Adsorption and Structure of Water on Kaolinite Surfaces: Possible Insight into Ice Nucleation from Grand Canonical Monte Carlo Calculations
Bibliographic record
Abstract
Grand canonical Monte Carlo calculations are used to determine water adsorption and structure on defect-free kaolinite surfaces as a function of relative humidity at 235 K. This information is then used to gain insight into ice nucleation on kaolinite surfaces. Results for both the SPC/E and TIP5P-E water models are compared and demonstrate that the Al-surface [(001) plane] and both protonated and unprotonated edges [(100) plane] strongly adsorb at atmospherically relevant relative humidities. Adsorption on the Al-surface exhibits properties of a first-order process with evidence of collective behavior, whereas adsorption on the edges is essentially continuous and appears dominated by strong water lattice interactions. For the protonated and unprotonated edges no structure that matches hexagonal ice is observed. For the Al-surface some of the water molecules formed hexagonal rings. However, the a o lattice parameter for these rings is significantly different from the corresponding constant for hexagonal ice ( Ih). A misfit strain of 14.0% is calculated between the hexagonal pattern of water adsorbed on the Al-surface and the basal plane of ice Ih. Hence, the ring structures that form on the Al-surface are not expected to be good building-blocks for ice nucleation due to the large misfit strain.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".