Akiyoshi M, Ikemoto K, Isobe H. Tier-grown Expansion of Design-of-Experiments Parameter Spaces for Synthesis of a Nanometer-scale Macrocycle.
Chem Asian J 2023;
18:e202201141. [PMID:
36424827 DOI:
10.1002/asia.202201141]
[Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2022] [Revised: 11/24/2022] [Accepted: 11/25/2022] [Indexed: 11/26/2022]
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.
Collapse