Food-grade algae modified Schiff base-chitosan benzaldehyde composite for cationic methyl violet 2B dye removal: RSM statistical parametric optimization
Bibliographic record
Abstract
This work aims to apply the use of food-grade algae (FGA) composited with chitosan-benzaldehyde Schiff base biopolymer (CHA-BD) as a new adsorbent (CHA-BA/FGA) for methyl violet 2B (MV 2B) dye removal from aqueous solutions. The effect of three processing variables, including CHA-BA/FGA dosage (0.02-0.1 g/100 mL), pH solution (4-10), and contact duration (10-120 min) on the removal of MV 2B was investigated using the Box-Behnken design (BBD) model. Kinetic and equilibrium dye adsorption profiles reveal that the uptake of MV 2B dye by CHA-BA/FGA is described by the pseudo-second kinetics and the Langmuir models. The thermodynamics of the adsorption process (ΔG°, ΔH°, and ΔS°) reveal spontaneous and favorable adsorption parameters of MV 2B dye onto the CHA-BA/FGA biocomposite at ambient conditions. The CHA-BA/FGA exhibited the maximum ability to absorb MV 2B of 126.51 mg/g (operating conditions: CHA-BA/FGA dose = 0.09 g/100 mL, solution pH = 8.68, and temperature = 25 °C). Various interactions, including H-bonding, electrostatic forces, π-π stacking, and n-π stacking provide an account of the hypothesized mechanism of MV 2B adsorption onto the surface of CHA-BA/FGA. This research reveals that CHA-BA/FGA with its unique biocomposite structure and favorable adsorption properties can be used to remove harmful cationic dyes from wastewater.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".