CO2 uptake – Relative humidity – Pore size relationship: A new tool for the design of amine-containing adsorbents
Bibliographic record
Abstract
• Synthesized periodic mesoporous silicas with 3 to 9 nm pore sizes, with similar morphology and amine loading. • Investigated the effect of pore size and relative humidity (RH) on CO2 uptake. • Impressive increase in CO2 uptake was achieved at vapor pressures corresponding to the water capillary condensation. • Maximum CO 2 uptake vs RH was pore-size dependent, allowing precise tailoring of adsorbents for feed gases with different RHs. Optimization of amine-containing CO 2 adsorbents, including CO 2 uptake, adsorption and desorption kinetics, energy requirements, and material stability, is a recurrent theme since the inception of this field. The objective of this work is (1) to establish a relationship between CO 2 uptake in amine-containing adsorbents as a function of relative humidity (RH) of feed gas and material pore size, and (2) to use this relationship to design adsorbents that maximize CO 2 uptake depending on RH. To this end, periodic mesoporous silicas with similar morphology and different pore sizes, ranging from 3 to 9.2 nm, are synthesized and grafted with comparable amounts of triaminosilane. All materials exhibit S-shaped water adsorption isotherms, with a steep rise as the water vapor reaches the capillary condensation pressure. All humid CO 2 adsorption isotherms versus RH, show prominent maxima at vapor pressures corresponding to the water capillary condensation. The enhanced CO 2 uptake at specific RHs is attributed to the occurrence of liquid-like water at capillary condensation pressure, which facilitates the formation of ammonium bicarbonate. Hence, maximum CO 2 uptake may be achieved using an adsorbent whose average pore size corresponds to the water capillary condensation at the feed gas RH.
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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 | 0.001 |
| 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) | 0.001 | 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".