ARTIFICIAL NEURAL NETWORK MODELLING of GREEN SYNTHESIS of SILVER NANOPARTICLES by HONEY

Abstract

Nanomaterials draw attention because of their unique physical, chemical and biological properties in areas such as catalysis, electronic, optics, medicine, solar energy conversion and water treatment. Green synthesis of silver nanoparticles has many superiorities compared to physical and chemical methods such as low cost, nontoxicity, eco-sensitive. In this paper, experimental conditions related to green synthesis of silver nanoparticles by honey were modelled using artificial neural network (ANN). While agitation time, agitation rate, pH, temperature, honey concentration, AgNO3 concentration were selected as input parameters, production of silver nanoparticles was used as an output parameter. According to the results, optimum hidden neuron number was found as 40 with Levenberg–Marquardt backpropagation algorithm. In this conditions, the percentages of training, validation and testing were 75, 20 and 5, respectively. After creating neural network separated input data set was applied and then experimental and ANN predicted data were compared. In conclusion, ANN can be an alternative modelling and robust approach that could help researchers in this field to estimate production of silver nanoparticles.