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相关概念视频

Olefin Metathesis Polymerization: Ring-Opening Metathesis Polymerization (ROMP)01:16

Olefin Metathesis Polymerization: Ring-Opening Metathesis Polymerization (ROMP)

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Ring-opening metathesis polymerization or ROMP involves strained cycloalkenes as starting materials. The mechanism of ROMP proceeds by reacting cycloalkene with Grubbs catalyst to give metallacyclobutane intermediate which undergoes a ring-opening reaction to form new carbene. The new carbene reacts with another molecule of cycloalkene. Repetition of these steps leads to the formation of an unsaturated open-chain polymer product. All these steps are reversible, however, relieving the ring...
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Olefin Metathesis Polymerization: Acyclic Diene Metathesis (ADMET)00:53

Olefin Metathesis Polymerization: Acyclic Diene Metathesis (ADMET)

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Acyclic diene metathesis polymerization or ADMET polymerization involves cross-metathesis of terminal dienes, such as 1,8-nonadiene, to give linear unsaturated polymer and ethylene. As ADMET is a reversible process, the formed ethylene gas must be removed from the reaction mixture to complete the polymerization process.
Similar to cross-metathesis, ADMET also involves the formation of metallacyclobutane intermediate by [2+2] cycloaddition of one of the double bonds of a terminal diene with...
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Olefin Metathesis Polymerization: Overview01:13

Olefin Metathesis Polymerization: Overview

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Recently, the development of olefin metathesis polymerization advanced the field of polymer synthesis. Simply put, the reorganization of substituents on their double bonds between two olefins in the presence of a catalyst is known as the olefin metathesis reaction. The use of metathesis reaction for polymer synthesis is called olefin metathesis polymerization.
Ruthenium-based Grubbs catalyst is the most commonly used catalyst for olefin metathesis polymerization. Grubbs catalyst consists...
2.1K
Molecular Weight of Step-Growth Polymers01:08

Molecular Weight of Step-Growth Polymers

2.2K
Step growth polymerization involves bi or multifunctional monomers. Bifunctional monomers react to form linear step growth polymers, whereas multifunctional monomers react to form non-linear or branched polymers.
As the step-growth polymerization involves step-wise condensation of monomers, the molecular weight also builds up eventually. Consequently, high molecular weight polymers are obtained at the late stages of the polymerization, where 99% of monomers have been consumed.
The extent of the...
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Step-Growth Polymerization: Overview01:03

Step-Growth Polymerization: Overview

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Step-growth or condensation polymerization is a stepwise reaction of bi or multifunctional monomers to form long-chain polymers. As all the monomers are reactive, most of the monomers are consumed at the early stages of the reaction to form small chains of reactive oligomers, which then combine to form long polymer chains in the late stages. Hence, the reaction has to proceed for a long time to achieve high molecular weight polymers.
Many natural and synthetic polymers are produced by...
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Radical Chain-Growth Polymerization: Chain Branching01:17

Radical Chain-Growth Polymerization: Chain Branching

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The skeletal structure of polymers synthesized via radical polymerization is always branched. For example, the polymerization of ethylene by radical polymerization results in a low-density grade of polyethylene with a heavily branched skeletal structure. Here, the radical site abstracts hydrogen from the growing chain, and the radical site shifts from the end (a primary carbon center) to anywhere within the growing chain (a secondary carbon center). Consequently, the part of the chain from the...
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Synthesis of Cyclic Polymers and Characterization of Their Diffusive Motion in the Melt State at the Single Molecule Level
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加速方案预测环开放聚合凝聚:模拟-实验数据融合和多任务机器学习.

Aubrey Toland1, Huan Tran1, Lihua Chen1

  • 1School of Materials Science & Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332, United States.

The journal of physical chemistry. A
|December 6, 2023
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概括
此摘要是机器生成的。

本研究引入了一种机器学习模型,用于预测可回收聚合物的环开启度 (ΔHROP). 这种方法可以加快新型脱聚合材料的设计,并具有高精度.

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科学领域:

  • 聚合物化学 聚合物化学
  • 计算化学的计算化学
  • 材料科学 材料科学 材料科学

背景情况:

  • 环开放度 (ΔHROP) 对于通过环开放聚合 (ROP) 设计可回收聚合物至关重要.
  • 目前用于计算ΔHROP的计算方法是计算密集的,阻碍了高效的聚合物设计.
  • 开发准确和快速的方法来预测ΔHROP对于推进可持续的聚合物化学至关重要.

研究的目的:

  • 开发一种可通用的机器学习 (ML) 模型,用于预测环开启力 (ΔHROP).
  • 为了使新型可脱聚合聚合物的设计更快,更有效.
  • 弥合计算成本与聚合物科学中准确热力学数据的需求之间的差距.

主要方法:

  • 为 ΔHROP 开发了一种基于实验数据和计算模拟结果的机器学习模型.
  • 利用实验测量和第一原则模拟数据的组合进行模型训练.
  • 专注于创建一个模型,它既准确又在计算上便宜,用于预测.

主要成果:

  • 对于 ΔHROP,ML 模型实现了大约 8 kJ/mol 的预测准确度,接近化学准确度.
  • 使用开发的ML模型进行预测几乎是即时的,大大减少了计算时间.
  • 该模型展示了通用性,允许对化学多样性的系统探索.

结论:

  • 开发的ML模型提供了一个计算效率高,准确的方法来预测ΔHROP.
  • 这项工作有助于加速设计和发现新的可回收和可脱聚合的聚合物.
  • 机器学习模型是推动可持续聚合物开发和应用的宝贵工具.