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Record W2068390622 · doi:10.1179/026708301101509115

Microstructure and properties of heat treated iron powder compacts intended for ac soft magnetic applications

2001· article· en· W2068390622 on OpenAlex
I. P. Swainson, Yves Deslandes, G. Pleizier, Patrice Chartrand

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueMaterials Science and Technology · 2001
Typearticle
Languageen
FieldEngineering
TopicAdvanced materials and composites
Canadian institutionsPolytechnique MontréalNational Research Council CanadaCanadian Nuclear Laboratories
Fundersnot available
KeywordsMaterials scienceMicrostructureLubricantIron powderZinc stearateComposite materialElectrical resistivity and conductivityParticle sizeParticle (ecology)MetallurgyIron oxideChemical engineering

Abstract

fetched live from OpenAlex

This paper describes the effect of heat treatments on the microstructure and properties of iron powder specimens intended for ac soft magnetic applications at 60 Hz. The specimens were fabricated by compacting iron powder–lubricant (zinc stearate) mixes and heat treating the compacted specimens at 450–550°C in nitrogen. The microstructure and chemical characteristics of particle interfaces (X-ray photoelectron spectrometry) and the presence of microstrains (neutron diffraction) were correlated with the electrical and magnetic properties of the material. During the heat treatments (450–550°C), lubricant burns out and iron oxide contacts are created at particle interfaces (thermal oxidation bonding). Iron powder oxidation, which depends on the heat treatment temperature, reduces the electrical resistivity of the material and affects eddy current loss. The heat treatments modify the microstrains in the specimens and allow reduction of the hysteresis portion of the magnetic loss.

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.

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 score0.002
Threshold uncertainty score0.281

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 studies0.0000.001
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 head0.204
Teacher spread0.197 · 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