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相关概念视频

Polymer Classification: Crystallinity01:21

Polymer Classification: Crystallinity

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Unlike ionic or small covalent molecules, polymers do not form crystalline solids due to the diffusion limitations of their long-chain structures. However, polymers contain microscopic crystalline domains separated by amorphous domains.
Crystalline domains are the regions where polymer chains are aligned in an orderly manner and held together in proximity by intermolecular forces. For example, chains in the crystalline domains of polyethylene and nylon are bound together by van der Waals...
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Polymer Classification: Stereospecificity01:26

Polymer Classification: Stereospecificity

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Polymerization generates chiral centers along the entire backbone of a polymer chain. Accordingly, the stereochemistry of the substituent group has a significant effect on polymer properties. Polymers formed from monosubstituted alkene monomers feature chiral carbons at every alternate position in the polymer backbone. Relative to the predominant orientation of substituents at the adjacent chiral carbons, the polymer can exist in three different configurations: isotactic, syndiotactic, and...
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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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Types of Step-Growth Polymers: Polyesters01:20

Types of Step-Growth Polymers: Polyesters

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The introduction of polyesters has brought major development to the textile industry. The wrinkle-free behavior of polyester blends has eliminated the need for starching and ironing clothes.
Polyesters are commonly prepared from terephthalic acid and ethylene glycol; the crude product is known as poly(ethylene terephthalate) or PET. However, polyesters are synthesized industrially by transesterification of dimethyl terephthalate with ethylene glycol at 150 °C. The two reactants and the...
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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...
2.2K
Polymer Classification: Architecture01:14

Polymer Classification: Architecture

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Polymers are classified as linear or branched on the basis of their chain architecture. The polymer chains in linear polymers have a long chain-like structure with minimal to no branching at all. Even if a polymer features large substituent groups on the monomer, which appear as branches to the skeleton, it is not considered a branched polymer. A branched polymer contains secondary polymer chains that arise from the main polymer chain. The branching occurs when the polymer growth shifts from...
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相关实验视频

Updated: Jun 16, 2025

Cooling Rate Dependent Ellipsometry Measurements to Determine the Dynamics of Thin Glassy Films
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玻璃过渡温度预测聚合物通过图形增强学习学习.

Caibo Dong1, Dazi Li1, Jun Liu2,3

  • 1College of Information Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China.

Langmuir : the ACS journal of surfaces and colloids
|August 21, 2024
PubMed
概括

这项研究引入了一种新的图形增强学习框架,用于预测聚合物特性,特别是聚胺的玻璃过渡温度 (Tg). 它有效地利用全球和本地分子结构来提高准确性.

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Synthesis of Programmable Main-chain Liquid-crystalline Elastomers Using a Two-stage Thiol-acrylate Reaction
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科学领域:

  • 聚合物科学 聚合物科学
  • 材料 信息学 信息学
  • 计算化学计算化学

背景情况:

  • 基于图形的模型被广泛用于预测聚合物结构-性质关系.
  • 现有的方法往往不充分利用不受监督的结构信息,限制了预测准确度.

研究的目的:

  • 开发一种先进的模型,用于预测 heterocyclic 聚合物的玻璃过渡温度 (Tg),重点是聚胺.
  • 充分利用全球和本地分子结构信息,以改善Tg预测.

主要方法:

  • 提出了一个新的图形强化学习框架:分子结构规则化图形卷积网络与强化学习 (MSRGCN-RL).
  • 集成图形神经网络 (GNN) 与强化学习 (RL) 进行聚合物属性预测.

主要成果:

  • 证明了全球和本地结构规范化对于精确的Tg预测的关键重要性.
  • 展示了通过RL优化MSRGCN培训的有效性,以提高性能.

结论:

  • 该MSRGCN-RL框架显著提高了聚合物玻璃过渡温度的预测精度.
  • 这项研究开创了GNN和RL的集成,用于先进的聚合物性质预测.