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Corrosion Characteristics of Al-B<sub>4</sub>C Metal Matrix Composites in Boric Acid Solution

2016· article· en· W2547674652 on OpenAlexafffund
Yu Han, X. Grant Chen

Bibliographic record

VenueMaterials science forum · 2016
Typearticle
Languageen
FieldEngineering
TopicAluminum Alloys Composites Properties
Canadian institutionsUniversité du Québec à Chicoutimi
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMaterials scienceCorrosionBoric acidMetalGalvanic corrosionPitting corrosionComposite numberComposite materialAnodePolarization (electrochemistry)MetallurgyElectrodeChemistryPhysical chemistry

Abstract

fetched live from OpenAlex

The corrosion behavior of Al-B4C metal matrix composites in H3BO3 solutions with different Cl- contents was investigated using potentiodynamic polarization and zero resistance ammetry techniques. Results show that the corrosion of Al-B4C composites in H3BO3 solution increases with increasing B4C volume fraction in the composites. The main corrosion characteristic of Al-B4C composites in H3BO3 solution is the galvanic corrosion between Al matrix and B4C particles. In the galvanic couple, B4C particle acts as cathode and Al matrix acts as anode. The cathodic reaction is hydrogen revolution reaction, which controls the corrosion mechanism of Al-B4C composites. Pitting is not observed on the composite surface in the H3BO3 solution with zero Cl-. However, with addition of Cl- in H3BO3 solution, pitting occurs and the corrosion resistance remarkably decreases with increasing Cl- content. The corrosion resistance of Al-B4C composites in H3BO3 solutions is compared with that in the standard 3.5% NaCl solution.

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 score0.004

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 integrity0.0010.000
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 head0.008
GPT teacher head0.209
Teacher spread0.201 · 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

Citations15
Published2016
Admission routes2
Has abstractyes

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