Conductivity Analysis of Carbon Black and Graphite Composites based on Percolation Theory

Yu FENG; Senlin HOU; Cong WU; Hui SUN; Meng CHEN; Guanglie ZHANG; Wen Jung LI

doi:10.1109/NSENS62142.2024.10561336

Conductivity Analysis of Carbon Black and Graphite Composites based on Percolation Theory

Yu FENG, Senlin HOU, Cong WU, Hui SUN, Meng CHEN, Guanglie ZHANG^*, Wen Jung LI^*

^*Corresponding author for this work

Department of Mechanical Engineering

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

3 Citations (Scopus)

Abstract

Conductive polymers, particularly carbon-based ones like carbon black and graphite, have gained attention for their unique electrical and mechanical properties. Carbon black, with its high conductivity and excellent electron transport, is a cost-effective additive that improves the overall conductivity of composites. Graphite, known for its conductivity and mechanical stability, offers extended electron movement and durability. These advantages make carbon-based conductive polymers suitable for flexible sensors. In this study, we optimize the properties of carbon black and graphite using percolation theory by manipulating the concentration and arrangement of conductive fillers. Simulations indicate that incorporating graphite enhances the conductivity of carbon black, enabling the theoretical analysis of high-performance flexible sensors. This research paves a pathway for the manufacture of flexible carbon-based sensors with superior electronic properties. ©2024 IEEE.

Original language	English
Title of host publication	Proceedings of the 3rd IEEE International Conference on Micro/Nano Sensors for AI, Healthcare and Robotics
Publisher	IEEE
Pages	120-123
ISBN (Electronic)	979-8-3503-7521-3
ISBN (Print)	979-8-3503-7522-0
DOIs	https://doi.org/10.1109/NSENS62142.2024.10561336
Publication status	Published - 2024
Event	3rd IEEE International Conference on Micro/Nano Sensors for AI, Healthcare and Robotics (IEEE-NSENS 2024) - Shenzhen Convention and Exhibition Center, Shenzhen, China Duration: 2 Mar 2024 → 3 Mar 2024 https://www.ieeensens.org/

Conference

Conference	3rd IEEE International Conference on Micro/Nano Sensors for AI, Healthcare and Robotics (IEEE-NSENS 2024)
Abbreviated title	NSENS 2024
Place	China
City	Shenzhen
Period	2/03/24 → 3/03/24
Internet address	https://www.ieeensens.org/

Funding

This work was supported by the Hong Kong Research Grants Council (Project Number: 11207222), the University Grants Committee (Project Number:T42-717/20-R), the CRF-Collaborative Research Fund (Project Number: 8739045), the Shenzhen Science and Technology Innovation Commission (Grant Number: JCYJ20190808181803703), and InnoHK Project at the Hong Kong Centre for Cerebro-cardiovascular Health Engineering (COCHE).

Research Keywords

carbon-based polymer composites
conductive analysis
percolation theory

RGC Funding Information

RGC-funded

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10.1109/NSENS62142.2024.10561336

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Cite this

@inproceedings{b2e5238f4f974970847b9828876f251b,

title = "Conductivity Analysis of Carbon Black and Graphite Composites based on Percolation Theory",

abstract = "Conductive polymers, particularly carbon-based ones like carbon black and graphite, have gained attention for their unique electrical and mechanical properties. Carbon black, with its high conductivity and excellent electron transport, is a cost-effective additive that improves the overall conductivity of composites. Graphite, known for its conductivity and mechanical stability, offers extended electron movement and durability. These advantages make carbon-based conductive polymers suitable for flexible sensors. In this study, we optimize the properties of carbon black and graphite using percolation theory by manipulating the concentration and arrangement of conductive fillers. Simulations indicate that incorporating graphite enhances the conductivity of carbon black, enabling the theoretical analysis of high-performance flexible sensors. This research paves a pathway for the manufacture of flexible carbon-based sensors with superior electronic properties. {\textcopyright}2024 IEEE.",

keywords = "carbon-based polymer composites, conductive analysis, percolation theory",

author = "Yu FENG and Senlin HOU and Cong WU and Hui SUN and Meng CHEN and Guanglie ZHANG and LI, {Wen Jung}",

year = "2024",

doi = "10.1109/NSENS62142.2024.10561336",

language = "English",

isbn = "979-8-3503-7522-0",

pages = "120--123",

booktitle = "Proceedings of the 3rd IEEE International Conference on Micro/Nano Sensors for AI, Healthcare and Robotics",

