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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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Chromatography: Introduction01:10

Chromatography: Introduction

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Chromatography is a technique used to separate compounds based on differences of partitioning between two phases, the stationary phase and the mobile phase.
The phase in which the compounds linger or on which the compounds adsorb is called the stationary phase, whereas the mobile phase is the solvent that carries the solutes to be analyzed. In traditional column chromatography, the mixture flows through the stationary phase, and the compounds partition between the stationary and mobile phases...
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相关实验视频

Updated: Jan 16, 2026

Monitoring Protein Adsorption with Solid-state Nanopores
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分段学习吸附模式和居住时间动力学在无形表面上的细分.

Mattia Turchi1, Ivan Lunati1

  • 1Laboratory for Computational Engineering, Swiss Federal Laboratories for Materials Science and Technology, Empa, 8600 Dübendorf, Switzerland.

Journal of chemical information and modeling
|October 3, 2025
PubMed
概括

无形表面的机器学习细分揭示了复杂的二氧化碳吸附模式. 该方法识别了关键的表面缺陷,并提供了预测催化剂吸附率的动力学.

科学领域:

  • 表面科学是一门科学.
  • 计算化学是一种计算化学.
  • 材料科学是一种材料科学.

背景情况:

  • 不同质的表面,如无形,具有复杂的原子结构,影响气体吸附.
  • 了解这些结构对于吸附和催化应用至关重要.
  • 这些表面的二氧化碳吸附呈现出复杂的空间模式.

研究的目的:

  • 开发和优化基于机器学习 (ML) 的细分协议,用于分析异质表面上的二氧化碳吸附景观.
  • 为了确定高密度的二氧化碳吸附区域,并提取它们的居住时间统计数据.
  • 为在无序表面上吸附的粗粒度模型提供必要的动力信息.

主要方法:

  • 使用修改后的随机森林 (RF) 分类器来对二氧化碳密度图进行ML细分.
  • 雇员的功能平滑和标准化的培训参数来控制细分.
  • 从确定的高密度吸附区域提取了居住时间统计数据.

主要成果:

  • 开发了一种针对异质表面的优化ML细分协议.
  • 确定了不同的高密度二氧化碳吸附区域及其空间特征.
  • 揭示了非指数的居住时间统计数据,表明与表面缺陷相关的多个吸附时间尺度.

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  • 提取的表面动力学与粗粒度建模相关.

    • 结论:

    • 优化的ML细分协议有效地分析了无形二氧化上复杂的CO2吸附场景.
    • 提取的多时间尺度动力学为开发可预测的粗粒度吸附模型提供了关键数据.
    • 这种方法可以预测杂乱的表面的宏观吸附/脱附率,弥合原子学模拟和实验验证.