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Record W2148729753 · doi:10.1002/maco.201307492

Effect of heat treatment on the microstructure and corrosion behaviour of Mg–Zn alloys

2014· article· en· W2148729753 on OpenAlexaff
Hamid Reza Bakhsheshi‐Rad, Esah Hamzah, Mamoun Medraj, M.H. Idris, Amir Fereidouni Lotfabadi, Mohammadreza Daroonparvar, Muhamad Azizi Mat Yajid

Bibliographic record

VenueMaterials and Corrosion · 2014
Typearticle
Languageen
FieldMaterials Science
TopicMagnesium Alloys: Properties and Applications
Canadian institutionsConcordia University
Fundersnot available
KeywordsMicrostructureCorrosionMaterials scienceMetallurgyAlloyPhase (matter)Matrix (chemical analysis)Galvanic cellComposite materialChemistry

Abstract

fetched live from OpenAlex

Microstructure and corrosion behaviour in simulated body fluid of as‐cast and heat treated Mg– x Zn ( x = 3 and 6) alloys for different heat treatment times were studied. The results revealed that as‐cast Mg–3Zn alloys consist of Mg 12 Zn 13 phase and α‐Mg matrix, while Mg–6Zn is composed of Mg 51 Zn 20 , Mg 12 Zn 13 compounds and α‐Mg matrix. After heat treatment of Mg–6Zn alloy at 340 °C, the Mg 51 Zn 20 phase decomposed to the matrix and Mg 12 Zn 13 while, the microstructure of Mg–3Zn remained unchanged. The results also indicated that heat treatment at 340 °C has little influence on the corrosion behaviour of Mg–3Zn. In contrast, heat treatment improved the corrosion resistance of the Mg–6Zn alloy as the decomposition of the Mg 51 Zn 20 phase decreased micro‐galvanic corrosion. The corrosion resistance of both as‐cast Mg–3Zn and Mg–6Zn alloys marginally improved with increasing heat treatment times.

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 score0.004
Threshold uncertainty score0.337

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 head0.008
GPT teacher head0.222
Teacher spread0.214 · 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

Citations54
Published2014
Admission routes1
Has abstractyes

Explore more

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