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Record W2408104405 · doi:10.1155/2016/5027243

Effects of La and Ce Addition on the Modification of Al-Si Based Alloys

2016· article· en· W2408104405 on OpenAlexaff
E. M. Elgallad, H. W. Doty, Saleh Alkahtani, F. H. Samuel

Bibliographic record

VenueAdvances in Materials Science and Engineering · 2016
Typearticle
Languageen
FieldEngineering
TopicAluminum Alloy Microstructure Properties
Canadian institutionsUniversité du Québec à Chicoutimi
Fundersnot available
KeywordsEutectic systemMaterials scienceLanthanumCeriumPrecipitationIntermetallicRare earthAlloyRecalescenceMetallurgyMelting temperatureComposite materialInorganic chemistry

Abstract

fetched live from OpenAlex

This study focuses on the effects of the addition of rare earth metals (mainly lanthanum and cerium) on the eutectic Si characteristics in Al-Si based alloys. Based on the solidification curves and microstructural examination of the corresponding alloys, it was found that addition of La or Ce increases the alloy melting temperature and the Al-Si eutectic temperature, with an Al-Si recalescence of 2-3°C, and the appearance of post-α-Al peaks attributed to precipitation of rare earth intermetallics. Addition of La or Ce to Al-(7–13)% Si causes only partial modification of the eutectic Si particles. Lanthanum has a high affinity to react with Sr, which weakens the modification efficiency of the latter. Cerium, however, has a high affinity for Ti, forming a large amount of sludge. Due to the large difference in the length of the eutectic Si particles in the same sample, the normal use of standard deviation in this case is meaningless.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0010.000

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 head0.004
GPT teacher head0.193
Teacher spread0.189 · 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 source (direct Gemma or distilled Codex), 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

Citations46
Published2016
Admission routes1
Has abstractyes

Explore more

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