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Record W2160679185 · doi:10.5006/1.3659505

Investigation on Corrosion Behavior of the Al-B<sub>4</sub>C Metal Matrix Composite in a Mildly Oxidizing Aqueous Environment

2011· article· en· W2160679185 on OpenAlexfundno aff
Ying Han, Danick Gallant, X.-G. Chen

Bibliographic record

VenueCORROSION · 2011
Typearticle
Languageen
FieldEngineering
TopicAluminum Alloys Composites Properties
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsX-ray photoelectron spectroscopyMaterials scienceCorrosionBoron carbideComposite numberAqueous solutionDielectric spectroscopyOxidizing agentMetalScanning electron microscopeChlorideMetallurgyChemical engineeringInorganic chemistryComposite materialElectrochemistryChemistryElectrode

Abstract

fetched live from OpenAlex

The corrosion behavior of Al-B4C (aluminum-boron carbide) metal matrix composites in a 0.5 M potassium sulfate (K2SO4) solution was investigated using electrochemical impedance spectroscopy and potentiodynamic polarization methods. Optical and scanning electron microscopes as well as profilometry were used to study the surface morphology of the material before and after corrosion. Moreover, infrared reflectionabsorption spectroscopy (IRRAS) and x-ray photoelectron spectroscopy (XPS) were used to identify the corrosion products. It was observed that SO4 2- species did not induce pitting of the AA1100 (UNS A91100)-16 vol% B4C. In contrast, it was found that the Al-B4C composite was highly susceptible to pitting attacks by chloride ions, especially at the Al/B4C interfaces. The B4C particles showed a cathodic character with respect to the peripheral matrix, and both the IRRAS and XPS results showed that bayerite (Al[OH]3) was the main corrosion product. © 2011, NACE International.

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 head0.027
GPT teacher head0.195
Teacher spread0.168 · 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

Citations31
Published2011
Admission routes1
Has abstractyes

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Same venueCORROSIONSame topicAluminum Alloys Composites PropertiesFrench-language works237,207