Sorption isotherms and kinetics of Crystal Violet dye uptake from aqueous solution by using polyaniline nanocomposite as adsorbent
Bibliographic record
Abstract
The ZnFe₂O₄-PANI nanocomposite has been developed as an adsorbent for the removal of Crystal Violet (CV) dye from aqueous solutions in the present study. The structural and functional characteristics of this material were systematically evaluated through various characterization techniques such as BET, FTIR and XRD. Fourier-transform infrared spectroscopy (FTIR) revealed significant vibrational bands associated with key functional groups that facilitate dye adsorption and confirmed the successful synthesis of the zinc-ferrite polyaniline nanocomposite, as evidenced by shifts in the peaks corresponding to ZnFe₂O₄ and PANi. The adsorption efficiency demonstrated a pH-dependent behaviour, increasing from 42 % at pH 3.0 to 88 % at pH 9.0, while a decline was observed above pH 9, attributed to electrostatic repulsion effects. The adsorption kinetics were effectively described by the pseudo-second-order model, with a maximum removal efficiency of 89 % achieved after a contact period of 60 mins. The analysis of the adsorption isotherm corroborated the applicability of the Langmuir model, indicative of a monolayer adsorption mechanism. Under optimal conditions (pH 9, 0.5 g of adsorbent in 50 ml of solution, and a 60-minute contact time), the ZnFe₂O₄-PANi nanocomposite exhibited endothermic and spontaneous adsorption characteristics. These findings suggest that this material possesses a high capacity and strong affinity for CV, thereby positioning it as a viable adsorbent for dye removal.
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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 | 0.000 |
| 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 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".