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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 919 records · Page 51
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Machine learning with knowledge constraints for process optimization of open-air perovskite solar cell manufacturing

Perovskite photovoltaics (PV) have achieved rapid development in the past decade in terms of power conversion efficiency of small-area lab-scale devices; however, successful commercialization still requires further development of low-cost, scalable, and high-throughput manufacturing techniques. One of the critical challenges of developing a new fabrication technique is the high-dimensional parameter space for optimization, but machine learning (ML) can readily be used to accelerate perovskite PV scaling. Herein, we present an ML-guided framework of sequential learning for manufacturing process optimization. We apply our methodology to the Rapid Spray Plasma Processing (RSPP) technique for perovskite thin films in ambient conditions. With a limited experimental budget of screening 100 process conditions, we demonstrated an efficiency improvement to 18.5% as the best-in-our-lab device fabricated by RSPP, and we also experimentally found 10 unique process conditions to produce the top-performing devices of more than 17% efficiency, which is 5 times higher rate of success than the control experiments with pseudo-random Latin hypercube sampling. Our model is enabled by three innovations: (a) flexible knowledge transfer between experimental processes by incorporating data from prior experimental data as a probabilistic constraint; (b) incorporation of both subjective human observations and ML insights when selecting next experiments; (c) adaptive strategy of locating the region of interest using Bayesian optimization first, and then conducting local exploration for high-efficiency devices. Furthermore, in virtual benchmarking, our framework achieves faster improvements with limited experimental budgets than traditional design-of-experiments methods (e.g., one-variable-at-a-time sampling). This framework shows the capability of incorporating researchers’ domain knowledge into the ML-guided optimization loop; therefore, it has the potential to facilitate the wider adoption of ML in scaling to perovskite PV manufacturing.

14 SOLAR ENERGY↗
DOE OSTI2021

Event reconstruction in a liquid xenon Time Projection Chamber with an optically-open field cage

nEXO is a proposed tonne-scale neutrinoless double beta decay (0νββ) experiment using liquid 136 Xe (LXe) in a Time Projection Chamber (TPC) to read out ionization and scintillation signals. Between the field cage and the LXe vessel, a layer of LXe (“skin” LXe) is present, where no ionization signal is collected. Only scintillation photons are detected, owing to the lack of optical barrier around the field cage. Here, in this work, we show that the light originating in the skin LXe region can be used to improve background discrimination by 5% over previous published estimates. This improvement comes from two elements. First, a fraction of the γ-ray background is removed by identifying light from interactions with an energy deposition in the skin LXe. Then, background from 222 Rn dissolved in the skin LXe can be efficiently rejected by tagging the α decay in the 214 Bi- 214 Po chain in the skin LXe.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