Recent advances and future perspectives for automated parameterisation, Bayesian inference and machine learning in voltammetry

Disruption Dynamics and Charge Transfer of a Single Attoliter Emulsion Droplet Revealed by Combined Fast-Scan Sinusoidal Voltammetry and Short Time Fourier Transform Analysis

Single-entity electrochemistry has gained significant attention for the analysis of individual cells, nanoparticles, and droplets, which is leveraged by robust electrochemical techniques such as chronoamperometry and cyclic voltammetry (CV) to extract information about single entities, including size, kinetics, mass transport, etc. For an in-depth understanding such as dynamic interaction between the electrode and a single entity, the unconventional fast electrochemical technique is essential for time-resolved analysis. This fast experimental technique is unfortunately hindered by a substantial nonfaradaic response. In this work, we introduce fast-scan sinusoidal voltammetry (FSSV) combined with a short-time Fourier transform (STFT) for analyzing single emulsion droplets. Utilizing ultramicroelectrode and fast potential sweeps up to apparent 200 V/s, we achieved high temporal resolution (8 ms per voltammogram) to capture the current signals during droplet collisions. STFT analysis reveals the amplitude and phase changes, allowing for the accurate detection of collision events even in the absence of redox species. By adopting an algorithm of drift-free baseline subtraction, a conventional CV shape was obtained in FSSV. The reacted charge from the single-entity voltammogram at every 8 ms was also plotted. This method effectively addresses limitations in traditional techniques, providing insights into emulsion dynamics such as droplet contact and droplet breakdown.

Practical Guide to Large Amplitude Fourier-Transformed Alternating Current Voltammetry-What, How, and Why

Fourier-transformed alternating current voltammetry (FTacV) is a technique utilizing a combination of a periodic (frequently sinusoidal) oscillation superimposed onto a staircase or linear potential ramp. The advanced utilization of a large amplitude sine wave induces substantial nonlinear current responses. Subsequent filter processing (via Fourier-transformation, band selection, followed by inverse Fourier-transformation) generates a series of harmonics in which rapid electron transfer processes may be separated from non-Faradaic and competing electron transfer processes with slower kinetics. Thus, FTacV enables the isolation of current associated with redox processes under experimental conditions that would not generate meaningful data using direct current voltammetry (dcV). In this study, the enhanced experimental sensitivity and selectivity of FTacV versus dcV are illustrated in measurements that (i) separate the Faradaic current from background current contributions, (ii) use a low (5 μM) concentration of analyte (exemplified with ferrocene), and (iii) enable discrimination of the reversible [Ru(NH3)6]3+/2+ electron-transfer process from the irreversible reduction of oxygen under a standard atmosphere, negating the requirement for inert gas conditions. The simple, homebuilt check-cell described ensures that modern instruments can be checked for their ability to perform valid FTacV experiments. Detailed analysis methods and open-source data sets that accompany this work are intended to facilitate other researchers in the integration of FTacV into their everyday electrochemical methodological toolkit.

Understanding the Decamethylferrocene FeIII/IV Oxidation Process in Tris(pentafluoroethyl)trifluorophosphate-Containing Ionic Liquids at Glassy Carbon and Boron-Doped Diamond Electrodes

Under voltammetric conditions, the neutral decamethylferrocene ([Me10Fc]) to cationic ([Me10Fc]+) FeII/III process is a well-known reversible outer-sphere reaction. A companion cationic [Me10Fc]+ to dicationic [Me10Fc]2+ FeIII/IV process has been reported under direct current (DC) cyclic voltammetric conditions at highly positive potentials in liquid SO2 at low temperatures and in a 1.5:1.0 AlCl3/1-butylpyridinium chloride melt. This study demonstrates that in room-temperature ionic liquids containing the hard to oxidize and hydrophobic tris(pentafluoroethyl)trifluorophosphate anion, the [Me10Fc]+/2+ process can be detected as a quasi-reversible reaction at glassy carbon (GC) and boron-doped diamond (BDD) electrodes. Large amplitude Fourier-transformed alternating current (FT-AC) voltammetry minimizes background current contributions occurring at potentials similar to those of the FeIII/IV process in the second and higher-order harmonics. This enables a straightforward determination of the thermodynamics and kinetics for both the FeII/III and FeIII/IV processes. Unlike the ideal outer-sphere FeII/III process, the parameters of the FeIII/IV process may be impacted by ion-interaction effects. For the faster FeII/III process, heterogeneous rate constants are approximately 10 times smaller at BDD than those at GC electrodes. This electrode dependence is less pronounced for the slower FeIII/IV process. The slower BDD kinetics may be attributed in part to a density of states lower than that at GC.

Maximizing Electrochemical Information: A Perspective on Background-Inclusive Fast Voltammetry

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.

