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Updated: Jan 8, 2026

Elemental-sensitive Detection of the Chemistry in Batteries through Soft X-ray Absorption Spectroscopy and Resonant Inelastic X-ray Scattering
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Elemental-sensitive Detection of the Chemistry in Batteries through Soft X-ray Absorption Spectroscopy and Resonant Inelastic X-ray Scattering

Published on: April 17, 2018

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通过机器学习揭示电子结构-化学吸收关系,以加速通过机器学习发现水性电池添加剂

Ravindra Kokate1, Dipan Kundu1, Priyank V Kumar1

  • 1School of Chemical Engineering, University of New South Wales, Kensington, NSW, Australia.

Small (Weinheim an der Bergstrasse, Germany)
|December 22, 2025
PubMed
概括

提高离子电池的可充电性需要了解电解质添加剂. 这项研究使用机器学习来预测基于电子结构的增材性能,从而实现更快的选,以改善电池周期寿命.

关键词:
添加剂 添加剂 添加剂吸附能量是一种吸附能量.水性离子电池水性离子电池密度函数理论密度函数理论机器学习是机器学习.状态的分子密度的状态.

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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...
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Anodic Stripping Voltammetry (ASV)
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