Optimization of microfluidic synthesis of silver nanoparticles: a generic approach using machine learning

TL;DR

This paper presents a machine learning-guided optimization method for synthesizing silver nanoparticles using a microfluidic system, improving efficiency and understanding of the process parameters.

Contribution

It introduces a decision tree-based experimental design integrating microfluidics and machine learning to optimize nanoparticle synthesis parameters.

Findings

Machine learning models effectively predict nanoparticle size.

Design of experiments enhances model accuracy over random sampling.

Hydrodynamics and temperature significantly influence nanoparticle properties.

Abstract

The properties of silver nanoparticles (AgNPs) are affected by various parameters, making optimisation of their synthesis a laborious task. This optimisation is facilitated in this work by concurrent use of a T-junction microfluidic system and machine learning approach. The AgNPs are synthesized by reducing silver nitrate with tannic acid in the presence of trisodium citrate, which has a dual role in the reaction as reducing and stabilizing agent. The study uses a decision tree-guided design of experiment method for the size of AgNPs. The developed approach uses kinetic nucleation and growth constants derived from an independent set of experiments to account for chemistry of synthesis, the Reynolds number and the ratio of Dean number to Reynolds number to reveal effect of hydrodynamics and mixing within device and storage temperature to account for particle stability after collection. The obtained model was used to define a parameter space for additional experiments carried out to improve the model further. The numerical results illustrate that well-designed experiments can contribute more effectively to the development of different machine learning models (decision tree, random forest and XGBoost) as opposed to randomly added experiments.