a symbol of purity in several Asian countries. Despite growing in muddy water, the leaves remain clean. Droplets on the surface cannot wet the surface and remain “suspended” maintaining a spherical shape. When rolling off the leaf surface, any contamination is effortlessly carried along, revealing the self-cleaning property

54.1 Introduction

(a)

(b)

(d)

(e)

Figure 54.2 A wide variety of superhydrophobic surfaces occurring in nature: (a) water droplets on a lotus leaf, leading to (b) self-cleaning performance of the nonsticky surface, (c) sticky superhydrophobic rose petal, and (d) superhydrophobic

(c)

(f) butterfly wing. (e) Duck feathers with strong water-repellent properties and (f ) superhydrophobic spider silk with dew drops. (Source: Koch et al. 2009 [1]. Reprinted with permission of Elsevier.)

also referred to as the “lotus effect” [2]. The lotus leaf, together with that of rice (Oryza sativa), cabbage (Brassica oleracea), and Taro (Colocasia esculenta), belongs to the category of nonsticky superhydrophobic surfaces [3]. In contrast to the aforementioned superhydrophobic plant leaves with negligible liquid–solid adhesion, there are other plants that exhibit superhydrophobic properties but where water droplets are firmly pinned to the surface (Figure 54.2c); in this case, there is a strong adhesion between the liquid and the solid surface. Rose petals (Rosa rehd), scallions (Allium wakegi), and garlic (Allium sativum) leaves are examples of such naturally occurring sticky superhydrophobic surfaces [4]. These superb nonwetting phenomena are also exhibited by feathers of, for example, ducks (Figure 54.2e) and swans. In addition to plant leaves and bird feathers, many insects also benefit from water-repellent properties. The most prominent example comprises the nonwetting legs of the water strider, enabling it to stand and “walk” on the surface of water [5]. The water repellency of butterfly wings (Figure 54.2d) serve several purposes: (i) dew and rain drops remove dust, (ii) enables them to fly in the rain, and (iii) directionality keeps their body dry and free from disastrous effects of osmosis [6]. Gecko feet, mosquito eyes, and spider silk (Figure 54.2f ) are other examples of naturally occurring superhydrophobic surfaces [7]. The wide diversity of water-regulating properties, including superhydrophobicity of natural surfaces, has raised the question where these characteristics originate

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from. Why do some surfaces show strong water repellency? And why is there a distinction between sticky and nonsticky behavior? The answers lie in the competition between cohesion and adhesion of liquid and surface entities. The wettability of a surface strongly depends on the surface energy of solid and liquid. However, even a smooth surface with a minimal surface energy [8] cannot exhibit superhydrophobicity as observed in nature, indicating that there is another relevant ingredient. With the help of advanced imaging methods, such as scanning electron microscopy (SEM), it became possible to study the surface morphology of naturally occurring superhydrophobic surfaces. Detailed examination of these surfaces revealed that the interesting nonwetting properties originate from a combination of surface chemistry and morphological roughness. It even turned out that the role of surface roughness is more prominent than the chemical nature [9]. For example, the lotus leaf surface structure consists of micron-sized papillae decorated with nanosized wax crystals [3]. Although the wax has a low surface energy, a smooth surface will not exhibit superhydrophobicity; a hierarchical roughness with low surface energy is required [10]. Furthermore, the morphology of the rough surfaces is described by the dimensions, arrangement, and pattering of its features. These quantities play a crucial role in tuning the superhydrophobic properties from sticky to nonsticky. For example, surfaces of both lotus leaf and rose petals are superhydrophobic, but the dynamic behavior of water is significantly different. Water droplets easily roll off the lotus leaf, while they remain firmly attached to the rose petal even when tilting it vertically. This originates from differences in size, spacing, and design of micro- and nanostructures of the hierarchically rough surfaces. The papillae on the lotus surface are well separated and decorated with wax crystals, while rose petal papillae are periodic and covered with nanofolds. Typical biological surfaces with their specific wetting properties and other functionalities are listed in Table 54.2; for details on the surface microstructure of selected examples, a nice overview is provided by Zhang et al. [11]. Table 54.2 Typical naturally occurring surfaces with different functionalities, including superhydrophobicity. Bio-surface

Functionalities

Lotus leaf Rose petal Rice leaf Water strider leg Cicada wing Gecko foot Peacock feather Butterfly wing

Superhydrophobic, low adhesion, self-cleaning Superhydrophobic, color, strong adhesion Superhydrophobic, anisotropic wetting Durable and robust superhydrophobic Superhydrophobic, antireflection Reversible adhesive, superhydrophobicity, self-cleaning Structural color, superhydrophobicity Superhydrophobicity, directional adhesion, structural color, self-cleaning, chemical sensing capability

54.1 Introduction

54.1.3 Biomimetic Surfaces

Biomimetics refers to the field in which naturally occurring phenomena are mimicked or copied from biological systems [12]. In the previous section, we summarized the amazing wetting properties of (super)hydrophobic substrates. A relevant question is whether it is possible to mimic these biosurfaces and manufacture artificial surfaces with similar properties. Natural phenomena have not only fascinated scientists and engineers for decades but it has also been an inspiration and source of information for great novel inventions [13, 14]. From morphological analysis of natural superhydrophobic surfaces, it is now widely established that the nonwetting properties derive from two essential factors, i.e. (i) the surface chemistry and (ii) the surface roughness. Moreover, the surface roughness turns out to be the most prominent of the two. As such, controlling the morphological and structural parameters allows tuning of the wettability characteristics. Inspired by naturally occurring surfaces, researchers have successfully replicated a range of biomaterial functionalities for practical purposes, which include lotus and rice leaves, butterfly wings, water strider legs, fish scales, and spider silk [13]. In the following section, we will briefly discuss the many techniques available to fabricate substrates with controllable wetting properties. 54.1.4 Fabrication Methods

Many different ways have been described, which enable fabrication of surfaces with predefined wetting properties including superhydrophobic substrates. The approaches can be divided into two categories: (i) top-down and (ii) bottom-up methods. 54.1.4.1 Top-down

Carving, molding, or machining of bulk materials by mechanical means, solution etching, and laser-induced modification are frequently used to achieve surface roughness. Top-down approaches often include templating and lithography methods. 54.1.4.1.1 Templation Molding of a master surface followed by removal of the template by dissolution, lift-off, or sublimation leaves a replica of the original surface. As master surfaces for the templates, not only artificial micro- and nanostructured substrates but also natural surfaces can be used, including leaves, butterfly wings, and reptile skin. 54.1.4.1.2 Lithography This category includes optical (photo)lithography, soft lithography, nanoprint lithography, and colloidal lithography. Owing to developments in the field of microfabrication and nanotechnology, lithography

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comprises a well-established class of top-down techniques to fabricate superhydrophobic surfaces. As an example, in photolithography, a surface covered with photoresist is irradiated by light, X-rays, or electrons through a mask with a predefined pattern. After developing and washing off the photoresist, etching of surface features provides the desired roughness. Subsequently, the surface energy is modified by applying a suitable chemical coating. 54.1.4.2 Bottom-up

In contrast to top-down approaches, bottom-up fabrication comprises the assembly of smaller components into larger well-defined superstructures to create the desired surface roughness. Chemical deposition, colloidal assembly, layer-by-layer deposition, and sol–gel processes are examples of bottom-up methods. 54.1.4.2.1 Chemical Deposition In this case, thin films of crystalline inorganic materials (for example, ZnS, CuSe, and CdS) are deposited on selected substrates by chemical reactions. Examples include chemical bath deposition (CBD), chemical vapor deposition (CVD), and electrochemical deposition. Desired roughness is created with different surface morphologies including nanopins, nanotubes, and nanorods. Superhydrophobic substrates can be manufactured using the proper materials and specific deposition conditions. 54.1.4.2.2 Layer-By-Layer Deposition In this relatively simple and potentially inexpensive technique, which does not require sophisticated equipment, multilayers of particles (or polymers) are repetitively adsorbed by taking advantage of different interactions between subsequent layers to obtain the desired surface roughness. Often the employed particles are hydrophilic and a hydrophobic coating is applied to achieve superhydrophobicity. Substrates with hierarchical roughness are created by using particles of different sizes, enabling control over the wetting properties. 54.1.4.2.3 Colloidal Assembly In this approach, which is effectively a derivative of the aforementioned layer-by-layer deposition, the desired hierarchical roughness is created by depositing multilayers of different length scale structures using processes such as dip coating, spin coating, or substrate immersion. Monodispersed particles can be self-assembled on different substrates through chemical bonding, van der Waals forces, or other interactions driving the colloidal assembly process. This method is cost-effective as no expensive lithography technique is required, is relatively easy to master, and can be applied under normal laboratory conditions. Furthermore, it can also be used in combination with other techniques such as lithography, sol–gel processing, and chemical deposition to prepare micro- and/or nanostructured surfaces. Finally, there are also methods that involve a combination of bottom-up and top-down approaches. Examples include electrospinning, electrospraying, polymer solution casting, and phase separation.

54.1 Introduction

54.1.5 Application in Technology

The tremendous interest in the fabrication of biomimetic superhydrophobic surfaces with special adhesion (sticky and nonsticky behavior) by various methods as mentioned above are due to their emerging application in modern technology and industry. These promising applications include self-cleaning surfaces, anti-icing/antifogging coatings, (micro)droplet manipulation, water collection, nonfouling coatings, corrosion-resistive surfaces, and water/oil separation, to name only a few [11, 15, 16]. Below we will comment on a few of these applications. 54.1.5.1 Self-cleaning Surfaces

“Self-cleaning” is one of the most amazing and well-documented applications of superhydrophobic surfaces. In nature, the lotus leaf demonstrates this unique property, the so-called “lotus effect.” As with many plants, the leaf surface always appears clean despite growing in a mire environment. The ability to remain free of contaminants even in dirty ambient has triggered research interest in the development of artificial superhydrophobic surfaces toward dirt-resistant applications. In fact, the morphological structure leads to dust particles residing as it were suspended on the surfaces asperities, which results a very low contact area and weak physical adhesion force, i.e. a weak van der Waals force [17] between particle and surface [3]. When water droplets come in contact with such particles, they are picked up by the droplets because of stronger capillary forces between particles and droplets [18] as these roll over the surfaces (as shown schematically and in an experiment in Figure 54.3).

(a)

(b)

Figure 54.3 Self-cleaning mechanism. (a) Schematic illustration of dust particles adhering to a water droplet, which rolls over a rough superhydrophobic surface [3].

(b) Experiment showing a water droplet on an artificial nonsticky superhydrophobic surface picking up dust particles as it rolls over the surface. (Source: Raza 2012 [19].)

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54.1.5.2 Anti-icing/Antifogging Coatings

In cold regions of the world, especially in winter, ice and fog pose serious problems by disturbing daily routing and aeronautics, for example slippery roads, frozen engines, attenuated signals, burst pipes, ice-covered airplane wings, and electrical transmission lines or towers. Preemptive deicing methods, which avoid ice formation or decrease the ice adhesion and accretion, are of great interest [16]. The superhydrophobic and antifogging properties of mosquito eyes [20, 21] have inspired researchers to mimic surfaces preventing ice accumulation, as droplets can bounce off before ice nucleation takes place even in supercooled conditions. Many successful attempts to manufacture anti-icing and antifogging surfaces have been reported [22–24]. As compared to current conventional (physical or chemical) deicing methods, superhydrophobic surfaces provide a relatively simple and cost-effective strategy to tackle the frost deposition problem [16]. 54.1.5.3 Microdroplet Manipulation

Loss-free transportation of microdroplets is of great importance for microfluidic devices, such as channel-free microfluidic systems, low-cost microfluidic chips, and surface microfluidic channels, and also for smart droplet microreactors. A wide range of applications is involved including liquid transportation, biochemical separation, localized chemical synthesis, and in situ detection. Superhydrophobic surfaces enable simple microdroplet manipulation owing to tuneable/adjustable droplet adhesion (switching between sticky and nonsticky). As such, they can be used as a “mechanical hand” [25, 26] for controllable and no-loss transportation. In Figure 54.4, an example of lossless transportation is shown. The adhesion is switched by varying the curvature of the surface, thereby enabling pick up and release of droplets at will [27]. 54.1.6 Outline of This Chapter

In the following sections, we will build up knowledge relevant to understand the basics of superhydrophobic surfaces. In Section 54.2, we first summarize the general aspects of surface wetting, such as surface tension, wetting regimes, and contact angles, both static and dynamic. We finish the section by describing a few relevant techniques to probe the wetting characteristics of surfaces. In Section 54.3, we give an overview of the effect and potential of patterning substrates to control wetting properties. First, the effect of chemical patterning on morphologically flat surfaces is presented, and the Cassie–Baxter model is introduced. After that, morphologically structured surfaces are considered; the Wenzel model and mixed wetting states are reviewed, and we touch upon the effect of hierarchical roughness. In Section 54.4, several features related to dynamic wetting properties are described. The concept of slip length is treated, and contact angle hysteresis and the impact and bouncing of droplets on superhydrophobic surfaces are also discussed.

54.2 Wetting of Isotropic Surfaces

Transitional Wenzel’ state

PDMS pillar arrays

Capture

(a)

Cassie’s state

Releasing

Superhydrophobic surface with low adhesion force to water dorplet

1

2

3

4

5

6

(b) Figure 54.4 Lossless water droplet transport using superhydrophobic surfaces. (a) Schematic illustration of curvature-driven switching between “pinned” and “roll down” states to capture and release, respectively. (b) Demonstration of “picking-up” and “sampling” a water droplet. A flat

superhydrophobic surface (the pinned state) picks up the water droplet without any mass loss and releases it on target by changing into the “roll-down” state by increasing the surface curvature. (Source: Wu et al. 2011 [27]. Reproduced with permission of Wiley.)

Finally, in Section 54.5, a number of timely new subjects in the realm of wetting properties in general and liquid-repellent superhydrophobicity are reviewed. The potential of these novel horizons are briefly summarized.

54.2 Wetting of Isotropic Surfaces 54.2.1 Surface Tension

Abundant in nature, water has unique, interesting properties. A water molecule, H2 O, consists of an oxygen atom covalently bound to two hydrogen atoms; the H–O bonds are at an angle of 104.5∘ as shown in Figure 54.5a. Owing to the difference in electronegativity of the oxygen and hydrogen atoms, the electron distribution within

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54 Toward Superhydrophobic Surfaces: Controlling Wetting Characteristics

Hydrogen bonding between water molecules δ+

Water molecule H

δ–

H O

(a)

δ+

δ+

δ– δ+

H-bond

(b)

Figure 54.5 Schematic representation of the water molecule (a) and hydrogen bonding between water molecules arising from molecular polarization (b).

the covalent bond is pulled toward the oxygen side, resulting in a net polarization of the water molecule with a slight negative charge 𝛿 − on the oxygen atom and a small positive charge 𝛿 + on the hydrogen atoms. As a result, neighboring water molecules experience a net attractive interaction, ultimately leading to the well-known hydrogen bonding (Figure 54.5b), which leads to some of the fascinating properties of water. Within a volume of water, but more generally for every liquid, sufficiently far from the surface, each molecule is surrounded by neighboring molecules on all sides. Because of this isotropic coordination, the average cohesive interactions are balanced and the liquid molecule experiences no net force (Figure 54.6). At the surface, the situation is different as a result of the broken symmetry. The absence of liquid molecules on the outside of the liquid–air interface gives rise to a net inward cohesive force. Because of this inward pull, the liquid surface behaves like a starched

Surface molecule

Interior molecule

Figure 54.6 Schematic overview of attractive forces acting on molecules within the interior and at the surface of a liquid.

54.2 Wetting of Isotropic Surfaces

elastic membrane or film and is in an energetically unfavorable state. This imbalance of intermolecular attractive forces gives rise to a natural resistance of the surface to external forces, generally referred to as “surface tension,” which is represented by the symbols 𝛾 or 𝜎 and has units of force over length or equivalently energy per unit area. Thus, it can be described as energy required to increase the surface area of a liquid by a unit area or alternatively the amount of external force required to break a stretched film of a unit length. Since the intermolecular forces depend on the nature of the liquid, various liquids exhibit different surface tension properties. As an example, the surface tension of water amounts to 𝛾 = 72.8 dynes∕cm, which means a force of 72.8 dynes is required to break a film of 1 cm length; alternatively for mercury 𝛾 = 465 dynes∕cm. In our everyday life, the action of surface tension and especially that of water is omnipresent: 54.2.1.1 Drops of Water

Water from a tap generally does not flow in a continuous stream Figure 54.7a but breaks up in a series of drops when the flow is sufficiently slow. Because of surface tension, the drops tend to minimize their surface area and thereby the total surface free energy and consequently adopt a spherical shape. The same is the case for example with falling rain drops. 54.2.1.2 Walking on Water

Water striders and a range of other insects can easily move on the water surface, as shown in Figure 54.7b, without actually penetrating the liquid–air interface due to the surface tension. Owing to the specially designed legs, the weight of the insect is

(a)

(c) Figure 54.7 (a) To minimize their surface energy, water droplets (from a tap) adopt a spherical shape. (b) Water striders are able to “walk” on water without penetrating the

(b) liquid surface. (c) Surface tension enables solid objects with a higher density than that of water, such as a paperclip, to float on the water surface.

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evenly distributed; moreover, directional wetting with a preferential flow of liquid along the legs allows it to move along the surface. 54.2.1.3 Floating Solid Objects

Similar to the water strider, a carefully placed needle or paper clip, Figure 54.7c, can be made to float on the surface of water without sinking into it, even though the density is markedly larger than that of water. Generally, when a solid object is deposited onto the water surface, its weight pushes the water molecules apart. If the weight of the object is greater than the attractive intermolecular forces, it will break through the surface layer and sink into the water. On the other hand, if the effect of the weight is limited, it will lead to depression of the meniscus and surface tension forces can balance the gravitational pull, effectively allowing the object to float on the water. Careful observation of a floating paperclip shows that the water surface curves down under the metal surface because of its weight; interfacial tension makes the surface of water behave like a “flexible skin” supporting the object. 54.2.2 Wetting Regimes

In the previous section, we considered the interactions within and at the surface of a free spherical droplet. When such a droplet is brought into contact with a perfectly flat substrate, different wetting regimes can be observed [28], as schematically summarized in Figure 54.8. When the liquid has a strong affinity for the solid surface, total wetting occurs. An example of such a complete wetting case is a water droplet on a clean glass surface. The liquid forms a thin film, which is in contact with the entire surface. When the same droplet is deposited on a plastic sheet, for which it has a low affinity, partial wetting leads to a finite contact angle 𝜃 of the sessile droplet.

θ=π

0 𝛼L . Alternatively, if 𝛼S < 𝛼L , the system is in a partial wetting state. 54.2.3 Static Contact Angle

In the case of partial wetting, liquid droplets form spherical caps characterized by a contact angle 𝜃. When 𝜃 ≤ 90∘ , the liquid is referred to as mostly wetting; for 𝜃 > 90∘ ,

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(a)

(b)

(c)

(d)

Figure 54.9 Natural surfaces exhibiting the different classes of wettability. (a) Hydrophobic Regnellidium diphyllum. (b) Superhydrophobic Brassica oleracea. (c) Hydrophilic Alocasia odora. (d) Superhydrophilic Ruellia devosiana. (Republished with permission [31].)

we speak of mostly nonwetting. The value 𝜃 = 90∘ has no particular implications from a thermodynamic point of view. However, as we will show later on, this is important in the case of rough surfaces when we deal with the Wenzel equation. Now, we turn to the magnitude of the droplet contact angle 𝜃 in relation to the liquid and substrate properties. There are two ways to approach this, which are schematically shown in Figure 54.10. The first method considers the capillary forces acting on the three-phase contact line (also often referred to as the triple line). In a

γlv

dx cos θ

θ

γsv

θ

γsl dx (a)

(b)

Figure 54.10 Determination of the equilibrium contact angle 𝜃 by considering the force balance (a) and from the work associated with a movement of the contact line (b).

54.2 Wetting of Isotropic Surfaces

stationary situation, the net force on a section of the triple line of unit length is balanced in the horizontal direction. Taking the projection 𝛾LV cos 𝜃 of the liquid–vapor surface tension, and equating the forces directed outward and inward, we find 𝛾SV = 𝛾SL + 𝛾LV cos 𝜃

(54.2)

The famous Young’s equation [33] follows from simple rewriting of Eq. (54.2) 𝛾 − 𝛾SL (54.3) cos 𝜃 = SV 𝛾LV We can substitute the spreading parameter (Eq. (54.1)) into Eq. (54.2) to obtain cos 𝜃 = 1 + S∕𝛾LV

(54.4)

from which it immediately follows that the contact angle 𝜃 is only defined for S < 0 corresponding to partial wetting. One may wonder what happens to the vertical components of the force balance, as the liquid–vapor surface tension 𝛾LV also has a vertical component. This component is balanced by a distortion of the substrate, the extent depending on the hardness of the surface; for hard surfaces, the distortion cannot be observed, while droplets on soft surfaces such as plastics indeed give rise to a notable deformation. The contact angle can also be derived by considering the work dW done by moving the contact angle a distance dx as schematically shown in Figure 54.10b. Calculating the changes of the different contributions, one obtains dW = (𝛾SV − 𝛾SL )dx − 𝛾LV dx cos 𝜃 = [(𝛾SV − 𝛾SL ) − 𝛾LV cos 𝜃]dx

(54.5)

For a droplet in equilibrium, the net work done for any arbitrary distance dx is zero. Setting dW = 0 and rewriting, the Young’s equation in Eq. (54.3) again follows. 54.2.4 Dynamic Contact Angles

On an ideal planar surface without any contaminants, the equilibrium contact angle is given by Eq. (54.3). However, in real life, a range of static contact angles can be observed, which differs from the equilibrium value. The origin of this range of different static contact angles is ascribed to defects, either physical in the form of surface roughness and contaminants or chemical arising from uncontrolled stains or deliberately fabricated patterns. The nonunique values of the contact angles are observed when a droplet is inflated or deflated, as is schematically shown in Figure 54.11a,b. Upon adding liquid to the droplet, the contact angle will increase to values exceeding the equilibrium value 𝜃. Up to the advancing angle 𝜃A , the contact line will not move; only when passing this threshold, the contact line will start to move. Similarly, when drawing liquid from the droplet, the contact angle will decrease until it reaches the receding angle 𝜃R . The advancing and receding contact angles, also often referred to as the dynamic contact angles, can also be determined by monitoring a droplet sliding on an inclined

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54 Toward Superhydrophobic Surfaces: Controlling Wetting Characteristics

θA

(a)

θR

Advancing angle

(b)

Receding angle

θR

θA (c) Figure 54.11 Advancing (a) and receding (b) contact angles when a droplet is inflated and deflated, respectively. (c) The dynamic contact angles 𝜃A and 𝜃R determined for a droplet on an inclined surface.

surface, as shown in Figure 54.11c. Upon tilting the surface away from horizontal, the contact angle on the low side will increase while that on the high side will decrease. At a certain tilt angle, the droplet will start to slide on the surface; this angle is often termed the sliding angle, or alternatively the roll-off angle. Once the droplet starts sliding, the contact angles on the front and rear sides of the droplet will be equal to 𝜃A and 𝜃R , respectively. The difference between the maximum and minimum angles is referred to as the contact angle hysteresis. In the literature, two definitions of the contact angle hysteresis are found. Simply taking the difference 𝜃A − 𝜃R is often used. Nonsticky surfaces with low hysteresis are characterized by 𝜃A − 𝜃R < 5∘ , while for sticky surfaces, the hysteresis can be as large as 50∘ . For more quantitative analyses, the hysteresis is defined as cos 𝜃A − cos 𝜃R , as this quantity in fact represents the difference in forces acting on the front and rear of the sliding droplet. 54.2.5 Characterization Techniques

To characterize the wettability of a surface, the ability to accurately measure fluid contact angles is essential as it provides information about the attraction of molecules within the droplet with respect to the (attractive or repulsive) interaction of the liquid with the surface. Wettability measurements can be divided into two categories: (i) based on optical detection or (ii) based on force tensiometer experiments. In the first category, both static and dynamic contact angles are easily obtained. Typically, a droplet is placed on the solid surface and the image of the

54.2 Wetting of Isotropic Surfaces

Fcapillary

d l

θc

Figure 54.12 Schematic representation of the Wilhelmy plate method. (Source: Berry et al. 2015 [34]. Reprinted with permission from Elsevier.)

drop is recorded, followed by fitting of the droplet profile to some model shape. In the second category, the force tensiometer is mostly used to assess dynamic wetting characteristics. Force tensiometry effectively measures the force a solid experiences as it is brought into contact with a liquid. An example of the second category is the Wilhelmy plate method (Figure 54.12), which is mainly used to measure equilibrium surface or interfacial tension at an air–liquid or liquid–liquid interface. A plate is oriented perpendicular to the interface and the force Fcapillary because wetting is measured via a tensiometer or a microbalance. The surface tension 𝛾 follows from the Wilhelmy equation [35] 𝛾=

Fcapillary 𝓁 cos 𝜃c

(54.6)

where 𝓁 is the wetted perimeter of the immersed Wilhelmy plate and 𝜃c is the observed contact angle. Of the aforementioned two approaches to assess wetting properties, the optical method is the most frequently used owing to its versatility and ease of use. Moreover, it enables studying the homogeneity of the sample by measuring contact angles at several different places. With force tensiometer methods, this is not possible as the calculated contact angle is the average over the entire immersed area. 54.2.5.1 Static Contact Angle Techniques

Frequently used techniques to measure static contact angles are shown in Figure 54.13. The most widely used method to measure contact angles of liquid droplets on solid surfaces is the “sessile drop” method. Typically, in an optical goniometer, a well-defined volume of liquid with known surface energy is placed on the substrate. By viewing the droplet profile from the side, and specifically the three-phase contact line, the contact angle is defined as the angle between

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54 Toward Superhydrophobic Surfaces: Controlling Wetting Characteristics

θ

γlv γsv

θ

θ γsl

(a)

(b)

(c)

Figure 54.13 Schematic representation of different ways to measure static contact angles: (a) sessile drop, (b) capillary rise, and (c) captive bubble.

the liquid/solid and liquid/air interface, as shown in Figure 54.13a. Nowadays, automated drop shape analysis systems are commercially available, which comprise a computer-controlled goniometer equipped with imaging hardware and software incorporating curve-fitting tools. To assess the wetting properties of tubular geometries and inner coatings, the “capillary rise” method can be used. In this method, which is schematically shown in Figure 54.13b, a hollow material is immersed into the test liquid; the liquid will typically rise under the influence of capillary interactions. When the adhesive intermolecular forces between the liquid and the inner surface of the tube are stronger than the cohesive intermolecular forces inside the liquid, a concave meniscus forms. The contact angle of the meniscus with the vertical surface is directly accessible in a side view geometry. The “captive bubble” method depicted in Figure 54.13c is very similar to the aforementioned sessile drop technique. Instead of placing a drop on the solid as in the case of the sessile drop, a gas bubble is formed in the liquid by an inverted syringe beneath the substrate. The contact angle is again measured by means of an optical goniometer. In this method, the solid–vapor interface is in equilibrium with the saturated vapor pressure of the liquid within the bubble. The contact angle measured within the bubble is equal to 180∘ − 𝜃. This method is particularly suitable for solids with a high surface-free energy on which liquids show considerable spreading. 54.2.5.2 Dynamic Contact Angles

Accurate measurement of dynamic contact angles and the hysteresis enable assessment of dynamic wetting properties of a solid surface, also in relation to its chemical functionality and morphological structure. The approaches relate to and are often adaptions of the static techniques. The sessile drop (or captive bubble) method is a very simple and often used approach. As described in relation to Figure 54.11, liquid (or gas, in the case of the captive bubble methods) is pumped into and out of the droplet (or bubble) at the solid–liquid interface to attain the advancing and receding contact angles, respectively. In many cases, the liquid volume is varied through an inserted syringe needle, which deforms the liquid–vapor interface. A more accurate setup is depicted in Figure 54.14; liquid is added or removed through a channel in the substrate, keeping the droplet meniscus intact. The sessile drop technique can be modified slightly to the so-called “evaporation method,” in which the receding angle is measured as a droplet evaporates.

54.3 Chemical Patterning and Morphological Structuring

Vapor θ

Liquid

Solid

Liquid flow

Figure 54.14 Schematic representation of a modified setup to determine dynamic contact angles. Volume variations are applied through a channel in the substrate; the droplet meniscus remains spherically shaped.

The “tilted plate” method is demonstrated in Figure 54.11c, where the liquid droplet is positioned on an inclined substrate. It can also be used in the captive bubble configuration. The inclination or tilt angle is increased up to the moment the droplet start to slide over the surface. At this moment, the difference of the dynamic contact angles (advancing and receding angles at the lower and higher side of the droplet, respectively) provide the contact angle hysteresis. Simultaneously, the latter method yields the sliding angle, which is equal to the substrate tilt angle at which the droplet starts to move.

54.3 Chemical Patterning and Morphological Structuring

In the previous section, we considered isotropic homogeneous surfaces. Contact angles were defined independent of direction, only relating to the omnidirectional surface energy. In this section, we deal with the effect of surface heterogeneities, i.e. regions of variable surface energy, and also morphological structuring. As such, the local contact angle varies as a function of spatial position on the surface. These heterogeneities may be on a large scale, with dimensions of the order of the droplet size, or much smaller, in which case we speak of microscopic heterogeneities. In the first section, we start with morphologically flat surfaces comprising areas of different wettability, followed by a description of the effect of surface roughness. In the second half of this section, we focus specifically on superhydrophobcity and briefly touch upon hierarchical roughness. 54.3.1 Cassie–Baxter Model

In many cases, the chemical heterogeneities on a surface are much smaller than the dimension of the droplet. As such, the contact line is not substantially distorted and a macroscopic effective contact angle can be observed. For isotropic surfaces, i.e. without any directionality of the chemical pattern, the droplet shape will be spherical; effects due to anisotropic wetting properties will be discussed in Section 54.5. We are interested in the relation between the effective contact angle of a droplet on such a heterogeneous surface in relation to that of the various composing elements

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54 Toward Superhydrophobic Surfaces: Controlling Wetting Characteristics

of the surface heterogeneity. The approach described below, which is generally accepted and widely used, is referred to as the Cassie–Baxter model [36]. Consider the surface to be composed of two species with different surface energies and thus characterized by their own local contact angles 𝜃A and 𝜃B . The fractional surface areas occupied by these two species are denoted as fA and fB , respectively; obviously, fA + fB = 1. We denote the apparent effective contact angle in the Cassie–Baxter model with 𝜃CB . As in the previous section, 𝜃CB can be evaluated by considering a small displacement of the contact line over a distance dx (see also Figure 54.15a). As a result, the surface energies change by dW (per unit length of the contract line). This can be written as dW = fA (𝛾SL,A − 𝛾SV,A )𝛿x + fB (𝛾SL,B − 𝛾SV,B )dx + 𝛾LV dx cos 𝜃CB

(54.7)

in which the indices A and B refer to the two different surface species. Minimizing the energy, i.e. the situation where dW = 0, combined with Young’s equation (Eq. (54.3)) for the different surface species, yields the well-established Cassie–Baxter relation cos 𝜃CB = fA cos 𝜃A + fB cos 𝜃B

(54.8)

Effectively, the cosine of the apparent macroscopic contact angle is the weighted average of the cosines of the individual contact angles. A straightforward extension of the derivation shows that in principle the Cassie–Baxter model is not restricted to two chemical species but can be applied in the case of many components. From Eq. (54.8), it follows that the effective contact angle 𝜃CB is always restricted between the values 𝜃A and 𝜃B of the constituting entities. As such, an isotropic surface consisting of various chemical species will never exhibit contact angles exceeding that of the most nonwetting species. As the most water-repellent chemical coatings give rise to contact angles of 115∘ –120∘ at most, only providing chemical patterning will not allow the manufacture of superhydrophobic coatings with effective contact angles exceeding 150∘ .

dx cos θ

dx cos θ

A (a)

B

θ

θ dx

dx (b)

Figure 54.15 Schemes representing the evaluation of the apparent macroscopic contact angle in the Cassie-Baxter (𝜃CB ; left) and Wenzel (𝜃W ; right) regimes in relation to the

local Young’s angle 𝜃 and the composition (two components A and B are schematically shown) or roughness r of the surface.

54.3 Chemical Patterning and Morphological Structuring

54.3.2 Wenzel Model

The Wenzel model is one of the first attempts to account for the wetting properties of chemically homogeneous but rough surfaces. The essential assumption in Wenzel’s theory [37] is that liquid penetrates into the voids and completely encloses asperities of the surface structure, as is schematically shown in Figure 54.15b. Using a similar approach as done earlier, it is assumed that the length scale of the roughness is well below the dimension of the liquid droplet. The local contact angle, i.e. on a microscopic level, is defined by Young’s equation (54.3) and equal to that of a perfectly flat surface. Using the Wenzel model, the effective macroscopic contact angle 𝜃W can again be evaluated by considering a small displacement dx of the three-phase contact line in a direction parallel to the surface, as schematically depicted in Figure 54.15b. Because of this displacement, the surface energy changes by an amount dW given by (again per unit length of the contact line) dW = r(𝛾SL − 𝛾SV )dx + 𝛾LV dx cos 𝜃W

(54.9)

where r is an important parameter describing the roughness; r is defined as the ratio between the actual and projected surface areas. By minimizing the overall change in surface energy, i.e. dW = 0, we obtain the Wenzel equation cos 𝜃W = r cos 𝜃

(54.10)

in which 𝜃 is the Young’s angle for a flat surface with identical chemical composition. Obviously, for a flat surface with r = 1, it follows that 𝜃W = 𝜃. It is interesting to note that in the Wenzel model, the surface roughness effectively acts as an amplifier for the wetting properties. In the case of hydrophilic surfaces with 𝜃 < 90∘ , we see that 𝜃W < 𝜃 as in all cases r > 1; the hydrophilic surface becomes more hydrophilic. Similarly, for hydrophobic substrates with a finite roughness, the nonwetting properties will be enhanced giving rise to larger contact angles than perfectly flat surfaces with the same chemical functionalization. More specifically focusing on superhydrophobicity, it is obvious that the Wenzel model can account for very large effective contact angles, with values approaching 180∘ . This will happen as soon as the product on the right side of Eq. (54.10) becomes equal to −1. In fact, for a specific Young’s angle 𝜃, there is an upper limit for the roughness rmax = −1∕ cos 𝜃 above which the substrate become totally nonwetting. As an example, more specific in numbers, for surfaces characterized by 𝜃 = 110∘ , total nonwetting already sets in at a roughness amounting to rmax = 2.9. In the next section, we further zoom in on superhydrophobicity and we will discuss the limits of the Wenzel model. 54.3.3 Superhydrophobicity

In the aforementioned Wenzel model, the liquid is in all cases assumed to wet the entire surface and penetrate into the cavities of the surface structure, as shown in

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54 Toward Superhydrophobic Surfaces: Controlling Wetting Characteristics

(a)

(b)

Figure 54.16 Schematic representation of a liquid droplet on a rough surface. In the Wenzel state (a), the liquid completely wets the surface structure, while in the Cassie–Baxter state (c), the liquid is

(c) suspended on the asperities. In a mixed or intermediate regime (Wenzel–Cassie state) as shown in (b), the liquid partially infiltrates the surface micro/nanostructure.

Figure 54.16a. However, for hydrophobic surfaces with 𝜃 > 90∘ , the surface energy of the dry substrate is less than that of the wet substrate. As such, it is not obvious that the liquid will wet the entire rough surface and the solid–liquid interface may not completely conform to the topography of the substrate. As the roughness increases above a certain critical value [38], liquid may not fully penetrate into the cavities of the surface structures , as shown in Figure 54.16c. Air can be entrapped below the liquid droplet as long as locally the Young’s equation is satisfied for the three-phase contact lines [39]. As such, the liquid partially wets the surface and effectively a droplet resides on a composite surface consisting of the solid and air pockets. To evaluate the apparent contact angle of a droplet on a composed air/solid surface, we follow the approach described by Bico et al. [38, 40] and consider a flat liquid meniscus resting on flat pillars (or ridges) as shown in Figure 54.16c. The fraction of the liquid–solid interface of this meniscus is denoted by f , while the liquid/air fraction amounts to 1 − f . Again the energy change upon movement of the three-phase contact line can be derived as done in previous cases [28, 40]. However, more straightforward and intuitive is to use the Cassie–Baxter model of Eq. (54.8) and insert contact angles and fractions for the solid (𝜃A = 𝜃; fA = f ) and air (𝜃B = 180∘ ; fB = 1 − f ) below the droplet meniscus. After rearrangement, we obtain an effective apparent, macroscopic contact angle 𝜃eff given by cos 𝜃eff = f (1 + cos 𝜃) − 1

(54.11)

When comparing this relation with the Wenzel model in Eq. (54.10), a number of pronounced differences are obvious. These can also be visualized in a plot of the effective contact angle 𝜃eff as a function of Young’s contact angle 𝜃 as shown in Figure 54.17. In the Cassie–Baxter state, the effective contact angle jumps to a value f − 1 and slowly increases to 180∘ , when the wetted area of the substrate is small. Moreover, Eq. (54.11) also suggests that the maximum value 𝜃eff = 180∘ can never be reached, as this would require either 𝜃 = 180∘ or f = 0; both are physically not realistic. This is markedly different from the Wenzel model, in which the effective contact angle already reaches the maximum value for a Young’s contact angle equal to 𝜃 = −1∕r.

54.3 Chemical Patterning and Morphological Structuring

cos θeff

θc

–1

Wen zel

cos θ

xter

f–1

Ba sie–

Cas

–1 –1/r

Figure 54.17 Apparent effective contact angle 𝜃eff as a function of Young’s angle 𝜃 for liquid on a morphologically structured surface. Only the hydrophobic

(negative–negative cosine) quadrant is shown. The green and red lines represent the Cassie–Baxter and Wenzel models, respectively, as described in the text.

In the aforementioned analysis, we have assumed that the liquid meniscus is completely flat, residing on pillars that are also characterized by a flat top. Most often the wetted fraction f is not smooth but also exhibits roughness on a different length scale. In the expression for the effective contact angle, the combined effects on the wettability of chemical composition by the presence of air at the interface and roughness can be incorporated to obtain [41, 42] cos 𝜃eff = f (r cos 𝜃 + 1) − 1

(54.12)

Extending this to the liquid–air meniscus underneath the droplet, the assumption that this interface is flat is certainly not valid because of the finite Laplace pressure. This dictates that the radius of curvature is identical for the entire droplet, so both on the outside and below the droplet. To incorporate this, a similar relation can be derived [43] cos 𝜃eff = f (rsolid cos 𝜃 + rliquid ) − rliquid

(54.13)

in which rsolid is equal to the roughness r defined earlier and rliquid relates the actual area of the liquid meniscus to the projected area parallel to the substrate. Analysis shows that the effects due to roughness of the solid can be substantial and essential for superhydrophobicity as we will discuss in the next section; the correction due to the (limited) curvature of the liquid meniscus is relatively small [43]. 54.3.4 Metastable Wetting States

By now, it is clear that the so-called “fakir” state, in which liquid droplets are suspended on top of surface asperities, is a requirement for superhydrophobicity

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54 Toward Superhydrophobic Surfaces: Controlling Wetting Characteristics

characterized by large contact angles and small sliding angles; the latter we will further deal with in the next section. In relation to Figure 54.17, one may wonder which of the two states is the stable one. For nonwetting surfaces with large Young’s contact angles, analysis of the overall surface energy of the system reveals that the Cassie–Baxter state with entrapped air is the stable configuration. For small Young’s contact angles, the complete wetting Wenzel state is energetically most favorable. The crossover between the two states in terms of energetics is defined by 𝜃c (see Figure 54.17), which is easily derived using Eqs. (54.8) and (54.10): 1−f (54.14) cos 𝜃c = r−f Therefore, when the surface chemistry defines 𝜃 > 𝜃c , the “fakir” state is the stable state. As an example, let us consider a few numbers. The maximum contact angle for a flat surface that has experimentally been obtained amounts to approximately 𝜃 = 120∘ , corresponding to cos 𝜃 = −0.5; this implies that in actual situations, the left half of the negative–negative quadrant in Figure 54.17 does not occur in actual experiments. Now let us assume the realistic values for the roughness and the wetted fraction, for example f = 0.1 and r = 2. Equation (54.14) yields cos 𝜃 = 0.9∕1.9 = 0.47. This implies that in most cases, the Wenzel state is the stable one. Despite the fact that in many situations the Wenzel state has a lower overall energy, experimental reports have shown that highly nonwetting systems do exist; we have provided many examples in Section 54.1. Further analysis of the vast amount of reports in the literature indeed confirm that the Wenzel state is very often energetically the most favorable wetting state. Nevertheless, the suspended “fakir” state turns out to be a metastable one [40]. As liquid droplets are generally deposited gently on the surface and thereby onto its extremities, the metastable Cassie–Baxter state is the initial state. If on the other hand droplets are formed for example by condensation of vapor or accumulation of much smaller droplets by means of a spray, it has been shown that the liquids completely wet the surface and there is no entrapment of air between liquid and structured solid [44]. As the “fakir” state is a metastable one, the question arises what would drive a transition to the more stable Wenzel state. In other words, what is needed to force the system into its energetically most favorable configuration? It has been shown [44] that increasing pressure may lead to irreversible impalement of the liquid into the surface structures. Alternatively, increasing the impact energy during droplet deposition will lead to a transition to the lower energy Wenzel state. In the aforementioned cases, the Cassie-to-Wenzel transition was induced by an external stimulus (pressure or impact energy). McHale et al. [45] analyzed the spontaneous impalement observed during evaporation of liquid droplets on a superhydrophobic surface consisting of flat-topped pillars. As described above, in the case of a metastable superhydrophobic surface, gently deposited large droplets will reside on top of the pillars while much smaller droplets sprayed onto the substrate will lead to complete wetting. This suggests that upon evaporation, the Cassie-to-Wenzel transition will at some point occur. Reyssat et al. [18] performed a more elaborate study focusing on the origin of the transition. Using dilute pillar arrays, the critical radius of the droplet at which

54.3 Chemical Patterning and Morphological Structuring

(a)

(b)

Figure 54.18 Schematic representation of liquid impalement on a surface consisting of pillars, with (a) low and (b) high pillars. Upon increasing the pressure within the liquid droplet, the meniscus radius of

curvature will decrease, as indicated by the lighter blue shade and gray lines. Depending on the height or mutual spacing of the pillars, impalement of the liquid can be due to touchdown (a) or depinning (b) of the liquid.

the impalement transition occurs was determined as a function of pillar height and spacing. We first assume relatively low pillars, on which a large droplet rests as shown in Figure 54.18a. Upon evaporation, the radius of the outer droplet surface decreases. However, the radius of curvature of the liquid–air interface should be identical everywhere and consequently the liquid meniscus near the substrate will be further deformed. Eventually, it will reach the surface between the pillars. As soon as this happens, the solid–liquid interface will spread since the complete wetting state is the energetically more favorable one. To prevent the aforementioned impalement transition, the substrate design can be modified to either (i) increase the density of the pillars or alternatively (ii) increase the pillar height. In the former case, adding more pillars with the same top area will increase the fraction f of liquid in contact with the solid, and thereby smaller effective contact angles. To prevent this, thinner pillars can be fabricated, which will pose challenges in terms of structural stability and robustness. The second option is to increase the height of the pillars, as schematically shown in Figure 54.18b. When the radius of curvature decreases, the liquid/air interface lowers but does not reach the bottom surface as was the case with the shorter pillars of Figure 54.18a. However, the liquid meniscus will not only bend down upon increasing the curvature. Also, the angle of the liquid–air interface at the edges of the pillar top will increase. As soon as the contact angle becomes larger than the advancing angle on the sides of the pillars, the three-phase contact line will move down as schematically depicted by the gray lines in Figure 54.18b. Eventually, the liquid will contact the bottom surface again initiating the impalement transition [18]. 54.3.5 Hierarchical Roughness

As discussed in the previous section, the superhydrophobic nature of nonwetting surfaces generally originates from the combination of a hydrophobic material with a certain degree of surface roughness. To obtain sufficiently large contact angles, and corresponding small roll-off angles, the contact area of the liquid with the underlying

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54 Toward Superhydrophobic Surfaces: Controlling Wetting Characteristics

substrate should be very high. However, this poses challenges as to the stability of the wetting state. In designing artificial superhydrophobic surfaces, dilute arrays of surface asperities are required, but impalement of the liquid, i.e. the Cassie-to-Wenzel transition, must be prevented. Over the past two decades, many studies have focused on the properties of superhydrophobic surfaces encountered in nature. The lotus leaf has become the most important example; the combination of large contact angles, small roll-off angles, and self-cleaning properties is generally referred to as the “lotus effect.” However, many plant leafs and also other naturally occurring surfaces exhibit similar nonwetting properties. Electron microscopy images of the lotus leaf, such as those shown in Figure 54.19, reveal that the surface is characterized by a hierarchical structure consisting of randomly oriented small hydrophobic wax tubules on top of convex cell papillae. As mentioned, the characteristic nonwetting properties are not unique for the lotus leaf. Other examples are shown in Figure 54.20. Both the rice leaf and the wings of the Blue Morpho butterfly exhibit specific wetting properties [47]. The hierarchy is clearly observed in the electron microscopy images. Moreover, in the examples of Figure 54.20, the surface structure also defines a directionality in the wetting properties. In turn, these give rise to specific directions in which the liquid will roll off the surfaces. In the vast amount of research reported in the literature over the past 15–20 years, it has been established that superhydrophobicity does not primarily originate from the chemical nature of the surface but that the roughness is the predominant cause for ultimate nonwetting properties of naturally occurring surfaces [11, 46, 48, 49]. The hierarchical roughness on two or more length scales has been identified as an essential ingredient for nonstick, water-repellent surfaces. Many theoretical model has been discussed over the past years [49–51], ranging from qualitative descriptions to quantitative analyses; a full review lies outside the scope of this chapter. In Section 54.1, the various fabrication routes to superhydrophobic surfaces have been summarized. One of these involves the assembly of well-defined particles of various shapes and sizes. Here, we briefly review a facile and inexpensive bottom-up colloidal approach to achieve two-tier superstructures using micro- and Lotus leaf (Nelumbo nucifera)

10 μm

Figure 54.19 SEM micrographs (shown at three magnifications) of the lotus (Nelumbo nucifera) leaf surface, which consists of a microstructure formed by papillose epidermal cells covered with epicuticular wax

2 μm

0.4 μm

tubules on surface, which create a nanostructure. (Source: Koch et al. 2009 [46]. Reprinted with permission of The Royal Society of Chemistry.)

54.3 Chemical Patterning and Morphological Structuring

Micropapilla

10 μm (a)

(b)

2 μm (c)

100 μm (d)

(e)

Figure 54.20 Macroscopic and microscopic images of a rice leaf (Oryza sativa; a) and a butterfly wing (Blue Morpho didius; d). SEM images show that the rice leaf consists of sinusoidal grooves (b) decorated with micropapillae (c). The butterfly wing

10 μm (f)

consists of shingle-like scales (e) with aligned microgrooves (f ). Arrows indicate the direction of anisotropic fluid flow. (Source: Bixler and Bhushan 2012 [47]. Adapted with permission of The Royal Society of Chemistry.)

nanoparticles [52]. A typical example is shown in Figure 54.21. Colloidal silica particles with a radius of 440 nm are assembled on a flat silicon substrate by means of spin-coating. Depending on the density of the silica suspension and the spin-coating parameters, deposited layer thicknesses range from a single layer to multilayered structures [43]. The untreated silica arrays are essentially complete wetting structures, characterized by very small contact angles. Silica itself is hydrophilic with low contact angles. The roughness only enhances the effective contact angles, in line with the Wenzel model as discussed in relation to Eq. (54.10). By chemical modification using a fluorinated long-chain alkanesilane (such as 1H,1H,2H,2H-perfluorodecyltrichlorosilane [PFDTS]), the silica can be modified to render the surface hydrophobic. This indeed leads to relatively high contact angles, but since, there is still a substantial part of the surface which is wetted in a mixed-wetting configuration (Eq. (54.12)), the threephase contact line experiences relatively strong pinning and liquid sticks to the surface. Even turning the substrate upside down (inset in Figure 54.21a) does not lead to detachment of the droplet. A second length scale is introduced by irreversible assembly of gold nanoparticles typically with diameters in the range of 15–50 nm, as shown in Figure 54.21b. Because of the polar nature of the gold surface, without any coating, it will be

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54 Toward Superhydrophobic Surfaces: Controlling Wetting Characteristics

500 nm

500 nm (a) Figure 54.21 Helium ion microscopy (HIM) images showing the surface morphology of bare (a) and gold nanoparticle decorated (b) silica spheres, revealing the single and double length scales, respectively. The insets

(b) demonstrate the sticky nature of the microsphere arrays (a) and the nonsticky, sliding properties of the dual-length scale arrays (b). (Raza et al. 2012 [52]. Reprinted with permission of Elsevier.)

hydrophilic. Derivatization by alkanethiols is the way to modify the gold to exhibit hydrophobic properties; typical contact angles of hydrophobized flat gold surfaces amount to 105∘ –110∘ . However, the roughness combined with the double-length scale superstructure gives rise to strong water repellency (contact angles exceeding 150∘ ) and very small sliding angles, as shown by the inset in Figure 54.21b. To end this section, we summarize the colloidal route to control wetting properties in Figure 54.22. Flat silicon substrates with a thin layer of natural oxide exhibit very low contact angles (a). After hydrophobizing, the contact angle markedly increases to values in the range 110∘ –120∘ . Roughness induced by assembly of nano- or microparticles (c and d, respectively) followed by derivatizing with waterrepellent coatings gives rise to even larger contact angles. However, these substrates cannot be categorized as superhydrophobic and self-cleaning as contact angles are below 150∘ and sliding angles are relatively small, leading to sticky surfaces. The only way to obtain true superhydrophobic surfaces using colloidal assembly requires at least two length scales (e). Large contact angles are accompanied by very small sliding angles in the range 1∘ –5∘ [52, 53].

54.4 Dynamic Wetting Behavior

In the previous sections, we focused on static wetting properties of liquids interacting with solid surfaces. The motion of liquids and/or droplets on such surfaces relates to the dynamic wetting characteristics of the interface. As before, factors that

54.4 Dynamic Wetting Behavior

(a)

CA ≈ 128°

CA ≈ 111°

CA < 5°

(b)

(c)

Figure 54.22 Schematic (top) and measured (bottom) wetting behavior of the various different substrates based on siliconoxide coated with various micro- and nanoparticles. (a) A water droplet on flat silicon without any chemical treatment spreads fully. (b) After chemical modification, the surface becomes hydrophobic. (c) The contact angle further increases by decorating the

CA ≈ 148°

(d)

CA ≈ 162°

(e)

surface with gold nanoparticles, combined with chemical derivatization. (d) Larger silica sphere arrays followed by chemical treatment gives rise to larger contact angles. (e) The largest contact angle is achieved on a chemically treated surface with hierarchical roughness composed of gold nanoparticles on silica spheres. (Source: Raza et al. 2012 [52]. Reprinted with permission of Elsevier.)

affect the fluid dynamics include surface roughness, chemical heterogeneities, and thereby the macroscopic surface hydrophobicity. Important parameters related to the hydrodynamics of a superhydrophobic solid surface are the slip length, contact angle hysteresis, and the sliding angle. 54.4.1 Slip Length

The interactions between a solid surface and the motion of fluid in contact with that surface determine the dynamic wetting behavior. One of the basic parameters of interest in fluid flow is slip. Different scenarios at the solid–liquid interface depend on the nature of solid and fluid, as schematically shown in Figure 54.23. Generally, the flow velocity increases for larger distances to the solid–liquid interface. In the no-slip boundary condition, the velocity at the interface is considered to reduce to zero (Figure 54.23a). The molecules in the liquid closest to the interface are effectively immobilized at the surface; adhesion of liquid molecules to the solid surface is (much) stronger than the cohesion between liquid molecules. Most often, the no-slip boundary condition is considered to be important at hydrophilic surfaces. On the other hand, when the adhesion is less strong and cohesive interactions within the liquid dominate, with the extreme case being that of superhydrophobic surfaces, the fluid velocity at the solid–liquid interface may not vanish. The “nonzero slip” boundary condition is schematically shown in Figure 54.23b. The degree of boundary slip at the solid–liquid interface is characterized by the slip length b, which

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54 Toward Superhydrophobic Surfaces: Controlling Wetting Characteristics

Solid surface

Solid surface

b

V

V Flow direction b

Solid surface (a)

Velocity Profile without slip

(b)

Figure 54.23 Velocity profiles of fluid flow between two solid surfaces showing (a) the no-slip boundary condition and (b) the situation with a finite, nonzero slip. The “slip

Solid surface

Velocity Profile with slip

length” and the velocity of the liquid are represented by b and V, respectively; arrows indicate the flow direction.

is defined as the length of the vertical intercept along the axis orthogonal to the interface when a tangent line is drawn along the velocity profile at the interface [54]. From the above, it is obvious that the average flow velocity in the case of finite slip increases for larger slip lengths. As such, to reduce the fluid drag and to enhance flow, design of functional surfaces focuses on increasing the slip length. As mentioned, the balance between adhesion and cohesion is important. Consequently, the slip length can be enlarged by lowering the surface energy of the solid surface, effectively making it more hydrophobic. Another option is to enlarge the roughness of the substrate in an attempt to minimize the fluid–solid contact area. Different scenarios can be envisaged when considering the effect of surface roughness on the slip; if the liquid completely impales the surface structures (Wenzel state), the “no-slip condition” holds as shown in Figure 54.24a. However, if the liquid does not penetrate into the openings of the substrate cavities, due to hydrophobicity of the material (Cassie state in Figure 54.24b), the finite effective slip length can give rise to reduced flow resistance [55]. 54.4.2 Contact Angle Hysteresis

As already outlined in Section 54.2, another important parameter in the dynamic wetting properties is the contact angle hysteresis, which can be observed in many

Liquid

Liquid

Gas Solid (a)

Solid (b)

Figure 54.24 Schematic illustrations of local flow profiles at the interface between a liquid and a solid, in the case of (a) the Wenzel state and (b) the Cassie state.

54.4 Dynamic Wetting Behavior

daily experiences. For example, when rain drops on glass windows creep downward, typically a characteristic stick-slip motion occurs. The competition of gravity pulling the liquid down and the adhesion of the water to the glass surface lead to an asymmetric shape of the droplet with a smaller contact angle at the top and a high contact angle on the lower side. When the droplet has attained a certain size, it will start to move and slide down in this asymmetric shape. The difference between the contact angles on the advancing and receding sides comprises the hysteresis, as shown in Figure 54.25. The contact angle hysteresis is strongly dependent on the specific material, chemical functionalization, and roughness of the underlying substrate [56, 57]. The hysteresis arises from the fact that on a nonideal surface, a range of static contact angles are possible, which are referred to as “metastable” states. The range of metastable contact angles is defined by the advancing and receding angles. Owing to the existence of barriers in the free energy between the metastable states, it is not possible to measure a true “equilibrium.” Only on an “ideal” surface with a pure liquid, there will be only one unique thermodynamically stable contact angle. As such, ideal surfaces do not exist in real life. Measuring of a static contact angle is generally not sufficient to characterize the wetting properties of the surface. It is important and relevant to always measure the contact angle hysteresis to identify the range of stable contact angles, especially when focusing on dynamic behavior of liquids and droplets. One more example of a system where advancing and receding contact angles are essential is shown in Figure 54.26; here, the motion of droplets on various surfaces with different wettabilities is induced by a laminar flow of air over the surface [58]. 54.4.3 Impinging Droplets

Solid surface

θR Liquid

Solid surface

The phenomenon of droplet impact on surfaces has been a topic of research interest since the nineteenth century with the pioneering contribution of Worthington [59]. The impact dynamics can be affected by a variety of parameters, such as impact velocity, liquid density, surface tension, viscosity, droplet size, and the roughness and

θA (a)

θR

Liquid

θA (b)

(c)

(d)

Figure 54.25 (a and b) Schematic representation and (c and d) experimental images of different asymmetric shapes of liquid droplets on morphologically structured surfaces with different roughness and structural dimensions.

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54 Toward Superhydrophobic Surfaces: Controlling Wetting Characteristics

Surface A

Surface B

Surface C 1 mm

Airflow

Z X

FCL θ R

(a)

Time

422

FD

θA

(b)

Figure 54.26 (a) Schematic representation of droplet motion induced by laminar air flow; the drag and capillary forces are indicated by FD and FCL , respectively. (b) Experimental results for a 13 μl droplet on surfaces with different wetting properties. Surface A:

𝜃 = 90.1∘ , 𝜃A = 99.5∘ and 𝜃R = 76.2∘ ; Surface B: 𝜃 = 85.5∘ , 𝜃A = 87.1∘ and 𝜃R = 60.2∘ ; Surface C: 𝜃 = 80.4∘ , 𝜃A = 82.5∘ and 𝜃R = 48.8∘ . (Source: Fan et al. 2011 [58]. Reprinted with permission of Elsevier.)

wettability of the solid surface [60]. The dynamic behavior of droplets impacting on solid surfaces is of fundamental importance because of its industrial applications in various technological areas including inkjet printing, spray painting and coating, spray cooling, pesticide spraying, metal forming, soil erosion because of rain drop impact, and turbine wear [61]. In the study of dynamic properties of impacting droplets, various parameters are of interest, such as the maximum spreading diameter, the “bouncing” on superhydrophobic surfaces, the rebound height, the contact time, and contact angle variation during and after impact on the surface. Different application areas dictate the relevant parameters to be investigated. For example, in herbicide treatment, the maximum spreading diameter is of interest to improve the herbicide efficiency; a key issue is to prevent droplet rebound. On the other hand, to improve the watercooling of a hot solid, the contact time of the droplet with the surface is an important quantity. When a liquid droplet with a certain velocity impacts on a solid surface, the combined role of potential energy, kinetic energy, and liquid surface energy give rise to an interesting phenomena [62]. The dynamics are governed by a competition of the liquid–solid adhesion force and the inertial behavior of the droplet. When inertia dominates, the droplet bounces off, which is often referred to as the rebounding or nonwetting regime. If adhesion exceeds the inertial rebound force, a permanent solid–liquid interface is formed and the droplet sticks to the solid surface; this is referred to as the pinning or wetting regime. As discussed in previous sections, chemical modifications and/or morphologically structuring of substrate surfaces markedly affects the interactions between the liquid droplet and solid substrate, resulting in considerable variation of the macroscopic surface wettability. In Figure 54.27, a series of snapshots are shown for two

54.4 Dynamic Wetting Behavior

3 ms

4 ms

6 ms

7 ms

8.5 ms

15 ms

33 ms

59 ms

9 ms

13 ms

16 ms

23 ms

30 ms

37 ms

44 ms

(a)

6 ms (b)

Figure 54.27 Time lapse of impact events exhibiting (a) jetting, complete rebounding and oscillation on a superhydrophobic surface and (b) jetting and oscillation on

a sticky hydrophobic surface. The impact velocity of the droplet in both cases was to 0.41 ± 0.02 ms; the liquid volume was 10 μl.

typical movies, as observed by high speed imaging, in which a 10 μl water droplet impinges on surfaces with different morphologies and thus different wetting properties. The results represent (a) the complete rebounding (nonwetting) on a superhydrophobic surface and (b) wetting by pinning of the contact line on a hydrophobic substrate [53]. In Figure 54.28, a typical transient is depicted of the height (center of mass) of a bouncing droplet such as the one in Figure 54.27a. The overall motion of the droplet can be divided into three regimes: (i) free fall (bold points), (ii) bouncing (dotted line), and (iii) sticky oscillation (solid line).

