How Microscopic Features of Mineral Surfaces Critically Influence Heterogeneous Ice Nucleation
Bibliographic record
Abstract
Heterogeneous ice nucleation is an important process in environmental, biological, and atmospheric science. A variety of mineral, organic, and biological materials function as ice nucleating particles (INPs). A major aim of the current research is to determine exactly why and how ice nucleates on certain surfaces but not on others. The lattice match to ice and the atomistic surface morphology have both emerged as important factors, but how these factors interact, and which aspects play a vital role in ice nucleation, is not yet understood. This is the central question addressed in the present paper. We focus on four atmospherically relevant minerals: kaolinite, α-alumina, gibbsite, and hematite. These minerals are structurally similar, but the former pair are excellent INPs, while the latter pair are not. These four minerals provide an opportunity to systematically examine how details of lattice match and/or surface morphology favor or inhibit ice nucleation. We use molecular simulations to examine the interaction of a realistic water model with protonated (001) mineral surfaces. As expected from earlier simulations, ice nucleates via the basal plane for α-alumina, but via the primary prism face for kaolinite. Ice nucleation was not observed for gibbsite and hematite, consistent with experiment. To analyze water structure in the surface layers, we introduce a two-dimensional (2D) lattice perspective. Basal and prism face ice bilayers can be decomposed into two, 2D lattices (triangular for basal, rectangular for prism), and a surface must stabilize both 2D lattices of a bilayer to initiate ice nucleation. We define a 2D lattice mismatch parameter, which, unlike the conventional lattice mismatch criterion, is sensitive to the atomistic structure of a surface. Combining this approach with simulations involving scaled lattices, we clearly show how lattice match, and, more importantly, details of the surface morphology determine the wide range of ice nucleating activity displayed by the four minerals. We believe that the approach followed in this paper will contribute to the general understanding and prediction of ice nucleation (or lack thereof) by other surfaces. Ice nucleation by β-AgI and PbI 2, and the absence of nucleation by the mineral boehmite are briefly discussed from a 2D lattice perspective.
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 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".