Evidence map›Paper›PMID 40077342›Full record

ArticleMaterials (Basel, Switzerland)2025

Effect of SiC Concentration on the Microstructure and Anti-Wear Performance of Electrodeposited Ni-SiC Composite Coatings Constructed for Piston Ring Application.

Fengwu Zhang, Qiuhua Wang, Huajie Shen, Caixia Bai, Chaoyu Li, Dehao Tian, Baojin Wang

Abstract read
In one paragraph

Article in Materials (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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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

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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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Fengwu ZhangSchool of Mechanical and Electrical Engineering, Sanming University, Sanming 365004, China.
Qiuhua WangYunnan Key Laboratory of Forest Disaster Warning and Control, College of Civil Engineering, Southwest Forestry University, Kunming 650224, China.
Huajie ShenSchool of Design, Fujian University of Technology, Fuzhou 350118, China.ORCID 0000-0002-5462-1262
Caixia BaiSchool of Design, Fujian University of Technology, Fuzhou 350118, China.
Chaoyu LiSchool of Mechanical and Electrical Engineering, Sanming University, Sanming 365004, China.
Dehao TianSchool of Mechanical Science and Engineering, Northeast Petroleum University, Daqing 163318, China.
Baojin WangSchool of Mechanical Science and Engineering, Northeast Petroleum University, Daqing 163318, China.

Funding

Natural Science Foundation of Heilongjiang Province LH2023A002
6 · The paper itself

Abstract

At present, the improvement of anti-wear performance of piston rings remains a challenge. In this article, Ni-SiC composite coatings fabricated at 3, 9, and 15 g/L SiC were denoted as NSc-3, NSc-9, and NSc-15 coatings. Meanwhile, the influence of SiC concentration on the surface morphology, phase structure, microhardness, and anti-wear performance of electrodeposited Ni-SiC composite coatings were investigated utilizing scanning electron microscopy, X-ray diffraction, a microhardness tester, and a friction-wear tester, respectively. The SEM images presented NSc-9 coatings with a compact, flat, or cauliflower-like surface morphology. The cross-sectional morphology and EDS results showed that the Si and Ni elements were uniformly distributed in the NSc-9 coatings with dense and flat microstructures. Moreover, the average grain size of the NSc-9 coatings was only 429 nm. Furthermore, the microhardness and indentation path of the NSc-9 coatings were 672 Hv and 13.7 μm, respectively. Also, the average friction coefficient and worn weight loss of the NSc-9 coatings were 0.46 and 29.5 mg, respectively, which were lower than those of the NSc-3 and NSc-15 coatings. In addition, a few shallow scratches emerged on the worn surfaces of the NSc-9 coatings, demonstrating their outstanding anti-wear performance when compared to the NSc-3 and NSc-15 coatings.

Indexed as

anti-wear performanceelectrodepositionmicrostructureNi-SiC composite coatingspiston ring

Identifiers

PMID40077342
PMCPMC11901545

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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.