Height (mm)

6

4

2

0

0

100

200

300

400

Time (ms) Figure 54.28 Typical transient of the height free fall regime, the dashed line represents bouncing (three times), and the solid line the of the droplet’s center of mass (10 μl) as damped oscillation of the droplet. extracted from a movie such as that in Figure 54.27a. The dotted line indicates the

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54 Toward Superhydrophobic Surfaces: Controlling Wetting Characteristics

After colliding with the substrate, the droplet adopts different shapes. Upon touching the surface, a surface capillary wave is excited and the droplet deforms into a pyramid-like shape as shown in the images at 3 ms and 4 ms in Figure 54.27a. The droplet spreads to achieve a maximum wetted area (at 6 ms in Figure 54.27a) due to inertia; during spreading, kinetic energy is converted into surface energy. The maximum spreading ratio Dmax ∕D0 , defined as ratio of the maximum width Dmax upon impact and the initial droplet diameter D0 , is often considered as a relevant parameter [63, 64], which generally depends on hydrophobicity of the substrate. For more wetting surfaces, the smaller advancing contact angles typically lead to higher maximum spreading ratios. Owing to the droplet deformation combined with the surface capillary waves, the droplet becomes toroidal, creating a cylinder-like cavity in the center as can be seen at 7 ms in Figure 54.27a. Because of air being entrapped when the droplet recoils, a jet is formed [60, 65] as shown in the image at 8.5 ms. The droplet continues to retract as a result of the surface energy and its inertial motion. On sufficiently hydrophobic surfaces, splashing or rebound of the droplet (dotted line in Figure 54.28) is often observed for a relatively high impact momentum. Just before detaching from the substrate, the droplet shape becomes elongated (at 15 ms) before taking off. The number of bounces increases with the substrate hydrophobicity, while lower impact velocities lead to a decrease of the number of bounces. After a number of bounces, the droplet resides on the surface asperities of the structured superhydrophobic substrate, with air-trapped underneath the droplet and thus a large contact angle. However, the remaining kinetic energy after the bouncing regime gives rise to oscillating behavior before reaching the equilibrium state (last four images in Figure 54.27a; the actual oscillation lasts longer, typically up to a few seconds). During the relaxation toward its final equilibrium shape, the dynamics are governed by a typical frequency and a relaxation rate, both of which depend on the wetting properties of the substrate [53]. In the case of impact on a sticky hydrophobic surface [19], the droplet is deformed to achieve maximum spreading (at 6 ms in Figure 54.27b), followed by recoiling behavior. Similar jetting as for the nonsticky superhydrophobic behavior can be observed, as shown in the image at 9 ms. Owing to the strong adhesion between liquid and substrate, the droplet is not able to completely rebound and does not bounce. This suggests that the liquid penetrates into the cavities formed by the surface structure corresponding to the Wenzel state. Again, owing to the kinetic energy, the droplet continues to oscillate like a sticky vibrating sphere as shown in the images after 13 ms in Figure 54.27b.

54.5 Novel Applications and New Horizons

In this last section, we briefly review the relevance of superhydrophobicity in modern society and its conceptual potential in competing with established

54.5 Novel Applications and New Horizons

technologies, such as anticorrosion coatings, drag reduction, and oil separation. Another intriguing development comprises omniphobic surfaces, which exhibit nonwetting properties for all types of liquids, polar and nonpolar. Slippery liquid infused porous surfaces (SLIPS) have properties very comparable to those of superhydrophobic surfaces, but the underlying mechanisms are very different. Finally, two topics that have attracted considerable attention over the past decade are reviewed. Recrystallizing natural wax onto artificial surfaces is an interesting and potentially viable approach to not only replicate natural phenomena but also use natural resources to recreate superhydrophobic surfaces. Finally, directional wetting is described with a focus on the application of wetting gradients to actively control liquid movement without any external stimulus. 54.5.1 Novel Applications of Superhydrophobic Surfaces 54.5.1.1 Anticorrosion Coatings

In humid atmosphere, many surfaces gradually deteriorate due to oxidation and corrosive reactions. Especially in the case of (re)active metals, this gives rise to decrease of their efficiency and in addition leads to environmental contamination and corresponding health hazards. Traditional methods to prevent corrosion include oilbased paint and chromium-containing coatings. Superhydrophobic coatings have been suggested as an effective alternative solution. Air trapped between the surface microstructures develops a neutral isolation and prevents direct contact between corrosive ions and the substrate. 54.5.1.2 Drag Reduction

For marine application, friction is a major issue leading to reduction of speed and increased consumption of fuel. Conventionally, methods to reduce drag include creating a gas layer at or near the solid–liquid interface by ionizing the liquid, by a cushion of air (e.g. below a hovercraft), or by creating bubbles at the interface. However, these methods require continuous energy input. Approaches involving superhydrophobic surfaces are considered as an alternative, as air-filled surface structures reduce the effective solid–liquid composite interfaces and give rise to enhanced slip lengths, thereby reducing drag. 54.5.1.3 Oil–Water Separation

Oil–water separation is of great importance for a range of biomedical, agricultural, environmental, and industrial applications. Accidental leakage of organic pollutants, such as crude oil and toxic aromatics into environmental water, poses a serious health threat. Available methods to limit pollution rely on absorption materials such as zeolites, activated carbon, natural clays, straw, and wool fibers. Owing to their hydrophilic nature, these absorbents usually show poor selectivity for organic pollutants. Superhydrophobic surfaces are a promising candidate for oil–water separation, owing to the low surface energy, i.e. oleophilic properties. In combination with the surface roughness, this effect is enhanced giving rise to superoleophilic surfaces.

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54 Toward Superhydrophobic Surfaces: Controlling Wetting Characteristics

54.5.2 Omniphobic Surfaces

As discussed in the previous sections, wetting properties of solid surfaces can be classified as superhydrophilic, hydrophilic, hydrophobic, and superhydrophoic, primarily based on the water contact angle on such substrates. Similarly, on the basis of interaction with oils (and other low surface tension liquids), surfaces can also be categorized into superoleophilic (oil CA < 10∘ ), oleophilic (CA < 90∘ ), oleophobic (CA > 90∘ ), and superoleophobic (CA > 150∘ ). In the latter case, true superoleophobicity is characterized by a contact angle hysteresis < 5∘ for oils, alcohols, or other organic solvents [66, 67]. If a surface exhibits nonwetting properties for high and low surface tension liquids, such as water and oil, respectively, these are referred to as “omniphobic” or amphiphobic surfaces, as shown in Figure 54.29. Thus, superomniphobic surfaces exhibit contact angles exceeding 50∘ and low contact angle hysteresis with essentially all liquids, organic or inorganic, polar or nonpolar, Newtonian or non-Newtonian [68, 69]. Superhydrophobic surfaces are abundant in nature, but to date, not a single example of naturally occurring superoleophobic surfaces has been observed. This originates from the much lower surface tension of oil as compared to that of water, leading to spreading on most substrates. However, a few examples of natural oleophobic surfaces have been reported; a prominent one is that of “Springtails” (Collembola). Microscopic analysis using SEM (shown in Figure 54.30) reveals that Springtail skin is composed of three types of surface features with different length scales [70]. The fascinating and durable omniphobic properties of the Springtail skin are ascribed to its unique structural design. As shown in the right part of Figure 54.30, the skin design provides various levels of antiwetting protection. The hairy cover constitutes the first wetting barrier; liquids can be pinned on the bristle tips. If external forces or very low surface tensions enable liquids to overcome this first barrier, a second stage comes into play: nanoscopic comb structures of interconnected primary granules can still pin liquids by effective retention of entrapped gas within the surface nanocavities. The third level that ensures gas retention is provided by the overhanging structural elements, which exhibit a negative curvature with

Methanol

Octane

Methylene iodide

Water

5 mm

Figure 54.29 Nonwetting characteristics of a dip-coated polyester fabric against various polar and nonpolar liquids. (Source: Cho et al. 2009 [68]. Adapted with permission of Wiley.)

Tetrodontophora bilanensis

4 mm

∼ 200 nm < 1 μm

SG

Primary granule with overhanging Comb pattern Interconnecting ridges

B Arrays of air filled nanocavities are A formed within the Bristle shield surface comb the surface by structure macroscopic air cushions

C Negative curvature on overhanging profiles prevents liquids from advancing

Figure 54.30 SEM images of Springtail skin showing the surface morphology consisting of microscopic bristles and the rhombic or hexagonal comb pattern formed by unique nanoscopic primary granules connected by ridges. The microelements papillous secondary granules (SG), which further provide stability of the antiwetting performance, are shown. (Source: Helbig et al. 2011 [70].)

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54 Toward Superhydrophobic Surfaces: Controlling Wetting Characteristics

θ

ψ

ψ

(a)

θ

(b)

Figure 54.31 Schematic diagrams highlighting the critical role of re-entrant texture in achieving a stable Cassie–Baxter state; liquid–vapor interfaces on two different surfaces have identical surface energy and the same equilibrium contact angle 𝜃,

but different geometric angles 𝜓: (a) nonre-entrant texture with 𝜓 > 𝜃 and (b) reentrant texture with 𝜓 < 𝜃. (Source: Tuteja et al. 2008 [71]. Reproduced with permission of National Academy of Sciences.)

respect to an orthogonal axis to the surface. Because of this negative curvature, an energy barrier must be overcome by the advancing liquid phase before wetting becomes irreversible even for liquids with very low surface tension, resulting in a dramatically reduced solid–liquid contact area [70]. In the previous section, we have seen that to obtain extreme nonwetting properties, two key ingredients are important: (i) surface chemistry and (ii) hierarchical roughness. The role of hierarchical roughness is essential to ensure the Cassie–Baxter state. However, to create superomniphobic surfaces, a special type of hierarchical roughness is required, the so-called “re-entrant texture” or “overhanging structures” similar to that of the Springtail skin. The critical role of such re-entrant texture can be illustrated qualitatively by considering the diagrams shown in Figure 54.31. In both cases, 𝜃 represents the local contact angle of any liquid (Eq. (54.3)) contacting the texture in the Cassie–Baxter state and is equal to the Young’s contact angle on a flat surface with the same surface energy. The angle 𝜓 is the local geometric angle, describing the shape of the surface structures. For 𝜓 > 𝜃 as shown in Figure 54.31a, there will be a downward net force on the liquid–vapor interface because of the capillary force, which will promote imbibition of the liquid into the solid texture, thereby potentially leading to a fully wetted Wenzel state. Thus, for liquids with low surface tensions and contact angles 𝜃 < 90∘ , a stable Cassie–Baxter state cannot be achieved on textures characterized by 𝜓 > 90∘ . On the other hand, when 𝜓 < 𝜃, the net force is directed upward and the liquid–vapor interface will recede to the top of the pillars, creating a composite liquid–solid–air interface. Even for contact angles 𝜃 < 90∘ , a robust Cassie–Baxter state is possible if the texture is such that 𝜓 < 90∘ (Figure 54.31b). Such re-entrant surface textures are found to be essential in the design of superomniphobic surfaces [66]. An example of re-entrant texture exhibiting true superomniphobicity is shown in Figure 54.32 [72].

54.5 Novel Applications and New Horizons

(a)

(b)

Figure 54.32 (a and b) Microscopic images of the “re-entrant texture” of two different silicon nanonail surfaces. (c) Droplets of water and ethanol exhibiting

(c) Water

Ethanol

superomniphobic behavior on these nanonail substrates. (Ahuja et al. 2008 [72]. Reprinted (adapted) with permission of American Chemical Society.)

54.5.3 Slippery Liquid-Infused Porous Surfaces (SLIPS)

In the case of “superhydrophobic” and “superomniphobic” surfaces as inspired by biomaterials such as the lotus leaf and Springtails, respectively, the primary role of hierarchical or re-entrant texture is to entrap air beneath the contacting liquid and thereby to minimize the contact area. In this way, a maximum contact angle and a minimum sliding angle (contact angle hysteresis) are achieved, aiding in the movement of fluid over the surface seemingly without any hindrance. Here, we describe how SLIPS are different from the aforementioned antiwetting surfaces. In fact, SLIPS exhibit extreme repellency and slippery properties repelling all types of liquids, ranging from water to blood to crude oil, with negligible roll-off angles. A fascinating feature of SLIPS is their self-healing capability after physical damage. Although the characteristics are very similar to superhydrophobic or superomniphobic surfaces, SLIPS are conceptually different. With the former surfaces, entrapped air plays a key role to achieve ultra-non-wetting properties, while in the case of SLIPS, “infused lubricating fluid” is locked in place between nano/microstructures of the substrate. This lubricant forms a stable, defect-free, and inert “slippery” interface, which repels various simple and complex liquids (water, hydrocarbons, crude oil, and blood) maintaining low contact angle hysteresis (< 2.5∘ ), quickly restores liquid repellency after physical damage, resists ice adhesion, and even operates at high pressures [73]. The basic idea of SLIPS is again inspired by nature, this time by the Nepenthes Pitcher plant, which provides a simple alternative approach that is different from that of the lotus effect. Instead of using surface structures to directly repel liquids, the surface features are used to lock-in a liquid, which acts as the repellent surface. Structural analysis reveals a hierarchical multilevel ridge morphology, of which the surface is perfectly wetting. The highly stable and slippery surface of the pitcher plant surface is a result of the combination of this microstructural roughness and compatibility of solid and liquid surface energies, where the liquid fills the spaces within the

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54 Toward Superhydrophobic Surfaces: Controlling Wetting Characteristics

Functionalized porous/textured solid

Test liquid (liquid A)

Lubricating film (liquid B)

Tilt

(a) Ordered nano-post array

5 μm

Random network of nanofibres

5 μm

(b) Figure 54.33 (a) Schematical representation of different SLIPS fabrication stages. (left) Functionalized porous/textured solid is prepared with a low surface energy; (middle) a physically smooth and chemically homogeneous lubricating film on the substrate is achieved by a chemically inert liquid; (right) slippery performance of the prepared

surface with a contacting liquid. (b) Electron micrographs showing the morphologies of porous/textured substrate materials: (left) an epoxy resin-based nanofabricated post array and (right) a Teflon-based porous nanofiber network. (Wong et al. 2011 [73]. Adapted with permission of Springer Nature.)

texture and forms a continuous overlying film. This aqueous film is sufficiently slippery to cause insects, on which the plant feeds, to slip from the rim into the digestive juices [74, 75]. In Figure 54.33a, a typical approach to prepare a SLIPS substrate is schematically shown. As a first step, the porous solid with suitable texture is prepared either from some low surface energy materials such as Teflon, or in the case of hydrophilic material, first, a porous substrate is created followed by functionalizing with a hydrophobic layer. In the next step, a lubricating fluid film (a low surface tension perfluorinated liquids, for example 3 M Fluorinert FC-70, or DuPont Krytox oils, that are nonvolatile and immiscible with both aqueous and hydrocarbon phases) is applied onto the porous solids to form an overcoating layer. With matching surface chemistry and roughness, the lubricating fluid will spread spontaneously onto the whole substrate through capillary wicking and is locked in place by the micro/nanoporous substrate. Typical microstructured surfaces used for SLIPS are shown in Figure 54.33b: (i) periodically ordered and random arrays of nanoposts functionalized with a low surface energy polyfluoroalkyl silane (left) and (ii) a random network of Teflon nanofibers distributed throughout the bulk substrate (right). For the successful fabrication of SLIPS, the following criteria must be met:

• the lubricating liquid must wick into, wet, and stably adhere to the substrate; • the solid must be preferentially wetted by the lubricating liquid rather than by the liquid to be repelled;

• the lubricating and impinging liquids must be immiscible.

54.5 Novel Applications and New Horizons

SLIPS Tilting = 5°

1 cm Physical damage

Tilting = 5°

Tilting = 5°

Crude oil

t=0s

t=1s

t=2s

Teflon AF treated flat surface Tilting > 10°

Tilting = 10°

Tilting = 5°

1 cm Crude oil Physical damage

Pinned droplet

t=0s Figure 54.34 Time-lapse showing the restoration of liquid repellency of a SLIPS substrate after physical damage, as compared to a typical hydrophobic flat surface (coated with DuPont Teflon AF amorphous

t=2s

t = 17 s

Pinned droplet

fluoropolymer) on which oil remains pinned at the damage site. (Source: Wong et al. 2011 [73]. Adapted with permission of Macmillan Publishers Ltd.)

When these design criteria are fulfilled, SLIPS provide a smooth, stable interface that completely eliminates pinning of the liquid contact line for both high- and lowsurface-tension liquids, minimizes pressure-induced impalement into the porous structures, self-heals and retains its function following mechanical damage, and can be made optically transparent. As mentioned, one of the remarkable characteristics of SLIPS is the self-healing capability. SLIPS can repeatedly restore the liquid-repellent function upon recurring, large-area physical damage as demonstrated in Figure 54.34; in this example, crude oil easily slides over the SLIPS substrate, even after physical damage. The lubricating film also serves as a self-healing coating to rapidly restore the liquid-repellent function after damage of the porous material by abrasion or impact. Due to the fluidic nature of the lubricant, the liquid simply flows toward the damaged area by surface energy-driven capillary action and spontaneously refills the physical voids within a time frame of milliseconds. 54.5.4 Recrystallization of Natural Epicuticular Waxes

As outlined in the Introduction section, nonwetting surfaces are omnipresent in nature. Inspired by their often amazing properties, there is a strong drive to mimic these natural surfaces [12]. By far, the most famous example of natural superhydrophobicity is the lotus leaf [76], but there are many more. High-resolution imaging combined with compositional analysis on many different plants has revealed that the microstructured epicuticular wax surface is decorated with a variety of nanostructures. Strong water repellency arises primarily from this hierarchical roughness; Figures 54.19 and 54.35 depict two examples (lotus and Euphorbia, respectively). The lotus leaf surface is covered with an epicuticular layer from which tubule-like nanostructures protrude outward. The nanofeatures not only give rise to the specific

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54 Toward Superhydrophobic Surfaces: Controlling Wetting Characteristics

100 μm

20 μm

5 μm

Figure 54.35 Microscopy images of Euphorbia myrsinites (bottom; field-of-view 100 μm, 20 μm and 5 μm, resp.).

wetting and self-cleaning properties, but the epicuticular wax layer also plays a major role in the optical properties of the plant surface [77]. Surface characterization of these wax layers has revealed that the tubules, with typical widths of 300 nm and lengths up to 1000 nm, are crystalline and primarily composed of nonacosan-10-ol and a small amount (0–5%) of nonacosan-4,40-diol. Most interesting for future applications is the ability to recrystallize these wax layers on a range of inorganic surfaces, including Highly Oriented Pyrolythic Graphite (HOPG), glass, silicon, and gold [78–80]. Recrystallization can be done either by solution casting or by thermal vapor deposition. In all cases, under the proper experimental conditions, the tubular wax structures form typically within a few hours. Surprisingly, the nanotubules also appear to be highly crystalline, while they originate from the amorphous precursor layer [81]. Moreover, the orientation of the nanotubules appears to depend on the nature of the supporting substrate. For example, on HOPG, the tubules are formed perpendicular to the substrate, while on a Au(111) surface, they grow parallel to the surface (see Figure 54.36). In both cases, the length and width seems to be self-regulated to specific dimensions. (A detailed description of Atomic Force Microscopy (AFM) can be found in Chapter 3.5 in Volume 1.) As another example in Figure 54.37, we show results obtained using wax extracted from leaves of the Euphorbia myrsinites. Superhydrophobicity on the leaves arises from a dense superstructure consisting of platelets. The primary component in the E. myrsinites wax has been identified as hexacosan-1-ol, which can be extracted very similar to that described above. The more symmetric shape of the molecules (no side groups) is assumed to give rise to the plate-like configurations. Preliminary results show that this wax can also be recrystallized on a silicon wafer. As shown in Figure 54.37b, a dense superstructure with nanoscale plate-like features appear, which gives rise to water contact angles up to 110∘ . 54.5.5 Directional Wetting

The ability to control liquid motion on surfaces has attracted the attention of a large scientific community, including fluid physicists, materials, and interface scientists.

54.5 Novel Applications and New Horizons

1 2

X 650 nm

(a)

980 nm

(b)

Figure 54.36 Atomic force microscopy (AFM) images of tubule growth (lotus) on HOPG (a) and Au(111) (b). Orientation perpendicular to the HOPG surface and parallel to the Au surface is clearly observed [80].

2 μm

(a)

1 μm

(b)

Figure 54.37 Nanostructured wax platelets of Euphorbia myrsinites. (a) Helium ion microscopy image of an actual plant surface and (b) redeposited wax on a Si/SiO2 wafer. The primary wax component is hexacosan-1-ol.

Smart surfaces with artificially designed wetting characteristics are highly relevant for a number of application areas, ranging from micro/nanofluidics to car windows. Directional anisotropic wetting can be achieved by a number of surface modifications. A substrate can be morphologically structured, for example by creating a set of parallel grooves [82–85]. An alternative way to create anisotropy is by selective chemical modification of a surface. Various methods to create a chemical pattern on a surface include CVD with different exposure times or diffusion-controlled silanization [86, 87], destruction of a monolayer with UV light after a CVD procedure [88], and microcontact printing [89]. An example we present here makes use of lithographic tools to pattern silicon wafers with a photoresist, which is subsequently employed to protect the wafer against CVD of PFDTS. After PFDTS deposition, the photoresist is removed, resulting in a chemically patterned surface with areas of different wettabilities, expressed by the two contact angles as shown in Figure 54.38. A recent review by Xia et al. [90] describes the latest advances in this field.

433

54 Toward Superhydrophobic Surfaces: Controlling Wetting Characteristics

θ

θ (b)

(a)

Figure 54.38 Water droplets on different surfaces. (a) Bare SiO2 with a high affinity for water and therefore a low contact angle 𝜃 = 40∘ . (b) SiO2 with a hydrophobic selfassembled monolayer with a low affinity for water and a large contact angle 𝜃 = 106∘ .

54.5.5.1 Anisotropic Wettability

A typical chemically patterned surface is displayed in Figure 54.39, with stripes of high and low wettability (referred to as dry and wet stripes, respectively). Droplets deposited on these stripes typically exhibit an anisotropic shape [88, 92, 93]. When Hydrophobic: PFDTS F F F

F F F FF F F

F

FF FF

F F

Cl Si Cl Cl

(a)

Parallel Θ⊥

Length

Hydrophilic: SiO2

10 μm

WPFDTS WSiO

434

Width

y Perpendicular Θ|| (b)

x

Figure 54.39 (a) Scanning electron microscopy image of a stripe-patterned surface consisting of hydrophilic SiO2 and hydrophobic PFDTS regions, as indicated. (b) Schematic top-view representation and photograph of an anisotropic glycerol

droplet on the striped surface. Note that the actual number of lines underneath the droplet is much larger than is shown in the sketch. (Source: Kooij et al. 2012 [91]. Reprinted with permission of Elsevier.)

435

54.5 Novel Applications and New Horizons

the three-phase contact line moves in the direction perpendicular to the stripes, it alternatingly crosses wet and dry stripes, giving rise to a stick-slip motion [94, 95]. For spreading in the direction parallel to the stripes, the contact line “feels” both stripes simultaneously. The energy barrier for spreading in the parallel direction does not depend on location, as it does for the perpendicular direction. The shape of a droplet on a chemically striped surface depends on the energy landscape, which, in the perpendicular direction, is mainly governed by the contact angle of the dry stripe, and in the parallel direction on the relative width of the stripes, which is expressed by the parameter 𝛼 𝛼=

𝑤PFDTS 𝑤SiO2

(54.15)

where 𝑤PFDTS and 𝑤SiO2 are the widths of the hydrophobic and hydrophilic stripes. The length of the droplet is always taken in the direction parallel to the stripes, while the width of the droplet is always taken in the direction perpendicular to the stripes. The aspect ratio of the droplet is defined as the length divided by the width. In Figure 54.40, the aspect ratio of 1 μl glycerol droplets is plotted as a function of 𝛼, as well as the contact angles. The contact angles for glycerol amount to 40∘ and 106∘ on the wet and dry pristine surfaces, respectively (see Figure 54.38). For low 𝛼, the droplet is markedly elongated; it is more favorable to spread in the direction parallel to the stripe than perpendicular to the stripes. The contact angle in the parallel direction is also close to the contact angle on the wet stripe. When 𝛼 is increased, the elongation decreases. The droplet becomes more spherical, exhibiting an aspect ratio slightly above unity for 𝛼 = 6.0. The perpendicular contact angle remains almost constant as a function of 𝛼, only for the smallest values, the contact angle becomes lower. The contact angle in the parallel direction shows a clear dependence on 𝛼 and is actually defined by the relative widths of the stripes. The 110

3.5

100 Contact angle (°)

Aspect ratio

3 2.5 2 1.5

80 70 60

CA|| CA⊥ CB eq

50 40

1 0

(a)

90

1

2

3 α

4

5

6

(b)

0

1

2

3 α

4

5

Figure 54.40 (a) Aspect ratio and (b) direc- represented by circles and squares, respectional contact angles as a function of the rel- tively. The dashed curve in (b) represents the ative stripe width 𝛼 for 1 μl glycerol droplets. modified Cassie–Baxter equation. Parallel and perpendicular contact angles are

6

436

54 Toward Superhydrophobic Surfaces: Controlling Wetting Characteristics

Cassie–Baxter equation was adapted to incorporate the parameter 𝛼 ( cos 𝜃 + 𝛼 cos 𝜃 ) wet dry 𝜃|| = arccos 1+𝛼

(54.16)

where 𝜃|| is the parallel contact angle, and 𝜃wet and 𝜃dry are contact angles on the high and low wettable stripe, respectively. A good agreement between the Cassie–Baxter equation and the data points is observed in Figure 54.40. 54.5.5.2 Wettability Gradients

In the aforementioned example of chemically patterned surfaces, the variation of the parallel contact angle can be used to achieve a surface energy gradient. Consecutive patterns with different 𝛼 values enable control over liquid motion on these surfaces. In Figure 54.41, the asymmetric droplet shape on a surface with different macroscopic wettability on both sides is shown. The droplet velocity is generally determined by the balance of a driving capillary force and opposing forces [96, 97]. Typically, the driving force originates from the unbalanced Young’s force related to the contact angles on either side of the droplet. An intuitive expression can be given for a ribbon of unit length F = 𝛾LV (cos 𝜃A − cos 𝜃B )

(54.17)

where 𝛾LV represents the surface tension of the liquid. Ideally, as soon as 𝜃B < 𝜃A , the ribbon will experience a net driving force and will start to move. For a droplet with a spherical cap shape with radius R, the driving force is given by [96] ) ( d cos 𝜃 (54.18) FY = πR2 𝛾LV dx V θB

θA A (a)

B Surface tension gradient PFDTS

SiO2

y

(b)

x

Figure 54.41 (a) Schematic representation between striped patterns with different valof a sessile droplet on a wettability gradient ues for 𝛼. (Source: Kooij et al. 2012 [91]. changing from hydrophobic to hydrophilic. Reprinted with permission of Elsevier.) (b) Schematic droplet shape in the boundary

54.5 Novel Applications and New Horizons

Two opposing forces hinder the movement of the droplet. Viscous drag, also referred to as the friction force, slows down the droplet as soon as its starts to move. However, typically, the viscous drag is orders of magnitude smaller than the driving force [98]. As such, droplets are expected to move on all surfaces as soon as the contact angles on both sides of the droplet are different. In actual experimental situations, contact angle hysteresis provides an additional energy barrier for droplet motion [97]. In Figure 54.42, typical sequential top-view images of droplet motion over linear (panel A) and radial (panel B) patterned surfaces are shown [99, 100]. When the three-phase contact line touches the boundary between striped patterns, wider SiO2 stripes result in higher overall surface energy inducing a preferential spreading direction. Sequentially, the contact line reaches patterns with decreasing 𝛼 values, i.e. with increasing hydrophilicity. At the outer border of the patterned areas, the droplet spreads on the unpatterned SiO2 . Although the liquid is on the stripe-patterned surface areas, motion in the direction perpendicular to the stripes is hindered effectively confining the droplet, thereby enhancing its motion in the direction along the stripe direction. Only when the droplet starts to spread isotropically on SiO2 , does it become wider in the perpendicular direction. Although a full quantitative analysis of the liquid motion lies outside the scope of this chapter, it is worth summarizing that using linear stripe-patterned surfaces, (a)

0.4 s

(b) 2.6 s

(c)

5.4 s (d)

(A)

(B)

Figure 54.42 Top-view images of droplets on surfaces patterned with a wettability gradient. (A) 2 μl glycerol/water droplet on a striped pattern constituting 1 mm wide regions with decreasing 𝛼 values of 0.9, 0.5, and 0.3. (B) 1 μl glycerol droplet on a radially

striped pattern. Around the circular central area (1.4 mm diameter) are two 1mm wide annular regions with 𝛼 values of 0.5 and 0.25. (Kooij et al. 2012 [91]. Adapted with permission of Elsevier.)

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54 Toward Superhydrophobic Surfaces: Controlling Wetting Characteristics

liquid droplets can be moved over distances up to several millimeters, typically 3–5 mm, with velocities as large as 10 mm/s. For the radial patterns, lower velocities are typically observed, which can be ascribed to the reduced confinement between the radially oriented stripes as compared to the parallel stripes of the linear patterns. Obviously, the viscosity of the liquid has a pronounced effect. For lower viscosity, the velocities will be markedly larger.

References 1. Koch, K., Bhushan, B., and Barthlott,

2. 3. 4.

5. 6. 7. 8. 9. 10.

11.

12.

13. 14. 15. 16. 17. 18. 19.

W. (2009). Progr. Mater. Sci. 54: 137–178. Koch, K., Bhushan, B., and Barthlott, W. (2008). Soft Matter 4: 1943–1963. Barthlott, W. and Neinhuis, C. (1997). Planta 202: 1–8. Chang, F.M., Hong, S.J., Sheng, Y.J., and Tsao, H.K. (2009). Appl. Phys. Lett. 95: 064102. Gao, X.F. and Jiang, L. (2004). Nature 432: 36. Zheng, Y.M., Gao, X., and Jiang, L. (2007). Soft Matter 3: 178–182. Zheng, Y.M., Bai, H., Huang, Z.B. et al. (2010). Nature 463: 640. Nishino, T., Meguro, M., Nakamae, K. et al. (1999). Langmuir 15: 4321–4323. Burton, Z. and Bhushan, B. (2006). Ultramicroscopy 106: 709–719. Li, X.M., Reinhoudt, D., and Crego-Calama, M. (2007). Chem. Soc. Rev. 36: 1350–1368. Zhang, Y.L., Xia, H., Kim, E., and Sun, H.B. (2012). Soft Matter 8: 11217–11231. Benyus, J.M. (2007). Biomimicry: Innovation Inspired by Nature. New York: Quill-William Marrow. Liu, K. and Jiang, L. (2011). Nano Today 6: 155–175. Darmanin, T. and Guitttard, F. (2014). J. Mater. Chem. A 2: 16319–16359. Yao, X., Song, Y., and Jiang, L. (2011). Adv. Mater. 23: 719–734. Lai, Y.K., Chen, Z., and Lin, C.J. (2011). J. Nanoeng. Nanomanuf. 1: 18–34. Chow, S.T. (2007). Nanotechnology 18: 115713. Reyssat, M., Yeomans, J.M., and Quéré, D. (2008). Europhys. Lett. 81: 26006. Raza, M.A. (2012). Colloidal routes to functionalize substrates: from selective

20. 21. 22. 23. 24. 25. 26.

27. 28.

29. 30.

31. 32. 33. 34.

35.

metallization to superhydrophobicity. PhD thesis. University of Twente. ISBN: 978-90-365-3328-7. Gao, X.F., Yan, X., Yao, X. et al. (2007). Adv. Mater. 19: 2213–2217. Parker, A.R. and Townley, H.E. (2007). Nat. Nanotechnol. 2: 347–353. Tourkine, P., Merrer, M.L., and Quéré, D. (2009). Langmuir 25: 7214–7216. Cao, L.L., Jones, A.K., Sikka, V.K. et al. (2009). Langmuir 25: 12444–12448. Mishchenko, L., Hatton, B., Bahadur, V. et al. (2010). ACS Nano 4: 7699–7707. Hong, X., Gao, X., and Jiang, L. (2007). J. Am. Chem. Soc. 129: 1478–1479. Cheng, Z.J., Feng, L., and Jiang, L. (2008). Adv. Funct. Mater. 18: 3219–3225. Wu, D., Wu, S.Z., Chen, Q.D. et al. (2011). Adv. Mater. 23: 545. de Gennes, P.G., Brochard-Wyart, F., and Quéré, D. (2004). Capillarity Ant Wetting Phenomena: Drops, Bubbles, Pearls, Waves. New York: Springer-Verlag. de Gennes, P.G. (1985). Rev. Mod. Phys. 57: 827–863. Chrader, M.E. and Loeb, G.I. (1992). Modern Approaches to Wettability. Theory and Applications. New York: Plenum Press. Koch, K. and Barthlott, W. (2009). Philos. Trans. R. Soc. A 367: 1487–1509. Fox, J. and Zisman, W. (1950). J. Colloid Interface Sci. 5: 514. Young, T. (1805). Philos. Trans. Soc. London 95: 65. Berry, J.D., Neeson, M.J., Dagastine, R.R. et al. (2015). J. Colloid Interface Sci. 454: 226–237. Holmberg, K. (2002). Handbook of Applied Surface and Colloid Chemistry, vol. 2. New York: Wiley.

References 36. Cassie, A.B.D. and Baxter, S. (1944). 37. 38. 39. 40. 41. 42. 43.

44. 45.

46.

47. 48.

49.

50.

51. 52.

53.

54. 55.

56.

57.

Trans. Faraday Soc. 40: 546. Wenzel, R.N. (1936). Ind. Eng. Chem. 28: 988. Bico, J., Tordeux, C., and Quéré, D. (2001). Europhys. Lett. 55: 214. Barbieri, L., Wagner, E., and Hoffmann, P. (2007). Langmuir 23: 1723. Bico, J., Thiele, U., and Quéré, D. (2002). Colloids Surf., A 206: 41–46. Marmur, A. (2003). Langmuir 19: 8343. Michielsen, S. and Lee, H.J. (2007). Langmuir 23: 6004. Raza, M.A., Kooij, E.S., van Silfhout, A., and Poelsema, B. (2010). Langmuir 26: 12962–12972. Lafuma, A. and Quéré, D. (2003). Nat. Mater. 2: 457–460. McHale, G., Aqil, S., Shirtcliffe, N.J. et al. (2005). Langmuir 2005: 11053–11060. Koch, K., Bhushan, B., Jung, Y.C., and Barthlott, W. (2009). Soft Matter 5: 1386–1393. Bixler, G.D. and Bhushan, B. (2012). Soft Matter 8: 11271–11284. Boreyko, J.B., Baker, C.H., Poley, C.R., and Chen, C.-H. (2011). Langmuir 27: 7502. Bormashenko, E.Yu. (ed.) (2013). Wetting of Real Surfaces. Berlin/Boston, MA: Walter de Gruyter GmbH. Hejazi, V., Moghadam, A.D., Rohatgi, P., and Nosonovsky, M. (2014). Langmuir 30: 9423–9429. Extrand, C.W. (2016). Langmuir 32: 7697–7706. Raza, M.A., Kooij, E.S., van Silfhout, A. et al. (2012). J. Colloid Interface Sci. 385: 73–80. Raza, M.A., van Swigchem, J., Jansen, H.P. et al. (2014). Surf. Topogr. Metrol. Prop. 2: 035002. Bhushan, B. (2011). Beilstein J. Nanotechnol. 2: 66–84. Vinogradova, O.I. and Belyaev, A.V. (2011). J. Phys. Condens. Matter 23: 184104. Eral, H.B., ’t Mannetje, D.J.C.M., and Oh, J.M. (2013). Colloid Polym. Sci. 291: 247–260. Wang, X., Rahman, Md.A., Jacobi, A.M., and Hrnjak, P.S. (2013). Heat Transfer Eng. 34: 1088–1098.

58. Fan, J., Wilson, M.C.T., and Kapur, N.

59. 60. 61.

62. 63.

64. 65. 66.

67. 68. 69.

70. 71.

72. 73. 74.

75. 76. 77. 78.

79. 80.

(2011). J. Colloid Interface Sci. 356: 286–292. Worthington, A.M. (1876). Proc. R. Soc. London 25: 261–272. Tsai, P., Pacheco, S., Pirat, C. et al. (2009). Langmuir 25: 12293–12298. Bolleddula, D.A., Berchielli, A., and Aliseda, A. (2010). Adv. Colloid Interface Sci. 159: 144–159. Yarin, A.L. (2006). Annu. Rev. Fluid Mech. 38: 159. Clanet, C., Béguin, C., Richard, D., and Quéré, D. (2004). J. Fluid Mech. 517: 199–208. Brown, P.S., Berson, A., Talbot, E.L. et al. (2011). Langmuir 27: 13897. Chen, L., Xiao, Z., Chan, P.C.H. et al. (2011). Appl. Surf. Sci. 257: 8857–8863. Susarrey-Arce, A., Marín, A.G., Nair, H. et al. (2012). Soft Matter 8: 9765–9770. Kota, A.K., Kwon, G., and Tuteja, A. (2014). NPG Asia Mater. 6: e109. Choi, W., Tuteja, A., Chhatre, S. et al. (2009). Adv. Mater. 21: 2190–2195. Pan, S., Kota, A.K., Mabry, J.M., and Tuteja, A. (2013). J. Am. Chem. Soc. 135: 578–581. Helbig, R., Nickerl, J., Neinhuis, C., and Werner, C. (2011). PLoS One 6: e25105. Tuteja, A., Choi, W., Mabry, J.M. et al. (2008). Proc. Natl. Acad. Sci. U.S.A. 105: 18200–18205. Ahuja, A., Taylor, J.A., Lifton, V. et al. (2008). Langmuir 24: 9–14. Wong, T.S., Kang, S.H., Tang, S.K.Y. et al. (2011). Nature 477: 443–447. Federle, W., Riehle, M., Curtis, A.S.G., and Full, R.J. (2002). Integr. Comp. Biol. 42: 1100–1106. Bauer, U. and Federle, W. (2009). Plant Signal. Behav. 4: 1019–1023. Bhushan, B. and Yung, Y.C. (2011). Progr. Mater. Sci. 56: 1–108. Müller, C. and Riederer, M. (2005). J. Chem. Ecol. 31: 2621–2651. Niemetz, A., Wandelt, K., Barthlott, W., and Koch, K. (2009). Prog. Org. Coat. 66: 221–227. Koch, K., Dommisse, A., Niemetz, A. et al. (2009). Surf. Sci. 603: 1961–1968. Dora, S.K. and Wandelt, K. (2011). Beilstein J. Nanotechnol. 2: 261–267.

439

440

54 Toward Superhydrophobic Surfaces: Controlling Wetting Characteristics 81. Koch, K., Dommisse, A., and Barthlott,

82.

83.

84. 85.

86. 87. 88.

89.

90.

W. (2006). Cryst. Growth Des. 6: 2571–2578. Gau, H., Herminghaus, S., Lenz, P., and Lipowsky, R. (1999). Science 283: 46–49. Chung, J.Y., Youngblood, J.P., and Stafford, C.M. (2007). Soft Matter 3: 1163–1169. Dorrer, C. and Rühe, J. (2007). Langmuir 23: 3179–3183. Kusumaatmaja, H., Vrancken, R.J., Bastiaansen, C.W.M., and Yeomans, J.M. (2008). Langmuir 24: 7299–7308. Daniel, S., Chaudhury, M.K., and Chen, J.C. (2001). Science 291: 633–636. Yu, X., Wang, Z., Jiang, Y., and Zhang, X. (2006). Langmuir 22: 4483–4486. Morita, M., Koga, T., Otsuka, H., and Takahara, A. (2005). Langmuir 21: 911–918. Drelich, J., Wilbur, J.L., Miller, J.D., and Whitesides, G.M. (1996). Langmuir 12: 1913–1922. Xia, D., Johnson, L.M., and Lopéz, G.P. (2012). Adv. Mater. 24: 1287–1302.

91. Kooij, E.S., Jansen, H.P., Bliznyuk, O.

92. 93.

94.

95.

96. 97. 98. 99.

100.

et al. (2012). Colloids Surf., A 413: 328–333. Léopoldès, J. and Bucknall, D.G. (2005). J. Phys. Chem. B 109: 8973–8977. Bliznyuk, O., Vereshchagina, E., Kooij, E.S., and Poelsema, B. (2009). Phys. Rev. E 79: 041601. Bliznyuk, O., Jansen, H.P., Kooij, E.S., and Poelsema, B. (2010). Langmuir 26: 6328–6334. Stapelbroek, B.B.J., Jansen, H.P., Kooij, E.S. et al. (2014). Soft Matter 10: 2641–2648. Brochard, F. (1989). Langmuir 5: 432–438. Chaudhury, M.K. and Whitesides, G.M. (1992). Science 256: 1539–1544. Suda, H. and Yamada, S. (2003). Langmuir 19: 529–531. Bliznyuk, O., Jansen, H.P., Kooij, E.S. et al. (2011). Langmuir 27: 11238–11245. Bliznyuk, O., Seddon, J.R.T., Veligura, V. et al. (2012). ACS Appl. Mater. Interfaces 4: 4141–4148.

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55 Cell-Penetrating Peptides Targeting and Distorting Biological Membranes Corina Ciobanasu and Ulrich Kubitscheck

55.1 Introduction

Biological membranes play a key role for all living beings. Membranes form hydrophobic barriers that allow living organisms to construct separate structural and functional compartments. The fundamental compartment is a biological cell representing the minimum unit of life, which can autonomously reproduce itself. Living organisms may comprise only one cell, such as bacteria, or several trillion cells, which then serve hundreds of specialized functions, as in mammals. Biological membranes are formed by lipid molecules and proteins. Lipids are small amphiphilic molecules, which can spontaneously form planar bilayers in aqueous solution. In that case, the polar residues of the lipids, the so-called lipid head groups, face the aqueous surroundings, whereas the nonpolar residues of lipids face each other and form a two-dimensional hydrophobic sheet. In biological membranes, these planar lipid bilayers contain a high concentration of proteins, to which carbohydrates may be conjugated. The composition of a native biological membrane is exceedingly complex containing numerous diverse components, which carry out a great number of specific functions. A very important function of membrane proteins is the specific and regulated transport of information and matter across the membrane barrier. Protein channels with complex structures can translocate ions such as Ca2+ or H+ in a welldefined manner. Other membrane proteins specifically bind ligands and transmit the information of ligand binding by structural rearrangements into the cellular interior. Large molecules such as soluble proteins or carbohydrates can only translocate across membranes by means of membrane pores formed by proteins of very complex composition and structure. The translocation of charged molecules across the nonpolar central layer is virtually impossible without the help of the mentioned transporter membrane molecules because of their high free energy of solvation. Notably, this was the scientific consensus until the 1980s. Then, a class of water-soluble biological molecules was discovered that could seemingly traverse biological membranes without any energy-supplying processes. These results were very controversially discussed for Surface and Interface Science: Liquid and Biological Interfaces, First Edition. Edited by Klaus Wandelt. © 2020 Wiley-VCH Verlag GmbH & Co. KGaA. Published 2020 by Wiley-VCH Verlag GmbH & Co. KGaA.

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55 Cell-Penetrating Peptides Targeting and Distorting Biological Membranes

many years until this surprising fact was generally accepted about 10 years later. In this chapter, we will shortly review the discovery of these so-called cell-penetrating peptides (CPPs), discuss the meanwhile accepted translocation modes of action, and highlight new developments in the field. For one of the most prominent and widely used CPPs, the so-called trans-activator of transcription (TAT) peptide corresponding to the amino acids 48–57 of a poly-cationic protein, the TAT protein of the human immunodeficiency virus (HIV-1), we will discuss the current state of knowledge and its applications in more detail.

55.2 Definition

CPPs are short sequences of up to 40 amino acids capable to traverse lipid bilayers, which usually represent strict barriers for water-soluble molecules. CPPs have been shown to pass the plasma membrane of living cells in a nondestructive manner and enter cells by various passive and active mechanisms. The direct passage of membranes without any cellular enzymatic processes is designated as translocation. Because of this ability, CPPs can facilitate the intracellular delivery of numerous molecular cargoes in a nontoxic manner, which is extensively employed in cell biology, medical, and pharmaceutical studies.

55.3 Discovery of CPPs

The first CPP was discovered in the late 1980s, when two laboratories independently found that the transcription trans-activating (TAT) protein of HIV-1 was efficiently internalized in different cell lines [1, 2]. In 1991, the Drosophila Antennapedia transcription factor (later named Penetratin) was shown to translocate across cell membranes and enter the cellular interior [3]. These discoveries opened the new field of protein transduction domains (PTDs) and were followed by the revelation that even short sequences of these proteins exhibited membrane-crossing properties. Thus, in 1997, the group of Lebleu identified the minimal section of the TAT protein necessary for cellular uptake [4]. One year later, the group of Langel introduced the term “cell-penetrating peptide” for the first chimeric peptide carrier, transportan, which was derived from the N-terminal of the neuropeptide galanin and linked to mastoparan, a wasp venom peptide [5]. Cellular uptake of transportan was not mediated by endocytosis, as it could not be blocked by treating cells with phenylarsine oxide and occurred efficiently at 37, 4, and 0 ∘ C. Since then, the field of CPPs became of great interest and the number of known CPPs increased considerably. Almost 2000 of such peptides were meanwhile identified (see CPPsite 2.0, the internet database of CPPs, http://crdd.osdd.net/raghava/ cppsite). CPPs have been demonstrated to enter a large number of different cell types and were used as vectors for a plethora of cargos such as drugs, proteins, imaging

55.4 Classification of CPPs

and radiotherapeutic agents, gold nanoparticles, nucleotides, DNA and even huge cargoes such as liposomes, or magnetic nanoparticles [6]. Low cytotoxicity and the capability to transport many different types of cargo across the cell membrane made CPPs optimal candidates for the delivery of therapeutic molecules. The exact molecular mechanisms, by which translocation of CPPs across membranes occurs, are not easy to uncover. Meanwhile, it became clear why this is so difficult: there is no unique translocation mechanism. Rather, the translocation process depends on numerous parameters such as the chemical nature of the CPPs in terms of their amino acid sequence and possible chemical modifications thereof, concentrations, the molecular composition of the membrane, the chemical nature of the cargo, and the topology of the membrane system. This creates a huge parameter space, in which numerous different translocation mechanisms exist. As a consequence, there exists a huge – mostly descriptive – research literature on CPP action and applications.

55.4 Classification of CPPs

CPPs can be classified according to their origin in naturally occurring and synthetic or chimeric peptides. However, a classification based on their structural and chemical properties is more meaningful in order to systematically study and understand the mechanisms of CPP internalization. Thus, CPPs can be divided into cationic, amphipathic, and hydrophobic peptides [7]. Here, we will focus on cationic and amphipathic peptides, as they are mostly used and studied. Figure 55.1 shows some representative peptides for these classes. TAT and nona-arginine (R9) possess a high positive net charge and represent typical cationic penetrating peptides. The number

TAT: GRKKRRQRRRPPQ VP22: NAKTRRHERRRKLAIER SV40: PKKKRKV (nuclear localization)

Cationic

Polyarginines: R8, R9, R10, R12 DPV3: RKKRRRESRKKRRRES DPV6: GRPRESGKKRKRKRLKP R6H4: RRRRRRHHHH

Natural

CPPs

Synthetic

Penetratin: RQIKIWFQNRRMKWK MPG: GLAFLGFLGAAGSTMGAWSQPKKKRKV pVEC: LLIILRRRIRKQAHAHSK

Amphipathic

MAP: KLALKLALKALKAALKLA Pep-1: KETWWETWWTEWSQPKKKRKV TP10: AGYLLGKINLKALAALAKKIL (chimeric of galanin and mastoparan transpotan) CADY: GLWRALWRLLRSLWRLLWRA (chimeric peptide PPTG1)

Figure 55.1 Examples of typical CPPs. Peptide sequences are given in the one-letter amino acid notation.

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of arginine residues can vary, and studies demonstrated that at least six positive charges are required for an efficient peptide uptake [8]. Amphipathic CPPs consist of hydrophilic and hydrophobic surface domains and can be subdivided into primary and secondary amphipathic peptides. The mentioned transportan 10 (TP10) is a primary amphipathic CPP and contains both polar and nonpolar regions [5]. The hydrophobicity of primary amphipathic CPPs is essential for the cellular uptake. They bind strongly to both neutral and anionic lipid membranes, and their internalization mechanism depends on the membrane potential and composition [9]. Secondary amphipathic CPPs are amphipathic only because of the secondary structure, usually an α-helix or β-sheet, that they assume in contact with hydrophilic/hydrophobic liquid interfaces. Then, they form one hydrophobic face while the opposite may be cationic, anionic, or polar. Unlike primary CPPs, the secondary amphipathic CPPs have a greater affinity to anionic than neutral membranes because of electrostatic interactions [10]. Proline-rich amphipathic CPPs show a well-defined secondary structure because of their pyrrolidine ring, which is designated as polyproline II (PPII). PPII is a lefthanded extended helix of three amino acids per turn [11]. Several proline-rich CPPs have been reported: bactenecin-7 (Bac7) [12], the synthetically designed peptides SAP [13], (PPR)n, and (PR)n (where n = 3, 4, 5, and 6) [14]. Hydrophobic peptides contain only nonpolar residues, having a low net charge or have a hydrophobic motif that is essential for uptake [11]. A recent review suggested a new classification for CPPs based on the respective mechanism of internalization, which are (i) efficient plasma membrane lysis, (ii) spontaneous membrane translocation, (iii) uptake by energy-dependent endocytosis, followed by nondestructive endosomal release, (iv) transient plasma membrane disruption, and (v) uptake by energy-dependent endocytosis followed by endosomal membrane disruption [15].

55.5 Modes of Action

CPPs can be used as vectors to deliver a great variety of cargo molecules or particles into living cells. In contrast to other traditional techniques to accomplish this goal, such as microinjection and electroporation, CPPs are internalized without largescale destruction of the membrane integrity [16]. Although CPPs have been widely used to deliver cargo molecules into cells, the exact uptake mechanism of these peptides is still a topic of debate, although significant progress has been made. Meanwhile, there is evidence for two fundamentally different mechanisms of internalization: energy-independent penetration of membranes, which is designated as translocation and sometimes as transduction, and endocytic uptake by one or more of the three endocytic routes for ingestion of molecules or small particles into cells: clathrin-dependent endocytosis, caveolae-mediated endocytosis, or macropinocytosis [17]. All endocytic uptake pathways require metabolic energy of the ingesting cell. The mode of entry into the cell of a specific CPP is influenced by a variety of

55.5 Modes of Action

Passive mechanism

Active mechanism

Caveolae and clathrin independent endocytosis CPP Cell membrane

Macropinocytosis Caveolae or clathrin mediated endocytosis Inverted micelles Barrel-stave/ toroidal pore

Carpet

Direct penetration

Endosome

Endosomal escape

Nucleus Cytoplasm

Figure 55.2 Different mechanisms for CPP internalization. CPPs or CPP–cargo complexes enter cells via endocytosis pathways (clathrin-dependent, caveolaedependent, clathrin-independent, and caveolae-independent) or macropinocytosis.

After endocytic capture, CPPs can escape from endosomes and be released into the cytosol. On the left side, energy-independent pathways are shown: micelle formation, pore formation, and carpet-mediated transfer.

factors: the physiochemical properties of the CPP such as concentration, length, secondary structure, charge distribution, and hydrophobicity and the properties of the conjugated cargo molecule such as its type, size, and charge (see Figure 55.2). For example, TAT conjugated to large protein cargoes was reported to be ingested by caveolae-mediated endocytosis [18]. However, TAT conjugated to small molecules, e.g. fluorophores, can be directly transduced [19] and was reported to be taken up by clathrin-mediated endocytosis [20] (although this was challenged in Ref. [19]). Presumably, the pathway of cell entry also depends on the target cell type [21, 22]. 55.5.1 Endocytosis

Macropinocytosis is a nonselective endocytic route used by cells to ingest bigger particles and fluids with solutes [23]. This process begins with the formation of large endocytic vesicles, the so-called macropinosomes, which may have diameters beyond 200 nm and are generated by actin-driven circular ruffles of the plasma membrane. Usually, cargoes are delivered to the early endosomes after

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internalization. Endosomes consist of either vesicular (clathrin- or caveolin-coated vesicles) or tubular intermediates. Clathrin-mediated endocytosis is the best studied active internalization pathway. It is initiated by the binding of a ligand molecule to its receptor. Then, the uptake of material and formation of an endocytic vesicle are mediated by the cytosolic protein clathrin. The size of formed vesicles depends on the cargo and is smaller than that of macropinosomes, usually significantly less than 200 nm in diameter [24]. A broad variety of cargo molecules are internalized by this pathway, which is often complementary to macropinocytosis [25]. Caveolae-mediated endocytosis, also known as lipid raft-mediated endocytosis, is a dynamin-dependent and receptor-mediated pathway. Caveolae are plasma membrane buds coated with the protein caveolin existing on the surface of many, but not all, cell types. Caveolin, the structural protein of caveolae, is a cholesterol-binding protein. The formed vesicles are relatively small, approximately 50–100 nm in diameter [26], limiting the uptake of big CPP–cargo complexes by this pathway. It is generally accepted that the electrostatic interactions mediate the first contact between the cationic CPPs and the cell surface. The extracellular matrix and the cell surface proteoglycan platform are also involved [27]. Their side chains are glycosaminoglycans, which are highly negatively charged. CPPs trigger the remodeling of the actin network and selectively activate the small GTPase Rho A or Rac1 [10, 28]. Concentration is a further parameter relevant for the cellular uptake of CPPs. Thus, at low concentrations of CPP-cargo conjugates, the internalization is associated with an energy-dependent endosomal pathway depending on cargo properties [29]. In contrast, at high concentrations, the uptake is partially associated with energy-independent processes [30]. In most cases, when CPP-cargo conjugates are internalized, only 2% of the delivered cargos are biologically active, the rest end up trapped in endosomes [31]. From here, they are directed to either recycling routes for exocytosis or to degradation. Thus, the CPP-cargo conjugates need to escape from the endosomes for exerting their intended biological effects. This can be achieved either by traversing or by rupturing the endosomal membrane. 55.5.2 Membrane Translocation 55.5.2.1 Types of Mechanisms

Three different mechanisms have been suggested, by which CPPs can be internalized without utilizing an enzymatic membrane transport system: direct membrane penetration [32], transfer by the formation of inverted micelles [33], or a translocation similar to the action of antimicrobial peptides killing bacteria [34, 35]. The classical models that describe the transport of antimicrobial peptides across bacterial membranes are the toroidal pore model, the barrel-stave model, and the carpet model [36]. The carpet model is a widely used model that describes defects in the lipid bilayer induced by coverage with CPPs. It is assumed that peptides accumulate on the membrane surface with their hydrophobic surfaces facing the membrane

55.5 Modes of Action

and their hydrophilic surfaces facing the solvent, thus forming a “carpet.” As soon as a critical local concentration is reached, the CPPs disrupt the membrane bilayer in a detergent-like manner. The pore model is related to the phenomenon of flip-flop of phospholipids between both leaflets of the membrane. Here, it is thought that a limited number of peptides assemble on the membrane surface and then enter into the bilayer with the hydrophobic regions associating with the lipid core, whereas the hydrophilic regions of the peptides remain associated with the phospholipid head groups. When a threshold concentration of peptides is reached, transient pores may be formed. In the barrel-stave model, the peptides enter with the hydrophobic regions interacting with the lipid bilayer and the hydrophilic regions facing the lumen of the pore, perpendicular to the plane of the bilayer forming the “staves” in a “barrel”-shaped cluster. Direct CPP translocation into cells can be studied by either knocking down the genes responsible for specific endocytic cellular pathways, by chemical inhibition of the different endocytic pathways, or by simply lowering the temperature to 4 or 0 ∘ C to inhibit any active transport processes [19]. However, experiments with model lipid systems completely avoid the presence of any possibly distorting active cellular processes and thus early proved their utility for the understanding of the molecular peptide–membrane interactions and translocation (see e.g. the review by [37]). In general, the interactions and orientations of peptides in lipid bilayers depend on the sequence of the CPPs, concentration of peptides, the nature of lipids, cargo, and the characteristics of the chosen model system. We will shortly present the current understanding for cationic and amphipathic CPPs. 55.5.2.2 Cationic Peptides

The observation that strongly charged cationic peptides can cross membranes was especially astonishing because of the high Born energy that such peptides should exhibit in the hydrophobic membrane environment [38]. At low-to-moderate cationic peptide concentrations or in model bilayers containing only small amounts of anionic lipids, charged CPPs such as TAT or R9 bind to, but neither translocate nor disrupt, membranes [37, 39]. At high concentrations, these peptides can effectively cross anionic model bilayers, e.g. membranes of giant unilamellar vesicles (GUVs), containing anionic lipids at a mole fraction greater than 0.3. Disruption of the bilayer structure by pore formation was also observed [37]. Not surprisingly, the affinity of cationic CPPs for anionic membranes is high and the electrostatic contribution leads to a decrease of the Born energy [40]. Herce et al. showed by molecular dynamics (MD) simulations that R9 is able to induce transient pores in the bilayer [41]. Also, R9 increases the membrane fluidity and make the lipid bilayer more prone to reorganization. Greater details for the behavior of cationic peptides are given below for a representative example of this class, the TAT peptide. 55.5.2.3 Amphipathic Peptides

In contrast to cationic peptides, the amphipathic TP10 does not require the presence of anionic lipids for translocation. Using confocal microscopy, it was shown that TP10 readily translocates into the interior of GUVs made from zwitterionic

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phosphatidylcholine (PC) only [42]. This observation suggested that the interaction between primary amphipathic CPPs and membranes is governed by hydrophobic interactions [43]. The affinity of Penetratin – a secondary amphipathic CPP – for membrane bilayers was studied using isothermal titration calorimetry of fluorescently labeled peptides [44]. The binding constant is ∼106 M−1 for large unilamellar vesicles (LUVs) containing ≥20% anionic lipids and drops significantly for less anionic LUVs [45]. It was also shown that the affinity of Penetratin for membrane is driven by enthalpy. Thus, the binding of this CPP to bilayer surfaces is rather governed by the “nonclassical” hydrophobic effect [46], although this notion has been questioned in Ref. [47]. Binding of secondary amphipathic CPPs to membranes involves an exothermic conformational transition of CPPs from a random coil to α or β motifs that contributes to the binding energy [48]. For example, Penetratin is a random coil in the presence of zwitterionic lipids. Increasing the percentage of anionic phosphatidylglycerol (PG) lipids in the composition of the membrane Penetratin adopts a helical conformation [49, 50]. Lamazière et al. showed that Penetratin induces a negative membrane curvature, which resulted in the induction of membrane tubulations and invaginations in GUVs that mimic an endocytic uptake [51]. Consequently, they designated this process as “physical endocytosis.” It is inhibited in liquid-ordered (Lo) membranes, but favored in liquid-disordered (Ld) membranes. This suggests that internalization of such peptides requires a certain lipid mobility. Lamaziere et al. further showed that Penetratin is able to induce phase separation of membrane bilayers in combination with Penetratin-rich cluster formation [52]. They suggested that the increase of peptide local concentration on separated membrane domains then leads to membrane-negative curvature causing undulations, vesiculation, and/or tube formation and possibly inverse micelles. These stark alterations of membrane topology would cause transient pores and influx of the peptide. In summary, for primary amphipathic CPPs, the hydrophobic contribution is essential to membrane binding, while for secondary amphipathic CPPs, the electrostatic contribution increases with the percent of anionic lipids in membranes. 55.5.2.4 Role of Membrane Composition and Lipid Topology

The lipid topology determines the local curvature of bilayers [53]. Cone-shaped lipids such as phosphatidylethanolamine (PE) can locally induce negative membrane curvatures, whereas cylinder-shaped lipids such as phosphatidylserine (PS), PG, and PC form planar bilayers. It was observed that accumulation of CPPs in LUVs is less efficient than in GUVs. To explain this behavior, Persson et al. proposed a correlation between membrane curvature, membrane tension, and the ability of Penetratin to translocate across model membranes [54]. In general, peptides favoring negative curvatures are more efficient in membrane translocation [55]. The orientation and insertion of a peptide in the bilayer depends not only on its secondary structure but also on the lipid composition of the membrane. Pep-1 is inserted perpendicular to the surface into the hydrophobic domain of

55.6 Application Aspects

1,2-dipalmitoyl-sn-glycero-3-phosphocholine (DPPC) membrane, whereas in more fluid membranes, such as 1-oleoyl, 2-palmitoyl-sn-glycero-3-phosphocholine (POPC), it shows no preferred orientation [56, 57]. The interaction of CPPs with charged vesicles strongly depends on the model system and is quite different for small unilamellar vesicles (SUVs) compared to LUVs [50]. Generally, the translocation depends on the membrane composition and peptide chain length [58, 59]. Recent experiments with vesicles made from native plasma membranes containing the native lipid mixture, membrane proteins, and glycoconjugates revealed that CPPs such as TAT, Penetratin, and TP10 are internalized without membrane disruption [60]. The translocation of CPPs across these native membranes usually occurs through Ld membrane domains low in cholesterol and sphingomyelin [61].

