Acta Phys. -Chim. Sin. ›› 2013, Vol. 29 ›› Issue (01): 30-34.doi: 10.3866/PKU.WHXB201210265


Quantitative Structure-Property Relationship of the Critical Micelle Concentration of Different Classes of Surfactants

ZHU Zhi-Chen1, WANG Qiang2, JIA Qing-Zhu2, TANG Hong-Mei2, MA Pei-Sheng3   

  1. 1 School of Science, Tianjin Institute of Urban Construction, Tianjin 300384, P. R. China;
    2 School of Material Science and Chemical Engineering, Tianjin University of Science and Technology, Tianjin 300457, P. R. China;
    3 School of Chemical Engineering and Technology, Tianjin University, Tianjin 300072, P. R. China
  • Received:2012-08-13 Revised:2012-10-26 Published:2012-12-14
  • Supported by:

    The project was supported by the National Natural Science Foundation of China (20976131, U1162104), and Natural Science Foundation of Tianjin University of Science and Technology, China (20050207).


Critical micelle concentration (CMC) is one of the most useful parameters for the characterization of surfactants; thus, CMC plays an important role in the investigation of the surfactants? properties for industrial applications and biological utilizations. The following study presents a stable and accurate structure-property relationship model for the prediction of CMC for a diverse set of 175 surfactants using a new topological index, the extended distance matrix. Research indicates that the new model based on this topological index is very efficient and provides satisfactory results. The high-quality prediction model is evidenced by an R2 (square correlation coefficient) value of 0.9295 and an average relative difference (ARD) value of 8.20% for the training set, an R2 value of 0.9257 and an ARD value of 6.76% for the testing set. Comparison results with reference models demonstrate that this new method based on the topological index results in significant improvements, both in accuracy and stability for predicting CMC of surfactants.

Key words: Surfactant, Critical micelle concentration, Structure-property relationship, Topological index, Prediction