Taste of Research Summer Scholarships

2025 Projects - School of Photovoltaic and Renewable Energy Engineering

Photovoltaic and Renewable Energy Engineering Research Areas

Photovoltaic and Renewable Energy Engineering Projects

No School Research Area

Project Title:	Adaption of solar cell characterisation techniques to light emitting diodes
Name of Supervisor:	Dr. Yan Zhu
Email of Supervisor:	yan.zhu@unsw.edu.au
Name of Joint/Co-Supervisor:	Prof. Ziv Hameiri
Email of Joint/Co-Supervisor:	.
School:	School of Photovoltaic and Renewable Energy Engineering
Faculty Research Area (Theme):	Energy Systems, Renewable and Non-Renewable
Applicable to other Engineering schools/disciplines:
Terms:	Term 2
Abstract:	A light emitting diode (LED) is a device converting electrical energy into light. With the advantages of lower power consumption and longer lifetime, it is widely used in the lighting and display market with an increasing market share. A solar cell is a device converting light into electricity, operating using the reverse process of an LED. The physics and device structures of LEDs and solar cells have many similarities. Many requirements for making a good solar cell also apply to making a good LED. Therefore, many characterisation techniques for solar cells can also be applied to inspect the material and device quality of LEDs. For example, luminescence imaging, which is widely used for measuring the uniformity of solar cells, can potentially be adapted to inspect the light uniformity of LED. Charge carrier lifetime spectroscopy, which is widely used to investigate recombination-active defects in solar cells, can be adapted to investigate the non-radiative recombination in LEDs. In this project, we will adapt several advanced characterisation techniques for solar cells to the inspection of LEDs. This project provides a unique opportunity to perform multi-disciplinary research. You will have a chance to learn knowledge about both solar cells and LEDs and use innovation to make connections between these two fields.
Research Environment:	In this project, you will work with Dr. Yan Zhu. He has extensive experience in the characterisation of solar cells and has developed several novel inspection techniques. He will share with you the necessary knowledge and skills to start the project and help you build your own knowledge during the project. You will also join the ACDC research team at UNSW (Artificial intelligence, Characterisation, Defects and Contacts). The research team is made up of around 20 researchers and students and we work on a wide spectrum of research activities to make the world a better place. The group has a friendly and open environment. You will enjoy not only research activities but also many social activities with us. We will ensure a smooth start to your project, and throughout its duration, you will have a better chance to develop independent research skills.
Novelty and Contribution:	.
Expected Outcomes:	The outcome of this project will be several novel characterisation methods for LEDs. These methods will be based on the advanced characterisation techniques we previously developed for solar cells. You will help us leverage our expertise in photovoltaics in the new field of LEDs. We will aim to develop a prototype of the new characterisation technique and perform some proof-of-concept measurements on LEDs. This is a chance to present the outcomes in academic conferences and/or publish them as a journal paper. Our previous ToR students presented their work in the US and Japan! More importantly, the major outcome of the project will be you, an undergraduate student who enjoys doing research, and understanding the basic skills needed for conducting good research.
Reference Material Links:	"In our lab, we have a wide range of optical and electrical components to develop customised characterisation systems. Numerous unique characterisation techniques have been previously developed by our group. Therefore, we have both the necessary hardware and experience to help you realise your novel ideas. We also collaborate with research institutes and industry to obtain suitable LED samples for testing the characterisation techniques to be developed. Through our regular group meetings, you will have the chance to learn various cutting-edge research topics. At the same time, you can also get advice from all the members of our research team. Website of our research group: https://www.acdc-pv-unsw.com/"
Will the student visit the premises of an industry partner, or undertake any activity on premises external to UNSW?	No

