• 文献检索
  • 文档翻译
  • 深度研究
  • 学术资讯
  • Suppr Zotero 插件Zotero 插件
  • 邀请有礼
  • 套餐&价格
  • 历史记录
应用&插件
Suppr Zotero 插件Zotero 插件浏览器插件Mac 客户端Windows 客户端微信小程序
定价
高级版会员购买积分包购买API积分包
服务
文献检索文档翻译深度研究API 文档MCP 服务
关于我们
关于 Suppr公司介绍联系我们用户协议隐私条款
关注我们

Suppr 超能文献

核心技术专利：CN118964589B侵权必究
粤ICP备2023148730 号-1Suppr @ 2026

文献检索

告别复杂PubMed语法，用中文像聊天一样搜索，搜遍4000万医学文献。AI智能推荐，让科研检索更轻松。

立即免费搜索

文件翻译

保留排版，准确专业，支持PDF/Word/PPT等文件格式，支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述，25分钟生成高质量综述，智能提取关键信息，辅助科研写作。

立即免费体验

基于定量结构-性质关系预测非离子表面活性剂的临界胶束浓度

Prediction of critical micelle concentration of nonionic surfactants by a quantitative structure - property relationship.

作者信息

Mozrzymas Anna, Rózycka-Roszak Bozenns

机构信息

Department of Physics and Biophysics, Wrocław University of Environmental and Life Sciences, Poland.

出版信息

Comb Chem High Throughput Screen. 2010 Jan;13(1):39-44. doi: 10.2174/138620710790218195.

DOI:10.2174/138620710790218195
PMID:20201824
Abstract

A quantitative structure - property relationship (QSPR) was used to predict the critical micelle concentration (cmc) of nonionic surfactants. The relation was developed for a diverse set of 23 nonionic surfactants (r = 0.99, F-test = 1042.5) employing the connectivity and valence connectivity indices only. The molecular connectivity indices were calculated for the whole molecule which was a simple and general approach.

摘要

采用定量结构-性质关系（QSPR）来预测非离子表面活性剂的临界胶束浓度（cmc）。该关系仅利用连接性指数和价连接性指数，针对23种不同的非离子表面活性剂建立（r = 0.99，F检验 = 1042.5）。分子连接性指数是针对整个分子计算的，这是一种简单且通用的方法。

相似文献

1
Prediction of critical micelle concentration of nonionic surfactants by a quantitative structure - property relationship.基于定量结构-性质关系预测非离子表面活性剂的临界胶束浓度
Comb Chem High Throughput Screen. 2010 Jan;13(1):39-44. doi: 10.2174/138620710790218195.
2
Quantifying the hydrophobic effect. 2. A computer simulation-molecular-thermodynamic model for the micellization of nonionic surfactants in aqueous solution.量化疏水效应。2. 水溶液中非离子表面活性剂胶束化的计算机模拟 - 分子热力学模型。
J Phys Chem B. 2007 Feb 8;111(5):1045-62. doi: 10.1021/jp065697a.
3
A review on progress in QSPR studies for surfactants.关于表面活性剂 QSPR 研究进展的综述。
Int J Mol Sci. 2010 Mar 8;11(3):1020-1047. doi: 10.3390/ijms11031020.
4
QSPR for the prediction of critical micelle concentration of different classes of surfactants using machine learning algorithms.基于机器学习算法的不同类型表面活性剂临界胶束浓度的定量构效关系预测。
J Mol Graph Model. 2024 Jun;129:108757. doi: 10.1016/j.jmgm.2024.108757. Epub 2024 Mar 11.
5
On the prediction of critical micelle concentration for sugar-based non-ionic surfactants.关于基于糖类的非离子表面活性剂的临界胶束浓度预测。
Chem Phys Lipids. 2018 Aug;214:46-57. doi: 10.1016/j.chemphyslip.2018.05.008. Epub 2018 May 30.
6
On the Spacer Group Effect on Critical Micelle Concentration of Cationic Gemini Surfactants Using Molecular Connectivity Indices.基于分子连接性指数研究间隔基团对阳离子双子表面活性剂临界胶束浓度的影响
Comb Chem High Throughput Screen. 2016;19(6):481-8. doi: 10.2174/1386207319666160504095717.
7
Combination of genetic algorithm and partial least squares for cloud point prediction of nonionic surfactants from molecular structures.遗传算法与偏最小二乘法相结合用于从分子结构预测非离子表面活性剂的浊点
Ann Chim. 2007 Jan-Feb;97(1-2):69-83. doi: 10.1002/adic.200690087.
8
Matrix trace operators: from spectral moments of molecular graphs and complex networks to perturbations in synthetic reactions, micelle nanoparticles, and drug ADME processes.矩阵迹算子：从分子图和复杂网络的谱矩到合成反应、胶束纳米颗粒及药物ADME过程中的扰动
Curr Drug Metab. 2014;15(4):470-88. doi: 10.2174/1389200215666140908101604.
9
Rhamnolipid CMC prediction.鼠李糖脂临界胶束浓度预测
J Colloid Interface Sci. 2017 Feb 15;488:10-19. doi: 10.1016/j.jcis.2016.10.055. Epub 2016 Oct 21.
10
Complementary use of simulations and molecular-thermodynamic theory to model micellization.结合使用模拟和分子热力学理论对胶束化进行建模。
Langmuir. 2006 Feb 14;22(4):1500-13. doi: 10.1021/la052042c.

引用本文的文献

1
Predicting the Key Properties of a Modified Product to Pre-Select a Pluronic F127 Modification Scheme for Preparing High-Quality Nano-Micelles.预测改性产物的关键性质以预先选择用于制备高质量纳米胶束的泊洛沙姆F127改性方案。
Polymers (Basel). 2025 Jan 27;17(3):349. doi: 10.3390/polym17030349.
2
Modelling of the Critical Micelle Concentration of Cationic Gemini Surfactants Using Molecular Connectivity Indices.使用分子连接性指数对阳离子 Gemini 表面活性剂的临界胶束浓度进行建模
J Solution Chem. 2013;42(11):2187-2199. doi: 10.1007/s10953-013-0095-6. Epub 2013 Nov 2.
3
Biomacromolecular quantitative structure-activity relationship (BioQSAR): a proof-of-concept study on the modeling, prediction and interpretation of protein-protein binding affinity.
生物大分子定量构效关系（BioQSAR）：蛋白质-蛋白质结合亲和力建模、预测和解释的概念验证研究。
J Comput Aided Mol Des. 2013 Jan;27(1):67-78. doi: 10.1007/s10822-012-9625-3. Epub 2013 Jan 10.