CO2 capture in activated pyrolytic coke/metal oxide nanoparticle composites
Bibliographic record
Abstract
Carbon dioxide (CO 2 ) capture from flue gas is of utmost importance in mitigating greenhouse gas emissions. This study focuses on enhancing the CO 2 adsorption capacity of activated carbon (AC) composites through the incorporation of metal oxides . AC, AC/Fe 3 O 4 , and AC/MgO composites were synthesized and characterized using various analytical techniques including Raman and FTIR spectroscopy , TGA , SEM, EDX , XRD , AFM, BET, and VSM analysis. The CO2 adsorption isotherms of the AC/MgO and AC/Fe 3 O 4 composites were measured at different temperatures and pressures using quartz crystal microbalance (QCM). The Langmuir model was successfully employed to fit the experimental data, with AC/MgO and AC/Fe 3 O 4 exhibiting adsorption capacities of 38.567 mmol g − 1 and 71.963 mmol g − 1 , respectively, at 298.15 K and 5 bar. These results demonstrate the significant enhancement of CO 2 adsorption capacity achieved by incorporating metal oxides into the AC composite structure. Furthermore, the regeneration efficiency of the adsorbents was evaluated through multiple adsorption/desorption cycles, revealing that AC, AC/MgO, and AC/Fe 3 O 4 maintained adsorption capacities of 94.1%, 97.9%, and 96.5%, respectively, after six consecutive cycles. This confirms their stability and reusability under practical conditions. These findings contribute to our understanding of CO 2 capture using activated pyrolytic coke and metal oxide nanoparticle composites, highlighting the promising potential of AC/MgO composites as effective adsorbents for CO 2 capture applications. Further investigation and optimization of composite structures and synthesis methods are warranted to enhance their overall performance and applicability
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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".