In situ Raman and <sup>13</sup>C NMR spectroscopic analysis of gas hydrates formed in confined water: application to natural gas capture
Bibliographic record
Abstract
This study investigates the formation characteristics of gas hydrate from bulk water as well as dispersed water in silica gel and dry water particles when they are exposed to natural gas. The inclusion process of methane, ethane, and propane molecules in hydrate cages were observed with in situ Raman spectroscopy, and the resulting cage occupancies were estimated from 13C NMR spectra. A high-pressure autoclave was used to monitor the formation process to determine hydrate onset time, initial growth rate, and conversion ratio. The obtained data from Raman spectra and gas consumption profiles suggested that hydrate formed within less than 20 min when the temperature is sufficiently lower than the hydrate equilibrium condition at a given pressure. Methane molecules started to occupy the small cages of structure II, but about 6 min later ethane and propane were also included in hydrate cages. 13C NMR spectroscopy confirms that only 23% of large cages of structure II are occupied by methane molecules when hydrate formed from dispersed water in silica gel, which was much less than 68% from dry water. These results suggest that the dispersion of water in silica gel and dry water would enhance the formation process by increasing gas-to-water ratio, although the composition of hydrate phase may vary depending on the formation condition. However the formation of hydrate in silica gel and dry water still provide an effective option to capture the natural gas without using complex rotating machineries.
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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".