A New Method for the Preparation of Co3O4 Nanoparticles

Abstract

The study of quantitative structure and retention index relationship (QSRR) is an important subject in chromatographic field, which has been used to obtain simple models to explain and predict the chromatographic behavior of various classes of compounds. One hundred twenty-seven topological descriptors of 207 methylalkane structures are calculated and investigated via the quantitative structure-property relationship (QSPR) model in the present paper. GAPLS method, which is a variable selection method combining genetic algorithms (GA), back stepwise, and partial least squares (PLS), is introduced in the variable selection of quantitative structure gas chromatographic (GC) retention index (RI) relationship. Seven topological descriptors are finally selected from 127 topological descriptors by GAPLS method to build a QSRR model with a high regression quality of squared correlation coefficient (R2) of 0.99998 and standard deviation (S) of 2.88. The errors of the model are quite close to the experimental errors. The validation of the model is then checked by leave-one-out cross-validation technique. The results of leave-one-out crossvalidation indicate that the built model is reliable and stable with high prediction quality, such as squared correlation coefficient of leave-one-out (R² ) of 0.99997 and standard deviation of leave-one-out predictions (Scv) of 2.95. A successful interpretation of the complex relationship between GC RIs of methyalkanes and the chemical structure is achieved using the QSPR method. The seven variables in the model are also rationally interpreted, which indicates that methylalkanes  RI is precisely represented by topological descriptors.