Ice nucleation on mineral dust particles: Onset conditions, nucleation rates and contact angles
Bibliographic record
Abstract
An optical microscope coupled to a flow cell was used to investigate the onset conditions for ice nucleation on five atmospherically relevant minerals at temperatures ranging from 233 to 246 K. Here we define the onset conditions as the humidity and temperature at which the first ice nucleation event was observed. Kaolinite and muscovite were found to be efficient ice nuclei in the deposition mode, requiring relative humidities with respect to ice (RHi) below 112% in order to initiate ice crystal formation. Quartz and calcite, by contrast, were poor ice nuclei, requiring relative humidities close to water saturation before ice crystals would form. Montmorillonite particles were efficient ice nuclei at temperatures below 241 K but were poor ice nuclei at higher temperatures. In several cases, there was a lack of quantitative agreement between our data and previously published work. This can be explained by several factors including the mineral source, the particle sizes, the surface area available for nucleation, and observation time. Heterogeneous nucleation rates (Jhet) were calculated from the measurements of the onset conditions (temperature and RHi) required from ice nucleation. The Jhet values were then used to calculate contact angles (θ) between the mineral substrates and an ice embryo using classical nucleation theory. The contact angles measured for kaolinite and muscovite ranged from 6° to 12°, whereas for quartz and calcite, the contact angles ranged from 25° to 27°. The reported Jhet and θ values may allow for a more direct comparison between laboratory studies and can be used when modeling ice cloud formation in the atmosphere.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".