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• W4220790111 abstract "Abstract: Multi-component chemical mixtures (MCMs) and their various effects always concerned in analytical chemistry, but current analytical techniques based on test-tube experiments often involves many high-cost and laborious operations. Today’s pop machine-learning (ML) technology has exhibited their successes in dealing with the analysis task of various complex systems. Predictably, the introduction of ML will radically accelerate the exploration of many fields involving mixture analysis. But the biggest challenge ahead for this process is how to provide some intelligible and sufficient data for various algorithms. In this study, we proposed a chemical imaging strategy to visualize various mixtures as some feature images by using ink-jet printing technology based on combinatorial chemistry. Here, these feature images were as a novel data form of chemical reaction spectrum (CRS), which can comprehensively describe and record the reaction characteristics of the complex sample. Compared with common imaging methods, the CRS with high-throughput chemical reaction dots is an efficient and economic information visualization way for the MCM sample. It is expected to be an important data acquisition approach for the application of ML in the field of chemistry in future." @default.
• W4220790111 created "2022-04-03" @default.
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• W4220790111 date "2022-03-18" @default.
• W4220790111 modified "2023-10-18" @default.
• W4220790111 title "Chemical Reaction Spectrum: A Holographic Image for Characterizing Multi-component Chemical Mixtures" @default.
• W4220790111 doi "https://doi.org/10.26434/chemrxiv-2022-b4c3v" @default.
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