Project Title:	Apply machine learning to model the dynamics of charge carriers in solar cells
Name of Supervisor:	Brendan Wright
Email of Supervisor:	brendan.wright@unsw.edu.au
Name of Joint/Co-Supervisor:	Ziv Hameiri
Email of Joint/Co-Supervisor:	.
School:	School of Photovoltaic and Renewable Energy Engineering
Faculty Research Area (Theme):	Energy Systems, Renewable and Non-Renewable
Applicable to other Engineering schools/disciplines:
Terms:	Term 2
Abstract:	"A fundamental understanding of material and device physics is key to the design of solar cells with high efficiency and long operational life. however the physical processes of light absorption (charge carrier generation) and subsequent electrical power generation or loss (transport and recombination) are complex and difficult to directly monitor. Transient charge extraction measurements can provide a snapshot of these processes, and are used to assess material and device characteristics (electrical performance, material quality, presence of defects). however current methods for processing these results are limited in scope and require significant manual analysis and interpretation before meaningful insights are obtained. This project will use machine learning systems (generative representation learning) to model the transient charge extraction measurement, thereby enabling an improved understanding of the dynamics of charge carriers (transport, recombination) in operational solar cells, and provide a predictive model to classify behaviour and investigate underlying causal relationships."
Research Environment:	Through this research project you will gain an understanding of the physics of charge carrier solar cells, from light absorption to current extraction, and how these processes are influenced by material type and device architecture, as well as develop valuable skills regarding time-series data analysis and the development and training of machine learning models Applicant suitability: familiarity (basic skills, experience desirable) with coding in python (data processing, analysis, visualisation); basic understanding of machine learning systems (statistics, optimisation).
Novelty and Contribution:	.
Expected Outcomes:	This will enable the characterisation and analysis of, as well as development of new design rules for, novel photovoltaic material systems and device architectures. Deliverables: prepared training datasets (charge extraction transients); performance validation (statistical assessment) and visual examples of model output (generated charge carrier transient prediction) for each configuration of learning model; analysis of the influence of varying training dataset, model architecture, and learning methodology.
Reference Material Links:	n/a
Will the student visit the premises of an industry partner, or undertake any activity on premises external to UNSW?	No

Project Title:	Contactless characterisation of the electrical properties of solar cells
Name of Supervisor:	Dr. Yan Zhu
Email of Supervisor:	yan.zhu@unsw.edu.au
Name of Joint/Co-Supervisor:	Prof. Ziv Hameiri
Email of Joint/Co-Supervisor:	.
School:	School of Photovoltaic and Renewable Energy Engineering
Faculty Research Area (Theme):	Energy Systems, Renewable and Non-Renewable
Applicable to other Engineering schools/disciplines:
Terms:	Term 2
Abstract:	The global market for photovoltaics (PV) has seen tremendous growth during the last few decades. With continuously increasing energy conversion efficiency and reduced cost, the electricity generated from PV is now cheaper than coal-fired power plants in Australia and many other places in the world. Characterisation plays a vital role in the optimisation of solar cells and making them more efficient. Conventional characterisation techniques usually require making electrical contacts with the measured solar cells, which limits the measurement throughput and increases the chance of damaging the measured cells. In this project, we aim to develop contactless characterisation techniques for solar cells. This involves the adaption of advanced characteristion techniques such as photoluminescence imaging; and the application of machine learning algorithms for advanced data processing. You will be able to develop inspection techniques that can help improve the efficiency of state-of-the-art silicon solar cells, as well as multijunction solar cells.
Research Environment:	In this project, you will work with the ACDC research team (www.acdc-pv-unsw.com). The team focuses on the characterisation of solar cells. It is made up of around 20 researchers and students. The group has a very friendly environment. We have students who finished a ToR project with us, stayed for their 4th-year thesis, and later transitioned to a PhD in the group. You will mainly work with Dr. Yan Zhu. He has developed several novel characterisation techniques which have been well recognised in international PV research conferences. Many other experts in PV, including the inventor of PL imaging, will also help and guide you during this project. We will ensure a smooth start to your project, and throughout its duration, you will have a better chance to develop independent research skills.
Novelty and Contribution:	.
Expected Outcomes:	Together, we will develop novel characterisation techniques to measure the current-voltage characteristics of solar cells without contacting them. Potentially, we will also develop a prototype of a new characterisation tool. There is a chance to publish the outcomes as a conference paper or journal paper. Our previous ToR students presented their work in the US and Japan! Nevertheless, the most important outcome of the project will be you, an undergraduate student who enjoys doing research, and understanding the basic skills needed for conducting good research.
Reference Material Links:	"The novel characterisation techniques to be developed in this project will benefit from the extensive research experience in novel luminescence imaging techniques of our group. We have several unique homemade luminescence imaging setups in our lab, all customised to meet different purposes. We will either modify one of our existing setups or build a completely new one. State-of-the-art solar cells to be investigated have also been provided by our industrial partners. Through the regular group meetings, all the other members of our research team will also use their expertise in characterisation to help you solve the problems you may encounter. We also have experts in numerical simulation, which is beneficial for our understanding of the measurement results. Website of our research group: https://www.acdc-pv-unsw.com/"
Will the student visit the premises of an industry partner, or undertake any activity on premises external to UNSW?	No

