Nucleation and Growth of Hydrates on Ice Surfaces: New Insights from <sup>129</sup>Xe NMR Experiments with Hyperpolarized Xenon
Bibliographic record
Abstract
The nucleation and growth of hydrate on the surface of ice was followed by NMR spectroscopy using hyperpolarized Xe. From the ice surface area and Xe pressure drop, the thickness of the hydrate film formed was ∼ 500−1000 Å before the reaction became extremely slow. The cage occupancy ratio θ L /θ S was measured as a function of time from the Xe spectrum and was used to monitor the nature of the material formed. The ratio changed from values close to 1 during the early part of the reaction (induction time) to its equilibrium value of ∼3−4 after nucleation processes were finished and rapid growth commenced. The low value can be seen as evidence of a precursor phase that is quite different from the equilibrium hydrate. The induction time was found to be a reproducible function of temperature and pressure for the conditions studied. The kinetics of gas uptake was analyzed according to the Avrami−Erofeyev equation used to describe solid−gas reactions. A surface memory effect was noted on successive cycles of xenon adsorption−desorption−readsorption, as the induction time was absent on the readsorption cycle. The new results are discussed in terms of models for nucleation and growth of hydrate and the various experiments that have been carried out in the past. It is essential to differentiate between later-stage diffusion-limited hydrate formation processes and the initial steps of hydrate formation at a surface, as they have opposite temperature coefficients.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".