Nucleation Curves of Carbon Dioxide Hydrate in the Absence of a Solid Wall
Bibliographic record
Abstract
Nucleation kinetics of clathrate hydrate is poorly understood because of the difficulties in determining the essential parameter, nucleation rate. Nucleation rate enables quantitative comparisons of the impacts of various additives and solid walls on nucleation. We made a setup that determines the nucleation curves of structure I─forming CO 2 hydrate in quiescent quasi-free water droplets supported by a bulk of liquid perfluoromethyldecalin under isobaric conditions. The results were compared to the nucleation rates of CO 2 hydrate in quiescent water that was in direct contact with stainless-steel walls. We assessed the convergence of the nucleation curves with the increasing numbers of nucleation data and compared our results to the nucleation rates of methane/propane mixed gas hydrate in quiescent quasi-free water droplets and the nucleation rates of gas hydrates of various guest types in the presence of solid walls reported in the literature. We found that (1) 400 nucleation events were sufficient to construct a reliable nucleation curve; (2) the addition of stainless-steel walls promoted the nucleation kinetics of CO 2 hydrate, as it did to methane/propane mixed gas hydrate; (3) the kinetic parameter was significantly lower than the theoretically expected value, whereas the thermodynamic parameter was comparable to both the theoretically expected value and the experimentally determined value reported in the literature; and (4) CO 2 hydrate nucleated over a substantially shallower supercooling range and had higher nucleation rates than those of methane/propane mixed gas hydrate, both in the presence and in the absence of a solid wall.
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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 | 0.001 | 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".