Project Title:	Development of SLAM-Based Autonomous Inspection System for Solar Power Plants
Name of Supervisor:	Jim Joseph John
Email of Supervisor:	j.joseph_john@unsw.edu.au
Name of Joint/Co-Supervisor:	Moonyong Kim, and Bram Hoex
Email of Joint/Co-Supervisor:	.
School:	School of Photovoltaic and Renewable Energy Engineering
Faculty Research Area (Theme):	Intelligent & Autonomous Systems
Applicable to other Engineering schools/disciplines:
Terms:	Term 2
Abstract:	This project aims to develop a Simultaneous Localization and Mapping (SLAM) system for autonomous inspection of solar power plants. The system will enable mobile robots or drones to navigate, map, and inspect large-scale solar farms in real time without reliance on GPS, which is often unreliable in certain terrains or under panel shading.
Research Environment:	The research will be conducted in a multi-disciplinary setting combining expertise in AI and solar energy systems.
Novelty and Contribution:	.
Expected Outcomes:	A SLAM-based navigation system for autonomous inspection robots or drones in solar farms. High-resolution 3D maps of solar plants with precise localisation.
Reference Material Links:	https://autowarefoundation.github.io/autoware-documentation/main/how-to-guides/integrating-autoware/creating-maps/open-source-slam/
Will the student visit the premises of an industry partner, or undertake any activity on premises external to UNSW?	No

Project Title:	High precision modelling for detection of system underperformance
Name of Supervisor:	Dr Phillip Hamer
Email of Supervisor:	p.hamer@unsw.edu.au
Name of Joint/Co-Supervisor:	Dr Moonyong Kim, Dr Shukla Poddar
Email of Joint/Co-Supervisor:	.
School:	School of Photovoltaic and Renewable Energy Engineering
Faculty Research Area (Theme):	Energy Systems, Renewable and Non-Renewable
Applicable to other Engineering schools/disciplines:
Terms:	Term 2
Abstract:	High precision models, whether physical or AI based, are widely used for detecting underperformance in utility scale solar plants. However, much of the data for modern plants is not effectively utilised due to the operation of the inverters. Most systems are "oversized", such that the inverter cannot effectively use the maximum power of all connected panels during periods of high irradiance, which results in "clipping". This is where the system output is fixed at the inverter capacity. In some cases this can occur for the majority of a clear-sky day. In this project the student will join a team looking at the development of high precision modelling methods that allow this data to be used for fault detection by accurately modelling the expected current and voltage characteristics at the inverter. It is expected this can be leveraged to create pseudo I_V curves that will not only aid in detection of underperformance but aid in diagnosing the underlying causes. Students with backgrounds in renewable energy, electrical engineering, computing science or software engineering are encouraged to apply.
Research Environment:	The student will work as part of the modelling group at SPREE, under the direction of Professor Bram Hoex. They will be supported by a team of 4 postdocs and HDR students. The group works with data from commercial partners, as well as publically available datasets. The work will consist of data analysis, programming and physical modelling, with support provided for area's outside the student's core competence.
Novelty and Contribution:	.
Expected Outcomes:	Demonstration of the ability to predict I_V characteristics of a PV system under inverter clipping and re-creation of a pseudo I_V curve. Quantification of the uncertainty in the resulting model parameters and suggestions for improvement. Students will experience a research environment including interaction with commercial partners, as well as broaden their existing skills to cover new areas.
Reference Material Links:	"[1] 2024 Solar Risk Assessment, kWh Analytics, Available at https://kwhanalytics.com/industry-report/ [2] Balfour, John, et al. ""Masking of photovoltaic system performance problems by inverter clipping and other design and operational practices."" Renewable and Sustainable Energy Reviews 145 (2021): 111067. [3] Micheli, Leonardo, et al. ""Quantifying the impact of inverter clipping on photovoltaic performance and soiling losses."" Renewable Energy 225 (2024): 120317."
Will the student visit the premises of an industry partner, or undertake any activity on premises external to UNSW?	No

