Integrated biomass algae @ cross-linked pyromellitic dianhydride chitosan biocomposite for methyl violet 2B adsorption: modelling and experiment design optimization
Bibliographic record
Abstract
A new adsorbent was developed by integrating algae biomass (AG) into a chitosan (CN) matrix, followed by structural enhancement via crosslinking with pyromellitic dianhydride (PMDA) through a hydrothermal synthesis approach. This process resulted in the formation of a robust AG@CN-PMDA composite with improved physicochemical characteristics suitable for advanced adsorption applications. The AG@CN-PMDA composite was evaluated for its efficiency in removal of the cationic dye methyl violet 2B (MV 2B) from aqueous solution. The adsorption process was refined through the Box-Behnken design (RSM-BBD), evaluating three essential parameters: adsorbent dosage (A: 0.02–0.1 g/100 mL), pH (B: 4–10), and time (C: 5–20 min). The ideal conditions for attaining the best removal rate for MV 2B (86%) were determined based on the desirability function optimisation results, corresponding to 0.09 g/100 mL of AG@CN-PMDA, at a pH of 6.9 and time of 9.45 min. The adsorption isothermal analysis revealed a close fit between the experimental data of MV 2B adsorption and both the Temkin and Langmuir models, with the Temkin model showing a slightly better correlation. Furthermore, the adsorption kinetics are well-described by the pseudo-second-order model. The maximum adsorption capacity of AG@CN-PMDA was 162.3 mg/g at 25°C. The adsorption of MV 2B onto AG@CN-PMDA was spontaneous, endothermic, and entropy-driven as evidenced by negative ΔG° values. The binding of MV 2B dye onto the AG@CN-PMDA composite was facilitated through mechanisms such as hydrogen bonding, π–π stacking, and electrostatic attraction. These findings demonstrate that AG@CN-PMDA is an effective and sustainable adsorbent for the removal of cationic dyes from industrial effluents.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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 | 0.000 | 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 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".