The Effects of Aminium and Ammonium Cations on the Ice Nucleation Activity of K‐Feldspar
Bibliographic record
Abstract
Abstract Mineral dust is one of the most abundant types of ice nucleating particles in the atmosphere. During atmospheric transport, mineral dust particles can become coated with inorganic and organic solutes, which can impact their ice nucleation activity. Aminium cations formed from amines are one type of organic solute that can coat mineral dust particles in the atmosphere, but their effects on the ice nucleation activity of mineral dust have not been studied. We investigated the effects of primary, secondary, and tertiary aminium cations with methyl and ethyl groups, as well as ammonium cations, on the ice nucleation activity of K‐feldspar, an important type of mineral dust, in the immersion freezing mode at low cation concentrations (0.2–20 mM) using a droplet‐freezing apparatus. Ammonium cations substantially increased the ice nucleation activity of K‐feldspar, consistent with previous studies. In contrast, primary aminium cations significantly reduced K‐feldspar ice nucleation activity, and secondary and tertiary aminium cations had no significant effect (the effect was less than the uncertainty of our measurements). Our combined results are consistent with the following mechanisms: ammonium cations undergo ion exchange with K‐feldspar, providing exposed N–H groups for hydrogen bonding with ice; primary aminium cations undergo ion exchange with K‐feldspar, exposing a hydrophobic tail that is not effective at nucleating ice; secondary and tertiary aminium cations do not undergo ion exchange with K‐feldspar due to steric effects caused by the multiple hydrophobic groups on the cation.
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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".