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• W4310081643 abstract "A method to find optimum synthetic conditions was devised by combining a data-driven empirical model with a traditional mechanistic model. In this method, an experimental parameter space was empirically obtained by Design-of-Experiments optimizations with machine-learning supplements and was strategically expanded by examination of the mechanistic model of the reaction paths. An extra tier grown on the original 3×3×3 parameter space succeeded in allocating an optimum reaction condition in the expanded 3×3×4 parameter space. The method was specifically devised for the synthesis of a macrocycle, [n]cyclo-meta-phenylenes ([n]CMP), and the largest congener with n=12 was synthesized and fully characterized for the first time. Crystallographic and photophysical analyses revealed favorable features of [12]CMP for the material applications." @default.
• W4310081643 created "2022-11-30" @default.
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• W4310081643 date "2022-12-08" @default.
• W4310081643 modified "2023-10-17" @default.
• W4310081643 title "Tier‐grown Expansion of Design‐of‐Experiments Parameter Spaces for Synthesis of a Nanometer‐scale Macrocycle" @default.
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• W4310081643 doi "https://doi.org/10.1002/asia.202201141" @default.
• W4310081643 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/36424827" @default.
• W4310081643 hasPublicationYear "2022" @default.
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