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相关概念视频

Polymers: Molecular Weight Distribution01:10

Polymers: Molecular Weight Distribution

4.7K
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.
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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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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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Molecular Models02:00

Molecular Models

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Physical models representing molecular architectures of chemical compounds play essential roles in understanding chemistry. The use of molecular models makes it easier to visualize the structures and shapes of atoms and molecules.
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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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Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

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Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...
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相关实验视频

Updated: Jan 18, 2026

Author Spotlight: Real-Time Imaging of Bonding in 3D-Printed Layers
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聚合物动态模型来自维度减小技术的模型.

Phillip Bement1, Jörg Rottler1

  • 1Department of Physics and Astronomy and Stewart Blusson Quantum Matter Institute, University of British Columbia, Vancouver, British Columbia V6T 1Z1, Canada.

The Journal of chemical physics
|September 8, 2025
PubMed
概括
此摘要是机器生成的。

线性维度缩小,包括时间滞后的独立组件分析 (tICA),有效地模拟了聚合物动态. 这种方法与Rouse模式和动态自相一致的场理论保持一致,扩展到复杂的不平衡过程.

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科学领域:

  • 聚合物物理 聚合物物理
  • 计算化学计算化学
  • 统计力学 统计力学

背景情况:

  • 了解聚合物动态对于材料科学至关重要.
  • 线性维度减小方法为复杂的聚合物行为提供了新的视角.
  • 时间滞后独立组件分析 (tICA) 是分析动态系统的强大工具.

研究的目的:

  • 使用线性维度减小技术分析聚合物动态,特别是主要成分分析 (PCA) 和tICA.
  • 为了证明tICA与动态自相一致的场理论 (D-SCFT) 对于理想的Rouse动态的等价性.
  • 将tICA扩展到模拟非平衡现象,如旋点分解.

主要方法:

  • 主要成分分析 (PCA) 和时间滞后的独立成分分析 (tICA) 应用于聚合物动态.
  • 对细分密度的里埃模式的分析.
  • 引入隐藏变量和时间局部方法来结合时间记忆.
  • 一般化用于构建不平衡过程的连续模型.

主要成果:

  • 为了获得理想的Rouse动态,tICA识别的慢模式与传统的Rouse模式相匹配.
  • 对里埃模式应用的tICA产生了相当于D-SCFT与特定修改的动态.
  • 开发的方法成功地模拟了双块共聚合物中的时间记忆和不平衡的旋点分解.

结论:

  • 线性维度缩小,特别是tICA,为研究聚合物动态提供了强大的框架.
  • tICA提供了微观聚合物行为和D-SCFT等连续理论之间的桥梁.
  • 一般化的tICA方法适用于复杂的,不平衡的聚合物系统.