Project Title:	Low-cost solar cell metallization via plating
Name of Supervisor:	Sisi Wang
Email of Supervisor:	sisi.wang@unsw.edu.au
Name of Joint/Co-Supervisor:	Dr Yuchao Zhang, Dr Li Wang
Email of Joint/Co-Supervisor:	.
School:	School of Photovoltaic and Renewable Energy Engineering
Faculty Research Area (Theme):	Energy Systems, Renewable and Non-Renewable
Applicable to other Engineering schools/disciplines:
Terms:	Term 2
Abstract:	"Metallization is the process of depositing metal contacts on the surface of the solar cell to collect and transport the electrical current generated by the solar cell. Plating is an electrochemical process which involves using an electrical current to reduce dissolved metal ions so that they form a layer of metal. The plated metal contacts are typically in crystalline structure with excellent conductivity. Plating, particularly electroplating, has been a common process in the semiconductor industry for metallization. It has been used in commercial scale in the PV industry in Buries Contact Solar Cells and Laser Doped and Selective Emitter Solar Cells. The adaption and use of plating technologies in the solar industry are growing. It has been driven by the need for more cost-effective alternatives to traditional screen-printed solver contacts. The PV industry required over 14% of global silver supply in 2022. Considering how the world needs to expand the scale by more than a factor of 10 within the coming decade, the material shortage may become a significant issue towards the sustainability of the industry. Materials with lower cost could be an ideal option to lower the silver consumption either by replacing silver fully or partially. Their conductivity and impact on cell performance need to be investigated. This project will aim to explore the possibility of other alternative materials with low cost as metal electrode via plating. The contact and series resistance of metal lines will be characterised. The impact on cell performance will be investigated. "
Research Environment:	The student will work closely with post-docs experienced in solar cell metallization including plating, screen-printing, and all the relevant characterisation skills and theoretical knowledge. The student will perform experiments in SPREE research labs. This ToR project will be mainly supervised and supported by Dr Sisi Wang, Dr Yuchao Zhang and Dr Li Wang. Besides, other technical support can also be provided by the rest of the research team, including A. Prof. Brett Hallam.
Novelty and Contribution:	.
Expected Outcomes:	"Throughout the project duration, students are expected to carry out/obtain: - In-depth literature review to understand the metallization of solar cells - Understanding of different and most recent metallization technologies - Investigation of plating metal contacts using alternative materials and its impact on conductivity and cell performance - Opportunity for conference/journal paper publication - Opportunity for thesis project/PhD program "
Reference Material Links:	"Y. Zhang et al. En. Environ. Sci. 2021 https://doi.org/10.1039/D1EE01814K Tepner S, Lorenz A. Printing technologies for silicon solar cell metallization: A comprehensive review. Prog Photovolt Res Appl. 2023; 31(6): 557-590. doi:10.1002/pip.3674 Lennon, A., Yao, Y. and Wenham, S. (2013), Evolution of metal plating for silicon solar cell metallisation. Prog. Photovolt: Res. Appl., 21: 1454-1468. https://doi.org/10.1002/pip.2221"
Will the student visit the premises of an industry partner, or undertake any activity on premises external to UNSW?	No

