Photocatalytic hydrogen production offers a promising route for the direct conversion of solar energy into green hydrogen [1-2]. Unlike electrochemical systems, it eliminates the need for solar panels and costly electrolysers, potentially reducing production costs. However, its overall efficiency remains relatively low.
Machine learning has recently gained traction as a tool to accelerate the discovery of efficient solar photocatalysts [3]. A crucial first step in implementing machine learning in this field is establishing a compatible data generation platform. High-throughput and combinatorial techniques are therefore vital for producing large, diverse datasets.
This project aims to develop a high-throughput experimental platform to screen visible-light-active photocatalyst libraries from our lab and global partners. The platform will combine automated X-ray diffraction, UV-vis spectroscopy, and high-throughput photocatalytic activity measurements. The data will be integrated into our CatDat database (https://solarcatalysis.ai) for machine learning analysis, enabling robust benchmarking of photocatalytic hydrogen production.
School
Chemical Engineering
Research Area
Chemical engineering | Photocatalysis | Renewable energy | Solar fuels
Suitable for recognition of Work Integrated Learning (industrial training)?
No
- Research environment
- Expected outcomes
- Supervisory team
- Reference material/links
The student will have the opportunity to join the Particles and Catalysis Research Group (PartCat) under the supervision of Dr Denny Gunawan and Prof Rose Amal. The student will have access to state-of-the-art laboratories equipped with advanced experimental facilities and computational tools for photocatalysis research. Additionally, the student will collaborate closely with international partners through the UNSW-led sunlight-to-X research hub (https://www.pcrg.unsw.edu.au/sunlighttoX), involving institutions from Australia, Japan, Singapore, Malaysia, and Indonesia. This project offers a multidisciplinary research environment where the student will develop a broad set of technical and professional skills, supporting future career opportunities in both academia and industry.
The student is expected to gain hands-on experience in photocatalyst synthesis, characterisation, activity measurement techniques, and the application of machine learning. The project also offers opportunities to collaborate with researchers from UNSW and external institutions, providing valuable interdisciplinary experience. Continuation of the research as an Honours thesis project is possible.
- Gunawan, D. et al. (2024). Materials Advances in Photocatalytic Solar Hydrogen Production: Integrating Systems and Economics for a Sustainable Future. Adv. Mater. 36, 2404618.
- oe, C. Y. et al. (2021). Advancing Photoreforming of Organics: Highlights on Photocatalyst and System Designs for Selective Oxidation Reactions. Energy Environ. Sci. 14, 1140-1175.
- Masood, H. et al. (2019). Machine Learning for Accelerated Discovery of Solar Photocatalysts. ACS Catal. 9, 12, 11774-11787.