Modeling and mechanistic approach for acid red 88 dye removal by hydrothermally synthesized magnetic chitosan-grafted with side chain salicylaldehyde
Bibliographic record
Abstract
Abstract This research used the hydrothermal process to cross-link biopolymer magnetic chitosan (CS/Fe3O4) with an aromatic aldehyde (salicylaldehyde, SA) for the adsorption of acidic azo dye (AR88) from an aqueous environment. Analyses of VSM, pHpzc, CHNS, XRD, SEM-EDX, FTIR, and BET were used to determine the properties of CS-SL/Fe3O4 material. Using the Box-Behnken design (BBD), the effects of A: CS-SL/Fe3O4 dose range from 0.02–0.1 g, B: [AR88] concentration (10–50 mg/L), C: pH (4–10), and D: duration (10–90 min) on the adsorption performance of CS-SL/Fe3O4 toward AR88 dye were systematically investigated. In this research, the Freundlich isotherm and pseudo-second-order kinetic models were applicable to describe the adsorption rate of AR888 molecules. The maximum adsorption capacity (qmax) of the hydrothermally cross-linked CS-SL/Fe3O4 for AR88 dye was 137.3 mg/g. Multiple mechanisms, including electrostatic attraction, π-π stacking, n-π interaction, and H-bonding, are responsible for AR88 adsorption by CS-SL/Fe3O4. This study demonstrates that hydrothermal preparation of cross-linked CS-SL/Fe3O4 offers an effective and promising adsorbent for removing acidic dyes from polluted water.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".