Production of activated carbon from food wastes (chicken bones and rice waste) by microwave assisted ZnCl <sub>2</sub> activation: an optimized process for crystal violet dye removal
Bibliographic record
Abstract
A major worldwide challenge that presents significant economic, environmental, and social concerns is the rising generation of food waste. The current work used chicken bones (CB) and rice (R) food waste as alternate precursors for the production of activated carbon (CBRAC) by microwave radiation-assisted ZnCl2 activation. The adsorption characteristics of CBRAC were investigated in depth by removing an organic dye (crystal violet, CV) from an aquatic environment. To establish ideal conditions from the significant adsorption factors (A: CBRAC dosage (0.02–0.12 g/100 mL); B: pH (4–10); and C: duration (30–420), a numerical desirability function of Box-Behnken design (BBD) was utilized. The highest CV decolorization by CBRAC was reported to be 90.06% when the following conditions were met: dose = 0.118 g/100 mL, pH = 9.0, and time = 408 min. Adsorption kinetics revealed that the pseudo-first order (PFO) model best matches the data, whereas the Langmuir model was characterized by equilibrium adsorption, where the adsorption capacity of CBRAC for CV dye was calculated to be 57.9 mg/g. CV adsorption is accomplished by several processes, including electrostatic forces, pore diffusion, π-π stacking, and H-bonding. This study demonstrates the use of CB and R as biomass precursors for the efficient creation of CBRAC and their use in wastewater treatment, resulting in a greener environment.
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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 | 0.001 |
| 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".