Bibliographic record
Abstract
Applications of clathrate hydrate require fast formation kinetics of it, which is the long-standing technological bottleneck due to mass transfer and heat transfer limitations. Although several methods, such as surfactants and mechanical stirring, have been employed to accelerate gas hydrate formation, the problems they bring are not negligible. Recently, a new water-in-air dispersion stabilized by hydrophobic nanosilica, dry water, has been used as an effective promoter for hydrate formation. In this review, we summarize the preparation procedure of dry water and factors affecting the physical properties of dry water dispersion. The effect of dry water dispersion on gas hydrate formation is discussed from the thermodynamic and kinetic points of view. Dry water dispersion shifts the gas hydrate phase boundary to milder conditions. Dry water increases the gas hydrate formation rate and improves gas storage capacity by enhancing water-guest gas contact. The performance comparison and synergy of dry water with other common hydrate promoters are also summarized. The self-preservation effect of dry water hydrate was investigated. Despite the prominent effect of dry water in promoting gas hydrate formation, its reusability problem still remains to be solved. We present and compare several methods to improve its reusability. Finally, we propose knowledge gaps in dry water hydrate research and future research directions.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".