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Movassaghi CS, Alcañiz Fillol M, Kishida KT, McCarty G, Sombers LA, Wassum KM, Andrews AM. Maximizing Electrochemical Information: A Perspective on Background-Inclusive Fast Voltammetry. Anal Chem 2024; 96:6097-6105. [PMID: 38597398 PMCID: PMC11044109 DOI: 10.1021/acs.analchem.3c04938] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2023] [Revised: 03/07/2024] [Accepted: 03/08/2024] [Indexed: 04/11/2024]
Abstract
This perspective encompasses a focused review of the literature leading to a tipping point in electroanalytical chemistry. We tie together the threads of a "revolution" quietly in the making for years through the work of many authors. Long-held misconceptions about the use of background subtraction in fast voltammetry are addressed. We lay out future advantages that accompany background-inclusive voltammetry, particularly when paired with modern machine-learning algorithms for data analysis.
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Affiliation(s)
- Cameron S. Movassaghi
- Department
of Chemistry and Biochemistry, University
of California, Los Angeles, Los Angeles, California 90095, United States
| | - Miguel Alcañiz Fillol
- Interuniversity
Research Institute for Molecular Recognition and Technological Development, Universitat Politècnica de València-Universitat
de València, Camino de Vera s/n, Valencia 46022, Spain
| | - Kenneth T. Kishida
- Department
of Translational Neuroscience, Wake Forest
School of Medicine, Winston-Salem, North Carolina 27101, United States
- Department
of Neurosurgery, Wake Forest School of Medicine, Winston-Salem, North Carolina 27101, United States
| | - Gregory McCarty
- Department
of Chemistry, North Carolina State University, Raleigh, North Carolina 27695, United States
| | - Leslie A. Sombers
- Department
of Chemistry, North Carolina State University, Raleigh, North Carolina 27695, United States
- Comparative
Medicine Institute, North Carolina State
University, Raleigh, North Carolina 27695, United States
| | - Kate M. Wassum
- Department
of Psychology, University of California,
Los Angeles, Los Angeles, California 90095, United States
- Brain Research
Institute, University of California, Los
Angeles, Los Angeles, California 90095, United States
- Integrative
Center for Learning and Memory, University
of California, Los Angeles, Los
Angeles, California 90095, United States
- Integrative
Center for Addictive Disorders, University
of California, Los Angeles, Los
Angeles, California 90095, United States
| | - Anne Milasincic Andrews
- Department
of Chemistry and Biochemistry, University
of California, Los Angeles, Los Angeles, California 90095, United States
- Brain Research
Institute, University of California, Los
Angeles, Los Angeles, California 90095, United States
- Department
of Psychiatry and Biobehavioral Science, University of California, Los Angeles, Los Angeles, California 90095, United States
- Hatos Center
for Neuropharmacology, University of California,
Los Angeles, Los Angeles, California 90095, United States
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2
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Consolidated designer waveform for maximizing analytical output of voltammetric measurements for complex chemical matrices. J Electroanal Chem (Lausanne) 2023. [DOI: 10.1016/j.jelechem.2023.117332] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/19/2023]
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3
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Chen H, Li D, Kätelhön E, Miao R, Compton RG. Experimental Voltammetry Analyzed Using Artificial Intelligence: Thermodynamics and Kinetics of the Dissociation of Acetic Acid in Aqueous Solution. Anal Chem 2022; 94:5901-5908. [PMID: 35381175 PMCID: PMC9082489 DOI: 10.1021/acs.analchem.2c00110] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
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Artificial intelligence (AI) is used
to quantitatively analyze
the voltammetry of the reduction of acetic acid in aqueous solution
generating thermodynamic and kinetic data. Specifically, the variation
of the steady-state current for the reduction of protons at a platinum
microelectrode as a function of the bulk concentration of acetic acid
is recorded and analyzed giving data in close agreement with independent
measurements, provided the AI is trained with accurate and precise
knowledge of diffusion coefficients of acetic acid, acetate ions,
and H+.
