Adsorptive removal of Carbamazepine from synthetic wastewater using Moringa oleifera seed coat
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 it