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Abstract
Lewis acidic boranes, especially tris(pentafluorophenyl)borane [B(C6F5)3], have emerged as metal-free catalysts for carbene transfer reactions of α-diazocarbonyl compounds in a variety of functionalization reactions. The established mechanism for how borane facilitates carbene generation for these compounds in the scientific community is based on the formation of a B-O (C═O) intermediate (path O). Herein, we report an extensive DFT study that challenges the notion of a ubiquitous path O, revealing that B-C(═N═N) bond formation (path C) for certain diazocarbonyl substrates proves to be the preferred pathway. This study elucidates, through the introduction of 22 various substituents on each side of the α-diazocarbonyl backbone, how the electron-donating and -withdrawing properties of substituents influence the competition between these B-O and B-C pathways. To elucidate the impact of the electronic features of diazo substrates on the competition between the O and C pathways in the studied dataset, we employed a machine learning approach based on the Random Forest model. This analysis revealed that substrates with higher electron density on the diazo-attached carbon, lower electron density on the carbonyl carbon, and more stable HOMO orbitals tend to proceed via path C. Furthermore, this study not only demonstrates that borane efficiency in facilitating N2 release is greatly affected by the nature of substituents on both sides of the α-diazocarbonyl functionality but also shows that for some substrates, borane is incapable of catalyzing the release of molecular nitrogen.
Original language | English |
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Pages (from-to) | 14486-14496 |
Number of pages | 11 |
Journal | ACS Catalysis |
Volume | 14 |
Issue number | 19 |
Early online date | 16 Sept 2024 |
DOIs | |
Publication status | Published - 4 Oct 2024 |
MoE publication type | A1 Journal article-refereed |
Keywords
- boron
- carbene transfer
- catalysis
- DFT
- machine learning
- tris(pentafluorophenyl)borane
- α-diazocarbonyl
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Dive into the research topics of 'Comparison of the Efficiency of B-O and B-C Bond Formation Pathways in Borane-Catalyzed Carbene Transfer Reactions Using α-Diazocarbonyl Precursors: A Combined Density Functional Theory and Machine Learning Study'. Together they form a unique fingerprint.Datasets
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Borane-Diazocarbonyl
Farshadfar, K. (Creator), GitHub, 11 Aug 2024
https://github.com/KavehFarshadfar/Borane-Diazocarbonyl
Dataset: Software or code
Projects
- 1 Finished
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XAI-BAT-Aka: Materials Development for Flow Batteries with Help of Explainable AI
Laasonen, K. (Principal investigator), Farshadfar, K. (Project Member), Mahdian, A. (Project Member), Khakpour, R. (Project Member) & Mäkinen, M. (Project Member)
EU The Recovery and Resilience Facility (RRF)
01/01/2022 → 31/12/2024
Project: Academy of Finland: Other research funding