Tuning the Composition of Guest Molecules in Clathrate Hydrates: NMR Identification and Its Significance to Gas Storage
Bibliographic record
Abstract
Gas hydrates represent an attractive way of storing large quantities of gas such as methane and carbon dioxide, although to date there has been little effort to optimize the storage capacity and to understand the trade-offs between storage conditions and storage capacity. In this work, we present estimates for gas storage based on the ideal structures, and show how these must be modified given the little data available on hydrate composition. We then examine the hypothesis based on solid-solution theory for clathrate hydrates as to how storage capacity may be improved for structure II hydrates, and test the hypothesis for a structure II hydrate of THF and methane, paying special attention to the synthetic approach used. Phase equilibrium data are used to map the region of stability of the double hydrate in P-T space as a function of the concentration of THF. In situ high-pressure NMR experiments were used to measure the kinetics of reaction between frozen THF solutions and methane gas, and (13)C MAS NMR experiments were used to measure the distribution of the guests over the cage sites. As known from previous work, at high concentrations of THF, methane only occupies the small cages in structure II hydrate, and in accordance with the hypothesis posed, we confirm that methane can be introduced into the large cage of structure II hydrate by lowering the concentration of THF to below 1.0 mol %. We note that in some preparations the cage occupancies appear to fluctuate with time and are not necessarily homogeneous over the sample. Although the tuning mechanism is generally valid, the composition and homogeneity of the product vary with the details of the synthetic procedure. The best results, those obtained from the gas-liquid reaction, are in good agreement with thermodynamic predictions; those obtained for the gas-solid reaction do not agree nearly as well.
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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 | 0.001 |
| 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".