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 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) | 0.000 | 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 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".