An Overview of Modified Chitosan Adsorbents for the Removal of Precious Metals Species from Aqueous Media
Bibliographic record
Abstract
This mini-review provides coverage of chitosan-based adsorbents and their modified forms as sustainable solid-phase extraction (SPE) materials for precious metal ions, such as gold species, and their complexes in aqueous media. Modified forms of chitosan-based adsorbents range from surface-functionalized systems to biomaterial composites that contain inorganic or other nanomaterial components. An overview of the SPE conditions such as pH, temperature, contact time, and adsorbent dosage was carried out to outline how these factors affect the efficiency of the sorption process, with an emphasis on gold species. This review provides insight into the structure-property relationships for chitinaceous adsorbents and their metal-ion removal mechanism in aqueous media. Cross-linked chitosan sorbents showed a maximum for Au(III) uptake capacity (600 mg/g), while S-containing cross-linked chitosan display favourable selectivity and uptake capacity with Au(III) species. Compared to industrial adsorbents such as activated carbon, modified chitosan sorbents display favourable uptake of Au(III) species, especially in aqueous media at low pH. In turn, this contribution is intended to catalyze further research directed at the rational design of tailored SPE materials that employ biopolymer scaffolds to yield improved uptake properties of precious metal species in aqueous systems. The controlled removal of gold and precious metal species from aqueous media is highly relevant to sustainable industrial processes and environmental remediation.
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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 | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".