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

Classification and Mechanical Properties of Synthetic Polymers01:28

Classification and Mechanical Properties of Synthetic Polymers

Synthetic polymers are classified as elastomers, fibers, or plastics based on their crystallinity. Crystallinity, the degree of long-range order in the solid state, influences the mechanical properties (stretching or contracting) of elastomers. Elastomers are flexible polymers that can expand or contract easily upon the application of an external force. They have numerous crosslinks that pull them back into their original shape when stress is removed. Silicones, for instance, are highly elastic...

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

Tuning Piezoelectricity and Pyroelectricity in Poly(vinylidene fluoride) through Ionic Liquid Anion-Size Directed Polymorph and Interface Engineering.

ACS applied materials & interfaces·2026
Same author

Synthesis and characterization of honeycomb-like carboxylate-bridged dimeric Cu(II) and Zn(II)-based coordination polymers with a pyridyl Schiff base linker: dye sorption and Schottky device applications.

Dalton transactions (Cambridge, England : 2003)·2026
Same author

Beyond Conventional Pyroelectrics: 2D-Layered Perovskites for Next Generation Pyro-Phototronic Devices.

Small (Weinheim an der Bergstrasse, Germany)·2026
Same author

Interfacial Nanoarchitectonics of Stimuli-Responsive Ortho-Fluorinated Alkoxy Azobenzene Monolayers.

Langmuir : the ACS journal of surfaces and colloids·2026
Same author

Defect-Conjugation Coupling in Sulfur and Carbon Co-doped Poly(triazine imide) for Visible-Light-Driven H<sub>2</sub>O<sub>2</sub> Production.

ACS applied materials & interfaces·2026
Same author

Correction: Chemically functionalized cellulose triboelectret nanogenerator for machine-learning-enabled tactile sensing.

Materials horizons·2026

相关实验视频

Updated: Jul 5, 2026

Production of Membrane-Filtered Phase-Shift Decafluorobutane Nanodroplets from Preformed Microbubbles
07:10

Production of Membrane-Filtered Phase-Shift Decafluorobutane Nanodroplets from Preformed Microbubbles

Published on: March 23, 2021

2.7K

机器学习启用纳米级阶段预测在工程化聚乙烯化物中)

Anand Babu1, B Moses Abraham2, Sudip Naskar1

  • 1Quantum Materials and Devices Unit, Institute of Nano Science and Technology, Knowledge City, Sector 81, Mohali, 140306, India.

Small (Weinheim an der Bergstrasse, Germany)
|October 15, 2024
PubMed
概括
此摘要是机器生成的。

本研究引入了一种机器学习方法,以准确区分聚乙烯化物 (PVDF) 阶段,这对于先进的材料应用至关重要. 该方法提高了相位识别的准确性和弹性,加速了材料选择.

关键词:
这是一个PVDF.电活性聚合物的电活性聚合物.机器学习是机器学习.阶段预测阶段预测它们是多态的.

更多相关视频

Formulation and Acoustic Modulation of Optically Vaporized Perfluorocarbon Nanodroplets
07:44

Formulation and Acoustic Modulation of Optically Vaporized Perfluorocarbon Nanodroplets

Published on: July 16, 2021

2.0K
Author Spotlight: Advances in Nanoscale Infrared Spectroscopy to Explore Multiphase Polymeric Systems
06:54

Author Spotlight: Advances in Nanoscale Infrared Spectroscopy to Explore Multiphase Polymeric Systems

Published on: June 23, 2023

787

相关实验视频

Last Updated: Jul 5, 2026

Production of Membrane-Filtered Phase-Shift Decafluorobutane Nanodroplets from Preformed Microbubbles
07:10

Production of Membrane-Filtered Phase-Shift Decafluorobutane Nanodroplets from Preformed Microbubbles

Published on: March 23, 2021

2.7K
Formulation and Acoustic Modulation of Optically Vaporized Perfluorocarbon Nanodroplets
07:44

Formulation and Acoustic Modulation of Optically Vaporized Perfluorocarbon Nanodroplets

Published on: July 16, 2021

2.0K
Author Spotlight: Advances in Nanoscale Infrared Spectroscopy to Explore Multiphase Polymeric Systems
06:54

Author Spotlight: Advances in Nanoscale Infrared Spectroscopy to Explore Multiphase Polymeric Systems

Published on: June 23, 2023

787

科学领域:

  • 材料科学 材料科学 材料科学
  • 聚合物科学 聚合物科学
  • 机器学习应用 机器学习应用

背景情况:

  • 工程化聚乙烯化物 (PVDF) 呈现出各种晶体相,这些相对于压电器,火电器,铁电器和电压电器设备至关重要.
  • 精确的相位检测对于理解PVDF材料中的结构属性关系至关重要.
  • 传统的表征方法在有效区分PVDF相方面存在局限性.

研究的目的:

  • 开发和验证一种多式数据驱动的机器学习 (ML) 方法,用于区分PVDF晶相.
  • 克服PVDF相位识别中的传统表征技术的局限性.
  • 通过提供自主相区分方法来加速基于PVDF的设备的材料选择.

主要方法:

  • 采用多式联运数据驱动技术与机器学习 (ML) 模型相结合.
  • 使用经验和理论数据的组合训练了ML模型.
  • 评估了模型在分类不同PVDF阶段的性能.

主要成果:

  • 实现了超过94%的分类准确性,用于区分PVDF相.
  • 与单模方法相比,噪声抗性得到了15%的改善.
  • 在使用多式联运数据时,准确度增加了11%.

结论:

  • 开发的多式联机模型为自主PVDF相位区分提供了有效的替代方案.
  • 这种方法大大减少了对重复实验的需求,节省了资源和时间.
  • 这些发现加速了用于各种PVDF应用的材料选择过程.