55.6 Application Aspects 55.6.1 Clinical Application of CPPs

CPP-modified nanocarriers are very promising tools for pharmaceutical research in the field of intracellular delivery of drugs to selected tissues [62]. A “smart” nanocarrier conjugated with TAT was recently developed to be sensitive to the extracellular matrix metalloprotease 2 (MMP2), an enzyme upregulated in tumors [63]. These delivery platforms are composed of liposomes loaded with the drug, hydrophilic, and flexible, long polyethylene glycol (PEG) chains to prevent nonspecific interactions and extend their circulation time, an antinucleosome monoclonal antibody (mAb 2C5) for specific targeting of the carrier, a linker sensitive to MMP2 between PEG and liposome, and TAT peptides. The nanocarriers specifically target tumor sites by action of mAb 2C5 and are retained there because of the enhanced permeability and retention (EPR) effect typical for malignant tumor tissue. Then, MMP2 from the tumor microenvironment cleaves the MMP2-sensitive linker and removes the protective long-chain PEG. Thus, TAT is exposed and facilitates the cellular internalization. Such nanocarrier delivery systems optimize the internalization of drug molecules in cancer cells. Another recent study exploited CPPs as bioenhancers for the nasal delivery of interferon beta (IFN-β) and IFN-β in its PEGylated form (PEG-IFN-β) in rats. It was shown that a noncovalent administration strategy of Penetratin combined with PEG-IFN-β significantly increased the nasal absorption of the drug and extended the retention time in a dose-dependent manner. Also, the toxicity assessments showed no damage to the epithelial membranes after nasal absorption [64]. Gene therapy is a further application field with promising perspectives for CPPs. CPPs conjugated with plasmid DNA represent a classical tool for restoration or replacement of functioning, respectively, malfunctioning genes [65]. Arginine-rich CPPs (SR9, HR9, and PR9) were successfully used to transfer plasmid DNA into human cells in a noncovalent manner [66]. Liu et al. also reported that the

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treatment with calcium chloride did not facilitate transfection efficiency with CPPs but improved the gene expression intensity. Delivery of small interfering RNA (siRNA) has also promising therapeutic applications in viral infection treatments, hereditary disorder, or cancer treatments [67]. Until now, phosphorodiamidate morpholino oligomers (PMOs) are the most widely used antiviral cargos conjugated to CPPs [68]. CPP-PMO conjugates are water-soluble, nuclease-resistant, and act as steric-blocking antisense agents because of the formation of stable duplexes with complementary RNA and reduce viral replication and increase survival in infected mice [69]. A new developed cationic lipid peptide, Deca-(R)8, was shown to inhibit duck hepatitis B virus replication by a novel antiviral mechanism, without targeting the viral polymerase [70]. A large number of preclinical studies on CPP-based therapies have been performed in the past two decades, but only a few peptides are currently in actual clinical trials. Thus, CPPs and CPP-drug conjugates were successfully used to deliver chemotherapeutics (e.g. doxorubicin, methotrexate, cyclosporine A, and paclitaxel) in muscular dystrophy, cardiology, prion diseases, and both viral and bacterial infections [69]. The first clinical trial involving a CPP was a cyclosporine– polyarginine conjugate for treatment of the skin disease psoriasis (PsorBan1 by CellGate Inc.) [71]. The drug entered phase II of clinical trials in 2003, but it was eventually discontinued. At present, CPPs involved in clinical trials include AZX100 (Capstone Therapeutics), a peptide that mimics heat shock proteins (HSP20), KAI9803 (KAI Pharmaceuticals), a protein kinase C𝛿 inhibitor-TAT conjugate for myocardial infarction, pain, and cytoprotection/ischemia, and XG-102 (Auris Medical) for curing intraocular inflammation and hearing loss [65]. 55.6.2 Targeting CPPs

CPPs are generally accepted as putative unique tools to overcome the cell membrane barrier and carry different cargo molecules into the cellular interior, but their low specificity limits their application for targeted delivery of drugs or imaging agents. In the attempt to overcome this drawback, numerous studies offering new ideas to increase the specificity of CPPs were performed. As mentioned above, one possibility is bioconjugation of CPPs with “smart” molecules. These “smart” nanocarrier delivery systems are sensitive to local environmental conditions typical for certain physiological conditions as they occur in cancer or in infarct inflammatory tissue such as lower pH, higher temperature, altered redox potentials, and diseaseassociated proteases [72]. Also, there exist smart carriers that can be activated by external triggers such as heat, radiation, ultrasound, radiofrequencies, or magnetic fields [73]. Improved active tumor targeting includes the use of protein ligands for special cell surface receptors [74] that can be constructed based on monoclonal antibodies, aptamers, or peptides. The major advantage of receptor-mediated nanocarrier targeting is their accumulation on or within targeted cells and tissues for longer periods of time, thus avoiding the fast redistribution into systemic circulation [75]. CPP-mediated delivery of bioactive molecules has the advantage to allow reducing

55.6 Application Aspects

the administered dose, which also moderates putative unwanted side effects of drugs on healthy tissues. There are two ways to deliver cargo molecules into cells with CPPs: either by covalent or by noncovalent conjugation of cargo and CPP. The first one requires chemical synthesis with the cargo involving methods usually based on thioether or thiolmaleimide ester formation and click chemistry [76]. Alternatively, noncovalent complexes of CPPs and cargo may be formed. This method is often used to deliver negatively charged molecules such as nucleotides, siRNA, or large DNA plasmids [77]. A major challenge in using CPPs as a cargo delivery system is the efficient release of cargo from endosomes after the endocytosis-mediated uptake into the cell of the peptide–cargo complex. Endosomes have an important role in the degradation of molecules and also in recycling of internalized molecules and receptors to the cell surface [7]. There are several strategies that exploit the low pH conditions of endosomes to enhance the intracytoplasmatic delivery such as fusogenic peptides, pH-sensitive polymers, pH-sensitive core–shell nanoparticles, and pH-sensitive liposomes [78]. Another solution is to use spontaneous membrane-translocating peptides, which deliver cargos under avoidance of endocytosis [79]. 55.6.3 Cell-Penetrating Homing Peptides

Over the past few years, much attention has been paid to so-called homing peptides, a class of peptides that bind after systemic delivery specifically to either normal tissues or tissues in pathological conditions [80]. Each healthy organ displays a specific molecular signature, a “zip code” system on their vasculature, which is modified under pathological conditions. This can be exploited to target cargo molecules using peptides that aim for such molecular markers. Peptides homing tumor blood vessels, lymphatic vessels, and/or tumor cells or normal organs can be isolated using in vivo phage display [81]. This technique is based on genetic engineering of bacteriophages (viruses that infect bacteria) and repeated rounds of antigen- or receptorguided selection and phage propagation. In vivo phage display uses phage libraries, in which each individual phage expresses a unique peptide sequence or protein fragment on its surface [82]. This method has also been used to identify cell- and tumortype-specific CPPs [83]. So far, homing peptides are the preferred ligands to achieve specificity for drug delivery systems because of their low molecular weight, low immunogenicity, long-term storage stability, easy synthesis, and modification and low interference in vivo [84]. The first homing peptides revealed by in vivo phage display include arginine– glycine–aspartic acid (RGD) and asparagine–glycine–arginine (NGR) [85]. The RGD peptide has affinity for αvβ3 and αvβ5 integrins, which are heterodimeric proteins that mediate interactions with the extracellular matrix. They play an important role in tumor cell growth, migration, and invasion. Poor penetration of anticancer drugs into tumors often limits their efficacy. Conjugation of the anticancer agent tumor necrosis factor-α (TNF-α) with either RGD or NGR peptides significantly enhanced their antitumor activity. The required dose was approximately 1000-fold

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lower than that of unconjugated free TNF-α. Therefore, the side effects of this highly toxic cytokine could dramatically be reduced [86]. A big step forward in the field of homing peptides was the discovery of multifunctional peptides that are capable not only of tumor homing but also of membrane penetration. The tumor-penetrating peptide iRGD (CRGDK/RGPD/EC) specifically homes to αv integrins that are expressed on tumor endothelium, tumor fibroblast, and tumor cells [87]. Coadministration of iRGD and antitumor drugs facilitates their penetration into tumor tissues. The delivery of the drug is efficient even without chemical conjugation to the peptide [88]. Tumor-penetrating iRGD peptides contain a C-terminal arginine (or lysine) CendR motif, R/KXXR/K, where X designates a random amino acid, which is essential for the internalization [87]. These peptides follow an active transport pathway. After binding to integrins, the peptide is proteolytically cleaved, which leads to the C-terminal exposure of the CendR motif as the binding motif for the cell surface receptor neuropilin-1, which finally mediates penetration into the tumor cells [89]. The iRGD peptide was already tested in phase I and II clinical trials [80]. Besides iRGD, further penetrating homing peptides containing the CendR motifs have been identified, such as Lyp-1 and F3 [83]. The cell-penetrating homing peptides differ from the classical CPPs in the internalization way into cell, being tumor-type specific, while CPPs are able to enter all cells they encounter, without any selection. Therefore, the development of such peptides means a considerable advance in current cancer treatment and diagnosis methods. 55.6.4 Toxicity

In general, CPPs display a low level of cellular toxicity, which is the one of the main reasons for their application in drug delivery systems. Cationic peptides such as Penetratin, TAT, or R9 showed only low or mild toxic effects [90]. Systematic studies on arginine-based peptides comprising between 5 and 12 arginines revealed that eight was the minimal number of arginines required for cellular internalization to occur [91]. The cellular uptake of peptides increases with the number of arginines, but so does the cytotoxicity. Nona-arginine displays the best combination of high translocation efficiency and tolerable toxicity. Among cationic peptides, cytotoxicity varies in the following order: TAT < Penetratin < oligoarginines [92]. The toxicity can be modified by the conjugation with cargo molecules. For example, TAT or Penetratin linked to large cargo peptides display an enhanced toxicity compared to small cargoes or unconjugated peptides [93]. Penetratin alone has a significantly higher toxicity than TAT inducing cell death at concentrations above 10–30 μM, whereas TAT peptide shows some toxicity at a concentration beyond 100 μM. There are also reports that low-molecular-weight molecules such as carboxyfluorescein would increase CPP toxicity [94]. Large molecular cargoes are internalized by endocytosis and the lower toxicity in this case may just result from the lower bioavailability of CPP-cargo molecules in the cytoplasm [95]. Not surprisingly, amphipathic peptides such as transportan and model amphipathic peptide (MAP), which induce

55.8 Internalization of TAT Peptides

membrane leakage, are generally more toxic than R9 or TAT. The toxicity of these peptides is probably associated with their hydrophobicity [96].

55.7 Focus on TAT

The TAT peptide is the best known and mostly utilized CPP. The HIV1-TAT protein comprising 86–101 amino acids (depending on the subtype) was discovered in the context of AIDS research in 1988. This 10 kDa protein displayed a remarkable ability to penetrate cell plasma membranes [1, 2]. Further studies revealed that the minimal section required for cell penetration of this molecule was much shorter, namely the amino acids 48–57 (GRKKRRQRRR) of the protein. This peptide is designated as HIV1-TAT [4] or simply TAT. TAT is highly cationic containing six arginines and two lysines. It has been shown that the amount and rate of cellular uptake of arginine-rich CPPs strongly depends on the number of basic residues and especially arginines [91]. Thus, substitution of a noncharged glutamine residue with alanine has no effect on cellular uptake, but substitution of any of the basic residues (arginine or lysine) significantly decreases the cellular uptake. TAT peptides show a great capability to deliver a plethora of cargoes such as fluorophores, nucleotides, proteins, drugs [97], imaging and radiotherapeutic agents [98], and genes into living cells [99]. TAT peptides could not only deliver large molecules such as proteins with a mass exceeding 100 kDa but also magnetic nanoparticles with diameters of 40 nm, quantum dots and even 200 nm liposomes into the cellular interior [100, 101].

55.8 Internalization of TAT Peptides 55.8.1 Experimental Results on Cellular Systems

Growing evidence revealed both active and passive, i.e. nonendocytic, internalization pathways for TAT. Caveolin-dependent endocytosis in HeLa and HL3T1 cells was shown for TAT-avidin and a fusion protein comprising the TAT peptide and an enhanced green fluorescent protein (EGFP) [18]. Clathrin-dependent endocytosis for fluorescently labeled TAT peptides was observed in HeLa, HepG2, and Chinese hamster ovary (CHO) cells [20, 102]. Wadia et al. had shown the evidence for a macropinocytotic mechanism of TAT internalization in live mouse cells [103]. Experiments with the inhibitor of lipid raft formation methyl-β-cyclodextrin suggested that uptake of fluorescently labeled TAT peptide and 30 kDa TAT fusion proteins is mediated by lipid raft-dependent macropinocytosis in Namalwa cells [104]. Duchardt et al. showed using inhibitors for different active mechanisms, i.e. chlorpromazine, 5-(N-ethyl-N-isopropyl) amiloride, and methyl-β-cyclodextrin,

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that TAT and other arginine-rich CPPs were internalized by all three endocytic pathways, clathrin- and caveolae-mediated endocytosis and macropinocytosis, depending on peptide concentration [17]. At low peptide concentrations up to 10 μM, TAT entered through caveolae/lipid-raft-mediated endocytosis and macropinocytosis and at higher concentrations clathrin-mediated internalization was more likely. Tünnemann et al. [95] showed that TAT traverses cell membranes with high efficiency by at least two mechanisms depending on the size of the cargo. Big molecules, such as proteins or quantum dots, enter via caveolae endocytosis and macropinocytosis. Small cargoes, such as peptides of less than 30–40 amino acids, enter slowly by endocytosis and rapidly by direct translocation designated as transduction that utilizes the membrane potential. Growing evidence does not support the idea of specific cell surface receptors for TAT as the experiments with L- and D-enantiomeric peptides suggested [105]. Ziegler et al. found that internalization of a fluorescein-labeled TAT peptide was heparan sulfate dependent [106]. Further studies showed that heparan sulfatedependent uptake rely on the concentration of the peptide [17]. At low peptide concentrations (1 μM), removal of cell surface heparan sulfates had no effect on TAT peptide uptake. Instead, TAT at 100 μM was internalized only in the presence of cell surface heparan sulfate. To clarify the question of endocytic or nonendocytotic pathways of TAT translocation, Ter-Avetisyan et al. used genetically modified cell systems to inhibit the various endocytic uptake routes [19]. They excluded the pathway of clathrin-mediated endocytosis by a carefully controlled knockdown experiment. Furthermore, they showed that also caveolin-mediated endocytosis was not involved in TAT translocation because caveolin knockout cells showed an identical transduction frequency to wild-type cells. Most importantly, TAT was not excluded from cells that were gently transferred to 4 ∘ C, a state where all potential active endocytic pathways including macropinocytosis were inhibited. Thus, these experiments demonstrated – in stark contrast to many of the above cited studies – that the translocation of TAT into living cells is not dependent on any endocytic or pinocytic process, although the frequency and kinetics of TAT uptake varied between different cell types. A significant breakthrough in the understanding of the translocation mechanism of arginine-rich peptides such as TAT or R9 was achieved by [107]. An elegant combination of theoretical computations, in vitro, and live cell experiments revealed an efficient translocation mechanism of arginine-rich peptides on the basis of fatty acids and the plasma membrane pH gradient [107]. They proposed that fatty acids contained in the cellular membrane bind extracellular arginine-rich peptides at high pH, mediate their membrane transport, and release them into the lower pH environment of the cytosol. They presented in vitro experiments that demonstrated all of the major steps of this mechanism. Computational results revealed that deprotonated fatty acids reduce the free energy of insertion of arginine-rich peptides into model phospholipid bilayers, and this insertion leads to the formation of a water-filled channel across the cellular lipid bilayer. Accordingly, live-cell experiments showed that both the extracellular pH and the cell membrane fatty acid content modulate the cell transduction of arginine-rich

55.8 Internalization of TAT Peptides

peptides into living cells. Furthermore, the proposed mechanism described the puzzling cell uptake differences observed between poly-arginine and poly-lysine peptides. The suggested mechanism should be universal across cells from different species. 55.8.2 TAT Peptide and Its Interaction with Model Membranes 55.8.2.1 Membrane Binding

Model membranes are very useful to elucidate the nonendocytic entry routes of peptides because enzyme-driven, energy-dependent processes do not exist. It is generally agreed that electrostatic forces between the positively charged peptide TAT and negative charges of phospholipids play an important role for the binding of the peptide to the membrane [108]. Ziegler et al. showed by isothermal calorimetry (ITC) experiments that the electrostatic contribution is large and accounts for 80% of the binding energy between peptides and membrane [109]. Nonelectrostatic forces, such as hydrogen bonding and hydrophobic or van der Waals forces, contribute about ∼20% to the binding energy. Ziegler et al. also showed that the affinity of peptides for membranes below 37 ∘ C is enthalpy driven and that the binding entropy increases with temperature. As a result, nonelectrostatic interactions are due to hydrophobic effects. As expected, the interaction between TAT peptide and the negatively charged membrane remains unaffected by changing the chemical nature of the negative charge, such as replacing PG with PS, and it is attenuated in the presence of a high salt concentration [109]. It was suggested that efficient binding of TAT requires a fluid membrane with lipids in the Ld state [108]. We could not corroborate these results. We studied the binding of fluorescently labeled TAT peptides to membranes of GUVs by singlemolecule microscopy [110]. To this end, we generated neutral and anionic GUVs containing DPPC, 1,2-dioleoyl-sn-glycero-3-phosphocholine (DOPC), and cholesterol and containing DPPC, DOPC, cholesterol, and 1,2-dipalmitoyl-sn-glycero-3phospho-L-serine (DPPS), respectively, by electroformation. Notably, the mole fraction of anionic DPPS was chosen as 0.15. In order to obtain reference values, we first measured the diffusion of single fluorescently labeled lipid tracers (Texas Redlabeled DHPE) within Lo and Ld lipid phases by single-molecule tracking yielding a DLo of 0.6 ± 0.05 μm2 s−1 and a DLd of 2.5 ± 0.05 μm2 s−1 , respectively. The mobility was identical for neutral and anionic lipids. Figure 55.3 shows the jump distance histograms, which were the basis of the analysis. To obtain these data, the diffusional motion of single fluorescent lipid tracer molecules was observed on the GUV surface by sensitive and fast video microscopy. Single-molecule signals could be well detected and connected to single-particle trajectories. Each single-molecule trajectory was defined by coordinates (xi , yi ). The probability that a molecule starting at a given position is found in a distance r within a shell of width dr from the start position at time t is given by: p(r, t) dr =

1 −r2 ∕4Dt 2𝜋 r dr e 4𝜋D t

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55 Cell-Penetrating Peptides Targeting and Distorting Biological Membranes

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Figure 55.3 Analysis of TR-DHPE diffusion on neutral and anionic GUVs. Jump distance analysis for TR-DHPE in neutral GUVs made from (a) DPPC/cholesterol (Lo, neutral) and (b) DOPC/cholesterol (Ld, neutral). TR-DHPE moves more rapidly within GUVs known to have an Ld phase with a diffusion coefficient of D = 2.53 ± 0.05 μm2 /s compared to GUVs known to comprise an Lo phase, D = 0.6 ± 0.02 μm2 /s. (c and d) Jump distance analysis for TR-DHPE in anionic GUVs made from (c) DPPC/cholesterol/DPPS

0

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(Lo, anionic) and (d) DOPC/cholesterol/1,2dioleoyl-sn- glycero-3-phospho-L-serine (DOPS) (Ld, anionic). Here, TR-DHPE diffuse faster within GUVs with an Ld phase with a diffusion coefficient of D = 2.54 ± 0.05 μm2 /s compared to GUVs, which exhibit an Lo phase, D = 0.55 ± 0.02 μm2 /s. Thus, lipid mobility does not depend on the charge of the membrane. (Source: Ciobanasu et al. 2009 [110]. Reprinted with permission of American Chemical Society.)

This equation is valid for a single-particle species diffusing in two dimensions. Experimentally, this probability distribution can be approximated by a frequency distribution, determined by counting the jump distances within respective intervals [r, r + dr] traveled by single particles after t. In the jump distributions shown in Figure 55.3, the distances between molecular positions in successive frames were evaluated. In further experiments, the behavior of fluorescently labeled TAT peptides on the GUV surface was examined. The peptides slightly accumulated on neutral GUVs but bound very efficiently to negatively charged GUVs. Single-molecule tracking revealed that HIV1 TAT peptides move on neutral GUVs with a DTAT of 5.3 ± 0.2 μm2 /s and on anionic GUVs with a DTAT of 3.3 ± 0.2 μm2 /s (Figure 55.4). Obviously, TAT diffusion was significantly faster than lipid diffusion. Also, we found it to be independent of the phase state of the GUV membrane. Thus, we concluded that TAT peptides are not integrated into the membrane bilayer but rather “floating” on the membrane surface. The lower diffusion coefficient on

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55.8 Internalization of TAT Peptides

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Figure 55.4 Analysis of R-TAT diffusion on neutral and anionic GUVs at low concentration. Jump distance analysis for rhodamine-TAT (R-TAT) in neutral GUVs made from (a) DPPC/cholesterol (Lo, neutral) and (b) DOPC/cholesterol (Ld, neutral). (c and d) Jump distance analysis for R-TAT in anionic GUVs made from (c) DPPC/cholesterol/DPPS (Lo, anionic) and

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(d) DPPC/cholesterol/DPPS (Ld, anionic). R-TAT moves more rapidly within or on membranes of neutral GUVs with a diffusion coefficient of 5.3 ± 0.2 μm2 /s compared to anionic GUVs, D = 3.3 ± 0.2 μm2 /s. Obviously, TAT mobility does not depend on the lipid phase. (Source: Ciobanasu et al. 2009 [110]. Reprinted with permission of American Chemical Society.)

negatively charged lipid bilayers suggested that they immerged deeper into the head group domain of anionic lipids. Furthermore, we determined the peptide mobility as a function of TAT concentration in a concentration range from 150 pM to 2 μM and did not find any dependence. From this, we concluded that the peptides were independent from each other, indicating that the peptides did not form any “carpet.” These results were consistent with those of previous studies, who also did not observe a significant distortion of the membrane structure by TAT [108, 109]. 55.8.2.2 Membrane Translocation

There are experimental reports that suggest that translocation of TAT peptides depends on the curvature of model membranes. Thus, it was seen that fluorescently labeled TAT peptides can directly traverse the lipid bilayers of GUVs but not that of LUVs of the same composition [111]. Together with the topology, the composition of the membrane seems to play a very important role in TAT peptide translocation. Mishra et al. [112] showed internalization of the peptide in GUVs with diameters of 5–30 μm comprising 40% of 1,2-dioleoyl-sn-glycero-3-phosphoethanolamine (DOPE). Using synchrotron-based small-angle X-ray scattering (SAXS), they

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55 Cell-Penetrating Peptides Targeting and Distorting Biological Membranes

detected that TAT induces a negative Gaussian (“saddle-splay”) membrane curvature that might eventually lead to disruption of membranes and actual pore formation. The formation of transient, water-filled pores by arginine-rich TAT peptides was initially predicted by MD simulations [113] and later experimentally observed at the structurally related example of R9 by electrophysiology experiments on planar lipid bilayers [41]. The extensive MD simulations revealed a possible molecular mechanism for TAT peptide translocation based on the strong interactions between the cationic TAT and the negatively charged phosphate groups of the lipid bilayer [113]. The proposed mechanism was composed of five steps: (i) At low peptide concentration, the arginines and lysines bind to the phospholipid phosphate and carbonyl groups and accumulate at the interface with the carbon chains of lipids. (ii) The binding leads to an increase of the local TAT concentration and the peptides start to sequester phosphate groups from neighboring phospholipids. At a certain concentration, the positive charges of the TAT peptides from the proximal layer begin to interact with the negatively charged phosphate groups in the distal bilayer. This distorts and thins the bilayer. (iii) Because of stochastic thermal motion, arginine side chains may translocate to the distal face of the bilayer and thus nucleate the formation of transient water pores with a size of 3 nm in the membrane with a half-life shorter than 1 ms. (iv) Once the pore is formed, a few peptides may translocate by diffusing along the walls of the pore. (v) Finally, the pore closes when the negative charges on the distal bilayer are locally compensated. An explicit pore formation was also observed in our own experiments. We used confocal laser scanning microscopy (CLSM) to systematically examine the interaction of fluorescently labeled TAT with GUVs of different lipid compositions and putative peptide and tracer translocation [34]. We observed that for GUVs made of neutral phosphatidylcholine (PC) and cholesterol, peptides just accumulate on the vesicle surface without internalization. Upon systematically increasing the molar ratio of the anionic PS in the model membranes containing PC, PS, and cholesterol, accumulation of the peptide on the GUVs dramatically increased (Figure 55.5a,b). Even more, at a threshold of 40 mol% anionic PS, peptides directly translocated into GUVs (Figure 55.5c). In this case, we also observed that the interaction between peptides and some anionic GUVs was so strong that it could provoke vesicle disruption caused by membrane destabilization. Furthermore, we performed efflux experiments of GUVs that were loaded with fluorescent tracers of different molecular weights before addition of the peptides (Figure 55.6). GUVs with 40 mol% PS released small dye molecules with molecular weight up to 4 kDa, but not large molecules such as 40 or 70 kDa dextran upon incubation with 2 μM TAT (Figures 55.5c and 55.6c). We concluded that the TAT translocation into the GUVs was due to the formation of membrane pores with diameters up to 1.3–2.0 nm. Similarly to PS, we gradually increased the concentration of PE in the composition of GUVs, a cone-shaped lipid that creates local and intrinsic negative curvature. In this case, the translocation of TAT peptide across the GUV membrane was observed already at 20 mol% PE. Efflux experiments showed that only small dye molecules (AlexaFluor 647) were completely released

55.8 Internalization of TAT Peptides 250

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Figure 55.5 Interaction of fluorescently labeled TAT peptides with anionic GUVs. Confocal imaging of TAT peptide labeled with Alexa Fluor 647 (AF-TAT) interaction with anionic GUV after 30 minutes of incubation: (a) membranes with 20 mol% PS, (b) membranes with 30 mol% PS, and (c) membranes with 40 mol% PS. The plots in the upper plane quantify the intensity of fluorescent TAT peptides along a central line across the

PS 30%

(c)

PS 40%

GUVs. The internalization of TAT peptide can be seen in (c) by the equilibration of the fluorescence across the GUV membrane. Also, it can be noted that the vesicles were deformed. All experiments were done in the presence of 40 kDa FITC-dextran (lower panel). It does not pass the membrane in any of the experiments. Scale bar, 20 μm. (Source: Ciobanasu et al. 2010 [34]. Reprinted with permission of Biophysical Society.)

from GUVs, suggesting a smaller diameter for induced pores in PE than for PS containing membranes. In conclusion, membrane staining of neutral GUVs by fluorescently labeled TAT can be observed and increases with the content of PS or PE but does depend on the lipid composition. In a recent paper, the role of PS and cholesterol for TAT peptide translocation was systematically investigated by coarse-grained MD simulations [114]. Hu and Patel use DPPC/DPPS lipids as model bilayers with 0 or 20 mol% cholesterol. Thus, they reproduced the same membrane compositions that we used in our experiments discussed above. As expected, Hu and Patel observed that the addition of cholesterol stabilized the liquid-ordered membrane phase. Simultaneously, this increased the energy barrier for peptide translocation. Upon addition of the anionic PS to the membrane, however, TAT peptide translocation was favored in agreement with our experimental observations. In the simulations, even the formation of distinct toroidal pores with a hydrophilic interior was observed. Once a pore was formed, peptide translocation occurred with little energetic costs. Notably, the size of the

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55 Cell-Penetrating Peptides Targeting and Distorting Biological Membranes

R-TAT

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Figure 55.6 Pore formation by TAT peptides in GUVs with 40 mol% PS. (a) The upper panel shows the binding and translocation of 2 μM R-TAT (not quantified here, but see Figure 55.5), and the lower panel shows the leakage of Alexa Fluor 647 out of the vesicle. (b) Upper panel shows the binding and translocation of AF-TAT peptide, and

the lower panel shows the partial release of 3 kDa dextran labeled with Alexa Fluor 546. (c) Upper panel, AF-TAT; lower panel, 70 kDa dextran labeled with Alexa Fluor 546. This large molecule cannot pass the membrane. Scale bar, 20 μm. (Source: Ciobanasu et al. 2010 [34]. Reprinted with permission of Biophysical Society.)

55.8 Internalization of TAT Peptides

observed pores in the simulation with a diameter of 1.5–2.0 nm agreed well with our previous experimental results. Interestingly, Hu and Patel also examined the mobility of TAT peptides on the membrane surface in silico [114]. The peptide diffusion was found to be faster on neutral lipid systems than on the anionic membranes, which again agreed with our data (Figure 55.4). However, the diffusion constants of the simulation yielded values that were 10 times faster than our experimental results. This difference may be due to several factors: our diffusion measurements were performed at room temperature (300 K) using fluorescently labeled TAT peptides, whereas the simulation resembled 350 K and pure, unlabeled TAT peptides. Also, it is well known that it is not straightforward to translate the time unit of MD simulations to real-world data. Lattig-Tunnemann and coworkers investigated the membrane translocation of TAT and other arginine-rich peptides as a function of their structural rigidity. They examined a rigid cyclic TAT peptide and its linear and more flexible counterparts by a combination of MD simulations, analytical ultracentrifugation, and live cell microscopy [115]. MD simulations of TAT peptides on membranes composed of DOPC or DOPC:DOPE (1 : 1) suggested that TAT peptides localize in the head group region of bilayers close to the carbonyl-glycerol group. The peptide entered more frequently into the hydrophobic core of PC : PE membranes than into bilayers composed of PC lipids only. The existence of PE facilitated translocations of the peptide by stabilizing intermediate states, in which hydrated peptides span the bilayer. Lattig-Tunnemann et al. found that the maximal separation of the guanidium groups due to the cyclization enhanced the transduction efficiency of arginine-rich CPPs. They proposed that this is due to increased membrane contacts facilitated by the rigid cyclic backbone structure. The data of Lattig-Tunnemann propose that rigidity resulting in an ideal interface for interaction with membrane constituents is more important than structural flexibility to facilitate peptide transduction. They demonstrated that “needle”-like peptide structures are not required for transduction. Their findings suggest interesting alternative molecular engineering strategies for putative, more efficient vectors for transporting cargo molecules into living cells. 55.8.3 TAT Peptides for the Delivery of Therapeutic Agents

TAT peptides can be used to increase the intracellular delivery of nanocarriers loaded with poorly water-soluble drugs and of inorganic nanoparticles including silica, iron, silver, and gold nanoparticles [7]. TAT-modified silver nanoparticles display antitumor activity in both multidrug-resistant and nonresistant cancer cells, while nanosilver alone is poorly internalized [116]. Dekiwadia et al. delivered gold nanoparticles with TAT into lysosomes and lysosome-like structures with minimal toxicity [117]. The intracellular targeting using gold nanoparticles has therapeutic applications such as the near-infrared thermal ablation of tumors.

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The TAT peptide can be administered via almost all clinical routes: orally, injection (intravenous, subcutaneous, and intramuscular), transdermal delivery [118], or mucosal drug delivery [119]. Recently, Zhang et al. developed a novel recombinant fusion protein, TAT-IL-24-KDEL, as an anticancer drug [120]. Interleukin-24 (IL24) selectively induces apoptosis in cancer cells through a mechanism involving endoplasmic reticulum (ER) stress response without harming normal cells. Thus, the fusion protein is composed of three distinct functional domains, IL-24, TAT, and finally the tetrapeptide Lys–Asp–Glu–Leu (KDEL), that link at its amino and carboxy terminal. KDEL targets the fusion protein to the ER and Golgi apparatus. The natural recombinant IL-24 does not penetrate into cancer cells, unlike TAT-IL-24, which enters cells but without the ability to locate at the ER as effective as TAT-IL24-KDEL. The in vitro results indicated that TAT-IL-24-KDEL inhibited growth in bladder cancer cells, in a lung cancer cell line, and in a breast cancer cell line, but the normal human lung fibroblast cell line was not affected. This indicated a significant cancer specificity of TAT-IL-24-KDEL. More recent publications suggest that TAT peptides have a great potential in treatments of neurodegenerative diseases, malignant brain tumors, cerebral ischemia, pain, and myocardial infarction (see reviews in Ref. [121, 122]). The field is immense; here, we can only give a few examples. The antiapoptotic protein Bcl-xL conjugated to TAT was used for neuroprotection in a murine middle cerebral artery occlusion (MCAO) model [123]. In addition, TAT-BH4 domain of the Bcl-xL conjugate could decrease apoptosis in primary cardiomyocytes after intravenous injection in a myocardial ischemia mouse model [124]. In vivo evidence showed the antitumor effects of Tat-conjugated P15, a peptide inhibiting the activity of casein kinase 2 (CK2), which induced apoptosis when conjugated to TAT in different tumor cell lines [125, 126]. TAT peptide was fused to therapeutic proteins to facilitate their entry in various cell types at a dose-dependent approach [127]. For example, a large protein (β-galactosidase, 480 kDa) could be carried across the blood–brain barrier by fusion with TAT after intraperitoneal injection [128]. TAT peptides have entered different phases of clinical trials, although no therapy has yet been approved by the American Food and Drug Administration. Some examples are as follows. In 2007, subcutaneous injection of protein kinase C𝛿 inhibitor-TAT conjugates was evaluated in phase I and II clinical trials for blood flow restoration after a heart attack, spinal cord injury, or postoperative pain and prevention of ischemic injury [129]. Only one successful clinical trial for TAT showed good results and was closed in 2012 (with number NCT00728182, ClinicalTrials.gov). In this trial, TAT peptide was conjugated with NA-1, a compound that disrupts the signaling pathways that maintain the neuronal viability and inhibits the brain damage caused by reduced blood flow. This conjugate could be tested in phase II of clinical trials for reducing small embolic strokes in patients that experienced neurosurgery to repair aneurysms [29, 129]. A further phase II clinical trial was performed using TAT peptides to deliver botulinum toxin type A/wrinkles removal (RT-001) [127].

References

55.9 Summary and Conclusions

In the last two decades, CPPs have been intensively investigated and developed as vectors to deliver various kinds of cargo molecules and particles into living cells. CPPs were shown to be able to transport macromolecules across membranes to the cytoplasm, nucleus, and even through the blood–brain barrier. Different biophysical factors and physiochemical properties including charge, amphipathicity, secondary structure, length, charge distribution, concentration, and the properties of the conjugated cargo molecule such as its type, size, and charge play important roles for entry of CPP-cargo into cells. Meanwhile, it is accepted that peptides are internalized by two fundamentally different mechanisms, energy-independent penetration of membranes and active, endocytic uptake. A major challenge in the field of CPPs is to target the delivery of drugs or imaging agents to specific cell types, tissues, or organs. Several options to improve the specificity of CPPs have been employed, i.e. bioconjugation with “smart” molecules sensitive to local environmental properties such as pH, temperature, altered redox potentials, and disease-associated proteases or with ligands for special cell surface receptors. For this purpose, monoclonal antibodies, aptamers, or peptides have been used. Nevertheless, translation into medical applications of CPPs still needs clarification of underlying mechanisms of internalization and delivery of CPP conjugates. Understanding the mechanisms driving the cell entry and toxic effects of CPPs will help to design improved and optimized peptides with low toxicity, high penetration activity, and efficient conjugation for the delivery of therapeutic molecules.

References 1. Frankel, A.D. and Pabo, C.O. (1988).

Cellular uptake of the tat protein from human immunodeficiency virus. Cell 55 (6): 1189–1193. 2. Green, M. and Loewenstein, P.M. (1988). Autonomous functional domains of chemically synthesized human immunodeficiency virus tat trans-activator protein. Cell 55 (6): 1179–1188. 3. Joliot, A., Pernelle, C., Deagostini-Bazin, H. et al. (1991). Antennapedia homeobox peptide regulates neural morphogenesis. Proc. Natl. Acad. Sci. U.S.A. 88 (5): 1864–1868. 4. Vives, E., Brodin, P., and Lebleu, B. (1997). A truncated HIV-1 Tat protein basic domain rapidly translocates through the plasma membrane and

5.

6.

7.

8.

accumulates in the cell nucleus. J. Biol. Chem. 272 (25): 16010–16017. Pooga, M., Hallbrink, M., Zorko, M. et al. (1998). Cell penetration by transportan. FASEB J. 12 (1): 67–77. Fischer, R., Fotin-Mleczek, M., Hufnagel, H. et al. (2005). Break on through to the other side-biophysics and cell biology shed light on cellpenetrating peptides. ChemBioChem 6 (12): 2126–2142. Cerrato, C.P., Kunnapuu, K., and Langel, U. (2016). Cell-penetrating peptides with intracellular organelle targeting. Expert Opin. Drug Deliv. 14 (2): 245–255. Wender, P.A., Galliher, W.C., Goun, E.A. et al. (2008). The design of guanidinium-rich transporters and

463

464

55 Cell-Penetrating Peptides Targeting and Distorting Biological Membranes

9.

10.

11.

12.

13.

14.

15.

16.

17.

18.

19.

their internalization mechanisms. Adv. Drug Delivery Rev. 60 (4–5): 452–472. Deshayes, S., Konate, K., Aldrian, G. et al. (2010). Interactions of amphipathic CPPs with model membranes. Methods Mol. Biol. 683: 41–56. Ziegler, A. (2008). Thermodynamic studies and binding mechanisms of cell-penetrating peptides with lipids and glycosaminoglycans. Adv. Drug Delivery Rev. 60 (4–5): 580–597. Milletti, F. (2012). Cell-penetrating peptides: classes, origin, and current landscape. Drug Discovery Today 17 (15–16): 850–860. Sadler, K., Eom, K.D., Yang, J.L. et al. (2002). Translocating proline-rich peptides from the antimicrobial peptide bactenecin 7. Biochemistry 41 (48): 14150–14157. Pujals, S. and Giralt, E. (2008). Prolinerich, amphipathic cell-penetrating peptides. Adv. Drug Delivery Rev. 60 (4–5): 473–484. Daniels, D.S. and Schepartz, A. (2007). Intrinsically cell-permeable miniature proteins based on a minimal cationic PPII motif. J. Am. Chem. Soc. 129 (47): 14578–14579. Kauffman, W.B., Fuselier, T., He, J. et al. (2015). Mechanism matters: a taxonomy of cell penetrating peptides. Trends Biochem. Sci. 40 (12): 749–764. Zhang, D., Wang, J., and Xu, D. (2016a). Cell-penetrating peptides as noninvasive transmembrane vectors for the development of novel multifunctional drug-delivery systems. J. Controlled Release 229: 130–139. Duchardt, F., Fotin-Mleczek, M., Schwarz, H. et al. (2007). A comprehensive model for the cellular uptake of cationic cell-penetrating peptides. Traffic 8 (7): 848–866. Fittipaldi, A., Ferrari, A., Zoppe, M. et al. (2003). Cell membrane lipid rafts mediate caveolar endocytosis of HIV1 Tat fusion proteins. J. Biol. Chem. 278 (36): 34141–34149. Ter-Avetisyan, G., Tunnemann, G., Nowak, D. et al. (2009). Cell entry of arginine-rich peptides is independent of endocytosis. J. Biol. Chem. 284 (6): 3370–3378.

20. Richard, J.P., Melikov, K., Brooks,

21.

22.

23.

24.

25.

26.

27.

28.

29.

30.

H. et al. (2005). Cellular uptake of unconjugated TAT peptide involves clathrin-dependent endocytosis and heparan sulfate receptors. J. Biol. Chem. 280 (15): 15300–15306. Jones, A.T. and Sayers, E.J. (2012). Cell entry of cell penetrating peptides: tales of tails wagging dogs. J. Controlled Release 161 (2): 582–591. Koren, E. and Torchilin, V.P. (2012). Cell-penetrating peptides: breaking through to the other side. Trends Mol. Med. 18 (7): 385–393. Swanson, J.A. (2008). Shaping cups into phagosomes and macropinosomes. Nat. Rev. Mol. Cell Biol. 9 (8): 639–649. McMahon, H.T. and Boucrot, E. (2011). Molecular mechanism and physiological functions of clathrin-mediated endocytosis. Nat. Rev. Mol. Cell Biol. 12 (8): 517–533. Petrescu, A.D., Vespa, A., Huang, H. et al. (2009). Fluorescent sterols monitor cell penetrating peptide Pep-1 mediated uptake and intracellular targeting of cargo protein in living cells. Biochim. Biophys. Biomembr. 1788 (2): 425–441. Kumari, S., Swetha, M.G., and Mayor, S. (2010). Endocytosis unplugged: multiple ways to enter the cell. Cell Res. 20 (3): 256–275. Ziegler, A. and Seelig, J. (2004). Interaction of the protein transduction domain of HIV-1 TAT with heparan sulfate: binding mechanism and thermodynamic parameters. Biophys. J. 86 (1 Pt 1): 254–263. Gerbal-Chaloin, S., Gondeau, C., Aldrian-Herrada, G. et al. (2007). First step of the cell-penetrating peptide mechanism involves Rac1 GTPasedependent actin-network remodelling. Biol. Cell 99 (4): 223–238. Heitz, F., Morris, M.C., and Divita, G. (2009). Twenty years of cell-penetrating peptides: from molecular mechanisms to therapeutics. Br. J. Pharmacol. 157 (2): 195–206. Deshayes, S., Morris, M., Heitz, F. et al. (2008). Delivery of proteins and nucleic

References

31.

32.

33.

34.

35.

36.

37.

38.

39.

acids using a non-covalent peptidebased strategy. Adv. Drug Delivery Rev. 60 (4–5): 537–547. Brasseur, R. and Divita, G. (2010). Happy birthday cell penetrating peptides: already 20 years. Biochim. Biophys. Acta 1798 (12): 2177–2181. Marks, J.R., Placone, J., Hristova, K. et al. (2011). Spontaneous membranetranslocating peptides by orthogonal high-throughput screening. J. Am. Chem. Soc. 133 (23): 8995–9004. Swiecicki, J.M., Bartsch, A., Tailhades, J. et al. (2014). The efficacies of cellpenetrating peptides in accumulating in large unilamellar vesicles depend on their ability to form inverted micelles. ChemBioChem 15 (6): 884–891. Ciobanasu, C., Siebrasse, J.P., and Kubitscheck, U. (2010). Cell-penetrating HIV1 TAT peptides can generate pores in model membranes. Biophys. J. 99 (1): 153–162. Patel, L.N., Zaro, J.L., Shen, W.C. et al. (2007). Cell penetrating peptides: intracellular pathways and pharmaceutical perspectives. Pharm. Res. 24 (11): 1977–1992. Shai, Y. (1999). Mechanism of the binding, insertion and destabilization of phospholipid bilayer membranes by alpha-helical antimicrobial and cell non-selective membrane-lytic peptides. Biochim. Biophys. Biomembr. 1462 (1–2): 55–70. Di Pisa, M., Chassaing, G., and Swiecicki, J.M. (2015). Translocation mechanism(s) of cell-penetrating peptides: biophysical studies using artificial membrane bilayers. Biochemistry 54 (2): 194–207. Esbjorner, E.K., Lincoln, P., and Nordén, B. (2007). Counterionmediated membrane penetration: cationic cell-penetrating peptides overcome Born energy barrier by ionpairing with phospholipids. Biochim. Biophys. Acta 1768 (6): 1550–1558. Swiecicki, J.M., Di Pisa, M., Burlina, F. et al. (2015). Accumulation of cell-penetrating peptides in large unilamellar vesicles: a straightforward screening assay for investigating the

40.

41.

42.

43.

44.

45.

46.

47.

48.

49.

internalization mechanism. Biopolymers 104 (5): 533–543. Walrant, A., Correia, I., Jiao, C.Y. et al. (2011). Different membrane behaviour and cellular uptake of three basic arginine-rich peptides. Biochim. Biophys. Biomembr. 1808 (1): 382–393. Herce, H.D., Garcia, A.E., Litt, J. et al. (2009). Arginine-rich peptides destabilize the plasma membrane, consistent with a pore formation translocation mechanism of cell-penetrating peptides. Biophys. J. 97 (7): 1917–1925. Wheaten, S.A., Ablan, F.D., Spaller, B.L. et al. (2013). Translocation of cationic amphipathic peptides across the membranes of pure phospholipid giant vesicles. J. Am. Chem. Soc. 135 (44): 16517–16525. Yandek, L.E., Pokorny, A., Floren, A. et al. (2007). Mechanism of the cellpenetrating peptide transportan 10 permeation of lipid bilayers. Biophys. J. 92 (7): 2434–2444. Persson, D., Thoren, P.E.G., Lincoln, P. et al. (2004). Vesicle membrane interactions of penetratin analogues. Biochemistry 43 (34): 11045–11055. Drin, G., Mazel, M., Clair, P. et al. (2001). Physico-chemical requirements for cellular uptake of pAntp peptide – role of lipid-binding affinity. Eur. J. Biochem. 268 (5): 1304–1314. Binder, H. and Lindblom, G. (2003). Interaction of the Trojan peptide penetratin with anionic lipid membranes – a calorimetric study. Phys. Chem. Chem. Phys. 5 (22): 5108–5117. Fernandez-Vidal, M., White, S.H., and Ladokhin, A.S. (2010). Membrane partitioning: “classical” and “nonclassical” hydrophobic effects. J. Membr. Biol. 239 (1–2): 5–14. Wieprecht, T. and Seelig, J. (2002). Isothermal titration calorimetry for studying interactions between peptides and lipid membranes. Current Topics in Membranes 52: 31–56. Eiriksdottir, E., Konate, K., Langel, U. et al. (2010). Secondary structure of cell-penetrating peptides controls membrane interaction and insertion. Biochim. Biophys. Acta 1798 (6): 1119–1128.

465

466

55 Cell-Penetrating Peptides Targeting and Distorting Biological Membranes 50. Magzoub, M., Kilk, K., Eriksson, L.E.G.

51.

52.

53.

54.

55.

56.

57.

58.

59.

et al. (2001). Interaction and structure induction of cell-penetrating peptides in the presence of phospholipid vesicles. Biochim. Biophys. Biomembr. 1512 (1): 77–89. Lamaziere, A., Chassaing, G., Trugnan, G. et al. (2009). Tubular structures in heterogeneous membranes induced by the cell penetrating peptide penetratin. Commun. Integr. Biol. 2 (3): 223–224. Lamaziere, A., Maniti, O., Wolf, C. et al. (2010). Lipid domain separation, bilayer thickening and pearling induced by the cell penetrating peptide penetratin. Biochim. Biophys. Biomembr. 1798 (12): 2223–2230. Parthasarathy, R. and Groves, J.T. (2007). Curvature and spatial organization in biological membranes. Soft Matter 3 (1): 24–33. Persson, D., Thoren, P.E.G., Esbjorner, E.K. et al. (2004). Vesicle sizedependent translocation of penetratin analogs across lipid membranes. Biochim. Biophys. Biomembr. 1665 (1–2): 142–155. Schmidt, N., Mishra, A., Lai, G.H. et al. (2010). Arginine-rich cellpenetrating peptides. FEBS Lett. 584 (9): 1806–1813. Deshayes, S., Heitz, A., Morris, M.C. et al. (2004). Insight into the mechanism of internalization of the cell-penetrating carrier peptide Pep1 through conformational analysis. Biochemistry 43 (6): 1449–1457. Henriques, S.T., Quintas, A., Bagatolli, L.A. et al. (2007). Energy-independent translocation of cell-penetrating peptides occurs without formation of pores. A biophysical study with pep-1. Mol. Membr. Biol. 24 (4): 282–U29. MacCallum, J.L., Bennett, W.F.D., and Tieleman, D.P. (2011). Transfer of arginine into lipid bilayers is nonadditive. Biophys. J. 101 (1): 110–117. Takechi, Y., Yoshii, H., Tanaka, M. et al. (2011). Physicochemical mechanism for the enhanced ability of lipid membrane penetration of polyarginine. Langmuir 27 (11): 7099–7107.

60. Saalik, P., Niinep, A., Pae, J. et al.

61.

62.

63.

64.

65.

66.

67.

68.

69.

70.

(2011). Penetration without cells: membrane translocation of cell-penetrating peptides in the model giant plasma membrane vesicles. J. Controlled Release 153 (2): 117–125. Pae, J., Saalik, P., Liivamagi, L. et al. (2014). Translocation of cell-penetrating peptides across the plasma membrane is controlled by cholesterol and microenvironment created by membranous proteins. J. Controlled Release 192: 103–113. Torchilin, V.P. (2014). Multifunctional, stimuli-sensitive nanoparticulate systems for drug delivery. Nat. Rev. Drug Discovery 13 (11): 813–827. Zhu, L., Kate, P., and Torchilin, V.P. (2012). Matrix metalloprotease 2responsive multifunctional liposomal nanocarrier for enhanced tumor targeting. ACS Nano 6 (4): 3491–3498. Iwase, Y., Kamei, N., Khafagy el, S. et al. (2016). Use of a non-covalent cellpenetrating peptide strategy to enhance the nasal delivery of interferon beta and its PEGylated form. Int. J. Pharm. 510 (1): 304–310. Cerrato, C.P., Lehto, T., and Langel, U. (2014). Peptide-based vectors: recent developments. Biomol. Concepts 5 (6): 479–488. Liu, B.R., Lin, M.D., Chiang, H.J. et al. (2012a). Arginine-rich cell-penetrating peptides deliver gene into living human cells. Gene 505 (1): 37–45. Kanasty, R., Dorkin, J.R., Vegas, A. et al. (2013). Delivery materials for siRNA therapeutics. Nat. Mater. 12 (11): 967–977. Delcroix, M. and Riley, L.W. (2010). Cell-penetrating peptides for antiviral drug development. Pharmaceuticals (Basel) 3 (3): 448–470. Copolovici, D.M., Langel, K., and Eriste, E. (2014). Cell-penetrating peptides: design, synthesis, and applications. ACS Nano 8 (3): 1972–1994. Abdul, F., Ndeboko, B., Buronfosse, T. et al. (2012). Potent inhibition of late stages of hepadnavirus replication by a modified cell penetrating peptide. PLoS One 7 (11): e48721.

References 71. Rothbard, J.B., Garlington, S., Lin, Q.

72.

73.

74.

75.

76.

77.

78.

79.

80.

81.

82.

et al. (2000). Conjugation of arginine oligomers to cyclosporin A facilitates topical delivery and inhibition of inflammation. Nat. Med. 6 (11): 1253–1257. Vaupel, P., Kallinowski, F., Neuringer, L.J. et al. (1989). Blood flow, oxygen and nutrient supply, and metabolic microenvironment of human tumors: a review. Cancer Res. 49 (23): 6449–6465. Krishnan, S., Diagaradjane, P., and Cho, S.H. (2010). Nanoparticle-mediated thermal therapy: evolving strategies for prostate cancer therapy. Int. J. Hyperthermia 26 (8): 775–789. Richter, M. and Zhang, H. (2005). Receptor-targeted cancer therapy. DNA Cell Biol. 24 (5): 271–282. Mehra, N.K., Mishra, V., and Jain, N.K. (2013). Receptor-based targeting of therapeutics. Ther. Deliv. 4 (3): 369–394. Lu, K., Duan, Q.P., Ma, L. et al. (2010). Chemical strategies for the synthesis of peptide-oligonucleotide conjugates. Bioconjug. Chem. 21 (2): 187–202. Kumar, P., Wu, H., McBride, J.L. et al. (2007). Transvascular delivery of small interfering RNA to the central nervous system. Nature 448 (7149): 39–43. Dominska, M. and Dykxhoorn, D.M. (2010). Breaking down the barriers: siRNA delivery and endosome escape. J. Cell Sci. 123 (Pt 8): 1183–1189. He, J., Kauffman, W.B., Fuselier, T. et al. (2013). Direct cytosolic delivery of polar cargo to cells by spontaneous membrane-translocating peptides. J. Biol. Chem. 288 (41): 29974–29986. Laakkonen, P. and Vuorinen, K. (2010). Homing peptides as targeted delivery vehicles. Integr. Biol. (Camb.) 2 (7–8): 326–337. Hyvonen, M. and Laakkonen, P. (2015). Identification and characterization of homing peptides using in vivo peptide phage display. Methods Mol. Biol. 1324: 205–222. Pasqualini, R. and Ruoslahti, E. (1996). Organ targeting in vivo using phage display peptide libraries. Nature 380 (6572): 364–366.

83. Laakkonen, P., Porkka, K., Hoffman,

84.

85.

86.

87.

88.

89.

90.

91.

92.

93.

J.A. et al. (2002). A tumor-homing peptide with a targeting specificity related to lymphatic vessels. Nat. Med. 8 (7): 751–755. Wang, X., Li, S., Shi, Y. et al. (2014). The development of site-specific drug delivery nanocarriers based on receptor mediation. J. Controlled Release 193: 139–153. Arap, W., Pasqualini, R., and Ruoslahti, E. (1998). Cancer treatment by targeted drug delivery to tumor vasculature in a mouse model. Science 279 (5349): 377–380. Curnis, F., Sacchi, A., Borgna, L. et al. (2000). Enhancement of tumor necrosis factor alpha antitumor immunotherapeutic properties by targeted delivery to aminopeptidase N (CD13). Nat. Biotechnol. 18 (11): 1185–1190. Teesalu, T., Sugahara, K.N., Kotamraju, V.R. et al. (2009). C-end rule peptides mediate neuropilin-1-dependent cell, vascular, and tissue penetration. Proc. Natl. Acad. Sci. U.S.A. 106 (38): 16157–16162. Sugahara, K.N., Teesalu, T., Karmali, P.P. et al. (2010). Coadministration of a tumor-penetrating peptide enhances the efficacy of cancer drugs. Science 328 (5981): 1031–1035. Sugahara, K.N., Teesalu, T., Karmali, P.P. et al. (2009). Tissue-penetrating delivery of compounds and nanoparticles into tumors. Cancer Cell 16 (6): 510–520. Saar, K., Lindgren, M., Hansen, M. et al. (2005). Cell-penetrating peptides: a comparative membrane toxicity study. Anal. Biochem. 345 (1): 55–65. Tunnemann, G., Ter-Avetisyan, G., Martin, R.M. et al. (2008). Live-cell analysis of cell penetration ability and toxicity of oligo-arginines. J. Pept. Sci. 14 (4): 469–476. El-Andaloussi, S., Jarver, P., Johansson, H.J. et al. (2007). Cargo-dependent cytotoxicity and delivery efficacy of cell-penetrating peptides: a comparative study. Biochem. J 407 (2): 285–292. Cardozo, A.K., Buchillier, V., Mathieu, M. et al. (2007). Cell-permeable

467

468

55 Cell-Penetrating Peptides Targeting and Distorting Biological Membranes

94.

95.

96.

97.

98.

99.

100.

101.

102.

103.

peptides induce dose- and lengthdependent cytotoxic effects. Biochim. Biophys. Acta 1768 (9): 2222–2234. Bechara, C. and Sagan, S. (2013). Cell-penetrating peptides: 20 years later, where do we stand? FEBS Lett. 587 (12): 1693–1702. Tunnemann, G., Martin, R.M., Haupt, S. et al. (2006). Cargo-dependent mode of uptake and bioavailability of TAT-containing proteins and peptides in living cells. FASEB J. 20 (11): 1775–1784. Kilk, K., Mahlapuu, R., Soomets, U. et al. (2009). Analysis of in vitro toxicity of five cell-penetrating peptides by metabolic profiling. Toxicology 265 (3): 87–95. Zhao, M., Kircher, M.F., Josephson, L. et al. (2002). Differential conjugation of tat peptide to superparamagnetic nanoparticles and its effect on cellular uptake. Bioconjug. Chem. 13 (4): 840–844. Polyakov, V., Sharma, V., Dahlheimer, J.L. et al. (2000). Novel Tat-peptide chelates for direct transduction of technetium-99m and rhenium into human cells for imaging and radiotherapy. Bioconjug. Chem. 11 (6): 762–771. Sebbage, V. (2009). Cell-penetrating peptides and their therapeutic applications. Biosci. Horiz. 2 (1): 64–72. Ruan, G., Agrawal, A., Marcus, A.I. et al. (2007). Imaging and tracking of tat peptide-conjugated quantum dots in living cells: new insights into nanoparticle uptake, intracellular transport, and vesicle shedding. J. Am. Chem. Soc. 129 (47): 14759–14766. Torchilin, V.P. and Levchenko, T.S. (2003). TAT-liposomes: a novel intracellular drug carrier. Curr. Protein Pept. Sci. 4 (2): 133–140. Potocky, T.B., Menon, A.K., and Gellman, S.H. (2003). Cytoplasmic and nuclear delivery of a TAT-derived peptide and a beta-peptide after endocytic uptake into HeLa cells. J. Biol. Chem. 278 (50): 50188–50194. Wadia, J.S., Stan, R.V., and Dowdy, S.F. (2004). Transducible TAT-HA fusogenic peptide enhances escape of

104.

105.

106.

107.

108.

109.

110.

111.

112.

TAT-fusion proteins after lipid raft macropinocytosis. Nat. Med. 10 (3): 310–315. Kaplan, I.M., Wadia, J.S., and Dowdy, S.F. (2005). Cationic TAT peptide transduction domain enters cells by macropinocytosis. J. Controlled Release 102 (1): 247–253. Derossi, D., Calvet, S., Trembleau, A. et al. (1996). Cell internalization of the third helix of the Antennapedia homeodomain is receptor-independent. J. Biol. Chem. 271 (30): 18188–18193. Ziegler, A., Nervi, P., Durrenberger, M. et al. (2005). The cationic cellpenetrating peptide CPP(TAT) derived from the HIV-1 protein TAT is rapidly transported into living fibroblasts: optical, biophysical, and metabolic evidence. Biochemistry 44 (1): 138–148. Herce, H.D., Garcia, A.E., and Cardoso, M.C. (2014). Fundamental molecular mechanism for the cellular uptake of guanidinium-rich molecules. J. Am. Chem. Soc. 136 (50): 17459–17467. Tiriveedhi, V. and Butko, P. (2007). A fluorescence spectroscopy study on the interactions of the TAT-PTD peptide with model lipid membranes. Biochemistry 46 (12): 3888–3895. Ziegler, A., Blatter, X.L., Seelig, A. et al. (2003). Protein transduction domains of HIV-1 and SIV TAT interact with charged lipid vesicles. Binding mechanism and thermodynamic analysis. Biochemistry 42 (30): 9185–9194. Ciobanasu, C., Harms, E., and Tunnemann, G. (2009). Cell-penetrating HIV1 TAT peptides float on model lipid bilayers. Biochemistry 48 (22): 4728–4737. Thoren, P.E., Persson, D., Esbjorner, E.K. et al. (2004). Membrane binding and translocation of cell-penetrating peptides. Biochemistry 43 (12): 3471–3489. Mishra, A., Gordon, V.D., Yang, L. et al. (2008). HIV TAT forms pores in membranes by inducing saddle-splay curvature: potential role of bidentate hydrogen bonding. Angew. Chem. Int. Ed. Engl. 47 (16): 2986–2989.

References 113. Herce, H.D. and Garcia, A.E. (2007).

114.

115.

116.

117.

118.

119.

120.

121.

