A QSPR model for prediction of diffusion coefficient of non-electrolyte organic compounds in air at ambient condition.

Evaluation of diffusion coefficients of pure compounds in air is of great interest for many diverse industrial and air quality control applications. In this communication, a QSPR method is applied to predict the molecular diffusivity of chemical compounds in air at 298.15K and atmospheric pressure.

Prediction of molecular diffusivity of pure components into air: a QSPR approach.

The molecular diffusivity of 378 pure components into air was predicted using genetic algorithm-based multivariate linear regression (GA-MLR) and feed forward neural networks (FFNN). GA-MLR was used to select the molecular descriptors, as inputs for FFNN. The correlation coefficient (R2) of obtained multivariate linear seven-descriptor model by GA-MLR is 0.