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相关概念视频

Radical Reactivity: Overview01:11

Radical Reactivity: Overview

2.6K
Radicals, the highly reactive species, gain stability by undergoing three different reactions. The first reaction involves a radical-radical coupling, in which a radical combines with another radical, forming a spin‐paired molecule. The second reaction is between a radical and a spin‐paired molecule, generating a new radical and a new spin‐paired molecule. The third reaction is radical decomposition in a unimolecular reaction, forming a new radical and a spin‐paired...
2.6K
Radical Reactivity: Electrophilic Radicals01:02

Radical Reactivity: Electrophilic Radicals

2.4K
Radicals adjacent to electron‐withdrawing groups are called electrophilic radicals. These radicals readily react with nucleophilic alkenes. For example, the malonate radical, in which the radical center is flanked by two electron‐withdrawing groups, reacts readily with butyl vinyl ether, which consists of an electron‐donating oxygen substituent. The reaction between electrophilic malonate radical and nucleophilic vinyl ether is favored because the radical has a...
2.4K
Radical Reactivity: Nucleophilic Radicals01:16

Radical Reactivity: Nucleophilic Radicals

2.6K
Radicals adjacent to electron-donating groups are called nucleophilic radicals. These radicals readily react with electrophilic alkenes. The SOMO–LUMO interactions are the driving force for the reaction, where the high-energy SOMO of the electron-rich, nucleophilic radicals interacts with the low-energy LUMO of the electron-deficient, electrophilic alkenes. Such SOMO–LUMO interactions are the basis of reactive radical traps, affecting the selectivity in radical reactions. For...
2.6K
Radical Reactivity: Intramolecular vs Intermolecular01:33

Radical Reactivity: Intramolecular vs Intermolecular

2.1K
Radical reactions can occur either intermolecularly or intramolecularly. In an intermolecular radical reaction, a nucleophilic radical adds to an electrophilic alkene or vice versa. In such reactions, the radical and generally the alkene, which is also called the radical trap, are two different molecules. Additionally, for such intermolecular reactions to occur, the radical trap must be active, present in an excess concentration, and the radical starting material must have a weak...
2.1K
Radical Reactivity: Steric Effects01:10

Radical Reactivity: Steric Effects

2.4K
The presence of electron-donating, electron-withdrawing, or conjugating groups adjacent to a radical center, imparts electronic stabilization to the radicals. Examples of such electronically-stabilized radicals are triphenylmethyl, tetramethylpiperidine‐N‐oxide, and 2,2‐diphenyl‐1‐picrylhydrazyl. These radicals are remarkably stable and are known as persistent radicals. Some of the persistent radicals can even be isolated and purified.
Along with electronic...
2.4K
Radical Formation: Addition00:47

Radical Formation: Addition

2.1K
Radicals can be formed by adding a radical to a spin-paired molecule. This is typically observed with unsaturated species, where the addition of a radical across the π bond leads to the production of a new radical by dissolving the π bond. For example, the addition of a Br radical to an alkene yields a carbon-centered radical.
Similar to charge conservation in chemical reactions, spin conservation is implicit for radical reactions. Accordingly, the product formed must possess an...
2.1K

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相关实验视频

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Free Radicals in Chemical Biology: from Chemical Behavior to Biomarker Development
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使用机器学习绘制玻利基基性质和反应性:B-Rad和React-B-Rad地图

Beatriz Peñín1, Nil Sanosa1, Cecilia Merino-Robledillo1

  • 1Department of Chemistry Instituto de Investigación Química de la Universidad de La Rioja (IQUR), Universidad de La Rioja, Madre de Dios 53, Logroño, 26006, Spain.

Angewandte Chemie (International ed. in English)
|October 11, 2025
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概括

这项研究引入了一个机器学习平台来分类基,提供了对它们对有机合成的反应性的见解. 它可以提供更好的实验选择,并有助于发现新的玻利基基试剂.

关键词:
玻利基激素是一种基.描述符是一个描述符.机器学习是机器学习.一个地图,一个地图.反应性 反应性 反应性

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科学领域:

  • 有机化学 有机化学
  • 计算化学计算化学
  • 机器学习 机器学习

背景情况:

  • 基在有机合成中至关重要,但其反应性很难预测.
  • 了解它们的硬质和电子特性是利用它们的合成潜力的关键.

研究的目的:

  • 开发一个全面的基的分类系统.
  • 创建一个可预测的工具,用于基在有机反应中的反应性.
  • 为了促进基试剂的合理设计和虚拟选.

主要方法:

  • 创建了一个141个基的数据库,具有DFT计算的特征.
  • 应用无监督机器学习 (k-means,PCA/UMAP) 来创建一个"B-rad地图".
  • 综合全球电友性/核友性指数和DFT计算的激活能量.
  • 训练有素的监督机器学习 (随机森林) 模型来预测反应性.

主要成果:

  • "B-rad地图"可视化了五个玻利基基团的固态和电子趋势.
  • 极度注释和React-B-rad地图将内在特性与反应性能联系起来.
  • 机器学习模型成功地预测了各种反应类型的基反应活性.

结论:

  • 开发的平台为实验人员提供了实用指南,并为数据驱动的发现奠定了基础.
  • 这种方法可以合理设计和虚拟选用于合成应用的基试剂.