Next Article in Journal
l-Cystine-Crosslinked Polypeptide Nanogel as a Reduction-Responsive Excipient for Prostate Cancer Chemotherapy
Previous Article in Journal
UV Light Induces Dedoping of Polyaniline
Article Menu

Export Article

Open AccessArticle
Polymers 2016, 8(2), 22; doi:10.3390/polym8020022

Artificial Neural Network and Response Surface Methodology Modeling in Ionic Conductivity Predictions of Phthaloylchitosan-Based Gel Polymer Electrolyte

1
Department of Chemistry, University of Malaya, Kuala Lumpur 50603, Malaysia
2
Centre of Ionics, University of Malaya, Kuala Lumpur 50603, Malaysia
These authors contributed equally to this work.
*
Author to whom correspondence should be addressed.
Academic Editor: Martin Kröger
Received: 13 December 2015 / Revised: 7 January 2016 / Accepted: 14 January 2016 / Published: 29 January 2016
View Full-Text   |   Download PDF [6622 KB, uploaded 29 January 2016]   |  

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. View Full-Text
Keywords: phthaloylchitosan; ionic conductivity; gel polymer electrolyte; artificial neural network; response surface methodology phthaloylchitosan; ionic conductivity; gel polymer electrolyte; artificial neural network; response surface methodology
Figures

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Supplementary material

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Azzahari, A.D.; Yusuf, S.N.F.; Selvanathan, V.; Yahya, R. Artificial Neural Network and Response Surface Methodology Modeling in Ionic Conductivity Predictions of Phthaloylchitosan-Based Gel Polymer Electrolyte. Polymers 2016, 8, 22.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Polymers EISSN 2073-4360 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top