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関連する概念動画

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....
20.5K
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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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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メタル・オーガニック・フレームワーク・データベースからの固体電解質の採掘

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
まとめ
この要約は機械生成です。

大型言語モデル (LLM) を使用する人工知能は,固体電解質 (SSE) のための新しい金属有機フレームワーク (MOF) の発見を加速します. このAIによるアプローチは,高いイオン伝導性と電気化学的安定性を持つ有望なMOF SSE材料を特定します.

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科学分野:

  • 材料科学
  • 電気化学
  • 人工知能

背景:

  • メタル・オーガニック・フレームワーク (MOF) は,リチウム+イオン伝導のための固体電解質 (SSE) として有望である.
  • MOF SSEの開発は,複雑さと設計ガイドラインの欠如によって制限されています.

研究 の 目的:

  • AI,特にLLMと機械学習を活用して,MOF SSEの発見と設計を加速する.
  • AI支援のマイニングを通じて 材料発見の新たなパラダイムを確立する

主な方法:

  • MOF SSEデータを抽出するためにLLMを使用するインタラクティブテキストマイニング.
  • MOFの構造的および電気化学的性質の専門データセットの構築.
  • 大規模なデータセットから有望なMOF SSE候補者を特定するための代表クラスタリング.

主要な成果:

  • LLMとクラスタリングを使用して,1万1,000人以上の候補者からMOF SSEを成功裏に採掘しました.
  • NOTT-400は高いLi+伝導率 (2.23 × 10−4 S cm−1) と高い電気化学的安定性 (0−4.79 V) を有する有望なMOFSSEとして特定された.
  • 物理化学的特徴と電気化学的実証を通じてAI駆動のアプローチを検証した.

結論:

  • AI,特にLLMは,新しいMOF SSEの特定を大幅に加速することができます.
  • AI主導の方法論は 材料発見の信頼性と効率性の高いアプローチを提供します
  • この研究は,望ましい性質を持つMOF SSEを設計するための新しいパラダイムを確立しています.