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Record W4408506010 · doi:10.2497/jjspm.15c-t1-21

Characterization of the Chemistry of Oxides Formed on the Surface of Water-atomized Steel Particles during Annealing

2025· article· en· W4408506010 on OpenAlexaff
Gabrielle Laramée, Simon Gélinas, Philippe Plamondon, Jean‐Philippe Masse, Gilles L’Éspérance, Carl Blais

Bibliographic record

VenueJournal of the Japan Society of Powder and Powder Metallurgy · 2025
Typearticle
Languageen
FieldEngineering
TopicMetallurgical Processes and Thermodynamics
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsAnnealing (glass)Materials scienceOxideSinteringStoichiometryMetalTransmission electron microscopyMetallurgyChemical engineeringScanning electron microscopeChemical compositionNanotechnologyComposite materialChemistryPhysical chemistryOrganic chemistry

Abstract

fetched live from OpenAlex

Water-atomized (WA) steel powders are inevitably oxidized due to the chemical reaction between molten metal and water vapor. Minimization of surface oxides is essential for forming strong metallic bonds during sintering. However, the efficiency of H2-annealing is dependant on oxide chemistry, which is rarely made of pure/stoichiometric species. The objective here was to perform an in-depth characterization of the oxides found on WA steel particles to understand their behaviour during H2-annealing. It investigates the relationship between the chemical composition of pre-alloyed steels(Cr, Mn, Mo, Si), the chemical composition of the surface oxides and their modification due to H2-annealing. Particular attention is given to the impact of the difference between Si(wt.%) and Mn(wt.%) contents on powder oxidation. Characterization was performed using scanning and transmission electron microscopy coupled with energy dispersive X-ray spectroscopy. Characterization before and after annealing provided insights into the dynamics of oxides reduction according to powder chemistry.

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)0.001
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 score0.282

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
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 head0.007
GPT teacher head0.200
Teacher spread0.192 · 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
Published2025
Admission routes1
Has abstractyes

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