A combination of combinatorial AACVD and machine learning for high-throughput investigation of photoelectrochemical performance of Mo-doped BiVO 4 Zhi-Peng Lin a , Haochen Li b , Alex M. Ganose a,b , Andreas Kafizas a,c a Department of Chemistry, Molecular Science Research Hub, Imperial College London, White City, London, UK, b Department of Chemistry, University College London, 20 Gordon Street, London, WC1H 0AJ, UK, c London Centre for Nanotechnology, Imperial College London, UK A combinatorial dual-inlet aerosol-assisted chemical vapor deposition (AACVD) approach was developed to grow libraries of Mo-doped BiVO 4 with ranging Mo-doping level, film thickness, morphology and crystallinity 1 ( Figure (a) ). By tuning the deposition temperature (350°C to 450°C), BiVO 4 precursor concentration (5 mM, 7.5 mM, 10 mM) and flow rate (0.2–1.3 L/min), Mo precursor concentration (0 mg/mL to 0.5 mg/mL) flask flow rate (0–0.5 L/min), 30 unique plates of samples were synthesized (i.e. 1080 distinct samples). The photoelectrochemical (PEC) water oxidation performance of these libraries was screened using a novel capillary- based photoelectrochemical (PEC) probe ( Figure (b) ).
A Machine Learning (ML) approach was employed to determine the numerical relationships between the physicochemical properties and PEC performances of the samples, leveraging the multidimensional analysis capabilities of ML 2 . This study demonstrates that combining combinatorial chemical vapour deposition with a high throughput performance screening method and ML enables the rapid, systematic investigation of metal-doped or multi-metal systems in terms, providing new insights for research in related fields 3 References 1. Zhao, S., Jia, C., Shen, X., Li, R., Oldham, L., Moss, B., Tam, B., Pike, S., Harrison, N., Ahmad, E. and Kafizas, A., 2024. The aerosol-assisted chemical vapour deposition of Mo-doped BiVO 4 photoanodes for solar water splitting: an experimental and computational study. Journal of Materials Chemistry A , 12 (39), pp.26645-26666. 2. Lin, Z. P., Li, Y., Haque, S. A., Ganose, A. M., & Kafizas, A. (2024). Insights from experiment and machine learning for enhanced TiO 2 coated glazing for photocatalytic NO x remediation. Journal of Materials Chemistry A , 12 (22), 13281-13298. 3. Chadwick, N., Sathasivam, S., Kafizas, A., Bawaked, S.M., Obaid, A.Y., Al-Thabaiti, S., Basahel, S.N., Parkin, I.P. and Carmalt, C.J., 2014. Combinatorial aerosol assisted chemical vapour deposition of a photocatalytic mixed SnO 2/TiO 2 thin film. Journal of Materials Chemistry A , 2 (14), pp.5108-5116.
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