Molecular dynamics simulations suggest a mechanism for translocation of the HIV-1 TAT peptide across lipid membranes. Proc. Natl. Acad. Sci. U.S.A. 104 (52): 20805–20810. Hu, Y. and Patel, S. (2016). Thermodynamics of cell-penetrating HIV1 TAT peptide insertion into PC/PS/CHOL model bilayers through transmembrane pores: the roles of cholesterol and anionic lipids. Soft Matter 12 (32): 6716–6727. Lattig-Tunnemann, G., Prinz, M., Hoffmann, D. et al. (2011). Backbone rigidity and static presentation of guanidinium groups increases cellular uptake of arginine-rich cell-penetrating peptides. Nat. Commun. 2. Liu, J., Zhao, Y., Guo, Q. et al. (2012b). TAT-modified nanosilver for combating multidrug-resistant cancer. Biomaterials 33 (26): 6155–6161. Dekiwadia, C.D., Lawrie, A.C., and Fecondo, J.V. (2012). Peptide-mediated cell penetration and targeted delivery of gold nanoparticles into lysosomes. J. Pept. Sci. 18 (8): 527–534. Nasrollahi, S.A., Taghibiglou, C., Azizi, E. et al. (2012). Cell-penetrating peptides as a novel transdermal drug delivery system. Chem. Biol. Drug Des. 80 (5): 639–646. Kamei, N., Nielsen, E.J., el Khafagy, S. et al. (2013). Noninvasive insulin delivery: the great potential of cellpenetrating peptides. Ther. Deliv. 4 (3): 315–326. Zhang, J., Sun, A., Xu, R. et al. (2016b). Cell-penetrating and endoplasmic reticulum-locating TAT-IL-24-KDEL fusion protein induces tumor apoptosis. J. Cell. Physiol. 231 (1): 84–93. Chugh, A., Eudes, F., and Shim, Y.S. (2010). Cell-penetrating peptides: nanocarrier for macromolecule delivery

122.

123.

124.

125.

126.

127.

128.

129.

in living cells. IUBMB Life 62 (3): 183–193. Zou, L., Peng, Q., Wang, P. et al. (2016). Progress in research and application of HIV-1 TAT-derived cell-penetrating peptide. J. Membr. Biol. Cao, G., Pei, W., Ge, H. et al. (2002). In vivo delivery of a Bcl-xL fusion protein containing the TAT protein transduction domain protects against ischemic brain injury and neuronal apoptosis. J. Neurosci. 22 (13): 5423–5431. Boisguerin, P., Redt-Clouet, C., Franck-Miclo, A. et al. (2011). Systemic delivery of BH4 anti-apoptotic peptide using CPPs prevents cardiac ischemia-reperfusion injuries in vivo. J. Controlled Release 156 (2): 146–153. Kristensen, M., Birch, D., and Nielsen, H.M. (2016). Applications and challenges for use of cell-penetrating peptides as delivery vectors for peptide and protein cargos. Int. J. Mol. Sci. 17 (2). Perea, S.E., Reyes, O., Puchades, Y. et al. (2004). Antitumor effect of a novel proapoptotic peptide that impairs the phosphorylation by the protein kinase 2 (casein kinase 2). Cancer Res. 64 (19): 7127–7129. Bolhassani, A., Jafarzade, B.S., and Mardani, G. (2017). In vitro and in vivo delivery of therapeutic proteins using cell penetrating peptides. Peptides 87: 50–63. Schwarze, S.R., Ho, A., Vocero-Akbani, A. et al. (1999). In vivo protein transduction: delivery of a biologically active protein into the mouse. Science 285 (5433): 1569–1572. Rizzuti, M., Nizzardo, M., Zanetta, C. et al. (2015). Therapeutic applications of the cell-penetrating HIV-1 Tat peptide. Drug Discovery Today 20 (1): 76–85.

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Index a ab initio molecular dynamics (AIMD) simulations 144–146, 482, 487, 488, 785 – Born–Oppenheimer approximation 146, 148 – electronic structure problem 146 adsorbate free electrode surfaces 547 adsorbed iodide 595 adsorption – computational methods 784–788 – electrocatalysis 782–784 advanced light source (ALS) 758 alcohol crossover 850 alcohol oxidation 848–850 1-alkyl-3-methylimidazolium bis(trifluoromethanesulfonyl)imide ionic liquids – alkyl chain length effect on surface structure 364–370 – anion size effect on surface structure 371–373 – HR-ERDA spectra 360, 361 – HR-RBS spectra 358, 360, 361, 363 – MD simulations 362–364 – molecular structure 358 – preferential orientation 362, 364 𝛼-reconstruction 611 ambient pressure XPS (APXPS) 758–760 amorphous solid water (ASW) layer 266 amphipathic cell-penetrating peptides 444, 447 angle resolved ion scattering spectroscopy (ARISS) 335, 336 – polymer-stabilized Pt nanoparticles embedded in ionic liquids 340 – of pure solvents (formamide and benzyl alcohol) 335–338 – surfactant solutions 337, 339

anionic O sites, hydroxylation and de-hydroxylation 766 anisotropic wettability 434 anodic polarization 765 anthropogenic CO2 emissions 733 anti-icing/anti-fogging coatings 398 antimicrobial activity 215, 216, 218 antimicrobial peptides (AMP) 213–218 apertureless IR version, of SNOM 111 arginine-rich – clinical application, of CPPs 449–450 – TAT peptides, translocation mechanism of 454 ARXPS 233 – 1-alkyl-3-methylimidazolium bis(trifluoromethylsulfonyl)imide ionic liquids 307 – concentration depth profile determination 267, 287 – POPC/TBABr/HPN system 322–324 – TBAI/formamide system 279 atomic cations adsorption – cadmium – Cu(111), Cu(100) 638–647 – copper – Au(111) 647–659 – metal deposition 635–638 atomic force microscopy (AFM) 106, 535 – biological systems 103, 108 – force-mapping mode 107 – freshly isolated outer mitochondrial membranes 107 – interaction forces between biomolecules 108 – liquid/solid interfaces – – asymmetric cationic Gemini surfactant on mica surface 109 – – limitation 106 – – in nonbiological systems 108 – – poly(2-vinylpyridine) conformations 110

Surface and Interface Science: Interfacial Electrochemistry, First Edition. Edited by Klaus Wandelt. © 2020 Wiley-VCH Verlag GmbH & Co. KGaA. Published 2020 by Wiley-VCH Verlag GmbH & Co. KGaA.

948

Index

atomic force microscopy (AFM) (contd.) – in living systems, identifying changes 107 – vs. scanning tunneling microscopy 106 – tip functionalization approach 108 – ultrathin liquid film deposition on solid surface 335 attenuated total reflectance (ATR) spectroscopy 4–10 Auger de-excitation (AD) 232, 244 Auger electron spectroscopy (AES) 545, 546, 793 Auger neutralization (AN) 231, 243–245

boron-doped diamond (BDD) 71, 879 bottom-up approach 396, 416 bovine serum albumin (BSA) 51, 202, 205–207 bromide – Cu(110) 583–585 Brønsted-Evans-Polanyi principle 782, 816 Butler–Volmer (BV) equation 532, 776, 778, 779 1-butyl-1-methylpyrrolidinium bis(trifluoromethylsulfonyl)imide ([BMP]+ [TFSA]– ) 509

c b bare metal 518, 547, 556 barrel-stave model 446, 447 basal planes 39, 98, 792, 793 battery-electric vehicles 733 bead electrode 544 Becke, Lee, Yang, and Parr (BLYP) functional 148 Belousov– Zhabotinsky reaction 891 Besocke-type scanner 539 binding energy (BE) 489, 760, 785, 815, 819, 825, 830, 831 biocompatibility 8, 85, 200, 209 biofilms – bacteria-resistant materials 212 – description 199 – formation 199 – functional and mature 199 – primary film 212 biofouling properties 221 biological membranes 343, 441 biomass electrocatalytic transformation 877–878 biomimetic surfaces 395 see also superhydrophobic surfaces biorecognition phenomena 199 biosensing 30, 199 bistable behaviour – CO electrooxidation – – cyclic voltammogram 908 – – electrooxidation mechanism 897 – – mathematical modeling 899–901 – CO oxidation – – bifurcation diagram 894, 895 – – hysteresis 894 – – mathematical modeling 896–897 – – UHV conditions 893–894 Bonn EC-STM 541, 542 Born–Oppenheimer approximation 146, 780 Born–Oppenheimer MD 145, 148

cadmium – Cu(111), Cu(100) 638–647 capillary rise method 408 capillary waves 335, 361, 363, 375–377, 424 captive bubble method 408 carbon model 904 Car–Parrinello molecular dynamics (CPMD) 532 carpet model 446 Cartesian coordinate system 497 Cassie–Baxter model 409–410 cationic peptides 447 caveolae-mediated endocytosis 444–446, 454 caveolin-dependent endocytosis 453 cell penetrating homing peptides 451–452 cell-penetrating peptides (CPPs) – bioconjugation 450 – bioenhancers 449 – cargo delivery system 451 – classification 443–444 – clinical applications 449–450 – definition 442 – discovery 442–443 – endocytosis 446 – gene therapy 449 – membrane translocation 446 – modes of action 444–449 – nanocarriers 449 – physiochemical properties 445 – targeting 450–451 – toxicity 452–453 – translocation 449 charge distribution, in foam film 298 chemical contrast enhancement agents 95 chemical deposition 396 chemical patterning 398, 409 – Cassie–Baxter model 409–410 – Wenzel model 411 chemical potential – 1-(palmitoyl)-2-(oleoyl)-sn-glycero3-phosphocholine 449

Index

– tetratbutylphosphonium bromide in formamide 316 chlor-alkali electrolysis 819 chlor-alkali process 820 chloride, bromide – Cu(100) 567–572 chloride, bromide – Cu(111) 554–565 chlorine evolution reaction (CER) – dimensionally stable anodes 820 – fundamental studies 821 clathrin-dependent endocytosis 444, 453 clathrin-mediated endocytosis 445, 446, 454 closed-cell TEM approach 112 13 C NMR 75, 76 CO electrooxidation – bistable behaviour – – cyclic voltammogram 908 – – electrooxidation mechanism 897 – – mathematical modeling 899–901 – oscillatory behaviour – – anion concentration 909 – – electrode rotation rate 909 – – electrolyte conductivity 911–912 – – mathematical modeling 914–918 – – negative feedback loops 911 – – polycrystalline Pt electrode 908, 909 – – S-NDR 907–908 – – supporting electrolytes 911 – – surface crystallographic orientation 913–914 – spatio-temporal patterns – – experimental techniques 925–927 – – galvanostatic conditions 927–932 – – potentiostatic conditions 932–934 CO oxidation 844–847 – bistable behaviour – – bifurcation diagram 894, 895 – – hysteresis 894 – – mathematical modeling 896–897 – – UHV conditions 893–894 – oscillatory behaviour – – mathematical modeling 904, 907 – – skeleton bifurcation diagram 907 – – surface reconstruction 905 – spatio-temporal patterns – – bistable behaviour 38, 39 – – oscillatory behaviour 39, 40 CO2 reduction 857–863, 873–876 cobalt protoporphyrin (Co-PP) 874 coherent anti-Stokes Raman scattering (CARS) 21, 23, 90, 93 colloidal assembly process 396 colloidal synthesis 796, 797 common black films (CBF) 294 complex self-organization 891

computational hydrogen electrode (CHE) 474, 477, 502, 786 concentration depth profile – description 268–269 – liquid surfaces – – ionic liquids 305–312 – – solutions with inorganic salts 303–305 – – surfactant solutions 280–282 – – thin foam films 289–303 – NICISS 238, 254 – – benzyl alcohol, TOF-spectrum of 254, 255 – – gauging the depth scale 256 – of tetra-N-butylammonium iodide 281 – top surface layer of liquid system 269 confocal fluorescence microscopy 85 confocal laser scanning microscopy (CLSM) 458 confocal microscopy 215, 447 constant current mode 97, 537, 539 constant height mode 537, 539 contact angle hysteresis 398, 406, 409, 419–421, 426, 429, 437 coordination number 56, 840 copper – Au(111) 647–659 copper dissolution reaction (CDR) 531, 573, 623, 711 copper iodide 592–599 copper redeposition reaction 569, 572, 573, 583, 623, 665, 671 copper reduction 531 core hole lifetime broadening 740 counter electrode 494, 499, 504, 535, 540, 702, 775 critical micelle concentration (CMC) 316 crystal truncation rods (CTRs) 579 cubo-octahedron 796 cyclic voltammetry – adsorbed state 532 – defined 528 – features of 531 – in situ STM 529 – Randles–Sevcik equation 533 – three electrode 528 cyclic voltammogram – dibenzylviologen – Cu(111) 703–708 – DPV 687 – polycrystalline platinum electrode 791 – Pt(100) electrode 795 – Pt(111) electrode 794 – sulfide – Cu(111) 600–608 – TMPyP – Cu(100) 668–674 – TMPyP – Cu(111) 664–668 – viologen molecules 681

949

950

Index

d Damascene 517 d-band centers 786 Debye–Hückel theory 319 Debye length 186, 292, 295, 479, 526 deconvolution procedure, NICISS 259, 260 Δμ technique 742, 743 density-derived atomic point charges (DDAPC) method 171 density-functional theory (DFT) 145–149, 472, 482, 582, 785 designer solvents 352 dialkylviologens – Cu(100) 681–685 1,1′ -dibenzyl-4,4′ (propane-1,3diyl)dipyridinium (C3-DBDP) molecules 679, 708 dibenzyl-dipyridinium – Cu(100) 708–710 dibenzylviologen – Cu(100) 693–700 – reactive vs. non-reactive adsorption 700–701 – tip-induce phase transition 701–703 dibenzylviologen – Cu(111) 703–708 dicarboxydiheptylviologen – Cu(100) 685–687 dicarboxydiheptylviologen (DCDHV) 679, 685 differentially pumped lens system 758 differential pumping 261, 262, 266, 267, 356, 758, 760, 761 diheptylviologen – Cu(100) 682–685 dimensionally stable anodes (DSA) – chlorine evolution 820–821 – Raman spectra 824 dimethyl (DMV)- and diethyl (DEV)-viologen 681 dimethyl – Cu(100) 681–682 diphenylviologen – Cu(100) 687–692 direct CPP translocation 447 directional wetting – anisotropic wettability 434–436 – chemical vapor deposition 433 – wettability gradients 436–438 direct methanol fuel cells (DMFCs) 848 direct recoil spectroscopy (DRS) 237, 267, 353 dispersion-corrected atom-centered potentials (DCACPs) 148 dissipative solitary waves 936–937 1,1′ -disubstituted 4,4′ -bipyridinium molecules 677 dodecyldimethyl phosphineoxide (C12 DMPO), TFPB data 297 double-layer region 790 double-reference method 495, 498

dynamic contact angles 405–406, 408–409 dynamic wetting properties – contact angle hysteresis 420–421 – impinging droplets 421–424 – slip length 419–420

e effective screening method 504, 507 elastic energy loss, NICISS 239–240 electric double layer (EDL) 144, 479 electroannealing 545, 584, 588 electrocatalysis – adsorption – – computational methods 784–787 – – importance of 782–784 – biomass transformation 877–878 – chlorine evolution reaction – – dimensionally stable anodes 820 – – fundamental studies 821–825 – defined 773 – electrocatalytic ammonia synthesis 876–877 – electrochemical kinetics – – Butler–Volmer equation 776–778 – – macroscopic approach 774–776 – – Marcus–Hush model 780 – – schematic fashion concepts 778 – – Tafel equation 774–776 – electrochemical oxidation processes 879 – features 774 – hydrogen evolution reaction – – kinetic reaction mechanisms 812–814 – – rationalizing trends, activity 814–819 – macromolecules and enzymes – – CO2 reduction 873–876 – – concepts 863–868 – – HER and HOR 868–870 – – OER and ORR 870–873 – metal catalysts 880 – multi-product reactions – – CO2 reduction 857–863 – – nitrate reduction 852–857 – oxygen evolution reaction – – energetics 841–844 – – fundamental 836–840 – – materials 844 – oxygen reduction reaction – – fundamental reaction 825–830 – – materials for 835–836 – – reaction trends and surface sensitivity 830–835 – small organic molecules – – alcohol oxidation 848–850

Index

– – – – – –

– CO oxidation 844–847 surface-and product-sensitive techniques – EC-SERS 809 – infrared spectroscopy 807–809 – online mass spectrometry 810 – scanning-probe microscopy techniques 811 – – semi-online liquid chromatography 810–811 – – sum-frequency generation 810 – well-defined surfaces – – and structure sensitivity 788–792 – – controlling composition 805 – – from single-crystal electrodes to shape-selective nanoparticles 792–799 – – reactions classification 799–801 – – surface modifications 801–805 electrocatalyst surfaces 741 electrocatalytic ammonia synthesis 876 electrochemical annealing 545, 569, 581, 587, 592, 634, 635 electrochemical atomic layer epitaxy (ECALE) 599 electrochemical cells 14, 70, 503, 528, 529, 540, 542, 545, 753, 760, 762, 768, 774–776, 793, 810, 903, 921 electrochemical desorption 636, 812–814 electrochemical double layer 542 – adsorption–desorption 524–525 – defined 518, 525, 542 – electrolytes properties 522 – Gouy–Chapman model 525–526 – Gouy–Chapman–Stern–Grahame model 519, 526–527 – Helmholtz model 525 – metal surfaces structure 518–522 electrochemical electrodes – electrode potential 474 – heterogeneous catalysis 473 – metal surfaces adsorption 476 – reaction intermediates 478 electrochemical equilibrium 476, 635, 775 electrochemical kinetics – Butler–Volmer equation 776–778 – macroscopic approach 774–776 – Marcus–Hush model 780 – schematic fashion concepts 778–779 – Tafel equation 774–776 electrochemical oxide 836 electrochemical preparation 545, 621 electrochemical process 1, 18, 101, 498, 734, 758, 760, 774, 788, 819, 852, 876, 892, 920

electrochemical scanning tunneling microscopy (EC-STM) 526, 811 electrochemical surface-enhanced Raman spectroscopy (EC-SERS) 809 electrochemistry – porphyrin molecules 663–664 – surface science approach 527 – underpotential deposition 490 – viologen molecules 678–681 electrocompression 562, 567, 573 electrode emersion technique 70 electrode flooding 760, 763, 764 electroetching 517 electrolyte meniscus 738 electrolytes 473 – properties 522–524 – quartz/water interface 170–175 electron energy loss spectroscopy (EELS) 264–265 electronic stopping power 245 electronic structure problem 146 electron paramagnetic spectroscopy (EPR) 80–83 electron spin resonance (ESR) 80–83 electron tunneling, principle 535–537 electropolishing 790 electrosorption valency 782 electrostatic energy 371, 496, 497 elegant method 504 ellipsometry 45–46 embedded atom method (EAM) 481 empirical valence bond (EVB) model 481 endosomes 445, 446, 451 energy loss straggling 247–253 evaporation method 408 exchange current density 778 extended X-ray absorption fine structure (EXAFS) 52–54, 56, 736 external reflection configuration 808, 853

f face-centered cubic (fcc) 518, 521 fakir state 413, 414 Faraday’s laws 776 Fe/Ni oxyhydroxides 752 fibronectin 202, 205, 206, 210, 211 flame annealing 543–544 flow cytometry 86, 116 fluorescein-labeled TAT peptide 454 fluorescence detection 32, 88, 110, 737–740 fluorescence emission spectroscopy 31–35 fluorescence lifetime imaging (FLIM) 87, 88 fluorescence microscopy – catalytic sites identification 89

951

952

Index

fluorescence microscopy (contd.) – confocal 85 – near-infrared multiphoton microscopy 85 – single-molecule time-resolved 87 – total internal reflection mode 85 fluorescence SNOM 111 fluorescently labeled TAT peptides 453, 455–459, 461 fluorite/water interfaces 182–188 foam films 289 force tensiometry 407 formamide (FA) 264 Förster (fluorescence) resonance energy transfer (FRET) 87, 88 Fourier transformation (FT) 3, 259, 267, 558, 566, 567 Franck–Condon principle 780 free energy, bimodal distribution of 324–334 frequency modulation atomic force microscopy (FM-AFM) 182 Frumkin effect 556, 569, 624 fuel cell stack 734

g Gaussian-shaped counter electrode 499, 504 generalized gradient approximation (GGA) 482, 785 Gibbs equation 312–316 Gibbs free energy 292, 473, 841, 842 Gouy–Chapman–Stern–Grahame model 519, 526–527 Gouy–Chapman theory 288, 289, 291, 526 grazing-incidence small-angle neutron scattering (GISANS) 68 grazing-incidence small-angle X-ray scattering (GISAXS) 52 grazing-incidence X-ray diffraction (GIXD) 52 – advantage 63 – calcite crystallization 65 – electrochemical systems 64 – phytosterol and sphingomyelin mixtures 63 – uses 64 green chemistry 877

h Haber–Bosch process 876 half-cell reactions 734, 775 halide anions adsorption – bromide – Cu(110) 583–585 – chloride and bromide – Cu(100) 567–572 – chloride and bromide – Cu(111) 554–565

– – – – –

chloride – Cu(110) 585–592 copper iodide 592–599 iodide – Cu(100) 572–578 iodide – Cu(111) 565–567 XRD of chloride, bromide, iodide on Cu(100) 578–583 Hamaker constant 293 hard and soft X-ray spectroscopy 735 hard sphere model 521, 567, 568, 571, 573, 585, 587, 604, 610, 626, 645, 669, 670 H2 electrooxidation 893, 901–902 Helmholtz layer 64, 479, 480, 526 Helmholtz model 525 HER/hydrogen oxidation reaction (HOR) 868 heterogeneous catalysis 473, 773 Heyrovsky reaction 505, 506, 812 hidden N-shaped negative differential resistance (HN-NDR) systems 892–893 – oscillations 918–919 – pattern formation – – global coupling 938 – – migration coupling 922, 932–934 hierarchical roughness 394, 396, 398, 409, 415–419, 428, 431 high energy ion scattering (HEIS) 267 high energy resolution fluorescence detection (HERFD) mode 740 high free reaction enthalpy 734 highly oriented pyrolytic graphite (HOPG) 99, 102, 539 high-resolution elastic recoil detection (HR-ERDA) spectra 354 – 1-alkyl-3-methylimidazolium bis(trifluoromethanesulfonyl)imide 364–370 – for [Cn C1 Im][Tf2 N] 360 – experimental setup 356 high-resolution Rutherford backscattering spectroscopy (HR-RBS) – 1-alkyl-3-methylimidazolium bis(trifluoromethanesulfonyl)imide 358 – [C2 C1 Im][BF4 ] equimolar mixture 374 – [C2 C1 Im][Tf2 N] 359, 374 – [C6 C1 Im][Cl] 371 – [C6 C1 Im] [Tf2 N]0.5 Cl0.5 380, 381 – drawback 354 – experimental setup 356 – for PbSe(111) single crystal 355 – rotating disc method 357, 358 high-resolution X-ray microscopy 95 HIV1-TAT protein 453 1 H NMR 75, 76 Hohenberg–Kohn (HK) theorems 146

Index

homing peptides 451, 452 homogeneous topography 335, 336 hydration enthalpy 523, 525 hydrogen-bonded network 480, 491, 510, 763 hydrogen evolution reaction (HER) – HOR 868 – kinetic reaction mechanisms 812–814 – rationalizing trends, activity 814–819 hydrogen fuel cell vehicles 733 hydrogen oxidation reaction (HOR) 812, 901 hyphenated techniques 784, 807, 811, 850, 853, 860, 877

i inelastic energy loss 244–245 infrared (IR) absorption spectroscopy – FT-IR approach 3 – liquid/solid interfaces – – attenuated total reflectance spectroscopy 4–10 – – reflection–absorption infrared spectroscopy 10–14 – – transmission modes 14–15 infrared (IR) microscopy 94–96 infrared reflection absorption spectroscopy (IRRAS) 10, 614, 808 infrared spectroscopy 807–809 inhomogeneous topography 335, 336 inner-sphere electron transfer 780 inner vs. outer Helmholtz plane 526 in situ infrared reflection absorption spectra (IRRAS) 697 in situ scanning tunneling microscopy – atomic force microscopy 535 – electrochemical STM 540–542 – electron tunneling principle 535–537 – sample preparation – – electrochemical preparation 545 – – flame annealing 543 – – UHV-EC transfer 545–546 – tip position control 537–540 in situ surface-sensitive technique 789 interfacial electron transfer 866 interferometric SNOM setup 111 internal reflection configuration 808, 809 in vivo phage display 451 iodide anions 551, 562, 572, 577, 578, 592, 593 iodide – Cu(100) 572–578 iodide – Cu(111) 565–567 ionic liquids (IL) 71, 303, 509

– 1-alkyl-3-methylimidazolium bis(trifluoromethylsulfonyl)imide 307 – 1-alkyl-3-methylimidazolium tetrafluoroborate 306 – cations and anions, examples of 352 – description 305–312 – ion concentration 300 – properties 351, 352 – protic 310 – surface mole fraction of 383 – surface structure of binary mixtures 373–374 ion scattering spectroscopy (ISS) 236–239, 267, 269, 306, 318, 353, 527, 640 iridium oxide 765, 767 isoelectric point, of foam film 298

j Jellium model

520

k K𝛼 fluorescence 738 K-edges (L-edges) 740 kinetic reaction mechanisms 812–814 Kohn–Sham DFT 146 Kolbe reaction 878 Koutecky–Levich plots 827, 828 Krasil’shchkov mechanism 837

l lactoglobulin 209 Langmuir–Hinshelwood mechanism 812, 893, 894, 897, 898, 907, 917, 920 Langmuir isotherm 782, 788 laser induced liquid bead ion desorption (LILBID) 266 lateral homogeneity 342–343 layer-by-layer deposition 396 length-extension resonator (LER) 539 Li intercalation/deintercalation reactions 734 Li-ion batteries 733 linear voltammogram 528, 534, 637, 653 lipid raft-mediated endocytosis 446, 454 lipid topology 448–449 liquid jet method 73 liquid surface analysis 265 liquid surfaces 3D surface topography, 334 liquid/solid interfaces, at molecular-level 1 – atomic force microscopy 97, 106 – electron-based surface-science techniques 2 – electron microscopy 112

953

954

Index

liquid/solid interfaces, at molecular-level (contd.) – electron spin resonance 80 – ellipsometry 45 – extended X-ray absorption fine structure 54–56 – fluorescence emission spectroscopy 31 – fluorescence microscopy 84 – infrared absorption spectroscopy 3 – neutron reflectivity 67 – nuclear magnetic resonance 74 – quartz crystal microbalance 48 – Raman scattering spectroscopy 17 – resonant inelastic X-ray scattering 57 – scanning electrochemical microscopy 102 – scanning near-field optical microscopy 110, 111 – scanning tunneling microscopy 96, 98 – second harmonic generation 35 – sum frequency generation 23 – surface plasmon resonance 40 – transmission electron microscopy 112-114 – UV–Vis absorption spectroscopy 29 – X-ray absorption near-edge spectroscopy 53–56 – X-ray absorption spectroscopy 53–57 – X-ray diffraction 62 – X-ray microscopy 96 – X-ray photoelectron spectroscopy 70 – X-ray reflectivity and scattering 58 Listeria innocua 215 Listeria monocytogens 215–216 lithography 395–396 local density approximation (LDA) 147, 148 local density of states (LDOS) 485, 486, 536 Lotus effect 393, 397, 416 low energy electron diffraction (LEED) 342, 527, 545, 341, 342, 793, 904 low energy ion scattering (LEIS) 237, 353, 641 lysozyme – biocidal mechanism 218 – molecular dynamics calculation 201 lysozyme from hen egg white (HEWL) 218, 219

m macromolecules – CO2 reduction 873–876 – HER and HOR 868–870 – molecular electrocatalysis 863 – OER and ORR 870–873 macropinocytosis 444–446, 453, 454

magic angle spinning (MAS) 74 Marcus–Hush model 780 mass spectrometry 810 medium energy ion scattering (MEIS) 239 membrane proteins, function of 441 meniscus method 74 metal deposition – cadmium – Cu(111), Cu(100) 638–647 – copper – Au(111) 647–659 – Nernst equation 635 metal-electrolyte interfaces – adsorbate free electrode surfaces 547 – atomic cations adsorption – – cadmium – Cu(111), Cu(100) 638–647 – – copper – Au(111) 647–659 – – metal deposition 635 – cyclic voltammetry 528 – electrochemical double-layer – – adsorption–desorption 524–525 – – electrolytes properties 522 – – Gouy–Chapman model 525–526 – – Gouy–Chapman–Stern–Grahame model 526–527 – – Helmholtz model 525 – – metal surfaces structure 518–522 – halide anions adsorption – – bromide – Cu(110) 583–585 – – chloride – Cu(110) 585–592 – – chloride, bromide – Cu(100) 567–572 – – chloride, bromide – Cu(111) 554–565 – – copper iodide 592–599 – – iodide – Cu(100) 572–578 – – iodide – Cu(111) 565–567 – – XRD of chloride, bromide, iodide on Cu(100) 578–583 – in situ scanning tunneling microscopy – – electrochemical STM 540–542 – – electron tunneling principle 535–537 – – imaging modes 542 – – sample preparation 543–546 – – tip position control 537–540 – – tip preparation 542–543 – – tunneling through an electrolyte 537 – perchlorate anions adsorption 618–621 – porphyrins adsorption – – porphyrin molecules electrochemistry 663–664 – – TMPyP – Cu(100) 668–674 – – TMPyP – Cu(111) 664–668 – – TTMAPP – Cu(111) and Cu(100) 674–677 – porphyrins and viologens, co-adsorption 710–713 – sulfate anions adsorption 621–635

Index

– – – – – – – – – –

sulfide anions adsorption – sulfide – Cu(100) 608–612 – sulfide – Cu(111) 600–608 surface science approach 527 thiocyanate anions adsorption – thiocyanate – Cu(100) 614–618 – thiocyanate – Cu(111) 613–614 viologens adsorption – dialkylviologens – Cu(100) 681–685 – dibenzyl-dipyridinium – Cu(100) 708–710 – – dibenzylviologen – Cu(100) 693–700 – – dibenzylviologen – Cu(111) 703–708 – – diphenylviologen – Cu(100) 687–692 – – viologen molecules electrochemistry 679–681 metal surfaces structure 518–522 metastable induced electron spectroscopy (MIES) 231, 269 – binary mixtures of two polar solvents 269–271 – lecithin/cholesterol 275–277 – lecithin and POPC 273–275 – liquid surface analysis 265 – NICISS and ARXPS 279–280 – orientation sensitivity 271 – PD/FA 277–279 – sodium oleate/FA 271–273 metastable wetting states 413–415 Michaelis–Menten kinetics 868 Michelson interferometer 3 (micro)droplet manipulation 398 microjet technique 265, 266 miscibility gap, in binary liquid mixture 330 Moirè pattern 606, 643 molecular dynamics (MD) simulation 458, 481, 784 – 1-alkyl-3-methylimidazolium bis(trifluoromethanesulfonyl)imide 362–364 – [C2 C1 Im][Tf2 N]0.5 [BF4 ]0.5 mixture 378 – [C2 C1 Im][Tf2 N] and [C2 C1 Im][BF4 ] equimolar mixture 374–379 molecular mechanics (MM) 481 Monte Carlo simulations (MC) 784, 787 multiproduct reactions – CO2 reduction 857–863 – nitrate reduction 852–857

near-infrared (NIR) multiphoton microscopy 85 negative differential resistance (NDR) 892 Nernst equation 635 neutral impact collision ion scattering spectroscopy (NICISS) – concentration depth profile 238, 253–254 – convolution function 259 – cross section 240–243 – deconvolution procedure 259 – elastic energy loss 239–243 – energy loss straggling 247–253 – experimental equipment 261–263 – inelastic energy loss 244–245 – to liquid surfaces 335 – neutralization 243–244 – POPC/TBABr/HPN system 322–324 – poly(3-hexylthiophene-2,5-diyl) 258 – vs. Rutherford backscattering 353 – simulations 260–261 – stopping power 246–247 – TBAI/formamide system 279 – thermal broadening 253 neutralization of projectiles, NICISS 243 neutron reflectivity (NR) 67–69, 230 – hexadecyltrimethylamonium bromide in water 289 Newton black films (NBF) 295 nisin 214–218 nitrate reduction 852–857 noncovalent interactions 804 nonequilibrium thermodynamic conditions 891 nonlinear optical methods 268 nonlinear VSFG spectroscopy, silica-water interfaces 165 nonwetting regime 422 nonzero slip boundary condition 419 Nosé–Hoover thermostat 507 N-shaped negative differential resistance (N-NDR) 892 nuclear magnetic resonance (NMR) – high-pressure 77 – vs. ESR 80 – liquid/solid interfaces – – advantage 78 – – limitations 74 nuclear Overhauser effect (NOE) 77 nuclear stopping power 245

n

o

nafion membrane 762 nanopipettes 811 nanosized single-crystals

oil–water separation 425 – anticorrosion coatings 425 – anti-icing/anti-fogging coatings

794

398

955

956

Index

oil–water separation (contd.) – bottom-up colloidal approach 416, 418 – categories 392 – drag reduction 425 – hierarchical roughness 416 – micro-droplet manipulation 398 – in nature 416 – omniphobic/amphiphobic surfaces 426 – self-cleaning 397 oleophilic properties 425 omniphobic surfaces 425, 426 online mass spectrometry 810 operando X-ray and electron spectroscopy – CoOOH and (Ni,Fe)OOH catalysts, for alkaline water electrolysis 750–757 – Δμ technique 742–743 – fuel cell model catalysts 743–749 – methods – – fluorescence detection 737–740 – – HERFD XAS 739–741 – – XAS transmission 737 – X-ray photoelectron spectroscopy – – ambient pressure 758–760 – – electrochemical XPS 760–762 – – oxygen reduction reaction, on Pt 762–765 – – oxygen evolution reaction, on IrO2 765–768 open circuit potential (OCV) 65, 765, 766 optical matrix method 67 optimized water structures 489 oscillatory behaviour – CO electrooxidation – – anion concentration 909 – – electrode rotation rate 909 – – electrolyte conductivity 911–912 – – mathematical modeling 914–918 – – negative feedback loops 911 – – polycrystalline Pt electrode 908, 909 – – S-NDR 907–908 – – supporting electrolytes 911 – – surface crystallographic orientation 913–914 – CO oxidation – – mathematical modeling 904, 907 – – skeleton bifurcation diagram 907 – – surface reconstruction 905 outer Helmholtz-plane 498, 506, 525, 526 overpotential deposition 636, 652, 656 oxide model 904 oxygen electrocatalysis 735 oxygen evolution reaction (OER) 663 – energetics 841–844 – free energy diagram 478

– fundamental reaction 477, 836 – Gibbs free energy 841, 842 – on IrO2 765–768 – materials 844 – ORR 870–873 – on Pt 765 oxygen reduction reaction (ORR) – fundamental reaction 825–830 – materials for 835–836 – OER 870–873 – reaction trends and surface sensitivity 830–835 – on Pt 762–764 oxygen-containing species 54, 743, 744, 791, 828

p 1-palmitoyl-2-oleoyl-sn-glycero-3phosphocholine (POPC)/tetrabutylammonium bromide (TBABr)/3-hydroxioropionitrile (HPN) system 322 penetratin 442, 448, 452 perchlorate – Cu(111) 618–621 perchlorate anions adsorption 618–621 phosphate buffer saline 210 phosphinoxide 318 phosphorodiamidate morpholino oligomers (PMOs) 450 photoelectron emission microscopy (PEEM) 922 physical endocytosis 448 physisorption processes 783 pluronic–lysozyme copolymers 219, 220 point of zero charge (PZC) 822 – of quartz surface 161, 170 Poisson–Boltzmann equation 288, 298, 479 Poisson equation 496, 497 Poisson statistic 251 polarization sensitivity, of SHG signal 93 polydimethylsiloxane (PDMS) 211 polymer electrolyte membrane (PEM) 734, 735 polyproline II (PPII) 444 pore model 447 porphyrin molecules electrochemistry 663 porphyrin adsorption – co-adsorption 710 – porphyrin molecules electrochemistry 663 – TMPyP – Cu(100) 668–675 – TMPyP – Cu(111) 664–668 – TTMAPP – Cu(111) and Cu(100) 674–677

Index

porphyrins and viologens, co-adsorption 710–713 potential of zero total charge (pztc) 784 Potential-dependent series 692 potentiostatic mode 542 proline-rich amphipathic CPPs 444 protein solution 78 – concentration and pH effect 209–211 protein–surface interactions 44, 200, 211 protein transduction domains (PTDs) 442 protein unfolding/denaturation 210–211 protic ionic liquids 310, 328 proton-coupled electron transfers 829, 858 pseudo-halides 612, 613 pseudomorphic Cu monolayer 649–653, 655 pseudopotentials 149, 156, 497, 498, 785 Pt catalyst electrode 764 Pt-skeleton structures 806 Pt-skin structures 806 195 Pt NMR 74 purely empirical model 779 pyrolitic edge graphite (PGE) 868

q quantum chemistry methods 481 quantum mechanical/molecular mechanics (QM/MM) methods 481 quartz crystal microbalance (QCM) 48–51, 203 quartz–water interfaces – electrolytes 170–175 – nonlinear VSFG vibrational spectroscopy 165–169 – water organisation 156–160

r Raman microscopy 90–93 Randles–Sevcik equation 533, 688 rate-determining step (rds) 779, 783, 813, 818, 821, 837, 853, 854, 860, 881 reaction intermediates 5, 8, 83, 478, 492, 493, 782, 783, 828–830, 841, 857, 877 reactive vs. non-reactive adsorption 700 receptor-mediated nanocarrier targeting 450 reconstruction model 903, 908 recrystallisation, of natural epicuticular waxes 431–432 redox reactions, catalysis 773 reference electrode 528, 775 reflectance anisotropy spectroscopy (RAS) 585

reflection–absorption infrared spectroscopy (RAIRS) 4, 10–14 relative humidity, of water vapor 760 renewable gasoline 733 resonant inelastic X-ray scattering (RIXS) spectroscopy 52, 750 resonant ionization (RI) 231 room temperature ionic liquids 351 rotating disc method 357 rotating ring-disk electrode (RRDE) 826 Rutherford backscattering (RBS) 230, 267 – 1-butyl-3-methylimidazolium hexafluorophosphate surface 306 – neutral impact collision ion scattering spectroscopy 239 – principle of 354–356

s Sabatier principle 783 sample preparation, in situ STM – electrochemical preparation 545 – flame annealing 543 – UHV-EC transfer 545 scaling relationships 786, 834 scanning electrochemical microscopy (SECM) 102–106 scanning near-field optical microscopy (SNOM) – advantage 110 – fluorescence detection 110 – liquid/solid interface 110, 111 scanning-probe microscopy techniques 811 scanning transmission X-ray microscopy 96 scanning tunneling microscopy (STM) 342, 509, 519 – vs. atomic force microscopy 106 – cyclic voltammetry 529 – electrochemical 540 – images 644 – liquid/solid interfaces 98 scattering process, in CM system 242 second harmonic generation (SHG) 35–40, 230, 618 – 1-alcyl-4-dimethylaminopyridinium bromide solutions in water 319 self-assembled monolayers (SAMs) 7, 599 self-cleaning surfaces 397 self-organization 891 – bistable behaviour see bistable behaviour – dissipative systems 891 – oscillatory behaviour see oscillatory behaviour – solid/gas interface 892, 893 – solid/liquid interface 892

957

958

Index

self-organization (contd.) – spatial pattern formation see spatio-temporal patterns sessile drop method 407, 408 shape-controlled nanoparticles 794 silica-water interfaces – electrolytes 170 – non-linear VSFG vibrational spectroscopy 165 – surface acidities 161 – water organization 156 siloxane 318 single-crystal metal surfaces 792 single-molecule time-resolved fluorescence microscopy 87 single-reflection IR absorption spectroscopy 10 slip length 398, 420 slippery liquid infused porous surfaces (SLIPS) 425, 429–431 small-angle neutron scattering (SANS) 68, 69 small-angle X-ray scattering (SAXS) 52, 59, 457 small organic molecules – alcohol oxidation 848–850 – CO oxidation 844–847 soft X-ray spectroscopy 740 solid/electrolyte interfaces – atomistic first-principles 480–493 – continuum models 479–480 – defined 471 – electrochemical electrodes 473–479 – electrode potentials 493–508 – non-aqueous electrolytes 508–510 – structure of 471 solid/gas interface 892 solid–liquid interfaces 892 – acidity constants calculation 149–151 – role in modern chemistry 143 – VSFG spectroscopy see vibrational sum frequency generation (VSFG) spectroscopy solid surfaces, types of 403 solvated ions 525 spatial resolution 84 spatio-temporal pattern – CO electrooxidation – – experimental techniques 925–927 – – galvanostatic conditions 927–932 – – potentiostatic conditions 932–934 – CO oxidation – – bistable behaviour 922 – – oscillatory behaviour 924, 925

– – UHV conditions 922 – diffusion coupling 921 – HN-NDR systems – – global coupling 938 – – migration coupling 937, 938 – local and global coupling 921 spectroelectrochemical flow cell 926 spectroscopy cell 760, 762 spin labeling method 80 spontaneous membrane-translocating peptides 451 Springtails 426 sputtering 263, 806 S-shaped negative differential resistance (S-NDR) 892, 893 – CO electrooxidation 907 – oscillations 918–919 stainless steel (SS) surface functionalization 216 standard hydrogen electrode (SHE) 474 Stanford Synchrotron Radiation Lightsource (SSRL) 760 state-of-the-art synthetic methods 796 static contact angle 403 – measurement techniques 407 steric forces 291, 295 Stern model 480 stick-and-ball model 661, 688 STM-derived structure model 646 stopping power 246, 354 subtractively normalized interfacial Fourier transform infrared spectroscopy (SNIFTIRS) 11 sulfate anions adsorption 621 sulfate – Cu(111) and Cu(100) 600, 608 – adsorption peak 622 – cyclic voltammograms 621 – electrochemical annealing 634 – Moiré formation 632 – Moiré-type superstructure 627 – spectroscopic STM 627 – sulfate-covered 624 sulfide anions adsorption – sulfide – Cu(100) 608–612 – sulfide – Cu(111) 600–608 – sulfur interaction 599 Sulfide Moiré-structure 607 sum frequency generation (SFG) 23, 26, 93, 810 – adsorption of aminoacids 27 – advantage 28 – corrosion studies 25 – electrochemical systems 25 – liquid/solid interfaces 23, 24

Index

– vibrational transitions 23 superhydrophobicity 391, 413 superhydrophobic surfaces 393 surface charge effect, on protein binding 206, 208 surface damage 263 surface-enhanced CARS (SE-CARS) 23 surface-enhanced Raman spectroscopy (SERS) 17, 20, 809, 850 – adsorption processes 19 – cytochrome c (Cyt c), uptake 19 – liquid/solid interfaces 18 – perchlorate ion detection 17 – self-assembled alkanethiols monolayers 18 – use of 17 surface excess 283, 313, 318 surface fabrication methods 395 – bottom-up approach 396 – chemical deposition 396 – colloidal assembly process 396 – layer-by-layer deposition 396 – lithography 395–396 – templation 395 – top-down approach 395 surface hydrophobicity/hydrophilicity 201–204 surface hydroxo-complex 840 surface miscibility gap 333, 334 surface modifications – catalytic effect 804 – inert adatoms blocking 802 – noncovalent interactions 804 surface plasmon resonance (SPR) 40 surface science approach 527 surface selection rule 614, 697 surface sensitive techniques – EC-SERS 809 – infrared spectroscopy 807 – online mass spectrometry 810 – scanning-probe microscopy techniques 811 – semi-online liquid chromatography 810 – sum-frequency generation 810 surface structuration 220 surface structure – binary mixtures of ionic liquids 373–374 – of [C4 C1 Im]0.5 [C12 C1 Im]0.5 [Tf2 N] 379–382 – of pure ionic liquids 358–373 surface tensiometry 269, 312, 324 surface tension 312 – alkyltrimethylammonium bromides in water 315

– BA and FA 271, 328, 330 surface topography 204 surface wettability 391, 392, 406, 422 surface X-ray diffraction (SXRD) 62

t Tafel equation 774–776 Tafel reaction 505, 506, 812 Tafel slope 814 TAT-IL-24-KDEL 462 TAT peptides 453 – delivery of therapeutic agents 461–462 – membrane binding 455–457 – membrane translocation 457–461 tetrakis (4-trimetyl ammonium-phenyl) porphyrin (TTMAPP) 662 tetra-N-alkylammonium halide salts 280 bis(terpyridine) (BTP) 510 3D CuI clusters 597 thermal desorption spectra 636, 638 thin film pressure balance (TFPB) 292, 294 thin foam film – charge distribution 298 – concentration depth profiles – – by ionic surfactant 298, 300 – – measurement equipment 295 – – by nonionic surfactant solution 300–302 – description 289–303 – electrochemical potential 293 – isoelectric point 298 – steric forces 291 – thickness 294, 297 – thin film pressure balance 292, 294 thin layer cell 614 thiocyanate anions adsorption – thiocyanate – Cu(100) 614–618 – thiocyanate – Cu(111) 613–614 third body effect 803, 804 tilted plate method 409 time-of-flight secondary ion mass spectrometry (ToF-SIMS) 203, 353 time-resolved neutron diffraction 69 tip-enhanced Raman spectroscopy (TERS) 111, 809 TMPyP – Cu(100) 668–675 TMPyP – Cu(111) 664–668 top-down approach 395, 396 total energy loss 245, 246 total internal reflection fluorescence microscopy 34, 85 transfer coefficient 506, 532, 777 transition metal oxide materials 765

959

960

Index

transmission electron microscopy (TEM) 112–114 transmission IR absorption spectroscopy 14 transportan 10 (TP10) 444 1,2,4-trichlorobenzene (TCB) 510 TTMAPP – Cu(111), Cu(100) 674–677 2D CuI film 596 2D reaction–diffusion systems 922

u UHV-EC transfer 545, 597 ultrahigh vacuum (UHV) 517, 535, 793, 891 ultra-microelectrode (UME) 811 underpotential deposition (UPD) 64, 490, 549, 636, 790 UV–Vis absorption spectroscopy 29–32 UV–Vis Raman, on liquid/solid interfaces 20, 21

v van-der-Waals forces 293, 294 vibrational sum frequency generation (VSFG)spectroscopy 182 – theory for 151 – velocity-velocity correlation functions 154 viologen adsorption 677 – co-adsorption 710 – dialkylviologens – Cu(100) 681–685 – dibenzyl-dipyridinium – Cu(100) 708–710 – dibenzylviologen – Cu(100) 693–700 – dibenzylviologen – Cu(111) 703–708 – dicarboxydiheptylviologen – Cu(100) 685–687 – dimethyl – Cu(100) 681–682 – diphenylviologen – Cu(100) 687–692 viologen molecules electrochemistry 679–681 volcano plot 783 volcano type relationship 735 Volmer reaction 500, 502, 812

w water bilayers 484–486, 491, 501, 505, 506 water electrolysis 734, 735, 750–757 water-mineral interactions see fluorite/water interfaces

water monomer 484–486 water-repellent properties 393 water-soluble biological molecules 441 water splitting process 768 Wenzel model 411 wettability 391 see also surface wettability – measurements, categories of 406 Wigner–Seitz radius 522 Wilhelmy plate method 407 working electrode 528, 775

x X-ray absorption near-edge spectroscopy (XANES) 53–56, 736 X-ray absorption spectroscopy (XAS) 52, 53, 55–57, 59, 736, 737 X-ray back illumination cell 754 X-ray diffraction (XRD) 62–66 X-ray microscopy 96 X-ray photoelectron spectroscopy (XPS) 2, 70–74, 546 – ambient pressure 758–760 – electrochemical 760–762 – 1-ethyl-3-methylimidazolium bis(trifluoromethylsulfonyl)imide thin film formation 71 – ionic liquid surfaces 353 – liquid/solid interfaces – – electrode emersion technique 70 – – platinum electrode and 1-ethyl-3-methylimidazolium tetrafluoroborate ionic liquid 70 – oxygen evolution reaction – – on IrO2 765–768 – – on Pt 765 – oxygen reduction reaction, on Pt 762–764 – spectra, water electrolyzer Pt anode 766 – ultrahigh vacuum conditions 70 – voltammetric characteristics of polycrystalline Au and W electrodes 71 X-ray reflection 343 X-ray reflectivity (XRR) 32, 58–62, 64, 230, 268 X-ray voltammetry 65, 747

z zero-point energy (ZPE) 786 Zisman’s rule 403

Edited by Klaus Wandelt Surface and Interface Science

Surface and Interface Science Edited by Klaus Wandelt Volume 1: Concepts and Methods Volume 2: Properties of Elemental Surfaces Print ISBN 978-3-527-41156-6 oBook ISBN 978-3-527-68053-5 (Volume 1) oBook ISBN 978-3-527-68054-2 (Volume 2) Volume 3: Properties of Composite Surfaces: Alloys, Compounds, Semiconductors Volume 4: Solid-Solid Interfaces and Thin Films Print ISBN 978-3-527-41157-3 oBook ISBN 978-3-527-68055-9 (Volume 3) oBook ISBN 978-3-527-68056-6 (Volume 4) Volume 5: Solid-Gas Interfaces I Volume 6: Solid-Gas Interfaces II Print ISBN 978-3-527-41158-0 oBook ISBN 978-3-527-68057-3 (Volume 5) oBook ISBN 978-3-527-68058-0 (Volume 6) Volume 7: Liquid and Biological Interfaces Volume 8: Interfacial Electrochemistry Print ISBN 978-3-527-41159-7 oBook ISBN 978-3-527-68059-7 (Volume 7) oBook ISBN 978-3-527-68060-3 (Volume 8) Volume 9: Applications of Surface Science I Volume 10: Applications of Surface Science II Print ISBN 978-3-527-41381-2 oBook ISBN 978-3-527-82249-2 (Volume 9) oBook ISBN 978-3-527-82250-8 (Volume 10)

Edited by Klaus Wandelt

Surface and Interface Science Volume 8: Interfacial Electrochemistry

The Editor Prof. Dr. Klaus Wandelt University of Bonn Institute of Physical and Theoretical Chemistry Germany and University of Wroclaw Institute of Experimental Physics Poland Cover Pictures: Left: Reprinted with permission from Morschl et al., J. Phys. Chem. C, 2008, 112 (26), pp 9548–9551. Copyright ©2008 American Chemical Society. Middle: Kindly provided by Prof. Groß (TU Ulm). Right: Kindly provided by Prof. Klaus Wandelt, University of Bonn, Germany. Cover Design: Klaus Wandelt and Grafik-Design Schulz

All books published by Wiley-VCH are carefully produced. Nevertheless, authors, editors, and publisher do not warrant the information contained in these books, including this book, to be free of errors. Readers are advised to keep in mind that statements, data, illustrations, procedural details or other items may inadvertently be inaccurate. Library of Congress Card No.: applied for British Library Cataloguing-in-Publication Data A catalogue record for this book is available from the British Library. Bibliographic information published by the Deutsche Nationalbibliothek The Deutsche Nationalbibliothek lists this publication in the Deutsche Nationalbibliografie; detailed bibliographic data are available on the Internet at . © 2020 Wiley-VCH Verlag GmbH & Co. KGaA, Boschstr. 12, 69469 Weinheim, Germany All rights reserved (including those of translation into other languages). No part of this book may be reproduced in any form – by photoprinting, microfilm, or any other means – nor transmitted or translated into a machine language without written permission from the publishers. Registered names, trademarks, etc. used in this book, even when not specifically marked as such, are not to be considered unprotected by law. Print ISBN: 978-3-527-41159-7 Typesetting SPi Global, Chennai, India Printing and Binding

Printed on acid-free paper 10 9 8 7 6 5 4 3 2 1

V

Contents Volume 8 About the Editor XI Preface XIII List of Abbreviations XVII

56 56.1 56.2 56.3 56.4 56.5 56.6 56.7

57 57.1 57.1.1 57.1.1.1 57.1.1.2 57.1.1.3 57.1.1.4 57.1.1.5 57.1.1.6 57.2 57.2.1 57.2.2 57.2.3 57.2.3.1 57.2.3.2

Theory of Solid/Electrolyte Interfaces 471 Axel Groß Introduction 471 Structure of Electrochemical Interfaces 473 Continuum Models of Solid/Electrolyte Interfaces 479 Atomistic First-Principles Description of Solid/Water Interfaces Explicit Consideration of Varying Electrode Potentials 493 Solid/Electrolyte Interfaces for Nonaqueous Electrolytes 508 Conclusions 511 References 512 Metal–Electrolyte Interfaces: An Atomic View 517 Marek Nowicki and Klaus Wandelt Introduction 517 Electrochemical Double Layer 518 Structure of Metal Surfaces 518 Properties of Electrolytes 522 Adsorption–Desorption 524 Helmholtz Model 525 Gouy–Chapman Model 525 Gouy–Chapman–Stern–Grahame Model 526 Experimental Methods and Procedures 527 Electrochemical “Surface Science Approach” 527 Voltammetry: Principle and Experimental Setups 528 In Situ Scanning Tunneling Microscopy 534 Principle of Electron Tunneling 535 Tunneling Through an Electrolyte 537

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57.2.3.3 57.3 57.3.1 57.3.2 57.3.2.1 57.3.2.2 57.3.2.3 57.3.2.4 57.3.2.5 57.3.2.6 57.3.2.7 57.3.2.8 57.3.3 57.3.3.1 57.3.3.2 57.3.4 57.3.4.1 57.3.4.2 57.3.5 57.3.5.1 57.3.6 57.3.6.1 57.4 57.4.1 57.4.1.1 57.4.1.2 57.5 57.5.1 57.5.1.1 57.5.1.2 57.5.1.3 57.5.1.4 57.5.2 57.5.2.1 57.5.2.2 57.5.2.3 57.5.2.4 57.5.2.5 57.5.2.6 57.5.3 57.6

Instrumental Aspects 537 Adsorption of Anions 547 Adsorbate-Free Electrode Surfaces 547 Adsorption of Halide Anions 549 Chloride, Bromide – Cu(111) 554 Iodide – Cu(111) 565 Chloride and Bromide – Cu(100) 567 Iodide – Cu(100) 572 XRD of Chloride, Bromide, and Iodide on Cu(100) 578 Bromide – Cu(110) 583 Chloride – Cu(110) 585 Surface Compound Formation: Copper Iodide 592 Adsorption of Sulfide Anions 599 Sulfide – Cu(111) 600 Sulfide – Cu(100) 608 Adsorption of Thiocyanate Anions 612 Thiocyanate – Cu(111) 613 Thiocyanate – Cu(100) 614 Adsorption of Perchlorate Anions 618 Perchlorate – Cu(111) 618 Adsorption of Sulfate Anions 621 Sulfate – Cu(111) and Cu(100) 621 Adsorption of Atomic Cations 635 Metal Deposition 635 Cadmium – Cu(111) and Cu(100) 638 Copper – Au(111) 647 Adsorption of Molecular Cations 660 Adsorption of Porphyrins 663 Electrochemistry of Porphyrin Molecules 663 TMPyP – Cu(111) 664 TMPyP – Cu(100) 668 TTMAPP – Cu(111) and Cu(100) 674 Adsorption of Viologens 677 Electrochemistry of Viologen Molecules 679 Dialkylviologens – Cu(100) 681 Diphenylviologen – Cu(100) 687 Dibenzylviologen – Cu(100) 693 Dibenzylviologen – Cu(111) 703 Dibenzyl-dipyridinium – Cu(100) 708 Coadsorption of Porphyrins and Viologens 710 Final Remark 713 References 713

Contents

58 58.1 58.2 58.2.1 58.2.2 58.2.2.1 58.2.2.2 58.2.2.3 58.2.3 58.2.3.1 58.2.3.2 58.2.3.3 58.3 58.3.1 58.3.2 58.3.3 58.3.4 58.3.5 58.4

59 59.1 59.1.1 59.2 59.2.1 59.2.1.1 59.2.1.2 59.2.1.3 59.2.1.4 59.2.2 59.2.2.1 59.2.2.2 59.2.3 59.2.3.1 59.2.3.2 59.2.3.3

X-ray Spectroscopy at Electro-catalytic Interfaces 733 Daniel Friebel, Hirohito Ogasawara, and Anders Nilsson Introduction 733 XANES and EXAFS, and HERFD XAS 736 Introduction 736 Methods 737 XAS in Conventional Transmission and Fluorescence Detection Modes 737 Fluorescence Detection Under Grazing Incidence 737 HERFD XAS 740 Examples 741 Δ𝜇 Technique 742 HERFD XAS and EXAFS Studies of Well-Defined Fuel Cell Model Catalysts 743 HERFD XAS and EXAFS Studies of CoOOH and (Ni,Fe)OOH Catalysts for Alkaline Water Electrolysis 750 Operando Electrochemical X-ray Photoelectron Spectroscopy 757 Ambient Pressure XPS 758 Electrochemical XPS 760 Oxygen Reduction Reaction on Pt 762 Oxygen Evolution Reaction on Pt 765 Oxygen Evolution Reaction on IrO2 765 Summary 768 References 769 Fundamental Aspects of Electrocatalysis 773 Matteo Duca and Marc T.M. Koper Introduction 773 Two Hundred Years of Electrocatalysis 773 Basics 774 Introduction to Electrocatalysis and Electrochemical Kinetics 774 Macroscopic Approach and Empirical Tafel Equation 774 Electrochemical Kinetics: Fundamental Laws and the Butler–Volmer Equation 776 Basic Electrochemical Kinetics: Further Concepts 778 Molecular Model of Electron Transfer: the Marcus Model 779 Adsorption and Electrocatalysis 781 Importance of Adsorption in Electrocatalysis 782 Modern Computational Approaches to Chemisorption and Electrocatalysis 784 Well-Defined Surfaces and Structure Sensitivity in Electrocatalysis 788 From Single-Crystal Electrodes to Shape-Selective Nanoparticles 792 Classification of Reactions 799 Surface Modifications 801

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Contents

59.2.3.4 59.2.4 59.2.4.1 59.2.4.2 59.2.4.3 59.2.4.4 59.2.4.5 59.2.4.6 59.3 59.3.1 59.3.1.1 59.3.1.2 59.3.2 59.3.2.1 59.3.2.2 59.3.3 59.3.3.1 59.3.3.2 59.3.3.3 59.3.4 59.3.4.1 59.3.4.2 59.3.4.3 59.4 59.4.1 59.4.1.1 59.4.1.2 59.4.2 59.4.2.1 59.4.2.2 59.4.3 59.4.3.1 59.4.3.2 59.4.3.3 59.4.3.4 59.5 59.5.1 59.5.2 59.5.3 59.5.4

Controlling the Composition 805 Surface- and Product-Sensitive Techniques Combined to Electrochemistry 807 Infrared Spectroscopy 807 Electrochemical Surface-Enhanced Raman Spectroscopy (EC-SERS) 809 Sum-Frequency Generation 810 Differential or Online Electrochemical Mass Spectrometry 810 Semi-online Electrochemical Liquid Chromatography 810 Scanning Probe Microscopy Techniques 811 Key Reactions of Electrocatalysis 812 Hydrogen Evolution (HER) 812 Kinetic Description of Reaction Mechanisms of HER 812 Modeling of HER: Rationalizing Trends in Activity 814 Chlorine Evolution (CER) 819 Anode Materials for Chlorine Evolution: DSA 820 Fundamental Studies 821 Oxygen Reduction Reaction (ORR) 825 Fundamental Considerations on ORR 825 Rationalization of Reaction Trends and Surface Sensitivity of ORR at Pt 830 Materials for ORR 835 Oxygen Evolution Reaction (OER) 836 Fundamental Considerations on OER 836 Intermediates of OER: Energetics 841 Materials for OER 844 Other Reactions in Electrocatalysis 844 Oxidation of Small Organic Molecules for Energy Applications 844 CO Oxidation 844 Alcohol Oxidation 848 Multiproduct Reactions: Selectivity Issues 850 Nitrate Reduction 852 CO2 Reduction 857 Electrocatalysis Driven by Macromolecules and Enzymes: An Overview 863 General Concepts 863 HER and HOR 868 OER and ORR 870 CO2 Reduction 873 Challenges in Electrocatalysis 876 Electrocatalytic Ammonia Synthesis 876 Electrocatalytic Transformation of Biomass 877 Advanced Electrochemical Oxidation Processes 879 Final Remarks 879 A Brief Guide to Cited Reference Books and the Literature 882 References 882

Contents

60

60.1 60.2 60.2.1 60.2.1.1 60.2.1.2 60.2.1.3 60.2.2 60.2.2.1 60.2.2.2 60.2.2.3 60.2.3 60.2.4 60.3 60.3.1 60.3.2 60.3.2.1 60.3.2.2 60.3.3 60.3.4 60.4 60.4.1 60.4.1.1 60.4.1.2 60.4.2 60.4.2.1 60.4.2.2 60.4.2.3

60.4.3 60.4.3.1 60.4.3.2 60.5

Complexity and Self-Organization Phenomena: From Solid/Gas to Solid/Liquid Interfaces 891 Antoine Bonnefont and Katharina Krischer Introduction 891 Bistable Kinetics 893 Bistable Kinetics in CO Oxidation Under UHV Conditions 893 Mechanism of CO Oxidation Under UHV Conditions 893 Bistable Region and Hysteresis in CO Oxidation 894 Mathematical Modeling of the Gas-Phase CO Oxidation 896 Bistable Kinetics in CO Electrooxidation at the Solid/Liquid Interface 897 Mechanism of CO Electrooxidation on Pt in Aqueous Solutions 897 Bistable Behavior of CO Electrooxidation 898 Mathematical Model of CO Electrooxidation 899 Bistable Kinetics in H2 Electrooxidation at the Solid/Liquid Interface 901 Comparison of Bistable Behaviors 902 Oscillatory Kinetics 903 Oscillations in Gas-Phase CO Oxidation Kinetics: Experiments and Modeling 903 Oscillations in CO Electrooxidation 907 S-NDR Oscillator 907 Oscillations During CO Electrooxidation on Pt in the Presence of Anions 908 Oscillations in (H)N-NDR Systems 918 Comparison of the Oscillation Mechanisms 919 Spatiotemporal Pattern Formation 921 Spatial Pattern Formation in CO Oxidation Under UHV Conditions 922 Front Propagation in Bistable CO Oxidation 922 Spatiotemporal Patterns in Oscillatory CO Oxidation 924 Pattern Formation in CO Bulk Electrooxidation 925 Experimental Methods 925 Stationary Domains During Galvanostatic CO Electrooxidation 927 Spatial Pattern Formation Under Potentiostatic Control and Migration Coupling: Formation of Turing-Type Patterns and Dissipative Solitons 932 Pattern Formation in (H)N-NDR Systems 937 Pattern Formation Induced by Migration Coupling: Transition to Turbulence 937 Pattern Formation Induced by Global Coupling 938 Conclusions and Perspectives 938 References 941 Index

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About the Editor

Klaus Wandelt is currently Professor Emeritus at the University of Bonn, Germany, where he was also Director of the Institute of Physical and Theoretical Chemistry until 2010. He received his PhD on electron spectroscopy of alloy surfaces in 1975 in München; spent a postdoctoral period at the IBM Research Laboratory in San Jose, California, in 1976/1977; and qualified as a professor in 1981 in München. Since then his research focuses on fundamental aspects of the physics and chemistry of metal surfaces under ultrahigh vacuum conditions and in electrolytes, on the atomic structure of amorphous materials, and more recently on processes at surfaces of plants. Professor Wandelt was visiting scientist at the University of Caracas, Venezuela; the University of Hefei, China; the University of Newcastle, Australia; and the University of California, Berkeley, and he was guest professor at the University of Messina, the University of Padua, and the University of Rome Tor Vergata, Italy; the University of Linz and the Technical University of Vienna, Austria; and the University of Wroclaw, Poland. He chaired the surface physics divisions of the German and European Physical Society as well as of the International Union of Vacuum Science Techniques and Applications, has organized numerous workshops and conferences, and was editor of journals, conference proceedings, and books.

