Authors: Daniel Torres | Jon Ustarroz
Source:  (article, 2023-12-18)
Field: Materials Science — Materials Chemistry

Multimicroscopy at the same scale: Studying phase growth through Local Electrochemistry and High-Throughput Data Analysis The distribution of active sites is ubiquitous in many natural and industrial processes, due to the diverse physical or chemical features on an interface discernible only at the local level. However, a comprehensive understanding remains elusive due to the challenge of probing micro/nanoscopic regions and extracting and adequate statistical interpretation of the interfacial phenomenon. Moreover, the electrochemical response is generally recorded taken from macroscopic areas, which are then correlated with surface characterization obtained on a vastly different scale (~μm2), which is not necessarily representative of the macroscopic surface. This discrepancy may arise from the inadequate assessment of active site distributions, further complicating the interpretation of electrochemical data. Furthermore, it has been shown that the electrochemical magnitudes are also distributed at the micro/nanoscopic scale, contingent on the nature of the substrate [1,2]. Consequently, a more robust approach is warranted, utilizing techniques like scanning probe methods, which offer high spatial-temporal resolution, or in-situ techniques providing real-time insights into dynamic nanoscale processes.In this work, we have employed Scanning Electrochemical Cell Microscopy (SECCM) as a powerful and versatile scanning probe technique, capable of surveying the surface with high spatial-temporal resolution, by independently probing hundreds of different surfaces, providing a novel perspective and insights into the activity distribution and role of the substrate state for the synthesis of nanostructures. Instead of relying on the response of a single region for interpretation, which may not offer a comprehensive representation, we emphasize the significance of acknowledging the diversity at the microscopic scale by summarizing the electrochemical profiles with statistical measures and observing their temporal- or potential-dependent evolution. This local approach offers a new perspective on interfacial phenomena, justifying the need for high-throughput experimentation and data-driven analysis. By combining the local electrochemical information with other imaging methods, such as Scanning Electron Microscopy (SEM), that can be co-located with the same area on the same scale, we can establish unambiguous correlations and a more complete understanding of the electrochemical response at the local scale. This so-called multi-microscopy approach facilitates studying the relationship between physicochemical features of a substrate and nanostructures, fundamental for relating microscopic events to macroscopic properties of new deposits. This understanding is the bridge connecting the microscopic world to macroscopic outcomes, shedding light on how surface heterogeneities influence reaction rates and nanostructure properties. Our multi-microscopy strategy, using data-driven high throughput local investigation to bring a new perspective to old phenomena, could be easily replicated for different interfacial phenomena such as electrocatalysis, phase growth, or corrosion [3,4]. References1. D. Torres et al, Distribution of Copper Electrochemical Nucleation Activities on Glassy Carbon: A New Perspective Based on Local Electrochemistry, J. Electrochem. Soc. 169 (2022) 102513. 2. M. Bernal, D. Torres et al, A microscopic view on the electrochemical deposition and dissolution of Au with scanning electrochemical cell microscopy – Part I, Electrochim. Acta. 445 (2023) 142023.3. L.B. Coelho, D. Torres et al, Estimating pitting descriptors of 316 L stainless steel by machine learning and statistical analysis, Npj Mater. Degrad. 7 (2023) 82. 4. L.B. Coelho, D. Torres, et al, Probing the randomness of the local current distributions of 316 L stainless steel corrosion in NaCl solution, Corros. Sci. 217 (2023) 111104. Throughput ; Computer science ; Scale (ratio) ; Electrochemistry ; Phase (matter) ; Electrode ; Chemistry ; Telecommunications ; Physics Machine Learning in Materials Science ; Hydrogen embrittlement and corrosion behaviors in metals ; Electrochemical Analysis and Applications