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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".