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Nonaqueous flow batteries hold promise given their high cell voltage and energy density, but their performance is often plagued by the crossover of redox compounds. In this study, we used permselective lithium superionic conducting (LiSICON) ceramic membranes to enable reliable long-term use of organic redox molecules in nonaqueous flow cells. With different solvents on each side, enhanced cell voltages were obtained for a flow battery using viologen-based negolyte and TEMPO-based posolyte molecules. The thermoplastic assembly of the LiSICON membrane realized leakless cell sealing, thus overcoming the mechanical brittleness challenge. As a result, stable cycling was achieved in the flow cells, which showed good capacity retention over an extended test time. 

In this comprehensive study, a novel MATLAB to Python (M-to-PY) conversion process is showcased, specifically tailored for an intricate image skeletonization project involving fifteen MATLAB files and a large dataset. The central innovation of this research is the adept use of ChatGPT-4 as an AI assistant, pivotal in crafting a prototype M-to-PY converter. This converter’s capabilities were thoroughly evaluated using a set of test cases generated by the Bard bot, ensuring a robust and effective tool. The culmination of this effort was the development of the Skeleton App, adept at image sketching and skeletonization. This live and publicly available app underscores the enormous potential of AI in enhancing the transition of scientific research from MATLAB to Python. The study highlights the blend of AI’s computational prowess and human ingenuity in computational research, making significant strides in AI-assisted scientific exploration and tool development.