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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
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
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
Ziegler–Natta Chain-Growth Polymerization: Overview01:17

Ziegler–Natta Chain-Growth Polymerization: Overview

3.4K
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...
3.4K
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
Polymer Classification: Architecture01:14

Polymer Classification: Architecture

2.8K
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...
2.8K

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相关实验视频

Updated: Jul 26, 2025

Synthesis of Monodisperse Cylindrical Nanoparticles via Crystallization-driven Self-assembly of Biodegradable Block Copolymers
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Published on: June 20, 2019

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模板设计复杂的区块共聚合物模式使用机器学习方法.

Zhihan Liu1, Yi-Xin Liu1, Yuliang Yang1

  • 1The State Key Laboratory of Molecular Engineering of Polymers, Department of Macromolecular Science, Fudan University, Shanghai 200433, China.

ACS applied materials & interfaces
|June 19, 2023
PubMed
概括
此摘要是机器生成的。

本研究介绍了一种机器学习方法,用于设计定向自组装 (DSA) 中的引导模板. 开发的神经网络模型在没有模拟的情况下准确地预测DSA模式的模板,达到97.1%的准确性.

关键词:
区块共聚物的区块共聚物.导向自组装的自组装方式反向设计的设计.石版印刷 石版印刷 石版印刷机器学习是机器学习.

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Fabricating Reactive Surfaces with Brush-like and Crosslinked Films of Azlactone-Functionalized Block Co-Polymers
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Self-assembling Morphologies Obtained from Helical Polycarbodiimide Copolymers and Their Triazole Derivatives
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Fabricating Reactive Surfaces with Brush-like and Crosslinked Films of Azlactone-Functionalized Block Co-Polymers
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Self-assembling Morphologies Obtained from Helical Polycarbodiimide Copolymers and Their Triazole Derivatives
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科学领域:

  • 材料科学 材料科学 材料科学
  • 计算化学计算化学
  • 机器学习 机器学习

背景情况:

  • 定向自组装 (DSA) 对于创建纳米尺度模式至关重要.
  • 为DSA设计指导模板是一个复杂的逆向设计问题.
  • 目前的方法通常依赖于计算上昂贵的前模拟.

研究的目的:

  • 开发一种基于机器学习的方法,用于DSA指导模板的反向设计.
  • 仅使用机器学习来预测DSA模板,消除了对前模拟的需求.
  • 评估各种神经网络架构的性能和概括能力.

主要方法:

  • 制定了反向设计问题作为一个多标签分类任务.
  • 训练了各种神经网络 (NN) 模型,包括带有残余块的卷积神经网络 (CNN).
  • 利用自相一致的场理论 (SCFT) 计算中的模拟图案样本进行训练.
  • 应用了针对形态预测的增强技术,以提高NN的性能.

主要成果:

  • 在模板预测的准确匹配准确度方面取得了显著的改进,从59.8% (基线) 增加到97.1% (最佳模型).
  • 证明机器学习模型可以预测模板,而不需要进行前模拟.
  • 性能最好的NN模型显示了人类设计的DSA模式的优秀概括能力.

结论:

  • 机器学习,特别是深度神经网络,为DSA指导模板的反向设计提供了强大而高效的解决方案.
  • 开发的方法通过消除对广泛模拟的需求,显著优于传统方法.
  • 该研究强调了人工智能在加速材料设计和制造过程中的潜力.