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Record W2098660511 · doi:10.1149/2.0781410jes

Microstructural Effects on Corrosion of AM50 Magnesium Alloys

2014· article· en· W2098660511 on OpenAlexafffund
R. Matthew Asmussen, W. Jeffrey Binns, Pellumb Jakupi, David W. Shoesmith

Bibliographic record

VenueJournal of The Electrochemical Society · 2014
Typearticle
Languageen
FieldMaterials Science
TopicMagnesium Alloys: Properties and Applications
Canadian institutionsWestern University
FundersGeneral Motors of CanadaMcMaster UniversityNatural Sciences and Engineering Research Council of CanadaMcGill University
KeywordsMicrostructureMaterials scienceScanning electron microscopeEutectic systemCorrosionMetallurgyMagnesiumDielectric spectroscopyAlloyGraphiteMagnesium alloyEnergy-dispersive X-ray spectroscopyAluminiumOptical microscopeElectrochemistryComposite materialChemistry

Abstract

fetched live from OpenAlex

The influence of microstructure and aluminum distribution on the corrosion of three different casts of the magnesium AM50 alloy (sand, graphite, die) was studied in 1.6 wt% NaCl solution. The microstructure of the alloys and the distribution of individual elements were characterized using scanning electron microscopy (SEM) and energy dispersive X-ray analyzes. Differences in the morphology and distribution of corrosion damage were determined using SEM and confocal scanning laser microscopy. Weight change measurements and electrochemical impedance spectroscopy showed that the corrosion resistance improved in the order sand cast < graphite cast ≪ die cast. This increased resistance was shown to be attributable to the increasing tightness of the α-Mg/β-phase/Al-containing eutectic microstructural network, which led to an improved protection of the surface by Al-enriched eutectic and a decrease in the probability of initiating a major damage site on an α-Mg region with low Al content.

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 score0.003
Threshold uncertainty score0.277

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 science0.0010.000
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 head0.005
GPT teacher head0.208
Teacher spread0.203 · 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

Citations36
Published2014
Admission routes2
Has abstractyes

Explore more

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