Removal of Nutrients from Water Using Biosurfactant Micellar-Enhanced Ultrafiltration
Bibliographic record
Abstract
The removal of NH4+, NO3−, and NH3− from wastewater can be difficult and expensive. Through physical, chemical, and biological processes, metals and nutrients can be extracted from wastewater. Very few scientific investigations have employed surfactants with high biodegradability, low toxicity, and suitability for ion removal from wastewater at different pH and salinity levels. This research employed a highly biodegradable biosurfactant generated from yeast (sophorolipid) through micellar-enhanced ultrafiltration (MEUF). MEUF improves nutrient removal efficiency and reduces costs by using less pressure than reverse osmosis (RO) and nanofiltration (NF). The biosurfactant can be recovered after the removal of nutrient- and ion-containing micelles from the filtration membrane. During the experiment, numerous variables, including temperature, pH, biosurfactant concentration, pollutant ions, etc., were evaluated. The highest amount of PO43− was eliminated at a pH of 6.0, which was reported at 94.9%. Maximum NO3− removal occurred at 45.0 °C (96.9%), while maximum NH4+ removal occurred at 25.0 mg/L (94.5%). Increasing TMP to 200 kPa produced the maximum membrane flow of 226 L/h/m2. The concentrations of the contaminating ion and sophorolipid were insignificant in the permeate, demonstrating the high potential of this approach.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 0.001 |
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 teacher head, 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".