Novel Synthesis and Characterization of Nano-Activated Carbon Derived from Agricultural Orange Peel Waste
Bibliographic record
Abstract
Orange peel, often discarded as waste, is a common byproduct of agricultural and food processing industries.Converting this waste into valuable materials reduces environmental pollution and promotes circular economy practices.The increasing recognition of orange peels may be attributed to their ease of obtaining from agricultural products.Here, we synthesize activated carbon from orange peels by carbonizing orange peel powder with N2 gas and activating it with CO2 gas.Characterization of synthesis activated carbon from orange peel OP-AC done by using FTIR, TEM, EDS, Raman spectroscopy, SEM, and BET.The specific surface area (SBET) of the activated carbon (AC) and orange peel (OP) are 7.9168 m 2 /g and 3.879 m 2 /g, respectively.Also, the total pore volume for AC and OP are 0.027785 cm 3 /g and 0.01789 cm 3 /g, respectively.Orange peel-activated carbon (OP-AC) exhibits a turbostratic structure and lamellar morphology, with the presence of inorganic impurities, and is primarily composed of micropores.In summary, the synthesis of nanostructured activated carbon from orange peel waste is a promising innovation with potential applications in environmental remediation, energy storage, wastewater and water treatment and industrial processes.It contributes to sustainability by turning agricultural waste into a high-value material, supporting both environmental and economic benefits.
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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".