Adsorptive removal of Carbamazepine from synthetic wastewater using Moringa oleifera seed coat
Bibliographic record
Abstract
Biosorption has demonstrated effectiveness in eliminating contaminants of emerging concern (CECs) that lack inclusion in current water quality standards. These pollutants impede progress towards UN Sustainable Development Goals 6 and 14, pertaining to clean water accessibility and marine life preservation. This research investigated the adsorption potential of the persistent pharmaceutical pollutant, carbamazepine, using Moringa oleifera (MO) seed coat. This research investigated the adsorption potential of the persistent pharmaceutical pollutant, carbamazepine, using MO seed coat biochar. Acid hydrolysis and subsequent carbonization produced a biochar with superior properties for adsorption as confirmed by FTIR, SEM, EDX, CHNOS, and XRD analyses. The influence of agitation speed (200 rpm, 300 rpm, and 400 rpm), adsorption duration (0 to 180 min), and adsorption temperature (30 °C, 40 °C, and 50 °C) on CBZ removal in synthetic wastewater were studied. The quadratic regression model obtained from Box Behnken experimental design (BBD) with Response surface methodology (RSM) showed a strong predictive ability with R² = 0.9754 and adjusted-R² = 0.9015. Agitation speed significantly influenced the adsorption capacity as evidenced by a p-value of 0.02848. The optimum conditions for CBZ adsorption were 400 rpm, 30 °C, and 2.15 h at which a maximum adsorption capacity of 51.87 mg/g was anticipated. The adsorption behaviour was best described by pseudo-second order kinetics and the Temkin isotherm model. The study confirmed that MO seed coat biochar as a promising green adsorbent for pharmaceutical contaminant removal in wastewater 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 | 0.001 |
| 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".