MétaCan
Menu
Back to cohort
Record W4311448415 · doi:10.2464/jilm.72.638

Internal corundum growth in alumina-silica refractory during exposure to molten Al-5Mg alloy

2022· article· en· W4311448415 on OpenAlexaff
Yosuke Tamura, H. Soda, Alexander McLean, H. Suzuki, Long Yun PIAO, Hideaki KATSUMATA, Syunsuke Torii

Bibliographic record

VenueJournal of Japan Institute of Light Metals · 2022
Typearticle
Languageen
FieldEngineering
TopicAluminum Alloys Composites Properties
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsCorundumCrucible (geodemography)Materials scienceSpinelAlloyIntermetallicMetallurgyAluminiumDegradation (telecommunications)SilicateChemical engineeringChemistry

Abstract

fetched live from OpenAlex

The present work is aimed at clarifying the internal degradation processes of an alumina-silica refractory through microstructural observations and analysis after exposure to a molten Al-5Mg alloy for an extended period. An alumina-silica crucible was pretreated by a molten Al-5Mg alloy to cause the initial stage of degradations at the inner crucible surface. Fresh alloy was melted in the pretreated crucible and held at 1150˚C for 96 hrs to cause further degradations into the crucible wall. Results indicate that Mg reacts with aggregate (Al6Si2O13, SiO2) and matrix materials to form MgAl2O4, Si, Al, and Al-Si-Ca intermetallic compounds that were crystallized around MgAl2O4 to form network-like structures. In the absence of Mg, Al reacts with the aggregate to produce Si and αAl2O3 and reacts with calcium-aluminum silicate in the matrix to form Si and CaO-Al2O3 compounds. These indicate that MgAl2O4 spinel forms preferentially before the corundum (αAl2O3) formation. Mg and Ca, segregated at the interface of " αAl2O3 / Al6Si2O13" or "αAl2O3 / SiO2", may promote corundum formation. The lower concentration of O and Si in the degraded areas in comparison with those in un-degraded areas suggests that gaseous SiO might have been generated during the degradation processes.

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 categoriesMeta-epidemiology (narrow)
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 score0.237
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
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 head0.011
GPT teacher head0.213
Teacher spread0.201 · 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.

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
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Japan Institute of Light MetalsSame topicAluminum Alloys Composites PropertiesFrench-language works237,207