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

2.6K
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...
2.6K
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...
3.0K
Polymers: Molecular Weight Distribution01:10

Polymers: Molecular Weight Distribution

3.7K
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.
3.7K
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
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

101
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
101
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

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

AI-Based Polymer Classification Using Ensemble Deep Learning and Heuristic Optimization: Implications for Recycling Applications.

Polymers·2026
Same author

An Artificial Intelligence-Based Melt Flow Rate Prediction Method for Analyzing Polymer Properties.

Polymers·2025
Same author

High-Accuracy Polymer Property Detection via Pareto-Optimized SMILES-Based Deep Learning.

Polymers·2025
Same author

Non-Enzymatic Glucose Sensors Composed of Polyaniline Nanofibers with High Electrochemical Performance.

Molecules (Basel, Switzerland)·2024
Same author

Intelligent Wireless Capsule Endoscopy for the Diagnosis of Gastrointestinal Diseases.

Diagnostics (Basel, Switzerland)·2023
Same author

Electrochemical Sensing of H<sub>2</sub>O<sub>2</sub> by Employing a Flexible Fe<sub>3</sub>O<sub>4</sub>/Graphene/Carbon Cloth as Working Electrode.

Materials (Basel, Switzerland)·2023
Same journal

RETRACTED: Alshabanah et al. Elastic Nanofibrous Membranes for Medical and Personal Protection Applications: Manufacturing, Anti-COVID-19, and Anti-Colistin Resistant Bacteria Evaluation. <i>Polymers</i> 2021, <i>13</i>, 3987.

Polymers·2026
Same journal

Correction: Kang et al. Energy-Saving Electrospinning with a Concentric Teflon-Core Rod Spinneret to Create Medicated Nanofibers. <i>Polymers</i> 2020, <i>12</i>, 2421.

Polymers·2026
Same journal

Influence of Self-Adhesive Resin Composite Deep Marginal Elevation on the Sealing Ability of CAD/CAM Lithium Disilicate Glass-Ceramic Inlays: An In Vitro Study.

Polymers·2026
Same journal

Modulating Exciton Dynamics Through Fluorescent Side Group Incorporation in Benzodithiophene-Benzotriazole-Isoindigo Terpolymers.

Polymers·2026
Same journal

PLA/PBSA Biocomposites Reinforced with Tangerine Tree-Derived Agro-Industrial Waste for Rigid Packaging: Effect of Extraction Treatment on Morphology and Thermo-Mechanical Performance.

Polymers·2026
Same journal

Synergistic Coatings Based on Chitosan and <i>Eugenia caryophyllata</i> Essential Oil to Improve Postharvest Quality of <i>Capsicum chinense</i>.

Polymers·2026
查看所有相关文章

相关实验视频

Updated: Sep 13, 2025

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

7.6K

数据驱动的聚合物分类使用BiGRU和混合元启发式优化算法.

Mohammad Anwar Parvez1, Ibrahim M Mehedi2

  • 1Department of Chemical Engineering, College of Engineering, King Faisal University, Al-Ahsa 31982, Saudi Arabia.

Polymers
|July 30, 2025
PubMed
概括
此摘要是机器生成的。

一个新的数据驱动的聚合物分类模型,OADLNN-DDPC,使用深度学习和优化算法来准确识别聚合物类型. 这种先进的方法显著改善了材料科学应用的现有技术.

关键词:
数据规范化的数据规范化.数据驱动的聚合物分类.深度学习 (DL) 是指深度学习.功能选择 功能选择斑马优化算法 斑马优化算法

更多相关视频

Polymer Microarrays for High Throughput Discovery of Biomaterials
13:37

Polymer Microarrays for High Throughput Discovery of Biomaterials

Published on: January 25, 2012

14.7K
Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules
10:58

Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules

Published on: July 25, 2013

17.2K

相关实验视频

Last Updated: Sep 13, 2025

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

7.6K
Polymer Microarrays for High Throughput Discovery of Biomaterials
13:37

Polymer Microarrays for High Throughput Discovery of Biomaterials

Published on: January 25, 2012

14.7K
Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules
10:58

Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules

Published on: July 25, 2013

17.2K

科学领域:

  • 材料科学 材料科学 材料科学
  • 计算机科学 计算机科学

背景情况:

  • 传统的聚合物分类方法是劳动密集型的,容易出现错误.
  • 越来越需要高效,数据驱动的方法来探索聚合物的广化学空间.
  • 深度学习 (DL) 模型为材料科学中的自动化分析和分类提供了强大的工具.

研究的目的:

  • 提出一个新的优化算法与数据驱动聚合物分类 (OADLNN-DDPC) 模型的基于深度学习的神经网络.
  • 为了提高数据驱动的聚合物分类的准确性和效率.
  • 为了利用先进的优化算法来改善聚合物表征.

主要方法:

  • 使用Z分数规范化的数据规范化.
  • 使用白搜索 (BES) 算法进行特征选择.
  • 聚合物分类采用双向门循环单元 (BiGRU) 技术.
  • 模型调整使用斑马优化算法 (ZOA).

主要成果:

  • 在19500个记录和2048个特征的数据集上,OADLNN-DDPC模型实现了98.58%的高精度.
  • 性能优于现有模型,包括LSTM (83.37%),PLS-DA (88.18%) 和K-NN (98.36%).
  • 与其他已知方法相比,在聚合物分类性能上显著改善.

结论:

  • 拟议的OADLNN-DDPC模型为数据驱动的聚合物分类提供了一种优越的方法.
  • 整合DL和优化算法有效地解决了聚合物材料分析中的挑战.
  • 这种数据驱动的方法为更准确,更有效地发现新型聚合物铺平了道路.