Project Title:	Machine Learning for Accurate Soiling Detection and Cleaning of Solar Panels
Name of Supervisor:	Abhnil Prasad
Email of Supervisor:	abhnil.prasad@unsw.edu.au
Name of Joint/Co-Supervisor:	Merlinde Kay and Ziv Hameiri
Email of Joint/Co-Supervisor:	.
School:	School of Photovoltaic and Renewable Energy Engineering
Faculty Research Area (Theme):	Energy Systems, Renewable and Non-Renewable
Applicable to other Engineering schools/disciplines:
Terms:	Term 2
Abstract:	As the world moves towards achieving net-zero emission targets, it is becoming increasingly important to harness renewable sources of energy such as solar power. While the installation costs for solar energy systems are rapidly declining, the costs associated with cleaning from soiling, which is the accumulated dirt on solar panels that can degrade performance and energy yield, remains a major concern for farm operators. This project aims to address this issue by exploring smart detection approaches for calculating soiling rates through the analysis of weather and panel output datasets gathered from a research facility in Australia. The project will compare physical and artificial intelligent approaches used for time-series analysis and change-point detection for soiling loss. The study will introduce concepts for data preparation, data analytics, machine learning, renewable energy systems modelling, and energy meteorology using open-source Python packages and publicly available datasets. By normalizing observed panel outputs by modelled power under clear and clean conditions, the study will accurately calculate soiling loss rates and provide insights into ways to reduce cleaning costs while maintaining optimal panel performance. Overall, this project will contribute to the development of efficient and cost-effective renewable energy systems, which are essential for achieving net-zero emissions targets.
Research Environment:	High-Performance Computing Environment with applicants having a basic understanding of time series analysis and renewable energy systems modelling. A student with coding experience in python is desirable.
Novelty and Contribution:	.
Expected Outcomes:	• A broad survey of artificial intelligent and physical-based approaches for calculating soiling rates. • Characterization of change points for soiling accumulation and cleaning and the calculation of soiling rates.
Reference Material Links:	Michael G. Deceglie, Leonardo Micheli and Matthew Muller, "Quantifying Soiling Loss Directly From PV Yield," in IEEE Journal of Photovoltaics, 8(2), pp. 547-551, 2018 DOI: 10.1109/JPHOTOV.2017.2784682
Will the student visit the premises of an industry partner, or undertake any activity on premises external to UNSW?	No

Project Title:	Multi-Agent AI System for Advanced Solar Power Plant Modeling and Optimization
Name of Supervisor:	Jim Joseph John
Email of Supervisor:	j.joseph_john@unsw.edu.au
Name of Joint/Co-Supervisor:	Moonyong Kim, and Bram Hoex
Email of Joint/Co-Supervisor:	.
School:	School of Photovoltaic and Renewable Energy Engineering
Faculty Research Area (Theme):	Energy Systems, Renewable and Non-Renewable
Applicable to other Engineering schools/disciplines:
Terms:	Term 2
Abstract:	This project aims to develop a multi-agent AI system to enhance the modeling, performance analysis, and optimization of solar power plants. The system will integrate multiple AI agents, each specialising in different tasks. These agents will collaborate and communicate through a shared framework, improving decision-making efficiency, reducing operational risks, and optimising power generation.
Research Environment:	The student will work in the UNSW Electric Drive Lab, collaborating with researchers in magnetic bearings, motor drives, and power electronics. They will utilize advanced transducers to analyze rotor stability and lift-up characteristics. High-power, high-speed inverters will enable controlled magnetic bearing excitation, while dSPACE MicroLabBox and microprocessor controllers will facilitate real-time control implementation. High-bandwidth measurement devices and digital oscilloscopes will capture transient responses, ensuring precise system evaluation. A three-phase power supply with protection systems will provide a safe testing environment, supporting the development of efficient and high-performance homopolar magnetic bearings.
Novelty and Contribution:	.
Expected Outcomes:	A multi-agent AI framework capable of real-time solar plant simulation, monitoring, and optimisation. A scalable and customisable AI solution that can be applied across different solar power plant configurations.
Reference Material Links:	"https://www.deeplearning.ai/short-courses/multi-ai-agent-systems-with-crewai/ https://towardsdatascience.com/multi-ai-agent-systems-101-bac58e3bcc47/"
Will the student visit the premises of an industry partner, or undertake any activity on premises external to UNSW?	No

