Evidence map›Paper›PMID 35729335›Full record

ArticleScientific reports2022

Modeling and characterization of the electrical conductivity on metal nanoparticles/carbon nanotube/polymer composites.

Yang Wang, Sijian Lu, Wenke He, Shen Gong, Yunqian Zhang, Xinsi Zhao, Yuanyuan Fu, Zhenghong Zhu

Abstract read
In one paragraph

Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing 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

8 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

8 authors.

Yang WangSchool of Materials Science and Engineering, Central South University, Hunan, 410083, Changsha, China.
Sijian LuSchool of Materials Science and Engineering, Central South University, Hunan, 410083, Changsha, China.
Wenke HeSchool of Materials Science and Engineering, Central South University, Hunan, 410083, Changsha, China.
Shen GongSchool of Materials Science and Engineering, Central South University, Hunan, 410083, Changsha, China. gongshen011@csu.edu.cn.
Yunqian ZhangSchool of Life Science, Central South University, Hunan, 410083, Changsha, China.
Xinsi ZhaoSchool of Materials Science and Engineering, Central South University, Hunan, 410083, Changsha, China.
Yuanyuan FuSchool of Materials Science and Engineering, Central South University, Hunan, 410083, Changsha, China.
Zhenghong ZhuDepartment of Mechanical Engineering, York University, 4700 Keele Street, Toronto, ON, M3J 1P3, Canada.

Funding

National Natural Science Foundation of China 51701241
6 · The paper itself

Abstract

Flexible conductive films have good deformability and conductivity, and are expected to be used in flexible electronic devices. In this paper, four kinds of flexible conductive films were successfully prepared by compounding nano-sized metal (Ni, Cu, Au or AuCu alloy) particles to CNT surface and then dispersing to polydimethylsiloxane matrix. Experiment results show that the conductivity of these prepared films are almost two orders of magnitude higher than that of CNT/polydimethylsiloxane films with the same CNT loadings. A simulation model based on percolation network theory and Monte Carlo technology is introduced to study the influence of nanoparticles on the composite conductivity. Results confirmed that the introduction of nanoparticles effectively reduces the effective resistance of CNT and the tunnelling resistance at CNT junctions. The intrinsic conductivity and the length diameter ratio of CNT, the intrinsic conductivity, the size and the coverage ratio of nanoparticles are the core parameters affecting the conductivity of composite. Compared with CNT/polydimethylsiloxane films, the optimized theoretical conductivity of these nano-sized particles enhanced composites can be further improved.

Identifiers

PMID35729335
PMCPMC9213557

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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.