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相关概念视频

Polymers: Defining Molecular Weight01:01

Polymers: Defining Molecular Weight

3.1K
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...
3.1K
Polymers: Molecular Weight Distribution01:10

Polymers: Molecular Weight Distribution

3.7K
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.
3.7K
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...
3.1K
Polymer Classification: Stereospecificity01:26

Polymer Classification: Stereospecificity

2.6K
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...
2.6K
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
Polymer Classification: Architecture01:14

Polymer Classification: Architecture

2.9K
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...
2.9K

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相关实验视频

Updated: Sep 11, 2025

Fabricating Superhydrophobic Polymeric Materials for Biomedical Applications
09:22

Fabricating Superhydrophobic Polymeric Materials for Biomedical Applications

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通过机器学习技术增强聚合物的导热计算.

Chengyang Tu1, Xin Li1, Junmin Chen1

  • 1Tsinghua SIGS, Tsinghua University, 518055 Shenzhen, China.

The journal of physical chemistry. B
|August 11, 2025
PubMed
概括

难以预测聚合物的导热率 (κ). 本研究介绍了一种混合机器学习 (ML) 方法,使用PhyNEO潜力和ML辅助的热流计算来准确地预测聚合物 κ.

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
  • 聚合物物理 聚合物物理

背景情况:

  • 由于复杂的结构,精确预测聚合物导热率 (κ) 是具有挑战性的.
  • 现有的ab initio方法 (例如,DFT-BTE) 在计算上昂贵.
  • 在分子动力学中,经典力场缺乏对 κ 预测所需的准确性.

研究的目的:

  • 开发一种计算效率高,准确的方法来预测聚合物导热率.
  • 为了从小量子集群数据中对散装聚合物 κ 进行定量预测.
  • 根据实验数据验证开发的方法.

主要方法:

  • 将初始混合机器学习 (ML) 与多极极化潜能 (PhyNEO) 结合起来.
  • 使用ML-方便的热量流量计算用于可靠的轨迹生成.
  • 使用聚乙烯氧化物作为验证的模型系统.

主要成果:

  • 该PhyNEO-ML方法提供可靠的热流轨迹.
  • 聚合物 κ 的定量预测得到了很好的协议.
  • 对聚乙烯氧化物的计算结果与来自时间域热反射度测量的实验数据相匹配.

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相关实验视频

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09:22

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Published on: August 28, 2015

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Environmental Dynamic Mechanical Analysis to Predict the Softening Behavior of Neural Implants
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结论:

  • 开发的混合ML/PhyNEO方法为聚合物导热率预测提供了一个计算上可行的和准确的方法.
  • 这种方法允许从最小的量子数据开始进行定量 κ 预测.
  • 该方法在未来的聚合物材料设计和分析中具有广泛的适用性.