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Record W4393163222 · doi:10.3390/ma17071493

Microstructure of Deposits Sprayed by a High Power Torch with Flash Boiling Atomization of High-Concentration Suspensions

2024· article· en· W4393163222 on OpenAlexafffund
Saeid Amrollahy Biouki, Fadhel Ben Ettouil, Andre C. Liberati, Ali Dolatabadi, Christian Moreau

Bibliographic record

VenueMaterials · 2024
Typearticle
Languageen
FieldEngineering
TopicHigh-Temperature Coating Behaviors
Canadian institutionsUniversity of TorontoConcordia University
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsMaterials scienceMicrostructureAnataseDeposition (geology)Suspension (topology)RutileBoilingChemical engineeringMetallurgyPlasma torchAnalytical Chemistry (journal)PlasmaChemistryChromatography

Abstract

fetched live from OpenAlex

The main objective of this study was to use flash boiling atomization as a new method to inject suspensions with high solid content into the high-power plasma flow. The water-based suspension was prepared with submicron titanium oxide particles with an average size of 500 nm. The investigated solid concentrations were 20, 40, 55 and 70 wt%. Two plasma torches operated at 33, 70 and 110 kW were used to investigate the effect of increasing power on the deposited microstructure and deposition efficiency. At low torch power, the deposition efficiency decreased with increasing solid concentration, and deposits with a high number of unmelted particles were obtained with 70 wt% suspensions. At high torch power, the deposition efficiency increased with increasing solid concentration, and dense deposits were obtained with 70 wt% suspensions. XRD analysis was performed on all deposits to determine the distribution of rutile and anatase phases. The percentage of the anatase phase varied from 35.7% to 66.9%, depending on the power input and solid concentration.

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 score0.003
Threshold uncertainty score0.543

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 head0.003
GPT teacher head0.193
Teacher spread0.191 · 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
Published2024
Admission routes2
Has abstractyes

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