The U.S government's Solar Energy Technologies Office (SETO) Fiscal Year 2020 funding program has been released, supporting projects that will improve the affordability, reliability, and value of solar technologies on the U.S. grid and tackle emerging challenges in the solar industry.

This program funds projects that advance early-stage photovoltaic (PV), concentrating solar-thermal power (CSP), and systems integration technologies, and reduce the non-hardware costs associated with installing solar energy systems. Two perovskite-related projects have been included in this program.

The first, is a $300,000 grant for the Georgia Institute of Technology, for a project named: Development of Organic-Inorganic Hybrid Selective Layers via Vapor Phase Infiltration to Enhance the Durability of Perovskite Solar Cells. This project will aim to improve perovskite solar cell stability by embedding metal oxide clusters within the small molecule layers using vapor phase infiltration (VPI). These embedded metal oxide clusters will restrict motion of the molecules and impede crystallization. This unique hybrid small molecule-metal oxide cluster layer should increase stability of these perovskite solar cells, a key step towards their commercialization.

The second project to receive (the same amount of) funding is MIT's "Machine Learning Accelerates Innovation in Perovskite Manufacturing Scale-Up". This project will use machine learning to improve the manufacturing scale-up process for perovskite PV technologies. The methodology will speed up the research and development cycle for emerging perovskite PV technologies via the machine-learning-assisted experimental design. The team will develop a framework that combines sequential machine learning and process engineering to maximize process improvements with fewer required experiments. This framework will enable rapid development of scalable deposition process for perovskite PV manufacturing.



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