Carbon Dioxide Adsorption on Amine-Impregnated Mesoporous SBA-15 Sorbents: Experimental and Kinetics Study
Bibliographic record
Abstract
In this work, an experimental and theoretical investigation was conducted on the adsorptive removal of CO 2 onto tetraethylenepentamine (TEPA) functionalized mesoporous SBA-15. The functionalization of SBA-15 silica with TEPA was achieved using a conventional wet impregnation technique. The structural properties of the mesoporous silica sorbents were characterized by nitrogen adsorption/desorption, SAXS, SEM, TEM, and FTIR techniques. The adsorption of CO 2 on the amine-impregnated sorbent was measured by thermogravimetric method over a CO 2 partial pressure range of 10–100 kPa and a temperature range of 30–100 °C under atmospheric pressure. The effects on CO 2 adsorption capacity of temperature, partial pressure of CO 2, amine loading, and moisture were evaluated. All the impregnated SBA-15 sorbents showed reversible CO 2 adsorption behaviors with fast adsorption kinetics. The CO 2 adsorption capacity measured at different temperatures suggests that the optimal adsorption temperature is 75 °C. The CO 2 uptake of the amine-impregnated sorbent increased significantly in the presence of moisture. SBA-15 containing 60 wt % TEPA showed the highest CO 2 adsorption capacity of 5.22 mmol/g in pure and humid CO 2 at 75 °C. Temperature swing adsorption/desorption cycles were also explored using simulated flue gas in both dry and humid conditions, and it was found that CO 2 uptake after ten cycles was within 90% of CO 2 uptake of the first cycle. Different adsorption kinetic models have also been investigated to analyze the experimental data of CO 2 uptake. The model was validated with the experimental results of isothermal adsorption measurements of CO 2 on SBA-15/TEPA. It has been found that Fractional Order kinetic model ( Chem. Eng. J. 2011, 173, 72) is very good over the entire adsorption region of the study with a maximum average absolute deviation between experimental CO 2 uptake and that calculated from the model of about 2.42%.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".