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相关概念视频

Analyte Adsorption and Distribution01:09

Analyte Adsorption and Distribution

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In certain chromatographic separations, solutes transfer between the mobile phase and the stationary phase via sorption, which typically refers to the process of adsorption. For many chromatographic systems, the sorption process often depends on the polarity of the compounds—an expression of the overall dipole moment within the molecule. During the separation process, there is competition between the solute and solvent for adsorption to the stationary phase. Highly polar compounds and...
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Atomic Absorption Spectroscopy: Lab01:21

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For AAS measurements, samples must be introduced as clear solutions, often requiring extensive preliminary treatment to dissolve materials like soils, animal tissues, and minerals. Common methods for sample preparation include treatment with hot mineral acids, wet ashing, combustion in closed containers, high-temperature ashing, or fusion with reagents.
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In situ FTIR Spectroscopy as a Tool for Investigation of Gas/Solid Interaction: Water-Enhanced CO2 Adsorption in UiO-66 Metal-Organic Framework
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用光谱指导的深度学习预测未见溶剂中的固体-液体表面吸附物质

Wenjie Du1,2,3, Fenfen Ma1,4,5, Baicheng Zhang1,4

  • 1Key Laboratory of Precision and Intelligent Chemistry, University of Science and Technology of China, Hefei, Anhui 230026, China.

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|December 29, 2023
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概括

这项研究引入了一种新的神经网络 (HMNN) 来从光谱数据中预测材料特性,克服了各种溶剂类型的挑战. 这种方法可以对看不见的溶剂进行非常准确的预测,从而实现实时的材料表征.

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科学领域:

  • 计算化学
  • 材料科学
  • 机器学习

背景情况:

  • 精确的物质性质获取对于催化和电化学至关重要.
  • 频谱学和机器学习为快速表征提供了潜力.
  • 在溶剂类型中数据分布不均,阻碍了可靠的机器学习模型训练.

研究的目的:

  • 利用光谱数据开发一种可靠的方法来预测微观材料的特性.
  • 解决由多种溶剂系统引起的数据变化的挑战.
  • 为了实现催化和电化学的快速和准确的材料表征.

主要方法:

  • 使用密度函数理论 (DFT) 来计算23种溶剂中CO-Ag吸附的光谱数据.
  • 提出了数据驱动模型的层次知识提取多专家神经网络 (HMNN).
  • 实施了两级培训策略:I级用于定量频谱与属性关系 (QSPR) 和II级用于溶剂特定知识整合.

主要成果:

  • 在预测分子吸附物质特性方面,HMNN表现出卓越的性能.
  • 在新型溶剂的零射击预测中,预测误差低于0.008 eV.
  • 成功弥合不同溶剂系统之间的知识差距.

结论:

  • HMNN提供了一种可用,可靠和方便的方法来预测材料属性.
  • 该方法通过利用QSPR来实现实时访问微观特性.
  • 这项工作促进了机器学习在催化和电化学材料表征方面的应用.