MétaCan
Menu
Back to cohort
Record W3161336272 · doi:10.31399/asm.cp.itsc2021p0515

Formation of Solid Solution and Metallic Nickel Phases During Suspension Plasma Spraying of Co Oxide and Ni Oxide

2021· article· en· W3161336272 on OpenAlexaff
Vahid Jalilvand, Ali Dolatabadi, Christian Moreau, Saeed Mohammadkhani, Lionel Roué, Daniel Guay

Bibliographic record

VenueThermal spray · 2021
Typearticle
Languageen
FieldEngineering
TopicHigh-Temperature Coating Behaviors
Canadian institutionsInstitut National de la Recherche ScientifiqueConcordia University
Fundersnot available
KeywordsMaterials scienceNickelInertGas dynamic cold sprayMetallurgyOxideChemical engineeringSuspension (topology)MetalDeposition (geology)MicrostructureCermetThermal sprayingSubstrate (aquarium)CoatingComposite materialCeramic

Abstract

fetched live from OpenAlex

Abstract The focus of this study is the formation of a solid solution and metallic nickel in the cobalt-nickel mixed oxide coatings during suspension plasma spray (SPS) deposition. The (Co,Ni)O solid solution is a potential material for inert anode applications in aluminum production. SPS coatings and in-flight collected particles are studied to gain further insight into the melting and mixing phenomena of the NiO and CoO powders as well as phase formation in the deposited coatings. Moreover, the role of suspension feedstock particle sizes on the microstructure of coatings is discussed. SEM, EDS and X-ray diffraction studies helped better understanding the formation of different crystalline phases within the as-sprayed coatings. It was found that the formation of metallic nickel is possible in the coatings. The results support the importance of substrate temperature on the formation of metallic Ni, so that keeping the substrate at low temperature results in an increase of the Ni content in the coatings. In this study, possible causes for the formation of metallic Ni during spraying are discussed.

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 score0.018
Threshold uncertainty score0.571

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 head0.015
GPT teacher head0.243
Teacher spread0.228 · 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
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueThermal spraySame topicHigh-Temperature Coating BehaviorsFrench-language works237,207