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相关概念视频

Batteries and Fuel Cells03:12

Batteries and Fuel Cells

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A battery is a galvanic cell that is used as a source of electrical power for specific applications. Modern batteries exist in a multitude of forms to accommodate various applications, from tiny button batteries such as those that power wristwatches to the very large batteries used to supply backup energy to municipal power grids. Some batteries are designed for single-use applications and cannot be recharged (primary cells), while others are based on conveniently reversible cell reactions that...
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相关实验视频

Updated: Jun 7, 2025

Three-electrode Coin Cell Preparation and Electrodeposition Analytics for Lithium-ion Batteries
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Three-electrode Coin Cell Preparation and Electrodeposition Analytics for Lithium-ion Batteries

Published on: May 22, 2018

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一个全面的数据网络,以数据驱动的研究电池材料的数据.

Yibin Xu1,2, Yen-Ju Wu1, Huiping Li1

  • 1Center for Basic Research on Materials, National Institute for Materials Science, Tsukuba, Japan.

Science and technology of advanced materials
|November 19, 2024
PubMed
概括
此摘要是机器生成的。

这项研究引入了一个用于电池材料研究的新型数据网络,集成了物质,材料和电池数据. 本资源支持用于属性预测和材料设计的先进机器学习.

关键词:
材料数据库 材料数据库电池材料是电池的材料.容量 容量 容量 容量 容量阴极阴极是指一个阴极.晶体结构 晶体结构数据策划数据的策划.离子导电性的离子导电性.自然语言处理自然语言处理.固体电解质是一种固体电解质.

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Elemental-sensitive Detection of the Chemistry in Batteries through Soft X-ray Absorption Spectroscopy and Resonant Inelastic X-ray Scattering
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Characterization of Electrode Materials for Lithium Ion and Sodium Ion Batteries Using Synchrotron Radiation Techniques
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Characterization of Electrode Materials for Lithium Ion and Sodium Ion Batteries Using Synchrotron Radiation Techniques

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Elemental-sensitive Detection of the Chemistry in Batteries through Soft X-ray Absorption Spectroscopy and Resonant Inelastic X-ray Scattering
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Characterization of Electrode Materials for Lithium Ion and Sodium Ion Batteries Using Synchrotron Radiation Techniques
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科学领域:

  • 材料科学 材料科学 材料科学
  • 计算化学的计算化学
  • 数据科学数据科学数据科学

背景情况:

  • 用于材料研究的机器学习需要广泛,多样化和高质量的数据集.
  • 电池材料,通常是多晶体,陶或复合材料,需要包括物质,材料和电池性能在内的多尺度数据.
  • 现有的数据资源可能无法充分捕捉先进电池材料发现所需的复杂性和相互关系.

研究的目的:

  • 为电池材料研究开发一个全面的,相互连接的数据网络.
  • 通过机器学习促进数据驱动的财产预测和材料设计.
  • 从科学文献中整合有关物质,材料和电池的多尺度数据.

主要方法:

  • 构建一个由三个相互连接的数据库组成的数据网络.
  • 使用自然语言处理 (NLP) 来选超过33万篇研究论文.
  • 数据提取和策划由材料科学专业编辑人员进行,他们接受了数据标准化培训.
  • 专注于固体电解质和阴极材料研究.

主要成果:

  • 一个统一的数据网络,提供关于晶体结构,电子结构,化学成分,材料特性和电池性能指标的全面数据.
  • 成功地从大量科学文献中提取和整理数据.
  • 为电池材料开发中的机器学习应用程序建立一个结构化资源.

结论:

  • 开发的数据网络是推动数据驱动电池材料研究的宝贵资源.
  • 这种综合方法增强了准确财产预测和新型材料设计的能力.
  • 该方法提供了一个可扩展的框架,用于从科学出版物中策划复杂材料数据.