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The influence of Al2O3 particle morphology on the coating formation and dry sliding wear behavior of cold sprayed Al–Al2O3 composites

2015· article· en· W2093936338 on OpenAlexafffund
J. Michael Shockley, Sylvie Descartes, Phuong Vo, Éric Irissou, Richard R. Chromik

Bibliographic record

VenueSurface and Coatings Technology · 2015
Typearticle
Languageen
FieldEngineering
TopicHigh-Temperature Coating Behaviors
Canadian institutionsNational Research Council CanadaMcGill University
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada
KeywordsMaterials scienceMorphology (biology)CoatingComposite materialTribologyParticle (ecology)Deposition (geology)Gas dynamic cold sprayRaw materialMetallurgy

Abstract

fetched live from OpenAlex

Five Al–Al2O3 coatings were deposited by gas dynamic cold spray, using feedstocks containing 0, 10, and 50 wt.% of Al2O3 particles admixed into commercially pure Al powders. Two feedstocks were made using Al2O3 powders with an angular, blocky Al2O3 morphology, two feedstocks used Al2O3 powders with a spherical morphology, and the fifth consisted of 100% Al. The influence of Al2O3 concentration and morphology in the feedstock powders on the cold spray coating formation was measured in terms of Al2O3 recovery in the coatings, deposition efficiency, and microstructural analysis. The Al–Al2O3 coatings were then subjected to dry sliding wear experiments from which the friction, wear, and microevolution of third body structures in the wear track and transfer film were observed. The spherical Al2O3 morphology was associated with improved tribological behavior compared to the angular morphology when comparing similar coating concentrations of Al2O3.

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 score0.003
Threshold uncertainty score0.006

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 head0.012
GPT teacher head0.230
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 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

Citations145
Published2015
Admission routes2
Has abstractyes

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