Single-entity electrochemistry of collision in sensing applications

Single entity electrochemistry of collision (SEEC) is an emerging electrochemical method with the great potential for ultra-sensitive analysis applications. Here, the possibilities and challenges of SEEC in sensing are discussed. The analytical characteristics of the collision-based detection method, such as sensitivity, detection range, the limit of detection, signal-to-noise ratio, and selectivity, are examined. Factors affecting these parameters and strategies to improve them are discussed. Potential target analytes in environmental and bioanalytical applications are overviewed based on the reported up to date literature. Finally, challenges and limitations currently preventing real-life applications of the method are highlighted

The Role of an Inert Electrode Support in Plasmonic Electrocatalysis

Plasmonic nanostructures loaded onto catalytically inert conductive support materials are believed to be advantageous for maximizing photocatalytic effects in photoelectrochemical systems due to the increased efficiency of Schottky barrier-free architectures in collecting hot charge carriers. However, the systematic mechanistic investigation and description of the inert electrode support contribution to plasmonic electrocatalysis is missing. Herein, we systematically investigated the effect of the supporting electrode material on the observed photocatalytic enhancement by comparing photoelectrocatalytic properties of AuNPs supported on highly oriented pyrolytic graphite (HOPG) and on indium tin oxide (ITO) electrodes using electrocatalytic benzyl alcohol (BnOH) oxidation as a model system. Upon illumination, only ~(3 ± 1)% enhancement in catalytic current was recorded on the AuNP/ITO electrodes in contrast to ~(42 ± 6)% enhancement on AuNP/HOPG electrodes. Our results showed that the local heating due to light absorption by the electrode material itself independent of localized surface plasmon effects is the primary source of the observed significant photo-induced enhancement on the HOPG electrodes in comparison to the ITO electrodes. Moreover, we demonstrated that an increased interfacial charge transfer at elevated temperatures, and not faster substrate diffusion is the main source of the enhancement. This work highlights the importance of systematic evaluation of contributions of all parts, even if they are catalytically inert, to the light-induced facilitation of catalytic reactions in plasmonic systems

The Potential of Molecular Electrocatalysis for Ammonia-to-Dinitrogen Conversion

Electrochemical ammonia oxidation reaction (eAOR) regains interest due to ammonia being an interesting alternative to hydrogen for fuel cell technologies. In the present review, we first discuss some of the most important findings on eAOR with solid catalysts, including mechanistic and feasibility aspects for practical implementation. We then examine the reports on molecular catalysis of eAOR that have recently emerged. We finally discuss immobilization strategies of these molecular catalysts, and discuss intrinsic advantages of those strategies, so as to guide the design of efficient catalytic systems able to compete with heterogeneous, solid catalysts

From protein film to single-entity protein electrochemistry

This mini-review discusses recent advancements in the singleentity electrochemistry technique for the analysis of catalytic activities of single redox protein molecules, highlighting papers of interest from the past three years. The diverse detection and experimental strategies, as well as the theoretical frameworks enabling the analysis of experimental data, are presented. Additionally, insights that can be obtained from comparing single-entity protein electrochemistry with protein film electrochemistry data are discussed

Automated Analysis of Nano-Impact Single-Entity Electrochemistry Signals Using Unsupervised Machine Learning and Template Matching

Nano-impact (NIE) (also referred to as collision) single-entity electrochemistry is an emerging technique that enables electrochemical investigation of individual entities, ranging from metal nanoparticles to single cells and biomolecules. To obtain meaningful information from NIE experiments, analysis and feature extraction on large datasets are necessary. Herein, a method is developed for the automated analysis of NIE data based on unsupervised machine learning and template matching approaches. Template matching not only facilitates downstream processing of the NIE data but also provides a more accurate analysis of the NIE signal characteristics and variations that are difficult to discern with conventional data analysis techniques, such as the height threshold method. The developed algorithm enables fast automated processing of large experimental datasets recorded with different systems, requiring minimal human intervention and thereby eliminating human bias in data analysis. As a result, it improves the standardization of data processing and NIE signal interpretation across various experiments and applications. Nano-impact (NIE) electrochemistry is an emerging technique for studying individual entities. Analyzing large NIE datasets, often with low signal-to-noise ratios, is challenging. Herein, an automated approach is introduced using unsupervised machine learning and template matching for accurate feature extraction from spike-shaped NIE signals. It improves data processing, accuracy and standardization, reducing human bias in signal interpretation across experiments.image (c) 2023 WILEY-VCH Gmb

