Low‐cost biosorbent based on <i>Moringa oleifera</i> residues for herbicide atrazine removal in a fixed‐bed column
Bibliographic record
Abstract
Abstract A low‐cost biosorbent based on Moringa oleifera Lam (MO) seed husks was used in order to remove atrazine (ATZ) from aqueous solutions in a continuous system. The influence of some parameters, such as bed depth, flow rate, pH, and inlet concentration, on ATZ biosorption was investigated. The breakthrough and saturation time increased with the increase of bed depth. However, the opposite behaviour was observed for the flow rate. The sorption capacity, breakthrough, and saturation time were similar at all values of pH tested. Increasing the inlet concentration, the MO sorption capacity also increased, while the breakthrough and saturation time decreased. The Adams‐Bohart, Thomas, dose‐response, and Yoon‐Nelson models were applied to predict the breakthrough curves. All models showed good agreement with the experimental data, presenting good values of the correlation coefficient (R2). The results showed that MO seed husks can be effectively used as a biosorbent to remove the atrazine in aqueous solutions, achieving five cycles of biosorption‐desorption without loss of biosorption capacity, which demonstrates the potential of MO as a biosorbent for the removal of ATZ being a low‐cost and efficient alternative to the use of conventional materials.
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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) | 0.001 | 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".