Project Title:	Optimising a Silicon Cell for Singlet Fission on Silicon Tandems
Name of Supervisor:	Shona McNab
Email of Supervisor:	shona.mcnab@unsw.edu.au
Name of Joint/Co-Supervisor:	Jingnan Tong, Alison Ciesla
Email of Joint/Co-Supervisor:	.
School:	School of Photovoltaic and Renewable Energy Engineering
Faculty Research Area (Theme):	Energy Systems, Renewable and Non-Renewable
Applicable to other Engineering schools/disciplines:
Terms:	Term 2
Abstract:	Single-junction silicon solar cells are reaching the limit of their efficiency. To fabricate solar cells with higher efficiencies, tandem devices are being developed, which have multiple junctions to utilise a greater proportion of the solar spectrum. An alternative method to achieve high efficiency is in a silicon/singlet fission device. In this structure the low energy photons are collected by the silicon cell, with minimal energy losses, while the high energy photons are absorbed in an organic layer which undergoes 'singlet fission' to create two excitons from each photon. These two excitons are transferred to the silicon cell and contribute twice as much energy from the photon, compared to in a conventional single-junction solar cell. To maximise the efficiency of these devices the silicon cell must be optimised to collect the excitons from the singlet fission material. This project will investigate the best method to fabricate the silicon cell to optimise the silicon cell efficiency and enhance the benefit of the singlet fission layer.
Research Environment:	The student will work closely with Shona McNab who specialises in fabrication and characterisation of silicon cells and have the oversight of Alison Ciesla and other members of the OMEGA silicon team. Experimental work will involve fabricating samples using evaporation and screen printing and testing using a range of industrially relevant characterisation techniques (Light IV, EQE, PL).
Novelty and Contribution:	.
Expected Outcomes:	The student will become familiar with some key fabrication tools for making silicon cells and a range of characterisation tools. It is hoped that by the end of the project the student will have opimised some of the key aspects in creating a silicon cell with an enhancement from singlet fission.
Reference Material Links:	https://www.omegasilicon.solar
Will the student visit the premises of an industry partner, or undertake any activity on premises external to UNSW?	No

Project Title:	Use machine learning to identify defects in solar cell luminescence images
Name of Supervisor:	Brendan Wright
Email of Supervisor:	brendan.wright@unsw.edu.au
Name of Joint/Co-Supervisor:	Ziv Hameiri
Email of Joint/Co-Supervisor:	.
School:	School of Photovoltaic and Renewable Energy Engineering
Faculty Research Area (Theme):	Energy Systems, Renewable and Non-Renewable
Applicable to other Engineering schools/disciplines:
Terms:	Term 2
Abstract:	Accurately identifying defects in solar modules from luminescence images is currently a manual process and requires experienced domain experts. this approach is time-consuming, prone to error, and does not sufficiently scale to that required by the growing photovoltaic energy industry. This project will use machine learning models (generative representation learning) to identify and classify visible defects in luminescence images of photovoltaic modules.
Research Environment:	Through this research project you will gain an understanding of common defects in solar cells and modules, and how these present themselves in luminescence images, as well as develop valuable skills regarding image data analysis and the development and training of machine learning models. Applicant suitability: familiarity (basic skills, experience desirable) with coding in python (data processing, analysis, visualisation); basic understanding of machine learning systems (statistics, optimisation).
Novelty and Contribution:	.
Expected Outcomes:	This will enable a robust, scalable, and automated methodology to monitor and accurately identify defective modules, such that they can be replaced, and either re-used, repaired or recycled. Deliverables: prepared training datasets (cell luminescence images); performance validation (statistical assessment) and visual examples of model output (generated solar cell luminescence images) for each configuration of learning model; analysis of the influence of varying training dataset, model architecture, and learning methodology.
Reference Material Links:	n/a
Will the student visit the premises of an industry partner, or undertake any activity on premises external to UNSW?	No

Projects offered by other Engineering Schools that may be of interest are:

