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

2016· article· en· W2521405205 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

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.

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 score0.008
Threshold uncertainty score0.262

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 science0.0000.000
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 head0.006
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