Potential effect of chemical and thermal treatment on the Kinetics, equilibrium, and thermodynamic studies for atrazine biosorption by the <i>Moringa oleifera</i> pods
Bibliographic record
Abstract
Abstract The effectiveness of chemical and thermal pretreatments on Moringa oleifera pods for the atrazine removal from water was investigated. The untreated pods (MOPun), the chemically treated pods (MOPC), and the chemically treated pods followed by the thermally treated pods (MOPT) were physico‐chemically and morphologically characterized by elemental, pHZPC, N2 physisorption, FTIR, and SEM analysis. The effect of modified pods was significant in increasing surface porosity, favouring the surface chemistry, and improving the atrazine biosorption capacity. Kinetics data were best explained by pseudo‐second order model for all biosorbents. The equilibrium data for biosorption were analyzed by using Langmuir, Freundlich, Sips, and Polanyi‐Manes (PMM) isotherm models to define the best correlation for atrazine biosorption capacity. Among the four isotherm models, both Sips and PMM were the models best fitted with the equilibrium isotherm for atrazine. The maximum atrazine adsorption capacities were 0.629, 1.580, and 7.47 mg · g−1 for MOPun, MOPC, and MOPT respectively with a maximum removal percentage of 65 %, 82 %, and 99 %. The thermodynamic studies showed that atrazine biosorption is favourable. The biosorption experiments demonstrated that Moringa oleifera pods have great potential for removing atrazine and other organic contaminants from water.
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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".