Preparation and application of magnetic chitosan in environmental remediation and other fields: A review
Bibliographic record
Abstract
Abstract Magnetic chitosan has received considerable attention over the decades due to its low cost, biodegradability, green sources, magnetic intensity. In this review, we reviewed the preparation methods of magnetic chitosan using co‐precipitation, cross‐linking and electrochemical. Therein cross‐linking methodologies involved in the reaction of amino groups are facile to introduce additional reaction groups and improve anti‐swelling of chitosan layers, mostly in an acidic environment. Besides, we focused on the applications of magnetic chitosan in various fields such as wastewater treatment, for example, removal of heavy metal ions, organic/inorganic dyes, fluorides, and pesticides. Moreover, magnetic chitosan also reveals great potential application in the field of medical, pharmaceutical, food and electronic screening. Above all, magnetic chitosan is economically and operationally beneficial as it can be easily separated and controlled with an external magnetic field and can be modified to maximize its functions.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| 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) | 0.003 | 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".