Effects of Inorganic Ions on Ice Nucleation by the Al Surface of Kaolinite Immersed in Water
Bibliographic record
Abstract
I) are considered. Simulations were performed at 300 K to obtain equilibrium surface-ion and surface-water density profiles. These simulations show no specific ion adsorption at the kaolinite surface. There are weak surface-ion correlations, with cations preferring to be closer to the surface than the anions. At a supercooling of 26 K (taking account of freezing point depression), 1 M salt solutions slowed ice nucleation by a factor of 2-3 compared with pure water and significantly reduced the rate of ice growth after nucleation. All salt solutions had similar influences on ice nucleation, and no specific ion effects were identified. Ice nucleation simulations for 1 M NaI(Cl), KI(Cl), and LiI solutions were performed for a range of temperatures. In all cases, the supercooling required for ice nucleation was larger by ∼1-6 K, after accounting for freezing point depression, than that required for pure water. For 1 M LiI solution an earlier laboratory study using kaolin as ice nucleating particles (INP) reported that the supercooling required for ice nucleation was ∼11 K smaller than that required for pure water. Our simulation results are not consistent with this finding. In this paper, we report new laboratory results for 1 M LiI solution employing kaolinite as INP. In our experiments ice nucleation in the LiI solution required the same supercooling as pure water, which is more consistent with our simulations.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".