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相关概念视频

Polymer Classification: Architecture01:14

Polymer Classification: Architecture

3.0K
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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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.
3.8K
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
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
Types of Step-Growth Polymers: Polyesters01:20

Types of Step-Growth Polymers: Polyesters

2.3K
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...
2.3K
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

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Self-assembling Morphologies Obtained from Helical Polycarbodiimide Copolymers and Their Triazole Derivatives
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使用机器学习对聚合物混合薄膜的形态预测.

Bishnu R1, Rabibrata Mukherjee2, Nandini Bhandaru1

  • 1Chemical Engineering Department, Birla Institute of Technology and Science (BITS) Pilani, Hyderabad Campus, Jawahar Nagar, Medchal District, Hyderabad-500078, Telangana, India. nandini@hyderabad.bits-pilani.ac.in.

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概括
此摘要是机器生成的。

这项研究引入了一个机器学习模型来预测聚合物混合薄膜形态. 支持矢量机 (SVM) 模型准确预测形态,帮助特定应用的实验设计.

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科学领域:

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

背景情况:

  • 聚合物混合薄膜表现出介质尺度形态,这取决于处理参数.
  • 形态学对于确定薄膜应用至关重要.
  • 了解不混合聚合物混合物中的相分离对于材料设计至关重要.

研究的目的:

  • 开发一种机器学习 (ML) 框架,用于预测聚乙烯/聚甲基甲酸盐 (PS/PMMA) 薄膜的形态.
  • 引导实验人员设计具有所需形态的薄膜.
  • 增强对聚合物混合物相位分离现象的理解.

主要方法:

  • 使用实验参数 (PS重量分数,PMMA分子量,度,基板表面能量) 作为ML模型的输入.
  • 采用多类分类来预测形态类型 (列,孔,岛).
  • 实现了支持向量机 (SVM) 和其他ML算法,以及可解释的AI技术.

主要成果:

  • 使用SVM算法实现了最高的93.75%的预测准确度.
  • 可解释的ML提供了与实验观测一致的见解,验证了模型.
  • 确定了影响阶段分离和形态形成的关键参数.

结论:

  • ML框架可靠地预测PS/PMMA混合薄膜形态.
  • 来自可解释AI的洞察力加深了对聚合物相位分离的理解.
  • 制定了指导方针和一个网络工具,以促进针对特定薄膜形态的实验设计.