Evidence map›Paper›PMID 36236185›Full record

ArticlePolymers2022

The Preparation and Wear Behaviors of Phenol-Formaldehyde Resin/BN Composite Coatings.

Chao Zang, Yaping Xing, Tingting Yang, Qi Teng, Jinming Zhen, Ran Zhang, Zhengfeng Jia, Weifang Han

Abstract read
PubMed Full text at PMC Publisher
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 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. Review
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.

Chao ZangSchool of Materials Science and Engineering, Liaocheng University, Liaocheng 252059, China.
Yaping XingSchool of Materials Science and Engineering, Liaocheng University, Liaocheng 252059, China.
Tingting YangSchool of Materials Science and Engineering, Liaocheng University, Liaocheng 252059, China.
Qi TengSchool of Materials Science and Engineering, Liaocheng University, Liaocheng 252059, China.
Jinming ZhenSchool of Materials Science and Engineering, Liaocheng University, Liaocheng 252059, China.
Ran ZhangSchool of Materials Science and Engineering, Liaocheng University, Liaocheng 252059, China.
Zhengfeng JiaSchool of Materials Science and Engineering, Liaocheng University, Liaocheng 252059, China.ORCID 0000-0002-9109-9776
Weifang HanSchool of Materials Science and Engineering, Liaocheng University, Liaocheng 252059, China.ORCID 0000-0002-1369-7607

Funding

Project of Natural Science Foundation of China 51905247, 52105190Shan Dong Province Nature Science Foundation ZR2020ME133; ZR2019MEE018 ; ZR2020QE044
6 · The paper itself

Abstract

Phenolic-matrix composites possess excellent synergistic effects on mechanical and tribological properties and can be used in the aerospace, medical, and automobile industries. In this work, a series of phenol-formaldehyde resin/hexagonal boron nitride nanocomposites (PF/BNs) were in situ synthesized using an easy method. PF/BN coatings (PF/BNCs) on 316L steels were prepared through a spin-casting method. The wear behaviors of these PF/BNCs were investigated by dry sliding with steel balls. The percentage of BN, the thickness of the coating, and the heat treatment temperature affected the coefficients of friction (COFs) and wear rates of these coatings. After heat treatment at 100 °C, the tribological properties of the PF/BNCs were remarkably improved, which might be attributed to both the transformation of carbon on the worn surfaces from C-O/C=O into C=N, carbide, and other chemical bonds and the cross-linking of the prepolymers.

Indexed as

coatingheat treatmentphenol–formaldehyde resin/BN compositeswear

Identifiers

PMID36236185
PMCPMC9572075

What OpenQuestion holds

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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.