Hydrophobic and hydrophilic functional groups and their impact on physical adsorption of CO2 in presence of H2O: A critical review
Bibliographic record
Abstract
Surface functional groups (SFGs) play a key role in adsorption of any target molecule and CO 2 is no exception. In fact, due to its quadrupole nature, different SFGs may attract either the oxygen or the carbon atoms to facilitate improved sorption characteristics in porous materials, hence the proliferation of this approach in the context of carbon capture via solid adsorbents. However, actual processes involve CO 2 capture/removal from a mixed gas stream that may have a non-negligible water content. The presence of humidity significantly hampers the sorption properties of classical physisorbents. To overcome this, the surface of the adsorbent can be modified to include hydrophobic/hydrophilic SFGs making the materials more resilient to moisture. However, the mechanisms behind H 2 O-tolerance depend greatly on the characteristics of SFGs themselves. Herein, a multitude of hydrophobic and hydrophilic SFGs (e.g. carbonyls, halogens, hydroxyls, nitro groups, phenyls, various alkyl chains and etc.) for physical CO 2 adsorption are reviewed within the context of their separation performance in a humid environment, highlighting their merits and limitations as well as their impact on cooperative or competitive H 2 O – CO 2 adsorption. • H 2 O hampers CO 2 adsorption on most physisorbents due to competitive adsorption. • Presence of surface functional groups impacts adsorption of both CO 2 and H 2 O. • Hydrophobic functionalities usually decrease uptake of both CO 2 and H 2 O. • Impact of hydrophilic functionalities on CO 2 uptake from humid environments varies. • Interplay between pore size and surface functional group is case-specific.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".