Potassium Ferro cyanide electrochemically detected by Differential Pulse and Square Wave Voltammetry in a Competition using Gradient Boosting as Machine Learning Algorithm

The electrochemical detection has a wide horizon to be one of the most competitive fields among other sciences in detection and analysis in recent papers. The voltammetric techniques were chosen in this work due to the less time consuming along with high sensitivity. Differential pulse voltammetry DPV and square wave voltammetry SWV are used for the detection of potassium ferrocyanide along with the artificial neural network. Different five concentrations of potassium ferrocyanide were prepared range from 2mM up to 10Mm solutions were detected by pencil graphite electrode, starting from the lowest concentration to the highest, under appropriate parameters and conditions for optimization. The voltammograms showed the varied peaks due to different concentrations were used, moreover, the outcome shows the higher sensitivity of SWV over DPV. Beyond the electrochemical analysis, the data obtained would be an output for the artificial intelligence model to be progressed via a mathematical model to have an outcome, and the comparison was done related to what has been obtained.