Polyethyleneimine Supported Mesoporous Silica for CO2 Capture: Adsorption Kinetics and Degradation Problems
Bibliographic record
Abstract
Polyethyleneimine supported mesoporous silica (PEI-MPS) has widely been investigated as solid sorbents for CO2 adsorption in laboratory. However, the adsorption kinetics of CO2 on this sorbent is still a disputed problem and the sorbent degradation could be a cause that hinders the sorbent from industrial application. In this study, the PEI-MPS sorbent was prepared by impregnating PEI onto mesoporous silica and then the adsorption kinetics was investigated using the pseudofirst order model, pseudo-second order model and intraparticle diffusion model. Results indicated that the CO2 adsorption kinetics on the sorbent occurred in a 2 stage process. In early stage, it is well fitted by the pseudo-first order model, but in later stage, it is well fitted by the intraparticle diffusion model. The pseudo second-order model proved to be the most suitable one to describe the adsorption of CO2 on the sorbent for the whole adsorption period with less than 5% absolute deviation. The sorbent with a CO2 adsorption capacity of 144.9 mg/g was calculated based on the fitting of the pseudo- second order model. The major degradation issues and their potential effects on the CO2 capture process were also discussed. The degradation significantly impacts on the performance and efficiency of CO2 capture process. The chemical degradation, however, can be considerably minimized by humidification.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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 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".