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Record W4392033857 · doi:10.32920/25267153

Grain Refinement of AZ91 Magnesium Alloy with a Novel Aluminum-Graphite Inoculant

2024· preprint· en· W4392033857 on OpenAlexaff
Michael Rinaldi

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEngineering
TopicAluminum Alloy Microstructure Properties
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsMicrobial inoculantMetallurgyMaterials scienceMagnesiumGraphiteAlloyAluminiumMagnesium alloyGeologyBacteria

Abstract

fetched live from OpenAlex

Magnesium is a lightweight metal with a high strength-to-weight ratio, making it an ideal material for improving fuel efficiency. However, its poor mechanical properties necessitate the need for researchers to find ways of improving them for this material to find wider acceptance in industry. This study examines the grain refinement capabilities of an aluminum-carbon (graphite) composite in cast AZ91E magnesium alloy. With increasing carbon addition, ultimate tensile strength (UTS) and ductility improved, while yield strength (YS) remained largely unchanged. The fracture mechanism also remained unchanged with increasing carbon addition, showing mixed-mode fracture in samples of highest and lowest ductility. The nucleation mechanism was determined to be duplex nucleation of Al4C3 and Al8Mn5. X-ray diffraction analysis confirmed that the Al-C inoculant contained Al4C3 after sintering; however, most Al4C3 was formed in-situ during the melting and casting process.

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)0.000
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 score0.002
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.009
GPT teacher head0.195
Teacher spread0.185 · 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

Citations0
Published2024
Admission routes1
Has abstractyes

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