使用智能方法,基于结构建模盐水中离子表面活性剂的临界细胞度 (CMC)
Danial Abooali1, Reza Soleimani2
1Young Researchers and Elite Club, Central Tehran Branch, Islamic Azad University, Tehran, Iran. danial.abooali@gmail.com.
Scientific reports
|August 17, 2023
概括
新的模型准确地预测了离子表面活性剂的临界微粒度 (CMC),考虑到分子性质和温度和pH等环境因素. 随机梯度提升方法显示出卓越的预测性能.
科学领域:
- 物理化学 物理化学
- 计算化学的计算化学
背景情况:
- 临界微粒度 (CMC) 是表面活性剂的关键性质,具有广泛的应用.
- 现有的CMC估计方法经常忽视温度,pH和盐度等环境因素.
研究的目的:
- 开发新的模型来估计离子表面活性剂的CMC.
- 将分子特性和环境因素 (温度,pH,盐度) 纳入CMC预测中.
主要方法:
- 使用了量化结构与属性关系 (QSPR) 技术.
- 收集了111种离子表面活性剂的488个CMC值.
- 计算了1410个分子描述符,并使用了描述符选择的增强替代.
- 开发了多变量线性,随机梯度增强 (SGB) 和基因编程 (GP) 模型.
主要成果:
- 使用SGB (R2=0.999395) 和GP (R2=0.954946) 模型实现了高精度.
- 证明了SGB方法对可靠的CMC预测的优越性.
- 确定了有效的分子描述因子和影响CMC估计的因素.
结论:
- 开发的QSPR模型提供了对阳离子表面活性剂的CMC的准确估计.
- SGB方法为预测CMC提供了一个强大的,高度准确的方法.
- 这项研究增强了对影响CMC的因素的理解,并为表面活性剂科学提供了有价值的预测工具.
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