Hybrid Chitosan Biosorbents: Tunable Adsorption at Surface and Micropore Domains
Bibliographic record
Abstract
Herein, we report a study that provides new insight on the knowledge gaps that relate to the role of biopolymer structure and adsorption properties for chitosan adsorbents that are cross-linked with glutaraldehyde. The systematic modification of chitosan cross-linked with glutaraldehyde (CG) and its quaternized forms (QCG) was studied in relation to the reaction conditions: mole ratios of reactants and pH conditions. Complementary adsorbent characterization employed 13C NMR/FTIR spectroscopy, TGA and DSC, point-zero-charge (PZC), solvent swelling, and sorption studies using selected dye probes. The spectral and thermal techniques provide complementary evidence that affirm the key role of cross-linker content and quaternization on variation of the physicochemical properties of chitosan. The PZC results reveal a neutral surface charge for the modified materials between pH 6.0 to 6.3 ± 0.3, as compared with pH 8.7 ± 0.4 for pristine chitosan. Solvent swelling in water decreased with greater cross-linking, while the QCG materials had greater swelling over CG materials due to enhanced hydration. The adsorption results reveal variable dye uptake properties according to the cross-linker content. Similarly, surface versus micropore adsorption was demonstrated, according to the nature and ionization state of the dye for the modified adsorbents, where the CG and QCG materials had tunable sorption properties that exceeded that of unmodified chitosan. A key step in tuning the structure and surface chemical properties of cross-linked chitosan involves pH control during synthesis. The facile tunability of the physicochemical properties of the modified biopolymers reported herein means that they possess features of biomimetics that are relevant to advanced drug delivery, antimicrobial materials for wound healing, biosensors, and biosorbents for biomedical applications.
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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) | 0.000 | 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) | 0.001 | 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".