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相关概念视频

Extraction: Advanced Methods00:56

Extraction: Advanced Methods

1.1K
Metal ions can be separated from one another by complexation with organic ligands–the chelating agent– to form uncharged chelates. Here, the chelating agent must contain hydrophobic groups and behave as a weak acid, losing a proton to bind with the metal. Since most organic ligands used in this process are insoluble or undergo oxidation in the aqueous phase, the chelating agent is initially added to the organic phase and extracted into the aqueous phase. The metal-ligand complex is...
1.1K
Metallic Solids02:37

Metallic Solids

20.5K
Metallic solids such as crystals of copper, aluminum, and iron are formed by metal atoms. The structure of metallic crystals is often described as a uniform distribution of atomic nuclei within a “sea” of delocalized electrons. The atoms within such a metallic solid are held together by a unique force known as metallic bonding that gives rise to many useful and varied bulk properties.
All metallic solids exhibit high thermal and electrical conductivity, metallic luster, and malleability....
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Metal-Ligand Bonds02:51

Metal-Ligand Bonds

23.9K
The hemoglobin in the blood, the chlorophyll in green plants, vitamin B-12, and the catalyst used in the manufacture of polyethylene all contain coordination compounds. Ions of the metals, especially the transition metals, are likely to form complexes.
In these complexes, transition metals form coordinate covalent bonds, a kind of Lewis acid-base interaction in which both of the electrons in the bond are contributed by a donor (Lewis base) to an electron acceptor (Lewis acid). The Lewis acid in...
23.9K
Crystal Field Theory - Octahedral Complexes02:58

Crystal Field Theory - Octahedral Complexes

30.6K
Crystal Field Theory
To explain the observed behavior of transition metal complexes (such as colors), a model involving electrostatic interactions between the electrons from the ligands and the electrons in the unhybridized d orbitals of the central metal atom has been developed. This electrostatic model is crystal field theory (CFT). It helps to understand, interpret, and predict the colors, magnetic behavior, and some structures of coordination compounds of transition metals.
CFT focuses on...
30.6K

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相关实验视频

Updated: Jan 14, 2026

Author Spotlight: Magnetometric Characterization of Intermediates in the Solid-State Electrochemistry of Redox-Active Metal-Organic Frameworks
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通过大型语言模型和表示集群从金属有机框架数据库中提取固态电解质

Jinglang Zhang1,2, Jiaxin Li1,2, Guanhua Zhao2

  • 1Tianjin Key Laboratory of Advanced Carbon and Electrochemical Energy Storage, School of Chemical Engineering and Technology, and National Industry-Education Integration Platform of Energy Storage, Tianjin University, Tianjin 300350, China.

Journal of the American Chemical Society
|October 24, 2025
PubMed
概括
此摘要是机器生成的。

使用大型语言模型 (LLM) 的人工智能加速了对固态电解质 (SSE) 的新型金属有机框架 (MOF) 的发现. 这种人工智能驱动的方法确定了具有高离子导电性和电化学稳定的有前途的MOF SSE材料.

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科学领域:

  • 材料科学
  • 电化学
  • 人工智能

背景情况:

  • 金属有机框架 (MOF) 作为固态电解质 (SSE) 显示出 Li+ 离子导电的潜力.
  • 由于复杂性和缺乏设计指导方针, MOF SSE 的发展受到限制.

研究的目的:

  • 利用人工智能,特别是LLM和机器学习,加速MOF SSE的发现和设计.
  • 通过人工智能辅助的采矿来建立材料发现的新范式.

主要方法:

  • 使用LLM提取MOF SSE数据的交互式文本挖掘.
  • 构建一个专门的MOF结构和电化学特性数据集.
  • 从大量数据中确定有前途的MOF SSE候选人.

主要成果:

  • 通过使用LLM和集群,成功地从超过11,000名候选人中挖掘出MOF SSE.
  • 确定NOTT-400是具有高Li+导电性 (2.23 × 10-4 S cm-1) 和广泛的电化学稳定性 (0-4.79 V) 的有希望的MOF SSE.
  • 通过物理化学表征和电化学演示验证了人工智能驱动的方法.

结论:

  • 人工智能,特别是LLM,可以显著加快新型MOF SSE的识别.
  • 人工智能驱动的方法为材料发现提供了可靠和高效的方法.
  • 这项工作为设计具有理想性质的MOF SSE建立了新范式.