Effect of Crystallinity on the Wettability of Petroleum Coke by Coal Tar Pitch
Bibliographic record
Abstract
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 machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".