Surface-Modified Chitosan: An Adsorption Study of a “Tweezer-Like” Biopolymer with Fluorescein
Bibliographic record
Abstract
Tweezer-like adsorbents with enhanced surface area were synthesized by grafting aniline onto the amine sites of a chitosan biopolymer scaffold. The chemical structure and textural properties of the adsorbents were characterized by thermogravimetric analysis (TGA) and spectral methods, including Fourier transform infrared (FT-IR), nuclear magnetic resonance (1H- and, 13C-NMR) and scanning electron microscopy (SEM). Equilibrium solvent swelling results for the adsorbent materials provided evidence of a more apolar biopolymer surface upon grafting. Equilibrium uptake studies with fluorescein at ambient pH in aqueous media reveal a high monolayer adsorption capacity (Qm) of 61.8 mg·g−1, according to the Langmuir isotherm model. The kinetic adsorption profiles are described by the pseudo-first order kinetic model. 1D NMR and 2D-NOESY NMR spectra were used to confirm the role of π-π interactions between the adsorbent and adsorbate. Surface modification of the adsorbent using monomeric and dimeric cationic surfactants with long hydrocarbon chains altered the hydrophile-lipophile balance (HLB) of the adsorbent surface, which resulted in attenuated uptake of fluorescein by the chitosan molecular tweezers. This research contributes to a first example of the uptake properties for a tweezer-like chitosan adsorbent and the key role of weak cooperative interactions in controlled adsorption of a model anionic dye.
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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 | 0.001 | 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".