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Record W4313435693 · doi:10.31399/asm.cp.itsc2019p0512

Microstructural and Performance Analyses of Thermally Sprayed Electric Resistance Heating Systems as De-Icing Elements

2019· article· en· W4313435693 on OpenAlexaff
André McDonald, Morvarid Mohammadian Bajgiran, Christian Moreau

Bibliographic record

VenueThermal spray · 2019
Typearticle
Languageen
FieldEngineering
TopicHigh-Temperature Coating Behaviors
Canadian institutionsConcordia UniversityUniversity of Alberta
Fundersnot available
KeywordsMaterials scienceNichromeThermal sprayingComposite materialGas dynamic cold sprayMicrostructureCoatingMetallurgyElectrical resistivity and conductivityAluminiumSuspension (topology)Corundum

Abstract

fetched live from OpenAlex

Abstract This study investigates the microstructure and efficiency of coating-based heating elements produced by deposition of various powders, including aluminum oxide (Al2O3), alumina-titania (Al2O3-TiO2), nickel-chromium (NiCr), and copper, using flame spraying, suspension plasma spraying, high-velocity oxyfuel (HVOF) spraying, and cold spraying techniques. The main goals are to assess the dielectric strength of flame and plasma sprayed alumina, compare the electrical resistivity of HVOF and flame sprayed NiCr, and obtain coating cross-sectional images to shed light on the challenges and potential of different heating element designs. The Al2O3 layer produced by suspension plasma spraying appeared to be more reliable due to its cauliflower-like structure, corundum content, and hygroscopic properties. Resistivity was found to be higher in the flame sprayed NiCr than in the HVOF deposit mainly due to discontinuities and imperfections such as cracks, pores, and oxygen content. The micrographs taken from sample cross-sections show penetration of flame-sprayed NiCr into the flame-sprayed Al2O3 and Al2O3-TiO2 layers, which decreases the effective thickness of the dielectric. However, interlocking between NiCr and Al2O3-TiO2 coatings can be beneficial when cohesion is a concern.

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 score0.111
Threshold uncertainty score0.778

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 head0.010
GPT teacher head0.249
Teacher spread0.239 · 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

Citations1
Published2019
Admission routes1
Has abstractyes

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