Discolouration of contaminated water with textile dye through a combined coagulation/flocculation and membrane separation process with different natural coagulants extracted from <scp> <i>Moringa oleifera</i> </scp> <i>Lam</i> . seeds
Bibliographic record
Abstract
Abstract The removal of reactive black 5 (RB5) dye present in aqueous solution was studied by applying individual coagulation/flocculation (CF) and microfiltration (MF) processes as well as the combination of both processes (CF‐MF). In the CF process, three natural coagulants produced from Moringa oleifera Lam . seeds (MOS) were used: saline extract (ES) and the purified protein fractions of albumin (ALB) and globulin (GLO). In the MF process, a commercial polyethersulfone (PES) membrane was used. It was noticed that without the combination of both processes, the obtained RB5 removal was only 20.38% for ALB, 52.38% for GLO, and 12.50% for ES. However, when the CF process was combined with a microfiltration process, it was possible to achieve a higher textile dye removal, due to the formation of aggregates in the CF process larger than the pores of the microfiltration membrane. Especially with the ALB coagulant, the combined CF‐MF process achieved a removal greater than 95%. The results demonstrate the potential use of the purified proteins fractions of Moringa oleifera Lam . coagulant in combination with microfiltration process for water treatment.
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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) | 0.000 | 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".