Modular Cross-Linked Chitosan Beads with Calcium Doping for Enhanced Adsorptive Uptake of Organophosphate Anions
Bibliographic record
Abstract
Chitosan beads were cross-linked at variable composition with glutaraldehyde (GA) and epichlorohydrin (EP), respectively. The beads were post-treated by impregnation with a CaCl 2 solution and characterized to evaluate the structure and physicochemical effect of calcium doping. The bead adsorption properties were studied at pH 8.5 with p -nitrophenyl phosphate (PNPP), where beads cross-linked with GA showed higher uptake relative to beads cross-linked with EP. Calcium doping of GA beads showed a 4-fold greater uptake (0.97 mmol g –1 ) over non-cross-linked (NCL) beads (0.23 mmol g –1 ). By comparison, EP-based beads with calcium doping showed a 2-fold enhancement for the uptake of PNPP (0.90 mmol g –1 ) over NCL beads. This work illustrates the utility of cross-linking and calcium doping as modular strategies for tuning the adsorption behavior of chitosan-based beads. Calcium-doped beads cross-linked with glutaraldehyde showed favorable adsorption–desorption properties where the uptake capacity of PNPP remained relatively constant (19.6–17.5%) over several regeneration cycles. The results of this work contribute significantly to the development of advanced materials for the controlled uptake and treatment of waterborne phosphate species.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".