Intelligent Control of the Synthesis of Luminescent Carbon Dots with the Desired Photoluminescence Quantum Yield Using Machine Learning

Abstract

In this study, the results of solving a “synthesis–properties” type problem using artificial neural networks have been presented. The purpose of the study has been to determine the optimal conditions for synthesis of carbon dots to obtain nanoparticles with a given luminescence quantum yield (QY). Carbon dots were synthesized by hydrothermal synthesis from citric acid and ethylenediamine at various conditions. A multilayer perceptron (MLP) type artificial neural network was used to approximate the dependence of the target variable (luminescence QY) on the synthesis parameters. The neural network approach was successfully applied to the spectral data of a set of carbon dots of 343 samples to determine the optimal conditions for their hydrothermal synthesis from citric acid and ethylenediamine while varying the precursor ratio, temperature and reaction time over wide ranges to obtain nanoparticles with a given luminescence QY. Optimal carbon dots synthesis parameters to maximize the luminescence QY at 350 nm have been determined. Testing of the proposed neural network approach on an independent database of spectral data specially synthesized for this purpose showed good agreement between the results obtained using MLP and the experimentally measured values of the QY (the root-mean-squared error of the QY prediction was 2.14%).