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Record W2124637642 · doi:10.31399/asm.cp.itsc2015p0673

Structure and Mechanical Properties of Thick Copper Coating Made by Cold Spray

2015· article· en· W2124637642 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueThermal spray · 2015
Typearticle
Languageen
FieldEngineering
TopicHigh-Temperature Coating Behaviors
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsMaterials scienceGas dynamic cold sprayCopperCoatingMicrostructureLayer (electronics)MetallurgyComposite materialSubstrate (aquarium)Deformation (meteorology)PropellantDeposition (geology)Adhesion

Abstract

fetched live from OpenAlex

Abstract The main purpose of this study was to form cold sprayed copper coatings on A 516 low carbon steel, which is considered a prospective material for manufacturing used nuclear fuel containers. The 3 mm-thick Cu coatings were formed using the high pressure cold spray method with N2 as the propellant gas. To increase the adhesion strength of the deposited coatings a copper sublayer was formed first, using He as the propellant gas. The deformation of copper particles during the deposition process was studied. The obtained SEM images of the Cu layer-A 516 low carbon steel substrate cross-sections demonstrated that the Cu sublayer had a dense microstructure, and local jet-metallic mixing areas. The Cu particles were deformed considerably more severely in the sub-layer than in the following layers. The steel substrate underwent severe deformation due to the impact of Cu particles. The mutual severe deformation of Cu particles and steel substrate resulted in a considerable increase of adhesion strength up to 120MPa. The structure of coatings and coating-substrate interface was studied by OIM, SEM and EDS.

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.

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 score0.002
Threshold uncertainty score0.644

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 head0.018
GPT teacher head0.214
Teacher spread0.196 · 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