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相关概念视频

Olefin Metathesis Polymerization: Acyclic Diene Metathesis (ADMET)00:53

Olefin Metathesis Polymerization: Acyclic Diene Metathesis (ADMET)

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Acyclic diene metathesis polymerization or ADMET polymerization involves cross-metathesis of terminal dienes, such as 1,8-nonadiene, to give linear unsaturated polymer and ethylene. As ADMET is a reversible process, the formed ethylene gas must be removed from the reaction mixture to complete the polymerization process.
Similar to cross-metathesis, ADMET also involves the formation of metallacyclobutane intermediate by [2+2] cycloaddition of one of the double bonds of a terminal diene with...
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[4+2] Cycloaddition of Conjugated Dienes: Diels–Alder Reaction01:16

[4+2] Cycloaddition of Conjugated Dienes: Diels–Alder Reaction

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The Diels–Alder reaction is an example of a thermal pericyclic reaction between a conjugated diene and an alkene or alkyne, commonly referred to as a dienophile. The reaction involves a concerted movement of six π electrons, four from the diene and two from the dienophile, forming an unsaturated six-membered ring. As a result, these reactions are classified as [4+2] cycloadditions.
10.2K
Cationic Chain-Growth Polymerization: Mechanism00:57

Cationic Chain-Growth Polymerization: Mechanism

2.3K
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...
2.3K
Radical Chain-Growth Polymerization: Chain Branching01:17

Radical Chain-Growth Polymerization: Chain Branching

1.9K
The skeletal structure of polymers synthesized via radical polymerization is always branched. For example, the polymerization of ethylene by radical polymerization results in a low-density grade of polyethylene with a heavily branched skeletal structure. Here, the radical site abstracts hydrogen from the growing chain, and the radical site shifts from the end (a primary carbon center) to anywhere within the growing chain (a secondary carbon center). Consequently, the part of the chain from the...
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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
Polymer Classification: Stereospecificity01:26

Polymer Classification: Stereospecificity

2.4K
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...
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适应性数据驱动的深度学习替代模型用于Dicyclopentadiene的前端聚合.

Qibang Liu1,2, Diab Abueidda3, Sagar Vyas1,2

  • 1Beckman Institute for Advanced Science and Technology, University of Illinois, Urbana-Champaign, Illinois 61801, United States.

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概括

本研究介绍了一种适应性深度学习模型,用于二cyclopentadiene (DCPD) 的正面聚合 (FP). 该模型显著加速模拟,使得聚合物制造工艺的分析和优化速度更快.

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科学领域:

  • 材料科学 材料科学 材料科学
  • 化学工程是化学工程的重要组成部分.
  • 计算科学 计算科学

背景情况:

  • 前端聚合 (FP) 是一种快速,节能的热固聚合物固化方法.
  • 像FEM这样的传统模拟技术是计算密集型的,阻碍了过程优化.
  • 有效的模拟对于分析灵敏度,不确定性和优化FP制造至关重要.

研究的目的:

  • 开发一种适应性的替代深度学习模型,用于二cyclopentadiene (DCPD) 的正面聚合 (FP).
  • 与FEM相比,在预测温度和治愈演变方面实现了数量级的加速.
  • 提高FP过程建模中的计算效率和准确性.

主要方法:

  • 开发了一个适应性深度学习替代模型用于FP模拟.
  • 使用自动分化来计算控制方程的剩余误差.
  • 采用基于残余误差的概率密度函数,用于高效的训练样本选择.
  • 使用2D有限元法 (FEM) 模拟生成的训练数据.

主要成果:

  • 适应性代孕模型预测温度和治愈演变的速度比FEM快得多.
  • 适应性抽样策略被证明比随机抽样更有效和更准确.
  • 该模型在预测能力方面实现了数量级的加速.
  • 快速提取了前方速度,形状和温度等关键FP特征.

结论:

  • 适应式深度学习模型为FP模拟提供了计算效率高,准确的方法.
  • 这种方法加速了热聚合物制造的分析和优化.
  • 替代模型能够从预测的领域快速提取关键过程参数.