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Affiliation(s)
- Haotian Chen
- Department of Chemistry, Physical and Theoretical Chemistry Laboratory, Oxford University, South Parks Road, Oxford OX1 3QZ, Great Britain
| | - Danlei Li
- Department of Chemistry, Physical and Theoretical Chemistry Laboratory, Oxford University, South Parks Road, Oxford OX1 3QZ, Great Britain
| | - Enno Kätelhön
- MHP Management- und IT-Beratung GmbH, Königsallee 49, 71638 Ludwigsburg, Germany
| | - Ruiyang Miao
- Department of Chemistry, Physical and Theoretical Chemistry Laboratory, Oxford University, South Parks Road, Oxford OX1 3QZ, Great Britain
| | - Richard G Compton
- Department of Chemistry, Physical and Theoretical Chemistry Laboratory, Oxford University, South Parks Road, Oxford OX1 3QZ, Great Britain
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4
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Alvarado JIM, Meinhardt JM, Lin S. Working at the interfaces of data science and synthetic electrochemistry. TETRAHEDRON CHEM 2022; 1. [PMID: 35441154 PMCID: PMC9014485 DOI: 10.1016/j.tchem.2022.100012] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Electrochemistry is quickly entering the mainstream of synthetic organic chemistry. The diversity of new transformations enabled by electrochemistry is to a large extent a consequence of the unique features and reaction parameters in electrochemical systems including redox mediators, applied potential, electrode material, and cell construction. While offering chemists new means to control reactivity and selectivity, these additional features also increase the dimensionalities of a reaction system and complicate its optimization. This challenge, however, has spawned increasing adoption of data science tools to aid reaction discovery as well as development of high-throughput screening platforms that facilitate the generation of high quality datasets. In this Perspective, we provide an overview of recent advances in data-science driven electrochemistry with an emphasis on the opportunities and challenges facing this growing subdiscipline.
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5
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Fenton Jr. AM, Brushett FR. Using voltammetry augmented with physics-based modeling and Bayesian hypothesis testing to identify analytes in electrolyte solutions. J Electroanal Chem (Lausanne) 2022. [DOI: 10.1016/j.jelechem.2021.115751] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
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6
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Gundry L, Kennedy G, Bond AM, Zhang J. Inclusion of multiple cycling of potential in the deep neural network classification of voltammetric reaction mechanisms. Faraday Discuss 2021; 233:44-57. [PMID: 34901986 DOI: 10.1039/d1fd00050k] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/26/2023]
Abstract
The use of deep neural networks (DNNs) for the classification of electrochemical mechanisms using simulated voltammograms with one cycle of potential for training has previously been reported. In this paper, it is shown how valuable additional patterns for mechanism distinction become available when a new DNN is trained simultaneously on images obtained from three cycles of potential using tensor inputs. Significant improvements, relative to the single cycle training, in achieving the correct classification of E, EC1st and EC2nd mechanisms (E = electron transfer step and C1st and C2nd are first and second order follow up chemical reactions, respectively) are demonstrated with noisy simulated data for conditions where all mechanisms are close to chemically reversible and hence difficult to distinguish, even by an experienced electrochemist. Challenges anticipated in applying the new DNN to the classification of experimental data are highlighted. Directions for future development are also discussed.
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Affiliation(s)
- Luke Gundry
- School of Chemistry, Monash University, Clayton, Vic. 3800, Australia.
| | - Gareth Kennedy
- School of Chemistry, Monash University, Clayton, Vic. 3800, Australia.
| | - Alan M Bond
- School of Chemistry, Monash University, Clayton, Vic. 3800, Australia.
| | - Jie Zhang
- School of Chemistry, Monash University, Clayton, Vic. 3800, Australia.
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7
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Adams AC, Jha S, Lary DJ, Slinker JD. Machine Learning for Estimating Electron Transfer Rates From Square Wave Voltammetry. Chempluschem 2021; 87:e202100418. [PMID: 34859611 DOI: 10.1002/cplu.202100418] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2021] [Revised: 11/11/2021] [Indexed: 11/12/2022]
Abstract
Electrochemistry of surface-bound molecules is of high importance for numerous electronic and sensor applications. Extracting the electron transfer rate is beneficial for understanding surface-bound processes, but it requires experimental or computational rigor. We evaluate methods to determine electron transfer rates from large voltammetry sets from experiments via machine learning using decision tree ensembles, neural networks, and gaussian process regression models. We applied these to reproduce computational measures of electron transfer rates modeled by first principles. The best machine learning models were a random forest with 80 decision trees and a neural network with Bayesian regularization, producing root mean squared errors of 0.37 and 0.49 s-1 , respectively, corresponding to mean percent errors of 0.38 % and 0.52 %, respectively. This work establishes machine learning methods for rapidly acquiring electron transfer rates across large datasets for widespread applications.
