Binary CO2/H2O adsorption on CO2 capture metal-organic frameworks CALF-20, Al-Fumarate and CAU-10H 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 CO2 and H2O isotherms on small quantities (≈ 200 mg) of metal-organic framework (MOF) samples. The binary CO2 and H2O equilibria were measured on CALF-20, Al-Fumarate and CAU-10-H, three MOFs of interest for post-combustion CO2 capture at 30 °C. For Al-Fumarate and CAU-10-H, pure CO2 and H2O competition was measured at 30 °C. For CALF-20, extensive mapping of the impact of CO2 concentration and various water concentrations was performed at 30 °C. All three MOFs feature an S-shape H2O isotherm and a type-I CO2 isotherm. In all three MOFs, the CO2 capacity is generally retained towards the left of the inflection point of the H2O isotherm. Water capacity drops after the inflection point, and the magnitude of the drop depends on the sharpness of the H2O isotherm. On the one hand, for Al-Fumarate and CAU-10-H the H2O isotherm was unaffected by CO2. On the other hand, for CALF-20, the presence of CO2 impacts the H2O water isotherm, extending the relative humidity range over which the CO2 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".