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相关概念视频

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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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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Ziegler–Natta Chain-Growth Polymerization: Overview01:17

Ziegler–Natta Chain-Growth Polymerization: Overview

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Ziegler–Natta polymerization is another form of addition or chain‐growth polymerization used for synthesizing linear polymers over branched polymers. The catalyst used for polymerization is the Ziegler–Natta catalyst, named after Karl Ziegler and Giulio Natta, who developed it in 1953. This catalyst is an organometallic complex of titanium tetrachloride and triethyl aluminum, with the active form of the catalyst being an alkyl titanium compound. Using the Ziegler–Natta...
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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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Molecular Weight of Step-Growth Polymers01:08

Molecular Weight of Step-Growth Polymers

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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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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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相关实验视频

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Designed for Molecular Recycling: A Lignin-Derived Semi-aromatic Biobased Polymer
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可持续高Tg聚合物的数据驱动建模和设计.

Qinrui Liu1, Michael F Forrester2, Dhananjay Dileep2

  • 1Department of Materials Design and Innovation, University at Buffalo, Buffalo, NY 14260, USA.

International journal of molecular sciences
|March 27, 2025
PubMed
概括

本研究引入了一种机器学习方法,用于预测聚合物的玻璃过渡温度 (Tg). 该方法通过分析聚合物化学和结构-性质关系,加速发现可持续的高温聚合物.

关键词:
玻璃过渡温度是玻璃过渡温度.图形理论中的图形理论.机器学习是机器学习.可持续的高分子.拓形容器的拓形容器是什么?

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科学领域:

  • 聚合物科学 聚合物科学
  • 材料科学 材料科学 材料科学
  • 机器学习 机器学习

背景情况:

  • 预测玻璃过渡温度 (Tg) 对于设计具有特定热性质的聚合物至关重要.
  • 传统的Tg预测方法往往耗时,可能无法捕捉复杂的结构-属性关系.

研究的目的:

  • 开发一种快速而强大的机器学习 (ML) 方法来预测聚合物玻璃过渡温度 (Tg).
  • 确定影响Tg的关键化学特征,用于设计可持续的高温聚合物.

主要方法:

  • 开发了一个全面的功能集,整合了聚合物化学的各个方面.
  • 采用ML技术将化学特征与Tg相关联,并构建一个高通量预测模型.
  • 利用非线性多重变换来捕捉聚合物拓和Tg.之间的复杂关系.

主要成果:

  • 确定了影响Tg的关键化学描述因素,包括旋转自由度和基于硬质障碍的骨干指数.
  • 在不同数据类型中实现了强大的Tg预测,通过对新型聚合物化学的实验测试进行验证.
  • 证明了聚合物刚性和相互作用动态对于调整Tg.g.至关重要.

结论:

  • 机器学习模型可以准确预测Tg,这表明拓描述符包含了必要的信息.
  • 聚合物结构和Tg之间的复杂,非线性关系需要先进的ML方法,而不是传统的回归.
  • 这种ML方法允许对聚合物化学物质进行快速优化,以满足针对性的高温应用.