Surfactant‐like Properties of Alkaline Extracts from Wastewater Biosolids
Bibliographic record
Abstract
Abstract In order to assess the potential for utilizing wastewater biosolids as a source of useful substances, the surface activity of materials extracted from wastewater biosolids (activated sludge) by simple incubation with sodium hydroxide solutions at room temperature was assessed. The surface activity, measured by surface and interfacial tension methods, of the extracts was shown to be dependent on the extraction pH and the concentration of the organic matter solubilized in the alkaline solution. Increasing the extraction pH increased the surface activity of the extract (lower surface tensions), which is linked to the presence of more hydrophobic species in the extract. After adjusting the pH to more acidic values (e.g., pH = 4), the extracts retained their surface activity. The apparent CMC (critical micelle concentration) of pH 12.6 extracts was approximately 1,000 mg/L (based on total organic carbon or TOC), and the surface tension after CMC approximately 35 mN/m. While the CMC of the extract is significantly higher, when compared to a conventional surfactant, sodium dodecyl benzene sulfonate (SDBS, CMC ~ 25 mg/L), its surface tension at CMC was comparable. Above its CMC, the pH 12.6 extract had similar interfacial tensions than SDBS against toluene, heptane and hexadecane. Furthermore, the extract and SDBS had similar detergency performance for the removal of hexadecane from cotton. Skin corrosivity tests of the neutralized extracts show that they have comparable toxicity to conventional anionic surfactants such as sodium dodecyl sulfate. The potential use of these extracts in commercial products is discussed.
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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".