Valorisation of <i>Ceratonia siliqua</i> leaves as an efficient green biosorbent for dye removal
Bibliographic record
Abstract
Water pollution by synthetic dyes such as methylene blue (MB) poses serious risks to aquatic ecosystems and human health, making the development of cost-effective and eco-friendly adsorbents essential. In this study, Ceratonia siliqua leaves (CSL), an abundant agricultural by-product, were valorised as a sustainable biosorbent for MB removal from aqueous solutions. CSL material was characterised using Fourier transform infrared spectroscopy, scanning electron microscopy, X-ray diffraction, and thermogravimetric/differential thermal analysis to elucidate its physicochemical and structural features. Batch adsorption experiments were conducted under varying pH levels (2–12), adsorbent doses (2–100 mg), contact times (2–60 min), dye concentrations (20–1000 mg/l), and temperatures (10°C–50°C) to determine the optimal conditions. Kinetic studies revealed that the pseudo-second-order model provided the best fit (R2 = 0.9992), while equilibrium data were described by both Langmuir (qmax = 1891.81 mg/g, R2 = 0.98) and Freundlich (R2 = 0.99) models. However, the superior correlation of Freundlich indicates that adsorption mainly occurs on a heterogeneous surface with sites of varying affinities, consistent with the lignocellulosic structure of CSL. Thermodynamic analysis confirmed that the adsorption was spontaneous and exothermic. Overall, CSL demonstrated excellent adsorption performance, highlighting its potential as a cheap, renewable, and sustainable adsorbent for large-scale wastewater treatment applications.
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How this classification was reachedexpand
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 | 0.001 | 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 teacher head, 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".