Simulations of water structure and the possibility of ice nucleation on selected crystal planes of K-feldspar
Bibliographic record
Abstract
Molecular dynamics simulations are employed to investigate the structure of supercooled water (230 K) in contact with the (001), (010), and (100) surfaces of potassium feldspar (K-feldspar) in the microcline phase. Experimentally, K-feldspar and other feldspar minerals are known to be good ice-nucleating agents, which play a significant role in atmospheric science. Therefore, a principal purpose of this work is to evaluate the possibility that the K-feldspar surfaces considered could serve as likely sites for ice nucleation. The (001) and (010) surfaces were selected for study because they are perfect cleavage planes of feldspar, with (001) also being an easy cleavage plane. The (100) surface is considered because some experiments have suggested that it is involved in ice nucleation. Feldspar is modeled with the widely used CLAYFF force field, and the TIP4P/Ice model is employed for water. We do not observe ice nucleation on any of the K-feldspar surfaces considered; moreover, the density profiles and the structure of water near these surfaces do not exhibit any particularly icelike features. Our simulations indicate that these surfaces of K-feldspar are likely not responsible for its excellent ice nucleating ability. This suggests that one must look elsewhere, possibly at water-induced surface rearrangements or some other "defect" structure, for an explanation of ice nucleation by K-feldspar.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".