XIII

Preface Surfaces and Interfaces: A “Divine Gift”

For decades books, book chapters, theses of generations of PhD students, and, more recently, also presentations on the Internet about subjects of surface and interface science, i.e. the research of physical and chemical properties and processes at solid surfaces, often start with the quotation God made the bulk, surfaces were invented by the devil attributed to Wolfgang Pauli, Nobel Prize Laureate in Physics 1945 [1]. Of course, quotes like this are to be understood from the respective era; a systematic experimental “surface science” did not exist at that time. A description of the field ion microscope (FIM), which for the first time enabled the visualization of individual surface atoms, was published only a few years later by Erwin W. Müller [2]. Now, nearly seventy years later, our profound scientific understanding of the fascinating peculiarities of solid surfaces presented in Volumes 1–8 of this series of books and their fundamental importance for so many vital technological areas emphasized below, and in part addressed in Volumes 9 and 10, make the “invention of surfaces” truly a gift from God. Surfaces and interfaces enrich our world in a double sense. On the one hand, they structure our world and make it so diverse and beautiful. On the other hand, surfaces and interfaces are locations of gradients, which drive spontaneous and mancontrolled processes. These processes change our world and, therefore, our all living conditions in a fundamental way. On the one hand, heterogeneous catalysis of chemical reactions at solid surfaces has enabled the large-scale production of (i) fertilizers and pesticides for agriculture, (ii) a vast variety of plastic commodities, and (iii) pharmaceuticals for medicine and the “health industry.” These products (i) have contributed to a better food supply of the world population and thereby its rapid growth, (ii) appear no longer indispensible in our daily life, and (iii) help to fight diseases and save lives, if produced and applied responsibly and sustainably. On the other hand, besides the growing world population itself, the profit-driven excess production of these products and the accompanying ruthless exploitation of our natural resources are

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an increasing thread for humanity’s survival. The excess production and thoughtless use and uncontrolled disposal of these products disturbs natural equilibria and leads to an increasing contamination of soil and groundwater, pollution of the atmosphere and oceans, and a weakening or failure of the natural immune systems. Insufficient or neglected air pollution control is most likely a reason for the obvious “global warming.” The concomitant rise of the sea level will cause dramatic erosion processes at ocean shores and dikes, the largest-scale solid/liquid interfaces. The consequent shrinkage of man’s living space will, at best, cause a process of mass migration of people. The physics of interfaces and low-dimensional systems has opened the door to modern electronic devices that are revolutionizing the collection, processing, and availability of information, which not only changes our own communication behavior but has also created the vision of the “Internet of things (IoT)” in which people and mobile and immobile physical objects including buildings communicate within a single and common network with each other, which in the opinion of some people will change the world for the better, while others fear that man may lose control. Biological processes function via processes at and through interfaces of membranes, which in turn can be influenced by traces of drugs. It is, thus, not only a great scientific challenge to investigate the properties and processes at surfaces and interfaces, but also of vital importance for mankind’s future, provided we make wise use of this knowledge. Although theoretical predictions about properties of surfaces as well as intuitive models of surface processes existed much earlier, modern experimental surface science started by now about 50 years ago with the commercial availability of ultrahigh vacuum (UHV) technology. Under UHV conditions, it became possible to prepare clean surfaces and to develop and apply a growing number of “surface-sensitive” methods based on particle beams. Unlike photon beams, for instance, used in X-ray crystallography, electron, ion, and atom beams interact only with the outermost layers of a solid and therefore provide information pertaining only to the surface. While in the beginning, practical surface investigations were concentrated on the changes of surface properties due to exposure to gases or vapors, it soon turned out that the properties of the bare surfaces themselves pose a lot of scientific surprises. Now 50 years later, the so-called reductionist “surface science approach,” that is, the use of well-defined, clean single-crystal surfaces under UHV conditions, enables a microscopic and spectroscopic characterization of these bare surfaces atom by atom. The overwhelming achievements of this research may ultimately be summarized by the general statement: Surfaces are a different state of matter! Moreover, nowadays, it is possible not only to study the interaction of individual atoms and molecules with a surface but also to manipulate them on the surface according to our will. The present series of books aims not only at giving a broad overview of the present state of understanding of the basic physics and chemistry at surface and interfaces but also at highlighting a number of technological applications that rely on the established knowledge about surfaces, like thin film and nanotechnology, highly integrated electronics, heterogeneous catalysis in gaseous and liquid phases,

Preface

electrochemical energy conversion and storage, and bio-functionalization of inorganic materials, to name a few. The intention of this series of books is, thus, not only to give an introduction for those who enter the field of surface research but also to provide an overview for those whose work needs conceptual and analytical input from surface science. According to the original concept, this book series should comprise six volumes. The first volume was planned to describe “bare surfaces and methods,” that is, all the physical properties of clean surfaces of elemental and composite solids as well as the most relevant surface analytical methods. However, it turned out immediately that an adequate treatment of just these topics exceeded by far the reasonable size of a single volume and instead filled three volumes, extending the number of intended volumes to eight. But also the material for Volumes 7 and 8 went beyond the limits of one book each, so, after all, the series comprises 10 volumes now: Volume 1: Concepts and Methods Volume 2: Properties of Elemental Surfaces Volume 3: Properties of Composite Surfaces: Alloys, Compounds, Semiconductors Volume 4: Solid/Solid Interfaces and Thin Films Volume 5: Solid/Gas Interfaces I Volume 6: Solid/Gas Interfaces II Volume 7: Liquid and Biological Interfaces Volume 8: Interfacial Electrochemistry Volume 9: Applications of Surface Science I Volume 10: Applications of Surface Science II. The first eight volumes emphasize the basic insights into the physics and chemistry at surfaces and interfaces and the most important experimental and theoretical methods, which led to these results. The methods are grouped according to the applied probe, namely, electrons, ions, photons, and proximity probes, and are described to an extent to give the reader enough confidence in “what surface scientists are able to do nowadays”; more detailed descriptions of these methods can be found in the existing specialized literature. The last two volumes present a selection of some daily phenomena and technological applications, which depend on and arise from surface-specific properties and processes. The vast material is laid out in 80 chapters and is structured according to increasing complexity of the subject in question. Each chapter is written by experts in the respective field and is supposed to start with an introduction of the basic phenomenon, to develop the problem from simple to more specific examples, and to end, if possible, with the identification of open questions and challenges for future research. This intended strategy “from simple to complex” is graphically expressed by the veil rising from left to right on all book covers. One person alone could hardly ever have written such an extensive and divers oeuvre. I am extraordinarily thankful to all authors who have contributed to this series of books. I am also very grateful to the publisher, namely, Ulrike Werner, Nina Stadthaus, Dr. Frank Weinreich, and Dr. Martin Preuss at Wiley, for their continuous

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support and their understanding and flexibility to adapt the original concept of the whole project to “new circumstances” and to agree with the expansion from 6 to 10 volumes. Altogether it took 12 years to realize this project, and obviously a great deal of patience and persistence was necessary to complete it, patience of the authors and the publisher with the editor, but also persistence of the editor and his patience with some authors. The result of this joint effort of all three parties is now in the hands of the critical readers. After all, surfaces and interfaces are a “divine gift” and as such by no means fully fathomed. Bonn, Wroclaw January 11, 2019

References 1. Quoted in: Jamtveit, B. and Meakin, P.

(eds.) (1999). Growth, Dissolution and Pattern Formation in Geosystems, 291. Kluwer Academic Publishers. 2. Müller, E.W. (1951). Z. Phys. 131: 136.

Klaus Wandelt

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List of Abbreviations AES AFM AIMD ALS APXPS ATR BDD BE [BMP+ TFSA]− BTP BV C3-DBDP CDR CE CER CHE CODH Co-PP CPMD CRR CTR CV CV DBV DC-DHV DCDHV DEV DFT DFT-GGA DHV DMDBV DMFC DMV

Auger electron spectroscopy atomic force microscopy ab initio molecular dynamics advanced light source ambient pressure X-ray photoelectron spectroscopy attenuated total reflection boron-doped diamond binding energy 1-butyl-1-methylpyrrolidinium bis(trifluoromethylsulfonyl)imide bis(terpyridine) Butler–Volmer 1,1′ -dibenzyl-4,4′ (propane-1,3-diyl)dipyridinium copper dissolution reaction calomel electrode chlorine evolution reaction computational hydrogen electrode carbon monoxide dehydrogenase cobalt protoporphyrin Car–Parrinello molecular dynamics copper reduction/redeposition reaction crystal truncation rods cyclic voltammetry cyclic voltammogram dibenzyl viologen dicarboxy-diheptylviologen dicarboxyheptyl viologen diethyl viologen density functional theory density functional theory-generalized gradient approximation diheptyl viologen dimethyl-dibenzylviologen direct methanol fuel cells dimethyl viologen

XVIII

List of Abbreviations

DOS DPV DSA EAM ECALE EC-STM EVB EXAFS fcc FPA FTIR GGA GI-XAS hdw HER HERFD HER–OER HOPG HOR IHP IL IR IRRAS ISS KL KSCN LDOS LEED LER MC MCT MD MM NDR N-NDR OEC OER OHP opd ORR ORR–HOR PEEK PEEM PEM PEMFC

density of states diphenyl viologen dimensionally stable anodes embedded atom method electrochemical atomic layer epitaxy electrochemical scanning tunneling microscopy empirical valence-bond extended X-ray absorption fine structure face-centered cubic focal plane array Fourier-transform infrared spectroscopy generalized gradient approximation grazing incidence X-ray absorption spectroscopy heavy domain wall hydrogen evolution reaction high energy resolution fluorescence detection hydrogen evolution reaction–oxygen evolution reaction highly oriented pyrolytic graphite hydrogen oxidation reaction inner Helmholtz plane ionic liquids infrared infrared reflection absorption spectroscopy ion-scattering spectroscopy Koutecky–Levich potassium thiocyanate local density of states low energy electron diffraction length-extension resonator Monte Carlo simulations mercury cadmium telluride molecular dynamics molecular mechanics negative differential resistance N-shaped negative differential resistance oxygen-evolving complex oxygen evolution reaction outer Helmholtz plane overpotential deposited oxygen reduction reaction oxygen reduction reaction–hydrogen oxidation reaction polyether ether ketone photoelectron emission microscopy polymer electrolyte membrane polymer electrolyte membrane fuel cells

List of Abbreviations

PGE PGM PI pzc pztc QM QMS RAS RDE rds RE RHE RIXS RRDE SAM SCE SECM SERS SFG shdw SHE SHG sn S-NDR SSRL STM STS TCB TDS THH TMDBV TMPyP TSV TTMAPP UHV UME UPD upd UPS UV VASP VB WE XANES

pyrolitic edge graphite platinum group metals path-integral potential of zero charge potential of zero total charge quantum mechanical quadrupole mass spectrometry reflectance anisotropy spectroscopy rotating disk electrode rate-determining step reference electrode reversible hydrogen electrode resonant inelastic X-ray scattering rotating ring-disk electrode self-assembled monolayers saturated calomel electrode scanning electrochemical microscopy surface-enhanced Raman spectroscopy sum-frequency generation “superheavy” domain wall standard hydrogen electrode second harmonic generation saddle node S-shaped negative differential resistance Stanford synchrotron radiation lightsource scanning tunneling microscopy scanning tunneling spectroscopy 1,2,4-trichlorobenzene thermal desorption spectroscopy tetra-hexahedral shape tetramethyl-dibenzylviologen 5,10,15,20-tetrakis(N-methyl-4-pyridinium)-21H,23H-porphine through-silicon via 5,10,15,20-tetrakis(4-trimetylammonium-phenyl)21H,23Hporphyrin ultrahigh vacuum ultra-microelectrode under potential deposited under potential deposition ultraviolet photoelectron spectroscopy ultraviolet vienna ab initio simulation package valence band working electrode X-ray absorption near edge structure

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List of Abbreviations

XAS XPS XRD ZPE

X-ray absorption spectroscopy X-ray photoelectron spectroscopy X-ray diffraction zero-point energy

471

56 Theory of Solid/Electrolyte Interfaces Axel Groß

56.1 Introduction

Processes at solid/electrolyte interfaces have witnessed an increasing interest in recent years. This is caused by the importance of electrochemical energy storage and conversion, which will play a central role in our future energy technology [1]. Among the devices in which such processes occur are batteries, fuel cells, and electrolyzers. However, solid/electrolyte interfaces have in fact been a research subject for a very long time. The first concepts describing solid/electrolyte interfaces were based on macroscopic approaches in which the electrolyte was described as a dielectric medium. Currently, there is a trend toward an electrochemical surface science approach [2] trying to identify structures and processes at solid/electrolyte interfaces with an atomic resolution. Still, in spite of the relevance of processes occurring at solid/electrolyte interfaces, it is fair to say that our knowledge about the atomistic structure is rather limited. Experimentally, this is due to the fact that the number of experimental probes with atomic resolution at the solid/liquid interface is still limited compared to the solid/vacuum interface. In particular, techniques based on the scattering and diffraction of electronic beams do not work in solution. From the theoretical point of view, the treatment of solid/electrolyte interfaces necessitates a proper description of the liquid, which requires to determine free energies instead of just total energies. Furthermore, in electrochemistry, structures and properties of the electrode/electrolyte interfaces are governed by the electrode potential. These two facts add considerable complexity to the theoretical description [3]. Consequently, even such elementary properties as the exact structure of water at electrode/electrolyte interfaces are still debated [4]. This lack of knowledge is illustrated in Figure 56.1. Figure 56.1a shows a typical schematic drawing of the structure of solid/electrolyte interfaces that can be found in textbooks in which the water molecules are depicted as balls with an arrow denoting their dipole moment. A figure like this is used to illustrate the differences between anions and cations at interfaces. Cations typically adsorb non-specifically, i.e. their solvation shell stays intact upon adsorption on the electrode. In contrast, anions often bind specifically to the electrode with their Surface and Interface Science: Interfacial Electrochemistry, First Edition. Edited by Klaus Wandelt. © 2020 Wiley-VCH Verlag GmbH & Co. KGaA. Published 2020 by Wiley-VCH Verlag GmbH & Co. KGaA.

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56 Theory of Solid/Electrolyte Interfaces

(a) Figure 56.1 Interface between a metal electrode and an aqueous electrolyte. (a) Schematic drawing and (b) optimized structure of a water layer on a

(b) halide-covered Pt(111) electrode obtained by density functional theory calculations. (Source: Courtesy of Florian Gossenberger.)

solvation shell being broken up. Yet, in drawings such as in Figure 56.1a, crucial assumptions about the arrangements of the water molecules are made that are not necessarily based on observations or calculations. Panel (a) suggests that all water molecules might bind to the electrode in the same configuration. On the other hand, panel (b) shows the optimized structure of a water layer on a Pt(111) electrode covered by fluorine atoms at a coverage of 1/12 determined by density functional theory (DFT) calculations. Although the water layer is wrapped around the fluorine atoms, it is obvious that because of the strong hydrogen bonding between the water molecules, there is still a water network present in which the water molecules assume different orientations. Note that thermal disorder is not considered neither in panel (a) nor in panel (b). This example shows that nowadays first-principles electronic structure calculations can be rather helpful in gaining information about the structure of solid/ electrolyte interfaces. Although, as mentioned above, some obstacles still have to be overcome, significant progress in the theoretical description of solid/electrolyte interfaces has been made recently, as will be shown in this chapter. In this chapter, first a simple, but elegant model to take the electrochemical environment within a thermodynamic approach into account is described. In a second step, continuum models of solid/electrolyte interfaces will be discussed. The atomistic description of solid/water interfaces will then be addressed, and current approaches to incorporate varying electrode potentials into the theoretical description will be covered. Nonaqueous electrolytes will be briefly discussed, and finally an outlook for future work will be given. It will become obvious that the theory of solid/electrolyte interfaces is far from being complete in spite of the numerous

56.2 Structure of Electrochemical Interfaces

theoretical studies on this subject. Because of this large number, an exhaustive and comprehensive review of important studies in this field is not possible. This chapter rather presents selective examples in order to illustrate our current understanding. Note also that we mainly concentrate on metal/electrolyte interfaces in this chapter. Interfaces between, for example, oxides or organic layers and aqueous electrolytes are usually much more complex because of the more open and chemically more inhomogeneous surface structure [5, 6]. Still, we hope that this chapter might encourage further studies about this subject, which is not only scientifically very interesting but also technologically rather relevant.

56.2 Structure of Electrochemical Interfaces

Processes at electrochemical electrodes occur in the presence of the electrolyte under potential control. As described above, this makes their theoretical description rather challenging. Electrolytes typically contain a certain amount of ions that serve as the charge carriers of the ionic transport. Because of the intimate contact of the electrode with the electrolyte, the presence of the ions in the electrolyte will lead to a certain coverage of them on the electrode, which is a function of their adsorption energy and thermodynamic variables such as electrode potential or the concentration of the electrolyte. Here, we will describe how the ion surface coverage can be estimated from DFT calculations and basic thermodynamic considerations. We will first address the case of gas/solid interfaces whose treatment is less complex because of the absence of varying electrode potentials. In heterogenous catalysis, processes occur at such solid/gas interfaces. Note that the impingement rate of the gas-phase particles is often so low, even at ambient conditions, that the presence of gas does not have to be explicitly considered in the theoretical modeling but implicitly through the corresponding chemical potential of species present in the gas phase. In equilibrium, the Gibbs free energy of adsorption Δ𝛾 for Nads adsorbates bound to a surface area As at a given temperature T and pressure p can be expressed [7] as Δ𝛾(T, p) = 𝛾(T, p, Nads ) − 𝛾clean (T, p, 0) 1 = ΔGads (T, p) As N = ads (Eads − Δ𝜇ads (T, p)) As

(56.1)

Here, ΔGads (T, p) is the change in the free energy upon adsorption. In the last line of Eq. (56.1), Δ𝜇ads (T, p) is the temperature- and pressure-dependent part of the chemical potential of the adsorbate. Any change in entropy and zero-point energies upon adsorption can be taken into account, but in fact, it is often neglected in theoretical studies addressing systems in heterogenous catalysis and surface science as these contributions are typically small [7]. Therefore, typically, only the total adsorption energy per particle Eads at the solid/vacuum interface derived from

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56 Theory of Solid/Electrolyte Interfaces

DFT calculations is used, which corresponds to the limit of zero temperature and pressure. Thus, the whole dependence of the free energy of adsorption Δ𝛾(T, p) on temperature and pressure is given by the corresponding dependence of the chemical potentials of the species in the gas phase, which acts as a reservoir. In the adsorption at electrochemical interfaces, the reference state does not correspond to the atom or molecule in gas phase – whose energy is relatively easy to determine – but rather to the species in solution in the presence of an electrode potential U. This means that the chemical potential 𝜇 has to be replaced by the electrochemical potential 𝜇̃ = 𝜇 + neU

(56.2)

where n is the charge of the particle. Furthermore, the problem remains that the electrochemical potential includes all solvation effects of the species. The determination of solvation energies requires computationally demanding thermal integration schemes [8]. These efforts can be avoided using the concept of the computational hydrogen electrode (CHE) [9, 10]. It is based on the fact that at standard conditions (pH= 0, p = 1 bar, T = 298 K), U = 0 is defined as the electrode potential at which there is an equilibrium between a proton and an electron in aqueous solution H+ (aq) + e− and hydrogen in the gas phase, 12 H2 (g). Furthermore, it is well known how the electrochemical potential of the proton and the electron change if the proton concentration and the electrode potential is varied [11], namely according to 1 (56.3) 𝜇(H2 (g)) − eUSHE − kB T ln(10)pH 2 where USHE is the electrode potential with respect to the standard hydrogen electrode (SHE). The success of the computational hydrogen electrode is among others based on the fact that it allows to derive adsorption energies with respect to solvated species without the need to determine any solvation energies. This concept does not only work for hydrogen and protons but it also works for any redox couple 12 A2 + e− ⇄ A− [12] where A could for example be a halogen. The electrochemical potential is then given by 𝜇(H ̃ + (aq)) + 𝜇(e− ) =

1 (56.4) 𝜇(A2 (g)) + e(USHE − U 0 ) + kB T ln aA− 2 where U 0 is the reduction potential of the corresponding halide and aA− its activity coefficient. Neglecting the change of zero-point energies and the entropy change upon adsorption, one arrives at the following expression for the free energy of adsorption as a function of the electrode potential at standard conditions, i.e. for an activity aA− = 1, 𝜇̃(A− (aq)) − 𝜇(e− ) =

Δ𝛾(USHE ) =

Nads (E − e(USHE − U 0 )) As ads

(56.5)

For other concentrations of species A in the electrolyte, the corresponding electrode potential has to be shifted by kB T ln aA− , which corresponds at room temperature to about 60 meV when the activity is changed by one order to magnitude. Within this approach, the adsorption energy Eads appearing in Eq. (56.5) is calculated without taking the electrochemical environment into account. Furthermore, the varying

56.2 Structure of Electrochemical Interfaces

excess charge at the metal electrodes as a function of the electrode potential is also not considered. Still this approach has been successfully used to derive electrochemical trends in the oxygen reduction [9, 13] and in the hydrogen evolution [10] on metal electrodes, which belong to the most important reactions in electrocatalysis. This success is most probably caused by the good screening properties of metallic electrodes, which make, for example, binding energies only weakly dependent on the applied electric fields [9, 14–16]. Here, we will illustrate this approach with respect to the equilibrium coverage of halides on metal electrodes [17] (see also Chapter 57 in this Volume). In Figure 56.2a, the adsorption energies of various halides on Cu(111) and Pt(111) as a function of the coverage with respect to the corresponding halogen molecules in the gas phase are plotted. Typically, adsorbed halides show a repulsive because √ √ interaction of the dipole–dipole interaction. Interestingly enough, the 3 × 3 structure corresponding to a coverage of 1/3 is rather√ stable,√especially for chlorine and bromine on Pt(111). Note that the close-packed 3 × 3 structure is at the same time the structure with the largest and smallest mutual distances among the adsorbates for a given density. This might explain its stability for adsorbates that repel each other. Still, from the adsorption energies alone, it is not directly evident what the thermodynamically stable structures under specific conditions are. In Figure 56.2b, the free energy of adsorption of iodine on Cu(111) determined according to Eq. (56.5) is√plotted √ as an example. Indeed, it is found that at potentials above −0.7 V, only the ( 3 × 3) structure is stable. This is consistent with the experiment [19]. For the sake of completeness, it should be mentioned that at positive potentials close to the onset of the copper dissolution, a uniaxially iodide adlayers caused √ √ incommensurate by an unidirectional compression of the 3 × 3-I structure has been observed [20] (see also Chapter 57 in this Volume), which, however, is hard to model in periodic DFT calculations. In contrast to Cu(111), Pt(111) is covered by hydrogen at low electrode potentials [21]. Hence, in the determination of the stable halide adsorption structure, the presence of hydrogen has to be taken into account. This is no problem in the CHE approach as more than one species in the electrolyte represented by their electrochemical potential can be taken into account. As a result, two-dimensional phase diagrams as a function of the two electrochemical potentials result [22, 23]. Note that in electrochemical experiments, the electrochemical potentials are not controlled but rather the electrode potential and the concentration of the solvated species. However, electrochemical potentials are a function of the electrode potential and the concentrations (see Eqs. (56.3) and (56.4)). Hence, the phase diagrams can, for example, be transformed into Pourbaix diagrams, i.e. into maps of the stable phases as a function of pH and electrode potential. Such a Pourbaix diagram based on the calculated adsorption energies of 117 different chlorine and hydrogen adsorption and chlorine–hydrogen coadsorption structures is plotted in Figure 56.3. Interestingly enough, over a wide range of electrode potentials and pH values, either pure hydrogen or pure chlorine adsorption phases or the clean surface are stable. There is only a small pocket at pH = 4 and USHE = 0.2 V in which a chlorine–hydrogen coadsorption phase is stable. Naively, one would expect that

475

56 Theory of Solid/Electrolyte Interfaces

–0.4

Cl/Cu(111) I/Cu(111) Cl/Pt(111) Br/Pt(111) I/Pt(111)

Adsorption energy (eV)

–0.6 –0.8 –1.0 –1.2 –1.4 –1.6 –1.8 –2.0

0

0.1

0.2

0.3

0.5

0.4

Halogen coverage Θ

(a) 20 2

I free energy of adsorption (meV/Å )

476

I/Cu(111) 0 –20 –40

Clean Cu(111)

ΘI = 0.333

–60 –80

ΘI = 1/16 ΘI = 1/8 ΘI = 3/16 ΘI = 1/4 ΘI = 1/3 ΘI = 3/8 ΘI = 1/2

–100 –120 –1.5

(b)

0 0.5 –1 –0.5 Electrode potential U (V vs. SHE)

Figure 56.2 Adsorption of halides on metal surfaces. (a) Adsorption energies of halogen atoms on Cu(111) and Pt(111) with respect to the free halogen molecule as a function of the coverage; (b) calculated electrochemical equilibrium coverage of iodine on Cu(111) at standard condition as a function of the

1

electrode potential vs. SHE. The inset √ illus√ trates the structure of the iodine 3 × 3 structure. Source: (a) Roman and Groß 2013 [18]. Reproduced with permission of American Physical Society. (b) Gossenberger et al. 2015 [17]. Reproduced with permission of Elsevier.

adsorbed anions (chloride) and cations (protons) would experience an attractive dipole–dipole interaction. However, because of the strong polarization of chlorine upon adsorption, chlorine adsorption on Pt(111) leads to a decrease of the work function for chlorine coverage below ΘCl = 0.5 [24, 25], as does hydrogen, leading to a dipole–dipole repulsion between hydrogen and chlorine upon coadsorption. Hence, upon increasing the electrode potential at low pH values, at a certain potential, adsorbed hydrogen becomes replaced by adsorbed chlorine. This

56.2 Structure of Electrochemical Interfaces

1.5

θ = 0.44 ML Cl

USHE /V

1.0

0.5

θ = 1ML H

0.0

θ = 0.08 ML Cl θ = clean surface

θ < 1ML H

θ > 1ML H –0.5 0

5

10

14

pH Figure 56.3 Calculated Pourbaix diagram, i.e. a map of the stable phases of coadsorbed chlorine and hydrogen on Pt(111) as a function of pH and electrode potential for

a fixed chlorine concentration corresponding to an activity of 0.1. Source: Gossenberger et al. 2015 [17]. Reproduced with permission of Elsevier.

competitive character of hydrogen and chlorine adsorption is in good agreement with corresponding experimental observations [26, 27]. The same competitive character as a function of electrode potential has been observed in the bromide and hydrogen adsorption [28, 29], which has also been confirmed in calculations [22, 23] based on the concept of the computational hydrogen electrode. This concept can in fact also be used to address electrocatalytic reactions at electrodes. This will be illustrated using the oxygen reduction reaction (ORR) [9, 30–32] as an example. The oxygen reduction reaction O2 + 4H+ + 4e− → 2H2 O

(56.6)

is the fundamental reaction that typically occurs at the cathode of fuel cells. Thermodynamically, this reaction should be associated with a potential of 1.23 V. However, under operating conditions at high current densities, the working potential of fuel cells drops below 0.8 V [33]. While some portion of this overpotential, i.e. the potential difference between the thermodynamically determined reduction potential and experimentally observed one, is associated with ohmic losses, most of it is directly due to the ORR. Obviously, it is thus a basic electrochemical reaction that represents the main obstacle for the improvement of the efficiency of fuel cells. Hence, the reduction of the ORR overpotential is of high technological relevance. This overpotential has been attributed to universal scaling relations in electrocatalysis, which are associated with the dependence of adsorption energies on a single descriptor [9, 30–32]. This is illustrated in Figure 56.4. We assume that the

477

56 Theory of Solid/Electrolyte Interfaces

4

Ideal OOH* U = 0V

O* OH* 2H2O(I)

2 0

U = 1.23 V

–2 (a)

Real catalyst O2(g)

U = 0V

OOH*

4

2 0

6

O2(g)

ΔG (eV)

6

ΔG (eV)

478

U = 0.8 V

O* OH* 2H2O(I)

U = 1.23 V

–2 Reaction coordinate

(b)

Reaction coordinate

Figure 56.4 Free energy diagram of the four steps in the oxygen reduction reaction on an ideal catalyst and a realistic catalyst.

ORR proceeds in a four-electron process through the reaction intermediates O2 (g) + 4H+ + 4e−

(56.7)

OOH ∗ +3H+ + 3e−

(56.8)

O ∗ +H2 O(l) + 2H+ + 2e−

(56.9)

OH ∗ +H2 O(l) + H+ + e−

(56.10)

2H2 O(l)

(56.11)

where * denotes an adsorbed species. On an ideal catalyst, the free energy difference between the successive reaction intermediates should be 1.23 eV so that at the equilibrium potential, a flat free energy diagram results (see Figure 56.4a). An infinitesimal decrease in electrode potential then leads to reaction steps that are all downhill in energy. For a realistic catalyst, however, the free energies are typically not equally spaced in energy, as illustrated in Figure 56.4b that reflects typical calculated properties of the ORR [31]. At the equilibrium potential of 1.23 V, there are two steps that are uphill in free energy, the OOH* formation and the OH* reduction, leading to a rather small current density in the ORR. The electrode potential in this particular example has to be reduced to 0.8 V in order to avoid any uphill step so that the overpotential becomes 0.43 V. One might now simply try to modify the free energies of the ORR intermediates in such a way that at the equilibrium potential, a flat free energy diagram results. However, all of the reaction intermediates bind through an oxygen atom to the catalyst so that the scaling relations discussed above are usually operative. Typically, one finds that on various catalyst surfaces, the free energy difference between OOH* and OH* assumes a constant value of about 3.2 eV [31] (as chosen in Figure 56.4b) instead of the desired value of 2.46 eV. Even for an optimum alignment of the OOH* and OH* level, then still an overpotential of 0.37 eV results.

56.3 Continuum Models of Solid/Electrolyte Interfaces

For catalytic substrates for which the scaling relations are valid, there is only a onedimensional parameter space for optimizing their performance. As already pointed out, this represents a severe restriction as binding energies of different intermediates cannot be modified independently. In order to overcome this restriction, it is important to develop strategies for designing catalysts that do not follow scaling relations depending on just one single parameter.

56.3 Continuum Models of Solid/Electrolyte Interfaces

The interface between a solid and a electrolyte typically corresponds to an interface between an electron conductor (the electrode) and an ionic conductor (the electrolyte) [11]. Usually, such interfaces are charged: there is an excess charge at the surface of the electrode, which is compensated by a charge layer of opposite sign at the solution side of the interface. This charge distribution acts as a capacitor with a small effective plate separation and is therefore called the electric double layer. The earliest model of the electric double layer is usually attributed to Helmholtz who assumed that the charge distribution in the solution is realized as a single layer of ions adsorbed at the surface [34]. The potential of this fixed Helmholtz layer exhibits a linear behavior as a function of separation from the electrode. This model was refined early in the twentieth century by Gouy [35] and Chapman [36]. They allowed for a diffuse thermal distribution of the ions. Applying Boltzmann statistics for the distribution of both anions and cations of absolute charge |ze| with a bulk density of n0 , one arrives at the Poisson–Boltzmann equation [11] for the potential 𝜙(x) ) ( )) ( ( zen0 ze𝜙(x) d2 𝜙 ze𝜙(x) − exp (56.12) exp − = − 𝜀𝜀0 kB T kB T dx2 For ze𝜙(x)∕kB T ≪ 1, the exponentials can be linearized so that the solution of the Poisson–Boltzmann equation yields an exponentially decreasing electric potential 𝜎 exp(−𝜅z) (56.13) 𝜙(x) = 𝜀𝜀0 𝜅 where 𝜎 is the surface charge density of the electrode and )1∕2 ( 2(ze)2 n0 𝜅= 𝜀𝜀0 kB T

(56.14)

is the inverse Debye length. Such a formulation is in fact close to the macroscopic treatment of semiconductor interfaces incorporating space charge layers. The Gouy–Chapman model, however, does not take into account that some ions adsorb specifically at the electrode surface so that they are not mobile. Therefore, it fails to describe highly charged double layers. For example, the Gouy–Chapman model predicts that the capacity as a function of the potential always has its minimum at the potential of zero charge, but at higher concentrations of the electrolyte, a much more complex dependence of the capacity on the potential is found [11].

479

480

56 Theory of Solid/Electrolyte Interfaces

Stern layer ψs Electrode

Charged diffusive layer ψd

Electrolyte

ξ

Electric potential

K–1 Debye length Slipping plane Stern plane

Figure 56.5 Illustration of the Stern model.

In order to improve the description of the double layer, a combination of the Helmholtz and Gouy–Chapman models was suggested by Stern [37]. This model is illustrated in Figure 56.5, giving an internal Stern layer (i.e. Helmholtz layer) in which the potential drops linearly, and an outer diffuse layer (i.e. Gouy–Chapman layer) in which the potential drops exponentially. The capacity C of this arrangement is then given 1 1 1 + = C CGC CH

(56.15)

where CGC is the Gouy–Chapman capacity and CH is the Helmholtz capacity that is independent of the electrolyte concentration. This model yields a phenomenological description of the solid/liquid interface. It is very helpful in order to understand and analyze general trends of the capacity at electrochemical interfaces. Still, as a continuum model, it does not take the atomistic structure of the interface into account. Hence, a more quantitative theory of solid/electrolyte interfaces is needed.

56.4 Atomistic First-Principles Description of Solid/Water Interfaces

In order to get a more realistic picture of the structure of solid/electrolyte interfaces, an atomistic modeling of solid/electrolyte interfaces is necessary. The proper treatment of the liquid side of the solid/electrolyte interface requires to perform statistical averages over sufficiently many different configurations, which leads to a considerable computational effort. One way to avoid this effort is to describe the

56.4 Atomistic First-Principles Description of Solid/Water Interfaces

electrode and any adsorbate layer atomistically, but the liquid through an implicit solvent model in which the electrolyte is modeled as a polarizable dielectric medium. Such an approach has widely been used to address solvation phenomena [38]. It has also been implemented in periodic DFT schemes [39]. Implicit solvent models are computationally very attractive, and they take the polarizable nature of electrolytes into account. Still, they correspond to an approximative description of the liquid nature of the electrolyte, and their validity is hard to judge because of the lack of direct comparisons of implicit and explicit solvent models for solid/electrolyte interfaces. Because of the computational effort associated with an explicit modeling of the solvent, numerically inexpensive treatments such as force field methods are desirable for simulations. In fact, parameterized classical interaction potentials are well-suited to treat the water–water interaction and describe the structural properties of water quite satisfactorily [40–42]. Because of the interest in the structure of solid/electrolyte interfaces, there have been numerous molecular dynamics studies addressing the structure of the metal–water interface at finite temperatures using parameterized potentials [43–46]. Because of the fact that they are computationally inexpensive, rather large systems can be addressed. It should be noted that important insights into the structure of solid/electrolyte interfaces have been gained from these studies. However, these studies suffer from the fact that a parameterized interaction potential typically only describes one class of materials well. Both the water–water and the water–metal interactions have to be described properly in a joint parametrization scheme. There is, for example, no empirical potential that reproduces water and metal properties satisfactorily at the same time. Numerically favorable classical molecular dynamics (MD) simulations based on force fields are not really suited to describe metals, whereas interpolation schemes including many-body effects such as the embedded atom method (EAM) [47, 48] cannot appropriately reproduce covalent bonding. Furthermore, classical interaction potentials usually do not allow to describe any bond-breaking and bond-making. Reactive force fields based on bond-order concepts [49] do not have this restriction, but their training effort rises exponentially with the number of elements considered in the parameterization. On the other hand, numerical interpolation schemes based on artificial networks can be rather flexible [50, 51] and yield a reliable description of the structural properties of liquid water [52] and of water/metal interfaces [53]. However, as no chemical insight goes into their construction procedure, their fitting usually requires large training sets [50]. Hence, appropriate quantum chemistry methods are required to yield a reliable description of the electrode, the electrolyte, and the electrode–electrolyte interaction. The empirical valence bond (EVB) model [54] can be viewed as an extension of force field approaches to include a region that is treated quantum mechanically in the spirit of hybrid quantum mechanical/molecular mechanics (QM/MM) methods [55]. Because of its relative simplicity, EVB methods allow to treat systems of several thousands of atoms over long time scales, which has been employed to describe the dynamics of the proton transfer from the electrolyte to metal electrodes [56–59]. Still, it suffers from the problem of all empirical interaction models: taking into

481

56 Theory of Solid/Electrolyte Interfaces

account a new element in the simulation that has not been parameterized before involves a considerable effort that makes these methods rather inflexible. First-principles methods avoid this problem as only the element number is needed as an input. In the fields of surface, interface, and condensed matter science, the method of choice are electronic structure calculations based on density functional theory (DFT) [4, 60, 61]. DFT calculations correspond to an electronic structure methodology that combines numerical efficiency with sufficient accuracy. Because of the improvements both in computer power and algorithms, it is nowadays possible to perform ab initio molecular dynamics (AIMD) simulations of sufficient length to obtain thermal averages [62, 63]. In these AIMD simulations, the forces necessary to integrate the classical equations of motion are determined “on the fly” by DFT calculations. There are still concerns with respect to the adequacy of current DFT functionals [64]. In DFT calculations, the quantum many-body effects are all included in the exchange–correlation functional. Unfortunately, the exact form of this functional is not known, hence approximations are needed. In studies addressing metal electrodes, typically semilocal functionals in the generalized gradient approximation (GGA) such as the Perdew-Burke-Ernzerhof (PBE) [65] or the revised PBE (RPBE) functional [66] are used. GGA hybrid functionals including a certain amount of exact Hartree-Fock exchange are rather popular for pure water simulations [67]. However, they are not appropriate for calculations including metals because Hartree–Fock exchange fails in reproducing certain metals properties [68]. Unfortunately, the popular PBE functional leads to an overstructuring of liquid water systems [69–71] because the directional H-bonds are overestimated by PBE. This overstructuring is illustrated in Figure 56.6. Bulk water simulations are typically performed in a periodic setup with 32, 64, or 128 water molecules in the unit cell. A snapshot of a

3.0

gOO(r)

482

PBE PBE-D3 RPBE RPBE-D3 Experiment (T = 300 K)

2.0

1.0

0.0

2

3

4

5

6

7

r (Å) (a)

(b)

Figure 56.6 (a) Snapshot of the water structure in a unit cell with 64 water molecules from a room temperature water simulation; (b) oxygen–oxygen (gOO (r)) radial distribution functions derived from experiment [73] and

from AIMD simulations at room temperature using the PBE and RPBE functional with and without semiempirical dispersion corrections. Source: Forster-Tonigold and Groß 2014 [72]. Adapted with permission of AIP Publishing.

56.4 Atomistic First-Principles Description of Solid/Water Interfaces

water configuration in a cell with 64 water molecules is shown in Figure 56.6a. The oxygen–oxygen radial distribution function of PBE-water derived from an ab initio molecular dynamics simulation is plotted in Figure 56.6b [72]. It is much more peaked than the measured distribution function [73], i.e. the shell structure of PBE water is too pronounced. A simple way out is to run the PBE water simulations at a higher temperature of 350 K [69–71], which, however, is not a satisfactory solution. Some real progress has recently been made through the introduction of dispersion-corrected DFT functionals [74–76]. The general idea is to replace the overestimated directional H-bond by the nondirectional van der Waals interaction [77]. Thus, not only the description of the water–metal interaction is improved [78, 79] but also the structural properties of water are better reproduced [72, 80–82], as illustrated for the case of the RPBE-D3 functional in Figure 56.6b. In contrast to PBE, RPBE does not overestimate the directional hydrogen bonding so that the oxygen–oxygen bulk water distribution function is already improved by going from the PBE to the RPBE functional. The agreement between simulations and experiment is further improved when dispersion corrections according to the D3 scheme [75] are added. These corrections are also required to obtain the correct water–water interaction energy and to reproduce the correct wetting behavior of water on metal electrodes [79]. Yet, with respect to the deviation between the experimentally and theoretically derived structure and energy of liquid water, there is another source, namely the possible role of nuclear quantum effects because of the low mass of hydrogen atoms. Quantum effects in the occupation of the O–H vibrations can be taken into account by applying quantum statistics to the calculated vibrational frequencies. Using this ansatz, a quantum correction of about 0.04 eV per water molecule was estimated [83]. In order to address quantum delocalization effects on the structural properties of water, for example, a path integral (PI) formalism can be applied. According to PI Car–Parrinello molecular dynamics (CPMD), nuclear quantum effects soften the structure of liquid water [84]. However, this conclusion is in conflict with another PI CPMD work [85], which arrived at the opposite conclusion, namely that nuclear quantum effects harden the structure of liquid water. Furthermore, a very recent path-integral study found that the O–O radial distribution function is hardly affected by nuclear quantum effects [86]. Given these conflicting results, it is fair to say that the exact role of nuclear quantum effects on the water structure is still not clear yet. For proton transfer in liquid water according to the Grotthus mechanism, on the other hand, quantum delocalization effects are apparently non-negligible [87]. In general, quantum tunneling effects and zero-point effects can to a certain extent cancel each other, as the comparison between classical and quantum H2 dynamics on the same potential energy surface demonstrated [88, 89]. Hence, it seems to be acceptable to ignore nuclear quantum effects, at least on a semiquantitative level. This is particularly true for MD simulations of solid/electrolyte interfaces, which typically include heavier atoms such as metal atoms. We will first address the geometric and electronic structure of water at the water/metal interface according to DFT calculations and then discuss the influence

483

484

56 Theory of Solid/Electrolyte Interfaces

Figure 56.7 Top view of the typical adsorption configuration of a water monomer on a close-packed metal surface.

of water on molecule–surface interactions in the absence of external fields. As a starting point, we consider the adsorption of a single water molecule on a flat metal surface (see also Chapter 37 in Volume 5). As illustrated in Figure 56.7, water monomers typically bind via their oxygen atom to the top sites of metal surfaces in an almost flat configuration, at distances between 2.25 Å (Cu) and 3.02 Å (Au) that are much larger than typical distances of specifically adsorbed or chemisorbed species. This is a consequence of the relatively weak binding of water monomers to metal surfaces with adsorption energies ranging from −0.1 eV to −0.4 eV. As for some particular important late transition metals, the interaction strength is ordered according to Au < Ag < Cu < Pd < Pt < Ru < Rh [4]. As far as the formation of water layers on metal surfaces is concerned, it is important to note that the water–water interaction is of comparable strength or even stronger than the water–metal interaction (see also Chapter 37 in Volume 5). Hence, the resulting structures are a compromise between optimal water–metal and water–water interaction. On closed-packed hexagonal metal surfaces, in fact, the energetically most favorable structures correspond to a water bilayer [4] whose structure is similar to that of the densest layer of ice [90]. In this structure, every second water molecule binds with its oxygen atom to the metal surface similar to the water monomer shown in Figure 56.7. For the other water molecules, there are in fact two different possible orientations, namely the so-called H-down and H-up structures with one hydrogen atom either pointing toward or away from the surface. These structures are illustrated in Figure 56.8a,b. In fact, for strongly interacting metal substrates, the water bilayers might not remain intact. Feibelman showed on the basis of DFT calculations that water on Ru(0001) should form a half-dissociated overlayer [91] where every second water molecule is dissociated to OH. As a matter of fact, Ru is not the only metal where half-dissociated water layers are more stable, but in Rh and Ni also, half-dissociated water layers should be energetically favorable [4].

56.4 Atomistic First-Principles Description of Solid/Water Interfaces

(a)

(b)

Figure 56.8 Side and top views of the water bilayer structures: (a) H-down bilayer and (b) H-up water bilayer.

The adsorption energies per water molecule of intact ice-like water layers on late transition metals with respect to the free water molecules range between −0.42 and −0.56 eV according to PBE calculations [4, 92]. The H-up structure is energetically favorable on Ni(111), Cu(111), and Ru(0001), whereas on Rh(111), Ag(111), Pt(111), Pd(111) [4], and Pd/Au(111) [93], the H-down structure is more stable. Note that these energies are less negative than the calculated PBE sublimation energy of water in a 32-molecule per cell model of ice-Ih, Esub = −0.666 eV [93]. This means that the considered water adlayers are not thermodynamically stable with respect to conversion to a three-dimensional ice cluster. However, this inconsistency is resolved if dispersion-corrected functionals such as RBPE-D3 [75] are used [78, 79]. An important information for the understanding of solid/electrolyte interfaces is the extent to which the presence of the electrolyte changes the properties of the electrode. The local density of states (LDOS) of the Pt(111) electrode in the absence and presence of water derived from periodic DFT calculations [94] is plotted in Figure 56.9. For the water bilayers shown in Figure 56.8, there are three inequivalent Pt surface atoms per surface unit cell, either noncovered or covered by a water molecule either lying flat or in the H-up or H-down configuration. The strongest change in the LDOS upon water adsorption is seen for the case of the adsorption of a water monomer in its most favorable configuration lying almost flat on the surface with the oxygen atom above a Pt atom [92, 95]. The increased LDOS at about −4.5 eV is caused by the hybridization of the water 1b1 orbital with the Pt d-band [95]. In contrast, upon adsorption of the H-down water bilayer, the LDOS is hardly changed compared to uncovered Pt(111). Even for the Pt atom beneath the water molecule in the bilayer that is bound via the oxygen atom,

485

56 Theory of Solid/Electrolyte Interfaces

Bare Pt(111) O-covered Pt H-covered Pt Uncovered Pt H2O monomer

2 Local density of states (a.u.)

486

1.5

1

0.5

0 –6

–4

–2

0

2

4

Energy ε– εf (eV) Figure 56.9 Local density of states (LDOS) of the Pt(111) surface atoms without and in the presence of a water monomer and a Hdown water bilayer. In the case of the water monomer, only the LDOS of the Pt atom directly below the water molecule is plotted,

whereas for the H-down water bilayer, the LDOS of the three inequivalent Pt atoms within the surface unit cell is drawn. Source: Gohda et al. 2008 [94]. Reproduced with permission of Royal Society of Chemistry.

there are only small modifications in the LDOS. This illustrates that the hydrogen bonding of the water molecules to other water molecules within the water bilayer weakens the interaction of the water molecules with the metal substrate [92]. The peak positions and the width of the Pt d-band are still hardly modified by the presence of water, even in the case of the water monomer adsorption. This indicates that the interaction of water with late transition metals is rather weak. It also explains why the chemical bonding of specifically adsorbed species to late transition metal electrode surfaces is only weakly influenced by the presence of water [92, 93]. Still, the question remains whether water structures at close-packed metal surfaces are really crystalline or are rather liquid-like. Izvekov et al. were the first to systematically study the structure of water layers on metal surfaces using AIMD simulations. Both the water structure on Cu(110) [96] and on Ag(111) [97] were addressed at room temperature. However, in these studies, the thermal stability of the hexagonal ice-like layer could not be assessed because the (110) surface does not correspond to a hexagonal structure, and for the AIMD simulations of the water–Ag(111) system, no hexagonal unit cell was chosen. Later, in a systematic study, the structure of two water layers on Ag(111), Au(111), Pt(111), Pd/Au(111), and Ru(0001) at room temperature was studied using AIMD simulations [62]. Figure 56.10 shows snapshots of the AIMD trajectories of water on Ag(111) and Pt(111). The simulations were started with the ice-like bilayer as the

56.4 Atomistic First-Principles Description of Solid/Water Interfaces

(a) H2O/Ag(111): 7.5 ps

(b) H2O/Pt(111): 7.5 ps

Figure 56.10 Snapshots of AIMD simulations of two water layers on (a) Ag(111) and (b) Pt(111) at 300 K. Only the first water layer is shown. Source: Schnur and Gro 2009 [62].

initial configuration. As Figure 56.10 demonstrates, for water on Ag(111) after 7.5 ps, no indication of the initial hexagonal geometry is left, the structure is disordered, there is some clustering, and most of the water molecules bind via their oxygen atom to the substrate. For water on Pt(111), on the other hand, there is still a hexagonal √ geometry visible. This structure might be an artifact of the relatively small 2 3 × √ 2 3 unit cell chosen, but still a higher lateral order than on Ag(111) is observed. Yet there is no indication of either the H-up or the H-down structure being left, the orientation of the water molecules is disordered. Note that the work function change of the metal electrodes upon water adsorption strongly depends on the orientation of the water molecules. The H-up and H-down structure causes work function changes that differ by about 2 eV because of their opposite dipole orientation [62, 98, 99]. Furthermore, neither the H-up structure nor the H-down structure yields work function changes [98] that agree with experimental findings for Au(111) [100], Pt(111) [101, 102], or Ru(0001) [103, 104]. In fact, the experimentally found work functions for these systems lie in between the calculated ones for the H-up and H-down structures. Only if the thermal motion of the water molecules is taken into account in the simulations, experimental and theoretical results become consistent [62, 98] because then the preferential orientation of the water molecules and thus of their dipole moments is significantly reduced. This gives strong evidence that indeed water layers at close-packed fcc(111) electrodes are disordered at room temperature. However, Figure 56.10 yields qualitative differences with respect to the degree of disorder. Water on the noble metal surface Ag(111) is also laterally disordered, whereas on the more strongly interacting Pt(111) electrode, still a hexagonal ordering exists. This might imply that the hexagonal ordering of the water layer is a consequence of the interaction between metal electrode and water. In this context, it is interesting to consider the structure of a water layer on stepped noble metal surfaces. In Figure 56.11, snapshots of a AIMD simulation of water on Au(511) at T = 140 K

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56 Theory of Solid/Electrolyte Interfaces

T = 140 K

(a)

T = 300 K

(b)

Figure 56.11 Top and side views of snapshots of an AIMD run of a water layer on Au(511) at (a) T = 140 K and (b) T = 300 K. Source: Lin and Gro 2012 [105]. Reproduced with permission of Elsevier.

and T = 300 K are shown [105]. This system was studied by vibrational spectroscopy by Ibach [106], and basically, the same results have also been found on Ag(511) [107]. Based on the vibrational spectra, Ibach made a proposal for the structure of the water layer on Au(511). It consists of an arrangement of the water network in tetragons, hexagons, and octagons (see Figure 56.11a). This structure was later confirmed to be an energy minimum structure in periodic DFT calculations [105]. AIMD simulations also allow to derive vibrational spectra through the Fourier transform of the velocity autocorrelation function. Using this technique, the measured vibrational spectra of water on Au(511) could be reproduced. Interestingly enough, this water network remained relatively stable also after 10 ps at Ts = 140 K and in the room temperature run (see Figure 56.11b), whereas on Au(111), the water network was already dissolved after 8 ps. A closer look at the water structure on Au(511) in Figure 56.11 shows that the oxygen atoms of the water molecules approximately form a flat layer, which is only attached to the metal through the oxygen atoms above the step atoms. An analysis of the charge density difference upon water adsorption also confirms that bonding between water and metal only occurs at the steps [105]. Thus, the water layer is pinned to the metal step atoms, whereas the water molecules above the small (100)-like terrace are not directly interacting with the metal substrate. This leads to a stronger water–water interaction, which apparently stabilizes the water network. The particular water structure on Au(511) obviously results as a compromise between the creation of the hydrogen-bonded network and the best-suited arrangement on the stepped (511) surface. Apparently, the surface structure matters as far as the network formation is concerned.

56.4 Atomistic First-Principles Description of Solid/Water Interfaces

However, not only the structure but also the lateral distances within the metal template matter, as far as the resulting water structure is concerned. This becomes obvious if one tries to form a hexagonal ice-like water layer on Pb(111). The lattice constant of Pb (a = 4.95 Å) is much larger than those of, e.g., the late transition metals such as Au that has a lattice constant of a = 4.08 Å. On Pb(111), according to periodic DFT calculations [108, 109], no closed hexagonal hydrogen-bonded rings are formed because the Pb–Pb distance is already too large, as illustrated in Figure 56.12a. It displays the energetically optimized structure within this arrangement corresponding to a water coverage of ΘH2 O = 2/3. The structure rather corresponds to an arrangement of water dimers with a large variation on the O–H separation of the hydrogen bonds. The reduced hydrogen bonding is also reflected in the low adsorption energy of −0.254 eV with respect to the free water molecule, which is considerably smaller than the corresponding adsorption energies at water coverages of ΘH2 O = 2/3 on late transition metals. When the coverage is increased to ΘH2 O = 1, i.e. there is one adsorbed water molecule per Pb atom on the surface, an energetically more favorable arrangement of the water molecule becomes possible, which is reflected in the higher energy gain per water molecule of −0.350 eV. The corresponding structure is illustrated in Figure 56.12b. Note that still no real two-dimensional water network results, and the water layer rather corresponds to an arrangement of water chains. Consequently, the binding energy per water molecule is still less than the binding energies in the hexagonal structure on the late transition metals. As far as electrolyte/metal interfaces are concerned, one has to take into account that there is typically a certain concentration of ions in the electrolyte, which might be adsorbed on the surface. In Section 56.2, we addressed how the coverage of the ions on the electrode can be estimated without taking the electrolyte explicitly into account. However, the presence of chemisorbed ions on the surface will modify the structure of the electrolyte directly above the electrode, which we will discuss here.

(a)

(b)

Figure 56.12 Optimized water structures of a water layer on Pb(111) for water coverages of (a) ΘH O = 2/3 and (b) ΘH O = 1. Source: Gro et al. 2014 [108]. Reproduced with permis2 2 sion of The Electrochemical Society.

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56 Theory of Solid/Electrolyte Interfaces

An ion that is always present in aqueous electrolytes is the proton. Depending on the adsorption energy of hydrogen on the electrode, the presence of protons might lead to a hydrogen layer on the electrode. If the hydrogen deposition occurs at potentials less negative than the equilibrium potential (which for hydrogen corresponds to USHE = 0 V), then this is called underpotential deposition (upd) in electrochemistry. It is well known that under electrochemical conditions at low but positive potentials, the Pt(111) electrode becomes covered by upd hydrogen [21, 110]. The presence of a hydrogen adlayer on Pt(111) significantly modifies the water structure above the electrode. This was shown using AIMD simulations at room temperature [111]. Snapshots of AIMD simulations of two water layers on clean and hydrogen-covered Pt(111) are shown in Figure 56.13a,b. It is obvious that the presence of hydrogen on Pt(111) weakens the interaction between water and Pt: when hydrogen is present on the surface, the lowest water layer is about 1 Å farther away from the surface than in the case of the clean electrode. Thus, the adsorption of hydrogen on Pt(111) leads to a passivation of the Pt electrode. Note that not only the distance is increased but also the thermal disorder within the water layers is reduced. Figure 56.13c displays the distribution in the O–O–O angle along the AIMD trajectories for water on clean Pt(111) and hydrogen-covered Pt(111) with different hydrogen coverages close to unity. For a perfect ice-like structure, the distribution would correspond to a delta function at 120∘ . Hence, the width of the distribution around 120∘ can be regarded as a descriptor for the deviation of the water layer from a perfect ice-like structure. Interestingly enough, although the interaction between metal and water is reduced because of the presence of the hydrogen overlayer, the angular distributions for hydrogen-covered Pt(111) are more peaked than the distribution for clean Pt(111).

Distribution (a.u.)

490

(a)

(b)

Figure 56.13 Structure of water on clean and hydrogen-covered Pt(111). (a,b) Snapshot of an AIMD trajectory of two water layers on clean and hydrogen-covered Pt(111) at room temperature, and (c) distribution

(c)

90 100 110 120 130 140 150 O–O–O angle γ (°)

of the O–O–O angle of water on clean and hydrogen-covered Pt(111) along the AIMD trajectories. Source: Roman and Groß 2013 [111]. Reproduced with permission of Elsevier.

56.4 Atomistic First-Principles Description of Solid/Water Interfaces

This indicates that a weaker metal–water interaction leads to a stronger order in the water layer. Recall that on clean metal surfaces, a lowered interaction strength caused a stronger disorder. Apparently, if the interaction strength is further reduced, again a higher order in the water layer at room temperature results. This can be rationalized by the fact that a weaker metal–water interaction leads to a stronger water–water interaction, which stabilizes the water hydrogen-bonded network. For an intermediate interaction strength as for clean substrates such as Au(111) or Ag(111), no order in the water layer is present any more. This suggests that in these cases, neither the water–metal nor the water–water interaction is strong enough in order to stabilize the hexagonal water network. In addition to protons, also anions that are present in the electrolyte can specifically adsorb or chemisorb at metal electrodes [112–115]. As shown in Section 56.2, halides adsorb on metal electrodes with a coverage in the order of 0.33–0.45 [17, 20, 114–117]. Of course, such a high coverage of species that reduce the interaction for further adsorbates [118] will also have a significant influence on the water structure. √ √ The calculated energy minimum structure of two water bilayers on a 3 × 3 chlorine-covered Pt(111) surface corresponding to a chlorine coverage of ΘCl = 1∕3 is shown in Figure 56.14. The anion structure is so dense that the water molecules do not penetrate it, and the water layer is instead located above the halide layer. Still, further issues require clarification, as far as the exact structure of the water layers at metal electrodes are concerned. It remains to be seen how the presence of more complex anions such as sulfate modifies the water configuration. Furthermore, so far no cations were considered which typically adsorb non-specifically, as illustrated in Figure 56.1. Still, it might be interesting to see how their solvation shell is modified when they are located close to the electrode. Finally, more complex structures can result if more than one adsorbate is present on the electrode, such as hydrogen atoms and anions.

