Development of a predictive response surface model for size of silver nanoparticles synthesized in a T-junction microfluidic device

Optimisation of the parameters governing the synthesis of silver nanoparticles (AgNPs) is a critical step in controlling particle size, which facilitates their application in diverse range of industrial and consumer related products. A T-junction microfluidic system was used together with design of experiments, regression-analysis and response surface methodology to build a predictive numerical model for the size of silver nanoparticles (AgNPs). Aqueous solutions of silver-precursor and reducing/stabilizing agent were supplied by two separate channels meeting at the T-junction, with the reaction occurring downstream in the outlet tube. To improve the mixing of the reagents, the output tube was coiled onto a 3D-printed helical shape device, exploiting the creation of Dean vortices. The effects of both reaction and hydrodynamic conditions including the solution pH, collection temperature, helical curvature, flow rates and concentration of stabilising agent were investigated using a D-optimal experimental design.

The obtained experimental size distributions for the AgNPs were fitted to a polynomial model with an average prediction error of around 13% and a 37 % maximum error. The model predicted the optimal synthesis conditions for the global and local-minimum sizes of AgNPs with an error of around 7.0% and 16.1% respectively. The average prediction error of the testing set was estimated to be 6.8% with 16.1% being the maximum error.