Biosorption of Silver using Metal-Imprinted Thiourea-Modified Glutaraldehyde-Crosslinked O-Carboxymethyl Chitosan Beads
Bibliographic record
Abstract
Chitosan, an abundant biopolymer obtained from deacetylation of chitin, has been proved to be a promising biosorbent for metal uptake. Grafting new functional groups on the chitosan backbone was also reported to be efficient in increasing either the sorption capacity or sorption selectivity for the target metal. In this study, silver-imprinted thiourea-modified glutaraldehyde-crosslinked O-carboxymethyl chitosan beads were prepared to selectively adsorb Ag (I) from a bimetallic aqueous solution. The biosorbent was synthesized by using prepared O-carboxymethyl chitosan beads to first absorb Ag (I) ion, then crosslinking with a polymeric Schiff’s base of thiourea/glutaraldehyde. To obtain the best performance sorbent, the degree of carboxymethylation substitution, and the amount of crosslinking agent used were studied. Results indicated the highest selective uptake capacity of Ag (I) occurred when a molar ratio of amino group of chitosan to carboxymethylation agent to crosslinking agent of 6:15:16 is used in the synthesis.Batch sorption tests were carried out for isotherm and kinetic studies using the sorbent which provides the best selective uptake capacity of Ag (I). All isothermal and kinetic experiments were performed at 25 °C. The maximum uptake of Ag (I) was found to be 137.5 mgg-1 at pH 5.0 with the initial concentration of Ag (I) being 1.75mmolL-1. The sorbent demonstrated extremely good selectivity towards Ag (I) as equal mole of Cu (II) present in the solution was scarcely absorbed over a pH range from 1.0 to 6.0. The experiments also revealed that the biosorption process of Ag (I) fits well with a Langmuir model and Lagergren’s Pseudo-second-order kinetic.
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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) | 0.000 | 0.001 |
| 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 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".