Prediction of Critical Micelle Concentration Using a Quantitative Structure−Property Relationship Approach. 1. Nonionic Surfactants

Abstract

A quantitative structure-property relationship study was performed on the critical micelle concentration (cmc) of nonionic surfactants using the CODESSA program. A known correlation between the logarithm of the cmc and counts of linear alkane carbon atoms and ethoxy groups in linear alkyl ethoxylates was improved (R 2) 0.996) by adding cross terms of these molecular descriptors. A general three-parameter structure-property relationship was developed for a diverse set of 77 nonionic surfactants (R 2) 0.984) employing topological descriptors calculated for the hydrophobic fragment of the surfactant molecule. The three descriptors represent contributions from the size of the hydrophobic group, the size of the hydrophilic group, and the structural complexity of the hydrophobic group.

QSPR Modelling of Lanthanide‐Organic Complex Stability Constants

“…The respective methodology has been described elsewhere 41 . The CODESSA program has already been successfully applied to correlate molecular structure with various properties including melting points 42 , response factors 43 , critical micelle concentrations 44,45 , aqueous solubility of gases 46 , glass transition temperatures of polymers 47 , and solvent polarity scales 48 .…”

Characterization of a Germination-Accelerating Factor from the Silkworm (Bombyx moriLinnaeus) of Entomopathogenic FungusNomuraea rileyi(Farlow) Samson

“…This can be explained by the higher CMC value of C 12 -Sph than that of C 14 -Sph, possibly due to differences in their carbon chain length. 16) On the other hand, when a high concentration of the detergent (1.0% Tween 80) was used in the bioassay, C 12 -Sph, C 14 -Sph and C 16 -Sph each showed a sigmoidal dose-response relationship ( Fig. 2A).…”

Prediction of Surfactants’ Properties using Multiscale Molecular Modeling Tools: A Review

“…During the last decades, the number of published QSPR studies have hugely increased, mainly due to the evolution of the computational power and to the development of powerfull statistical approaches and softwares. The first QSPR models for the prediction of the CMC were reported by Huibers et al who proposed equations for nonionic [89] and anionic [90] surfactants. Both of these QSPR models were obtained using MLR approach over a set of descriptors based on molecular topology and constitution.…”

“…Both of these QSPR models were obtained using MLR approach over a set of descriptors based on molecular topology and constitution. Apart from the work of Anoune et al [91], QSPR models intended to the CMC prediction were trained over specific chemical families that are: nonionic [89,[92][93][94][95][96][97][98] …”

“…Both of these QSPR models were obtained using MLR approach over a set of descriptors based on molecular topology and constitution. Apart from the work of Anoune et al [91], QSPR models intended to the CMC prediction were trained over specific chemical families that are: nonionic [89,[92][93][94][95][96][97][98], cationic [99][100][101], and anionic [90,94,[102][103][104][105][106][107] surfactants. Table 1 presents statistical coefficients of QSPR models listed in the literature.…”