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Affiliation(s)
- Austen C Adams
- Department of Physics, The University of Texas at Dallas, 800W. Campbell Rd., SCI 10, Richardson, TX 75080, USA
| | - Sauraj Jha
- Department of Materials Science and Engineering, The University of Texas at Dallas, 800W. Campbell Rd., SCI 10, Richardson, TX 75080, USA
| | - David J Lary
- Department of Physics, The University of Texas at Dallas, 800W. Campbell Rd., SCI 10, Richardson, TX 75080, USA
| | - Jason D Slinker
- Department of Physics, The University of Texas at Dallas, 800W. Campbell Rd., SCI 10, Richardson, TX 75080, USA
- Department of Materials Science and Engineering, The University of Texas at Dallas, 800W. Campbell Rd., SCI 10, Richardson, TX 75080, USA
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8
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Movassaghi CS, Perrotta KA, Yang H, Iyer R, Cheng X, Dagher M, Fillol MA, Andrews AM. Simultaneous serotonin and dopamine monitoring across timescales by rapid pulse voltammetry with partial least squares regression. Anal Bioanal Chem 2021; 413:6747-6767. [PMID: 34686897 PMCID: PMC8551120 DOI: 10.1007/s00216-021-03665-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2021] [Revised: 09/11/2021] [Accepted: 09/14/2021] [Indexed: 11/12/2022]
Abstract
Many voltammetry methods have been developed to monitor brain extracellular dopamine levels. Fewer approaches have been successful in detecting serotonin in vivo. No voltammetric techniques are currently available to monitor both neurotransmitters simultaneously across timescales, even though they play integrated roles in modulating behavior. We provide proof-of-concept for rapid pulse voltammetry coupled with partial least squares regression (RPV-PLSR), an approach adapted from multi-electrode systems (i.e., electronic tongues) used to identify multiple components in complex environments. We exploited small differences in analyte redox profiles to select pulse steps for RPV waveforms. Using an intentionally designed pulse strategy combined with custom instrumentation and analysis software, we monitored basal and stimulated levels of dopamine and serotonin. In addition to faradaic currents, capacitive currents were important factors in analyte identification arguing against background subtraction. Compared to fast-scan cyclic voltammetry-principal components regression (FSCV-PCR), RPV-PLSR better differentiated and quantified basal and stimulated dopamine and serotonin associated with striatal recording electrode position, optical stimulation frequency, and serotonin reuptake inhibition. The RPV-PLSR approach can be generalized to other electrochemically active neurotransmitters and provides a feedback pipeline for future optimization of multi-analyte, fit-for-purpose waveforms and machine learning approaches to data analysis.
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Affiliation(s)
- Cameron S Movassaghi
- Department of Chemistry & Biochemistry, University of California, Los Angeles, Los Angeles, CA, 90095, USA
- California NanoSystems Institute, University of California, Los Angeles, Los Angeles, CA, 90095, USA
| | - Katie A Perrotta
- Department of Chemistry & Biochemistry, University of California, Los Angeles, Los Angeles, CA, 90095, USA
| | - Hongyan Yang
- Department of Psychiatry and Biobehavioral Sciences, Semel Institute for Neuroscience and Human Behavior, and Hatos Center for Neuropharmacology, University of California, Los Angeles, Los Angeles, CA, 90095, USA
| | - Rahul Iyer
- Department of Electrical Engineering, University of California, Los Angeles, Los Angeles, CA, 90095, USA
| | - Xinyi Cheng
- Department of Chemistry & Biochemistry, University of California, Los Angeles, Los Angeles, CA, 90095, USA
| | - Merel Dagher
- Molecular Toxicology Interdepartmental Program, University of California, Los Angeles, Los Angeles, CA, 90095, USA
| | - Miguel Alcañiz Fillol
- Interuniversity Research Institute for Molecular Recognition and Technological Development, Universitat Politècnica de València - Universitat de València, Camino de Vera s/n, 46022, Valencia, Spain.
| | - Anne M Andrews
- Department of Chemistry & Biochemistry, University of California, Los Angeles, Los Angeles, CA, 90095, USA.
- California NanoSystems Institute, University of California, Los Angeles, Los Angeles, CA, 90095, USA.
- Department of Psychiatry and Biobehavioral Sciences, Semel Institute for Neuroscience and Human Behavior, and Hatos Center for Neuropharmacology, University of California, Los Angeles, Los Angeles, CA, 90095, USA.
