Production of W-based nanoparticles via spark erosion process along with their characterization and optimization for practical application in gas sensor

摘要

Tungsten-based (W-based) nanoparticles are produced through electrochemical spark erosion process. In this investigation, the parametric effects of voltage, tool rotation and pulse on time on production rate of W-based nanoparticles are analyzed. The shape and size of the produced nanoparticles are controlled through proper controlling of the referred parameters. Small size particles are obtained with low voltage and pulse on time, but with high tool rotation speed. The ANN-predicted values of this study are in close agreement with the observed experimental values for all the test formulations. It can be concluded that the process optimization via ANN modeling has been found to be very efficient for determining the performance linked with the electrochemical spark erosion process. The devised neural network provided an average prediction error of 1.52% for training and 3.78% in case of testing. The formulated models can predict results which are in close agreement with the test results. The produced W-based nanoparticles are used for sensing the NO2 and CO2 gases.