Particle Size Analysis of Nanopowders Using Neural Networks and Electron Microscopy

Abstract

The purpose of the research is to develop an application capable of automatically determining the particle size distribution of nanopowder using neural network technology in order to simplify the process of preparing documentation during its manufacture.Methods. To determine the physical properties of nanopowders during their fabrication, it is necessary to analyze the particle size distribution. A methodology for determining the size distribution of nanopowder particles based on light neural networks is proposed. Images obtained by electron microscopy are used for processing, which allows to speed up the preparation of manufactured powders for sale. The dataset collected for training contains real images of samples of different powders, augmented data and generated images. The Python language, LabVIEW graphical programming environment, YOLO convolutional neural network and various Python language libraries were used in the development.Results. The study resulted in a model trained on the collected dataset that is capable of recognizing particles in images. A software interface was created to work with the model to analyze nanopowder samples.Conclusion. The developed application allows to automatically determine the size of each powder particle on the basis of the obtained images, as well as to build graphs of their size distribution. This greatly simplifies the work of nanopowder producers and facilitates the preparation of the necessary documentation for the produced product.