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Record W4392182636 · doi:10.3390/met14030271

Heterogenous Grain Nucleation in Al-Si Alloys: Types of Nucleant Inoculation

2024· article· en· W4392182636 on OpenAlexaff
E. Isaac Samuel, Hicham Tahiri, F. H. Samuel

Bibliographic record

VenueMetals · 2024
Typearticle
Languageen
FieldEngineering
TopicAluminum Alloy Microstructure Properties
Canadian institutionsUniversité du Québec à Chicoutimi
Fundersnot available
KeywordsNucleationMaterials scienceMetallurgyInoculationThermodynamicsMedicinePhysics

Abstract

fetched live from OpenAlex

The objective of the current work is to establish, on the one hand, the conventional mechanisms of grain refining and, on the other hand, the effect of the refining-modification interaction in Sr-modified Al-Si alloys on the achieved grain refining and the modification of eutectic silicon. For this purpose, the hypereutectic alloy A390.1 (~17%Si) was used. Various grain refiners were used, namely, Al-10%Ti, Al-5%Ti-1%B, and Al-4%B. After the preparation of the liquid metal, several concentrations of these master alloys were added to the liquid bath according to the desired objective. The different melts prepared were heated at 750 °C and cast in a preheated graphite mold with a solidification rate of around 0.8 °C/s. The liquid metal was. The presence of strontium (added in the form of Al-10%Sr master alloy) and boron completely affects the microstructure of the alloy. An atom of Sr unites with 6 atoms of B to form a compound whose stoichiometric formula is of the SrB6 type, leading to a significant reduction in the modification. A strong relationship exists between the addition of B and the recovery level of Sr. The affinity between titanium and boron is stronger than the affinity between boron and strontium. Both B and TiB2 phase particles do not react with Si; it is only the Ti part of the Al-Ti-B master that forms (Al, Si)3Ti. Regardless of the amount of Si content in the alloy, the Al-4%B master alloy achieves the best grain refining compared to Ti-containing master alloys.

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 score0.394
Threshold uncertainty score0.376

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 head0.011
GPT teacher head0.216
Teacher spread0.206 · 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

Citations8
Published2024
Admission routes1
Has abstractyes

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