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Record W2593330578 · doi:10.3390/met7030079

Application of Boron Oxide as a Protective Surface Treatment to Decrease the Air Reactivity of Carbon Anodes

2017· article· en· W2593330578 on OpenAlexafffund
Ramzi Ishak, Donald Picard, Gaétan Laroche, Donald Ziegler, Houshang Alamdari

Bibliographic record

VenueMetals · 2017
Typearticle
Languageen
FieldMaterials Science
TopicAdvanced ceramic materials synthesis
Canadian institutionsUniversité LavalAlcoa (Canada)Natural Sciences and Engineering Research Council of Canada
FundersNatural Sciences and Engineering Research Council of CanadaUniversité Laval
KeywordsAnodeMaterials scienceBoronCarbon fibersOxideBoron oxideElectrolysisReactivity (psychology)GraphiteCoatingMetallurgyChemical engineeringInorganic chemistryElectrolyteChemistryNanotechnologyElectrodeComposite materialComposite numberOrganic chemistry

Abstract

fetched live from OpenAlex

The oxidation of a carbon anode with air and CO2 occurs during the electrolysis of alumina in Hall-Héroult cells, resulting in a significant overconsumption of carbon and dusting. Boron is well known to decrease the rate of this reaction for graphite. In this work, the application of boron oxide has been investigated to evaluate its inhibition effect on the air oxidation reaction, and to provide an effective protection for anodes. Different methods of impregnation coating have been explored. Impregnated anode samples were gasified under air at 525 °C according to the standard measurement methods. X-ray tomography was used to obtain the microstructural information of the samples before and after air-burning tests. The impregnated samples showed a very low oxidation reaction rate and dust generation.

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 score0.001
Threshold uncertainty score0.002

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 head0.019
GPT teacher head0.292
Teacher spread0.273 · 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

Citations9
Published2017
Admission routes2
Has abstractyes

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