Ice Nucleation Ability of Tree Pollen Altered by Atmospheric Processing
Bibliographic record
Abstract
Atmospheric subpollen particles (SPPs) released from pollen grains can act as ice nucleating particles. Using a droplet–freezing approach to assess immersion freezing–ice nucleating (IN) activity, we investigate the effects of simulated atmospheric processing on IN ability in aqueous suspensions for SPPs exposed to (A) light arising from artificial solar radiation and 310 nm UV radiation, (B) acidic water conditions, and (C) oxidation by hydrogen peroxide and a hydroxyl radical. The most studied IN–active SPP, gray alder, loses IN activity upon exposure to all processing modes, with less active IN sites more deactivated relative to their more active counterparts. The two other investigated SPPs, silver birch and red mulberry, are less responsive to atmospheric aging. The most significant impact arose from simulated solar radiation, where a 24 h exposure lowered the IN-activation temperature of gray alder SPPs by up to 5 °C. Similar changes observed after 4 h of exposure to 310 nm light indicate that UV radiation is the most important aging pathway for active-site deactivation. Additionally, we found that water acidity (pH 4–3) negatively impacts the IN activity of all SPPs. Oxidation induced by hydrogen peroxide at high concentrations of 10–500 mM reduced IN activity, consistent with chemical processing altering a specific active-site configuration. Changes in IN activity via aqueous OH radical oxidation were not observable above the effects arising from hydrogen peroxide when present as the OH precursor. Overall, these results imply that the role SPPs play in cloud ice formation may be reduced with longer atmospheric processing times.
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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) | 0.001 | 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".