Identifying protons trapped in hematite photoanodes through structure-property analysis

A robust methodology for PEC performance analysis of photoanodes using machine learning and analytical data

Machine learning (ML) is increasingly applied across various fields, including chemistry, for molecular design and optimizing reaction parameters. Yet, applying ML to experimental data is challenging due to the limited number of synthesized samples, which restricts its broader application in device development. In energy harvesting, photoanodes are crucial for solar-driven water splitting, generating hydrogen and oxygen. We explored electrodes like hematite and bismuth vanadate for photocatalytic uses, noting varied photoelectrochemical performances despite similar preparations. To understand this variability, we applied a data-driven ML approach, predicting photocurrent values and identifying key performance influencers even with limited experimental data in the research development of inorganic devices. We have utilized multiple machine learning algorithms to predict the target value in the calculation process, where the contributions of the dominant descriptors were unknown. We introduced a novel methodology, incorporating clustering to manage multicollinearity from correlated analytical data and Shapley analysis for clear interpretation of contributions to performance prediction. This method was validated on hematite and bismuth vanadate, showing superior predictability and factor identification, and then extended to tungsten oxide and bismuth vanadate heterojunction photoanodes. Despite their complexity, our approach achieved determination coefficients (R2) with a prediction accuracy over 0.85, successfully pinpointing performance-determining factors, demonstrating the robustness of the new scheme in advancing photodevice research.

Correlating activities and defects in (photo)electrocatalysts using in-situ multi-modal microscopic imaging

Photo(electro)catalysts use sunlight to drive chemical reactions such as water splitting. A major factor limiting photocatalyst development is physicochemical heterogeneity which leads to spatially dependent reactivity. To link structure and function in such systems, simultaneous probing of the electrochemical environment at microscopic length scales and a broad range of timescales (ns to s) is required. Here, we address this challenge by developing and applying in-situ (optical) microscopies to map and correlate local electrochemical activity, with hole lifetimes, oxygen vacancy concentrations and photoelectrode crystal structure. Using this multi-modal approach, we study prototypical hematite (α-Fe2O3) photoelectrodes. We demonstrate that regions of α-Fe2O3, adjacent to microstructural cracks have a better photoelectrochemical response and reduced back electron recombination due to an optimal oxygen vacancy concentration, with the film thickness and extended light exposure also influencing local activity. Our work highlights the importance of microscopic mapping to understand activity, in even seemingly homogeneous photoelectrodes.

Differentiating Defects and Their Influence on Hematite Photoanodes Using X-ray Absorption Spectroscopy and Raman Microscopy

A high degree of variability in behavior and performance of hematite as photoanodes for the oxygen evolution reaction signifies a need to improve our understanding of the interplay between defects and photoelectrochemical performance. We approach this problem by applying structure-property analysis to a series of hematite samples synthesized under either O₂ or N₂ environments such that they exhibit highly variable performance for photoelectrocatalytic oxygen evolution. X-ray absorption fine-structure spectroscopy and Raman spectroscopy provide parameters describing the structure of samples across the series. Systematic comparisons of these parameters to those describing photoelectrochemical performance reveal different defects in samples prepared under N₂ or O₂. Distinct correlations between both the iron oxidation state and charge carrier density with photoelectrocatalytic performance lead to assignment of the primary defects as oxygen vacancies (N₂) and iron vacancies (O₂). Differences in the structural distortions caused by these defects are seen in correlations between short-range structural parameters and photoelectrochemical behavior. These distortions are readily observed by Raman spectroscopy, suggesting that it may be possible to calibrate the width, energy, and intensity of peaks in Raman spectra to enable direct analysis of defects in hematite photoanodes.