Cold, Hot, Dry, and Wet: Locations and Dynamics of CO<sub>2</sub> and H<sub>2</sub>O Co-Adsorbed in an Ultramicroporous MOF
Bibliographic record
Abstract
Climate change from anthropogenic carbon dioxide (CO 2 ) emissions poses a severe threat to society. A variety of mitigation strategies currently include some form of CO 2 capture. Metal–organic frameworks (MOFs) have shown great promise for carbon capture and storage, but several issues must be solved before feasible widespread adoption is possible. MOFs often exhibit reduced chemical stabilities and CO 2 adsorption capacities in the presence of water, which is ubiquitous in nature and many practical settings. A comprehensive understanding of water influence on CO 2 adsorption in MOFs is necessary. We have used multinuclear nuclear magnetic resonance (NMR) experiments at temperatures ranging from 173 to 373 K, along with complementary computational techniques, to investigate the co-adsorption of CO 2 and water across various loading levels in the ultra-microporous ZnAtzOx MOF. This approach yields detailed information regarding the number of CO 2 and water adsorption sites along with their locations, guest dynamics, and host–guest interactions. Guest adsorption and motional models proposed from NMR data are supported by computational results, including visualizations of adsorption locations and the spatial distribution of guests in different loading scenarios. The wide variety and depth of information presented demonstrates how this experimental methodology can be used to investigate humid carbon capture and storage applications in other 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".