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相关概念视频

Polymer Classification: Crystallinity01:21

Polymer Classification: Crystallinity

2.9K
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...
2.9K
Polymer Classification: Stereospecificity01:26

Polymer Classification: Stereospecificity

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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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Polymer Classification: Architecture01:14

Polymer Classification: Architecture

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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...
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Step-Growth Polymerization: Overview01:03

Step-Growth Polymerization: Overview

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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...
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相关实验视频

Updated: Jul 19, 2025

Polymer Microarrays for High Throughput Discovery of Biomaterials
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通过显微镜图像自动预测材料特性:以高分子混合性为例

Zhilong Liang1, Zhenzhi Tan2, Ruixin Hong1

  • 1Institute for Artificial Intelligence of Tsinghua University (THUAI), Beijing National Research Center for Information Science and Technology (BNRist), and Department of Automation, Tsinghua University, Beijing 100084, P. R. China.

Journal of chemical information and modeling
|August 17, 2023
PubMed
概括
此摘要是机器生成的。

本研究介绍了一种使用机器学习的自动化方法,用于分析扫描电子显微镜 (SEM) 图像的聚合物可混合性. 人工智能模型达到94%的准确性,为人工分析提供了定量和高效的替代方案.

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Preparation and Friction Force Microscopy Measurements of Immiscible, Opposing Polymer Brushes
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Author Spotlight: Advances in Nanoscale Infrared Spectroscopy to Explore Multiphase Polymeric Systems
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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
  • 聚合物科学 聚合物科学

背景情况:

  • 材料属性通常通过显微镜成像进行评估,就像扫描电子显微镜 (SEM).
  • 聚合物可混合性至关重要,但通常从SEM图像主观地判断,这是低效和难以量化的.
  • 评估聚合物可混合性的现有方法是耗时和劳动密集的.

研究的目的:

  • 利用计算机视觉开发一种自动化,准确和定量化的方法来识别聚合物可混合性.
  • 克服主观人类判断的局限性,分析SEM图像进行材料特征.
  • 建立聚合物混合性评估的定量标准.

主要方法:

  • 利用卷积神经网络 (CNN) 和转移学习进行图像分析.
  • 开发了一种机器学习模型,用于从SEM图像中自动识别聚合物混合性.
  • 实现计算机图像识别以提供定量判断.

主要成果:

  • 在自动聚合物混合性识别中达到高达94%的准确性.
  • 成功开发了一种用于评估聚合物可混合性的定量标准.
  • 证明了准确和定量材料表征的潜力.

结论:

  • 拟议的人工智能驱动的方法比SEM图像的手动分析提供了显著的改进,用于聚合物可混合性.
  • 这种方法提供了准确,定量和高效的聚合物微观结构的特征.
  • 该方法广泛适用于各种材料的微观结构和性质表征.