Data-Guided Exploration of BiVO4 Synthesis Conditions for Enhanced Photocatalytic Oxygen Evolution

BiVO4 is a visible-light-responsive oxygen-evolution photocatalyst whose activity depends strongly on synthesis-derived phase and microstructural characteristics. Here, Bayesian optimization (BO), used as an established sequential experimental-design method rather than as a newly proposed algorithm, was applied to explore liquid–solid phase BiVO4 synthesis conditions. Gaussian process regression related synthesis variables to oxygen evolution activity, and a genetic algorithm maximized acquisition functions to propose experiments. In a literature-initialized exploratory study, the highest observed operational activity index was 101.2 μmol h–1 g–1 L, and X-ray diffraction (XRD) indicated coexisting monoclinic and tetragonal BiVO4. In an experiment-initialized study conducted under a common evaluation protocol, all four proposed conditions exceeded the initial best observed activity of 40.5 μmol h–1, and the highest observed value was 52.0 μmol h–1; the corresponding sample was predominantly monoclinic by XRD. The best-performing conditions in the two studies shared nearly equimolar Bi and V precursor concentrations of approximately 0.19 M, HNO3 concentrations of 0.51–0.57 M, solution volumes near 51 mL, and prolonged room-temperature stirring. Because each condition was evaluated once, no statistical significance or experimental variance could be determined. Moreover, the literature-derived activity index does not correct for all differences in irradiation and reactor configuration, and no alternative-search benchmark or additional physicochemical characterization was performed. Accordingly, the results are interpreted as proof-of-concept evidence that sequential data-guided experimentation can identify promising BiVO4 synthesis conditions, while the mechanistic origin and comparative search efficiency remain subjects for future study.