Sodium alginate- and chitosan-based hydrogels with different network charges for selective removal of cationic and anionic dyes from water
Bibliographic record
Abstract
ABSTRACT The grafting of chitosan (CH) and sodium alginate (SA) biopolymers with glycidyl methacrylate (GMA) and acrylamide (AAm) monomers, combined with graphene oxide (GO), led to the formation of bio-based hydrogels. These hydrogels, named CH -GO -hydrogel (GO/CH-g-poly (AAm-co-GMA)), CH -GO -hydrogel (CH-g-poly (AAm-co-GMA)), SA -GO -hydrogel (GO/SA-g-poly(AAm-co-GMA)), and SA -hydrogel (SA-g-poly(AAm-co-GMA)), were tested as selective dye adsorbents. While the chitosan-based hydrogels exhibited positive zeta potential values ranging from +27.5 to +0.1 mV, alginate-based samples had negative values between −10.4 to −41.7 mV in pH conditions from 3.0 to 9.0. Adding GO nano-fillers reduced the swelling capacity of both hydrogels, with water absorption (WA) values for SA -GO -hydrogel and SA -hydrogel recorded at 10.1 and 22.2 g/g, respectively. The ability of these materials to adsorb dyes, specifically crystal violet (cationic) and Congo red (anionic), was confirmed. Factors such as adsorbent dosage, initial pH, dye concentration, shaking time, and temperature were analyzed to determine dye adsorption capacity. Interestingly, the pristine hydrogels, free of GO, performed better than their nanocomposite counterparts. Adsorption capacities (qm) for crystal violet and Congo red with SA -hydrogel, SA -GO -hydrogel, CH -hydrogel, and CH -GO -hydrogel was 909.1, 714.3, 454.5, and 400.0 mg/g, respectively.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 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 | 0.001 | 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 teacher head, 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".