Using The GMDH Neural Network Model in Carbon Paste Electrodes Modified with Different Metal Oxide Nanoparticles for the Electrochemical Measurement of Tramadol and Acetaminophen Sensor, Nanoparticle, Detection, Acetaminophen, Tramadol, Carbon paste.

Abstract

Various nanoparticles, such as metal oxide nanoparticles, are extensively applied in the field of producing electrodes related to electrochemical processes.  The present study uses the GMDH neural network model in carbon paste electrodes modified with different metal oxide nanoparticles for the electrochemical measurement of tramadol and acetaminophen.  Thus, nanoparticles of zinc oxide, copper oxide, and titanium dioxide were synthesized by sol-gel method and then inserted into carbon paste through a process to obtain the working electrodes for the electrochemical process. The results of the GMDH neural network in the previous model showed that the impact of the input parameters on the objective function can be concluded by counting the number of repetitions of that variable.  The results revealed that the initial concentration of the pollutant and temperature with 8 repetitions, pH with 7 repetitions, and gram of catalyst material and time with 6 repetitions indicate the level of effect on the target function.