通过机器学习算法为HPLC使用温度响应的聚合物柱的保留机制的物理化学建模
Elena Bandini1, Rodrigo Castellano Ontiveros2, Ardiana Kajtazi3
1Separation Science Group, Department of Organic and Macromolecular Chemistry, Univeristy of Ghent, Krijgslaan 281 S4bis, Ghent, 9000, Belgium. elena.bandini@ugent.be.
Journal of cheminformatics
|June 21, 2024
概括
温度响应液态色谱 (TRLC) 使用水作为移动相进行绿色分离. 在低温和高温之间,保留机制显著不同,由不同的分子描述器驱动.
科学领域:
- 分析化学 分析化学
- 聚合物化学 聚合物化学
背景情况:
- 温度响应液态染色学 (TRLC) 是传统逆相液态染色学 (RPLC) 的环保替代方案.
- 在颗粒上,TRLC使用温度敏感的聚合物,使用水作为移动相,以避免使用有机溶剂.
- 了解TRLC的保留机制是优化列设计和分离选择性的关键.
研究的目的:
- 在不同温度下研究控制TRLC分离的物理化学参数.
- 为了确定分子描述符 (MDs),预测TRLC的保留行为.
- 评估TRLC保留预测的不同建模技术的性能.
主要方法:
- 使用TRLC对139个不同温度的分子进行分析.
- 计算分析以确定相关的分子描述符 (MDs).
- 应用和验证线性回归,支向量回归 (SVR) 和基于树的集合模型.
主要成果:
- 在TRLC中,保留机制取决于温度.
- 在较低的温度下 (),logP是保留的主要驱动因素,类似于RPLC.
- 在更高的温度下 (),保留受脂性和负性MDs以及特定功能组的组合的影响.
结论:
- 在不同的温度下,TRLC表现出不同的保留行为,提供可调节的选择性.
- 分子描述器为TRLC保留机制提供了宝贵的见解.
- 这些发现有助于开发TRLC方法和优化列表的性能.
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