Microwave-assisted preparation of mesoporous-activated carbon from coconut (<i>Cocos nucifera</i>) leaf by H<sub>3</sub>PO<sub>4</sub>activation for methylene blue adsorption
Bibliographic record
Abstract
Mesoporous-activated carbon was prepared from fallen coconut (Cocos nucifera) leaf, an agricultural waste through a microwave-induced H3PO4 activation process. The characterization of the coconut leaf–activated carbon (CAC) was evaluated through the iodine number, ash content, bulk density, and moisture content. Fourier transform infrared spectroscopy, scanning electron microscope, Brunauer–Emmett–Teller (BET) surface area, X-ray diffraction, and pHPZC. CAC has a mesopore content of 84% with an average pore size of 36.5 Å and a large BET surface area of 632 m2/g. The uptake properties of the CAC with methylene blue was evaluated at different CAC dosage levels (0.2–10 g/L), initial pH (3–10), methylene blue concentration (50–350 mg/L), and time (0–360 min) using batch mode operation. The kinetic profiles were described by the pseudo-second-order kinetics. The equilibrium data were well fitted to the Langmuir model with a maximum monolayer adsorption capacity of 250 mg/g at 30°C. Thermodynamic functions indicate a spontaneous and exothermic nature of the adsorption process. This study indicates that coconut leaves are a promising renewable precursor that can be utilized to develop an efficient mesoporous-activated carbon.
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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".