Adsorption of naphthenic acids on high surface area activated carbons
Bibliographic record
Abstract
In oil sands mining extraction, water is an essential component; however, the processed water becomes contaminated through contact with the bitumen at high temperature, and a portion of it cannot be recycled and ends up in tailing ponds. The removal of naphthenic acids (NAs) from tailing pond water is crucial, as they are corrosive and toxic and provide a substrate for microbial activity that can give rise to methane, which is a potent greenhouse gas. In this study, the conversion of sawdust into an activated carbon (AC) that could be used to remove NAs from tailings water was studied. After producing biochar from sawdust by a slow-pyrolysis process, the biochar was physically activated using carbon dioxide (CO2) over a range of temperatures or prior to producing biochar, and the sawdust was chemically activated using phosphoric acid (H3PO4). The physically activated carbon had a lower surface area per gram than the chemically activated carbon. The physically produced ACs had a lower surface area per gram than chemically produced AC. In the adsorption tests with NAs, up to 35 mg of NAs was removed from the water per gram of AC. The chemically treated ACs showed better uptake, which can be attributed to its higher surface area and increased mesopore size when compared with the physically treated AC. Both the chemically produced and physically produced AC provided better uptake than the commercially AC.
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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".