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Record W4403501010 · doi:10.1016/j.jallcom.2024.177046

Refractory high entropy TiTaZrHfW-N/Si3N4 nano-layered alloy thin film’s oxidation resistance

2024· article· en· W4403501010 on OpenAlexaff
Abdelhakim Bouissil, Sofiane Achache, Djallel Eddine Touaibia, Jaafar Ghanbaga, Павел С. Постников, Mohamed M. Chehimi, Benoît Panicaud, Fabrice Parent, Frédéric Sanchette, Mohamed El Garah

Bibliographic record

VenueJournal of Alloys and Compounds · 2024
Typearticle
Languageen
FieldEngineering
TopicHigh Entropy Alloys Studies
Canadian institutionsCentre for Excellence in Mining Innovation
FundersUniversité de Technologie de TroyesGroupement d'Intérêt Public Haute-Marne
KeywordsAlloyNano-Materials scienceRefractory (planetary science)Thin filmMetallurgyChemical engineeringComposite materialNanotechnologyEngineering

Abstract

fetched live from OpenAlex

Refractory high entropy TiTaZrHfW-N/Si 3 -N 4 nano-layered alloy thin films are investigated to study the effect of nano-layered architecture and silicon (Si) mean content on their structural, mechanical, thermal properties and oxidation behavior. The films are deposited using direct current (DC) magnetron sputtering of separate Si and TiTaZrHfW targets. The Si mean content is controlled by tailoring the power discharge applied to the Si target. The deposition process led to a nano-layered architecture where Si 3 N 4 (amorphous) and TiTaZrHfW-N (nano-crystalized NaCl FCC type structure) are alternated. By increasing the thickness of Si 3 N 4 nano-layers, the Si mean content increases. All coatings are found to have good thermal stability after annealing under vacuum at 900 °C. Increasing Si mean content reduces the film’s hardness; however, the annealing treatment at 900 °C improves it. A super-hardness of 41 GPa is found for the post-annealed Si-free film. Si 3 N 4 nano-layers enhance the oxidation resistance at elevated temperatures of 600, 700, and 800 °C. This oxidation resistance is further enhanced by increasing the nano-layer’s period and also by increasing the density of the films.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.534
Threshold uncertainty score0.776

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 head0.007
GPT teacher head0.211
Teacher spread0.204 · 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 designNot applicable
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
Published2024
Admission routes1
Has abstractyes

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