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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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Identifying Bounds of Inorganic Content in Solventless Processing of Hybrid Solid Electrolytes

Solid-state lithium batteries require safe, robust electrolytes to enable higher energy densities and improved safety over conventional cells. Hybrid polymer–ceramic electrolytes are a promising solution, combining the processability of polymers with the high ionic conductivity and mechanical strength of inorganic fillers. In this work, we demonstrate a solventless, UV-curing method to produce hybrid solid electrolytes using a poly(ethylene glycol) dimethyl ether (PEGDME)-based photocurable matrix incorporating Li 1.5 Al 0.5 Ge 1.5 (PO 4 ) 3 (LAGP) or Li 7 La 3 Zr 2 O 12 (LLZO) ceramic electrolyte. Inorganic filler loadings up to ∼55 wt.% could be successfully incorporated via this process which was the highest inorganic content at which the slurry remains processable and cured into a uniform film. The resulting UV-cured composite electrolytes remain flexible and exhibit room-temperature ionic conductivities on the order of 10 −4 S·cm −1 , along with notably improved lithium-ion transference numbers compared to conventional polymer electrolytes. Similar performance and processing limits were observed for both LAGP and LLZO, indicating that ceramic filler chemistry does not significantly affect the UV-curing process or the electrolyte's ion transport properties in this regime. Eliminating solvents from fabrication not only simplifies processing and mitigates environmental concerns but also enables higher solid contents that enhance mechanical strength and help suppress lithium dendrite formation. In conclusion, this scalable approach thus paves the way for manufacturing robust composite solid electrolytes for next-generation solid-state batteries (SSBs).

Batteries

Machine learning enhanced characterization and optimization of photonic cured MAPbI 3 for efficient perovskite solar cells

Photonic curing (PC) can facilitate high-speed perovskite solar cell (PSC) manufacturing because it uses high-intensity light pulses to crystallize perovskite films in milliseconds. However, optimizing PC conditions is challenging due to its many variables, and using power conversion efficiency (PCE) as the optimization metric is both time-consuming and labor-intensive. This work presents a machine learning (ML) approach to optimize PC conditions for fabricating methylammonium lead iodide (MAPbI 3 ) films by quantitatively comparing their ultraviolet-visible (UV-vis) absorbance spectra to thermal annealed (TA) films using four similarity metrics. We perform Bayesian optimization coupled with Gaussian process regression (BO-GP) to minimize the similarity metrics. Refining PC conditions using active learning based on BO-GP models, we achieve a PC MAPbI3 film with an absorbance spectrum closely matching a TA reference film, which is further verified by its crystalline and morphological properties. Thus, we demonstrate that the UV-vis absorption spectrum can accurately proxy film quality. Additionally, we use an AI-based segmentation model for a more efficient grain size analysis. However, when we use the optimized PC condition to fabricate PSCs, we find that interaction between MAPbI 3 and the hole transport layer (HTL) during PC critically degrades the PSC performance. By adding a buffer layer between the HTL and MAPbI 3 , the optimized PC PSCs produce a champion PCE of 11.8%, comparable to the TA reference of 11.7%. Using UV-vis similarity metrics instead of device PCE as the objective in our BO-GP method accelerates the optimization of PC processing conditions for MAPbI 3 films.

14 SOLAR ENERGY