A Kinetic-Driven Framework for Catalytic Oxygen Evolution Reaction Mechanisms

Thesis Abstract

The Oxygen Evolution Reaction (OER) remains a bottleneck for efficient electrochemical energy conversion due to sluggish kinetics and complex multi-step mechanisms that complicate catalyst design. This study addresses the need for a kinetic-centric framework capable of integrating mechanistic ambiguity with observable rate phenomena to predict catalyst performance and guide rational design of OER electrocatalysts. The aim is to develop and validate a kinetic-driven framework that links elementary steps, active-site dynamics, and real-time reaction rates to establish predictive relationships for OER mechanisms across transition-metal oxide and oxyhydride catalysts. Specific objectives are (i) to formulate a kinetic model that decomposes OER into discrete, charge-transfer–limited and chemistry-limited steps; (ii) to quantify rate constants and transfer coefficients for representative catalysts using spectroelectrochemical data; (iii) to test competing mechanistic hypotheses (e.g., lattice-oxygen participation vs. conventional adsorbate evolution pathways) within a unified framework; (iv) to employ machine-assisted parameter inference to identify dominant pathways under varying pH, potential, and electrolyte composition; and (v) to provide design rules that correlate kinetic descriptors with material properties. The methodology adopts a mixed-methods design combining experimental electrochemistry, operando spectroscopic interrogation, and computational parameterization. The population consists of commercially relevant catalysts including RuO2, IrO2, and grouped transition-metal oxides (Co, Ni, Fe-based systems) supported on conductive substrates. A representative set of 12 catalytic samples is synthesized via hydrothermal and sol-gel routes to ensure comparable surface areas and crystalline states. Electrochemical measurements are conducted in 0.1 M to 1.0 M KOH with deliberate variation of pH and temperature (20–60°C). Operando techniques include online electrochemical mass spectrometry (OLEMS) for gas evolution, operando X-ray absorption spectroscopy (XAS) to monitor oxidation state dynamics, and in situ Raman spectroscopy to track intermediate species. Data collection instruments also encompass chronoamperometry, cyclic voltammetry, and differential electrochemical mass spectrometry (DEMS) for Faradaic efficiency determination. The analytical framework employs a kinetic modeling approach grounded in transition-state theory and microkinetic analysis with rate constants inferred via Bayesian parameter estimation and Markov chain Monte Carlo sampling. Regression analyses (multivariate and ridge) and sensitivity analyses identify key kinetic descriptors, while network-based pathway analysis evaluates the plausibility of competing mechanisms. The theoretical basis integrates the electron-transfer–dominant Marcus theory with the lattice-oxide participation model, and the framework is instantiated as a modular set of coupled differential equations representing elementary steps with distinct potential dependencies. Model validation uses cross-validation against a reserved set of experimental traces and bootstrapping to quantify uncertainty. An a priori mechanistic hypothesis testing protocol compares lattice-oxygen involvement against conventional adsorbate evolution, using information criteria (AIC/BIC) to evaluate model adequacy. Expected findings include (i) quantitative ranking of rate-limiting steps across catalysts and operating conditions, (ii) identification of distinct kinetic fingerprints corresponding to different OER mechanisms, and (iii) robust descriptors that predict performance trends from material properties such as metal oxidation state adaptability, surface hydroxyl density, and electronic conductivity. The study anticipates that certain oxides will exhibit lattice-oxygen participation under alkaline conditions, while others will align with conventional adsorbate pathways, with kinetic parameters offering a unified explanation for observed overpotentials and Tafel slopes. Contributions to knowledge include a generalized kinetic framework enabling direct comparison of mechanistic hypotheses, a methodology for extracting mechanistic rate constants from operando data, and design guidelines linking kinetic descriptors to material design strategies. The main conclusion will articulate how kinetic modeling can reconcile disparate mechanistic narratives into a coherent predictive tool for OER catalyst development. Recommendations include adopting the kinetic framework for broader catalyst classes, integrating real-time spectroscopic monitoring into routine catalyst evaluation, and extending the approach to acidic media and photo-assisted OER contexts to further enhance transferability and impact.