Adsorption of volatile organic compounds on peanut shell activated carbon
Bibliographic record
Abstract
The aims of this work were to prepare porous activated carbon from peanut shell by chemical activation using ZnCl2 and study its volatile organic compounds adsorption capacities. The adsorption properties of ethyl on the prepared activated carbon were experimentally determined at different temperatures. The surface textural characteristic of the activated carbon was evaluated by N2 adsorption isotherm measurements. The average BET surface area, pore size, and micro‐pore volume of the prepared activated carbon were 1025 m2/g, 0.70 nm, and 0.37 cm3/g, respectively, with narrow pore size distribution. Higher adsorption capacity of toluene on the activated carbon was observed compared to ethyl benzene and p‐xylene, in particular at low vapour concentration ranges. The experimental isotherm data were also analyzed using the Langmuir, Langmuir‐Freundlich, and multisite Langmuir isotherm model. The Langmuir‐Freundlich and multisite Langmuir model provide the best fit for volatile organic compound adsorption isotherms. In addition, the surface and thermal properties of the activated carbon were also investigated using FT‐IR, Zeta‐potential, and TGA. Overall, the peanut shell activated carbon prepared in this study exhibited comparable surface properties and adsorption performance with the available commercial activated carbons and activated carbons prepared from other various sources reported in other literature.
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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".