Evidence map›Paper›PMID 38399924›Full record

ReviewPolymers2024

Advances in Monte Carlo Method for Simulating the Electrical Percolation Behavior of Conductive Polymer Composites with a Carbon-Based Filling.

Zhe Zhang, Liang Hu, Rui Wang, Shujie Zhang, Lisong Fu, Mengxuan Li, Qi Xiao

Abstract readReview
In one paragraph

Review in Polymers, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

11 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Zhe ZhangSchool of Textile Science and Engineering, Tiangong University, Tianjin 300387, China.
Liang HuSchool of Physical Science and Technology, Tiangong University, Tianjin 300387, China.
Rui WangSchool of Textile Science and Engineering, Tiangong University, Tianjin 300387, China.
Shujie ZhangSchool of Textile Science and Engineering, Tiangong University, Tianjin 300387, China.
Lisong FuSchool of Textile Science and Engineering, Tiangong University, Tianjin 300387, China.ORCID 0000-0001-5163-3535
Mengxuan LiCollege of Fine Arts & Design, Tianjin Normal University, Tianjin 300387, China.
Qi XiaoSchool of Textile Garment and Design, Changshu Institute of Technology, Changshu 215500, China.

Funding

Natural Science Foundation of Tianjin 15JCZDJC38400The Natural Science Foundation of the Jiangsu Higher Education Institutions of China No. 23KJD540001
6 · The paper itself

Abstract

Conductive polymer composites (CPCs) filled with carbon-based materials are widely used in the fields of antistatic, electromagnetic interference shielding, and wearable electronic devices. The conductivity of CPCs with a carbon-based filling is reflected by their electrical percolation behavior and is the focus of research in this field. Compared to experimental methods, Monte Carlo simulations can predict the conductivity and analyze the factors affecting the conductivity from a microscopic perspective, which greatly reduces the number of experiments and provides a basis for structural design of conductive polymers. This review focuses on Monte Carlo models of CPCs with a carbon-based filling. First, the theoretical basis of the model's construction is introduced, and a Monte Carlo simulation of the electrical percolation behaviors of spherical-, rod-, disk-, and hybridfilled polymers and the analysis of the factors influencing the electrical percolation behavior from a microscopic point of view are summarized. In addition, the paper summarizes the progress of polymer piezoresistive models and polymer foaming structure models that are more relevant to practical applications; finally, we discuss the shortcomings and future research trends of existing Monte Carlo models of CPCs with carbon-based fillings.

Indexed as

carbon-based materialsconductive networksCPCselectrical percolation behaviorMonte Carlo simulations

Identifiers

PMID38399924
PMCPMC10891544

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.