Discovering Electrochemistry with an Electrochemistry-Informed Neural Network (ECINN)

Machine learning is increasingly integrated into chemistry research by guiding experimental procedures, correlating structure and function, interpreting large experimental datasets, to distill scientific insights that might be challenging with traditional methods. Such applications, however, largely focus on gaining insights via big data and/or big computation, while neglecting the valuable chemical prior knowledge dwelling in chemists' minds. In this paper, we introduce an Electrochemistry-Informed Neural Network (ECINN) by explicitly embedding electrochemistry priors including the Butler-Volmer (BV), Nernst and diffusion equations on the backbone of neural networks for multi-task discovery of electrochemistry parameters. We applied the ECINN to voltammetry experiments ofF e 2 + / F e 3 + ${{\rm F}{{\rm e}}^{2+}/{\rm F}{{\rm e}}^{3+}}$ andR u N H 3 6 2 + / R u N H 3 6 3 + ${{\rm R}{\rm u}{\left({\rm N}{{\rm H}}_{3}\right)}_{6}^{2+{\rm \ }}/{\rm R}{\rm u}{\left({\rm N}{{\rm H}}_{3}\right)}_{6}^{3+{\rm \ }}}$ redox couples to discover electrode kinetics and mass transport parameters. Notably, ECINN seamlessly integrated mass transport with BV to analyze the entire voltammogram to infer transfer coefficients directly, so offering a new approach to Tafel analysis by outdating various mass transport correction methods. In addition, ECINN can help discover the nature of electron transfer and is shown to refute incorrect physics if imposed. This work encourages chemists to embed their domain knowledge into machine learning models to start a new paradigm of chemistry-informed machine learning for better accountability, interpretability, and generalization.

The effect of chelators on additives in the surface characterization and electrochemical properties of an eco-friendly electroless copper nano deposition

We represent the results of a study on as the chelators used in the environmentally friendly electroless deposition bath changed depending on the amounts of hydroxides were present. The baths were prepared using polyhydroxides, glycerol and sorbitol, as chelators with copper methanesulfonate as the metal ion. Dimethylamine borane (DMAB) was used as the reducing agent with N-methylthiourea and cytosine, as additives in both the glycerol and sorbitol contained baths. Potassium hydroxide was used as the pH adjuster, with glycerol and sorbitol baths maintained at a pH of 11.50 and 10.75 respectively at a room temperature of 28 ± 2 °C. XRD, SEM, AFM, cyclic voltammetry studies, Tafel and Impedance studies, as well as additional methods, were employed to monitor and record the surface, structural, and electrochemical characteristics of the deposits and bath. The reports of the study gave interesting results, which clearly the effect of chelators on additives in the nano deposition of copper in an electroless deposition bath.

While educating electrochemists, do not forget we live in a computer era

The appearance of computers has led to considerable changes in research practices of natural sciences, including electrochemistry. The current status of the computerization in electrochemistry is briefly discussed, with the conclusion that the progress in this area is not as fast as in other natural science disciplines. Some postulates are formulated, referring to the education of young generations of electrochemists, that might bring improvements.

Electrochemical Mechanistic Analysis from Cyclic Voltammograms Based on Deep Learning

For decades, employing cyclic voltammetry for mechanistic investigation has demanded manual inspection of voltammograms. Here, we report a deep-learning-based algorithm that automatically analyzes cyclic voltammograms and designates a probable electrochemical mechanism among five of the most common ones in homogeneous molecular electrochemistry. The reported algorithm will aid researchers' mechanistic analyses, utilize otherwise elusive features in voltammograms, and experimentally observe the gradual mechanism transitions encountered in electrochemistry. An automated voltammogram analysis will aid the analysis of complex electrochemical systems and promise autonomous high-throughput research in electrochemistry with minimal human interference.

Active Knowledge Extraction from Cyclic Voltammetry

Cyclic Voltammetry (CV) is an electro-chemical characterization technique used in an initial material screening for desired properties and to extract information about electro-chemical reactions. In some applications, to extract kinetic information of the associated reactions (e.g., rate constants and turn over frequencies), CV curve should have a specific shape (for example an S-shape). However, often the characterization settings to obtain such curve are not known a priori. In this paper, an active search framework is defined to accelerate identification of characterization settings that enable knowledge extraction from CV experiments. Towards this goal, a representation of CV responses is used in combination with Bayesian Model Selection (BMS) method to efficiently label the response to be either S-shape or not S-shape. Using an active search with BMS oracle, we report a linear target identification in a six-dimensional search space (comprised of thermodynamic, mass transfer, and solution variables as dimensions). Our framework has the potential to be a powerful virtual screening technique for molecular catalysts, bi-functional fuel cell catalysts, and other energy conversion and storage systems.