√ √ Figure 56.14 Structure of two water bilayers on a 3 × 3 chlorine-covered Pt(111). Source: Groß et al. 2014 [108]. Reproduced with permission of The Electrochemical Society.

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56 Theory of Solid/Electrolyte Interfaces

As shown above, the statistical averages necessary to determine the atomistic structure of solid/electrolyte interfaces can nowadays been obtained from first principles using AIMD simulations. However, in spite of the progress in computer power and the development of efficient algorithms, AIMD simulations are still numerically rather demanding. This represents a severe obstacle for the first-principles modeling, in particular when many different configurations are required to be calculated, as for example in the determination of electrocatalytic reaction networks. As an alternative, computationally attractive approach, the electrolyte can be represented in an implicit solvent model as a continuous dielectricum [39, 119–121], as mentioned in the beginning of this section. The difference between the explicit and the implicit solvent model is illustrated in Figure 56.15. The implicit solvent model is justified when the averaged behavior of many highly dynamic solvent molecules can be approximated by a potential of mean force [8]. Although the validity of this approach is limited, it is useful to derive trends for chemical processes at solid/electrolyte interfaces. This will be demonstrated using the methanol electro-oxidation on Pt(111) [122] as an example, which has been studied using a recent implementation of an implicit solvent model into a periodic DFT code [39]. An adsorbed methanol molecule is depicted in Figure 56.15. Interestingly enough, experimentally, it was shown that on Pt(111) in an electrochemical environment, methanol is predominantly converted to hydroxymethyl (H2 COH) involving the breaking of one of the C–H bonds of the methyl group of methanol [123]. In contrast, at the Pt/vacuum interface, methanol also decays to methoxy (CH3 O) associated with the breaking of the O–H bond. In order to compare the electrochemical and the gas-phase decomposition of methanol on Pt(111), the adsorption energies of the reaction intermediates were determined in the absence and the presence of the implicit solvent [23, 122]. A convenient way to estimate the influence of the solvent on reaction intermediates is

Solvent

Dielectric continuum theory (implicit solvent)

Solute

(a)

Quantum mechanics (explicit solute) (b)

Figure 56.15 Illustration of the difference between an explicit and an implicit solvent model. Source: Courtesy of Dr. Sung Sakong.

Stabilization of hydrophilic groups ∆HOH (eV)

56.5 Explicit Consideration of Varying Electrode Potentials

0.00 –0.02 –0.04 COOH/HCOO

–0.06 HCOOH/H2COO

–0.08 –0.10

H2COH/CH3O

–0.12 –0.14 COH/HCO –0.16 Isomeric pairs

Figure 56.16 Stabilization ΔHOH of reaction intermediates in the methanol oxidation in an aqueous electrolyte containing hydrophilic OH groups with respect to their

isomers without OH groups determined according to Eq. (56.16). Source: Lin et al. 2016 [23]. Adapted with permission of ACS Publications.

to compare the stability of isomers in the liquid and gas phase according to ΔHOH = (ΔH l (H2 COH) − ΔH l (CH3 O)) − (ΔH g (H2 COH) − ΔH g (CH3 O)) (56.16) where ΔH l is the formation enthalpy in the liquid phase appropriate for electrocatalysis and ΔH g in the gas phase appropriate for heterogenous catalysis, and where the isomeric pair hydroxymethyl-methoxy has been used as an example. ΔHOH has been plotted for four isomeric pairs in Figure 56.16. For all pairs, the species containing hydrophilic OH groups become stabilized in the presence of an aqueous electrolyte, but to a different extent. As far as the first dehydrogenation step in methanol oxidation is concerned, hydroxymethyl becomes energetically more favorable than methoxy by about 0.1 eV in the presence of an aqueous electrolyte compared to the gas phase. The same mechanism does not only stabilize the final product hydroxymethyl but it also lowers the C–H bond breaking barrier compared to the O–H bond breaking barrier [122], explaining why hydroxymethyl is preferentially formed at the electrode/electrolyte interface [123]. Thus, the implicit solvent model is at least qualitatively able to reproduce experimentally found trends in electrocatalysis.

56.5 Explicit Consideration of Varying Electrode Potentials

In Section 56.2, we have shown how varying electrode potentials can be implicitly included in the expression for the corresponding electrochemical potentials.

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56 Theory of Solid/Electrolyte Interfaces

However, the explicit influence of varying electrode potentials on, e.g. adsorption energies can thus not been assessed. Note that varying electrode potentials lead to changes in the excess surface charge of the electrodes and the emergence of electric fields. Dealing with charged systems in periodic electronic structure calculations is not trivial as the unit cell has to stay charge-neutral. Hence, the excess charge has to be balanced by counter charges. There are in fact two different modes to deal with charged systems in periodic DFT setups [124] that are illustrated in Figure 56.17. Originally, DFT was formulated for systems with a constant number of electrons Ne , but then it was realized [125] that there is an equivalent grand canonical formulation of DFT in which the chemical potential 𝜇 of the electrons instead of the number of electrons is one of the basic quantities. Systems with a constant number of electrons correspond to a slab that is isolated from its environment. In order to obey charge neutrality, a counter electrode carrying charge of equal amount as the slab but opposite sign has to be placed within the unit cell. This can be realized in different manners, for example, as a capacitor, as shown in Figure 56.17a, but the compensating charge does not need to be locally separated from the charged slab. It can also be distributed uniformly over the unit cell as a compensating charge background [126]. This is in fact the default procedure in periodic DFT codes. They automatically include a homogenous compensating charge background when the system is charged because of the fact that in the Ewald summation typically done in reciprocal space, the monopole term is excluded because it leads to divergence. The “𝜇 = constant” mode, on the other hand, corresponds to a metallic slab that is part of an electric circuit in which the voltage between charged electrode Constant charge +q

S

(a)

Constant potential ΔV

–q

R

F(T, V , Ne) Ne = const

S

(b)

Figure 56.17 Illustration of (a) the constant charge (Ne =const.) and (b) the constant chemical potential (𝜇 =const.) mode to treat charged systems within periodic DFT calculations. The corresponding

R

Ω(T, V , μ) μ = const thermodynamic potentials to describe a slab together with a reference electrode are the Helmholtz free energy F and the grand potential Ω, respectively.

56.5 Explicit Consideration of Varying Electrode Potentials

and counter electrode is specified. This mode is in fact much closer to an electrochemical setup in which the electrode potential is a crucial input parameter. There are implementations of periodic DFT calculations that perform self-consistent iterations within the grand-canonical formulation of DFT [124, 127]. The electron density is calculated in each iteration step as a sum of partial densities over Kohn–Sham orbitals with eigenvalues up to a given chemical potential 𝜇. As a consequence, the number of electrons is not necessarily conserved. However, this mode is not as easy to implement into periodic DFT codes as the “Ne = constant” mode. Furthermore, it typically exhibits a much slower convergence of the self-consistent field cycles than calculations with a fixed number of electrons. Hence, the vast majority of first-principles studies addressing electrochemical systems so far have been performed at a constant number of electrons. Still, calculations in the “Ne = constant” mode can also be related to the “𝜇 = constant” mode in an a posteriori manner. First calculations are performed for a given number of electrons. Then, the corresponding electrode potential is specified. In general, any different configuration calculated for a given number of electrons corresponds to a different electrode potential as dipole moments and consequently the work function vary. In practice, constant charge calculations are performed for a number of different charge states, the corresponding electrode potentials are determined, and then the desired quantity is interpolated as a function of the electrode potential so that it can be obtained for a given arbitrary potential. This will be demonstrated below using the oxygen dissociation [128] as an example. We will now address specific approaches to realize constant charge calculations and derive the corresponding electrode potentials. As already mentioned above, the default procedure in periodic DFT codes to treat charged systems is charge compensation through a homogenous background. Two problems arise in such an approach. First, the compensating charge background interacts with all charges present in the supercell. This artificial interactions has to be corrected for. Second, in the presence of a compensating charge background, there is no vacuum region in the unit cell. Hence, neither the work function can be directly calculated nor the electrode potential be derived. These two problems were addressed in the so-called “double-reference method” [126, 129], which is illustrated in Figure 56.18. In a first step, a DFT calculation with a well-defined vacuum region is performed. This is achieved by constructing a solvated slab with a vacuum region introduced in the middle of the unit cell between the slabs, as illustrated in Figure 56.18a. The potential in the middle of the vacuum layer is used as the first reference by setting 𝜙∞ = 0. Then, it is assumed that the electrode potential does not change when the vacuum layer in the water region is omitted as depicted in Figure 56.18b. In regions deep in the interior, the potential variation does not depend on the presence of the vacuum region. Note also that electric fields do not penetrate into perfect metallic conductors but are shielded at the surface through the build-up of a surface charge. Hence, the potential 𝜙0 (m) = 𝜙′0 (m) − 𝜙∞ inside of the slab taken with respect to the vacuum level is taken as the first reference point, where the primed

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56 Theory of Solid/Electrolyte Interfaces

One–electron potential (eV)

496

(a)

ϕ∞

0

(b)

ϕ′0 (w)

μ –5

ϕ′ (m)

ϕ′0 (m)

–10 0

5

10

15

20 25 30 40 0 5 10 Position along the surface normal (Å)

Figure 56.18 Electrostatic energy profile across the unit cell for solvated water slabs in a periodic slab calculation with (a) and without (b) a vacuum region in the middle between the metal slabs. 𝜇e denotes the chemical potential of the electrons, which corresponds to the Fermi energy at T = 0 K.

15

20

25

𝜙∞ is the vacuum level and 𝜙′ (m) and 𝜙′0 (m) are the bulk metal potential with and without the presence of the vacuum layer in the calculations, respectively. Source: Taylor et al. 2006 [126]. Reproduced with permission of American Physical Society.

values indicate the unshifted values and the subscript 0 denotes the uncharged calculations without vacuum. For a charged slab, however, the presence of the excess charge q leads to the existence of an additional electric field at the interface and in the region between the metallic slabs. Because there is no longer any region where the one-electron potential is flat, a vacuum reference point cannot be established. The following procedure has been suggested within the “double-reference method” [129]: A region far from the electrode is fixed at its configuration in the q = 0 calculation, and the oneelectron potential 𝜙0 (𝑤) at this site (see Figure 56.18b) is used as the second reference point. The remaining system is relaxed under the influence of the applied charge, and the potential at all other positions is shifted with respect to the second reference point. Finally, to obtain an absolute electrode potential, for example, versus the normal hydrogen electrode, the work function for the H2 ∕H+ couple on Pt in standard conditions is subtracted. In the definition of the electrode potential, one has to take into account that in electrochemistry, potentials are typically defined with respect to a positive probe charge, whereas work functions and one-electron potentials are defined for a negative probe charge. Naively, one could think that the introduction of a constant charge background does not affect the variation of the one-electron potential as no electric field might be associated with a homogenous charge that is translationally invariant. However, one has to consider that the constant charge background is embedded in the varying charge density of the water–metal system, and the resulting electrostatic potential as a solution of the Poisson equation is a consequence of the whole charge distribution subject to the appropriate boundary conditions. Even in vacuum regions where the charge distribution is entirely given by the uniform background charge, this introduces an electric field caused by a varying potential as the general solution of the

56.5 Explicit Consideration of Varying Electrode Potentials

Poisson equation for a region with a constant charge background ∇2 𝜙(x) = 4𝜋𝜌0

(56.17)

in Cartesian coordinates is given by ( 3 ) ∑ ∑ Ci,j xi xj + Ci x i + C0 𝜙(x) = 4𝜋𝜌0 i,j=1

(56.18)

i

∑ with Cii = 1. Note first that for an infinitely extended isolated uniform charge background, i.e. without any compensating charges, there is no proper solution of Eq. (56.17). This reflects the fact that the electrostatic energy density diverges for such a system. In the case of a finite region of constant charge density, however, the potential follows a quadratic profile according to Eq. (56.18). This is illustrated in Figure 56.19 where in the upper panel the calculated potential difference Δ𝜙 [126] between a charged and uncharged Cu(111) slab with the excess charge compensated by a constant charge background as a function of the vertical position (indicated by −1e) is plotted. This 0 Na

Potential difference (eV)

–2

–1e

–4

(a)

0 –1e –2 Na –4 (b) 0

5 10 15 Position along the surface normal (Å)

Figure 56.19 Calculated potential difference Δ𝜙 between a charged and uncharged Cu(111) slabs with the excess electron density compensated either by a constant charge background (denoted by −1e) or by a Na ion pseudopotential without (a) and with

20

a water layer (b) in front of the electrode (after [126]. Note that the plotted atoms are only included as an illustration and do not correspond to the actual positions of the atoms in the calculations.

497

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56 Theory of Solid/Electrolyte Interfaces

variation can be understood if one takes into account that for positions displaced from the center of the vacuum region, there are unequal amounts of charge in both directions along the surface normal. One may also explicitly include counter ions at the approximate position of the outer Helmholtz plane as a model for the electrochemical interface instead of balancing the excess charge of the slab by a constant charge background. The corresponding potential difference with a sodium ion pseudopotential as the counter ion is also plotted in Figure 56.19. This leads to the same formal surface charge density as the compensating charge background. Yet the resulting potentials are quite different for the two methods. Already close to the metal slab, the slope of the two potential curves differs significantly. In the case of the constant charge background, an electric field results that is more than a factor of 2 larger close to the electrode compared to the case of the explicit counter ion. This illustrates that a uniform compensating charge background and an explicit counter charge lead in general to rather different potentials. Similar problems arise in the modeling of charged vacancies in semiconductors. Figure 56.19a, however, does not correspond to an electrochemical situation as the electrolyte is not properly taken into account. In the lower panel of Figure 56.19a, the same counter charge distributions are considered as in panel(a), but in an aqueous environment [126]. Now the difference is considerably reduced apparently because of screening effects of the polarizable water layers. In particular, across the inner water layer directly above the metal electrode, the resulting electric fields are now rather similar. This indicates that the continuum technique might be appropriate to model electrochemical processes occurring in the inner layer as long as the electrolyte is represented through a polarizable medium. Still, one has to be cautious that the introduction of a constant charge background can introduce artifacts in the description of electrochemical interfaces. There is a further problem arising in the modeling of counter charges. As a positive counter charge is attracting electrons, it lowers the potential for the electrons. If this lowering is large enough so that the work function of the metal is smaller than the depth of the potential minimum because of the counter charge, then there will be an artificial charge flow from the metal slab to the counter charge. This always needs to be checked when performing slab calculations with a positive compensating charge background. The artifacts associated with a uniform compensating charge background can be avoided if the counter electrode is explicitly considered. This can be done by representing the counter electrode as a localized planar charge distribution [98, 130–134], for example, in the form of a Gaussian profile perpendicular to the surface: ( z − z )2 q 0 exp − 𝜌ce (r) = √ 2𝛼 2π𝛼

(56.19)

where q is the total charge of the counter electrode and z0 corresponds to the position of the counter electrode. The width of the Gaussian charge distribution 𝛼 should be chosen for the sake of numerical convenience [98].

56.5 Explicit Consideration of Varying Electrode Potentials

Counter

Electrode

Electrostatic potential (eV)

20

10

0

–10 ΔεF –20

0.2e 0.1e Neutral –0.1e –0.2e –0.3e –0.4e

Pt 0

5

20 10 15 Position along the surface normal (Å)

Figure 56.20 Illustration of the implementation of an explicit Gaussian-shaped counter electrode in a periodic DFT setup together with laterally averaged one-electron potential

25

30

of a symmetrically constructed metal–water slab for varying electron numbers given per unit cell. Source: Schnur and Groß 2011 [98]. Reproduced with permission of Elsevier.

This particular approach is illustrated in Figure 56.20. The extra electronic charges on the electrodes are compensated by the planar Gaussian-shaped counter electrode. Figure 56.20 also shows some examples of laterally averaged one-electron potentials for different excess electron numbers per unit cell for a symmetric slab covered by one water layer. The varying electron number leads to a shift of the Fermi energy whose range is indicated as Δ𝜀F . Note that for a negatively charge slab, the counter electrode has to be positively charged, which makes it attractive for electrons. However, for a sufficiently large positive counter charge, the one-electron potential in the counter electrode drops below the Fermi energy defined by the number of electrons in the slabs. This would lead to an artificial electron transfer from the slabs to the counter electrode, as mentioned above. Hence, the range toward negative charges on the slabs corresponding to low electrode potentials is limited. The electric field strength in the vacuum region caused by the counter electrode can be deduced from the slope of the linear regions. It does not penetrate deep into the Pt electrode because it is screened by the metallic properties of the Pt(111) slab. Hence, inside of the slab, the averaged one-electron potential is just shifted in a parallel manner. The counter charge has been introduced in order to compensate the excess charges on the electrodes; otherwise, it has no physical relevance. Still, there is a direct electrostatic interaction between the electrons and the ion core with the counter

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56 Theory of Solid/Electrolyte Interfaces

electrode, in spite of the fact that the counter electrode and the considered atoms are separated in space. Therefore, the resulting total energy has to be corrected for this interaction, which can be done, as derived by Taylor et al. [126], by integrating the electrochemical potential 𝜇 over the applied charge, i.e. ] q q[ Vtot 3 𝜇 dQ = EDFT + (56.20) d x dQ E= ∫0 ∫ Ω ∫0 A further correction is required to take the varying number of electrons in each system into account. Thus, the total grand canonical free energy energy of the electrons, except for entropic effects, is given by Efree = E + 𝜇q

(56.21)

Here, E is the total energy from the DFT calculations corrected for the electrostatic interaction with the counter electrode as given by Eq. (56.20), and q is the total charge of the electron ion system. The electrochemical potential 𝜇 has been taken with respect to the reference system with q = 0. It is obvious from Figure 56.20 that for nonzero excess Charge, there is no field free region in this implementation, similar to the case with a compensating uniform charge background. Hence, also here, no vacuum level can be straightforwardly defined, which also means that no work function and hence no electrode potential [135] can be directly deduced. One way to determine the electrode potential in this method is to relate the charge to the potential via experimentally derived capacities [131]; however, this introduces an empirical component to this approach. In principle, a similar first-principles approach as in the double reference method [126] described above could be used to specify the electrode potential. However, as Figure 56.20 illustrates, there is a very convenient second reference point as requested by the double-reference method. All the one-electron potentials drawn in Figure 56.20 cross outside the water layer at a position zref where the electrode potential in the neutral case already assumes its vacuum level. Using zref as a second reference point, simply the difference between the potential at this reference point and the Fermi energy can be regarded as a measure of the work function Φ = 𝑣(zref ) − 𝜀F

(56.22)

The corresponding electrode potential U of the water-covered metal electrode relative to the normal hydrogen electrode (NHE) can then be estimated [126, 135, 136] by U = Φ − ΦNHE

(56.23)

where the value of ΦNHE can be taken from the literature. There is still some debate about the exact value of ΦNHE , often ΦNHE = 4.44 V [137] is assumed. This approach has been used to address the Volmer reaction H3 O+ + e− ↔ Had + H2 O

(56.24)

56.5 Explicit Consideration of Varying Electrode Potentials

on Pt(111) by calculating √the energy of a Pt(111) substrate covered with one ice√ like bilayer in a 2 3 × 2 3 geometry with one hydrogen atom per unit cell either adsorbed on the electrode or incorporated into the water bilayer leading to H3 O+ . The free energies of both structures as a function of the electrode potential vs. NHE are plotted in Figure 56.21a. The difference Efree (Had + H2 O) − Efree (H3 O+ )

q=0

Energy Efree (eV)

1.5 H3O+ Had + H2O

q=0 1.0

0.5

0.0

–1

0

(a)

1

2

Potential U vs. NHE (V)

Free energy of adsorption (eV)

0.6 0.4

pH = 0 U=0V

Had Au

0.2 0.0

H+ + e–

1/2 H2 Pt

–0.2 Ni –0.4

Mo

–0.6 (b)

Reaction coordinate

Figure 56.21 (a) Free energy calculated according to Eq. (56.21) as a function of the electrode potential vs. NHE for a water bilayer on Pt(111) with an adsorbed hydrogen atom (Had + H2 O) and with a solvated proton in the water bilayer (H3 O+ ) in a √ √ 2 3 × 2 3 geometry, calculated using the explicit counter electrode according to Eq. (56.19). The symbols correspond to the

calculated values, the line to a quadratic fit to these results; (b) free energy diagram for hydrogen evolution at equilibrium (U = 0 vs. NHE) derived from hydrogen adsorption energies at various metal electrodes. Source: (a) Schnur and Groß 2011 [98]. Reproduced with permission of Elsevier. (b) Norskov et al. 2005 [10]. Adapted with permission of The Electrochemical Society.

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56 Theory of Solid/Electrolyte Interfaces

between these two curves can be regarded as an estimate for the adsorption energy of the hydrogen atom with respect to a proton in solution. The equilibrium of the Volmer reaction is at about −0.08 V vs. NHE, which means that it is rather close to the equilibrium of the hydrogen evolution at standard conditions. As a consequence, the hydrogen evolution on Pt(111) with the intermediate adsorbed hydrogen state is essentially thermoneutral, which has in fact been realized as the reason why Pt is an excellent catalyst for hydrogen evolution [10]. This conclusion is illustrated in Figure 56.21b where the calculated hydrogen adsorption energies on Au, Pt, Ni, and Mo using the concept of the computational hydrogen electrode [10] according to Eq. (56.3) are plotted. For the hydrogen evolution reaction (HER), as for any catalytic reaction, the Sabatier principle is valid: the interaction between the catalyst and the substrate should be neither too strong nor too weak. For a very weak interaction, the reactant will not bind to the catalyst and no reaction will occur. For a rather strong interaction, the reactant and/or the reaction products are not able to desorb from the catalyst again. This concept is the basis for the volcano plots in heterogenous and electrocatalysis [138, 139]. Hence, an intermediate interaction strength between the reactant and the catalyst is optimal for any catalytic reaction. As Figure 56.21b shows, Pt indeed exhibits this intermediate interaction with hydrogen, whereas Au binds hydrogen too weakly and Ni and Mo too strongly. The free energies calculated as a function of the electrode potential in Figure 56.21a fully confirm the exceptional role of Pt for the HER illustrated in Figure 56.21b. It should still be noted that for the determination of the free energies in Figure 56.21, the Pt(111) electrode has been assumed to be clean, whereas at low potentials, Pt(111) electrodes should be hydrogen-covered, as illustrated in Figure 56.13. The consideration of coverage effects might lead to some quantitative modifications, but probably not to any qualitative changes. As a further application of a constant charge scheme, we will present the oxygen dissociation on Pt(111) [128], which has been addressed using the double-reference method [126]. The initial, transition, and final state of the O2 dissociation on Pt(111) in the explicit presence of water molecules without and with Na coadsorption has been calculated for various system charges, and the corresponding potential has been derived. Figure 56.22a shows the free energies as a function of potential for the case without the Na atom. In order to derive the activation barrier for O2 dissociation for a specific potential, the difference between the interpolated quadratic fits is taken. These barriers in the “𝜇 = constant” mode are plotted in Figure 56.22b where the charge of the initial state is used as a reference. However, Figure 56.22b also illustrates the difference between the constant charge and constant potential mode, as also the activation barrier at constant charge is plotted. In particular, for positively charged Pt(111), i.e. at positive electrode potentials, there is a large difference between the results in these two modes, the corresponding activation barriers differ by up to 0.2 eV. This deviation can be traced back to the difference in the respective electrode potentials between the initial and the transition state in Figure 56.22b (boxes and circles, respectively) at positive charge. If the states for a given charge are aligned above each other, as in the case of negative potential or charge, then there is only a small difference between the

56.5 Explicit Consideration of Varying Electrode Potentials

Transition state

3

Adsorbed O2 molecule

Free energy (eV)

Adsorbed O atoms

2

1

0 –1

O2 dissociation activation energy (eV)

(a)

1 0 0.5 Potential vs. NHE (eV)

1.5

2

0.5 0.4 0.3 O2 + 23 H2O: q constant

0.2

O2 + 23 H2O: μ constant

0.1

O2 + Na + 22 H2O: μ constant

O2 + Na + 22 H2O: q constant

0.0 (b)

–0.5

–1.0

–0.5 0.0 0.5 System charge q of the initial state (e)

Figure 56.22 (a) Calculated free energies as a function of potential for the initial, transition, and final state of O2 dissociation on solvated Pt(111) obtained with the doublereference method [126]. The symbols correspond to the results obtained for different charge states of the Pt electrode, whereas the solid curves are quadratic fits to the

1.0

results. (b) Dissociation barrier of O2 on solvated Pt(111) without and with Na coadsorption, respectively, for various constant system charges q of the system and for constant potential, kept at the corresponding value of the initial state. Source: Wasileski and Janik 2008 [128]. Adapted with permission of Royal Society of Chemistry.

503

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56 Theory of Solid/Electrolyte Interfaces

constant charge and the constant potential mode. Basically, this means that the involved states are associated with rather similar work functions. If, however, the states are not aligned, as in the case of positive charges, then significant differences can result. This also means that AIMD simulations in the constant charge mode might give unreliable results as barriers can be severely under- or overestimated. The differences between the constant charge and the constant potential mode in fact decrease with increasing size of the surface unit cell. If the studied reaction involves only a small fraction of the considered atoms, then the associated work function change will only be small, even if locally the dipole moment changes. Until here, we have presented two ways to realize compensating charges in a constant charge mode for periodic DFT calculations, through a uniform charge background and through an explicit Gaussian-shaped counter electrode. Note that there are also other possibilities to implement an explicit counter charge into a periodic DFT arrangement, for example, through a perfect conducting continuum with a nonvanishing surface charge above the slab in a two-dimensional periodic approach [140–143]. This so-called effective screening method has among others been used to address the structure of water under acidic conditions [141, 142, 144]. All these constant-charge methods use excess charges in order to model varying electrode potentials. However, in an electrochemical cell, changing the electrode potential is accompanied by a rearrangement of the counter ions in the electrolyte, which supplies in fact the charges at the electrode/electrolyte interface. Hence, in fact, no charged unit cells are needed but just the concentration of counter ions at the electrode surface has to be varied to reach another electrode potential. This idea has been employed by Nørskov, Rossmeisl and coworkers [136, 145] in a periodic DFT setup. They have changed the corresponding electrode potential by introducing hydrogen atoms in the water layer close to the metal electrode. The added hydrogen atoms either become solvated as protons leading to the formation of hydronium ions (H3 O+ ) or adsorb at the metal electrode. In either case, they change the work function of the electrode/electrolyte system and thus the corresponding electrode potential. This elegant method is illustrated in Figure 56.23. In this setup, the whole supercell always remains neutral so that no countercharges are needed. By changing the hydrogen concentration, the surface charge and hence the electrode potential can then be varied. In Figure 56.23, the laterally averaged one-electron potential is shown for two different concentrations of hydrogen atoms, one or four atoms per (6 × 4) supercell. The particular atomic configuration is illustrated in the inset. It can be clearly seen that the two different hydrogen concentrations lead to vastly different work functions, which are given by the difference between the flat potential in the vacuum region and the Fermi level. The corresponding electrode potential, for example, vs. the normal hydrogen electrode, can be derived using Eq. (56.23). This approach has also been used to address the hydrogen evolution reaction on Pt(111) [136]. The potential was varied by adding varying amounts of hydrogen atoms to supercells of different sizes. In detail, the elementary processes occurring in the hydrogen evolution reaction on Pt(111), namely the Volmer reaction H+ + e− → Had

(56.25)

56.5 Explicit Consideration of Varying Electrode Potentials

One–electron potential (eV)

20 15

4H per (6 × 4) cell 1H per (6 × 4) cell

10 5

Φ1H+

0

Φ4H+

Fermi level

–5 0

5 10 15 Position along the surface normal (Å)

Figure 56.23 Illustration of how the work function and thus the electrode potential is changed by varying the number of counter ions at the electrode/electrolyte interface. The one-electron potential is changed by varying the number of hydrogen atoms in the water layer at Pt(111) within a (6 × 4)

geometry. In the vacuum layer, there is a potential drop because of the presence of a dipole layer. The inset illustrates the structure of the interface with additional protons. Source: Skulason et al. 2007 [136]. Adapted with permission of Royal Society of Chemistry.

the Tafel reaction 2Had → H2

(56.26)

and the Heyrovsky reaction Had + H+ + e− → H2

(56.27)

were addressed using this approach. In spite of its importance in electrochemistry, there is still some debate about the exact mechanism of the hydrogen evolution reaction [146, 147]. The barriers for the three reactions given above were calculated for a set of given hydrogen concentrations. This ansatz faces the same problem as constant charge simulations, the configurations along the reaction path correspond to different dipole moments and thus electrode potentials. Therefore, as a representative of the potential at which the reaction occurs, the average of the initial and final state potentials was taken. Note that the uncertainty associated with this procedure decreases with the size of the unit cell. The calculated activation barriers as a function of the potential for the Tafel reaction at the electrode/vacuum interface and in the presence of a water bilayer and for the Heyrovsky reaction in a water bilayer are plotted in Figure 56.24. Additionally calculated data points for the Heyrovsky reaction lie outside of the plotted potential range. There is a large gap between the results for negative and for positive potentials. This is due to the fact that there is a discontinuity in the differential hydrogen adsorption energies once a complete hydrogen layer is formed. The gap in

505

56 Theory of Solid/Electrolyte Interfaces

1.0

Activation energy (eV)

506

Tafel mechanism without water Tafel mechanism with water Heyrovsky mechanism with water

0.8

Tafel: y = 0.64 x + 0.80 0.6

Heyrovsky: y = 0.42 x + 0.59

0.4

0.2 –0.7 –0.6 –0.5 –0.4 –0.3 –0.2 –0.1 Potential vs. NHE (V) Figure 56.24 Calculated activation energy for the Tafel reaction as a function of potential without (diamonds) and with (triangles) a water bilayer and for the Heyrovsky reaction with water (circles). For the Heyrovsky reaction, two further barriers

0.0

0.1

0.2

were determined, which lie outside the plotted potential range. The dashed lines correspond to linear fits to the data. Source: Skulason et al. 2007 [136]. Adapted with permission of Royal Society of Chemistry.

Figure 56.24 could only be closed if larger unit cells were chosen or if some extrapolation scheme [145] is employed. As Figure 56.24 indicates, the dependence of the barriers on the potential is approximately linear. The so-called transfer coefficient 𝛼 𝛼=

dEa d𝜙

(56.28)

is given by the slope of the linear fit to the data. It is regarded as a measure of the symmetry of the activation barrier. The value of 𝛼 = 0.64 for the Tafel reaction indicates that the barrier location for the Tafel reaction is closer to the initial state at the electrode, whereas 𝛼 = 0.42 for the Heyrovsky mechanism means that in this case, the barrier is located closer to the outer Helmholtz plane. At potentials around 0 V, the Pt electrode becomes covered by a monolayer of hydrogen. Further hydrogen adsorption only occurs at potentials below −0.5 V, which corresponds to the so-called opd hydrogen (over-potential deposited). While the activation barriers are rather large at positive potentials, both the Tafel and the Heyrovsky reaction exhibit moderate barriers at negative potentials where hydrogen evolution becomes thermodynamically possible. As Figure 56.24 shows, the calculated barriers for the Heyrovsky mechanism are smaller than for the Tafel mechanism. This suggests that the Heyrovsky reaction should dominate the hydrogen evolution. Experimentally, the mechanism for the hydrogen evolution on Pt electrodes has been found to depend on the electrode termination [146]. As far as Pt(111) is concerned, the exact reaction mechanism could not be unambiguously

56.5 Explicit Consideration of Varying Electrode Potentials

deduced, the measured activation energy of 0.18 eV [146], however, is smaller than the one calculated in the DFT study [136]. Thus, there is still room for further improvements in the theoretical description. So far, we have only discussed calculations performed within the constant charge mode, as this mode is much easier to implement as the constant potential mode. In the constant potential mode, the number of electrons has to be allowed to vary. Thus, a grand-canonical formulation of DFT has to be invoked in which the chemical potential 𝜇 of the electrons instead of the number of electrons N is one of the basic quantities [125]. The theoretical framework for such a constant potential approach has been discussed by Lozovoi et al. [124]. They have proposed a method in which the number of electrons is allowed to vary in the self-consistent field procedure such that the Fermi level stays at a preset value. However, as it turns out, such an approach can lead to instabilities in the practical implementation [148] caused by oscillations in the number of electrons [149]. In order to avoid these problems, Bonnet et al. [148] implemented a scheme in which the system described within the effective screening method mentioned above is connected to a fictitious potentiostat. The setup is illustrated in Figure 56.25. In this particular implementation [140, 141], the system is described periodically in two dimensions but not in the dimension perpendicular to the electrode. In detail, the laterally periodic slab that can be covered by water and/or adsorbates is placed between two polarizable continua characterized by their dielectric constant 𝜖. For electrochemical systems, vacuum (𝜖 = 1) can be chosen at the metal side and a perfect conductor (𝜖 = ∞) at the other side. The potentiostat operates analogously to a thermostat. In the Nosé–Hoover thermostat [150], the temperature is controlled by allowing the exchange of kinetic energy with an external thermostat at temperature T. Analogously, in this constant potential scheme, the system is allowed to exchange electronic charge with an external potentiostat at a specified potential so that the total electronic charge of the 0 (V) ε=∞ Vacuum with barrier potential

H2O with H3O+

Pt(111) slab Ф (V) Vacuum Figure 56.25 Computational setup to perform constant potential calculations within the effective screening method. Source: Bonnet et al. 2012 [148]. Reproduced with permission of American Physical Society.

507

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56 Theory of Solid/Electrolyte Interfaces

system becomes a dynamical variable. Dynamical equations for the total electronic charge are used such that the calculated trajectory samples the grand-canonical distribution at a given electrode potential. Recently, another constant potential scheme was proposed for DFT calculations in a nonperiodic finite setup [149]. This avoids the introduction of compensating counter charges. Still, a straightforward implementation of a grand-canonical DFT scheme by inserting the requested chemical potential 𝜇 of the electrons into the Fermi–Dirac distribution functions also leads in a finite setup to numerical instabilities. Instead, a numerical evaluation of d𝜇∕dN is employed in order to arrive at a good guess for the correct number of electrons N. Both of these presented constant potential implementations have not been routinely applied in the description of structures and processes at the electrochemical solid/electrolyte interface. Hence, their robustness and reliability still has to assessed. It is in general true that still several different approaches have been suggested to model varying electrode potentials, and there is no consensus yet about what is the most appropriate method. This is an indication that the first-principles description of electrochemical electrode/electrolyte interfaces is still in its infancy. However, this also means that there is still room to develop new theoretical and numerical approaches. This makes this field challenging but at the same time rather exciting and rewarding. There is one further challenge in the theoretical description of processes at solid/electrolyte interfaces related to the liquid nature of the electrolyte that has not been addressed so far. Many of the structural properties in thermodynamic equilibrium discussed so far are collectively known as mechanical properties [8], which can be routinely obtained from AIMD simulations [62, 63]. However, for the proper description of electrocatalytic reactions, the determination of reaction paths and barriers is important. Here, it is important to note that it is the free energy including the entropy that is the crucial quantity determining the effective barrier heights. This means that entropic effects have to be taken into account in order to evaluate the potential of mean force along the reaction path. Typically, free energy differences are obtained by performing constraint MD simulations using either umbrella sampling schemes [151, 152], free energy perturbation methods [153], or some other appropriate thermodynamic integration scheme. As these methods are computationally rather time-consuming, there is also a need for the development of more efficient free energy sampling schemes [154].

56.6 Solid/Electrolyte Interfaces for Nonaqueous Electrolytes

So far, we have almost entirely focused on aqueous electrolytes in our theoretical description of solid/electrolyte interfaces. This can be justified by the important role aqueous electrolytes play in electrochemistry. However, often other electrolytes are used, for example, because of the small window of stability of water with respect to the electrode potential or if specific solutes are not solvable in water.

56.6 Solid/Electrolyte Interfaces for Nonaqueous Electrolytes

In the field of transition metal catalysis [155], but also in electrochemical energy storage [156], recently, ionic liquids (IL) have become rather popular. Ionic liquids are salts that are liquid around room temperature. They have a vanishing vapor pressure and are nonflammable, which make them very safe electrolytes. At the same time, they exhibit a high ionic conductivity and electrochemical stability. The liquid properties are caused by the typical combination of a large cation and a chargedelocalized anion [156] resulting in weak interactions. The direct electrostatic interaction is in fact so weak that van der Waals forces significantly contribute to the bonding within the ionic liquid. The same is in fact true for the bonding of ionic liquids to electrodes. In a joint experimental and theoretical study, the structure of an 1-butyl-1methylpyrrolidinium bis(trifluoromethylsulfonyl)imide ([BMP]+ [TFSA]− ) ionic liquid on Ag(111) was analyzed [157]. At room temperature, a liquid phase was found, but at about 100 K two-dimensional solid phases were observed by scanning tunneling microscopy (STM). Based on DFT simulations of the STM images, the [BMP]+ cation and the [TFSA]− anion could be unambiguously identified. The calculated structure of the adsorbed ion pair is illustrated in Figure 56.26. The butyl group of the cation points away from the Ag(111) surface. The anion adsorbs in a cis-conformation of the S–N–S plane, with the SO2 groups binding to the metal and the trifluoromethyl groups pointing toward the interface IL – vacuum. Interestingly enough, without dispersion corrections, the stabilization upon adsorption compared to the ionic liquid pair in gas phase only amounts to 0.06 eV. Including

Figure 56.26 Structure of the [TFSA]− [BMP]+ ionic liquid ion pair on Ag(111) determined by periodic DFT calculations. Source: Buchner et al. 2013 [157]. Reproduced with permission of American Chemical Society.

509

510

56 Theory of Solid/Electrolyte Interfaces

dispersion, the adsorption energy increases to 1.30 eV, which shows that the ionic liquid pair is basically van der Waals bonded to Ag(111) [157]. In general, dispersion effects play an important role in the interaction of organic molecules with substrates. This is also true for the adsorption of bis(terpyridine) (BTP) molecules on graphite where 95% of the adsorption energy with respect to the molecule in gas phase are due to van der Waals bonding [158]. These molecules are known to form ordered hydrogen-bonded networks on graphite when exposed to the substrate from a 1,2,4-trichlorobenzene (TCB) solvent [159, 160]. Because of the large size of the molecules, the first-principles modeling of the adsorption of the BTP molecules in the presence of the TCB solvent is computationally too demanding, so that classical force field simulations are the method of choice [161]. In order to estimate the adsorption energy of BTP on graphite with respect to the BTP molecule in the TCB solvent, molecular dynamics simulations of the molecules in the solvent above the graphite surface and on the graphite surface as depicted in Figure 56.27 were performed [161]. This setup allows to determine the change of enthalpy associated with the adsorption process, entropic contributions are not taken into account. Interestingly enough, according to the force field simulations, the adsorption of BTP from the TCB solvent is almost thermoneutral. Apparently, the interaction of the TCB solvent with the BTP molecules is as strong as the graphite–BTP interaction, which makes sense as both interactions are mainly due to dispersion effects. Hence, the substrate simply acts as a template to allow a planar arrangement of the network. The energetic stabilization of the adsorbed BTP adlayers is thus mainly caused by the formation of the hydrogen-bonded network, i.e. by the intermolecular interaction that amounts to about 0.4 eV [162].

(a)

(b)

Figure 56.27 Illustration of the setup to derive the adsorption energy of BTP from solution on graphite at a finite temperature. (a) BTP molecule dissolved in TCB above the graphite/solvent interface, and (b) adsorbed

BTP molecule at the graphite/solvent interface. Source: Kuenzel and Groß 2013 [161]. Adapted with permission of American Chemical Society.

56.7 Conclusions

56.7 Conclusions

In this chapter, we have tried to describe the current status of the theory of solid/electrolyte interfaces. These systems are not only interesting from a fundamental point of view but they are also of high technological relevance, for example, in electrochemical energy storage and conversion devices that are crucial for our future energy technology. Still, the theoretical description of solid/electrolyte interfaces faces many challenges. The liquid nature of the electrolyte requires a proper averaging over the many configurations compatible with the macroscopic variables defining the system. The computationally least demanding approach is first to average and then to perform the calculations, which leads to a macroscopic description. Many of the concepts still used today to discuss properties of solid/electrolyte interfaces have been developed more than one century ago using such a continuum approach. They form the basis of our current understanding. While they give qualitative guidelines, these macroscopic concepts alone are not sufficient for a quantitative description. For that purpose, an atomistic modeling is needed, which, however, has to be combined with a proper thermodynamic treatment in the spirit of statistical mechanics. This atomistic approach then requires the sampling over many possible configurations, which represents a considerable computational challenge. Therefore, numerically inexpensive methods to describe the interatomic and intermolecular interactions are desirable. However, classical force fields typically do not describe both the interaction within the electrolyte as well as the electrode–electrolyte interaction equally reliably. More advanced interpolation schemes, on the other hand, often require a considerable training effort to obtain a proper parameter set. Hence, a quantum chemical approach from first principles is needed for an accurate treatment of the interatomic interactions. Here, electronic structure calculations based on density functional theory are the method of choice as they combine numerical efficiency with an acceptable accuracy and reliability. Still, their numerical effort is so large that thermal averages over electrolyte configurations are not routinely done. Hence, the influence of the electrolyte is often effectively taken into account either as a thermodynamic reservoir or within implicit solvent models. The validity of these reasonable models cannot be completely assessed as there is still hardly any realistic first-principles reference calculation for solid/electrolyte interfaces. The presence of varying electrode potentials for electrochemical solid/electrolyte interfaces adds further complexity to the theoretical treatment. Several different theoretical approaches to represent external fields and varying electrode potentials exist, and all have their advantages and disadvantages. Thus, there is still room for improvements in the realistic theoretical description of electrochemical solid/liquid interfaces. It is certainly fair to say that the first-principles treatment of these systems has not matured yet. Nevertheless, there has been significant progress in the theoretical description of solid/electrolyte interfaces, at least as far as the conceptual and qualitative point of

511

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56 Theory of Solid/Electrolyte Interfaces

view is concerned. The still incomplete status of our knowledge about the microscopic nature of structures and processes at the electrochemical solid/liquid interface on a quantitative level makes this research field not only demanding but also exciting and rewarding.

References 1. Schlögl, R. (2010). ChemSusChem 3: 2. 3.

4. 5. 6. 7. 8.

9.

10.

11.

12.

13. 14.

15. 16.

17.

209. Kolb, D.M. (2002). Surf. Sci. 500: 722. Groß, A. and Schnur, S. (2011). Computational chemistry applied to reactions in electrocatalysis. In: Catalysis in Electrochemistry: From Fundamentals to Strategies for Fuel Cell Development (ed. E. Santos and W. Schmickler), 165196. Wiley. Michaelides, A. (2006). Appl. Phys. A 85: 415. Cheng, J. and Sprik, M. (2014). J. Phys. Condens. Matter 26: 244108. Bonthuis, D.J. and Netz, R.R. (2013). J. Phys. Chem. B 117: 11397. Reuter, K. and Scheffler, M. (2001). Phys. Rev. B 65: 035406. Leach, A.R. (2001). Molecular Modelling: Principles and Applications, 2e. Harlow: Pearson. Nørskov, J.K., Rossmeisl, J., Logadottir, A. et al. (2004). J. Phys. Chem. B 108: 17886. Nørskov, J.K., Bligaard, T., Logadottir, A. et al. (2005). J. Electrochem. Soc. 152: J23. Schmickler, W. and Santos, E. (2010). Interfacial Electrochemistry, 2e. Berlin: Springer-Verlag. Hansen, H.A., Man, I.C., Studt, F. et al. (2010). Phys. Chem. Chem. Phys. 12: 283. Keith, J.A., Jerkiewicz, G., and Jacob, T. (2010). ChemPhysChem 11: 2779. Rossmeisl, J., Nørskov, J.K., Taylor, C.D. et al. (2006). J. Phys. Chem. B 110: 21833. Wang, H.-F. and Liu, Z.-P. (2009). J. Phys. Chem. C 113: 17502. Jinnouchi, R., Kodama, K., and Morimoto, Y. (2014). J. Electroanal. Chem. 716: 31. Gossenberger, F., Roman, T., and Groß, A. (2015). Surf. Sci. 631: 17.

18. Roman, T. and Groß, A. (2013). Phys.

Rev. Lett. 110: 156804. 19. Inukai, J., Osawa, Y., and Itaya, K.

(1998). J. Phys. Chem. B 102: 10034. 20. Obliers, B., Broekmann, P., and

21. 22. 23. 24.

25.

26.

27.

28.

29.

30.

31.

32.

33.

Wandelt, K. (2003). J. Electroanal. Chem. 554–555: 183. Markovi´c, N.M. and Ross, P.N. Jr. (2002). Surf. Sci. Rep. 45: 117. Gossenberger, F., Roman, T., and Groß, A. (2016). Electrochim. Acta 216: 152. Lin, X., Gossenberger, F., and Groß, A. (2016). Ind. Eng. Chem. Res. 55: 11107. Gossenberger, F., Roman, T., Forster-Tonigold, K., and Groß, A. (2014). Beilstein J. Nanotechnol. 5: 152. Roman, T., Gossenberger, F., Forster-Tonigold, K., and Groß, A. (2014). Phys. Chem. Chem. Phys. 16: 13630. Garcia-Araez, N., Climent, V., Herrero, E. et al. (2005). J. Electroanal. Chem. 576: 33. Garcia-Araez, N., Climent, V., Herrero, E. et al. (2005). J. Electroanal. Chem. 582: 76. Garcia-Araez, N., Climent, V., Herrero, E. et al. (2006). J. Electroanal. Chem. 591: 149. Garcia-Araez, N. and Koper, M.T.M. (2010). Phys. Chem. Chem. Phys. 12: 143. Greeley, J., Stephens, I.E.L., Bondarenko, A.S. et al. (2009). Nat. Chem. 1: 552. Stephens, I.E.L., Bondarenko, A.S., Gronbjerg, U. et al. (2012). Energy Environ. Sci. 5: 6744. Viswanathan, V., Hansen, H.A., Rossmeisl, J., and Nrskov, J.K. (2012). ACS Catal. 2: 1654. Gasteiger, H.A., Kocha, S.S., Sompalli, B., and Wagner, F.T. (2005). Appl. Catal., B 56: 9.

References 34. Helmholtz, H. (1853). Pogg. Ann. 89: 35. 36. 37. 38. 39.

40. 41.

42.

43. 44. 45.

46. 47. 48. 49.

50. 51. 52.

53. 54. 55. 56.

211. Gouy, L.G. (1910). J. Phys. 9: 457. Chapman, D.L. (1913). Philos. Mag. 25: 475. Stern, O. (1924). Z. Electrochem. 30: 508. Klamt, A. (1995). J. Phys. Chem. 99: 2224. Mathew, K., Sundararaman, R., Letchworth-Weaver, K. et al. (2014). J. Chem. Phys. 140: 084106. Siepmann, J.I. and Sprik, M. (1995). J. Chem. Phys. 102: 511. Crozier, P.S., Rowley, R.L., and Henderson, D. (2000). J. Chem. Phys. 113: 9202. Bukowski, R., Szalewicz, K., Groenenboom, G.C., and van der Avoird, A. (2007). Science 315: 1249. Spohr, E. (1989). J. Phys. Chem. 93: 6171. Spohr, E. (1997). J. Chem. Phys. 107: 6342. Raghavan, K., Foster, K., Motakabbir, K., and Berkowitz, M. (1991). J. Chem. Phys. 94: 2110. Xia, X. and Berkowitz, M.L. (1995). Phys. Rev. Lett. 74: 3193. Daw, M.S. and Baskes, M.I. (1984). Phys. Rev. B 29: 6443. Daw, M.S., Foiles, S.M., and Baskes, M.I. (1993). Mater. Sci. Rep. 9: 252. Goddard, W.A. III, Merinov, B.V., van Duin, A.C.T. et al. (2003). Mol. Simul. 32: 251. Lorenz, S., Scheffler, M., and Groß, A. (2006). Phys. Rev. B 73: 115431. Morawietz, T. and Behler, J. (2013). J. Phys. Chem. A 117: 7356. Morawietz, T., Singraber, A., Dellago, C., and Behler, J. (2016). Proc. Natl. Acad. Sci. U.S.A. 113: 8368. Natarajan, S.K. and Behler, J. (2016). Phys. Chem. Chem. Phys. 18: 28704. Warshel, A. and Weiss, R.M. (1980). J. Am. Chem. Soc. 102: 6218. Lin, H. and Truhlar, D.G. (2007). Theor. Chem. Acc. 117: 185. Wilhelm, F., Schmickler, W., Nazmutdinov, R.R., and Spohr, E. (2008). J. Phys. Chem. C 112: 10814.

57. Wilhelm, F., Schmickler, W.,

58. 59. 60. 61. 62. 63.

64.

65. 66.

67. 68. 69. 70.

71. 72. 73. 74. 75.

76. 77.

78.

79.

Nazmutdinov, R.R., and Spohr, E. (2011). Electrochim. Acta 56: 10632. Schmickler, W., Wilhelm, F., and Spohr, E. (2013). Electrochim. Acta 101: 341. Wiebe, J., Kravchenko, K., and Spohr, E. (2015). Surf. Sci. 631: 35. Groß, A. (2002). Surf. Sci. 500: 347. Hafner, J., Wolverton, C., and Ceder, G. (2006). MRS Bull. 31: 659. Schnur, S. and Groß, A. (2009). New J. Phys. 11: 125003. Marx, D. and Hutter, J. (2009). Ab Initio Molecular Dynamics: Basic Theory and Advanced Methods. Cambridge: Cambridge University Press. Santra, B., Michaelides, A., Fuchs, M. et al. (2008). J. Chem. Phys. 129: 194111. Perdew, J.P., Burke, K., and Ernzerhof, M. (1996). Phys. Rev. Lett. 77: 3865. Hammer, B., Hansen, L.B., and Nørskov, J.K. (1999). Phys. Rev. B 59: 7413. Do, H. and Besley, N.A. (2012). J. Chem. Phys. 137: 134106. Paier, J., Marsman, M., and Kresse, G. (2007). J. Chem. Phys. 127: 024103. Fernández-Serra, M.V. and Artacho, E. (2004). J. Chem. Phys. 121: 11136. VandeVondele, J., Mohamed, F., Krack, M. et al. (2005). J. Chem. Phys. 122: 014515. Liu, L.-M., Krack, M., and Michaelides, A. (2009). J. Chem. Phys. 130: 234702. Forster-Tonigold, K. and Groß, A. (2014). J. Chem. Phys. 141: 064501. Soper, A.K. (2007). J. Phys. Condens. Matter 19: 335206. Grimme, S. (2004). J. Comput. Chem. 25: 1463. Grimme, S., Antony, J., Ehrlich, S., and Krieg, H. (2010). J. Chem. Phys. 132: 154104. Tkatchenko, A. and Scheffler, M. (2009). Phys. Rev. Lett. 102: 073005. Møgelhøj, A., Kelkkanen, A.K., Wikfeldt, K.T. et al. (2011). J. Phys. Chem. B 115: 14149. Carrasco, J., Santra, B., Klime˘s, J., and Michaelides, A. (2011). Phys. Rev. Lett. 106: 026101. Tonigold, K. and Groß, A. (2012). J. Comput. Chem. 33: 695.

513

514

56 Theory of Solid/Electrolyte Interfaces 80. Zhang, C., Wu, J., Galli, G., and Gygi,

81.

82.

83. 84. 85.

86.

87. 88. 89. 90. 91. 92. 93. 94. 95.

96.

97. 98. 99. 100. 101. 102. 103.

F. (2011). J. Chem. Theory Comput. 7: 3054. Lin, I.-C., Seitsonen, A.P., Tavernelli, I., and Rothlisberger, U. (2012). J. Chem. Theory Comput. 8: 3902. Sakong, S., Forster-Tonigold, K., and Groß, A. (2016). J. Chem. Phys. 144: 194701. Lie, G.C. and Clementi, E. (1986). Phys. Rev. A 33: 2679. Morrone, J.A. and Car, R. (2008). Phys. Rev. Lett. 101: 017801. Chen, B., Ivanov, I., Klein, M.L., and Parrinello, M. (2003). Phys. Rev. Lett. 91: 215503. Fritsch, S., Potestio, R., Donadio, D., and Kremer, K. (2014). J. Chem. Theory Comput. 10: 816. Marx, D. (2006). ChemPhysChem 7: 1848. Groß, A. and Scheffler, M. (1997). J. Vac. Sci. Technol., A 15: 1624. Groß, A., Wei, C.M., and Scheffler, M. (1998). Surf. Sci. 416: L1095. Thiel, P.A. and Madey, T.E. (1987). Surf. Sci. Rep. 7: 211. Feibelman, P.J. (2002). Science 295: 99. Roudgar, A. and Groß, A. (2005). Surf. Sci. 597: 42. Roudgar, A. and Groß, A. (2005). Chem. Phys. Lett. 409: 157. Gohda, Y., Schnur, S., and Groß, A. (2008). Faraday Discuss 140: 233. Michaelides, A., Ranea, V.A., de Andres, P.L., and King, D.A. (2003). Phys. Rev. Lett. 90: 216102. Izvekov, S., Mazzolo, A., Van Opdorp, K., and Voth, G.A. (2001). J. Chem. Phys. 114: 3284. Izvekov, S. and Voth, G.A. (2001). J. Chem. Phys. 115: 7196. Schnur, S. and Groß, A. (2011). Catal. Today 165: 129. Filhol, J.S. and Bocquet, M.-L. (2007). Chem. Phys. Lett. 438: 203. Heras, J.M. and Viscido, L. (1980). Appl. Surf. Sci. 4: 238. Langenbach, E., Spitzer, A., and Lüth, H. (1984). Surf. Sci. 147: 179. Kiskinova, M., Pirug, G., and Bonzel, H. (1985). Surf. Sci. 150: 319. Hoffmann, W. and Benndorf, C. (1997). Surf. Sci. 377–379: 681.

104. Lilach, Y., Romm, L., Livneh, T., and

105. 106. 107. 108.

109. 110.

111. 112. 113. 114. 115.

116.

117. 118. 119. 120. 121.

122. 123. 124.

125. 126.

127.

Asscher, M. (2001). J. Phys. Chem. B 105: 2736. Lin, X. and Groß, A. (2012). Surf. Sci. 606: 886. Ibach, H. (2010). Surf. Sci. 604: 377. Ibach, H. (2012). Surf. Sci. 606: 1534. Groß, A., Gossenberger, F., Lin, X. et al. (2014). J. Electrochem. Soc. 161: E3015. Lin, X., Evers, F., and Groß, A. (2016). Beilstein J. Nanotechnol. 7: 533. Schmidt, T.J., Ross, P.N. Jr., and Markovic, N.M. (2002). J. Electroanal. Chem. 524: 252. Roman, T. and Groß, A. (2013). Catal. Today 202: 183. Schmickler, W. (1996). Chem. Rev. 96: 3177. Guidelli, R. and Schmickler, W. (2000). Electrochim. Acta 45: 2317. Magnussen, O.M. (2002). Chem. Rev. 107: 679. Tripkovic, D.V., Strmcnik, D., van der Vliet, D. et al. (2009). Faraday Discuss 140: 25. Broekmann, P., Wilms, M., Kruft, M. et al. (1999). J. Electroanal. Chem. 467: 307. Gründer, Y., Drünkler, A., Golks, F. et al. (2011). Surf. Sci. 605: 1732. Groß, A. (2013). Surf. Sci. 608: 249. Andreussi, O., Dabo, I., and Marzari, N. (2012). J. Chem. Phys. 136: 064102. Andreussi, O. and Marzari, N. (2014). Phys. Rev. B 90: 245101. Sakong, S., Naderian, M., Mathew, K. et al. (2015). J. Chem. Phys. 142: 234107. Sakong, S. and Groß, A. (2016). ACS Catal. 6: 5575. Franaszczuk, K., Herrero, E., Zelenay, P. et al. (1992). J. Phys. Chem. 96: 8509. Lozovoi, A.Y., Alavi, A., Kohanoff, J., and Lynden-Bell, R.M. (2001). J. Chem. Phys. 115: 1661. Mermin, N.D. (1965). Phys. Rev. 137: A1441. Taylor, C.D., Wasileski, S.A., Filhol, J.-S., and Neurock, M. (2006). Phys. Rev. B 73: 165402. Alavi, A., Kohanoff, J., Parrinello, M., and Frenkel, D. (1994). Phys. Rev. Lett. 73: 2599.

References 128. Wasileski, S.A. and Janik, M.J. (2008). 129. 130. 131. 132. 133. 134. 135. 136.

137. 138. 139. 140. 141. 142.

143.

144.

145.

Phys. Chem. Chem. Phys. 10: 3613. Filhol, J.S. and Neurock, M. (2006). Angew. Chem. Int. Ed. 45: 402. Fu, C.L. and Ho, K.M. (1989). Phys. Rev. Lett. 63: 1617. Bohnen, K.P. and Kolb, D.M. (1998). Surf. Sci. 407: L629. Lozovoi, A.Y. and Alavi, A. (2003). Phys. Rev. B 68: 245416. Che, J.G. and Chan, C.T. (2003). Phys. Rev. B 67: 125411. Zhao, J., Chan, C.T., and Che, J.G. (2007). Phys. Rev. B 75: 085435. Trasatti, S. (1995). Surf. Sci. 335: 1. Skúlason, E., Karlberg, G.S., Rossmeisl, J. et al. (2007). Phys. Chem. Chem. Phys. 9: 3241. Trasatti, S. (1986). Pure Appl. Chem. 58: 955. Logadottir, A., Rod, T.H., Nørskov, J.K. et al. (2001). J. Catal. 197: 229. Cheng, J. and Hu, P. (2008). J. Am. Chem. Soc. 130: 10868. Otani, M. and Sugino, O. (2006). Phys. Rev. B 73: 115407. Sugino, O., Hamada, I., Otani, M. et al. (2007). Surf. Sci. 601: 5237. Otani, M., Hamada, I., Sugino, O. et al. (2008). Phys. Chem. Chem. Phys. 10: 3609. Hamada, I., Sugino, O., Bonnet, N., and Otani, M. (2013). Phys. Rev. B 88: 155427. Qian, Y., Hamada, I., Otani, M., and Ikeshoji, T. (2013). Catal. Today 202: 163. Rossmeisl, J., Skúlason, E., Björketun, M.J. et al. (2008). Chem. Phys. Lett. 466: 68.

146. Markovi´c, N.M., Grgur, B.N., and Ross,

P.N. (1997). J. Phys. Chem. B 101: 5405. 147. Greeley, J., Nørskov, J.K., Kibler, L.A.

148.

149. 150. 151. 152. 153. 154. 155. 156. 157. 158. 159.

160. 161. 162.

et al. (2006). Chem. Phys. Chem. 7: 1032. Bonnet, N., Morishita, T., Sugino, O., and Otani, M. (2012). Phys. Rev. Lett. 109: 266101. Schneider, W.B. and Auer, A.A. (2014). Beilstein J. Nanotechnol. 5: 668. Nosé, S. (1984). J. Chem. Phys. 81: 511. Torrie, G.M. and Valleau, J.P. (1977). J. Comput. Phys. 23: 187. Hartnig, C. and Koper, M.T.M. (2001). J. Chem. Phys. 115: 8540. Zwanzig, R.W. (1954). J. Chem. Phys. 22: 1420. Christ, C.D. and van Gunsteren, W.F. (2007). J. Chem. Phys. 126: 184110. Wasserscheid, P. and Keim, W. (2000). Angew. Chem. Int. Ed. 39: 3772. Armand, M., Endres, F., MacFarlane, D.R. et al. (2009). Nat. Mater. 8: 621. Buchner, F., Forster-Tonigold, K., Uhl, B. et al. (2013). ACS Nano 7: 7773. Künzel, D., Tonigold, K., Kuˇcera, J. et al. (2011). ChemPhysChem 12: 2242. Meier, C., Landfester, K., Künzel, D. et al. (2008). Angew. Chem. Int. Ed. 47: 3821. Meier, C., Roos, M., Künzel, D. et al. (2010). J. Phys. Chem. C 114: 1268. Künzel, D. and Groß, A. (2013). Beilstein J. Nanotechnol. 4: 269–277. Künzel, D., Markert, T., Groß, A., and Benoit, D.M. (2009). Phys. Chem. Chem. Phys. 11: 8867.

