Facilitating guest transport in clathrate hydrates by tuning guest-host interactions
Bibliographic record
Abstract
The understanding and eventual control of guest molecule transport in gas hydrates is of central importance for the efficient synthesis and processing of these materials for applications in the storage, separation, and sequestration of gases and natural gas production. Previously, some links have been established between dynamics of the host water molecules and guest-host hydrogen bonding interactions, but direct observation of transport in the form of cage-to-cage guest diffusion is still lacking. Recent calculations have suggested that pairs of different guest molecules in neighboring cages can affect guest-host hydrogen bonding and, therefore, defect injection and water lattice motions. We have chosen two sets of hydrate guest pairs, tetrahydrofuran (THF)-CO2 and isobutane-CO2, that are predicted to enhance or to diminish guest-host hydrogen bonding interactions as compared to those in pure CO2 hydrate and we have studied guest dynamics in each using (13)C nuclear magnetic resonance (NMR) methods. In addition, we have obtained the crystal structure of the THF-CO2 sII hydrate using the combined single crystal X-ray diffraction and (13)C NMR powder pattern data and have performed molecular dynamics-simulation of the CO2 dynamics. The NMR powder line shape studies confirm the enhanced and delayed dynamics for the THF and isobutane containing hydrates, respectively, as compared to those in the CO2 hydrate. In addition, from line shape studies and 2D exchange spectroscopy NMR, we observe cage-to-cage exchange of CO2 molecules in the THF-CO2 hydrate, but not in the other hydrates studied. We conclude that the relatively rapid intercage guest dynamics are the result of synergistic guest A-host water-guest B interactions, thus allowing tuning of the guest transport properties in the hydrates by choice of the appropriate guest molecules. Our experimental value for inter-cage hopping is slower by a factor of 10(6) than a published calculated value.
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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".