Advanced Spatiotemporal Voltammetric Techniques for Kinetic Analysis and Active Site Determination in the Electrochemical Reduction of CO2

ConspectusElectrochemical reduction of the greenhouse gas CO₂ offers prospects for the sustainable generation of fuels and industrially useful chemicals when powered by renewable electricity. However, this electrochemical process requires the use of highly stable, selective, and active catalysts. The development of such catalysts should be based on a detailed kinetic and mechanistic understanding of the electrochemical CO₂ reduction reaction (eCO₂RR), ideally through the resolution of active catalytic sites in both time (i.e., temporally) and space (i.e., spatially). In this Account, we highlight two advanced spatiotemporal voltammetric techniques for electrocatalytic studies and describe the considerable insights they provide on the eCO₂RR. First, Fourier transformed large-amplitude alternating current voltammetry (FT ac voltammetry), as applied by the Monash Electrochemistry Group, enables the resolution of rapid underlying electron-transfer processes in complex reactions, free from competing processes, such as the background double-layer charging current, slow catalytic reactions, and solvent/electrolyte electrolysis, which often mask conventional voltammetric measurements of the eCO₂RR. Crucially, FT ac voltammetry allows details of the catalytically active sites or the rate-determining step to be revealed under catalytic turnover conditions. This is well illustrated in investigations of the eCO₂RR catalyzed by Bi where formate is the main product. Second, developments in scanning electrochemical cell microscopy (SECCM) by the Warwick Electrochemistry and Interfaces Group provide powerful methods for obtaining high-resolution activity maps and potentiodynamic movies of the heterogeneous surface of a catalyst. For example, by coupling SECCM data with colocated microscopy from electron backscatter diffraction (EBSD) or atomic force microscopy, it is possible to develop compelling correlations of (precatalyst) structure-activity at the nanoscale level. This correlative electrochemical multimicroscopy strategy allows the catalytically more active region of a catalyst, such as the edge plane of two-dimensional materials and the grain boundaries between facets in a polycrystalline metal, to be highlighted. The attributes of SECCM-EBSD are well-illustrated by detailed studies of the eCO₂RR on polycrystalline gold, where carbon monoxide is the main product. Comparing SECCM maps and movies with EBSD images of the same region reveals unambiguously that the eCO₂RR is enhanced at surface-terminating dislocations, which accumulate at grain boundaries and slip bands. Both FT ac voltammetry and SECCM techniques greatly enhance our understanding of the eCO₂RR, significantly boosting the electrochemical toolbox and the information available for the development and testing of theoretical models and rational catalyst design. In the future, it may be possible to further enhance insights provided by both techniques through their integration with in situ and in operando spectroscopy and microscopy methods.

Inclusion of multiple cycling of potential in the deep neural network classification of voltammetric reaction mechanisms

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, EC_1^st and EC_2^nd mechanisms (E = electron transfer step and C_1^st and C_2^nd 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.

Machine Learning for Estimating Electron Transfer Rates From Square Wave Voltammetry

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.

Simultaneous serotonin and dopamine monitoring across timescales by rapid pulse voltammetry with partial least squares regression

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.

Machine learning and chemometrics for electrochemical sensors: moving forward to the future of analytical chemistry

Electrochemical sensors and biosensors have been successfully used in a wide range of applications, but systematic optimization and nonlinear relationships have been compromised for electrode fabrication and data analysis. Machine learning and experimental designs are chemometric tools that have been proved to be useful in method development and data analysis. This minireview summarizes recent applications of machine learning and experimental designs in electroanalytical chemistry. First, experimental designs, e.g., full factorial, central composite, and Box-Behnken are discussed as systematic approaches to optimize electrode fabrication to consider the effects from individual variables and their interactions. Then, the principles of machine learning algorithms, including linear and logistic regressions, neural network, and support vector machine, are introduced. These machine learning models have been implemented to extract complex relationships between chemical structures and their electrochemical properties and to analyze complicated electrochemical data to improve calibration and analyte classification, such as in electronic tongues. Lastly, the future of machine learning and experimental designs in electrochemical sensors is outlined. These chemometric strategies will accelerate the development and enhance the performance of electrochemical devices for point-of-care diagnostics and commercialization.

A Voltammetric Perspective of Multi-Electron and Proton Transfer in Protein Redox Chemistry: Insights From Computational Analysis of Escherichia coli HypD Fourier Transformed Alternating Current Voltammetry

This paper explores the impact of pH on the mechanism of reversible disulfide bond (CysS-SCys) reductive breaking and oxidative formation in Escherichia coli hydrogenase maturation factor HypD, a protein which forms a highly stable adsorbed film on a graphite electrode. To achieve this, low frequency (8.96 Hz) Fourier transformed alternating current voltammetric (FTACV) experimental data was used in combination with modelling approaches based on Butler-Volmer theory with a dual polynomial capacitance model, utilizing an automated two-step fitting process conducted within a Bayesian framework. We previously showed that at pH 6.0 the protein data is best modelled by a redox reaction of two separate, stepwise one-electron, one-proton transfers with slightly “crossed” apparent reduction potentials that incorporate electron and proton transfer terms (Eapp20 > Eapp10). Remarkably, rather than collapsing to a concerted two-electron redox reaction at more extreme pH, the same two-stepwise one-electron transfer model with Eapp20 > Eapp10 continues to provide the best fit to FTACV data measured across a proton concentration range from pH 4.0 to pH 9.0. A similar, small level of crossover in reversible potentials is also displayed in overall two-electron transitions in other proteins and enzymes, and this provides access to a small but finite amount of the one electron reduced intermediate state.