Predicting Perovskite Thin-Film Photovoltaic Performance from Photoluminescence Videos
Photovoltaics are a key technology to decarbonize the generation of energy. While perovskite thin-films are a promising option to build powerful next generation photovoltaics demonstrating high power conversion efficiencies, their manufacturing process remains unstable. We build a model that directly predicts the solar cell performance based on a video capturing the perovskite layer formation prior to finalizing the solar cell. This helps to uncover process insights that lead to a better scalable and more reliable production and facilitates experimentation, which may lead to overcoming the current commercialization hurdles faster.
Publication
Klein, L., Ziegler, S., Laufer, F., Debus, C., Götz, M., Maier‐Hein, K., Paetzold, U. W., Isensee, F., & Jäger, P. F. (2023). Discovering Process Dynamics for Scalable Perovskite Solar Cell Manufacturing with Explainable AI. Advanced Materials, 2307160. https://doi.org/10.1002/adma.202307160
Other Collaborations
Extracting clinically relevant parameters from real-time MRI images of fontan hearts
Obtaining accurate segmentations of the heart in real-time MRI allows a more realistic view on clinically relevant parameters, such as the stroke volume. Cardiac real-time MRI can assess diastolic filling under breath maneuvers or other cardiac load situations which potentially enhances diagnostics other than CINE breath hold cardiac MRI. Real-time MRI allows rapid acquisitions during […]
Optimizing electrode geometry and surface for hydrogen production
Electrolysis of water into oxygen and hydrogen is a cornerstone of modern energy storage, electric mobility and the transition towards a net-zero-emissions industry. Maximizing the efficiency of this technology is key to its economically viable wide scale adoption. One approach to both reduce the costs and improve the overall efficiency is to enhance the bubble […]