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相关概念视频

Characteristics and Nomenclature of Copolymers01:24

Characteristics and Nomenclature of Copolymers

2.6K
Copolymers are the products obtained from the polymerization of multiple monomer species. So, in a polymer chain itself, there can be multiple repeating units that come from different monomers. The process of synthesizing a polymer from different monomer species is called copolymerization. When two monomers are involved, the polymer is known as a bipolymer. Polymers with three and four monomers are termed terpolymers and quaterpolymers, respectively. Figure 1 depicts the copolymerization of...
2.6K
Cationic Chain-Growth Polymerization: Mechanism00:57

Cationic Chain-Growth Polymerization: Mechanism

2.4K
The cationic polymerization mechanism consists of three steps: initiation, propagation, and termination. In the initiation step of the polymerization process, the π bond of a monomer gets protonated by the Lewis acid catalyst, which is formed from boron trifluoride and water. The protonation of the π bond generates a carbocation stabilized by the electron‐donating group. In the propagation step, the π bond of the second monomer acts as a nucleophile and attacks the...
2.4K
Anionic Chain-Growth Polymerization: Overview01:20

Anionic Chain-Growth Polymerization: Overview

2.1K
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,...
2.1K
Step-Growth Polymerization: Overview01:03

Step-Growth Polymerization: Overview

3.5K
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...
3.5K
Anionic Chain-Growth Polymerization: Mechanism01:04

Anionic Chain-Growth Polymerization: Mechanism

2.1K
The mechanism for anionic chain-growth polymerization involves initiation, propagation, and termination steps. In the initiation step, a nucleophilic anion, such as butyl lithium, initiates the polymerization process by attacking the π bond of the vinylic monomer. As a result, a carbanion, stabilized by the electron‐withdrawing group, is generated. The resulting carbanion acts as a Michael donor in the propagation step and attacks the second vinylic monomer, which acts as a Michael...
2.1K
Polymers: Molecular Weight Distribution01:10

Polymers: Molecular Weight Distribution

3.5K
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.
3.5K

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相关实验视频

Updated: Jul 16, 2025

Disentangling High Strength Copolymer Aramid Fibers to Enable the Determination of Their Mechanical Properties
06:02

Disentangling High Strength Copolymer Aramid Fibers to Enable the Determination of Their Mechanical Properties

Published on: September 1, 2018

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开发高效的深度学习模型,用于预测共聚合物特性.

Himanshu1, Kaushik Chakraborty1, Tarak K Patra1

  • 1Department of Chemical Engineering and Center for Atomistic Modeling and Materials Design, Indian Institute of Technology Madras, Chennai, TN 600036, India. tpatra@iitm.ac.in.

Physical chemistry chemical physics : PCCP
|September 15, 2023
PubMed
概括
此摘要是机器生成的。

深度学习模型可以预测聚合物特性,但需要最佳的架构和足够的数据. 本研究介绍了使用最小数据和高参数进行高效的深度学习模型开发的方法,用于聚合物科学.

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Fabricating Superhydrophobic Polymeric Materials for Biomedical Applications
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Fabricating Superhydrophobic Polymeric Materials for Biomedical Applications

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Anionic Polymerization of an Amphiphilic Copolymer for Preparation of Block Copolymer Micelles Stabilized by &#960;-&#960; Stacking Interactions
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Anionic Polymerization of an Amphiphilic Copolymer for Preparation of Block Copolymer Micelles Stabilized by π-π Stacking Interactions

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相关实验视频

Last Updated: Jul 16, 2025

Disentangling High Strength Copolymer Aramid Fibers to Enable the Determination of Their Mechanical Properties
06:02

Disentangling High Strength Copolymer Aramid Fibers to Enable the Determination of Their Mechanical Properties

Published on: September 1, 2018

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Fabricating Superhydrophobic Polymeric Materials for Biomedical Applications
09:22

Fabricating Superhydrophobic Polymeric Materials for Biomedical Applications

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Anionic Polymerization of an Amphiphilic Copolymer for Preparation of Block Copolymer Micelles Stabilized by &#960;-&#960; Stacking Interactions
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Anionic Polymerization of an Amphiphilic Copolymer for Preparation of Block Copolymer Micelles Stabilized by π-π Stacking Interactions

Published on: October 10, 2016

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科学领域:

  • 聚合物科学 聚合物科学
  • 材料科学 材料科学 材料科学
  • 计算化学计算化学

背景情况:

  • 深度学习模型显示出预测聚合物性质的前景.
  • 模型性能在很大程度上取决于网络拓和训练数据量.
  • 缺乏标准化协议的架构选择和有限的同质聚合物数据阻碍了发展.

研究的目的:

  • 解决深度学习模型开发中的瓶问题.
  • 提出有效的模型创建策略,使用最小的数据和超参数.
  • 评估拓和数据量对模型性能的影响.

主要方法:

  • 线性层次扩展以确定最佳的神经网络拓.
  • 特性提取用于将离散的聚合物序列映射到连续的潜空间.
  • 实现用于预测共聚物旋转半径和共聚物兼容器特性.

主要成果:

  • 展示了一种系统识别合适深度学习架构的方法.
  • 开发了一种技术,以减少训练聚合物模型的数据要求.
  • 成功地应用了方法来预测特定的序列定义的聚合物特性.

结论:

  • 用最小的数据和超参数可以构建高效的深度学习模型来预测聚合物属性.
  • 拟议的策略克服了当前对聚合物深度学习应用的关键局限性.
  • 这项工作促进了聚合物科学中预测模型的快速发展.