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相关概念视频

Polymer Classification: Crystallinity01:21

Polymer Classification: Crystallinity

3.1K
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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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...
37.1K
Step-Growth Polymerization: Overview01:03

Step-Growth Polymerization: Overview

3.6K
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...
3.6K
Molecular Weight of Step-Growth Polymers01:08

Molecular Weight of Step-Growth Polymers

2.3K
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...
2.3K
Anionic Chain-Growth Polymerization: Overview01:20

Anionic Chain-Growth Polymerization: Overview

2.2K
The polymerization process that involves carbanion as an intermediate is called anionic polymerization. It is also a type of addition or chain-growth polymerization. Anionic polymerization gets initiated by a strong nucleophile such as an organolithium or a Grignard reagent. The most commonly used initiator for anionic polymerization is butyl lithium. Monomers involved in anionic polymerization must possess a vinyl group bonded to one or two electron-withdrawing groups. For instance,...
2.2K
Ziegler–Natta Chain-Growth Polymerization: Overview01:17

Ziegler–Natta Chain-Growth Polymerization: Overview

3.5K
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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相关实验视频

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Polymer Microarrays for High Throughput Discovery of Biomaterials
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Polymer Microarrays for High Throughput Discovery of Biomaterials

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人工智能应用在聚合物材料中的最新进展

Teng Long1,2, Qianqian Pang1,2, Yanyan Deng1,2

  • 1School of Materials Science & Engineering, Shandong University, Jinan 250061, China.

Polymers
|June 27, 2025
PubMed
概括

本综述主张将聚合物研究从传统方法转向数据驱动的人工智能 (AI) 方法. 人工智能为聚合物设计,性能预测和优化提供了显著的优势,为可持续创新铺平了道路.

关键词:
算法算法是一种算法.人工智能的人工智能是人工智能.我们的数据库数据库数据库数据库.描述者描述者是指描述者.聚合物材料是一种聚合物材料.

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科学领域:

  • 聚合物科学与工程 聚合物科学与工程
  • 材料科学 材料科学 材料科学
  • 计算化学计算化学

背景情况:

  • 聚合物科学界传统上依赖于经验驱动的研究范式.
  • 尽管人工智能 (AI) 取得了重大进展,但其在聚合物研究中的应用仍然有限.
  • 人工智能的潜力与其在聚合物材料开发中的当前应用之间存在差距.

研究的目的:

  • 倡导转向数据驱动,人工智能支持的聚合物科学研究的范式转变.
  • 评估AI在聚合物研究中的计算优势和持续障碍.
  • 为有效地将AI整合到聚合物材料设计,性能预测和流程优化中提出解决方案.

主要方法:

  • 审查当前的AI应用在聚合物设计,性能预测和过程优化.
  • 分析诸如数据稀缺,材料描述符和算法复杂性等挑战.
  • 讨论潜在的解决方案,包括协作数据平台,适应领域的描述符和主动学习.

主要成果:

  • 人工智能已经在加速聚合物研究方面展示了变革性的潜力.
  • 人工智能采用的主要障碍包括数据限制和方法复杂性.
  • 建议的解决方案可以提高数据质量,并确保人工智能驱动结果的可信性.

结论:

  • 向数据驱动,人工智能驱动的研究过渡对于推进聚合物科学至关重要.
  • 克服当前的挑战需要创新的数据管理方法和人工智能方法.
  • 这项工作为人工智能在聚合物研究中的可持续整合提供了路线图,以加速创新.