Waste Biomass-Extracted Surfactants for Heavy Oil Removal
Bibliographic record
Abstract
The potential synergism between biobased surfactants, produced from the alkaline extraction of waste biomass, and a synthetic surfactant was assessed. This synergism was explored in terms of surface and interfacial tension reduction, and the ability of mixtures to remove heavy oil from oil-bearing sand. The waste biomass sources investigated were return activated sludge (RAS) from municipal wastewater from Toronto, Canada, and urban refuse (UR) matter from municipal solid waste compost treatment facilities in Piemonte, Italy. Surfactants from both sources were extracted using alkaline extraction methods. Mixtures of these waste biobased surfactants with the synthetic surfactant, sodium dioctyl sulfosuccinate (AOT), at a total concentration of 1g TOC /L, were able to achieve low interfacial tensions (<1 mN/m) with toluene and hexane, without the addition of electrolytes. The mixtures generally achieved interfacial tensions (IFTs) an order of magnitude below that of the pure biobased surfactant. At an increased total concentration of 10 g TOC /L, an UR extract (FORSUD), mixed at 40% with AOT, reached an ultralow IFT of 0.019 mN/m against hexane, without the need to add salt into the system. Furthermore, the RAS–AOT and UR–AOT mixtures were examined for their use in the removal of heavy oil, bitumen, from contaminated sand. The low IFTs of the mixtures proved to be useful in removing heavy oil (bitumen) from sand particles.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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".