Adsorption of single‐ring model naphthenic acid from oil sands tailings pond water using physically activated petroleum coke
Bibliographic record
Abstract
Abstract Petroleum coke‐derived activated cokes were prepared and used for the adsorptive removal of a single‐ring naphthenic acid (NA) from synthetic oil sands process affected water (OSPW). CO 2 activation produced carbon with a larger mesopore volume fraction (0.67) than steam activation (0.25). Interestingly, prolonging the activation time of CO 2 from 6 to 9 h led to a simultaneous increase in specific surface area (276–405 m 2 /g) and mesopore volume (0.51–0.67). Furthermore, a positive relationship was found between the pseudo‐second‐order kinetic rate constant and the mesoporous volume of the activated coke. This suggests both the importance of pore size on kinetics and the fact that physical activation with a reagent such as CO 2 may be better suited than chemical activation due to its ability to create mesopores. Although enlarging the pores and accelerating the adsorption rate, post‐oxidation had detrimental effects on adsorption capacity, resulting in a decrease in equilibrium adsorbed amount from 115 to 34 mg/g, a 70% decrease, when post‐oxidized with O 2 , due to the negative charge of oxygen‐containing functional groups. On the other hand, the measured adsorption capacity increased by over 60% when activated coke was treated with ammonia, a result of the positively charged nitrogen‐containing surface groups. Finally, in real OSPW, the activated coke had a much lower capacity for total acid‐extractable organics than for the model NA. Therefore, activated petroleum coke may not be the best choice for treating raw tailings pond water and may be better suited for polishing.
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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 | 0.000 |
| 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".