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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 polymer...
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Polymers: Molecular Weight Distribution01:10

Polymers: Molecular Weight Distribution

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For any given polymer, the weight average molecular weight (Mw) is higher than, if not equal to, the number average molecular weight (Mn). The only situation in which the weight average molecular weight and the number average molecular weight are equal is when a polymer consists only of chains with equal molecular weight. However, this never happens in a synthetic polymer, since it is difficult to control the polymerization process up to a molecular level with accuracy to a hundred percent.
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Polymers: Defining Molecular Weight01:01

Polymers: Defining Molecular Weight

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Unlike small molecules with definite molecular weights, polymers are a mixture of individual polymer chains of varying lengths, each with a unique molecular weight.  So, the molecular weight of a polymer is expressed as an average value based on the average size of the polymer chains. The two most common forms of averages used for polymers are the number average molecular weight and weight average molecular weight.
The number average molecular weight (Mn) is the summation of the number...
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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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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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Polymers02:34

Polymers

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The word polymer is derived from the Greek words “poly” which means “many” and “mer” which means “parts”. Polymers are long chains of molecules composed of repeating units of smaller molecules, known as monomers. They either occur naturally, such as DNA and proteins, or can be constructed synthetically, like plastics. They have varied structural characteristics, such as linear chains, branched chains, or complex networks, that contribute to the...
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相关实验视频

Updated: Jan 16, 2026

Synthesis of Soft Polysiloxane-urea Elastomers for Intraocular Lens Application
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多模式机器学习与3D权重矩阵编码,用于高通量设计高性能聚氨.

Shushuai Zhou1,2, Wanchen Zhao1,2, Zilong Wan1,2

  • 1State Key Laboratory of Polymer Science and Technology, Changchun Institute of Applied Chemistry, Chinese Academy of Sciences, Changchun, China.

Macromolecular rapid communications
|September 27, 2025
PubMed
概括

研究人员开发了一种机器学习框架,以预测聚氨的机械性能. 这加速了通过选数百万种组合来发现先进的聚氨材料.

关键词:
高通量计算选的计算选.机器学习是机器学习.多式多样化的多式模式聚氨弹性体的聚氨弹性体房地产预测 房地产预测结构与财产关系的关系

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科学领域:

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

背景情况:

  • 聚氨 (PU) 广泛使用,但由于其复杂的结构,设计它们以满足特定的机械性能是具有挑战性的.
  • 预测PU的机械性质需要理解复杂的结构-性质关系.

研究的目的:

  • 创建一个高通量计算框架来预测聚氨的机械性质.
  • 通过先进的计算选加速高性能聚氨材料的开发.

主要方法:

  • 开发了一种用于表示聚氨单体的3D加权矩阵编码,其性能优于常规描述器.
  • 集成数字化合成参数与结构特征,使用早期融合深度学习架构.
  • 创建了一种多式深度学习模型,用于预测Young的模量,拉伸强度和断裂时的延伸.

主要成果:

  • 3D-Weighted-Matrix编码显示,特征可辨别性得到了23%的改善.
  • 多式联动深度学习模型在机械性质预测方面实现了超过0.86的R2值.
  • 选了超过1.5亿个分子和工艺组合,以确定最佳候选人,以提高机械性能.

结论:

  • 这项研究为加速开发高性能聚氨提供了一个强大的计算框架.
  • 实现了对聚氨中结构性质相关性的更深入的理解.
  • 开发的模型能够有效地预测和优化针对特定应用的材料特性.