Project Title:	Photoreforming of Glycerol for the Coproduction of Green Hydrogen and High-Value Chemicals
Name of Supervisor:	Rose Amal
Email of Supervisor:	r.amal@unsw.edu.au
Name of Joint/Co-Supervisor:	Denny Gunawan
Email of Joint/Co-Supervisor:	denny.gunawan@unsw.edu.au
School:	School of Chemical Engineering
Faculty Research Area (Theme):	Energy Systems, Renewable and Non-Renewable
Applicable to other Engineering schools/disciplines:
Terms:	Term 2
Abstract:	Photoreforming uses solar energy to activate photocatalysts for green hydrogen production while simultaneously facilitating organic oxidation. Compared to overall water splitting, photoreforming provides a more energy-efficient pathway to convert solar energy into hydrogen, significantly enhancing the hydrogen production rate [1]. Glycerol, a major byproduct of biodiesel manufacturing, is an attractive organic substrate for photoreforming [2]. The large quantity and low cost (US$0.11/kg) of glycerol from biodiesel production raise concerns about its disposal and environmental impact. Photoreforming offers a solution by upgrading glycerol to simultaneously generate hydrogen and valuable chemicals like dihydroxyacetone (US$150/kg). This approach can reduce costs for green hydrogen production while addressing environmental challenges [3]. This research aims to design zinc indium sulphide photocatalysts loaded with various metal cocatalysts, optimising hydrogen evolution activity and directing glycerol oxidation towards high-value products.
Research Environment:	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 future career in academic or industry.
Novelty and Contribution:	.
Expected Outcomes:	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 work with other research students to gain valuable interdisciplinary experience. The generated knowledge and data will result in a scientific journal publication. Continuing of the research as an 4th year honour thesis project is possible.
Reference Material Links:	"Toe, C. Y., Tsounis, C., Zhang, J., Masood, H., Gunawan, D., Scott, J., Amal, R. (2021). Advancing Photoreforming of Organics: Highlights on Photocatalyst and System Designs for Selective Oxidation Reactions. Energy Environ. Sci. 14, 1140-1175. Wen, L., Zhang, X., Abdi, F. F. (2024). Photoelectrochemical Glycerol Oxidation as a Sustainable and Valuable Technology. Mater. Today Energy 44, 101648. Gunawan, D., Zhang, J., Li, Q., Toe, C. Y., Scott, J., Antonietti, M., Guo, J., Amal, R. (2024). Materials Advances in Photocatalytic Solar Hydrogen Production: Integrating Systems and Economics for a Sustainable Future. Adv. Mater. 2404618."
Will the student visit the premises of an industry partner, or undertake any activity on premises external to UNSW?	No

Project Title:	Techno-Economic Assessment of Sunlight-to-X Conversion Processes
Name of Supervisor:	Denny Gunawan
Email of Supervisor:	denny.gunawan@unsw.edu.au
Name of Joint/Co-Supervisor:	Dr Shujie Zhou, Prof Rose Amal
Email of Joint/Co-Supervisor:	.
School:	School of Chemical Engineering
Faculty Research Area (Theme):	Energy Systems, Renewable and Non-Renewable
Applicable to other Engineering schools/disciplines:
Terms:	Term 2
Abstract:	Sunlight-to-X conversion, which harnesses abundant solar energy to produce fuels and chemicals (X), has recently emerged as a promising solution to address energy intermittency and decarbonise hard-to-abate sectors. By generating renewable feedstocks such as green hydrogen, ammonia, and methanol, these processes can support the development of alternative fuels, reducing dependence on finite fossil resources. Various technologies—including photovoltaic-electrocatalysis (PV-EC), photoelectrocatalysis (PEC), and photocatalysis (PC)—enable the conversion of solar energy into fuels and chemicals, each with distinct advantages and limitations [1,2]. Despite their potential, limited techno-economic modelling has been conducted to comparatively evaluate the feasibility of different sunlight-to-X pathways [3]. This research aims to assess the techno-economic viability of standalone PV-EC, PEC, and PC processes for producing renewable feedstocks such as hydrogen, ammonia, and methanol. The findings are expected to help identify key challenges and viable pathways for the commercialisation of these technologies.
Research Environment:	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 computational tools for techno-economic studies. The student will work in a multidisciplinary research environment and learn various functional skills to facilitate a future career in academia or industry.
Novelty and Contribution:	.
Expected Outcomes:	The student is expected to gain experience in process design and economic feasibility analysis. The project will also provide an opportunity for the student to collaborate with other research students, gaining valuable interdisciplinary experience. The knowledge and data generated will contribute as input to industry stakeholders and will result in a publication in a scientific journal.
Reference Material Links:	[1] Gunawan, D. et al. (2024). Materials Advances in Photocatalytic Solar Hydrogen Production: Integrating Systems and Economics for a Sustainable Future. Adv. Mater. 36, 42, 2404618. [2] Wang, Q. et al. (2021). Strategies to improve light utilization in solar fuel synthesis. Nat. Energy 7, 13-24. [3] Pinaud, B. A. et al. (2013). Technical and Economic Feasibility of Centralized Facilities for Solar Hydrogen Production via Photocatalysis and Photoelectrochemistry. Energy Environ. Sci. 6, 1983-2002.
Will the student visit the premises of an industry partner, or undertake any activity on premises external to UNSW?	No