prediction of the size of silver nanoparticles prepared via green synthesis: a gene expression programming approach

This study presents a new prediction model for estimating the size of agnps prepared by green synthesis via gene expression programming (gep). firstly, 30 different experiments were used to construct the gep models. plant extract, reaction temperature, concentration of agno3 and stirring time parameters were considered as input and the size of agnps parameter selected as output variables. by consideration of correlation coefficient (r2), mean absolute error (mae), root relative square error (rrse) as criteria, the performance of proposed models by gep were compared each other. finally, the best model (i.e., gep-1) with r2=0.9961, mae=0.2545 and rrse=0.0668 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 are the most effective parameters on the size of agnps, respectively. proposed model via gene expression is satisfactory and can be extended for a wide range of applications. moreover, the proposed model provides the possibility of preparation of the minimum materials consumption for preparation of the lowest size of agnps by consideration of practical or economical constraints.