Hydrate Kinetics Study in the Presence of Nonaqueous Liquid by Nuclear Magnetic Resonance Spectroscopy and Imaging
Bibliographic record
Abstract
The dynamics of methane hydrate growth and decomposition were studied by nuclear magnetic resonance (NMR) spectroscopy and imaging (MRI). Three well-known large molecule guest substances (LMGS) were used as structure H hydrate formers: 2,2-dimethylbutane (NH), methylcyclohexane (MCH), tert-butyl methyl ether (TBME). In addition, the impact of a non-hydrate former (n-heptane/nC7) was studied. The methane diffusion and hydrate growth were monitored by recording the 2H NMR spectra at 253 K and approximately 4.5 MPa for 20 h. The results revealed that methane diffuses faster in TBME and NH, slower in nC7, and slowest in MCH. The TBME system gives the fastest hydrate formation kinetics followed by NH, MCH, and nC7. The conversion of water into hydrate was also observed. The imaging study showed that TBME has a strong affinity toward ice, which is not the case for the NH and MCH systems. The degree of ice packing was also found to affect the LMGS distribution between ice particles. Highly packed ice increases the mass transfer resistance and hence limits the contact between LMGS and ice. It was also found that "temperature ramping" above the ice point improves the conversion significantly. Finally, hydrates were found to dissociate quickly within the first hour at atmospheric pressure and subsequently at a much slower rate. Methane dissolved in LMGS was also seen. The residual methane in hydrate phase and dissolved in LMGS phase explain the faster kinetics during hydrate re-formation.
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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".