Characterizing Electrocatalysts with Scanning Electrochemical Microscopy

Single Entity Electrocatalysis

Making a measurement over millions of nanoparticles or exposed crystal facets seldom reports on reactivity of a single nanoparticle or facet, which may depart drastically from ensemble measurements. Within the past 30 years, science has moved toward studying the reactivity of single atoms, molecules, and nanoparticles, one at a time. This shift has been fueled by the realization that everything changes at the nanoscale, especially important industrially relevant properties like those important to electrocatalysis. Studying single nanoscale entities, however, is not trivial and has required the development of new measurement tools. This review explores a tale of the clever use of old and new measurement tools to study electrocatalysis at the single entity level. We explore in detail the complex interrelationship between measurement method, electrocatalytic material, and reaction of interest (e.g., carbon dioxide reduction, oxygen reduction, hydrazine oxidation, etc.). We end with our perspective on the future of single entity electrocatalysis with a key focus on what types of measurements present the greatest opportunity for fundamental discovery.

Scanning Bubble Electrochemical Microscopy: Mapping of Electrocatalytic Activity with Low-Solubility Reactants

Determining electrocatalytic activity for reactions that involve reactants with limited solubility presents a significant challenge due to the reduced mass-transport to the electrocatalyst surface. This limitation is seen in important reactions such as the oxygen reduction reaction, nitrogen reduction reaction, and carbon dioxide reduction reaction. We introduce a new approach, which we call scanning bubble electrochemical microscopy, to enable the detection and high-resolution mapping of electrocatalytic activity with low-solubility reactants at high mass-transport rates. Using a scanning probe approach, a dual-barreled nanopipette is used to precisely deliver the gas-phase reactant within micrometers of an electrocatalyst surface, which results in high mass-transport rates at the electrocatalyst surface directly under the probe. We demonstrate the scanning bubble electrochemical microscopy approach by mapping the oxygen reduction reaction on model platinum microelectrode surfaces. We anticipate that scanning bubble electrochemical microscopy will provide an effective tool for measuring the electrocatalytic activity of reactants that have limited solubility.

Deconvoluting Kinetic Rate Constants of Catalytic Substrates from Scanning Electrochemical Approach Curves with Artificial Neural Networks

Extracting information from experimental measurements in the chemical sciences typically requires curve fitting, deconvolution, and/or solving the governing partial differential equations via numerical (e.g., finite element analysis) or analytical methods. However, using numerical or analytical methods for high-throughput data analysis typically requires significant postprocessing efforts. Here, we show that deep learning artificial neural networks can be a very effective tool for extracting information from experimental data. As an example, reactivity and topography information from scanning electrochemical microscopy (SECM) approach curves are highly convoluted. This study utilized multilayer perceptrons and convolutional neural networks trained on simulated SECM data to extract kinetic rate constants of catalytic substrates. Our key findings were that multilayer perceptron models performed very well when the experimental data were close to the ideal conditions with which the model was trained. However, convolutional neural networks, which analyze images as opposed to direct data, were able to accurately predict the kinetic rate constant of Fe-doped nickel (oxy)hydroxide catalyst at different applied potentials even though the experimental approach curves were not ideal. Due to the speed at which machine learning models can analyze data, we believe this study shows that artificial neural networks could become powerful tools in high-throughput data analysis.

Nanogap-Resolved Adsorption-Coupled Electron Transfer by Scanning Electrochemical Microscopy: Implications for Electrocatalysis

Here, we demonstrate for the first time that the mechanism of adsorption-coupled electron-transfer (ACET) reactions can be identified experimentally. The electron transfer (ET) and specific adsorption of redox-active molecules are coupled in many electrode reactions with practical importance and fundamental interest. ACET reactions are often represented by a concerted mechanism. In reductive adsorption, an oxidant is simultaneously reduced and adsorbed as a reductant on the electrode surface through the ACET step. Alternatively, the non-concerted mechanism mediates outer-sphere reduction and adsorption separately when the reductant adsorption is reversible. In electrocatalysis, reversibly adsorbed reductants are ubiquitous and crucial intermediates. Moreover, electrocatalysis is complicated by the mixed mechanism based on simultaneous ACET and outer-sphere ET steps. In this work, we reveal the non-concerted mechanism for ferrocene derivatives adsorbed at highly oriented pyrolytic graphite as simple models. We enable the transient voltammetric mode of nanoscale scanning electrochemical microscopy (SECM) to kinetically control the adsorption step, which is required for the discrimination of non-concerted, concerted, and mixed mechanisms. Experimental voltammograms are compared with each mechanism by employing finite element simulation. The non-concerted mechanism is supported to indicate that the ACET step is intrinsically slower than its outer-sphere counterpart by at least four orders of magnitude. This finding implies that an ACET step is facilitated thermodynamically but may not be necessarily accelerated or catalyzed by the adsorption of the reductant. SECM-based transient voltammetry will become a powerful tool to resolve and understand electrocatalytic ACET reactions at the elementary level.

Two-dimensional materials for electrochemical CO2 reduction: materials, in situ/operando characterizations, and perspective

Electrochemical CO₂ reduction (CO₂ ECR) is an efficient approach to achieving eco-friendly energy generation and environmental sustainability. This approach is capable of lowering the CO₂ greenhouse gas concentration in the atmosphere while producing various valuable fuels and products. For catalytic CO₂ ECR, two-dimensional (2D) materials stand as promising catalyst candidates due to their superior electrical conductivity, abundant dangling bonds, and tremendous amounts of surface active sites. On the other hand, the investigations on fundamental reaction mechanisms in CO₂ ECR are highly demanded but usually require advanced in situ and operando multimodal characterizations. This review summarizes recent advances in the development, engineering, and structure-activity relationships of 2D materials for CO₂ ECR. Furthermore, we overview state-of-the-art in situ and operando characterization techniques, which are used to investigate the catalytic reaction mechanisms with the spatial resolution from the micron-scale to the atomic scale, and with the temporal resolution from femtoseconds to seconds. Finally, we conclude this review by outlining challenges and opportunities for future development in this field.

Numerical Correction of In Situ AFM-SECM Measurements

Mass-transport-limited catalysis and membrane transport can be characterized by concentration profiles surrounding active surfaces. Scanning electrochemical microscopy (SECM) is a tool that has been used to measure concentration profiles; however, the presence and geometry of the tip can distort these profiles due to hindered diffusion, which in turn alters chemical behavior at the catalytic surface. To fully characterize the behavior of surface features such as catalytic sites, it is essential to account for and analytically remove the effect of tip presence. In this work, atomic force microscopy-based SECM (AFM-SECM) measurements over poly(tetrafluoroethylene) (PTFE) and gold electrode surfaces are used to measure negative and positive-feedback approach curves, respectively. By inversely fitting these approach curves with a finite element method (FEM) model, we derive kinetic and geometric tip parameters that characterize the effect of tip presence. Tip effects may be removed in the model to estimate concentration profiles and reaction properties for the case where no tip is present. A maximum 120% increase in the concentration at one tip radii above the surface is observed due to the presence of the tip, where the concentration field is compressed vertically, in proportion to surface feature size and tip separation. Conical AFM-SECM tips, with a higher ratio of tip height to the base size, introduce less concentration distortion than disk-shaped AFM-SECM tips.