Freundlich, Langmuir, Temkin, DR and Harkins-Jura Isotherm Studies on the Adsorption of CO2 on Various Porous Adsorbents
Bibliographic record
Abstract
Abstract The objective of the study was to prepare low-cost activated carbon from cornelian cherry stones (Cornus mas L.) and compare its adsorption capacities for CO2 with that of commercial multiwalled carbon nanotubes, activated carbon, MCM-41 and four zeolites (silver exchanged, 13X, faujasite type and linde type A). BET, t-plot and DFT methods were used for the characterization of all adsorbent samples based on N2 adsorption-desorption data obtained at 77 K. The produced activated carbon samples (KAC and NAC) were also characterized by scanning electron microscopy (SEM). The adsorption data were evaluated by several isotherm models such as Freundlich, Langmuir, Temkin, DR and Harkins-Jura isotherm models. Langmuir adsorption isotherms constants related to adsorption capacity, Wm were found as 11.496, 6.175, 4.189, 3.986 and 3.50 mmol/g for NAC, KAC, faujasite type zeolit, MCM-41 and AC, respectively. The CO2 adsorption capacities of the silver exchanged, 13X, faujasite type and linde type A zeolites were 10.814, 10.241, 14.608 and 13.343 wt%, respectively. Microporous zeolites showed higher CO2 adsorption capacities than the mesoporous MCM-41 and MWCNT. The CO2 adsorption capacity of commercial activated carbon (AC) was found to be 9.428 wt%, while the CO2 adsorption capacities of activated carbons obtained using NaOH and KOH were found to be 13.685 and 15.057 wt%, respectively. The highest CO2 adsorption capacity of 15.057 wt% was achieved with KAC. It could be confirmed that NaOH or KOH activation plays an important role to determine the porous structure and amount of CO2 adsorbed. Results showed that activated carbon produced from cornelian cherry stones is suitable for the adsorption of CO2 and could be used as a low cost effective adsorbent in the capture of CO2.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".