Binary CO<sub>2</sub>/H<sub>2</sub>O Adsorption on CO<sub>2</sub> Capture Metal–Organic Frameworks CALF-20, Al-Fumarate and CAU-10-H Using Microscale Dynamic Column Breakthrough
Bibliographic record
Abstract
This study reports the development of a quantitative microscale dynamic column breakthrough instrument to measure the competitive equilibrium loadings of CO 2 and H 2 O isotherms on small quantities (≈150 mg) of metal–organic framework (MOF) samples. The binary CO 2 and H 2 O equilibria were measured on CALF-20, Al-Fumarate and CAU-10-H, three MOFs of interest for postcombustion CO 2 capture at 30 °C and specifically since they show an S -shaped water isotherm. For Al-Fumarate and CAU-10-H, pure CO 2 and H 2 O competition was measured at 30 °C. For CALF-20, extensive mapping of the impact of CO 2 concentration and various water concentrations was performed at 30 °C. In all three MOFs, the CO 2 capacity is generally retained toward the left of the inflection point of the H 2 O isotherm. However CO 2 capacity drops toward the right of the inflection point, and the magnitude of the drop depends on the sharpness of the H 2 O isotherm. On the one hand, for Al-Fumarate and CAU-10-H the H 2 O isotherm was unaffected by CO 2 . On the other hand, for CALF-20, the presence of CO 2 impacts the H 2 O water isotherm, extending the relative humidity range over which the CO 2 capacity is retained. Using the ideal adsorbed solution theory to predict the binary equilibria revealed deviations from ideality for all three MOFs.
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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) | 0.000 | 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 | 0.000 | 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".