Protein fractionation of <i>Moringa oleifera</i> Lam. seeds and functionalization with magnetic particles for the treatment of reactive black 5 solution
Bibliographic record
Abstract
Abstract Industrial effluents are sewage from industrial processes, such as polluted cooling waters and waters used for cleaning. It is extremely important that these contaminated waters undergo treatment to allow for water reuse or safe disposal, without damage to the environment. In the present work, the efficacy of coagulating/flocculating aqueous solutions containing reactive black 5 (RB5) dye was evaluated using protein fractions derived from Moringa oleifera seeds (albumin and globulin) as natural coagulants functionalized with iron oxide magnetic nanoparticles. The application of a magnetic field during sedimentation allowed for faster settlement of the dye particles and their separation. Variations in parameters such as the protein concentration, nanoparticle concentration, solution pH, dye concentration, and the presence or absence of an alkalizing agent were assessed. Moreover, the sedimentation time was analyzed with and without the magnetic field and with the reuse of iron oxide nanoparticles. The functionalized magnetic coagulants were able to remove more than 90 % of RB5 dye within 5 min of sedimentation.
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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".