烯丙基与亲自由基共聚机理的密度泛函理论指导:光驱动自由基介导的 [3 + 2] 环化。

IF 2.1 4区 化学 Q4 BIOCHEMISTRY & MOLECULAR BIOLOGY Journal of Molecular Modeling Pub Date : 2024-08-13 DOI:10.1007/s00894-024-06104-x
Ou Liu, Piaoyi Chen, Qinglin Xiao, Chengfeng Yue, Yugang Huang, Guodong Ye
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引用次数: 0

摘要

背景:激活惰性烯丙基单体进行聚合一直是个难题,这促使我们提出了光驱动自由基介导的[3 + 2]环化反应(PRMC)。这种创新方法大大加快了多烯丙基单体的均聚速度,使用于肝癌干预的栓塞微球的合成成为可能。PRMC 涉及烯丙基单体形成烯丙基自由基,然后自由基作为亲自由基与不饱和烯烃发生环加成反应,形成环戊烷基自由基产物。虽然在理论和实验均聚合方面进行了广泛的研究,但 PRMC 在共聚合方面的应用仍有待探索。为了填补这一知识空白,我们通过密度泛函理论(DFT)分析,选择烯丙基甲基醚自由基(AMER)和α,β-不饱和酮作为亲自由基,对该基本反应进行了探索。我们量化了反应物基态和激发态之间的能量差异,阐明了前沿分子轨道,并评估了共聚可行性的热力学数据。我们还评估了反应物的电子特性,预测了亲基物的反应性和分子间反应的相互作用。此外,我们还应用了过渡态理论和相互作用/变形模型,并进行了局部轨道分析,以全面研究环状自由基产物的过量电子分布和回旋半径。我们的研究结果为 PRMC 在共聚方面的潜力提供了重要见解。这项研究为实际应用提供了坚实的理论基础,促进了聚合领域的发展:基于密度泛函理论(DFT),在高斯 16 软件包的 M06-2X/6-311 + + G(d,p)级进行了计算。随后,我们的分析结果应用了时间相关密度泛函理论(TD-DFT)和溶剂模型(SMD)。单点能量计算确定了自由基与亲自由基反应背后的驱动力。此外,我们还评估了反应物的静电位(ESP)。计算结果由 Multiwfn 3.6 和 VMD 1.9 程序直观显示。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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Density functional theory guide for copolymerization mechanism between allyl radical with radicalophile: photo-driven radical mediated [3 + 2] cyclization

Context

The challenge of activating inert allyl monomers for polymerization has persisted, prompting our proposal of the photo-driven radical mediated [3 + 2] cyclization reaction (PRMC). This innovative approach significantly expedites the homopolymerization of multi-allyl monomers, enabling the synthesis of embolic microspheres for hepatocellular carcinoma interventions. PRMC involves allyl monomers to form allylic radicals and then radicals participating in a cycloaddition reaction with unsaturated olefins as radicalophiles to form cyclopentane-based radical products. While extensively studied in the theoretical and experimental homopolymerization, PRMC’s application in copolymerization remains unexplored. To address this knowledge gap, we explored the elementary reaction, selecting allyl methyl ether radicals (AMER) and α,β-unsaturated ketones as radicalophiles for copolymerization investigations by density functional theory (DFT) analysis. We quantified energy differences between ground and excited states of reactants, elucidated frontier molecular orbitals, and assessed thermodynamic data for copolymerization feasibility. We also evaluated the electronic properties of reactants, predicting the reactivity of radicalophiles and the interactions of intermolecular reactions. Additionally, we applied transition state theory and interaction/deformation models and conducted a local orbital analysis to comprehensively study excess electron distribution and gyration radius of cyclic radical product. Our findings offer vital insights into PRMC’s potential in copolymerization. This research provides a robust theoretical foundation for practical application, enhancing the polymerization field.

Methods

Based on density functional theory (DFT), the calculations were performed at the M06-2X/6–311 +  + G(d,p) level in/by Gaussian 16 package. Subsequently, our analytical results apply time-dependent density-functional theory (TD-DFT) and solvent modeling (SMD). Single-point energy calculations determine the driving force behind the radicals’ reaction with radicalophiles. Furthermore, we assessed the electrostatic potential (ESP) of the reactants. The results of the calculations were visualized by the Multiwfn 3.6 and VMD 1.9 programs.

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来源期刊
Journal of Molecular Modeling
Journal of Molecular Modeling 化学-化学综合
CiteScore
3.50
自引率
4.50%
发文量
362
审稿时长
2.9 months
期刊介绍: The Journal of Molecular Modeling focuses on "hardcore" modeling, publishing high-quality research and reports. Founded in 1995 as a purely electronic journal, it has adapted its format to include a full-color print edition, and adjusted its aims and scope fit the fast-changing field of molecular modeling, with a particular focus on three-dimensional modeling. Today, the journal covers all aspects of molecular modeling including life science modeling; materials modeling; new methods; and computational chemistry. Topics include computer-aided molecular design; rational drug design, de novo ligand design, receptor modeling and docking; cheminformatics, data analysis, visualization and mining; computational medicinal chemistry; homology modeling; simulation of peptides, DNA and other biopolymers; quantitative structure-activity relationships (QSAR) and ADME-modeling; modeling of biological reaction mechanisms; and combined experimental and computational studies in which calculations play a major role.
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