Automated Analysis of Nano-Impact Single-Entity Electrochemistry Signals using Unsupervised Machine Learning and Template Matching

Nano-impact single-entity electrochemistry (NIE) is an emerging technique that enables electrochemical investigation of individual entities, ranging from metal nanoparticles to single cells and biomolecules. To extract meaningful information from NIE experiments, statistical analysis of large datasets is necessary. In this study, we developed a method for the automated analysis of NIE data based on unsupervised machine learning and template matching approaches. Template matching not only facilitates downstream processing of the NIE data but also provides a more accurate analysis of the NIE signal characteristics and variations that are difficult to discern with conventional data analysis techniques, such as the height threshold method. The developed algorithm enables fast automated processing of large experimental datasets recorded with different systems, requiring minimal human intervention and thereby eliminating human bias in data analysis. As a result, it improves the standardization of data processing and NIE signal interpretation across various experiments and applications

Correspondence on "Can Nanoimpacts Detect Single-Enzyme Activity? Theoretical Considerations and an Experimental Study of Catalase Impacts"

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Electron Transfer to the Trinuclear Copper Cluster in Electrocatalysis by the Multicopper Oxidases

International audienceHigh-potential multicopper oxidases (MCOs) are excellent catalysts able to perform the oxygen reduction reaction (ORR) at remarkably low overpotentials. Moreover, MCOs are able to interact directly with the electrode surfaces via direct electron transfer (DET), that makes them the most commonly used electrocatalysts for oxygen reduction in biofuel cells. The central question in MCO electrocatalysis is whether the type 1 (T1) Cu is the primary electron acceptor site from the electrode, or whether electrons can be transferred directly to the trinuclear copper cluster (TNC), bypassing the rate-limiting intramolecular electron transfer step from the T1 site. Here, using sitedirected mutagenesis and electrochemical methods combined with data modeling of electrode kinetics we have found that there is no preferential superexchange pathway for DET to the T1 site. However, due to the high reorganization energy of the fully oxidized TNC, electron transfer from the electrode to the TNC does occur primarily through the T1 site. We have further demonstrated that the lower reorganization energy of the TNC in its two-electron reduced, alternative resting, form enables DET to the TNC, but this only occurs in the first turnover. This study provides insight into the factors that control the kinetics of electrocatalysis by the MCOs and a guide for the design of more efficient biocathodes for the ORR

Single-Entity Protein Electrochemistry of Diffusion-Limited Enzymes

Single-entity electrochemistry has recently emerged as a promising method for label-free exploration of the catalytic functions of individual enzymes. However, skepticism within the scientific community regarding the applicability of the method for single enzyme measurements has arisen due to issues in the experimental data presented in the literature and limited theoretical modeling of such data. Here, we address these concerns through a thorough experimental investigation of two diffusion-limited enzymes, catalase and superoxide dismutase, employing a combination of protein film voltammetry and single-entity protein electrochemistry measurements. We then introduce a novel theoretical model for simulating the current responses, generated by the reduction of the product of the enzymatic reaction of single enzyme molecules at the electrode. This model is based on a combination of finite element simulations using COMSOL Multiphysics and random walk simulations. It incorporates the diffusion-limited enzymatic kinetics of the investigated enzymes and introduces a geometry that mimics the substrate diffusion channel of the enzyme. Our work demonstrates that the experimentally detected current signals align with the simulated current signals, affirming that they can be attributed to the catalytic activity of single enzymes detected via the product of the enzymatic reaction

Evaluation of the Electrochemically Active Surface Area of Microelectrodes by Capacitive and Faradaic Currents

Two experimental methods to estimate the electrochemically active surface area (EASA) of microelectrodes are investigated. One method is based on electrocapacitive measurements and depends significantly on the surface roughness as well as on other parameters. The other method is based on faradaic current measurements and depends on the geometric surface area. The experimental results are supplemented with numerical modeling of electrodes with different surface roughness. A systematic study reveals a strong influence of the scale and arrangement of the surface roughness, the measurement potential and the electrolyte concentration on the EASA of microelectrodes estimated from the electrocapacitive measurements. The results show that electrocapacitive measurements should not be used to estimate the faradaic EASA of microelectrodes with a non-negligible surface roughness