Process Optimization and Adsorptive Mechanism for Reactive Blue 19 Dye by Magnetic Crosslinked Chitosan/MgO/Fe3O4 Biocomposite
Bibliographic record
Abstract
A new biocomposite cross-linked glutaraldehyde-chitosan/MgO/Fe3O4 (CTS-GL/MgO/Fe3O4) adsorbent with magneto-responsiveness was prepared and applied for the removal of reactive blue 19 (RB-19), a synthetic textile dye. The prepared CTS-GL/MgO/Fe3O4 was structurally characterized using spectroscopic (XRD, FTIR, SEM–EDX), and its physicochemical properties were evaluated using potentiometry and pHpzc analyses. The influence of various adsorption parameters (A: CTS-GL/MgO/Fe3O4 dosage; B: initial solution pH; C: process temperature; and D: contact time) on the removal efficiency of RB-19 was statistically optimized using Box-Behnken design (BBD). The analysis of variance (ANOVA) indicates the presence of five significant statistical interactions between the adsorption parameters, as follows: AB, AC, AD, BC, and BD. The equilibrium dye uptake by the Freundlich isotherm model indicates heterogeneous adsorption, while the kinetics of adsorption was well-described by the pseudo-second-order model. The maximum adsorption capacity of CTS-GL/MgO/Fe3O4 towards RB-19 was 193.2 mg/g at 45 °C. This work highlights the development of a recoverable magnetic biocomposite adsorbent with favourable adsorption capacity towards a model textile dye with good separation ability by using an external magnetic field. Moreover, separation of the magnetic adsorbents from the treated solution is easy and convenient apply to continuous flow systems, which is highly preferred for industrial 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 | 0.001 |
| 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".