Holistic Analysis of Electrochemical Images

<jats:p>Electrochemical imaging maps localized electrochemical behavior with micro- and nanoscale resolution, generating rich, high-dimensional datasets. Imaging has enabled spatially resolved studies of electrochemical reactions and interfacial processes in many fields, including electrocatalysis, corrosion, and energy-storage materials. Quantifying spatial heterogeneity in electrochemical response helps link local measurements to system-level behavior, enabling interpretation and prediction of experimental outcomes. Conventional methods of analyzing electrochemical images require expertise and care. Multiple plots and images must be compared, with judicious choices made about which locations and potentials to analyze, increasing the risk of overlooking subtle but important signatures of electrochemical reactivity and slowing experimental throughput. Principal component analysis (PCA) simplifies the analysis of large datasets by isolating key features while retaining essential information. In this work, we implement a PCA-based approach to electrochemical data analysis and demonstrate its utility on an open-access dataset acquired via scanning electrochemical cell microscopy. Our approach combines the strengths of single-potential current maps and single-location voltammetry in a holistic visualization that simultaneously presents spatial and voltammetric information. This intuitive visualization makes spatial and voltammetric trends easier to identify and reduces the need for manual selection of potentials or locations. In the dataset analyzed here, these visualizations highlight voltammetric differences among crystal grains, grain boundaries, and point outliers. The tools introduced allow investigators to focus on interpretation, reduce the risk of oversight, and accelerate the route from experiment to insight.</jats:p>