Refined Analysis of the Nucleation Curves for Ice and Gas Hydrate Generated from the Linear Cooling Ramp Method
Bibliographic record
Abstract
We previously experimentally determined the nucleation curves of ice and clathrate hydrates using the linear cooling ramp method, and we used Classical Nucleation Theory (CNT) to determine the thermodynamic parameter (the Arrhenius activation barrier) and the kinetic parameter (the frequency of the system’s attempt for nucleation). However, our past analyses using CNT led to a curvature in a (ln J – Δ S eq Δ T / kT ) versus (1/ T Δ T 2 ) plot, which rendered deduction of the thermodynamic and kinetic parameters from the slope and the intercept of the plot challenging and consequently limited the utility of CNT in the analyses of the nucleation data. In the present study, we improved the analysis by adjusting the size of the “unit building block” in CNT, the only parameter that could be adjusted and still impact the linearity of a (ln J – Δ S eq Δ T / kT ) versus (1/ T Δ T 2 ) plot. The results showed that the linearity of the plot was highly sensitive to the choice of the size of the “unit building block”, and most samples in our previous studies achieved good linearity by using this approach. However, the linearity of the plot in the presence of the two most effective ice nucleation promoters investigated, Snomax and dispersed AgI suspension, remained poor, which offered new insights and highlighted the limitations of CNT in the presence of effective nucleation promoters.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".