Open AccessNeural network modeling and simulation of the synthesis of CuInS 2 /ZnS quantum dots
Author(s) -
Fu Min,
Mrziglod Thomas,
Luan Weiling,
Tu ShanTung,
Mleczko Leslaw
Publication year - 2020
Publication title -
engineering reports
Language(s) - English
Resource type - Journals
ISSN - 2577-8196
DOI - 10.1002/eng2.12122
Subject(s) - quantum dot , quantum yield , materials science , molar ratio , yield (engineering) , artificial neural network , quantum , nanotechnology , optoelectronics , computer science , chemistry , catalysis , fluorescence , physics , optics , biochemistry , quantum mechanics , machine learning , metallurgy
The development of recipes for synthesis of quantum dots (QDs), a novel semiconductor material for application in optoelectronic devices, is currently purely based on experiments. Since the quality of QDs represented by quantum yield (QY) and emission peak strongly depends on a number of different parameters (route, precursors, conditions, etc), a large number of experiments is necessary. In this article, we show that data‐driven modeling can be used as a supporting tool for optimization and a better understanding of the synthesis process. By using the results collected during the development of CuInS 2 /ZnS (CIS/ZnS) QDs, a neural network model has been established. The model is able to predict the optical properties (QY and emission peak) of CIS/ZnS QDs as a function of the most important synthesis parameters, such as reaction temperature, time of CIS core formation and ZnS shell growth, feed molar ratio of Cu/In and Zn/Cu, various starting precursors, and types of ligands. Finally, a model analysis under various effects influencing the quality of QDs is performed.