- Molecular Toxicology Interdepartmental Program, University of California, Los Angeles, Los Angeles, CA, 90095, USA.
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9
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Lloyd-Laney HO, Robinson MJ, Bond AM, Parkin A, Gavaghan DJ. A Spotter’s guide to dispersion in non-catalytic surface-confined voltammetry experiments. J Electroanal Chem (Lausanne) 2021. [DOI: 10.1016/j.jelechem.2021.115204] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
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10
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Gundry L, Kennedy G, Keith J, Robinson M, Gavaghan D, Bond AM, Zhang J. A Comparison of Bayesian Inference Strategies for Parameterisation of Large Amplitude AC Voltammetry Derived from Total Current and Fourier Transformed Versions. ChemElectroChem 2021. [DOI: 10.1002/celc.202100391] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Affiliation(s)
- Luke Gundry
- School of Chemistry Monash University Clayton Vic. 3800 Australia
| | - Gareth Kennedy
- School of Chemistry Monash University Clayton Vic. 3800 Australia
| | - Jonathan Keith
- School of Mathematics Monash University Clayton Vic. 3800 Australia
| | - Martin Robinson
- Department of Computer Science University of Oxford, Wolfson Building Parks Road Oxford OX1 3QD United Kingdom
| | - David Gavaghan
- Department of Computer Science University of Oxford, Wolfson Building Parks Road Oxford OX1 3QD United Kingdom
| | - Alan M. Bond
- School of Chemistry Monash University Clayton Vic. 3800 Australia
| | - Jie Zhang
- School of Chemistry Monash University Clayton Vic. 3800 Australia
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11
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Ryzhkov NV, Nikolaev KG, Ivanov AS, Skorb EV. Infochemistry and the Future of Chemical Information Processing. Annu Rev Chem Biomol Eng 2021; 12:63-95. [PMID: 33909470 DOI: 10.1146/annurev-chembioeng-122120-023514] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Nowadays, information processing is based on semiconductor (e.g., silicon) devices. Unfortunately, the performance of such devices has natural limitations owing to the physics of semiconductors. Therefore, the problem of finding new strategies for storing and processing an ever-increasing amount of diverse data is very urgent. To solve this problem, scientists have found inspiration in nature, because living organisms have developed uniquely productive and efficient mechanisms for processing and storing information. We address several biological aspects of information and artificial models mimicking corresponding bioprocesses. For instance, we review the formation of synchronization patterns and the emergence of order out of chaos in model chemical systems. We also consider molecular logic and ion fluxes as information carriers. Finally, we consider recent progress in infochemistry, a new direction at the interface of chemistry, biology, and computer science, considering unconventional methods of information processing.
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Affiliation(s)
- Nikolay V Ryzhkov
- Infochemistry Scientific Center of ITMO University, 191002 Saint Petersburg, Russia; , , ,
| | - Konstantin G Nikolaev
- Infochemistry Scientific Center of ITMO University, 191002 Saint Petersburg, Russia; , , ,
| | - Artemii S Ivanov
- Infochemistry Scientific Center of ITMO University, 191002 Saint Petersburg, Russia; , , ,
| | - Ekaterina V Skorb
- Infochemistry Scientific Center of ITMO University, 191002 Saint Petersburg, Russia; , , ,
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12
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Gundry L, Guo SX, Kennedy G, Keith J, Robinson M, Gavaghan D, Bond AM, Zhang J. Recent advances and future perspectives for automated parameterisation, Bayesian inference and machine learning in voltammetry. Chem Commun (Camb) 2021; 57:1855-1870. [PMID: 33529293 DOI: 10.1039/d0cc07549c] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
Advanced data analysis tools such as mathematical optimisation, Bayesian inference and machine learning have the capability to revolutionise the field of quantitative voltammetry. Nowadays such approaches can be implemented routinely with widely available, user-friendly modern computing languages, algorithms and high speed computing to provide accurate and robust methods for quantitative comparison of experimental data with extensive simulated data sets derived from models proposed to describe complex electrochemical reactions. While the methodology is generic to all forms of dynamic electrochemistry, including the widely used direct current cyclic voltammetry, this review highlights advances achievable in the parameterisation of large amplitude alternating current voltammetry. One significant advantage this technique offers in terms of data analysis is that Fourier transformation provides access to the higher order harmonics that are almost devoid of background current. Perspectives on the technical advances needed to develop intelligent data analysis strategies and make them generally available to users of voltammetry are provided.
