Recent developments in chitosan-based adsorbents for tetracycline removal: A mini-review
Why this work is in the frame
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Bibliographic record
Abstract
Tetracyclines (TCs) are widely used antibiotics that have raised concerns due to their presence in the environment, posing risks to human health and ecosystems. This mini-review explores recent advancements in utilizing chitosan-based adsorbents to remove TCs from wastewater efficiently. Our review reveals that adsorption performance is highly influenced by temperature and pH, with most studies reporting effective TC removal between 25-45 °C and pH values of 2-12. The Langmuir and Freundlich isotherm models are both applicable, depending on the specific adsorbent, indicating both monolayer and heterogeneous adsorption behavior, with maximum adsorption capacities ranging from 19.32 mg/g to 940 mg/g, with the highest capacity shown for BCM Char/CS /PEI. Kinetic studies predominantly followed the pseudo-second-order model, suggesting chemisorption as a rate-limiting step, while some followed a pseudo-first-order model. High removal rates (≈ 90-99%) were reported for materials like ZIF-8-chitosan, BCM Char/CS/PEI, and CMC-modified Na-Mt. This review highlights the significant potential of chitosan-based adsorbents. At the same time, further research is needed to optimize adsorption conditions, understand the mechanisms involved, and address the diverse sources of TC pollution. Given the global impact of TCs, a comprehensive approach encompassing enhanced monitoring, stricter regulations, the development of advanced treatment technologies like chitosan-based adsorbents, and public awareness campaigns is imperative to mitigate their environmental risks effectively.
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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