Accelerated Discovery of Solar Hydrogen Photocatalyst
R&D Focus Areas:
Photochemical and photocatalytic processes, Thermal water splitting, Materials modelling
Lead Organisation:
University of New South Wales (Sydney)
Partners:
City University of Hong Kong
Status:
Active
Start date:
May 2020
Completion date:
Estimated April 2023
Key contacts: Scientia Professor Rose Amal: r.amal@unsw.edu.au
Funding: AUD$540,000 – Australian Research Council (Discovery Project)
Project total cost:
AUD$1.129 million – combined cash and in-kind contributions
Project summary description:
Hydrogen is a desirable energy carrier which will be produced sustainably through solar photocatalysis, contributing to priority area of ‘Energy’ by meeting the challenge ‘New clean energy sources and storage technologies that are efficient, cost-effective and reliable’. The discovery of cost effective and active photocatalysts will promote environmental sustainability; extensive understanding of structure-property-activity mapping will enable process optimisation through efficient pathways leading to sizeable economic impact. Successful implementation of augmented intelligence will reduce decision-making errors facilitating chemical/process industries in waste management and preventing threats to human health and global resources. The adaptation of sophisticated machine learning to innovate solar photocatalysis hydrogen evolution is under question.
The aim of this project is to develop a novel hybrid photocatalyst design and discovery system, combining knowledge on photocatalysis (domain knowledge) with data-driven processing, as a rational and robust platform to accelerate the discovery of solar hydrogen photocatalysis. We aim to harvest scientific principles and integrate with robust protocols to obtain a machine-augmented rational workflow guiding and accelerating discovery of optimal catalysts for solar hydrogen production, solving a major bottleneck.
The project will contribute largely to Australia’s renewable energy sector; fundamental knowledge-based cognitive photocatalysis platform would be conveniently scalable and transferable to mechanistically relevant processes, such as ammonia synthesis and greenhouse gas reduction.
Related publications and key links:
- Machine learning for accelerated discovery of solar photocatalysts, ACS Catalysis, 2019, 9,12, p11774-11787, https://doi-org.wwwproxy1.library.unsw.edu.au/10.1021/acscatal.9b02531
Higher degree studies supported:
One PhD student based at University of New South Wales (Sydney)