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Affiliation(s)
- Luke Gundry
- School of Chemistry, Monash University, Clayton, Vic. 3800, Australia.
| | - Si-Xuan Guo
- School of Chemistry, Monash University, Clayton, Vic. 3800, Australia.
| | - Gareth Kennedy
- School of Chemistry, Monash University, Clayton, Vic. 3800, Australia.
| | - Jonathan Keith
- School of Mathematics, Monash University, Clayton, Vic. 3800, Australia
| | - Martin Robinson
- Department of Computer Science, University of Oxford, Wolfson Building, Parks Road, Oxford, OX1 3QD, UK
| | - David Gavaghan
- Department of Computer Science, University of Oxford, Wolfson Building, Parks Road, Oxford, OX1 3QD, UK
| | - Alan M Bond
- School of Chemistry, Monash University, Clayton, Vic. 3800, Australia.
| | - Jie Zhang
- School of Chemistry, Monash University, Clayton, Vic. 3800, Australia.
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13
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Lloyd-Laney HO, Yates NDJ, Robinson MJ, Hewson AR, Firth JD, Elton DM, Zhang J, Bond AM, Parkin A, Gavaghan DJ. Using Purely Sinusoidal Voltammetry for Rapid Inference of Surface-Confined Electrochemical Reaction Parameters. Anal Chem 2021; 93:2062-2071. [PMID: 33417431 DOI: 10.1021/acs.analchem.0c03774] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
Alternating current (AC) voltammetric techniques are experimentally powerful as they enable Faradaic current to be isolated from non-Faradaic contributions. Finding the best global fit between experimental voltammetric data and simulations based on reaction models requires searching a substantial parameter space at high resolution. In this paper, we estimate parameters from purely sinusoidal voltammetry (PSV) experiments, investigating the redox reactions of a surface-confined ferrocene derivative. The advantage of PSV is that a complete experiment can be simulated relatively rapidly, compared to other AC voltammetric techniques. In one example involving thermodynamic dispersion, a PSV parameter inference effort requiring 7,500,000 simulations was completed in 7 h, whereas the same process for our previously used technique, ramped Fourier transform AC voltammetry (ramped FTACV), would have taken 4 days. Using both synthetic and experimental data with a surface confined diazonium substituted ferrocene derivative, it is shown that the PSV technique can be used to recover the key chemical and physical parameters. By applying techniques from Bayesian inference and Markov chain Monte Carlo methods, the confidence, distribution, and degree of correlation of the recovered parameters was visualized and quantified.
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Affiliation(s)
- Henry O Lloyd-Laney
- Department of Computer Science, University of Oxford, Wolfson Building, Parks Road, Oxford, OX1 3QD United Kingdom
| | - Nicholas D J Yates
- Department of Chemistry, University of York, Heslington, York, YO10 5DD, United Kingdom
| | - Martin J Robinson
- Department of Computer Science, University of Oxford, Wolfson Building, Parks Road, Oxford, OX1 3QD United Kingdom
| | - Alice R Hewson
- Department of Chemistry, University of York, Heslington, York, YO10 5DD, United Kingdom
| | - Jack D Firth
- Department of Chemistry, University of York, Heslington, York, YO10 5DD, United Kingdom
| | - Darrell M Elton
- School of Engineering and Mathematical Sciences, La Trobe University, Bundoora, Victoria 3086, Australia
| | - Jie Zhang
- School of Chemistry and the ARC Centre of Excellence for Electromaterials Science, Monash University, Clayton, Victoria, 3800 Australia
| | - Alan M Bond
- School of Chemistry and the ARC Centre of Excellence for Electromaterials Science, Monash University, Clayton, Victoria, 3800 Australia
| | - Alison Parkin
- Department of Chemistry, University of York, Heslington, York, YO10 5DD, United Kingdom
| | - David J Gavaghan
- Department of Computer Science, University of Oxford, Wolfson Building, Parks Road, Oxford, OX1 3QD United Kingdom
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14
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Scholz F. Essay for the Rosarium Philosophicum on Electrochemistry Electrochemical Analysis – What it was, is, and Possibly will be. Isr J Chem 2020. [DOI: 10.1002/ijch.202000078] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/03/2023]
Affiliation(s)
- Fritz Scholz
- Institut of Biochemistry University of Greifswald Germany 17489 Greifswald Felix-Hausdorff-Str. 4
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