publisher = "IEEE",

address = "United States",

note = "3rd IEEE International Conference on Micro/Nano Sensors for AI, Healthcare and Robotics (IEEE-NSENS 2024), NSENS 2024 ; Conference date: 02-03-2024 Through 03-03-2024",

url = "https://www.ieeensens.org/",

}

FENG, Y , HOU, S , WU, C , SUN, H , CHEN, M, ZHANG, G & LI, WJ 2024, Conductivity Analysis of Carbon Black and Graphite Composites based on Percolation Theory. in Proceedings of the 3rd IEEE International Conference on Micro/Nano Sensors for AI, Healthcare and Robotics. IEEE, pp. 120-123, 3rd IEEE International Conference on Micro/Nano Sensors for AI, Healthcare and Robotics (IEEE-NSENS 2024), Shenzhen, China, 2/03/24. https://doi.org/10.1109/NSENS62142.2024.10561336

Conductivity Analysis of Carbon Black and Graphite Composites based on Percolation Theory. / FENG, Yu ; HOU, Senlin ; WU, Cong et al.
Proceedings of the 3rd IEEE International Conference on Micro/Nano Sensors for AI, Healthcare and Robotics. IEEE, 2024. p. 120-123.

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

TY - GEN

T1 - Conductivity Analysis of Carbon Black and Graphite Composites based on Percolation Theory

AU - FENG, Yu

AU - HOU, Senlin

AU - WU, Cong

AU - SUN, Hui

AU - CHEN, Meng

AU - ZHANG, Guanglie

AU - LI, Wen Jung

PY - 2024

Y1 - 2024

N2 - Conductive polymers, particularly carbon-based ones like carbon black and graphite, have gained attention for their unique electrical and mechanical properties. Carbon black, with its high conductivity and excellent electron transport, is a cost-effective additive that improves the overall conductivity of composites. Graphite, known for its conductivity and mechanical stability, offers extended electron movement and durability. These advantages make carbon-based conductive polymers suitable for flexible sensors. In this study, we optimize the properties of carbon black and graphite using percolation theory by manipulating the concentration and arrangement of conductive fillers. Simulations indicate that incorporating graphite enhances the conductivity of carbon black, enabling the theoretical analysis of high-performance flexible sensors. This research paves a pathway for the manufacture of flexible carbon-based sensors with superior electronic properties. ©2024 IEEE.

AB - Conductive polymers, particularly carbon-based ones like carbon black and graphite, have gained attention for their unique electrical and mechanical properties. Carbon black, with its high conductivity and excellent electron transport, is a cost-effective additive that improves the overall conductivity of composites. Graphite, known for its conductivity and mechanical stability, offers extended electron movement and durability. These advantages make carbon-based conductive polymers suitable for flexible sensors. In this study, we optimize the properties of carbon black and graphite using percolation theory by manipulating the concentration and arrangement of conductive fillers. Simulations indicate that incorporating graphite enhances the conductivity of carbon black, enabling the theoretical analysis of high-performance flexible sensors. This research paves a pathway for the manufacture of flexible carbon-based sensors with superior electronic properties. ©2024 IEEE.

KW - carbon-based polymer composites

KW - conductive analysis

KW - percolation theory

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DO - 10.1109/NSENS62142.2024.10561336

M3 - RGC 32 - Refereed conference paper (with host publication)

SN - 979-8-3503-7522-0

SP - 120

EP - 123

BT - Proceedings of the 3rd IEEE International Conference on Micro/Nano Sensors for AI, Healthcare and Robotics

PB - IEEE

T2 - 3rd IEEE International Conference on Micro/Nano Sensors for AI, Healthcare and Robotics (IEEE-NSENS 2024)

Y2 - 2 March 2024 through 3 March 2024

ER -

Conductivity Analysis of Carbon Black and Graphite Composites based on Percolation Theory

Abstract

Conference

Funding

Research Keywords

RGC Funding Information

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Cite this

Conductivity Analysis of Carbon Black and Graphite Composites based on Percolation Theory

Abstract

Conference

Funding

Research Keywords

RGC Funding Information

Access Output

Other Access Options

Permanent Link

Other Files and Links

Fingerprint

Projects

GRF: A Flexible Sponge-based Vibration Sensor for Conversion of Neck-skin Vibration to Digital Voice

TBRS-ExtU-Lead: Intelligent Robotics for Elderly Assistance in Hong Kong

CRF-ExtU-Lead: Super Reality for Hands-on Online Education

Cite this