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Updated: Jan 26, 2026

Synthesis of Ionic Liquid Based Electrolytes, Assembly of Li-ion Batteries, and Measurements of Performance at High Temperature
Published on: December 20, 2016
Artificial Neural Network and Response Surface Methodology Modeling in Ionic Conductivity Predictions of
Ahmad Danial Azzahari1, Siti Nor Farhana Yusuf2, Vidhya Selvanathan3
1Department of Chemistry, University of Malaya, Kuala Lumpur 50603, Malaysia. o_danny@siswa.um.edu.my.
Abstract:
A gel polymer electrolyte system based on phthaloylchitosan was prepared. The effects of process variables, such as lithium iodide, caesium iodide, and 1-butyl-3-methylimidazolium iodide were investigated using a distance-based ternary mixture experimental design. A comparative approach was made between response surface methodology (RSM) and artificial neural network (ANN) to predict the ionic conductivity. The predictive capabilities of the two methodologies were compared in terms of coefficient of determination R² based on the validation data set. It was shown that the developed ANN model had better predictive outcome as compared to the RSM model.
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