Exploring the nonlinear conductive properties of polymer/graphene composites at the molecular level: a machine learning approach-Reference-Cited by-同舟云学术

Exploring the nonlinear conductive properties of polymer/graphene composites at the molecular level: a machine learning approach

Published:2025 Issue:22 Volume:15 Page:17711-17719
ISSN:2046-2069
Container-title:RSC Advances
language:en
Short-container-title:RSC Adv.

Author:

Li Hongfei¹^ORCID,Chen Yazhou¹^ORCID,Zhou Linsen²^ORCID,Xie Zun³,Cao Wei¹,Qu Zhaoming¹

Affiliation:

1. National Key Laboratory on Electromagnetic Environment Effects, Army Engineering University of PLA, Shijiazhuang, 050003, China

2. Institute of Materials, China Academy of Engineering Physics, Mianyang, 621907, China

3. Department of Physics and Hebei Advanced Thin Film Laboratory, Hebei Normal University, Shijiazhuang 050024, China

Abstract

Machine learning-driven enables the rational design of polymer/graphene (Py/GN) composites with specific nonlinear conduction behaviors under external electric fields.

Funder

National Natural Science Foundation of China

Publisher

Royal Society of Chemistry (RSC)

Link

http://pubs.rsc.org/en/content/articlepdf/2025/RA/D5RA00705D

Reference59 articles.

1. Carbon nanotube–polymer composites: Chemistry, processing, mechanical and electrical properties

2. Positive temperature coefficient characteristic and structure of graphite nanofibers reinforced high density polyethylene/carbon black nanocomposites

3. Carbon nanotube–graphene nanoplatelet hybrids as high-performance multifunctional reinforcements in epoxy composites

4. A review of the fabrication and properties of vapor-grown carbon nanofiber/polymer composites

5. Polymer/Pristine Graphene Based Composites: From Emulsions to Strong, Electrically Conducting Foams