Development of sludge-based activated char sorbent with enhanced hydrophobicity for oil spill cleanup
Bibliographic record
Abstract
Recovery of oil spilled on surface waters by the use of sorbents remains one of the primary oil spill response options available. To improve on this response measure, we have successfully fabricated an activated char (AC) sorbent material by pyrolysis of sewage sludge (SS), a readily available waste product generated across the world from wastewater treatment plants. The inherent Fe-minerals in SS texture were converted to magnetic Fe3O4 particles during the pyrolysis reaction. The AC provided a unique means to recover the sorbent after the oil sorption process with a magnetic field. Meanwhile, a superhydrophobic sorbent material with a water contact angle of 152.2° was created by the treatment of AC with myristic acid which could float on the water surface. Feasibility studies at the laboratory-scale were conducted with motor oil and light crude oil to evaluate its potential use in spill response operations. Results showed a sorption capacity of about 8.5 and 10.7 g/g for motor oil and light crude oil, respectively. Following the recovery of the test oils by ethanol stripping, the material could be recycled up to 5 times with trivial loss in sorption capacity. This research proposes a framework for the development of a highly efficient sorbent material for oil spill response operations from SS waste.
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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".