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相关概念视频

Surface Active Agents01:27

Surface Active Agents

Surfactants, named for their behavior at interfaces, positively adsorb at the interfaces of two phases, reducing interfacial tension. Their versatility as emulsifiers, detergents, and foaming agents stems from this ability. Surfactants, often termed amphiphiles, share the property of amphipathy, with molecules having both hydrophilic and hydrophobic portions. The hydrophilic part is called the head, and the hydrophobic part, including an elongated alkyl substituent, forms the tail.Surfactants...
Micelles01:30

Micelles

Micelle formation is an intricate process that hinges on the properties of amphiphilic or amphipathic molecules and the conditions of the system in which they are found. Amphiphilic molecules, which have both hydrophilic (water-attracting) and hydrophobic (water-repelling) parts, play a critical role in this process.In aqueous environments, these molecules arrange themselves such that their hydrophilic heads are turned towards the water phase, while their hydrophobic tails are oriented away...

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相关实验视频

Updated: Jun 20, 2026

Extraction and Characterization of Surfactants from Atmospheric Aerosols
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使用图形神经网络预测表面活性CMC的温度依赖性.

Christoforos Brozos1,2, Jan G Rittig2, Sandip Bhattacharya1

  • 1BASF Personal Care and Nutrition GmbH, Henkelstrasse 67, 40589 Duesseldorf, Germany.

Journal of chemical theory and computation
|June 26, 2024
PubMed
概括

本研究引入了一个图形神经网络 (GNN) 模型,以预测表面活性剂的温度依赖的临界微粒度 (CMC). 该模型实现了高精度,即使对于看不见的表面活性剂和复杂的基于糖的分子.

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科学领域:

  • 物理化学 物理化学
  • 计算化学计算化学
  • 材料科学 材料科学 材料科学

背景情况:

  • 对于表面活性剂的应用,临界细胞度 (CMC) 是至关重要的.
  • 现有的定量结构属性关系 (QSPR) 和图形神经网络 (GNN) 模型预测室温的CMC,但忽略了温度依赖性.
  • 取决于温度的CMC对于现实世界的工业应用至关重要.

研究的目的:

  • 开发一个GNN模型来预测各种表面活性剂类别的温度依赖的CMC.
  • 评估模型在不同温度条件和表面活性剂类型的预测性能.
  • 评估模型对新型和复杂的表面活性剂结构的概括性,包括可持续的基于糖的表面活性剂.

主要方法:

  • 从公开来源收集了约1400个离子,非离子和离子表面活性剂的数据点,跨越多种温度.
  • 开发并训练了一个图形神经网络 (GNN) 模型来预测温度依赖的CMC.
  • 在两个场景中验证了模型的预测质量:在不同温度下对特定表面活性剂具有或没有先前的CMC数据.
  • 对基于糖的表面活性剂,具有复杂分子结构的模型的性能进行了测试.

主要成果:

  • 在两个测试场景中,GNN模型在测试数据上实现了高预测性能,R2 ≥0.95.
  • 该模型表现出强大的预测能力,可以将其推广到未见的表面活性剂.
  • 观察到模型性能在不同的表面活性剂类别之间有所不同.
  • 该模型成功预测了基于糖的复杂表面活性剂的CMC.

结论:

  • 开发的GNN模型有效地预测了不同的表面活性剂类别的温度依赖的CMC.
  • 该模型显示出优异的概括性和准确性,即使对于未包括在训练套件中的表面活性剂.
  • 这种方法对于预测个人和家庭护理行业中可持续表面活性剂的行为是有价值的.