Optimization and mechanistic approach for removal of crystal violet and methylene blue dyes <i>via</i> activated carbon from pyrolyzed-ZnCl <sub>2</sub> bamboo waste
Bibliographic record
Abstract
In this study, bamboo waste (BW) was subjected to pyrolysis-assisted ZnCl2 activation to produce mesoporous activated carbon (BW-AC), which was then evaluated for its ability to remove cationic dyes, specifically methylene blue (MB) and crystal violet (CV), from aqueous environments. The properties of BW-AC were characterized using various techniques, including potentiometric-based point of zero charge (pHpzc), scanning electron microscopy with energy dispersive X-rays (SEM-EDX), X-ray diffraction (XRD), gas adsorption with Brunauer–Emmett–Teller (BET) analysis, infrared (IR) spectroscopy. To optimize the adsorption characteristics (BW-AC dosage, pH, and contact time) of PBW, a Box-Behnken design (BBD) was employed. The BW-AC dose of 0.05 g, solution pH of 10, and time of 8 min are identified as optimal operational conditions for achieving maximum CV (89.8%) and MB (96.3%) adsorption according to the BBD model. The dye removal kinetics for CV and MB are described by the pseudo-second-order model. The dye adsorption isotherms revealed that adsorption of CV and MB onto BW-AC follow the Freundlich model. The maximum dye adsorption capacities (qmax) of BW-AC for CV (530 mg/g) and MB (520 mg/g) are favorable, along with the thermodynamics of the adsorption process, which is characterized as endothermic and spontaneous. The adsorption mechanism of CV and MB dyes by BW-AC was attributed to multiple contributions: hydrogen bonding, electrostatic forces, π-π attraction, and pore filling. The findings of this study highlight the potential of BW-AC as an effective adsorbent in wastewater treatment applications, contributing to the overall goal of mitigating the environmental impact of cationic dyes and ensuring the quality of water resources.
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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) | 0.000 | 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".