Nicholas Lee
Mechanical engineering
Hometown: Litchfield Park, AZ, United States
Graduation date: Spring 2029
FURI | Fall 2026, Needs Review
Physics-Informed Bayesian Optimization of Scalable Spray-Coated SnO₂ Electron Transport Layers for Perovskite Solar Cells
This research develops a physics-informed Bayesian optimization framework for scalable open-air spray-coating of nanoparticle tin oxide (SnO2) electron transport layers (ETLs) in perovskite solar cells. Solution-processable SnO2 offers a low-cost, scalable alternative to conventional fullerene-based ETLs that constrain high throughput photovoltaic manufacturing. Parameters including substrate temperature, nozzle height, flow rate, translation-speed, and SnO2 concentration are optimized using thin-film data characterized through profilometry, optical microscopy, and computer vision. Physics-based spray and curing descriptors are incorporated into Gaussian-process modeling to assist in optimization for desired film thickness and overall uniformity. This approach combines physical modeling and machine learning to accelerate perovskite manufacturing.
Mentor: Nick Rolston
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