Recent developments in chitosan-based adsorbents for tetracycline removal: A mini-review
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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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".