Next Article in Journal
l-Cystine-Crosslinked Polypeptide Nanogel as a Reduction-Responsive Excipient for Prostate Cancer Chemotherapy
Next Article in Special Issue
Functionalized Cellulose Networks for Efficient Oil Removal from Oil–Water Emulsions
Previous Article in Journal
UV Light Induces Dedoping of Polyaniline
Previous Article in Special Issue
Seaweed Polysaccharide-Based Nanoparticles: Preparation and Applications for Drug Delivery
 
Search for Articles:
Title / Keyword
Author / Affiliation
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
Journals
Polymers
Volume 8
Issue 2
10.3390/polym8020022
polymers-logo
Submit to this Journal Review for this Journal Edit a Special Issue
► Article Menu

Article Menu

share announcement question_answer thumb_up
...
textsms
...
Article

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

by
Ahmad Danial Azzahari
1,†,
Siti Nor Farhana Yusuf
1,†,
Vidhya Selvanathan
1,† and
Rosiyah Yahya
1,2,*
1
Department of Chemistry, University of Malaya, Kuala Lumpur 50603, Malaysia
2
Centre of Ionics, University of Malaya, Kuala Lumpur 50603, Malaysia
*
Author to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Academic Editor: Martin Kröger
Polymers 2016, 8(2), 22; https://doi.org/10.3390/polym8020022
Received: 13 December 2015 / Revised: 7 January 2016 / Accepted: 14 January 2016 / Published: 29 January 2016
(This article belongs to the Collection Polysaccharides)
View Full-Text Download PDF
Browse Figures

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
►▼ Show Figures

Graphical abstract

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

Supplementary Material

SciFeed

Share and Cite

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. https://doi.org/10.3390/polym8020022

AMA Style

Azzahari AD, Yusuf SNF, 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(2):22. https://doi.org/10.3390/polym8020022

Chicago/Turabian Style

Azzahari, Ahmad D., Siti N.F. Yusuf, Vidhya Selvanathan, and Rosiyah Yahya. 2016. "Artificial Neural Network and Response Surface Methodology Modeling in Ionic Conductivity Predictions of Phthaloylchitosan-Based Gel Polymer Electrolyte" Polymers 8, no. 2: 22. https://doi.org/10.3390/polym8020022

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

Article Metrics

Article Access Map by Country/Region

1
Back to TopTop