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Record W4412931275 · doi:10.1016/j.matdes.2025.114509

Corrosion behavior and interface characterizations of CoCrFeNiMoNb/WC high-entropy alloy composite coatings in molten aluminum

2025· article· en· W4412931275 on OpenAlexaff
Tao Wu, Guang Chen, Litao Yu, C.W. Wang, Yu Chen, Yanpeng Xue, Yonghao Lu, Benli Luan

Bibliographic record

VenueMaterials & Design · 2025
Typearticle
Languageen
FieldEngineering
TopicHigh Entropy Alloys Studies
Canadian institutionsWestern University
FundersState Key Laboratory of Material Processing and Die and Mould TechnologyKey Research and Development Program of Zhejiang ProvinceHuazhong University of Science and Technology
KeywordsMaterials scienceAlloyComposite numberMetallurgyCorrosionAluminiumComposite material

Abstract

fetched live from OpenAlex

Molten aluminum corrosion presents a major challenge in industrial applications because of its extreme aggressiveness at high temperatures. In this study, novel CoCrFeNiMo 0.2 Nb 0.2 /WC composite coatings are developed via laser cladding, and the WC ceramic is demonstrated to be beneficial for improving the corrosion resistance of the coatings in molten aluminum. As the WC content increases to 60 wt%, the corrosion rate of the coatings reaches a minimum value of 2.0 × 10 -14 m 2 /s. The corrosion mechanism involves two key processes: (1) the formation of a protective Fe/Cr oxide layer, followed by (2) its reaction with molten Al, forming brittle intermetallics that degrade the integrity of the coating. A critical finding is that WC particles delay failure by resisting dissolution, but degradation of the surrounding binder phase generates crack-prone transition layers under thermal stress. This work provides both a high-performance coating solution and fundamental insights into molten-metal corrosion mechanisms.

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 score0.029
Threshold uncertainty score0.862

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.009
GPT teacher head0.227
Teacher spread0.218 · 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

Citations2
Published2025
Admission routes1
Has abstractyes

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