The use of <i>Moringa oleifera</i> seeds and their fractionated proteins for <i>Microcystis aeruginosa</i> and microcystin‐LR removal from water
Bibliographic record
Abstract
The aim of this study was to evaluate the coagulation, flocculation, and dissolved air flotation (C/F/DAF) process using four types of coagulants based on Moringa oleifera (MO) seeds for the removal parameters of colour, turbidity, UV 254 nm, dissolved organic carbon (DOC), chlorophyll‐a, and microcystin‐LR. Tests were performed using water contaminated with Microcystis aeruginosa in a concentration of 104 cells mL−1. The coagulants analyzed were fractionated proteins (albumin and globulin), proteins extracted from saline solution, and the integral powder of MO seeds. The results obtained presented the same optimum dosage for globulin and albumin removal (1.5 mg L−1), in which globulin reached removal percentages of 83.87 and 80.88 % for chlorophyll‐a and microcystin‐LR, respectively, and in which albumin reached removal percentages of 79.44 and 48.14 % for chlorophyll‐a and microcystin‐LR, respectively. The optimal dosage of proteins extracted from the saline solution was 8.0 mg · L−1, which achieved removal percentages of 74.80 and 73.52 % for chlorophyll‐a and microcystin‐LR, respectively. For the DOC, removal was only observed after the use of fractionated proteins, which emphasizes the need for methods based on the use of active purified agents of MO seeds involved in flocculation. Therefore, it is concluded that globulin, as a coagulant, is the most suitable for the present study, since it improves the C/F/DAF process efficiency in Microcystis aeruginosa and microcystin‐LR reduction in addition to removing other analyzed parameters.
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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".