Evidence map›Paper›PMID 42461931›Full record

ArticlePloS one2026

Study on electrochemical corrosion behavior of gray cast iron processed via laser cavitation peening.

Chunhui Luo, Jiayang Gu, Jiahao Yang, Yanfen Sun, Yuwen Ding, Aihua Xu, Xiaohu Sang

Abstract read
In one paragraph

Article in PloS one, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

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.

Chunhui LuoSchool of Mechanical Engineering, Changzhou Vocational Institute of Mechatronic Technology, Changzhou, PR China.ORCID https://orcid.org/0009-0001-8716-8169
Jiayang GuSchool of Intelligent manufacturing, Jiangsu College of Engineering and Technology, Nantong, PR China.
Jiahao YangSchool of Mechanical Engineering, Changzhou Vocational Institute of Mechatronic Technology, Changzhou, PR China.
Yanfen SunSchool of Mechanical Engineering, Changzhou Vocational Institute of Mechatronic Technology, Changzhou, PR China.
Yuwen DingSchool of Normal Education, Changzhou Institute of Technology, Changzhou, PR China.
Aihua XuSchool of Mechanical Engineering, Changzhou Vocational Institute of Mechatronic Technology, Changzhou, PR China.
Xiaohu SangSchool of Mechanical Engineering, Changzhou Vocational Institute of Mechatronic Technology, Changzhou, PR China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Laser cavitation peening (LCP) is a novel material surface treatment method. In this study, we investigated the electrochemical corrosion performance of LCP-treated HT250 gray cast iron, with and without a coverage layer, under various laser energies, defocusing amounts, and immersion times. In the LCP process, a 0.04 mm aluminum coverage layer was employed. The influence of the coverage layer on LCP processing was analyzed. The results show that the presence of a coverage layer during the LCP treatment improves the specimen's anti-corrosion ability, with this alignment corresponding with an increase in laser energy. In comparison with laser energy, the effect of a coverage layer on corrosion resistance at various defocusing amounts is not very significant, and at a smaller defocusing amount, the treated specimen presents higher corrosion resistance. The corrosion resistance of gray cast iron in the case with a coverage layer first increases with the increase in laser energy and then decreases. The presence of a coverage layer helps to avoid laser ablation and cavitation erosion.

Indexed as

Electrochemical TechniquesIronLasersCorrosionSurface PropertiesIron

Identifiers

PMID42461931
PMCPMC13374986

What OpenQuestion holds

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LicenceCC BY
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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.