Steam Regeneration of Polyethylenimine-Impregnated Silica Sorbent for Postcombustion CO<sub>2</sub> Capture: A Multicyclic Study
Bibliographic record
Abstract
Steam regeneration of polyethylenimine (PEI)-impregnated commercial grade silica was investigated in a packed bed reactor. Adsorption was performed at 75 °C under 10% CO 2 /N 2, and desorption was carried out under steam at 110 °C for 20 consecutive cycles. CO 2 adsorption capacity was found to decrease by 9 mol % over the period of 20 cycles. No evident signs of sorbent degradation due to PEI leaching or changes in surface morphology and amine functionalities were observed upon characterization of the sorbent after the cyclic study. Most of the loss in adsorption capacity was associated with thermal degradation of the sorbent during drying under N 2 after steam stripping at 110 °C. The desorption kinetics during steam stripping was found to be much faster than during N 2 stripping. Over 80% of the total CO 2 was released within the first 3 min of steam injection into the reactor. A separate packed bed study was conducted to investigate the influence of moisture content (5.3–14.7 vol %) in flue gas on the CO 2 adsorption capacity of PEI-impregnated silica. The presence of moisture had a positive impact on CO 2 uptake of the sorbent; a 4–9 mol % increase in CO 2 uptake was observed in comparison to the adsorption under dry conditions. However, the presence of moisture increased the heat of regeneration of the sorbent significantly. It was calculated that the energy demand increased approximately 2-fold on introduction of 14.7% moisture compared to that of dry flue gas.
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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".