Modular Chitosan-Based Adsorbents for Tunable Uptake of Sulfate from Water
Bibliographic record
Abstract
The context of this study responds to the need for sorbent technology development to address the controlled removal of inorganic sulfate (SO42−) from saline water and the promising potential of chitosan as a carrier system for organosulfates in pharmaceutical and nutraceutical applications. This study aims to address the controlled removal of sulfate using chitosan as a sustainable biopolymer platform, where a modular synthetic approach was used for chitosan bead preparation that displays tunable sulfate uptake. The beads were prepared via phase-inversion synthesis, followed by cross-linking with glutaraldehyde, and impregnation of Ca2+ ions. The sulfate adsorption properties of the beads were studied at pH 5 and variable sulfate levels (50–1000 ppm), where beads with low cross-linking showed moderate sulfate uptake (35 mg/g), while cross-linked beads imbibed with Ca2+ had greater sulfate adsorption (140 mg/g). Bead stability, adsorption properties, and the point-of-zero charge (PZC) from 6.5 to 6.8 were found to depend on the cross-linking ratio and the presence of Ca2+. The beads were regenerated over multiple adsorption-desorption cycles to demonstrate the favorable uptake properties and bead stability. This study contributes to the development of chitosan-based adsorbent technology via a modular materials design strategy for the controlled removal of sulfate. The results of this study are relevant to diverse pharmaceutical and nutraceutical applications that range from the controlled removal of dextran sulfate from water to the controlled release of chondroitin sulfate.
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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 | 0.000 |
| 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 | 0.000 | 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".