A Study of Artificial Neural Networks for Electrochemical Data Analysis

Abstract

Artificial neural network has been demonstrated the capability of identifying the chemical composition from various analytical methods. This study tested the feasibility of using a single network to calculate data obtained from polarography, linear scanning voltammetry (LSV), and electrochemical impedance spectroscopy (EIS). Computer generated data were used for network learning and testing. The network property of a single layer network was calculated at various learning cycles, learning rates, and β values of the neuron activation function. This study also evaluated two‐layer, three‐layer, and four‐layer networks. Test results conclude that a single artificial neural network could analyze various analytical data by using corresponding weight matrices. To produce accurate results, the network requires high training cycle, small β value of sigmoid function, and small learning rate. Network accuracy increase as the number of neuron increased to certain extents. Network structure plays minimum effects on the accuracy of network for a given number of neurons.

Ultrahigh stable laminar graphene membranes for effective ionic and molecular nanofiltration with a machine learning-assisted study

“…Moreover, the ion permeability of the membranes was also demonstrated using machine learning, specifically the artificial neural network due to its versatility and high performance. 30,31 The prediction has been succeeded and well correlated with the experimental data, which overcome the limitation on the preparation of thin graphene membranes. The distinct ion rejection behavior of graphene membranes with a wide range of thicknesses was elucidated based on the calculations.…”