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DC Field | Value | Language |
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dc.citation.startPage | 139707 | - |
dc.citation.title | CHEMICAL ENGINEERING JOURNAL | - |
dc.citation.volume | 453 | - |
dc.contributor.author | Ahn, Gwang-Noh | - |
dc.contributor.author | Kang, Ji-Ho | - |
dc.contributor.author | Lee, Hyune-Jea | - |
dc.contributor.author | Park, Byung Eon | - |
dc.contributor.author | Kwon, Minjun | - |
dc.contributor.author | Na, Gi-Su | - |
dc.contributor.author | Kim, Heejin | - |
dc.contributor.author | Seo, Dong-Hwa | - |
dc.contributor.author | Kim, Dong-Pyo | - |
dc.date.accessioned | 2023-12-21T13:07:51Z | - |
dc.date.available | 2023-12-21T13:07:51Z | - |
dc.date.created | 2022-10-20 | - |
dc.date.issued | 2023-02 | - |
dc.description.abstract | The rapid development of novel synthetic routes for pharmaceutical compounds is highly attractive for overcoming pandemic and epidemic-prone diseases like COVID-19. Herein, we report an automated microreactor platform (AMP) with Bayesian optimization (BO) that can autonomously explore the optimal conditions for ultrafast synthesis of biologically active thioquinazolinone. First, AMP operation is successfully demonstrated with full control of quantitative variables, specifically reaction volume, temperature, and flow rate, allowing to sequentially conduct a total of 80 experiments planned by the user. Next, BO enables the AMP to autonomously self-optimize the reaction conditions, demonstrating the high efficiency of the fully automated AMP. The fully automated approach is extended to optimize more complex variables including a categorical variable (i.e. the type of organolithium for synthesis), revealing that phenyllithium (PhLi) gives superior yield for synthesizing thioquinazolinone. In addition, the autonomous AMP is utilized for combinatorial chemistry to sequentially synthesize a library composed of nine types of S-benzylic thioquinazolinone under autonomously optimized conditions within only 20 min. | - |
dc.identifier.bibliographicCitation | CHEMICAL ENGINEERING JOURNAL, v.453, pp.139707 | - |
dc.identifier.doi | 10.1016/j.cej.2022.139707 | - |
dc.identifier.issn | 1385-8947 | - |
dc.identifier.scopusid | 2-s2.0-85140275341 | - |
dc.identifier.uri | https://scholarworks.unist.ac.kr/handle/201301/59787 | - |
dc.identifier.wosid | 000933397800004 | - |
dc.language | 영어 | - |
dc.publisher | Elsevier BV | - |
dc.title | Exploring ultrafast flow chemistry by autonomous self-optimizing platform | - |
dc.type | Article | - |
dc.description.isOpenAccess | FALSE | - |
dc.relation.journalWebOfScienceCategory | Engineering, Environmental;Engineering, Chemical | - |
dc.relation.journalResearchArea | Engineering | - |
dc.type.docType | Article | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.subject.keywordAuthor | Automated microreactor | - |
dc.subject.keywordAuthor | Bayesian optimization | - |
dc.subject.keywordAuthor | Ultrafast chemistry | - |
dc.subject.keywordPlus | BAYESIAN OPTIMIZATION&apos | - |
dc.subject.keywordPlus | FLASH CHEMISTRY&apos | - |
dc.subject.keywordPlus | AUTOMATED OPTIMIZATION | - |
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