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Record W4408181943 · doi:10.1016/j.jmrt.2025.03.037

Frictional wear properties of different nano La2O3 composite FeCoNiCrMo high-entropy alloy coatings under soil conditions

2025· article· en· W4408181943 on OpenAlexaff
Zuyang Li, Wengang Chen, Dongyang Li, Jiawei Yang, Yao Zhang, Xiaodong Yang, Binggui Dai, Jihao Zhang, Zhaoling Qiu

Bibliographic record

VenueJournal of Materials Research and Technology · 2025
Typearticle
Languageen
FieldEngineering
TopicHigh Entropy Alloys Studies
Canadian institutionsUniversity of Alberta
FundersYunnan Provincial Department of Education Science Research Fund Project
KeywordsMaterials scienceAlloyNano-Composite numberHigh entropy alloysComposite materialMetallurgy

Abstract

fetched live from OpenAlex

In order to mitigate the problem of wear during the working process of earth-touching implements, in this study, nano La 2 O 3 -doped HEA (FeCoNiCrMo) coatings with different dopant contents were fabricated on the surface of 65Mn steel by laser cladding technology, Effects of the coatings on tribological properties of 65Mn steel were investigated in both dry ambient and dry soil environments. The microstructure, chemical composition, hardness, and tribological properties of the wear-resistant La 2 O 3 -doped composite coatings were evaluated. Results of the study show that La 2 O 3 can reduce the incidence of pores in HEA coatings. La 2 O 3 can effectively increase the hardness and wear resistance of the coatings, but too much La 2 O 3 may negatively affect the hardness of the coatings and thus their wear resistance. In dry friction and soil environments, coatings at 0.8% consistently show the lowest wear and the best abrasion resistance properties. In soil environments, the modes are mainly oxidative and adhesive wear, and soil particles act as a buffer medium to greatly reduce the amount of wear.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.031
Threshold uncertainty score0.375

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.019
GPT teacher head0.269
Teacher spread0.250 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations14
Published2025
Admission routes1
Has abstractyes

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