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Record W4384694587 · doi:10.22215/etd/2023-15581

Characterization and Performance Evaluation of High Entropy Nitride Coatings Produced by Cathodic Arc Evaporation

2023· dissertation· en· W4384694587 on OpenAlexaff
Alexandra Rachel Lothrop

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldEngineering
TopicMetal and Thin Film Mechanics
Canadian institutionsCarleton University
Fundersnot available
KeywordsMaterials scienceCathodic protectionNitrideMetallurgyMicrostructureAlloyCathodic arc depositionThermal sprayingEvaporationCoatingComposite materialThermodynamics

Abstract

fetched live from OpenAlex

Protective coatings are a widely used technique to improve surface properties. Recent developments in alloy design have identified many unique properties of high entropy alloys which have been incorporated into nitride coatings to produce high entropy nitrides. The research conducted for this thesis investigated the use of high entropy nitrides (AlCrTiVZr)N, (AlCrTiMoV)N and (AlCrTiMoVNi)N with varying Ni concentration for protective coatings produced by cathodic arc evaporation. All coatings were evaluated for their composition, microstructure, and surface morphology. The coatings thermal stability, mechanical properties of hardness and Young’s modulus were evaluated along with the coatings resistance to wear and erosion. The coatings produced in this study showed promise as protective coatings, however future efforts will be dedicated to reducing the droplet formation during deposition to further improve performance.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.017
GPT teacher head0.234
Teacher spread0.217 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2023
Admission routes1
Has abstractyes

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