Understanding the Effect of Water on CO<sub>2</sub> Adsorption
Bibliographic record
Abstract
Carbon capture from large sources and ambient air is one of the most promising strategies to curb the deleterious effect of greenhouse gases. Among different technologies, CO 2 adsorption has drawn widespread attention mostly because of its low energy requirements. Considering that water vapor is a ubiquitous component in air and almost all CO 2 -rich industrial gas streams, understanding its impact on CO 2 adsorption is of critical importance. Owing to the large diversity of adsorbents, water plays many different roles from a severe inhibitor of CO 2 adsorption to an excellent promoter. Water may also increase the rate of CO 2 capture or have the opposite effect. In the presence of amine-containing adsorbents, water is even necessary for their long-term stability. The current contribution is a comprehensive review of the effects of water whether in the gas feed or as adsorbent moisture on CO 2 adsorption. For convenience, we discuss the effect of water vapor on CO 2 adsorption over four broadly defined groups of materials separately, namely (i) physical adsorbents, including carbons, zeolites and MOFs, (ii) amine-functionalized adsorbents, and (iii) reactive adsorbents, including metal carbonates and oxides. For each category, the effects of humidity level on CO 2 uptake, selectivity, and adsorption kinetics under different operational conditions are discussed. Whenever possible, findings from different sources are compared, paying particular attention to both similarities and inconsistencies. For completeness, the effect of water on membrane CO 2 separation is also discussed, albeit briefly.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".