Prediction of the size of silver nanoparticles prepared via green synthesis: A gene expression programming approach

Abstract

This study presents a new prediction model for estimating the size of silver nanoparticles (AgNPs) prepared by green synthesis via Gene Expression Programming (GEP). First, 30 different experiments were carried out to construct the GEP models. Plant extract, reaction temperature, concentration of silver nitrate (AgNO3), and stirring time parameters were considered as input variables and the size of AgNPs was selected as the output variable. The collected experimental data were randomly divided into eight testing sets and 22 training sets for further analysis. By considering the correlation coefficient (R2), Mean Absolute Error (MAE), and Root Relative Square Error (RRSE) as the criteria, the performances of proposed models by GEP were compared. Finally, the best model (i.e., GEP-1) with R2 = 0:9961, MAE = 0.2545, and RRSE = 0.0668 was proposed as a new model with simplified mathematical expressions to estimate the size of AgNPs. The results of sensitivity analysis showed that the amount of plant extract, the concentration of AgNO3, stirring time, and reaction temperature were the most effective parameters on the size of AgNPs, respectively. The proposed model can be extended for a wide range of applications and it provides the possibility of minimum materials consumption in the preparation of the lowest-size AgNPs with regard to practical or economic constraints.