Evidence map›Paper›PMID 35335501›Full record

ArticlePolymers2022

Microscopic Pyrolytic and Electric Decomposition Mechanism of Insulating Polyimide/Boron Nitride Nanosheet Composites based on ReaxFF.

Xiaosong Wang, Tong Zhao, Yihan Wang, Li Zhang, Liang Zou

Abstract read
In one paragraph

Article in Polymers, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 citing papers in PubMed.

  1. Article
  2. Review
  3. Article
  4. Review
  5. Article
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

5 authors.

Xiaosong WangSchool of Electrical Engineering, Shandong University, Jinan 250061, China.
Tong ZhaoSchool of Electrical Engineering, Shandong University, Jinan 250061, China.ORCID 0000-0003-0523-4466
Yihan WangState Grid Jinan Power Supply Company, Jinan 250010, China.
Li ZhangSchool of Electrical Engineering, Shandong University, Jinan 250061, China.
Liang ZouSchool of Electrical Engineering, Shandong University, Jinan 250061, China.ORCID 0000-0002-9523-3020

Funding

Natural Science Foundation of Shandong Province ZR2021ME011State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources LAPS20001
6 · The paper itself

Abstract

High thermal conductivity insulating materials with excellent comprehensive properties can be obtained by doping boron nitride nanosheets (BNNSs) into polyimide (PI). To study the microscopic mechanism of composite material decomposition in an actual working environment and the inhibitory effect of BNNS doping on the decomposition process, molecular dynamics simulations were carried out at high temperatures, in intense electric fields, and with various reactive species in plasma based on the reactive force field (ReaxFF). The results showed that the decomposition was mainly caused by hydrogen capture and adsorption, which broke the benzene ring and C-N bond on the PI chains and led to serious damage to the PI structure. The BNNS filling was shown to inhibit the decomposition of the PI matrix at high temperatures and in intense electric fields. Moreover, the BNNS filling also inhibited the material decomposition caused by ·OH and ·NO. The erosive effect of the positive corona on the PI composites was more obvious than that of the negative corona. In this paper, the microscopic dynamic reaction paths of material pyrolysis in various environments were revealed at the atomic level, and it was concluded that BNNS doping could effectively inhibit the decomposition of PI in various environments.

Indexed as

boron nitride nanosheetselectric decompositionmolecular dynamicspolyimidepyrolyticreactive species in plasma

Identifiers

PMID35335501
PMCPMC8950269

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Registered trials

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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.