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57 Metal–Electrolyte Interfaces: An Atomic View Marek Nowicki and Klaus Wandelt

57.1 Introduction

Many, and increasingly more, important modern technologies are based on processes at solid/liquid interfaces [1], such as electrocatalysis [2], grafting [3], and biofunctionalization of surfaces [4] as well as electrodeposition [5, 6] and electroetching [7] of metals down to the nanometer scale. The on-chip wiring in modern electronic devices is nowadays carried out by a sequence of copper deposition and etching processes, the so-called Damascene and through-silicon via (TSV) processes, controlled by means of organic additives called as “accelerators, suppressors, and levelers” [8]. Functionalization of surfaces and nanoparticles with layers of biocompatible molecules serves to build biosensors [9, 10] in medical therapies [11, 12]. Biofunctionalization of nanoparticles enables their direct transport in body fluids for localized therapies. Such organic additives and biomolecules would not withstand evaporation and deposition via the gas phase and must therefore be applied from a solution phase. Vice versa, destructive processes such as corrosion [13] and fouling [14] cause huge economic losses and call for protective measures. In order to understand and ultimately optimize all these processes, the underlying reaction mechanisms should be investigated and understood on the atomic scale – as in the case of ultrahigh vacuum (UHV)-based surface research. As a consequence, there is growing interest and motivation to study solid/liquid interfaces in general and metal/electrolyte interfaces in particular, with the same precision as is standard nowadays in UHV-based surface science. On the one hand, the basic questions are the same: what are the properties such as atomic composition and structure at the interface? And how do interactions leading to adsorption, atomic/molecular arrangement, reaction, and desorption at the interface depend on the interfacial properties and external parameters? On the other hand, the interface between a metal and an electrolyte, i.e. two condensed phases, entails three new aspects. Firstly and most obviously, most of those wonderful methods used in UHV surface science, which are based on particle beams (electrons, ions, and atoms) [15], are not straightforwardly applicable to study solid/liquid interfaces. Secondly, any situation at the interface is in permanent contact and exchange with the liquid bulk phase, which Surface and Interface Science: Interfacial Electrochemistry, First Edition. Edited by Klaus Wandelt. © 2020 Wiley-VCH Verlag GmbH & Co. KGaA. Published 2020 by Wiley-VCH Verlag GmbH & Co. KGaA.

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57 Metal–Electrolyte Interfaces: An Atomic View

thirdly opens the possibility to apply an electric potential to the immersed metal sample. In particular, these latter two aspects make it mandatory to study the properties and processes at the interface not only in situ but preferably also “in operando.” Even though Chapter 49 in volume 7 [16] gives an exhaustive overview of the available analytical techniques to study solid/liquid interfaces on the molecular level, the development of in situ methods with the same spatial, energetic, and temporal resolution remains an ongoing challenge. Occasionally, it may therefore still be necessary to remove the sample from the liquid and transfer it under best possible conditions into UHV in order to take advantage of the plethora of high-resolution spectroscopic and microscopic surface science methods. In this chapter, we place emphasis on the combined use of in situ photon and scanning probe-based methods and occasionally make use of a so-called “transfer system” in which the sample can be transferred without contact to air, contamination-free, between the liquid environment and UHV, and vice versa in order to complement the in situ results by ex situ measurements (see Section 57.2.3.3.5). 57.1.1 Electrochemical Double Layer

The key to understand metal/electrolyte interfaces is the “electrochemical double layer,” which consists of a charged metal surface and a near-surface regime enriched with ions of opposite sign (see also Chapter 56 in this Volume). A positively charged electrode, for instance, causes attraction of anions and their adsorption on the surface. The overall interface must be electrically neutral, and the excess ion charge qion near the surface counterbalances the charge on the metal surface qMe , so that qMe + qion = 0. The charge on the metal surface can be carefully controlled by the applied “Galvani potential” of the electrode [17]. The components of the electrochemical double layer are, thus, a metal (or semiconductor) surface and an ion-containing solution layer (Figure 57.1). We shall briefly recall some relevant properties of both separated components, namely the atomistic and electronic properties of a metal surface on the one hand and the properties of electrolytic solutions on the other hand. 57.1.1.1 Structure of Metal Surfaces

First, we focus on the solid side of the double layer, i.e. the metal surface. Most of our knowledge about the atomic and electronic structure of bare and adsorbate-covered metal surfaces is based on surface science studies in UHV and has been laid out in great detail in volumes 1–6 of this series of books. Hereafter, even the properties of bare metal (and other solid) surfaces, even in vacuum, as well as liquid surfaces may deviate significantly from those of a bulk plane parallel to the surface [18–28]. In this chapter, we concentrate on the low-index (111), (100), and (110) surfaces of face-centered cubic (fcc) metals such as copper, silver, gold, and platinum (see Figure 57.2A). Merely because of the asymmetric bonding situation of the surface atoms, most metal surfaces show the phenomenon of “surface relaxation,” i.e.

57.1 Introduction

Ψ ΨIH ΨOH

+ + + + + + + + +

–

–

–

+ + + + + + + + +

+

–

–

–

– + +

–

–

– –

– – –

–

– +

Ψ

Electrode surface Inner Helmholtz plane Outer Helmholtz plane

–

x

+

–

+

–

+

–

+

+

– –

+

–

+ +

–

–

D if f x=0

(a)

+

–

Electrode surface Inner Helmholtz plane

Diffuse charge layer

x = 0x = δ

–

use

c h a r ge

layer

x

ΨIH (b)

Figure 57.1 (a) Gouy–Chapman–Stern–Grahame model of the “electrochemical double layer” (see text). (b) Model of a densely packed and rigid anion layer leading to an overcompensation of the surface charge.

different, mostly shortened, interplanar distances between the first few near-surface atomic layers compared to those in the bulk; unbalanced forces perpendicular to the surface simply pull the surface atoms closer toward the bulk. Moreover, some metal surfaces (e.g. Pt, Ir, and Au) exhibit the phenomenon of “surface reconstruction,” that is a deviation of the 2D lattice of (at least) the outmost layer of surface atoms from that of a parallel bulk plane. As a result, the lack of simple registry of the unlike lattices of the first two atomic layers causes atoms of the first layer to sit in varying positions with respect to those of the second layer, which leads to the occurrence of a height modulation and a long-range superstructure [32]. As examples, Figure 57.2B shows scanning tunneling microscopy (STM) images of the reconstructed √ Au(111), Pt(100), and Au(110) surface [29, 30, 33]. The Au(111) surface exhibits a ( 3 × 22) superstructure resulting in the so-called “herringbone” or “chevron” pattern as can be seen in Figure 57.2B(a). The Pt(100) surface assumes a “cable-stitch” motif, and the Au(110) surface shows a (2 × 1) structure with grooves twice as broad as those of an unreconstructed fcc(110) surface. Driving force for this “reconstruction” is a lowering of the excess surface-free energy resulting from the “unsaturated bonds” of the undercoordinated surface atoms. This is achieved by increasing the atomic number density of the first layer; the resultant “effective” increase of the coordination number within the layer partly compensates for the unsaturation of the out-of-plane bonds. This explains the quasi-hexagonal (111)-like reconstruction of the Ir(100), Pt(100), and Au(100) surface into Ir(100) − 5 × 1, Pt(100)-hex, and Au(100)-hex, respectively [32]. Obviously, the tendency of fcc(110) surfaces to reconstruct is highest because their surface atoms are the least coordinated

519

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57 Metal–Electrolyte Interfaces: An Atomic View

ones. Their reconstruction leads to (111)-like facets forming the grooves at the fcc(110)(2 × 1) surface of, e.g. Pt(110) and Au(110). Most interestingly, although already hexagonally densely packed, is the further densification of the Au(111) surface leading to the herringbone superstructure (Figure 57.2B(a)). The increased surface atomic density of the reconstructed surface phases requires the transport and incorporation of extra atoms, e.g. ∼20% in the case of reconstructed quasi-hexagonally terminated fcc(100) Ir, Pt, and Au surfaces and ∼4% in the case of the reconstructed Au(111) surface, and is thus an activated process [32]. Under UHV conditions, this is simply achieved by heating the sample to an elevated temperature, which activates diffusion of the extra atoms from the bulk reservoir to the surface. In parenthesis, we also note that the Pt(111) surface exhibits a similar reconstruction as the Au(111) surface, however, only if the required extra Pt atoms are delivered from outside, i.e. if they are deposited via the vapor phase onto the hot Pt(111) surface [34]. This makes the Pt(111) reconstruction easily reversible, by simply heating the reconstructed Pt(111) surface in the absence of Pt vapor. All other reconstructions are irreversible, the reservoir of extra atoms is unavoidably there, and cooling does not drive the extra atoms back into the bulk; their reconstructed surface phase is the energetically favored one. Both phenomena, relaxation and reconstruction, are strongly affected by adsorbates on the respective surface depending on the strength of the adsorbate bond. The surface relaxation may be lifted or even overcompensated by an adsorbate, resulting in an outward relaxation, and the surface reconstruction may be lifted. In fact, a very small coverage of a “chemisorbed” species often suffices to lift the relaxation and reconstruction in UHV. This raises the obvious question whether both phenomena exist at metal surfaces in contact with a liquid phase at all. In Section 57.3.1, we will see that reconstruction also happens, potential dependent, in electrolytic solutions. Quite a different phenomenon is the severe restructuring or faceting of a surface altogether, because of strongly chemisorbed adsorbates. As an example, we refer to the oxygen-induced faceting of the rather open Ir(210) surface in both UHV and solution [35, 36]. This adsorbate-induced restructuring or faceting is often regarded as a precursor toward the formation of a new (surface) compound. A simple model to describe the fundamental electronic properties of surfaces (before its contact with the electrolyte) is the so-called Jellium model [37] (see also Chapter 5 in Volume 2). In this model, the positive charge of the ion cores is spread uniformly over the whole solid while the valence electrons move in the potential produced by this positive background charge (“jellium”). At the surface, determined by the plane of the surface atom nuclei, the positive background charge terminates abruptly (jellium edge) but the electron density does not. Instead, the electron density oscillates near the surface (Friedel oscillations) before decaying exponentially outside the solid (see Figure 57.3). This distribution of charge density produces already an electrostatic dipole layer at the surface (even in UHV), which hinders electrons to leave the surface and is a major contribution to the work function [38, 39]. The higher the charge density, parametrized through the “Wigner–Seitz

57.1 Introduction

fcc(111)

fcc(100)

a

a

a

– [112] [1 01]

–

a

a

[011]

––

–

[011]

fcc(110)

[001]

–

[010]

[001]

–

–

[112]

[112]

[211]

[121]

–

–

[110]

[110]

–

–

[011]

[011]

–

–

[110]

[110]

– –

––

[121]

[211] –

–

[101]

–

[112]

–

–

[010] ––

[011]

––

[001]

[112]

[011]

– –

–

[112]

[001]

Nearest neighbor distances Copper

Silver

Gold

Platinum

aCu–Cu = 0.256 nm

aAg–Ag = 0.288 nm

aAu–Au = 0.288 nm

aPt–Pt = 0.277 nm

(A) (a)

(b)

(c)

–

[11

0]

(B) Figure 57.2 (a) Crystallographic orientations and hard sphere models of the (111), (100), and (110) surfaces of face-centered cubic (fcc) metals as well as nearest interatomic distances of Cu, Ag, Au, and Pt. (b) STM images of reconstructed metal sur√ faces in UHV showing (a) the ( 3 × 22)

“herringbone” structure of the reconstructed Au(111) surface (51 nm × 51 nm), (b) the “cable-stitch” structure of the Pt(100) surface (11 nm × 11 nm), and (c) the missing row structure of the Au(110)-(2 × 1) surface. Insets: Atomic resolution [29–31].

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57 Metal–Electrolyte Interfaces: An Atomic View

ρ(z)/ρ0 1.0

rs = 5 rs = 2 0.5

–1.0

–0.5

0

0.5

z/2πkF–1 Figure 57.3 Charge density distribution at a jellium surface for two different electron densities, expressed in terms of the dimensionless density parameter rs (“Wigner–Seitz radius”).

radius,” the more and further negative charge leaks out of the surface, and the higher the work function, whose value also depends on the crystallographic orientation of the surface because of the so-called Smoluchowski effect [39, 40]. The presence of adsorbates on a surface can add two different dipolar contributions to the work function. The first contribution arises from the charge transfer between the surface and the adsorbate. An electropositive adsorbate such as an alkali metal forms a chemical bond with a transition metal surface by donating charge into the metal substrate, which causes a decrease of the work function [39, 41, 42]. Conversely, an electronegative adsorbate, such as oxygen, sulfur, or halogens, withdraws charge from the metal and increases the work function [39]. The second contribution arises when a molecular adsorbate has an intrinsic dipole. Whether this contribution increases or decreases the work function depends on the relative orientation of the molecular dipole with respect to the surface [39]. Both contributions, of course, also affect the electrostatics within the electrochemical double layer at metal–electrolyte interfaces. 57.1.1.2 Properties of Electrolytes

Here, we exclusively concentrate on aqueous electrolyte solutions (in the following in short “electrolyte”) and neglect solid electrolytes as well as ionic liquids. The microscopic constituents of an electrolyte are water molecules (solvent) and dissolved polar molecules AC (solute) in equilibrium with their dissociation products, namely anions Az− and cations Cy+ : AC ↔ aAz− + cCy+ .

(57.1)

57.1 Introduction

From the charge neutrality of the overall solution, it follows az− + cy+ = 0.

(57.2)

This dissociation is possible because of the dipolar character of the water molecules. The water dipoles “hydrate” (solvent molecules “solvate”), i.e. surround the separated anions and cations with the appropriate orientation by one or two shells (see Figure 57.1), and thereby shield them from mutual electrostatic interaction. Assuming that the dielectric constant of water, ϵ = 80, also holds on the molecular level, the Coulomb attraction between ions in aqueous solution is damped by a factor of 1/80. The “hydration strength” of the ions depends on their “charge density” and is expressed and measurable in terms of the “hydration enthalpy.” The hydration enthalpy increases with increasing charge density, i.e. with decreasing radius at a given charge, of the central ion. Some values of hydration enthalpies are listed in Table 57.1 (http://www.rsc.org/ education/teachers/resources/databook/ds_hydration_enthalpies.htm). If the total hydration enthalpy of all ionic fragments from one parent molecule exceeds the bond energy of this molecule, it “dissociates.” Likewise, the ions get in direct contact with the metal surface only if their hydration sphere is stripped off again. Thus, both the existence of ions in solution as well as their mode of interaction with Table 57.1 Selected hydration enthalpies. Ion

H+ Li+ Na+ K+ Cu+ Cu2+ Cd2+ F− OH− Cl− Br− I− S2− SCN− ClO4 − SO3 2− SO4 2−

Hydration enthalpy

Hydration enthalpy

calculated (kJ/mol)

experimental (kJ/mol)

−1015 −510 −385 −305 −400 −1920 −1575 −345 −345 −270 −250 −220 −1280 −230 −180 −1230 −1145

−1050 −475 −365 −295 −525 −2010 −1575 −465 −430 −340 −315 −275 −1315 −280 −430 −1295 −1080

[Yizhak Marcus, J.CHEM. SOC. FARADAY TRANS., 1991, 87(18), 2995-2999]

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57 Metal–Electrolyte Interfaces: An Atomic View

the electrode surface depends on the “competition” between hydration energy vs. dissociation and adsorption energy, respectively. Thus, all interactions of a metal surface in contact with an electrolyte follow from the scenario sketched in Figure 57.1. If an electric potential is applied between the sample surface and a counter electrode, the ions in solution are selectively attracted to the electrode of opposite charge, anions toward the anode and cations toward the cathode. This field-driven directional motion of the ions as charge carriers supports a current through the electrolyte. Discharge processes, i.e. electron transfer reactions of the ions at the two electrode surfaces, connect this “ion current” in the solution to the “electron current” in the outer circuit. 57.1.1.3 Adsorption–Desorption

The interactions of the components of an electrolyte with an electrode surface obviously depend on the chemical nature of the species, their charge state, the properties of the surface, and on all external parameters, such as concentrations, temperature, electrode potential, and the type of the solvent. They can be of van der Waals type, electrostatic, metallic, covalent, or a mixture of all. Neutral molecules and hydrated ions bind “nonspecifically,” i.e. “physisorb” via van der Waals or electrostatic forces. After displacement of the hydration sphere, ions (anions and cations) adsorb “specifically” on the electrode surface, i.e. “chemisorb,” via chemical or “metallic” bonds of different ionicity, the latter depending on the electronegativity of both bonding partners, the adsorbate and the electrode metal. For example, the interaction between copper and chloride is expected to be more ionic than that between iodide and copper, which is more likely covalent; some values of Pauling electronegativities are listed in Table 57.2 (https://en.wikipedia.org/wiki/Electronegativities_of_the_elements_(data_page)). Adsorption and desorption of ionic species is obviously strongly affected by the electric potential applied to the electrode. The surface charge determines the attraction/repulsion of ions of opposite/same charge and, thereby, also the surface coverage Θ of the respective species, which, in turn, may be accompanied by structural phase transitions within the adsorbed layer, as, for instance, demonstrated in Sections 57.3.2.2, 57.3.2.4, 57.3.3.1, and 57.5.1.3. Different species coexisting within the same solution will compete for adsorption sites on the surface. The stronger, at a given electrode potential, their interaction with the surface and the higher their Table 57.2 Electronegativities (Pauling scale). https://de.wikipedia.org/wiki/ Elektronegativit%C3%A4t

H Li Na K Rb Cs

2.20 0.98 0.93 0.82 0.82 0.79

Be Mg Ca Sr Ba

1.57 1.31 1.00 0.95 0.89

Ni Pd Pt

1.91 2.20 2.28

Cu Ag Au

1.90 1.93 2.54

Zn Cd Hg

1.65 1.69 2.00

O S Se Te

3.44 2.58 2.55 2.1

F Cl Br I

3.98 3.16 2.96 2.66

57.1 Introduction

partial concentration in solution, the more likely they adsorb. As a consequence, this may lead to displacement reactions on the surface. The following sequence describes the tendency of anions to adsorb “specifically” [43]: PF6 − ≈ BF4 − ≪ ClO4 − < SO4 2− < Cl− < Br− < I−

(57.3)

Thus, decisive for the energetics and kinetics of adsorption and reaction processes of neutral molecules AC, anions Az− , and cations Cy+ at electrode surfaces are (i) their concentrations, (ii) the relative values of hydration enthalpies and interaction energies with the surface, (iii) the externally applied potential, and, last but not least, (iv) the temperature. The consideration of these parameters has led to the development of various models of the so-called “electrochemical double layer” at metal/electrolyte interfaces as described in the following sections. 57.1.1.4 Helmholtz Model

The earliest model of the electrochemical double layer was proposed by Helmholtz in 1879 [44]. According to this model, a rigid single monolayer of solvated ions is adsorbed at the surface. The arrangement resembles a plate capacitor with a separation of d = r between the two plates, where r is the radius of the solvated ions. The solvated ions are held at the surface by pure electrostatic forces, i.e. they are “physisorbed,” and the plane through their centers of charge is called the “outer Helmholtz plane” (see Figure 57.1a). The potential drop Ψ between the metal surface and the “outer Helmholtz plane” is linear: dΨ = constant dx

(57.4)

as in a coplanar capacitor. This model, however, does neither take into account the thermal motion of ions, which counteracts the rigidity of the double layer, nor the possible loss of the hydration sphere upon adsorption of the ions, i.e. specific adsorption [17]. 57.1.1.5 Gouy–Chapman Model

A more sophisticated model was proposed by Gouy and Chapman independently from each other in the years 1910–1917 [17, 45, 46]. Here, the ionic particles in solution are assumed to be point charges whose thermal fluctuations lead to a diffuse distribution of the charge carriers in the near-surface solution layer. As long as the potential drop across the double layer is small (i.e. ΔΨ 2 (at intermediate potentials, see Section 57.3.2.4 and Figure 57.39). Because of the higher density of adsorption sites on the Cu(111) surface, already bromide (and even chloride at very high potentials) forms such incommensurate structures on this surface as demonstrated in detail in the following √sections. √ Instead, all three halides tend to form simple commensurate c(2 × 2) or ( 3 × 3)R30∘ structures on the wider (100) and (111) surface meshes of silver, gold, and platinum [448]; see Tables 57.3–57.5. The influence of charge density is reflected by the – at first sight counterintuitive – adsorption/desorption behavior of the three halides on, e.g. Cu(111).

551

552

57 Metal–Electrolyte Interfaces: An Atomic View

Table 57.4 References on metal underpotential deposition on silver, gold, and platinum low-index single-crystal electrode surfaces. Ag(111)

Cu Cu Cu Cd Cd Pb Pb Pb Pb PbS

STM STM, XRD CV, STM CV, STM CV, STM STM SPM, EIS STM XRD AFM

[362] [363] [364] [365] [366] [367] [368] [369] [370] [371]

Cu Cd Cd Pb Pb

CV, STM CV, STM CV, AFM CV, LSV CV, EIS, SPM CV, STM

[372] [373] [374] [375] [376] [377]

Cu Cu Cd Cd Cd Pb Pb

CV, STM SXS CV, AFM CV, AFM CV, AFM CV, STM LSV, RS

[378] [379] [380] [381] [382] [383] [384]

Cu Cu Cu Cu Cu Cu Cu Cu Cu Cu Cd Cd Cd Cd

STM CV, STM CV, STM CV, STM CV SXS CV, XRD CV, STM CV, STM, IS CV, STM. IS CV, STM EQCM CV, STM CV, STM

[385] [386] [387] [388] [389] [390] [391] [392] [393] [394] [395] [396] [397] [398]

Ag(100)

Au(100)

Au(111)

57.3 Adsorption of Anions

Table 57.4 (Continued)

Cd Pb

STM LSV, RS

[399] [400]

Cu Pb

STM LSV, RS

[401] [402]

Cd Pb Pb

CV, STM STM, SEM CV

[403] [404] [405]

Cd Cd Cd Cd Pb Pb Pb Pb

CV, STM CV, AFM CV, XPS, ISS, LEED CV, in situ AFM CV, IRRAS CV, XRD, STM CV, STM XRD

[406] [407] [408] [409] [410] [411] [412] [413]

Cd

CV

[414]

Cu Pb

LEED, RHEED RRDE, SXS

[415] [416]

Cu Cu Cu Cu Cu Cu Cu Cu Cu Cd Cd Pb Pb

CV, RRDE, SXS LSV, RS CV, LSV CV, AES, LEED, RHEED, CV CV, PCT, SEM, ex situ STM RDE, SXS, LEED CV, STM RDE, SXS, LEED CV CV CV, DEMS, MPTW SXS, STM

[417] [418] [419] [420] [421] [422] [423] [424] [425] [426] [427] [428] [429]

Cu Cu Pb

CV, STM CV, LEED, RHEED CV, SXS, STM

[430] [431] [432]

Au(110)

Cu(100)

Cu(111)

Cu(110) Pt(100)

Pt(111)

Pt(110)

553

554

57 Metal–Electrolyte Interfaces: An Atomic View

Table 57.5 References on organic molecular self-assembly on silver, gold, and platinum low-index single-crystal electrode surfaces. Au(111)

Graphite

Cu(111)

Viologen Porphyrin Hexadecane Porphyrin Porphyrin Adenine, thymine, guanine, cytosine Thiol Thiol Methylene blue Crystal violet, porphyrin Sulfur Porphyrin Xanthine Fe–porphyrin Zn–porphyrin Benzene, naphthalene, anthracene

STM CV, STM CV, STM STM CV, STM CV, STM, AFM CV, STM CV, STM CV, CV, STM CV, STM, XPS CV, STM CV, STM, AFM STM,AFM CV, STM

[433] [434] [435] [436] [437] [438] [439] [440] [441] [442] [443] [444] [445] [446] [447]

Figure 57.20 shows CV traces of Cu(111) in chloride-, bromide-, and iodidecontaining acid solution. The anodic adsorption (cathodic desorption) potential is the higher, i.e. the more positive relative to the hydrogen evolution (I < Br < Cl), the higher the charge density of the ion. In other words, the higher the negative charge density of the halide ion, the more positively charged, i.e. more attractive, the electrode must be in order to adsorb the ion. This behavior appears counterintuitive because the strength of interaction with copper decreases in the order Cl- >Br- > I- as judged by the enthalpies of formation listed in Table 57.6. The explanation for this behavior is the hydration energy that increases with the charge density of the ion (see Table 57.1). The electrostatic force needed to remove the hydration sphere to enable specific adsorption increases from iodide to chloride. Vice versa, the higher the hydration energy, the less negative, i.e. the less repulsive, the electrode needs to be in order to cause desorption. 57.3.2.1 Chloride, Bromide – Cu(111)

The cyclic voltammogram of a Cu(111) surface in 10 mM HCl solution (Figure 57.21a) is characterized by an anodic Cl adsorption peak at −755 mV and a cathodic desorption peak at −980 mV (vs. Hg/Hg2 SO4 1) ), whereby the latter 1) We prefer to cite the actual value measured versus that reference electrode used in the respective study, instead of converting all values to one common reference electrode, e.g. RHE (reversible hydrogen electrode) because a reliable conversion would have to consider different concentrations, which are often not precisely given.

57.3 Adsorption of Anions

Current density (μA/cm2)

Ads.

Des.

HER Cl/Cu(111)

(a)

Potential (mV)

Current density (μA/cm2)

Ads.

Des.

Br/Cu(111)

HER (b)

Potential (mV)

Current density (μA/cm2)

Ads.

HER (c)

I/Cu(111) Potential (mV)

Figure 57.20 Cyclic voltammograms of a Cu(111) electrode in 10 mM HCl, HBr, and HI acid solution, respectively. Note the shift of both the anion adsorption (Ads.) and anion

desorption (Des.) signal to negative potentials when going from chloride to bromide and iodide (see text).

555

556

57 Metal–Electrolyte Interfaces: An Atomic View

Table 57.6 Selected enthalpies of formation. Compound

Enthalpy of formation (kJ/mol)

CuCl CuCl2 CuBr2 CuI CuI2 CuSO4 CuSO4 ⋅H2 O CuSO4 ⋅3H2 O CuSO4 ⋅5H2 O Cu(ClO4 )⋅6H2 O Cu2 S CuS CuSCN

−134.7 −205.9 −130 −68.2 −7.1 −771 −1083 −1682 −2277 – −83.43 −48.5 –

[D’Ans ⋅ Lax Taschenbuch für Chemiker und Physiker, Springer Verlag, 1967]

overlaps already with the hydrogen evolution current. Surprisingly, the area of the Cl desorption peak – even after subtraction of the exponential Butler–Volmer background (dashed trace in Figure 57.20a and Section 57.2.2) – is significantly larger than that of the Cl adsorption peak. This is due to the so-called Frumkin effect [450]: the extra negative surface charge due to the still adsorbed anions enhances the H3 O+ concentration near the surface, which causes hydrogen evolution before this process starts on the bare metal surface. This process subsides with progressing chloride desorption, until at more negative potentials, the exponential increase of the hydrogen evolution current from the bare copper surface sets in. Adsorption of chloride leads to a new surface structure, the formation, and decay of which can be followed by potentiodynamic in situ STM measurements (Figure 57.21b–e) [449]. Note that in this detection mode, the y-coordinate (see panel d) of each image not only represents the geometric position in this slow scan direction but also a continuous change of the electrode potential: while taking image b, the potential changes along part b (full line) of the CV in panel (a), and while scanning image c, the potential changes along the dotted section c in panel (a), and so on. As a result, the left parts of panels b and e show the anion-free Cu(111) surface with atomic resolution (see also Figure 57.17b). Conversely, in the rightmost part of panels (b) and (e), the distinctly different, i.e. more coarse, structure agrees with that seen in panels (c) and (d), respectively, and must therefore be associated with the formation/decay of the anion adlayer, which dominates in panels (c) and (d).

57.3 Adsorption of Anions

(C)

(B)

Chloride adsorbate

Cu(111) 30

(A)

20

Adsorption

j/(μA/cm2)

10

(c)

0 (b)

–10

(d)

–20 –30 –40 –50 –60 –1.2

(e) –1.1

–1.0

Desorption –0.9

–0.8

–0.7

–0.6

–0.5

–0.4

E vs. Hg | Hg2SO4 (V) Cu(111)

(E)

Chloride adsorbate

(D)

y

x

Figure 57.21 (a) Cyclic voltammogram of Cu(111) in 10 mM HCl solution. (b–e) Potentiodynamic in situ STM images registered while sweeping the electrode potential along the corresponding full line or dashed parts b,

c and d, e of the cyclic voltammogram in anodic and cathodic direction, respectively (see text). (Source: Broekmann et al. 1999 [449]. Reproduced with permission of Elsevier.)

557

558

57 Metal–Electrolyte Interfaces: An Atomic View

A potentiostatic image of the bare Cu(111) surface is already shown in Figure 57.17b. The structure of the adsorbate layer, shown in Figure 57.21c,d (somewhat distorted) and better in the potentiostatic image in Figure 57.23a, √ resolved √ is first identified as a ( 3 × 3)R30∘ -Cl superstructure.√This structure, however, undergoes a uniaxial compression into domains of a c(p × 3) phase at very positive potentials as described further below. Using STM, it is also possible to determine the adsorption site of the individual ad-particles. The most intuitive way would be to register the surface before and after adsorption of the anions and to superimpose the two images. Thermal drift between the two measurements, however, makes this approach unreliable. In cases where at certain potentials the substrate is only partially covered with adsorbate islands, an extrapolation of the adsorbate mesh onto the substrate lattice also yields the adsorption sites. The most reliable method, however, consists in the quasi-spectroscopic imaging √ √mode: the three panels (a)–(c) in Figure 57.22 are all registered from the ( 3 × 3)R30 – Cl covered surface but at different (constant) bias voltages, namely (a) −420 mV, (b) −480 mV, and (c) −520 mV [449]. At these different bias voltages, electrons from different electronic states of the surface constituents contribute to the STM image, namely dominantly states of the adsorbate in panel (a) and dominantly states of the copper substrate in panel (c), whereas in panel (b), the bias voltage is chosen such that the image shows contributions of both. A Fourier transformation of this latter image (panel d) and a back-transformation of the low-frequency and the high-frequency part enables a separation of the adsorbate (low frequency) and the substrate (high frequency) contribution as shown in panels (e) and (f ) in Figure 57.22. As both partial images originate from one and the same measurement, namely panel (b), their superposition clearly yields the positions of the ad-particles with respect to the substrate lattice. In the present case – not unexpectedly – the chloride anions are found to reside in threefold hollow sites (as in UHV [451–454]) as illustrated in Figure 57.22g. At more positive potentials (−400 mV vs. Ag/AgCl), more chloride is attracted to the surface, resulting in a compression of√ the chloride layer and a concomitant √ phase transition from the commensurate ( 3 × 3)R30∘ (Figure 57.23a) to an incommensurate phase as concluded from ex situ LEED measurements [455], in situ STM [456], and in situ XRD studies [457]. However, the latter two in situ methods arrive at different models of the formation and structure of the incommensurate phase. On the one hand, STM images of the incommensurate phase are shown in √ √ Figure 57.23b,c [456]. Although in the commensurate ( 3 × 3)R30∘ structure all anions reside in equivalent threefold hollow sites (see Figure 57.22), in the incommensurate phase, anions occupy different adsorption sites. The wavy superstructure in√Figure 57.23b,c has been interpreted in terms of a uniaxial compression of the √ √ ( 3 × 3)R30∘ structure to a c(p × 3) structure as explained in Figure 57.23d, with variable p [456]. The inequivalence of the adsorption sites along the [110] direction leads to the wavy height modulation in this direction. As a consequence, three domains rotated by 120∘ must exist on a (111) surface and are actually found as seen in Figure 57.23b. The very same behavior was also detected for bromide and iodide on Cu(111) [458, 459].

57.3 Adsorption of Anions

UB = –480 mV

UB = –420 mV

UB = –520 mV

(b)

(a)

(c)

(√3 × √3)R 30°-Cl

Cu(111) (√3 × √3)R 30°-Cl + Cu(111)

(f)

(e) (d)

Low frequency

High frequency

(√3 × √3)R 30° Cl/Cu(111)

{211}

(g)

Cl

Cu

Figure 57.22 (a–c) In situ STM images of a chloride-covered Cu(111) surface registered with different tunneling √ √parameters such that in panel (a) the ( 3 × 3)R30∘ chloride overlayer and in panel (c) the underlying Cu(111) substrate structure dominates. (b) Comprising contributions from both the chloride overlayer and the Cu substrate, enables after (d) Fourier analysis, a decomposition into the contributions from (e) the chloride

{110} overlayer on the one hand and (f ) the (1 × 1) lattice of the Cu(111) substrate on the other hand. As (e) and (f ) originate from the same experimental image (b), their superposition yields the absolute adsorption site of the Clanions, namely the threefold hollow sites as sketched in panel (g). (Source: Broekmann et al. 1999 [449]. Reproduced with permission of Elsevier.)

559

57 Metal–Electrolyte Interfaces: An Atomic View

(a)

(b)

(c)

α – [011] – [101] – [110]

⇀

b

⇀

a

(d)

√3

p

[211]

560

– [110]

Figure 57.23 In situ STM images of the chloride-covered Cu(111) electrode surface. √ √ (a) ( 3 × 3)R30∘ -Cl structure, which above −600 mV (vs. Ag/AgCl) transforms into a

√ c(p × 3) structure (b) of which two out of three possible rotational domains are shown in (c). (d) A structure model (see also Figure 57.27).

On the other hand, an analysis of in situ surface XRD data [457] of the incommensurate phase as shown in Figure 57.24a deliberately excluded a uniaxial compression of the chloride layer and, instead, favored a uniform compression and the formation of a Moiré superstructure. The reciprocal vectors of the unit cell of the copper → − → − substrate ( b 1 , b 2 ), a first-order vector of the incommensurate Cl√adlayer struc√ − ture (→ q ic ), and the reciprocal vector of the ideal commensurate ( 3 × 3)R30∘ − − q ic is rotated by structure (→ q c ) are indicated by arrows in Figure 57.24a. The vector → → − ∘ Φ = ±6 relative to q c , leading to pairs of spots. The resultant real space structure, deduced from this diffraction pattern, is displayed in Figure 57.24b and predicts a −r ≈ 44 Å. The uniform comMoiré superstructure characterized by the vector → M pression of the chloride layer leading to the Moiré superstructure was attributed

57.3 Adsorption of Anions

(a) Φ b2 qc qic

b1

(b)

a2

Φ

rM a1

Figure 57.24 (a) In-plane diffraction pattern of a chloride-covered Cu(111) electrode taken at a potential 220 mV higher than the chloride desorption peak maximum. The black spots arise from the copper substrate, whereas the gray spots originate from the Cl overlayer. The reciprocal space vectors − → − → of the substrate surface unit cell (b1 , b2 ), a first-order vector of the Cl adlayer structure (− q→ ic ), and the reciprocal √ √ position of the commensurate ( 3 × 3)R30∘ -Cl struc− ture (→ qc ) are indicated by arrows. b) Derived

rc

ric

real-space structure model of a uniformly compressed hexagonal, 6∘ -rotated chloride adlayer (green atoms) on the Cu(111) surface (orange atoms). From the mismatch between the (111) substrate lattice and the chloride overlayer, a Moiré superstructure with the coincidence vector − r→ M is expected. The surface unit cells of the substrate, the √ √ ( 3 × 3)R30∘ structure, and the adlayer are shown in the inset. (Source: Adapted from Gründer et al. 2011 [457] with permission of Elsevier.)

561

57 Metal–Electrolyte Interfaces: An Atomic View

to comparable adsorbate–adsorbate and adsorbate–substrate interactions [457], which is difficult to reconcile with the fact that the copper–chloride interaction is attractive, but the chloride–chloride interaction is repulsive. Moreover, the supposed Moiré superstructure fails to explain the existing 120∘ domains of the wavy superstructure in Figure 57.23b. One possible explanation for this discrepancy could be the different detection mode of STM vs. XRD. While STM measures strictly local, XRD is sensitive to long-range order.√However, regardless of this conflict, both the uniaxially incommensurate c(p × 3)- and the uniformly compressed, 6∘ -rotated Moiré structure model reflect a reversible potential-dependent variation of the chloride–chloride distance and, as a consequence, of the chloride coverage (“electrocompression”), which, all the more, is also found for the larger bromide and iodide anions [458, 459]. It is important to note that neither the CV signals nor the STM images nor their suggestive coincidence say anything about the chemical nature of the adsorbed species causing the coarse structure in Figure 57.21c,d and Figure 57.22a. An unambiguous identification of the adsorbed species can be achieved by ex situ XPS, UPS, and ISS measurements after transfer of the Cu(111) electrode, without contact to air, into an UHV spectrometer chamber as shown in Figure 57.16 [460, 461]. The XPS survey spectrum in Figure 57.25 shows the characteristic copper valence band (VB), 3p and 3s signals, as well as the spin–orbit split Cl 2p3/2,1/2 doublet (e.g. [463]). The top UPS (HeI) spectrum (1) in Figure 57.26 was obtained from the well-prepared Cu(111) surface in UHV, and the following series of alternating

Cl 2p3/2 Intensity

562

Cl 2p1/2

202

200

XPS

198

196

Cu 3p Cl 2p

200

VB

Cu 3s

0

100 Electron binding energy (eV)

Figure 57.25 XPS spectrum taken after emersion of a chloride-covered Cu(100) electrode showing the copper 3d valence band (VB) and the Cu(3p) and Cu(3s) core levels as well as the spin–orbit split Cl2p3/2,1/2 emission. The latter (see inset) shows no

indication of more than one adsorption state of chloride, for instance, from dried remnants of adhering solution during the emersion and transfer into the XPS spectrometer. (Source: Wandelt 2018 [462]. Reproduced with permission of Elsevier.)

57.3 Adsorption of Anions

Intensity (arb. unit)

Intensity (arb. unit)

UPS

1 ISS

Cl ×20

Cu

2 O 3

4 5 0

2 4 6 Eb (eV)

8 10 400

600

800

1000

Ekin (eV)

Figure 57.26 Sequence of UPS and ISS spectra registered (1) from a clean Cu(111) surface in UHV and (2–5) from the same surface after immersion in and emersion

from hydrochloric acid solution. Spectra 2–5 are taken in chronological order (see text). (Source: Stuhlmann et al. 2003 [461]. Reproduced with permission of Springer.)

UPS and ISS spectra was registered after air-free transfer into hydrochloric acid solution and back-transfer into UHV. UPS spectrum (2) as well as the following first ISS spectrum (3) show clear chloride signals. However, as the ISS method is not nondestructive, the repeated ion bombardment causes successive sputter removal of the adsorbed chloride. As a consequence, the chloride signals decrease, whereas the copper signals increase, so that after a total ion dose of ∼2mAs, all chloride is sputtered away (spectra (4) and (5)). The slight dissimilarity between spectra (1) and (4) is due to a different roughness of the well-annealed (1) and the ion-bombarded copper surface (4). Besides the mere chemical identification, the XPS spectrum in Figure 57.25 and the ISS spectrum (3) convey further important information. The XPS line shape points to a single chloride species, which excludes remnants from the liquid electrolyte on the surface, i.e. dried KCl. Moreover, the first ISS spectrum (3) does not show any trace of oxygen, which clearly excludes adhering or occluded water. As bromide desorbs at more negative potentials than chloride (Figure 57.20) already in the regime of strong hydrogen evolution current, it is difficult to image the uncovered Cu(111) surface in a bromide-containing electrolyte. In the whole

563

564

57 Metal–Electrolyte Interfaces: An Atomic View

accessible potential range, the surface is covered by a highly ordered√ bromide √ layer, which in contrast to chloride is never a simple commensurate ( 3 × 3)R30∘ structure, even at most negative potentials. Because of its larger size, the bromide √ anions form a uniaxially compressed c(p × 3) structure with 2.62 < p < 2.83 throughout the whole accessible potential range [464], resulting in the wavy superstructure as can be seen in Figure 57.27. At the cathodic (anodic) limit, the bromide √ layer √ is compressed in [110] directions by 5.6% (9%) compared to a simple ( 3 × 3)R30∘ structure. In the state of highest compression, the shortest Br–Br distance is 4.0 Å [465], which is longer than the Pauling distance between two singly charged bromide ions (3.9 Å) as well as the van der Waals distance between two neutral bromide atoms (3.7 Å), supporting the notion that even in the most compressed phase, the adsorbed bromide particles retain largely their ionic character. The step edge seen in Figure 57.27a has the same √ direction as the densely packed Br rows, which is explicitly not√the commensurate 3-direction but a direction parallel to a diagonal of the c(p × 3) unit cell shown in Figure 57.27b. It is the packing density of the bromide particles that is important for the stabilization of step edges. As a consequence, the step edge in Figure 57.27a does not run parallel but at an angle of 55∘ off the direction of the wave crests. Obviously, this angle varies with the electrode potential, i.e. the parameter p. The condition of step edges running parallel to the direction of densely packed bromide rows also determines the anodic corrosion morphology of the bromidecovered Cu(111) surface (and Cu(100), see below). Figure 57.28 shows a bromidecovered Cu(111) surface in the anodic potential regime resulting from a combination of local corrosion and (accompanying) deposition processes. The steps are running parallel to the densely packed Br rows, and not parallel to the densely packed Cu rows [464]. (b)

(a)

55°

Figure 57.27 In situ STM images of a bromide-covered Cu(111) surface showing √ the uniaxially incommensurate c(p × 3)Br superstructure. The step edge in (a) runs along and is stabilized by a close

√3aCu p > 2aCu

packed row of Br− anions with an orientation 55∘ off the direction of the wave valleys/crests (i.e. the commensurate direction). (a) 15.4 nm × 15.4 nm and (b) 8.3 nm × 8.3 nm.

57.3 Adsorption of Anions

(a)

(b)

–

[101] –

[211]

–

[101]

Figure 57.28 In situ STM images showing the morphology of a bromide-covered Cu(111) surface at positive potentials. (a) 285 nm × 285 nm; (b) 112 nm × 112 nm.

–

[211]

Note the orientation and the anisotropy of extending islands (a) and depressions (b) of monoatomic height/depth.

57.3.2.2 Iodide – Cu(111)

A cyclic voltammogram of Cu(111) in 10 mM HClO4 + 0.1 mM KI solution is displayed in Figure 57.29 [466]. The extra shoulder (Des.) superimposed on the exponentially growing hydrogen evolution current corresponds to the onset of the iodide desorption, which compared to chloride and bromide is shifted to even

–20 Des.

1.5 1.0

–40

I (μA/cm2)

Current density (μA/cm2)

P

Ads.

0

P

0.5 0.0 –0.5 –1.0

–60

–1.5

HER

–300 –250 –200 –150 –100 –50

E vs. Ag/AgI (mV)

–80 –700

–600

–500

–400

–300

–200

–100

0

E vs. Ag/AgI (mV) Figure 57.29 Cyclic voltammogram of Cu(111) in a 0.1 mM KI containing HClO4 solution; sweep rate dE/dt = 10 mV/s. Besides the iodide adsorption (Ads.) and iodide desorption (Des.) signal, the cyclic

voltammogram indicates a phase transition within the adsorbed iodide layer at P; HER, hydrogen evolution reaction. (Source: Obliers et al. 2003 [466]. Reproduced with permission of Elsevier.)

565

57 Metal–Electrolyte Interfaces: An Atomic View

more negative potentials. In the anodic scan direction, the peak Ads and peak P with a pre-shoulder (see inset) correspond to the re-adsorption of iodide and a phase transition within the adsorbed iodide layer, respectively, as concluded from STM images shown in the following figures. At very negative potentials (−500 mV √ √ vs. Ag/AgI), iodide forms a hexagonal ( 3 × 3)R30∘ structure (Figure 57.30a) √ with a nearest-neighbor distance of 4.4 ± 0.15 Å (= 3aCu–Cu ). This structure is rather perfect as revealed by the 2D Fourier transformation in Figure 57.30b; the hexagon is ideal and the individual spots (inset) are sharp and circular, indicating a sharp distribution of nearest-neighbor distances. This structure corresponds to a coverage of 𝜃 I = 0.33 ML and was also found in UHV [467–472]. Changing the potential toward more positive values causes a coverage increase and a uniaxial compression of the iodide overlayer which, similar to chloride and bromide on Cu(111), leads to the wavy incommensurate superstructure seen in Figure 57.30c. In the direction of the brighter wave crests, the ad-particles are in registry with the substrate (commensurate direction). In the direction perpendicular to the wave crests (incommensurate direction), they are out of registry and (b)

[01– 1]

(a)

[2–1

1]

(d)

(c) [01– 1]

566

[21– 1

]

Main spot Satellite

d*

Figure 57.30 In situ STM images of the (a) commensurate and (c) incommensurate iodide layer on Cu(111) in 0.05 mM KI containing HClO4 solution √ √ at different electrode potentials. (a) ( 3 × 3)R30∘ -I structure close to the onset of hydrogen evolution, E = −510 mV (Ag/AgI); 10.4 nm × 10.4 nm; (b) two-dimensional Fourier spectrum of

(a); (c) uniaxially compressed iodide layer at more positive potentials, E = −139 mV, 15 nm × 15 nm; (d) two-dimensional Fourier spectrum of (c). The insets in (b) and (c) are an enlargement of the white rectangle showing the fine structure of the respective spot. (Source: Obliers et al. 2003 [466]. Reproduced with permission of Elsevier.)

57.3 Adsorption of Anions

occupy no longer equivalent adsorption sites. In the potential interval from −510 to −139 mV (Ag/AgI), the interatomic distance shrinks from 0.44 to 0.42 nm. The corresponding Fourier transformation displayed in Figure 57.30d reflects a somewhat distorted hexagon, and the individual spots are split into a broadened main spot and a satellite spot. The broadening indicates a broader distribution of interatomic distances and the spot splitting d* arises from the long-range periodicity of the wavy superstructure. This “electrocompression” of the incommensurate structure occurs within the potential range −300 to −100 mV and correlates with the pre-shoulder plus peak P in the CV (Figure 57.29). Within the potential range −168 to −114 mV, the distance between adjacent wave crests decreases from 39.4 ± 0.15 Å to 37.1 ± 0.15 Å, respectively. The concomitant symmetry reduction of the iodide overlayer compared to the substrate causes the appearance of three equivalent rotational domains [466]. √ √ The transition from the ideally commensurate ( 3 × 3)R30∘ to a uniaxially √ incommensurate (p × 3)R30∘ structure can be explained by two possible mechanisms, namely (i) an insertion of domain walls or (ii) a uniform and continuous uniaxial compression of the adlayer. The iodide layer on Cu(111) follows the domain wall mechanism. For instance, at −114 mV (Ag/AgI), the average nearestneighbor distance has shrunk to 3.9 ± 0.15 Å (compared to 4.4± 0.15 Å in the ideal √ √ ( 3 × 3)R30∘ structure), but the nearest-neighbor distance within the darker √ √ valleys in Figure 57.30c is identical to that of an ideal ( 3 × 3)R30∘ structure while that within the brighter wave crests is found to be only 3.7± 0.15 Å. Figure 57.31a √ √ shows a hard sphere model of a uniaxially compressed ( 3 × 3)R30∘ structure with a straight “superheavy” domain wall (shdw), in which the iodide–iodide distance would be unrealistically short, namely equal to aCu–Cu = 2.56 Å. Instead, such a domain wall may partially relax by local atom displacements and meandering as sketched in Figure 57.31b,c, in agreement with the experimental observation in Figure 57.30c [466], and similar findings for iodine adsorption on Cu(111) in UHV [472]. 57.3.2.3 Chloride and Bromide – Cu(100)

A full cyclic voltammogram of Cu(100) in 10 mM HCl solution is shown in Figure 57.32. As mentioned before, the double-layer regime between the HER and the CDR is surprisingly structureless and shows no clear chloride adsorption. The only cathodic peak near +200 mV corresponds to the CRR. In situ STM images, however, prove that chloride adsorbs spontaneously within the double-layer regime forming a highly ordered commensurate structure. This √ structure is 2 times wider, centered, and 45∘ rotated compared to that of the bare √ √ Cu(100) surface unit cell and represents a ( 2 × 2)R45∘ , or in short c(2 × 2), structure (Figure 57.33a,b) as also found for chlorine adsorption in UHV [474–476]. The chloride anions reside in fourfold hollow sites of the Cu(100) surface as verified by XRD studies (see below). The surface coverage of the chloride layer is 0.5 ML and √ the nearest Cl–Cl distance is 2aCu = 0.362 nm (3.3% larger than the van der Waals bonding distance).

567

568

57 Metal–Electrolyte Interfaces: An Atomic View

shdw (a)

NND√3-Domäne = 0.44 nm

√3aCu √3a

Cu

√3aCu √3a

Cu

NNDshdw = 0.256 nm

shdw (b)

√3a

Cu

√3a

Cu

NNDshdw = 0.256 nm

shdw

(c)

√3a

Cu

√3a

Cu

NNDshdw > 0.256 nm

Figure 57.31 Schematic hard sphere models of (a) an ideal superheavy domain wall (shdw); (b) a meandering shdw, and (c) a partially relaxed shdw within a

√ √ ( 3 × 3)R30∘ iodide layer on Cu(111). (Source: Obliers et al. 2003 [466]. Reproduced with permission of Elsevier.)

The specific adsorption of the chloride ions causes also a significant restructuring of the surface as can be seen in panels (c) and (d) of Figure 57.33. In contrast to the frizzy step edges of an absorbate-free copper surface (see, e.g. Figures 57.17a and 57.57), the steps are strictly aligned in [010] and [001] direction, stabilized by

57.3 Adsorption of Anions

1.5 CDR

Current density (μA/cm2)

1.0 0.5

″Double layer regime″

0.0 CRR

–0.5 –1.0 HER –1.5 –400

–200

0

200

400

Electrode potential (vs. RHE) Figure 57.32 Steady-state cyclic voltammogram of Cu(100) in 10 mM HCl solution. The voltammogram is limited at high (anodic) potentials by the oxidative copper dissolution (CDR) and at low (cathodic) potentials

by the decomposition of the electrolyte and hydrogen evolution reaction (HER). The current wave in negative scan direction near E = +200 mV vs. RHE corresponds to the copper redeposition reaction (CRR).

densely packed chloride rows at the upper step edges (Figure 57.33d). This ordering and step faceting is found after repeated cycling of the electrode potential between high and low potentials and involves significant copper mass transport. During the positive scan, low-coordinated Cu atoms are dissolved as [CuCl2 ]− complexes (see Section 57.3.2.7), which in the reverse scan are decomposed and redeposited at energetically more favorable sites, i.e. vacancies and kink sites. In analogy to healing of surface defects by thermal annealing, this process has – not quite correctly – been termed “electrochemical annealing” (see Section 57.2.3.3.5). The resultant straight step edges may run for hundreds of nanometers until they meet another step in orthogonal direction. The full cyclic voltammogram of Cu(100) in 10 mM HBr solution is shown in Figure 57.34 and looks very similar to that in Figure 57.32. Like chloride, bromide also forms a c(2 × 2) structure of ΘBr = 0.5 ML (Figure 57.35b) both in solution and in UHV [477–480]. Consistent with the CV and the lower hydration energy of bromide (Table 57.1), the formation of the c(2 × 2) structure occurs already at less positive potentials than with chloride. It is difficult to register in situ STM images of the bromide-free surface in the bromide-containing electrolyte because the HER begins already on the bromide-covered surface because of the Frumkin effect [450] as already mentioned in the context of the chloride-covered Cu(100) surface. Figure 57.35 shows (a) the large-scale morphology and (b) the atomic c(2 × 2) structure of a bromide-covered Cu(100) surface at anodic potentials. Even though bromide is larger than chloride, the bromide anions still fit into a c(2 × 2) structure on the Cu(100) surface. The perfectly aligned steps in the [010] and [001] directions

569

57 Metal–Electrolyte Interfaces: An Atomic View

(b)

(a) 2a 2a

0] [01

(d)

[00

1]

Figure 57.33 In situ STM images of a chloride-covered Cu(100) √ electrode √ surface. (a) Atomically resolved c( 2 × 2)R45∘ - or (2 × 2)-Cl structure, (b) correlation of the c(2 × 2)-Cl structure (upper half ) with the bare Cu(100) lattice (lower half ), (c) largescale morphology of the chloride-covered

1]

(c)

0] [01

45°

[00

Cu(100) surface; note the long, straight, and orthogonal step edges (69 × 69 nm2 ), and (d) step edges stabilized by densely packed Clrows. (Source: Pham et al. 2009 [473]. Reproduced with permission of Swiss Chemical Society.)

CRD Current density (mA/cm2)

570

0.00 CRR

–0.05

HER

–800

–600

–400

–200

0

200

E vs. Ag/AgBr (mV) Figure 57.34 Steady-state cyclic voltammogram of Cu(100) in 10 mM HBr solution. CDR, CRR, and HER are defined in Figure 57.32.

57.3 Adsorption of Anions

(a)

(c)

(b)

2a

] 10 [0

] 10 [0

2a ]

01

[0

Figure 57.35 In situ STM images of Cu(100) in 10 mM HBr solution; (a) large-scale morphology with extended terraces and orthogonal step edges in [001] and [010] direction; arrows indicate “triple points” of double-step

]

01

[0

√ √ height, (86 × 86 nm2 ); (b) c( 2 × 2)R45∘ - or c(2 × 2)-Br structure on Cu(100); and (c) steps are aligned along densely packed Br− anion rows. Kinks like the one seen in (c) are energetically less favorable and therefore rare.

are again stabilized by densely packed rows of bromide anions (panel c). Occasionally, step edges cross each other, leading to the so-called “triple points” of double-step height (dashed arrows in panel a). The detailed adsorbate configuration at step edges on an fcc(100) surface covered with a c(2 × 2) adlayer is illustrated in Figure 57.36a on the basis of a monoatomically high island. Depending on the translational phase relation between adsorbate rows on the island and the adjacent terrace, there are “out-of-phase” (A,A′ ) and “in phase” (B) steps. As a consequence, ad-particles in up-step positions may be coordinated to either four (A,B) or three Cu atoms (A′ ). Along an A-step (A′ -step), the nearest distance between up- and down-step ad-particles is larger (smaller) than along a B-step. For sterical and probably electrostatic reasons, the energetic stability of the three configurations is different. The STM images in Figure 57.36b,c confirm the existence of both configurations, A and B, and the energetic anisotropy manifests itself in the anodic corrosion behavior of the surface. Figure 57.37a–c shows a sequence of STM images of the same surface area (see hole in the center) of a bromide-covered Cu(100) electrode. As the potential increases, the images show a (a)

(b) B

(c) B

A

A′

Figure 57.36 (a) Hard sphere model of the c(2 × 2)-Br-covered Cu(100) surface with a rectangular Cu island in the center (brighter) illustrating the two translational phase relations B and A/A′ between Br rows

A

on the island and the surrounding terrace, respectively. Both situations are verified in the experimental STM images in panels (b) and (c).

571

572

57 Metal–Electrolyte Interfaces: An Atomic View

(b)

(a) [001]

T1

[010]

(c)

T2

T3 T4 T5

E = –90 mV (d)

E = –90 mV

T1

T6

(f)

(e)

T2

5

T3

E = –65 mV

4 2

2

4 5

3

3

T4 1

T5 [001]

1

1

[010]

Figure 57.37 (a–c) Sequence of in situ STM acid (173 × 173 nm2 ). The same anisotropic images showing the clear unidirectional pref- behavior is observed upon Cu redepositon in erence of B-type steps (see Figure 57.36) panels (d–f ) (64 × 64 nm2 ). upon dissolution of Cu(100) in hydrobromic

pronounced unidirectional growth of grooves with bromide-stabilized B-type edges. The same anisotropic behavior is found during copper redeposition upon lowering the electrode potential (Figure 57.37d–f ). 57.3.2.4 Iodide – Cu(100)

Iodide anions are significantly larger than chloride or bromide ions. As a consequence, they do no longer fit into a simple c(2 × 2) structure on Cu(100) but form a series of different phases, which as a function of electrode potential and iodide concentration in solution differ in surface coverage [481]. The cyclic voltammogram in Figure 57.38 was taken with a Cu(100) electrode in 10 mM HClO4 + 1 mM KI solution. Except the anodic CDR, the pronounced CRR, and the HER regime (−420 and −600 mV), two weak current features (arrows) are visible, which correspond to surface phase transitions as concluded from the following STM data. At potentials near the copper dissolution, the large specifically adsorbed iodide anions form a uniaxially incommensurate structure resulting in a one-dimensional height modulation perpendicular to the commensurate direction (Figure 57.39a,b). Parallel to the commensurate [011] direction, the iodide–iodide distance of 5.1± 0.1 Å is exactly twice the copper–copper distance of aCu = 2.56 Å, whereas the nearest-neighbor distance between iodide particles in adjacent commensurate adsorbate rows is only 3.7± 0.1 Å. As a result, the iodide particles form a centered rectangular unit cell with a smaller lateral edge of 2aCu and a longer side with

57.3 Adsorption of Anions

6 CDR

Current density (μA/cm2)

4 2 0 –2

CRR

–4 –6 HER –8

–700 –600 –500 –400 –300 –200 –100

0

100

E vs. Ag/Agl (mV) Figure 57.38 Steady-state cyclic voltammogram of Cu(100) in 1 mM KI containing 10 mM HClO4 solution, CDR, copper dissolution reaction; CRR, copper redeposition reaction; and HER, hydrogen evolution reaction. The two arrows mark current waves

associated with structural phase transitions within the adsorbed iodide layer as verified by the following in situ STM images shown in Figures 57.39–57.47. (Source: Broekmann et al. 2002 [481]. Reproduced with permission of Elsevier.)

p > 2aCu , i.e. a c(p × 2) structure (Figure 57.39d). This structure can be regarded as a distorted c(2 × 2) phase, which is expanded parallel to the [011] direction. A schematic hard sphere model comparing the original c(2 × 2) as observed with chloride and bromide and the derivative c(p × 2) of iodide is presented in Figure 57.40. The p-vector decreases (increases) linearly with increasing (decreasing) electrode potential in terms of a so-called electrocompression (electrodecompression) process within the potential range between −400 and −5 mV (Ag/AgI). As a result of the reduced symmetry of the iodide lattice compared to that of the quadratic substrate, two rotational domains rotated by 90∘ against each other must exist and are indeed observed in Figure 57.39c(I, II). The iodide adlayer shown in Figure 57.39 corresponds to a saturation coverage of Θ = 0.46 ML (at E = −100 mV), which is close to 0.5 ML, the theoretical value of the c(2 × 2) structure. Lowering the potential to −430 mV leads to a decrease of the coverage to Θ = 0.38 ML. Although the iodide–iodide spacing along the commensurate direction remains unaffected, the average interatomic distance in the incommensurate direction increases. This expansion, however, is not uniform as can be seen in Figure 57.41. Although building block A consists of three atomic rows of iodide particles, building block B consists only of a double row, both separated by a larger distance (dark). The interatomic spacing within both building blocks is quite different, as can be seen in the line scan in Figure 57.41b. Even though this iodide structure

573

57 Metal–Electrolyte Interfaces: An Atomic View

(a)

(b) c

a b

[011] –

[011]

(d)

α

(c)

Cu

I

2a

574

a b

II

p>2 [011]

aC

u

–

[011]

Figure 57.39 In situ STM images of the uniaxially incommensurate iodide structure on Cu(100) at positive potentials, (a) 12.6 nm × 12.6 nm, E = −100 mV (Ag/AgI); (b) 5.7 nm × 5.7 nm, E = −100 mV; (c) 12 nm × 12 nm, E = −300 mV; and (d) correlation image of b) showing the

incommensurate iodide unit cell. I, II in panel (c) indicate different domains and 𝛼 the rotational angle between their densely packed anion rows. (Source: Broekmann et al. 2002 [481]. Reproduced with permission of Elsevier.)

