Unraveling the roles of network and tunnels in the conductivity of carbon nanofiber composites

Herein, an advanced conductivity model for polymer-carbon nanofiber (CNF) samples is introduced, stated as PCNFs. This model considers the length (l), radius (R) and amount of CNFs, interphase depth, percolation onset (PT), waviness, network portion, and tunneling length (d), by reasonable and meaningful equations. The proposed model is verified through the measured conductivity of samples and by studying the features' influences on the PCNF conductivity. The model calculations display respectable fitting with the experimented facts from numerous CNF samples. Additionally, all factors sensibly affect the conductivity of PCNFs. Longer and thinner CNFs, higher CNF amount, thicker interphase, shorter tunnels, and lower percolation onset lead to higher conductivity in PCNF. R > 50 nm and l < 15 μm produce an insulative composite, but the top conductivity of 0.21 S/m is displayed at R = 10 nm and l = 25 μm. Accordingly, narrower and bigger nanofibers can improve the conductivity. Furthermore, an insulative material is produced by PT = 0.04 nm and d > 5 nm, nevertheless the conductivity maximizes to 0.25 S/m at the least values of PT = 0.01 nm and d = 2 nm. These results disclose that the lowest percolation onset and narrowest tunnels yield the highest conductivity in the composite.

Authors

Institutions

Publication Details

Journal
Scientific Reports
Published
2025-10-10
DOI
https://doi.org/10.1038/s41598-025-19376-x
Citations
6
Primary Topic
Carbon Nanotubes in Composites
Type
article
Field-Weighted Citation Impact
3.11
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Unraveling the roles of network and tunnels in the conductivity of carbon nanofiber composites

Muhammad Naqvi, Yasser Zare, Soo‐Jin Park, Kyong Yop Rhee
6 citations
Scientific Reports
Carbon Nanotubes in Composites
3.11
article

Unraveling the roles of network and tunnels in the conductivity of carbon nanofiber composites

Muhammad Naqvi, Yasser Zare, Soo‐Jin Park, Kyong Yop Rhee
article en
6 citations

Abstract

Herein, an advanced conductivity model for polymer-carbon nanofiber (CNF) samples is introduced, stated as PCNFs. This model considers the length (l), radius (R) and amount of CNFs, interphase depth, percolation onset (PT), waviness, network portion, and tunneling length (d), by reasonable and meaningful equations. The proposed model is verified through the measured conductivity of samples and by studying the features' influences on the PCNF conductivity. The model calculations display respectable fitting with the experimented facts from numerous CNF samples. Additionally, all factors sensibly affect the conductivity of PCNFs. Longer and thinner CNFs, higher CNF amount, thicker interphase, shorter tunnels, and lower percolation onset lead to higher conductivity in PCNF. R > 50 nm and l < 15 μm produce an insulative composite, but the top conductivity of 0.21 S/m is displayed at R = 10 nm and l = 25 μm. Accordingly, narrower and bigger nanofibers can improve the conductivity. Furthermore, an insulative material is produced by PT = 0.04 nm and d > 5 nm, nevertheless the conductivity maximizes to 0.25 S/m at the least values of PT = 0.01 nm and d = 2 nm. These results disclose that the lowest percolation onset and narrowest tunnels yield the highest conductivity in the composite.

Scientific ReportsVol. 15(1)
Academic Center for Education, Culture and Research (IR), American University of the Middle East (KW), Kyung Hee University (KR), Motamed Cancer Institute (IR)
Openalex Percentile: Top 8%
Carbon Nanotubes in Composites
3.11
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.