Effects of sulfate coatings on the ice nucleation properties of a biological ice nucleus and several types of minerals
Bibliographic record
Abstract
An optical microscope coupled to a flow cell was used to study the ice nucleation properties of uncoated and coated mineral dust and SNOMAX (a proxy for biological ice nucleators made from cells of Pseudomonas syringae ) at temperatures ranging from 234 to 247 K. We define the onset conditions as the relative humidity (RH) and temperature at which the first ice nucleation event was observed. The results show that H 2 SO 4 coatings modified the ice nucleation properties of all the minerals studied. For kaolinite and illite, the acid coatings increased the RH over ice (RH i ) required for ice nucleation by ∼30% RH i ; for montmorillonite and quartz, the acid coatings increased the RH i by ∼20% RH i . NH 4 HSO 4 coatings also influenced the ice nucleation properties of kaolinite particles. In addition, our results indicate that SNOMAX is a reasonably good ice nucleus, having onset values between 110 to 120% RH i . In contrast to the mineral studies, sulfuric acid coatings did not hinder the ice nucleating ability of SNOMAX particles. Combined, our results support the idea that anthropogenic emissions of SO 2 and NH 3 may influence the ice‐nucleating properties of mineral dust particles. From our laboratory data, we also determined contact angles (θ) between the heterogeneous nuclei and ice embryos according to classical nucleation theory to parameterize the laboratory data for inclusion in atmospheric models. The data show that for uncoated ice nuclei the contact angles are small (below ∼20°), but for mineral particles coated with sulfuric acid, the contact angles are larger (above ∼60°).
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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 | 0.000 | 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) | 0.000 | 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".