Study of the Removal of Aniline from Wastewater via MEUF Using Mixed Surfactants
Bibliographic record
Abstract
In this paper, the anionic surfactant sodium dodecyl sulfate (SDS) and the bio-surfactant rhamnolipid are mixed to achieve micellar-enhanced ultrafiltration, and the associated aniline removal efficiency is investigated. The impacts of five factors, including the mixing ratio of the SDS concentration to the rhamnolipid concentration, the pH, the salinity, the operating pressure, and the aniline concentration in the feed solution, on the aniline rejection and the membrane permeation flux are explored. The aniline rejection mechanism of the rhamnolipid mixture surfactant is analyzed. This study shows that the effect of these factors on the aniline rejection is in the order of pH > mixing ratio > operating pressure > aniline concentration > salinity; the factors that affect the membrane permeation flux are in the order of mixing ratio > operating pressure > salinity > pH > aniline concentration. Under the optimal test conditions (i.e., SDS:rhamnolipid = 8:2, pH = 5, salinity = 250 mmol/L, operating pressure = 3.5 bar, aniline concentration = 1.5 mM), the verification test results showed an aniline rejection of 78.36%.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".