[011] –

[011]

2aCu 2aCu

β1

β2

2aCu p1 > 2aCu

2aCu p2 > 2aCu

Figure 57.40 Structure models of the hypo- between iodide anion rows. (Source: Broekmann et al. 2002 [481]. Reproduced with perthetical c(2 × 2)-I structure on Cu(100) and mission of Elsevier.) two incommensurate structures defining the lattice vectors and the related angles 𝛽

57.3 Adsorption of Anions

(a)

A

A

B

(b) z-Corrugation (nm)

B

4.1 Å

0.03

3.7 Å

5.6 Å

0.02 0.01 0.00 A 0

Figure 57.41 Domain wall structure of the iodide anion layer on Cu(100) at E = −450 mV (Ag/AgI); (a) 5.3 nm × 5.3 nm; (b) height profile along the white line in (a)

B 1

A

3 4 2 Distance (nm)

B 5

showing the discontinuous separation of iodide rows along the incommensurate direction. (Source: Broekmann et al. 2002 [481]. Reproduced with permission of Elsevier.)

with discontinuous interatomic spacings can strictly speaking no longer be described by a c(p × 2) unit cell, we will, for the sake of convenience, continue to denote this structure as c(p × 2)-I. The potential-dependent variation of the iodide structure along the incommensurate direction also affects the surface morphology. This is demonstrated in Figure 57.42, which enables a correlation between the symmetry properties of the iodide lattice and the step orientation. Most surprisingly, unlike the chloride and bromide-covered Cu(100) surface, the step orientations do not coincide with a commensurate directions, i.e. the wave crests of the long-range superstructure of the c(p × 2) structure, because the step edge in Figure 57.42b encloses an angle of 𝛽 = 40± 2∘ with the commensurate [011] direction. Instead, it is again the direction parallel to the close-packed iodide rows, which determines the energetically most (a)

(b) I

I

I

(c) I

β

I II

I

II [011] [011]

α

I II

Figure 57.42 Gross morphological features of an iodide-covered Cu(100) surface; (a) 29 nm × 29 nm, E = −200 mV (Ag/AgI); (b) 16.04 nm × 16.04 nm, E = −300 mV; (c) 15.7 nm × 15.7 nm, E = −300 mV. I and II mark different domains; note in panel (c) that only step edges between equal

II II

domains are straight; 𝛼 and 𝛽 are defined in Figures 57.39 and 57.40. White lines in panel (b) accentuate the translational phase relation between the upper and the lower terrace. (Source: Broekmann et al. 2002 [481]. Reproduced with permission of Elsevier.)

575

576

57 Metal–Electrolyte Interfaces: An Atomic View

favorable step orientation. As a consequence, the presence of two rotational domains on one and the same terrace manifests itself by step directions which deviate by the same angle 𝛼, which distinguishes the directions of close-packed rows of two different rotational domains (Figures 57.39c and 57.42a). The fact that the symmetry of the iodide lattice changes with increasing or decreasing electrode potentials obviously means that the direction of the densely packed iodide rows and thereby that of the steps changes. Hence, the step orientation in Figure 57.42a is characteristic only for the given electrode potential (see Figure 57.40 with p1 < p2 giving 𝛽 1 < 𝛽 2 ). At very negative potentials, i.e. from −450 to −550 mV, and further reduced iodide coverage, the discontinuous expansion seen in Figure 57.41 eventually leads to the formation of a new ordered commensurate iodide structure shown in Figure 57.43a. This structure remains stable even under massive hydrogen evolution. A quantitative analysis of the unit cell based on a Fourier analysis (Figure 57.43b) yields 2D lattice parameters of a = 5.1± 0.1 Å, b = 8.0± 0.1 Å, and 𝛾 = 109.5∘ ± 2∘ and corresponds √ to a so-called (2 × 10) unit cell in “quasi”-Wood notation (see Figure 57.44), or alternatively to a rectangular c(6 × 2) unit mesh (Figure 57.43a). One approach to (a)

(b)

(c)

b

γ

b* a

a*

6aCu 2aCu

Figure 57.43 c(6 × 2)-iodide structure on Cu(100) at E = −440 mV (Ag/AgI) close to the onset of hydrogen evolution; (a) 3.3 nm × 3.3 nm; (b) Fourier spectrum of the

2aCu 6aCu

0.44 nm

0.38 nm

√10aCu

2aCu

iodide structure; and (c) two-domain power spectrum of the c(6 × 2)-I phase. (Source: Broekmann et al. 2002 [481]. Reproduced with permission of Elsevier.) Figure 57.44 Structure model √ and parameters of the c(6 × 2)- or (2 × 10)-unit cell of the iodide superstructure at negative potentials. (Source: Broekmann et al. 2002 [481]. Reproduced with permission of Elsevier.)

57.3 Adsorption of Anions

understand this iodide structure is to assume characteristic building blocks of iodide “zigzag” chains (see dashed line lower left corner in Figure 57.44), in which one of the two iodide species resides in a fourfold hollow sites, while the other one is placed in an interstitial site. Because of the reduced symmetry of this adsorbate lattice, two rotational domains of this structure are again observable in the STM experiments (Figures 57.39 and 57.43c) [481]. Precisely, the same structure was also found in ex situ LEED studies in UHV after dissociative iodine adsorption on Cu(100) for an iodine coverage of Θ = 0.33 ML [483]. The phase transition from the c(p × 2)-I to the c(6 × 2)-I structure proceeds over a wide potential range (−450 to −600 mV) indicating kinetic hindrance. At the beginning, narrow stripes of the c(6 × 2)-I structure are formed, with the zigzag motif mentioned above as the smallest building block (see Figure 57.45a). These c(6 × 2) stripes broaden perpendicular to the wave crests of the incommensurate c(p × 2) structure (Figure 57.45b) until in the final stage of the phase transition large areas of the c(6 × 2)-I phase dominate which, however, are often still separated by narrow stripes of the c(p × 2)-I structure (Figure 57.45c). All these stripes reveal the same width of exactly one periodicity of the long-range superstructure (denoted l# in Figure 57.45b). Correspondingly, the width of the c(6 × 2)-I regions (denoted l in Figure 57.45b) is a multiple of l# [481]. As mentioned above, a change in iodide coverage changes the direction of densely packed iodide anion rows and, thereby, also the direction of step edges. This is evidenced in Figure 57.46, showing the atomic adsorbate structure on adjacent terraces close to a step edge. The orientation of the step edges within the narrow stripes of c(p × 2)-I structure (indicated by two white arrows in Figure 57.46b) differs significantly from the step direction within regions where the c(6 × 2)-I phase crosses the step [481]. The c(6 × 2)-I structure is stable down to very low potentials, no desorption of iodide is observed. An even lower coverage can only be realized by adsorbing a priori less iodide [482]. This can be achieved by injecting very small doses of (a)

(b)

× c(p

c(6

(c)

l

2)

) ×2 )

×2 c(p

l# c (p

c (p

×

× c(6

)

×2

2)

2)

Figure 57.45 In situ STM images showing the transition from the c(p × 2)- to the c(6 × 2)-iodide phase on Cu(100). (a) Initial stage of the phase transition, 72 nm × 72 nm, E = −450 mV (Ag/AgI); (b, c) advanced stages of this phase transition: (b) 13 nm × 13 nm,

c(6

×2

)

E = −460 mV; (c) 15.2 nm × 15.2 nm, E = −460 mV. The width l of the c(6 × 2) stripes in (b) corresponds to multiples of the wave length l# of the c(p × 2) superstructure. (Source: Broekmann et al. 2002 [481]. Reproduced with permission of Elsevier.)

577

578

57 Metal–Electrolyte Interfaces: An Atomic View

(a)

(b)

β [011] [011]

Figure 57.46 Morphological features of the transition from the c(p × 2)- to the c(6 × 2)-iodide phase on Cu(100) at E = −565 mV (Ag/AgI); (a) 40.5 nm × 40.5 nm,

(a)

(b)

2)

c(6 ×

2)

c( p ×

2)

(b) 14.4 nm × 14.4 nm. The angle 𝛽 is defined in Figure 57.40. (Source: Broekmann et al. 2002 [481]. Reproduced with permission of Elsevier.) )

c(p

c( p ×

×2

2 p(

×2

)

(c)

)

c(p

×2

2 p(

×2

)

)

c(p

×2

1]

[01

]

11

[0

hdw

Figure 57.47 Coexistence of the uniaxially incommensurate c(p × 2)- and the low coverage p(2 × 2)-iodide phase on Cu(100) obtained after a dosing experiment; (a) 20.7 nm × 20.7 nm, E = −280 mV (Ag/AgI);

(b) 11.5 nm × 11.5 nm, E = −200 mV; and (c) 4 nm × 4 nm, E = −200 mV. Atoms marked with * from a heavy domain wall (hdw). Source: Hommes et al. 2003 [482]. Reproduced with permission of Elsevier.

a low concentration iodide solution into an iodide-free KClO4 solution, which leads to the formation of domains of a p(2 × 2)-I structure (𝜃 I = 0.25 ML) as shown in Figure 57.47. This structure, of course, does not form distinguishable rotational but still translational domains as visible in Figure 57.47c, which exhibits a so-called “heavy domain wall (hdw).” In the ideal case, the iodide anions within this hdw would occupy positions of a c(2 × 2) structure-like Cl and Br (black stars in Figure 57.47c). However, in reality, because of the larger size of the iodide anions, which does not allow this density, the domain wall will laterally be “relaxed” slightly. 57.3.2.5 XRD of Chloride, Bromide, and Iodide on Cu(100)

The STM results presented above as well as early XRD studies [484–486] provided mainly information about the in-plane structure of the adsorbed halide layers

57.3 Adsorption of Anions

including order–disorder phase transitions. In situ XRD measurements, however, also yield information about the out-of-plane structure, for instance, adsorptioninduced relaxation effects perpendicular to the substrate surface, which are well established for surfaces under UHV conditions [32]. Such data provide important information about the bonding mechanism between adsorbates and substrates. The additional electrification of metal–electrolyte interfaces due to an applied potential is a reason to expect different relaxation effects than in UHV. Very detailed XRD analyses have been published for the interaction of all three halides, Cl, Br, and I with Cu(100) surfaces as a function of electrode potential. As an example, Figure 57.48 shows two symmetrically inequivalent so-called “crystal truncation rods” (CTRs) obtained from a Cu(100) surface at +260 and + 95 mV (RHE) in 10 mM HCl solution. Their analysis not only confirms the formation of a c(2 × 2)-Cl adlayer structure as already concluded from in situ STM images in Section 57.3.2.3 but also provides information about the atomic occupancy of the adsorbate layer, the surface roughness, and the first two interlayer spacings, i.e. between the adsorbate and the first Cu layer as well as between the first and second Cu layer [487]. As a result, the interlayer spacing between the first and second layer of the chloride-covered Cu(100) electrode in HCl electrolyte at +95 mV (RHE) is 2.2% larger than the bulk Cu interlayer spacing. By contrast, in UHV, the bare Cu(100) surface exhibits a 1% inward relaxation [488]. This difference is not unexpected for two reasons. Firstly, at +95 mV, the copper surface is positively polarized. This causes a depletion of negative charge between the outer Cu layers, which weakens the Cu–Cu bond perpendicular to the surface. Secondly, the presence of the adsorbed electronegative Cl species (see Table 57.2) is expected to enhance this charge depletion even further. This, however, leads to the fundamental question what the charge state of the adsorbed Cl species actually is, in particular also as a function of electrode potential. The first answer to this question can be obtained by comparing the XRD-derived Cu–Cl bond length obtained in solution (2.61 Å) with that obtained with LEED for chloride adsorbed on Cu(100) in UHV (2.41 Å), which also exhibits a c(2 × 2)-Cl structure [489–491]. These values support the notion that in the electrochemical environment, the chloride anions on Cu(100) are larger and, thus, mainly retain their negative charge upon adsorption. Conversely, XRD results obtained for a c(p × 2)-I structure on Cu(100) with p = 2.5, at the same potential +95 mV (RHE) (in order to avoid formation of CuI surface compound described in Section 57.3.2.8), are best described by the model shown in Figure 57.49, which includes positional relaxations not only perpendicular but also parallel to the surface [487]. In particular, the best fit to the XRD data suggests an (average) bond length between first-layer Cu atoms and the adsorbed iodine particles (sitting actually in inequivalent surface sites) of 2.51 Å and an inward relaxation of the topmost Cu layer by 3% compared to the ideal layer spacing in the Cu bulk. A similar inward relaxation was measured after bromide adsorption on Pt(111) [491]. However, in contrast to the expansion between the outer Cu layers by 2.2% upon chloride adsorption (see above), iodide adsorption causes a 3% compression. These results clearly point to a different bonding mechanism between these two halides and copper.

579

57 Metal–Electrolyte Interfaces: An Atomic View

1000

Intensity (arb. units)

Data Cl/Cu(100)-c(2 × 2) Clean Cu(100)

XRD

100

(2,0,L) 10

1

(2,2,L) 0.1

0.5

(a)

1.0

1.5

2.0

2.5

3.0

3.5

qz[2π/c] Top view

Cu

Cl

x

580

dad-layer y

d12 d23 Z

Side view (b)

(c)

Figure 57.48 Plot of the XRD intensity distribution of the (2,0,L) and (2,2,L) CTRs as a function of the momentum transfer perpendicular to a c(2 × 2)Cl-covered Cu(100) surface. The filled circles represent the experimental values, and the solid red line represents the best fit based on the structure

model shown in panels (b) and (c). The solid black line shows the calculated intensity based on an uncovered relaxed Cu(100) surface under UHV conditions. The CTR data were collected for t >40 minutes. (Source: Huemann et al. 2006 [487]. Reproduced with permission of ACS Publications.)

The size of an ion may be correlated with its (partial) charge; the less ionic an anion is, the smaller it is. In other words, the shorter its bond length to the surface, the more charge it has dumped between the atoms of the outer metal layers, which in turn reduces the metal core–core repulsion. Thus, both the elongated Cu–Cl bond and the expansion of the outer Cu–Cu bond interlayer spacing upon chloride adsorption indicate a largely ionic bond between metal and the chlorine adparticles,

57.3 Adsorption of Anions

x

Top view

y

1 Cu 2 1

2′ 3

z

Side view 1

2

3

Figure 57.49 Top and side view of the c(5 × 2)-iodide structure model on Cu(100). The solid rectangle represents the c(5 × 2) unit cell. The dashed circles indicate the positions of iodide anions before relaxation

x

(see text). The labels 1, 2, and 3 mark iodide species in fourfold, quasi-bridge, and bridge sites, respectively. (Source: Huemann et al. 2006 [487]. Reproduced with permission of ACS Publications.)

while the reduced Cu–I bond length and the Cu–Cu interlayer compression upon iodide adsorption are consistent with a more covalent Cu–I bond. The chloride induced Cu–Cu interlayer expansion and, hence, Cu–Cu bond weakening is also consistent with the increased Cu surface mobility (in the form of easily detaching and soluble [CuCl2 ]− complexes) and the chloride-driven “electrochemical annealing” effect [133–139]. Conversely, the iodide-induced stabilization of the Cu–Cu bond in conjunction with the less ionic (more covalent) Cu–I bond explains the formation of stable Cu–I surface compounds as described in Section 57.3.2.8. Turning to bromide, which forms the same two-dimensional c(2 × 2) surface lattice on Cu(100) as adsorbed chloride, in situ XRD measurements with Cu(100) in 10 mM KBr containing dilute sulfuric acid solution yield significantly different outof-plane results [492]. In contrast to chloride, adsorbed bromide leads to a decrease of the distance between the Br adlayer and the first Cu layer with increasing electrode potential (see Figure 57.50), which indicates strengthening of the Br–Cu bond. The spacing between the first two copper layers underneath the Br adlayer is always expanded (compared to the Cu–Cu spacing in the bulk), namely on average (see below) by 3.2% at −150 mV and 1.1% at +50 mV. This is, as explained with chloride above, counterintuitive because the mere positive polarization of the electrode, i.e. charge withdrawal, should result in a stronger repulsion between the Cu cores and, hence, in a Cu–Cu expansion. It must therefore be the presence of the bromide adlayer which, unlike chloride, overcompensates this effect by an increasing charge transfer from bromide into the Cu surface.

581

57 Metal–Electrolyte Interfaces: An Atomic View

Side view Br

Br dad-1

[100]

2b

2a 2b

2b

d2a-2b

1.09 1.08

Br c(2 × 2)/Cu(100)

1.07 1.06 1.05 1.04 1.03

1.03

d23

1.02

[010]

Cu

1.01

Cu

(a) Figure 57.50 (a) Side view of the p(2 × 2)Br structure on Cu(100) showing a series of layers and their interlayer distances, used in a fitting procedure. The rectangle represents the reduced unit cell commonly used in XRD. (b) Plots of potential-dependent interlayer

1.00

(b)

–100 –150 –100 –50 0 E vs. RHE (mV)

50

0.99 100

Cu1 - Cu2 distance (a/2)

2a

d1-2ab

Halide - Cu1 distance (a/2)

582

spacings between the c(2 × 2)-Br anion overlayer and the first Cu layer (upper plot), and the first and second Cu layer (lower plot). (Source: Saracino et al. 2009 [492]. Reproduced with permission of American Physical Society.)

The analysis of the c(2 × 2)Br–Cu(100) XRD data, however, was carried one step further than the previously described one for Cl–Cu(100) and I–Cu(100), in which even a buckling of the second Cu layer was considered. This buckling was found to improve the fit quality significantly [492] and arises from the fact that every other Cu atom (2a in Figure 57.50a) in the second layer directly underneath a bromide anion has a different distance, and thereby interaction, with the adsorbed halide than a Cu atom (2b) underneath an empty fourfold hollow site of the first Cu layer. The interaction of chloride and bromide with Cu(100) in solution was also examined by density functional theory (DFT) calculations [492] using the Vienna Ab initio Simulation Package (VASP) code [493–495]. The solvent is explicitly taken into account by placing a number of water molecules in the unit cell. Changes of the electrode potential were achieved by varying the total charge within the unit cell, and electroneutrality of the electrolyte itself was preserved by adding the appropriate number of counterions (here Ca2+ ) in the unit cell (see also Chapter 56 in this Volume). In the vacuum case, to begin with, the calculations confirm that both chloride and bromide adsorb most stably in the fourfold hollow sites of the Cu(100) surface, with the Br–Cu (Cl–Cu) interlayer spacing being 1.83 Å (1.67 Å), which is 0.8% larger (8.1% smaller) than the optimized Cu–Cu bulk layer spacing. The distance between the first two copper layers is increased by 0.3% (Br) and 0.6% (Cl) compared to the Cu bulk value, i.e. the Cu–Cu interaction is weakened more with adsorbed Cl than with Br. The subsequent consideration of the outer space charge layer with water molecules and counterions results in a weakening of the Br–Cu bond and a decrease of the Br–Cu and Cu–Cu interlayer spacing with increasing potential in agreement with the experimental findings. For adsorbed chloride, however, the theoretical calculations did not reproduce the experimental trend. Although the measured Cl–Cu

57.3 Adsorption of Anions

distance is almost independent on the electrochemical potential, the calculations predicted a similar trend as for Br [492]. 57.3.2.6 Bromide – Cu(110)

A seen in Figure 57.51, bromide adsorbs at −520 mV (vs. Ag/AgBr) and desorbs at about −560 mV [496], very near the HER. Just before readsorption, STM images (Figure 57.52a,b) confirm the expected rectangular unit cell with interatomic distances of 0.362± 0.01 nm in [001] and 0.265± 0.01 nm in [110] direction. (The noise in Figure 57.52a arises from concomitant hydrogen evolution.) At potentials above the adsorption peak, Figure 57.52c shows a quasi-hexagonal atomic structure of the bromide adlayer. Looking at this image under grazing angle clearly reveals a wavy superstructure with wave valleys and wave crests running in [001] direction, in contrast to earlier results by Wan and Itaya [497]. Both observations, the different symmetry of substrate and overlayer and the wavy superstructure, indicate that the bromide particles are located in inequivalent adsorption sites, which is clearly supported by the power spectrum of this structure (Figure 57.52d). The outer (quasi-) hexagon indicates the symmetry of the adlayer structure, while the weaker spots inside the hexagon arise from the long-ranged superstructure. Parallel to the [001] direction, the nearest interatomic distance is 0.720± 0.01 nm, whereas it is 0.384± 0.01 nm in the [110] direction. This results in a c(3 × 2) unit cell corresponding to a coverage of 𝜃 Br = 0.67 ML (Figure 57.53). A further very interesting property of the c(3 × 2)-Br-covered surface was discovered when scanning the surface with different imaging parameters [496]. 1

Adsorption (–520 mV)

–0.02

Desorption (–560 mV)

–0.04

0.06 j (mA/cm2)

Current density (mA/cm2)

0.00

–0.06

0.04 0.02

1

0.00

–0.02

Cu(110) Br/Cu(110)

–0.08 –600

–500

–300 –200 –100 0 100 E vs. Ag/AgBr (mV)

–400 –300 E vs. Ag/AgBr

Figure 57.51 Cyclic voltammogram of Cu(110) in 10 mM HBr solution. The inset shows in anodic direction the onset of copper dissolution followed by the copper

–200

redeposition in the reverse potential sweep, v = 10 mV/s. (Source: Obliers et al. 2004 [496]. Reproduced with permission of Elsevier.)

583

57 Metal–Electrolyte Interfaces: An Atomic View

(a)

(b) ]

(c)

[001]

10

01

]

[0

[1

584

[110]

(d)

[001]

[110]

Figure 57.52 In situ STM images of (a) a bromide-covered Cu(110) surface (16.5 nm × 16.5 nm, E = −480 mV (Ag/AgBr)); (b) a bare Cu(110) surface region at very negative potential, 3.52 nm × 3.52 nm, E = −619 mV (Ag/AgBr));

(c) the atomic structure of the bromide adlayer (8.5 nm × 8.5 nm, E = −203 mV (Ag/AgBr); and (d) power spectrum of panel (c). (Source: Obliers et al. 2004 [496]. Reproduced with permission of Elsevier.)

Figure 57.54a shows an image that was obtained after the central dark part had been scanned before with higher bias voltage and a slower scan rate, respectively, a longer residence time per tip position. Imaging this obviously damaged part of the surface 30 minutes later, again yields a regular structure of parallel furrows of mostly similar width and separation as indicated by the height profile in Figure 57.54c taken along the line in panel (b). These furrows are the result of a tip-induced electrocorrosion process (well below the copper dissolution potential) [498], followed by some “electroannealing” process. The furrows run in [001] direction and have, except the left-most, a width of 2.048± 0.13 nm, which corresponds to eight times the Cu–Cu interatomic distance in the [110] direction. These findings are consistent with a structure model as shown in Figure 57.55 according to which five, four, three, two, and one Cu atoms are missing in the first, second, third, fourth, and fifth Cu layer, respectively. As a result, the two side walls of each furrow form Cu(100) facets, which are now covered by a c(2 × 2)-Br structure as known from Cu(100) (see Section 57.3.2.3). The fact that the desorption of bromide from Cu(100) occurs at more negative potentials than from Cu(110) proves a stronger

57.3 Adsorption of Anions

0.410 nm

z-Corrugation (nm)

0.384 nm

1

0.724 nm 2

0.06

1

0.04

0.02

0.00 0

3aCu [110] Br in different adsorption positions

3

4

2 0.04

0.02

0.00 0

(a)

2

0.06

z-Corrugation (nm)

[001]

2aCu

1

Distance (nm)

(b)

Cu

Figure 57.53 (a) Schematic hard sphere model of the bromide adlayer on Cu(110) at positive potentials with relevant distances and the two possible unit cells (full and dashed line rectangle). The arrows 1

(c)

2

4

Distance (nm)

and 2 indicate the directions of measured height profiles displayed in panels (b) and (c). (Source: Obliers et al. 2004 [496]. Reproduced with permission of Elsevier.)

Br–Cu(100) than Br–Cu(110) interaction. Thus, the reason for the formation of the furrows is the formation of the energetically more favorable Br-covered (100) facets. The locally tunneling electrons just provide the necessary activation energy for the transformation process. These results are not only very much in line with the tendency of adsorbate-induced and thermally activated faceting of fcc(110) surfaces, well known from adsorption experiments under UHV conditions, but also nicely lead to the chloride-induced reconstruction of Cu(110) described in the following section. 57.3.2.7 Chloride – Cu(110)

Because of its more open atomic structure and, as a consequence, lower surface charge density, the bare Cu(110) surface is expected to be more reactive toward anions than the Cu(111) or Cu(100) surface. First in situ STM results of a Cu(110) surface in hydrochloric acid solution published by Wan et al. [497] and Li et al. [499] showed massive restructuring of the surface very much in line with subsequent STM studies of the Cu(110) surface exposed to HCl- or Cl2 -gas in UHV. [500–502]. Also, optical reflectance anisotropy spectroscopy (RAS) studies by Barritt et al. [503] showed a severe modification of an electronic surface state at 2.5 eV binding energy upon contact with hydrochloric acid solution. More recently, combined in situ STM, RAS, and CV measurements within the same electrochemical cell [504, 505] revealed additional rather detailed information about the potential dependence

585

57 Metal–Electrolyte Interfaces: An Atomic View

(a)

(b)

[001]

– [110]

0.6 0.5 Height (nm)

586

0.4 0.3 0.2 0.1 0.0 –2

(c)

0

2

4 6 8 10 Distance (nm)

Figure 57.54 In situ STM images of a tipinduced surface modification of a bromidecovered Cu(110) surface at E = −230 mV (Ag/AgBr). (a) 49.1 nm × 49.1 nm; (b)

12

14

14.5 nm × 14.5 nm; and (c) height profile along the line in b). (Source: Obliers et al. 2004 [496]. Reproduced with permission of Elsevier.)

of the interaction of chloride anions with a Cu(110) electrode surface. In contrast to the Br–Cu(110) interaction, this Cl-induced Cu(110) reconstruction is spontaneous and does not require extra tip activation because it is detectable by the optical RAS experiments. The cyclic voltammogram of a Cu(110) sample in 10 mM HCl solution shown in Figure 57.56 [504] is distinctly different from those of Cu(111) and Cu(100) in the same solution (see Figures 57.20 and 57.32) immediately expressing the difference in reactivity. The scan interval in Figure 57.56 is limited from about −600 to −300 mV (Ag/AgCl) in order to avoid both the cathodic hydrogen evolution and the anodic metal dissolution. In both scan directions, two pairs of peaks A, B and C, D are detected. The same shift of 30 mV between B and C, as well as A and D, suggests the sequential occurrence of two electrochemical surface processes in both scan directions. Also, the persistently high anodic current beyond peak B indicates some ongoing charge transfer reaction. This latter reaction continues even after reversal of the scan direction at −300 mV, until at ∼−430 mV the “product” of this slow process

57.3 Adsorption of Anions

2.048 nm a b

[001]

a

d – [110]

a = 0.256 nm b = 0.362 nm

Cu 6th layer

Cu 3rd layer

Cu 7th layer

Cu 4th layer

Bromide

)

Cu 2nd layer

00

Cu 5th layer

(1

Cu 1st layer

(110)

f (b)

(a) Figure 57.55 (a) Schematic hard sphere model explaining the result of an “electrochemical annealing” process of the bromidecovered and tip-modified Cu(110) surface shown in Figure 57.54, namely the formation

of bromide-covered Cu(100) facets. (b) Cross section of the hard sphere model in (a) showing possible adsorption sites within a furrow. (Source: Obliers et al. 2004 [496]. Reproduced with permission of Elsevier.)

4

B A J (μA/cm2)

1

3

2

6

5

4 8

9

7

12

D

11

C 10 Cu(110)/HCl 10 mM –600

–550

–500

–450

–400

–350

–300

–250

Electric potential (mV) Figure 57.56 Cyclic voltammogram of a Cu(110) surface in 10 mM HCl solution. A and B indicate chloride-related adsorption/reconstruction processes, C and D the corresponding deconstruction/desorption processes whose effects on the surface

structure are displayed in Figure 57.57. The numbers 1–12 along the CV refer to the potential regime in which the corresponding image in Figure 57.57 has been registered. (Source: Goletti et al. 2015. [504]. Reproduced with permission of ACS Publications.)

587

588

57 Metal–Electrolyte Interfaces: An Atomic View

decays and gives rise to the pronounced peak C. In particular, the ongoing reaction above peak B rules against a mere adsorption, and indeed, the following in situ STM and RAS results support the notion of a massive restructuring [504, 505]. Figure 57.57, panel 1, shows the anion-free Cu(110) surface at a constant electrode potential of −605 mV (Ag/AgCl) with atomic resolution and the expected unit cell with a = 2.54± 0.04 Å and b = 3.66 ± 0.04 Å [500] (see also Figure 57.2a). A few noisy, bright spots with atomic dimensions and heights of the order of about 1 Å float on the surface, as concluded from consecutive images. The spots have been assigned to mobile [CuCl2 ]− species formed with highly reactive low-coordinated Cu atoms at defect sites (the basis of “electroannealing”; see Section 57.2.3.3.5) at low potentials or to residues from the decay of those products formed at potentials beyond peak B (see below). The selected STM images in panels 2–12 in Figure 57.57 are taken in potentiodynamic mode, i.e. during a full cycle of the electrode potential [504]; the numbers of the panels refer to those along the CV in Figure 57.56. The potential interval in which the respective image was registered is indicated next to each panel. The spotty appearance of the large terrace in panel 2 is ascribed to an enhanced formation of mobile [CuCl2 ]− species compared to panel 1 taken at lower potential. The bottom of panel 2 corresponds to the onset of peak A in Figure 57.56 and shows, starting from step edges, the beginning formation of long dark channels of –605 mV

–550 mV

–500 mV –500 mV

–450 mV –350 mV

–300 mV

1

2

3

6

12

11

10

7

a b

Cu(110)

– [110] [001]

–605 mV –550 mV

–550 mV –500 mV

Figure 57.57 In situ STM images of a Cu(110) surface in 10 mM HCl solution. The selected numbers 1–12 refer to the numbered potential regime along the CV on Figure 57.56. Image 1 (4 nm × 4 nm, E = −605 mV vs. Ag/AgCl) is dominated by bare Cu(110) regions, whereas all other

–500 mV –450 mV

–350 mV –300 mV

images (81 nm × 81 nm, E given next to each image) show drastic structural changes with increasing and redecreasing potential (see text). (Source: Goletti et al. 2015. [504]. Reproduced with permission of ACS Publications.)

57.3 Adsorption of Anions

up to several tens of nanometers aligned along the [001] surface direction (arrow in panel 2). Their sharp boundaries are reminiscent of the chloride-stabilized step edges on the Cu(100) surface as discussed in Section 57.3.2.3. The formation of these furrows is very fast, and already at the top of panel 3, i.e. close to the onset of peak B in Figure 57.56 (−470/480 mV), their massive development is accompanied by the formation of bright stripes, all well aligned along the [001] direction. Passing the second anodic current peak (B) in Figure 57.56, the number and density of stripes increase until near the end of the positive scan (−300 mV, panel 6) and the restructuring process slows down. The stripes are up to several tens of nanometers long and 2–3 nm wide; some stripes have overgrown lower ones, and height profiles perpendicular to the stripes and channels suggest facets with an inclination angle of 18∘ ± 3∘ [504, 505]. Ex situ XPS Cl 2p spectra taken at this stage prove the presence of chloride on the surface. LEED images showing additional stripes in the [110] direction [504] are in agreement with earlier results of Stickney [506] and consistent with the anisotropic morphology seen in the in situ STM images (Figure 57.57, panels 3 and 6). Panels 7–12 in Figure 57.57 show the STM images registered along the negative scan direction in Figure 57.56 [504]. Basically, no change can be seen between panels 7 and 10 until below ∼−500 mV (onset of the desorption peak D), and the stripes suddenly disappear and large spotty copper (110) terraces with fuzzy steps edges reappear again showing mobility at the surface. The persistence of a few channels near −550 mV in panel 11 in contrast to the top of panel 2 implies a different kinetics of the defaceting compared to the faceting process. Below −550 mV, the original clean copper surface including some mobile spots is completely restored (panel 12). This chloride-induced surface restructuring is expected to also perturb the electronic states at the metal/liquid interface. The inherent anisotropy of the bare Cu(110) surface and even more so that of the new chloride-induced structure suggest to follow the restructuring process by means of RAS. Using linearly polarized light, RAS measures the difference in reflection along different surface directions. The inset in Figure 57.58 shows the difference between the spectra taken at E = −600 and −300 mV (Ag/AgCl) along the orthogonal [110] and [001] directions of the bare and chloride-covered Cu(110) surface, respectively. The pronounced maximum at h𝜈 = 2.5 eV can be taken as an optical fingerprint of the restructured surface [503–505] and can, thus, be used to follow the restructuring process as a function of the electrode potential. Figure 57.58 displays a plot of the height of this maximum of the ΔRAS signal measured again at the same electrode potentials labeled by the numbers along the CV in Figure 57.56. In the positive scan direction, the signal starts to rise at the onset of the first adsorption peak A in Figure 57.56. The slope changes once the second peak B has been passed at about −425 mV and is still not zero at −300 mV, indicating that the restructuring process continues at this electrode potential. In the reverse potential scan, the ΔRAS intensity remains basically constant until −480 mV and then decreases rather sharply reflecting the evolution of the morphological changes seen in the STM images in Figure 57.57. Like STM, this “optical tracking” also indicates a slow formation and a more abrupt lifting of the chloride-induced grooves and stripes. This hysteresis manifests itself

589

57 Metal–Electrolyte Interfaces: An Atomic View 3

10

2.0

ΔRAS (×10–3)

2.5

ΔRAS (×10–3)

590

9

8

7

11

2

1

6 0

1.5

5 –1

2

3

4

Energy (eV)

1.0

12 4

0.5 3

0.0 1 –800

–700

2

–600

–500 Potential (mV)

Figure 57.58 Plot of the intensity of a 𝛥RAS signal (see inset) at the fixed photon energy of 2.5 eV as a function of electrode potential (vs. Ag/AgCl). The numbers 1–12 along the hysteresis loop refer to the numbers along the cyclic voltammogram in Figure 57.56 as well as the corresponding STM images in

–400

–300

Figure 57.57. The two arrows indicate the scan direction of the electrode potential. The steep flank marked in red relates to the corresponding one in Figure 57.59. (Source: Goletti et al. 2015. [504]. Reproduced with permission of ACS Publications.)

immediately as a function of time by switching the potential repeatedly between −500 and −550 mV, i.e. the top and the bottom of the hysteresis loop as displayed in Figure 57.59 [504]. In summary, the combined CV, in situ STM, and RAS data support the following scenario: at potentials below −550 mV, step edges are fuzzy and chloride anions start to react with individual, particularly low-coordinated Cu surface atoms, forming mobile [CuCl2 ]− complexes, which show up as mobile bright spots in the STM images. Beyond −550 mV, the enhanced consumption of copper surface atoms leads to the appearance of missing copper rows (dark grooves in the STM images), which, starting at ∼500 mV, is accompanied by the formation of added rows and stripes (bright) of predominantly monoatomic height. The added rows first grow in number and length until higher (brighter) layers start to appear. In particular, this thickening beyond −420 mV is a slower process, which, however, continues even at −300 mV as suggested by the elevated current in the CV cycle above peak B as well as by the nonzero slope of the ΔRAS signal at −300 mV in positive scan direction. This ongoing reconstruction process at all potentials above peak B explains the sharp and huge “desorption/decay” peak C in the negative scan. All data together show that the decomposition of the chlorination product and the lifting of the surface reconstruction are faster than the chlorination process in the positive scan direction

ΔRAS (10–3) 3.0 2.5 2.0

–500 mV

–500 mV

1.5 1.0

–550 mV

–550 mV

0.5 0.0 –0.5 0

180 360 540 720 900 1080 1260 1440 1620 Time (s)

Figure 57.59 𝛥RAS signal from a Cu(110) electrode surface in 10 mM HCl solution as a function of time between repeated potential steps from E = −500 to −550 mV (Ag/AgCl) and from E = −550 mV back to E = −500 mV. The corresponding STM images (81 nm × 67.8 nm) display the concomitant structural changes. The different slope of the 𝛥RAS decrease (red) and reincrease is reflected in the different abruptness of structural changes seen in the STM images.

57 Metal–Electrolyte Interfaces: An Atomic View

because a growing thickness of the surface compound limits the outward diffusion of copper ions. The same restructuring was observed for a Cu(110) surface subjected to high exposures of molecular Cl2 in UHV [502], and the observed inclination angle of 18± 3∘ of the added stripes was interpreted in terms of the formation of (210) facets. Obviously, a similar faceting of the Cu(110) surface also occurs in solution at potentials >−510 mV. In solution, however, this restructuring is easily and fully reversible. 57.3.2.8 Surface Compound Formation: Copper Iodide

The previous sections dealt largely with the mere adsorption of chloride, bromide, and iodide anions on the copper electrode surfaces. Depending on the reactivity of the substrate, e.g. of Cu(110) vs. Cu(100) and Cu(111), or of defect sites, some restructuring of the first copper layer was already observed, namely in the form of the “electrochemical annealing” effect or in the form of the faceting of the Cu(110) surface in the presence of chloride and bromide anions. These effects are the stronger, the more positive the applied potential. Therefore, next we will address the influence of the halide anions on the copper dissolution process itself. Only in the case of iodide, this leads to the formation of stable copper iodide surface films, while the formed copper chloride and copper bromide compounds go into solution. Figure 57.60 shows cyclic voltammograms of Cu(111) in both the blank and a 0.1 mM KI containing 5 mM H2 SO4 electrolyte [507]. Between the anodic CDR, and the cathodic HER, a pair of additional peaks P at −30 mV and P′ at −200 mV in the blank H2 SO4 solution represents the adsorption and desorption of SO4 2− anions, which will be discussed in great detail in Section 57.3.6.1. In the iodide-containing CDR

P1-2 Current density (μA/mm2)

592

2

3 P

1

2

0

HER

P′ P′3

–2

P′1

CRR

P′2 –400

–200

200

0

400

E vs. RHE (mV) Figure 57.60 Cyclic voltammogram of a Cu(111) surface in pure 5 mM H2 SO4 (black curve) and in 5 mM H2 SO4 + 1 mM KI solution (gray curve). Sweep rate v = 10 mV/s.

The different peaks P and the arrows 1–3 are explained in the text. (Source: Hai et al. 2007 [507]. Reproduced with permission of ACS Publications.)

57.3 Adsorption of Anions

electrolyte, the adsorption/desorption peaks of sulfate anions on Cu(111) are fully suppressed, the iodide anions displace adsorbed sulfate anions, and cause the large and broad anodic peak system denoted as P1-2 centered at +214 mV close to the onset of the CDR. The corresponding cathodic peaks are P1 ′ at +100 mV, P3 ′ at +40 mV, and P2 ′ at −110 mV. The correlation of the corresponding peak pairs P1 /P1 ′ and P2 /P2 ′ is possible by measuring a series of cyclic voltammograms with constant cathodic but increasing anodic limit as shown in Figure 57.61 [507]. The signal P1-2 arises from the formation of a two-dimensional CuI surface film (see below). A very similar voltammetric behavior was also reported for Cu(100) exposed to the same electrolyte [508], for polycrystalline copper in an iodide-containing electrolyte at pH = 9.2 [509], and for Cu(111) exposed to an iodide-containing perchloric acid solution [510]. Obviously, the crystallographic orientation of the substrate and the nature of the supporting electrolyte have little influence on the underlying reaction between iodide and copper; however, the structure of the formed CuI film is found to depend on the substrate symmetry as described in detail below. After passing the signal P1-2 , the anodic current does not drop to zero (Figure 57.60), indicating that the underlying reaction is slowed down but not P1

CV 1

P′1 P1

CV 2 P′2

P2 P1 P′3 P′2 P′1 P2 P1

CV 3 CV 4 Current density

P′1

P′2 P′3P′1

CV 5

P′2

CV 6

P1-2

CDR

P′3 P′1 P′3 P′1

P1-2

P′2

CV 7 P′3 P′1 P1-2 P′2

CV 8 2 μA/mm2 P′2

–400

–200

0

200

400

E vs. RHE (mV) Figure 57.61 Formation of solid CuI on a Cu(111) electrode surface in 5 mM H2 SO4 + 1 mM KI solution upon gradual shifting of the anodic limit to more positive

potentials, which enables a correlation of anodic and cathodic peaks P (see text). (Source: Hai et al. 2007 [507]. Reproduced with permission of ACS Publications.)

593

594

57 Metal–Electrolyte Interfaces: An Atomic View

fully suppressed. Obviously, the grown CuI film does not effectively passivate the copper electrode against further anodic reaction, only the copper dissolution is shifted by about 60 mV to higher potentials compared to the situation in the pure supporting H2 SO4 electrolyte. The three cathodic current peaks P′ 1, P′ 2, and P′ 3 correspond to the electroreduction and dissolution of the various previously formed solid CuI phases. Although the origin of the current wave P′ 3 in Figure 57.60 is unclear [508], the two other peaks have been assigned to two different solid CuI phases, which differ in their structural relationship to the copper substrate surface (see below) [508]. The first CuI phase exhibits only a small potential hysteresis between its formation and dissolution (P1 /P1 ′ ) and has a close structural relationship to the underlying copper surface, whereas the second phase with a significantly larger hysteresis between growth and dissolution (P2/P′ 2) consists of CuI clusters with a loose structural relationship to the electrode surface. This interpretation is based on in situ STM measurements described next. By sweeping the electrode potential to values E >120 mV, i.e. beyond formation of the uniaxially compressed layer of adsorbed iodide with the wavy long-range superstructure shown in Figure 57.30c, drastic change of the surface structure occurs as shown in Figure 57.62. Figure 57.62a shows six terraces (T1–T6) of a Cu(111) surface covered with the saturated and uniaxially compressed iodide layer at E = +122 mV as shown in Figure 57.30. Already at +130 mV (Figure 57.62b), the copper steps start to recede with time, indicating some ongoing surface reaction. For comparison, in pure H2 SO4 , copper dissolution starts only at about E = +280 mV as seen in Figure 57.60. The white dashed lines in each image of Figure 57.62 indicate the position of the respective step in the preceding image. In panel (c), all of a sudden, from one scan line to the next (arrow), a new phase appears in the STM image because of the rapid nucleation and growth of a 2D CuI film. Copper terraces that are (a) T6 T5

M

T4

(c) T6

(b) T6 M

T5

T3

T4 T3

M

T5 T4

T3 T2 T2

T2 T1

Figure 57.62 Successive in situ STM images of a Cu(111) surface in 5 mM H2 SO4 + 1 mM KI solution showing copper dissolution at steps (a, b) and the sudden growth of a two-dimensional CuI film (c). T1–T6 denote terraces with adsorbed iodide (see inset in panel a) and Figure 57.39), whereas T1′ and T2′ denote terraces covered with a 2D CuI compound film; note the sudden change from T2 to T2′ (white arrow).

T2′ T1

T1′ 2D-Cul

2D-Cul

M marks a stationary defect. The white dashed lines indicate the position of the step edges in the preceding image. All images 109 nm × 109 nm. (a) E = +122 mV, (b, c) E = +130 mV (Ag/AgI). Recording time per image 16.8 seconds with no delay between the images. (Source: Hai et al. 2007 [507]. Reproduced with permission of ACS Publications.)

57.3 Adsorption of Anions

already covered with this 2D CuI film are labeled by T1′ and T2′ , whereas terraces still covered by merely adsorbed iodide are denoted with T1–T6. From Figure 57.62, it becomes evident that the dissolution processes at steps and the nucleation of this 2D CuI film are correlated with each other: depending on the applied potential, an equilibrium concentration of intermediate mobile cuprous species, namely CuI or [CuI2 ]− monomers (in the following simply denoted CuI), is assumed to exist on the surface [509, 511] in equilibrium with step edges that are the sources for these species. The concentration of these intermediate species on the terraces increases with increasing potential until their solubility product is exceeded, which results in the surface-confined nucleation and growth of the 2D CuI film [508]. Shortly after its formation, the film exhibits a high defect density (black pits in in Figure 57.62c), which, however, because of post-growth ripening processes, decreases with time, leading to a highly ordered 2D CuI film. The sudden growth of the 2D film is accompanied by a step along the white line in Figure 57.62c of height dCuI = 0.35±0.015 nm. This value agrees with the spacing of 0.35 nm between iodide layers along the (111) direction in crystalline zinc blende-type CuI. Figure 57.63 represents an I–Cu–I triple layer of this bulk CuI phase parallel to a (111) plane including one central layer of cuprous ions “sandwiched” between two iodide layers. The same stacking sequence is proposed for the 2D CuI film formed on Cu(111) in Figure 57.62c [507], such that the 2D CuI film consists of a layer of CuI species on top of the pre-existing layer of adsorbed iodide. In this picture, the a priori specifically adsorbed iodide does not form a chemical bond with the mobile cuprous CuI species but serves as a chemically inert structural template for the CuI layer formation on top. All dynamics (growth, ripening, and decay) as observed in the STM images solely affects the CuI overlayer on top of the preadsorbed iodide layer. The same explanation was also given by Andryushechkin et al. for 2D CuI films grown on Cu(100) and Cu(111) under UHV conditions by dissociative iodine adsorption [512]. First, strong support for this model of the 2D CuI film “floating” on the iodide precovered Cu(111) surface comes here from an analysis of the in-plane structure and from a similar behavior on iodide precovered Cu(100) [513]. As shown in Figure 57.64a,b the 2D film on Cu(111) is characterized by a pseudo-hexagonal Moiré pattern, which arises from a mismatch between the CuI overlayer and the underlying monolayer of adsorbed iodide, and not from a mismatch between the complete I–Cu–I triple layer and the Cu(111) surface lattice. The corrugation amplitude of the Moiré pattern amounts to 0.05 ± 0.01 nm, which is about 1 order

2D CuI film

Iad

0.2625 nm 0.0875 nm

Figure 57.63 I–Cu–I triple layer in bulk CuI parallel to the (111) plane of face-centered cubic crystalline CuI (zinc blende type), with interlayer distances.

595

596

57 Metal–Electrolyte Interfaces: An Atomic View

(a)

(b)

Figure 57.64 In situ STM images of the 2D CuI film on I-precovered Cu(111) in 5 mM H2 SO4 + 1 mM KI solution. (a) Moiré-type pseudo-hexagonal long-range height modulation of the 2D CuI film, 27 nm × 27 nm, E = +125 mV vs. Ag/AgI; (b) high-resolution image of the CuI film, enabling correlation between the atomic-scale structure and

(c)

the Moiré superstructure, 12 nm × 12 nm, E = +125 mV. (c) Even higher resolution image of the CuI film showing clearly dislocations in the CuI film (follow white dashed lines), 5.2 nm × 5.2 nm, E = +125 mV. (Source: Hai et al. 2007 [507]. Reproduced with permission of ACS Publications.)

of magnitude larger than the corrugation amplitude of the wavy long-range height modulation of the uniaxially incommensurate layer of preadsorbed iodide in Figure 57.30c. Distances between the almost hexagonally arranged Moiré maxima in Figure 57.64a vary between 4.3 and 5.8 nm. This broad distribution of distances points to an imperfect long-range order within this CuI overlayer. On the atomic scale, the CuI overlayer also reveals an almost hexagonal arrangement (Figure 57.64b) of the terminating iodide layer. The iodide nearest-neighbor distance is 0.41 ±0.03 nm, very similar to the interatomic spacing of iodide and cuprous ions of 0.4287 nm within the (111) plane of bulk CuI. The observed slight compression of the CuI overlayer with respect to an ideal (111) plane of bulk CuI may originate from a “template” effect of the pre-existing iodide underlayer [507]. As indicated above, a very similar 2D CuI film was also found on iodide-precovered Cu(100) under UHV and electrochemical conditions [512, 513]. As this film also shows hexagonal packing, its mismatch with the quadratic structure of the Cu(100) substrate, of course, results in the different long-range superstructure as shown in Figure 57.64c,d and supports the notion of a weak interaction with the preadsorbed iodide layer, which in this case has c(p × 2) symmetry. A similar effect was also reported for an epitaxial pseudo-hexagonal CuBr(111) film on a square c(2 × 2)-Br template structure [514]. Strongest support for the weak chemical interaction between the “floating” 2D CuI film on the pre-adsorbed iodide layer comes from ex situ XPS measurements presented below. At the same time, these XPS results also shine light on the product, which forms beyond completion of the 2D CuI film at higher potentials. The 2D CuI film is stable only within a narrow potential window ranging from about +100 to +125 mV. At higher electrode potential, the copper dissolution continues despite the presence of the 2D CuI film as evidenced by the high current in the CV beyond P1-2 (Figure 57.60). This ongoing copper dissolution leads to the nucleation and growth of 3D CuI clusters of enormous height on both Cu(111) and Cu(100) (Figure 57.65). These STM images, of course, do not reveal whether the

57.3 Adsorption of Anions

(b)

z-Corrugation (nm)

(a)

10 13 nm 5

0

2D-CuI 0

Figure 57.65 (a) In situ STM image of 3D CuI clusters, 293 nm × 293 nm. The image was registered at E = +120 mV after the potential had shortly been raised beyond peak P1-2 in Figure 57.60 (arrow 3), the arrows point

50

150 100 Distance (nm)

200

to regions (dark) of still 2D CuI structure. (b) Height profile along the white line in panel (a). (Source: Hai et al. 2007 [507]. Reproduced with permission of ACS Publications.)

3D CuI clusters grow directly on top of the 2D CuI film or nucleate in the surface near solution layer followed by precipitation onto the 2D CuI film. Their rather irregular appearance and distribution on the surface, however, seems to support the latter process. In any case, a disruption of the 2D CuI film after appearance of the 3D CuI growth on top can be excluded because atomically flat areas between the clusters still show the structure of the 2D CuI film. The 2D CuI film grown on the pre-existing iodide adsorbate can thus be regarded as a thermodynamically stable “wetting layer” [507]. Definitive evidence for the different chemical nature of the pre-existing iodide adlayer, the thermodynamically stable CuI overlayer on top, and the ultimate formation of 3D CuI clusters comes from ex situ XPS measurements [515] (see also Chapter 3.2.2 in Volume 1). For these experiments, the copper sample was emersed from the electrochemical environment at three different potentials (see arrows 1–3 in Figure 57.60) being characteristic for the presence of (1) the adsorbed iodide layer (Eemersion = −100 mV), (2) the 2D CuI film (Eemersion = +125 mV), and (3) the 3D CuI clusters (Eemersion = +175 mV). In the latter case, before emersion, a potential higher than P1-2 was applied in order to produce the 3D CuI clusters before the sample was removed from the electrolyte at +175 mV and transferred into the analytical UHV chamber (see Section 57.2.3.3.5; UHV-EC transfer). The photoelectron spectra were registered with synchrotron radiation (BESSY, Berlin) of different photon energy in order to vary the escape depth of the photoelectrons and thereby the information depth [507]. Very small oxygen (O 1s ≈ 529–535 eV) and the absence of sulfur (S 2p ≈ 164.5–170.5 eV)-related signals in wide survey spectra (not shown here) clearly indicate that the surfaces is free of undesired remnants of the electrolyte. A detailed

597

57 Metal–Electrolyte Interfaces: An Atomic View

350

hν = 245 eV I 4d5/2

300

I 4d3/2 II

Intensität (× 103)

598

250 (3)

I

200

3D-CuI cluster II

150 (2)

I 2D-CuI film

100 I

(1)

50

I Adsorbate 54

53

52

51

50

49

48

47

Binding energy (eV) Figure 57.66 XPS spectra monitored with synchrotron radiation of E photon = 720 eV after the sample was emersed from the 5 mM H2 SO4 + 1 mM KI solution at (1) E = −100 mV (I adsorbate), (2) E = +125 mV (2D CuI film), and (3) E = +175 mV (3D CuI clusters). The enlarged I(4d5/2,3/2 ) spectra

taken with the more surface-sensitive photon energy of E photon = 245 eV clearly distinguish between adsorbed (1) and iodine incorporated in the 2D film or 3D clusters (2, 3). (Source: Hai et al. 2007 [507]. Reproduced with permission of ACS Publications.)

analysis of the I 4d, as well as Cu 3p photoemission, and I M4 N4,5 N4,5 -Auger signals (only the I 4d lines are shown in Figure 57.66) yields the following picture: all data enable a clear distinction between the adsorbed iodide (in contact with the copper substrate) and an iodine species in the 2D/3D overgrowth. The I 4d emission (Figure 57.66) of the adsorbed layer (1) indicates just one single iodine component with a spin–orbit splitting of 1.70 eV and the 4d5/2 maximum at EB = 49.27 eV. Both values are in excellent agreement with results obtained after dissociative iodine adsorption in UHV [516]. Conversely, the I 4d emission of the 2D CuI film (2) is a superposition of two components (Figure 57.66). Component I corresponds to the adsorbed iodide species in direct contact with the metallic copper surface, whereas component II is shifted by ΔE = 0.5 eV to higher binding energy and attributed to the terminating iodide species of the I–Cu–I triple layer with the I 4d5/2 peak maximum at EB = 49.90 eV [507]. It is interesting that the I 4d emission originating from the 3D CuI clusters (spectrum (3)) can be fitted with the same two components as the spectrum of the 2D film. The persistence of the component at EB = 49.27 eV points to cluster-free patches still exposing the 2D film, which was also concluded from the STM observations in Figure 57.65. For completion, it shall be mentioned that also an analysis of the

57.3 Adsorption of Anions

I M4 N4,5 N4,5 Auger spectra of adsorbed iodide, the 2D film and the 3D clusters, yields the same picture, namely a clear distinction between adsorbed I on the metal electrode and just one further iodide species bound within either the 2D CuI film or the 3D CuI clusters. A similar surface compound formation with Cl− and Br− on copper is not found because of the higher solubility of the relevant compounds. Table 57.7 summarizes the relevant values of solubility products. 57.3.3 Adsorption of Sulfide Anions

The interaction of sulfur or sulfur-containing species with metal surfaces is strong and plays an important role in many areas of materials science and technology. Investigations on the adsorption of elemental sulfur in UHV are often motivated by the role of sulfur being a catalyst poison [517–519]. The electroless adsorption of sulfur-containing molecules from solution serves to prepare self-assembled monolayers (SAMs), e.g. from thiols [520, 521], and plays a role in mineral processing by flotation [522, 523]. SAM layers are applied to modify the physicochemical properties of surfaces, for instance, to improve their hydrophobicity, friction behavior, and corrosion resistance, and serve as a material for masks in the production of nanostructures. The electrochemistry of sulfide anions with metal surfaces has been studied in the context of corrosion [524, 525] and, more recently, the fabrication of light-emitting, photovoltaic, and optoelectronic devices. In this context, the electrochemical atomic layer epitaxy (ECALE) was introduced by Stickney et al. [526]. Compared to the halide anions, the interfacial electrochemistry of sulfide is per se more complex because sulfide (and the other chalcogenides, selenium and tellurium) anions not only react with the substrate but also with themselves by forming dimers, trimers, rings, and chains. Similar to the pH-dependent equilibrium between HSO4 2− and SO4 2− anions (see Section 57.3.6.1), sulfide (S2− ) anions will also be in equilibrium with hydrosulfide (HS− ) in acidic solution. For the sake of simplicity, however, we will use only the term “sulfide” in the following. Table 57.7 Selected solubility products. Compound

Solubility product K L (mol2 /l2 )

CuCl CuBr CuI

1 × 10−6 4 × 10−8 5 × 10−12

ETH Zürich – Experiments on the Internet (Prof. Dr. Reinhard Nesper) http://www.cci.ethz.ch/vorlesung/de/al1/node38.html

599

57 Metal–Electrolyte Interfaces: An Atomic View

Here, we present results on the interaction of sulfide anions with a Cu(111) and Cu(100) electrode in sulfuric acid solution as worked out by Spänig [527]. In both cases, the more strongly interacting sulfide anions displace any adsorbed sulfate species, but because of their different atomic density, both surfaces show a very different electrochemical behavior toward sulfide anions. Although sulfide interaction with the more densely packed Cu(111) surface leads to a series of reversible true adsorption phases leaving the substrate surface unreconstructed, adsorption of sulfide on the more open Cu(100) surface causes a massive restructuring of the surface, very much reminiscent of the interaction of chloride with Cu(110) compared to Cu(100) and Cu(111). 57.3.3.1 Sulfide – Cu(111)

Figure 57.67 displays the cyclic voltammogram of a Cu(111) electrode in pure and 1 mM Na2 S containing 0.5 mM H2 SO4 solution, respectively, together with in situ STM images of different sulfide structures registered in the indicated potential ranges [527]. The gray curve shows the relevant part of the cyclic voltammogram of Cu(111) in pure sulfuric acid with the characteristic SO4 2− adsorption (Ads.) and desorption (Des.) peaks (see Section 57.3.6.1). Both these signals are completely missing in the black cyclic voltammogram measured in the S2− -containing solution, suggesting the replacement of adsorbed SO4 2− anions by sulfide (S2− ) anions. Verification of this displacement comes again from ex situ XPS. Figure 57.68 shows spectra in the regime of the S(2p) emission with the 2p3/2 spin–orbit component

(a)

Current density (μA/cm2)

600

(b)

(c)

(2√7 × 2√7)R19.1° (√7 × √7)R19.1°

Moiré

“Chains”

Ads.

1

4

HER

–800

(d)

–600

3

2

Des.

–200 –400 Potential vs. RHE (mV)

0

200

Figure 57.67 Cyclic voltammograms of a Cu(111) electrode in pure 0.5 mM H2 SO4 (gray curve) and 1 mM Na2 S containing 0.5 mM H2 SO4 (black curve) solution, together with sulfide-induced surface structures displayed in the potential regimes of their appearance.

57.3 Adsorption of Anions

(c)

Intensity

170

168

166

(b)

Cu(111)/S2– S2–(1) S2–(2) Cu(111)/SO42–

(a) SO42– SO32– 172

170

162 168 166 164 Binding energy (eV)

Figure 57.68 XPS S(2p) spectra of a Cu(111) electrode emersed from (a) pure 0.5 mM H2 SO4 and (b) 1 mM Na2 S containing 0.5 mM H2 SO4 solution. The two sulfide components S2− (1) and S2− (2) may possibly be assigned

160

158

to the two species (1) and (2) within the unit cell in Figure 57.69b. (c) The evolution of the SO4 2− /SO3 2− related 2p emission as a function of irradiation time; E photon = 245 eV.

for SO4 2− (167.9 eV), SO3 2− (166.2 eV), and S2− (161.5 eV and 162.7 eV). After emersion from pure sulfuric acid, the spectrum indicates dominantly adsorbed SO4 2− anions. The small contributions from SO3 2− and S2− arise from radiation damage effects as verified by the series of spectra in the inset registered over a period of 16 minutes, in which the SO4 2− signal continuously decreases while the SO3 2− (and S2− ) signal increases [528]. This “dry reduction” caused by the emitted, mainly secondary, electrons was also observed with other adsorbates (see Section 57.5.2.3). The spectrum registered after emersion from the S2− -containing sulfuric acid exhibits no more signals of SO4 2− /SO3 2− , but signals from two S2− species of rather different intensities, a dominant one with EB (2p3/2 ) = 161.5 eV and a minor one with EB (2p3/2 ) = 162.7 eV. The origin of the latter one is not totally clear yet but may originate from S2− anions adsorbed at defects or possibly from an S2− species incorporated between the first and second copper layer (see below). Besides a shift of the HER by ∼300 mV to more negative potential in Figure 57.67 because of a higher pH value of the mixed (SO4 2− , S2− ) solution, the black trace shows one anodic peak (1) at −120 mV (RHE) and three weak current waves (2, 3, 4) at 20, −310, and −480 mV (RHE). Starting at most positive potentials, the in situ STM images first show a sulfide-induced “chain” structure, which with poten√ decreasing √ ∘ , and tial and sulfide coverage transforms gradually into a Moiré, a ( 7 × 7)R19.1 √ √ ∘ finally a (2 7 × 2 7)R19.1 structure. At very negative potentials (