Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

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...
2.4K
Polymers: Molecular Weight Distribution01:10

Polymers: Molecular Weight Distribution

3.3K
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.
3.3K
Molecular Weight of Step-Growth Polymers01:08

Molecular Weight of Step-Growth Polymers

2.2K
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...
2.2K
Characteristics and Nomenclature of Homopolymers01:00

Characteristics and Nomenclature of Homopolymers

3.0K
Polymers that are made up of identical monomer units are called homopolymers. Only one repeating unit is involved in the construction of the homopolymer structure. For example, as depicted in Figure 1, polypropylene is a homopolymer constituted of propylene monomers. Here, the only repeating unit in the polymer chain is propylene.
3.0K
Polymers: Defining Molecular Weight01:01

Polymers: Defining Molecular Weight

2.8K
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...
2.8K
Polymer Classification: Architecture01:14

Polymer Classification: Architecture

2.7K
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...
2.7K

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

Biostimulants for Plant Growth and Mitigation of Abiotic Stresses: A Metabolomics Perspective.

Metabolites·2020
查看所有相关文章

相关实验视频

Updated: Jun 15, 2025

Author Spotlight: Real-Time Imaging of Bonding in 3D-Printed Layers
04:36

Author Spotlight: Real-Time Imaging of Bonding in 3D-Printed Layers

Published on: September 1, 2023

3.2K

工业影响聚烯的数据驱动优化:机器学习洞察力

Randy D Cunningham1, Veronica Patterson1, Ebert Cawood1

  • 1SASOL Secunda Chemical Operations, Secunda 2302, Republic of South Africa.

Journal of chemical information and modeling
|June 13, 2025
PubMed
概括

机器学习模型可以实时预测聚烯的物理性能. 随机森林模型使用关键结构数据准确预测拉伸模量,屈曲模量和冲击强度.

科学领域:

  • 材料科学 材料科学 材料科学
  • 聚合物工程 聚合物工程
  • 计算科学 计算科学

背景情况:

  • 实验性确定冲击聚烯 (ICP) 的物理性质是耗时的.
  • 工业生产中的实时决策经常因这些漫长的测试而延迟.
  • 开发更快的方法来预测材料属性对于高效的制造至关重要.

研究的目的:

  • 探索机器学习 (ML) 模型的使用,以实时确定ICP物理属性.
  • 确定能准确预测材料性能的关键结构参数.
  • 为了减少实验开销,提高ICP制造过程的效率.

主要方法:

  • 训练和评估了三个ML模型:线性回归,随机森林和神经网络.
  • 使用一个工业数据集的ICP结构性质 (MFR,C2,RCC2,R21).
  • 使用随机森林和SHAP进行特征重要性分析,以确定关键预测因素.

主要成果:

  • 随机森林模型表现出优异的性能,R2值为0.78 (拉伸模量),0.75 (弹性模量) 和0.88 (冲击强度).
  • 化流速 (MFR) 和无形相位指标 (R21) 被确定为预测最关键的特征.
  • 仅使用MFR和R21重新训练模型显著降低了复杂性,并保持了预测准确性.

更多相关视频

Forming Micro-and Nano-Plastics from Agricultural Plastic Films for Employment in Fundamental Research Studies
08:21

Forming Micro-and Nano-Plastics from Agricultural Plastic Films for Employment in Fundamental Research Studies

Published on: July 27, 2022

4.1K
Quantification of Polybutylene Adipate Terephthalate-based Micro- and Nano-plastics from Soil Using Proton Nuclear Magnetic Resonance Spectroscopy
05:05

Quantification of Polybutylene Adipate Terephthalate-based Micro- and Nano-plastics from Soil Using Proton Nuclear Magnetic Resonance Spectroscopy

Published on: June 6, 2025

42

相关实验视频

Last Updated: Jun 15, 2025

Author Spotlight: Real-Time Imaging of Bonding in 3D-Printed Layers
04:36

Author Spotlight: Real-Time Imaging of Bonding in 3D-Printed Layers

Published on: September 1, 2023

3.2K
Forming Micro-and Nano-Plastics from Agricultural Plastic Films for Employment in Fundamental Research Studies
08:21

Forming Micro-and Nano-Plastics from Agricultural Plastic Films for Employment in Fundamental Research Studies

Published on: July 27, 2022

4.1K
Quantification of Polybutylene Adipate Terephthalate-based Micro- and Nano-plastics from Soil Using Proton Nuclear Magnetic Resonance Spectroscopy
05:05

Quantification of Polybutylene Adipate Terephthalate-based Micro- and Nano-plastics from Soil Using Proton Nuclear Magnetic Resonance Spectroscopy

Published on: June 6, 2025

42

结论:

  • 机器学习模型,特别是随机森林,为实时ICP属性预测提供了一个可扩展和可解释的解决方案.
  • 使用MFR和R21简化了预测过程,减少了对广泛实验表征的依赖.
  • 这种方法支持数字产品的开发,并提高工业ICP制造过程的效率.