Enhancement of photoelectrochemical performance of Bismuth vanadate (BiVO4)-Based photoanode by building phase-junction configurations

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.

Identification of the Contributing Factors to the Photoelectric Conversion Efficiency for Hematite Photoanodes by Using Machine Learning

Hematite has gained significant attention in the field of photocatalysis as one of the most promising materials for the photoanode of photoelectrochemical (PEC) water splitting due to visible light absorption and the abundance of availability. However, its performance improvement process suffers from a serious bottleneck due to "sample variation" and "inactivity". However, the physical origin of them has not yet been elucidated. To address these issues, we have developed a machine learning (ML) strategy using a combination of various analytical data of hematite photoanodes to discern "active/inactive" and identify the dominant factors. For the demonstration purpose of the ML strategy, we picked up one of the dominant factors, the interfacial resistivity between hematite and FTO, which has not generally been explored as a first candidate in the improvement of photocatalytic materials. The operational parameters for the sample preparation were optimized to modify the selected physical property. Along with the improvement of the selected resistivity, we found that the other dominant descriptors related to the properties of bulk hematite and the surface facet were also modified and help improve the PEC performance.

Study on g-C3N4/BiVO4 Binary Composite Photocatalytic Materials

Recent studies have shown that the composite of semiconductor photocatalytic materials and g-C₃N₄ can effectively inhibit photocatalytic carrier recombination and enhance the adsorption performance of the composite photocatalytic materials, so that the composite photocatalyst has stronger photocatalytic activity. In this paper, three kinds of graphitic carbon nitride photocatalyst g-C₃N₄ with different morphologies were prepared using the same precursor system by the chemical cracking method. After characterization and application, the sample with the most significant photocatalytic activity was selected and the g-C₃N₄/BiVO₄ heterostructure was synthesized by the simple solvent evaporation method, then the photocatalytic experiment was carried out. The results show that, when the content of BiVO₄ in the composite sample is 1%, the photocatalytic activity of RhB was the highest, and the degradation rate could reach 90.4%. The kinetic results showed that the degradation of RhB was consistent with the quasi-primary degradation kinetic model. The results of the photocatalytic cycle experiment show that the photocatalytic performance remains unchanged and stable after four photocatalytic cycles. The existence of a g-C₃N₄/BiVO₄ binary heterojunction was confirmed by UV/Visible diffuse reflection (UV-DRS) and photoluminescence (PL) experiments. Owing to the Z-type charge process between BiVO₄ and g-C₃N₄, efficient carrier separation was achieved, thus enhancing the photocatalytic capacity. This work provides a new idea for the study of heterojunction photocatalytic materials based on g-C₃N₄.