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Record W4294702184 · doi:10.31399/asm.cp.itsc2007p0744

Fragmentation and Splashing of Plasma-Sprayed Nickel on Oxidized Metals

2007· article· en· W4294702184 on OpenAlexaff
Allison E. McDonald, S. Chandra, Mario Lamontagne, Christian Moreau

Bibliographic record

VenueThermal spray · 2007
Typearticle
Languageen
FieldEngineering
TopicHigh-Temperature Coating Behaviors
Canadian institutionsNational Research Council CanadaUniversity of Toronto
Fundersnot available
KeywordsSplashMaterials sciencePyrometerNickelPlasmaMetallurgyNichromeThermalComposite materialSubstrate (aquarium)Temperature measurement

Abstract

fetched live from OpenAlex

Abstract Plasma-sprayed, molten nickel particles (60 µm diameter) were photographed during impact on oxidized 304L stainless steel surfaces that were maintained at room temperature or at 350oC. The steel samples were oxidized at different temperatures. Droplets approaching the surface were sensed using a photo detector and after a known delay, a fast charge-coupled device (CCD) camera was triggered to capture time-integrated images of the spreading splat from the substrate front surface. A two-color pyrometer was used to collect the thermal radiation from the particles to follow the evolution of their temperature after impact. Molten nickel particles impacting on oxidized steel at room temperature fragmented significantly, while heating the surfaces produced splats with disk-like morphologies. Impact on steel that was highly oxidized induced the formation of finger-like splash projections at the splat periphery. The splat cooling rate and thermal contact resistance between the splat and non-heated oxidized steel varied significantly as the degree of oxidation increased; heating the oxidized steel greatly reduced the variations. It was suggested that the large variations in splat cooling rates and thermal contact resistances on the non-heated oxidized steel was due primarily to the presence of adsorbates on the steel surface.

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 score0.017
Threshold uncertainty score0.559

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 head0.011
GPT teacher head0.241
Teacher spread0.230 · 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

Citations0
Published2007
Admission routes1
Has abstractyes

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