Photocatalytic H2 production has emerged as an attractive approach for directly converting solar energy into green H2 [1]. A key advantage is avoiding the need for huge solar PV power stations and expensive electrolysers, resulting in potentially lower capital expenditures compared to PV-electrolysis [2]. However, the poor solar-to-H2 (STH) efficiency, currently between 1% and 2%, hampers its widespread application. Our preliminary techno-economic analysis suggests that increasing the STH efficiency from 1% to 5% could reduce costs by up to 75% [3]. To achieve this significant improvement, this research focuses on designing sulphide-based heterostructure photocatalysts for highly efficient, stable, and cost-effective photocatalytic solar H2 production.


Chemical Engineering

Research Area

Renewable Energy | Solar Fuel Conversion | Nanomaterials | Photocatalysis

The student will have the opportunity to work in the Particles and Catalysis Research Group (PartCat) under the guidance of Scientia Professor Rose Amal. The student will have the access to well-equipped laboratories with experimental facilities and computational tools for photocatalysis research. The student will work in a multidisciplinary research environment and learn various functional skills to facilitate a future career in academia or industry.

The student is expected to gain experience in nanomaterials synthesis and characterisation, as well as photocatalytic activity measurements. The project will also allow the student to collaborate with other research students, gaining valuable interdisciplinary experience. The generated knowledge and data will contribute to a peer-reviewed international journal publication. Continuing the research as a 4th year honours thesis project is possible.

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[1] D. Soldani, M. Shore, J. Mitchell, M. Gregory, "The 4G to 5G network architecture evolution in Australia," Australian Journal of Telecoms and Digital Economy, Vol. 6, N. 4, Dec. 2018.

[2] M. Lanzagorta, “Underwater communications,” Synthesis lectures oncommunications, vol. 5, no. 2, pp. 1–129, 2012

[3] Z. Wei et al., "Orthogonal Time-Frequency Space Modulation: A Promising Next-Generation Waveform," in IEEE Wireless Communications, vol. 28, no. 4, pp. 136-144, August 2021

[4] Y. K. Enku et al., "Two-Dimensional Convolutional Neural Network-Based Signal Detection for OTFS Systems," in IEEE Wireless Communications Letters, vol. 10, no. 11, pp. 2514-2518, Nov. 2021

[5] R. A. McCarthy and A. Sengupta , "Underwater channel estimation exploiting multipath feature morphology", The Journal of the Acoustical Society of America, vol. 149, pp 983-996, 2021

[6] N. Ansari, A. S. Gupta and A. Gupta, "Underwater acoustic channel estimation via CS with prior information," OCEANS 2017 - Aberdeen, 2017, pp. 1-5, doi: 10.1109/OCEANSE.2017.8084965.

[7] Y. Fu, S. Wang, C. Wang, X. Hong, and S. McLaughlin, "Artificial intelligence to manage network traffic of 5G wireless networks," IEEE/ACM Trans. Netw., vol. 32, no. 6, pp. 58–64, Nov./Dec. 2018.