Collaborative Effort Seeks to Enhance Efficiency, Reliability, and Commercial Viability of Solar PV Technologies

PASADENA, Calif., Sept. 19, 2023 /PRNewswire/ -- Caelux, a pioneer in utilizing perovskites to make solar energy more powerful and cost-effective, today announced its partnership with the University of New South Wales (UNSW, Sydney, Australia) ACDC Research Group on the ARENA-funded project, 'High-Throughput Inspection Methods for High-Efficiency Multijunction Solar Cells.' This solar cells project will improve the commercial readiness of solar PV technologies and enable the next generation of solar innovation.

The Artificial Intelligence, Characterisation, Defects, and Contacts (ACDC) Research Group at the University of New South Wales (UNSW) is a leader in photovoltaic luminescence imaging and applied machine learning (ML). This project aims to develop novel, contactless methods to characterize perovskite solar cells during process development and inline manufacturing, which will improve production yields and device performance, while taking into consideration the differences between silicon and perovskites. This collaboration will also undertake commercialization activities including: testing the techniques on pilot production lines and techno-economic analysis of the potential market for the developed inspection tools.

"We are delighted to partner with the ACDC Research Group on this revolutionary solar cell project," said Scott Graybeal, CEO of Caelux. "Together, we are looking forward to accelerating the commercial development and long-term reliability of perovskite solar cells. We are excited to team up with leaders in the PV and luminescence imaging space to bring our ideas to life."

This partnership with the ACDC team at UNSW will greatly enhance the rapid development of perovskite solar cells and, through machine learning techniques, the long-term reliability of this class of solar cells to rapidly approach silicon, the ubiquitous technology.

"We are thrilled to collaborate with Caelux on this pivotal project. Caelux and its visionary leadership team have conceived an innovative product poised to redefine the photovoltaic (PV) industry," said Professor Ziv Hameiri, Principal Investigator at University of New South Wales. "We are honored to join forces with them to refine this groundbreaking product and accelerate the commercialization of perovskite solar cells, with a focus on pioneering characterisation and machine learning techniques to improve their long-term durability and reliability."

The appropriate interpretation of luminescence images requires consideration of the excitation conditions, measurement calibration, and complex photo-physics including photon reabsorption and recycling, waveguide effects, ion migration, varying phases, and phase segregation. The development of new perovskite characterisation techniques is crucial for the translation from R&D labs to large-scale manufacturing.

To learn more about the project, visit arena.gov.au. To learn more about Caelux, visit www.caelux.com.

About Caelux
Caelux's proprietary technologies improve the performance of any new crystalline silicon module, making solar energy more powerful and cost-effective. Headquartered in Pasadena, California, Caelux is at the forefront of the emerging science of perovskites, a special class of nanomaterials. Its flagship product, Caelux One, is an innovative product that integrates seamlessly into existing PV module manufacturing processes, boosting performance, reducing installed costs, and accelerating the proliferation of solar. For more information, visit www.caelux.com or connect on LinkedIn or Twitter.

About University of New South Wales and The ACDC Research Group
UNSW is a globally recognized research institute located in Sydney, Australia. UNSW is committed to pushing the boundaries of knowledge and innovation across a wide spectrum of disciplines, including science, engineering, and sustainability. The ACDC Research Group at UNSW specializes in the development of innovative measurements for solar cells and modules, and is a pioneer in utilizing machine learning algorithms for PV applications.

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