Development of Series-Parallel and Neural-Network Based Models for Predicting Electrical Conductivity of Polymer Nanocomposite-Reference-Cited by-同舟云学术

Development of Series-Parallel and Neural-Network Based Models for Predicting Electrical Conductivity of Polymer Nanocomposite

Published:2023 Issue: Volume:11 Page:92875-92886
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Folorunso Oladipo¹^ORCID,Olukanmi Peter O.¹^ORCID,Shongwe Thokozani¹^ORCID,Sadiku Rotimi²,Hamam Yskandar³

Affiliation:

1. Department of Electrical and Electronic Engineering Technology, University of Johannesburg, Johannesburg, South Africa

2. Institute of Nano Engineering Research (INER), Department of Chemical, Metallurgy and Material Engineering, Tshwane University of Technology, Pretoria, South Africa

3. Department of Electrical Engineering, French South African Institute of Technology (F’SATI), Tshwane University of Technology, Pretoria, South Africa

Funder

University of Johannesburg, South Africa

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Engineering,General Materials Science,General Computer Science,Electrical and Electronic Engineering

Link

http://xplorestaging.ieee.org/ielx7/6287639/10005208/10231322.pdf?arnumber=10231322

Reference45 articles.

1. Comparison of carbon nanotubes and nanodisks as percolative fillers in electrically conductive composites

2. Gasification of food waste in supercritical water: An innovative synthesis gas composition prediction model based on Artificial Neural Networks

3. Statistical characterization and simulation of graphene-loaded polypyrrole composite electrical conductivity

4. Modeling of the energy demand of the residential sector in the United States using regression models and artificial neural networks

5. Modeling the evolution of graphene agglomeration and the electrical and mechanical properties of graphene/polypropylene nanocomposites

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