ArticleHeliyon2025
Synergetic effect of TiC particles on cavitation erosion resistance of AZ31-TiC surface composites.
Article in Heliyon, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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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.
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.
Who cites it
1 citing paper in PubMed.
- Machine learning models based wear performance prediction of AZ31/TiC composites.Scientific reports · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
AZ31/TiC surface composites were produced using Friction Stir Processing (FSP), with varying amounts of TiC particles. The microstructure and hardness measurements, as well as the evaluation of erosion wear resistance, were carried out on AZ31/TiC composites. X-ray diffraction tests were carried out to identify the phase composition. The presence of α-Mg and TiC phases was observed in all composites and no chemical interactions between the AZ31 matrix and TiC were observed at the interface. The AZ31 alloy is shown to have a hardness of 62 The AZ31 alloy reinforced with 15 vol% of TiC particles showed the highest resistance to cavitation with volume loss of 44 mm
Identifiers
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Registered trials
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.