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Study of the Wettability of Coke by Different Pitches and Their Blends

2016· article· en· W2521405205 on OpenAlexafffund
Ying Lu, Duygu Kocaefe, Yaşar Kocaefe, Xianai Huang, Dipankar Bhattacharyay, Patrick Coulombe

Bibliographic record

VenueEnergy & Fuels · 2016
Typearticle
Languageen
FieldEngineering
TopicFiber-reinforced polymer composites
Canadian institutionsAluminerie Alouette (Canada)Université du Québec à Chicoutimi
FundersNatural Sciences and Engineering Research Council of CanadaUniversité du Québec à Chicoutimi
KeywordsWettingContact angleCokeMaterials sciencePetroleum cokeSessile drop techniqueCoal tarComposite materialChemical engineeringCoalMetallurgyChemistryOrganic chemistry

Abstract

fetched live from OpenAlex

The properties of coal tar pitch, which is used as the binder material for carbon anode production, strongly affect the anode properties. Pitches have significant differences in their chemical compositions depending on their origin. In this study, four different coal tar pitches and their blends were studied with the aim of understanding the wettability of a calcined petroleum coke by pitch using the sessile-drop test. In this test, contact angle, which is an indication of wettability, is measured. Contact angles decrease with increasing time, and smaller contact angle means better wettability. The chemical properties of pitches and coke were studied using XPS to investigate their interactions and, consequently, the wetting mechanism. The results showed that blending different pitches influences the wettability. The presence of acidic, basic, and heteroatom containing functional groups in pitch might cause acid–base/condensation reactions when they are blended and, thus, influence the wetting behavior of the pitch blend.

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 score0.003

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 head0.006
GPT teacher head0.178
Teacher spread0.172 · 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

Citations24
Published2016
Admission routes2
Has abstractyes

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