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Effect of Crystallinity on the Wettability of Petroleum Coke by Coal Tar Pitch

2016· article· en· W2302795028 on OpenAlexafffund
Arunima Sarkar, Duygu Kocaefe, Yaşar Kocaefe, Dipankar Bhattacharyay, D.K. Sarkar, Brigitte Morais

Bibliographic record

VenueEnergy & Fuels · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicRecycling and Waste Management Techniques
Canadian institutionsAluminerie Alouette (Canada)Université du Québec à Chicoutimi
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsCokePetroleum cokeWettingMaterials scienceX-ray photoelectron spectroscopyChemical engineeringCrystallinityFourier transform infrared spectroscopyAnodeScanning electron microscopeContact angleMetallurgyComposite materialChemistry

Abstract

fetched live from OpenAlex

Undercalcined coke gained interest as raw material for anodes used in aluminum production since it is reported in the literature that anodes produced with this coke might have lower CO 2 reactivity in the electrolytic cell. For any anode-grade coke, it is important to identify a suitable pitch which will bond well with that coke during baking and yield dense anodes. The wettability of petroleum coke by molten pitch indicates the quality of bonding between them and influences the final anode properties. In this study, the effect of coke crystallinity on its wettability by pitch has been studied using the sessile-drop test. Also, the chemical and physical properties of coke and pitch have been studied using Fourier transform infrared (FTIR) spectroscopy, X-ray photoelectron spectroscopy (XPS), energy dispersive X-ray spectroscopy (EDX), and scanning electron microscopy (SEM). It was found that the coke physical and chemical properties depend on its calcination temperature and, thereby, greatly affect the wetting properties. The study showed that the wettability of coke by pitch increases with decreasing crystalline length. The presence of porosity, C═C bonds, C═O, COO, and heteroatoms (O and N) are important factors which control the wetting of coke by pitch.

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 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)0.001
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 score0.147
Threshold uncertainty score0.760

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.005
GPT teacher head0.216
Teacher spread0.211 · 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 teacher head, 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

Citations22
Published2016
Admission routes2
Has abstractyes

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