Stability of amine-functionalized CO<sub>2</sub> adsorbents: a multifaceted puzzle
Bibliographic record
Abstract
This review focuses on important stability issues facing amine-functionalized CO2 adsorbents, including amine-grafted and amine-impregnated silicas, zeolites, metal-organic frameworks and carbons. During the past couple of decades, major advances were achieved in understanding and improving the performance of such materials, particularly in terms of CO2 adsorptive properties such as adsorption capacity, selectivity and kinetics. Nonetheless, to pave the way toward commercialization of adsorption-based CO2 capture technologies, in addition to other attributes, adsorbent materials should be stable over many thousands of adsorption-desorption cycles. Adsorbent stability, which is of utmost importance as it determines adsorbent lifetime and operational costs of CO2 capture, is a multifaceted issue involving thermal, hydrothermal, and chemical stability. Here we discuss the impact of the adsorbent physical and chemical properties, the feed gas composition and characteristics, and the adsorption-desorption operational parameters on the long-term stability of amine-functionalized CO2 adsorbents. We also review important insights associated with the underlying deactivation pathways of the adsorbents upon exposure to high temperature, oxygen, dry CO2, sulfur-containing compounds, nitrogen oxides, oxygen and steam. Finally, specific recommendations are provided to address outstanding stability issues.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".