Effects of Inorganic Acids and Organic Solutes on the Ice Nucleating Ability and Surface Properties of Potassium-Rich Feldspar
Bibliographic record
Abstract
Mineral dust particles can initiate the freezing of cloud droplets in the atmosphere. The freezing efficiency of these particles can, however, be strongly affected by solutes, such as inorganic acids, polyols, and carboxylic acids. Here, we report the effects of inorganic acids (HNO3 and HCl), polyols, and carboxylic acids at low concentrations on the ice nucleating ability of potassium-rich feldspar (K-rich feldspar) using the droplet freezing technique. The inorganic acids and carboxylic acids decreased the median freezing temperature of droplets containing K-rich feldspar by up to 7 °C, while the polyols had no significant effect on the median freezing temperature. For the inorganic acids and carboxylic acids, the median freezing temperature was a strong function of the pH of the droplets, with the median freezing temperature decreasing as the pH decreased. By examining the surface properties of K-rich feldspar exposed to different concentrations of HCl with cryogenic X-ray photoelectron spectroscopy, we show that the decrease in the ice nucleating ability of K-rich feldspar by the inorganic acids and carboxylic acids was likely caused by ion exchange (H3O+ with parent K+ in microcline) and the incongruent dissolution of Al with respect to Si at K-rich feldspar surfaces. The decrease in the ice nucleating ability of K-rich feldspar by the carboxylic acids only related to the pH of the droplets rather than the type of carboxylic acid and their expected binding mechanisms on K-rich feldspar. This study focuses on rare ice nucleating active sites (with an ice nucleating active site density of 10–600 cm–2) of the K-rich feldspar and short exposure times between the solutes and the K-rich feldspar. Further studies are needed to investigate more abundant ice nucleating active sites and longer exposure times, as well as K-rich feldspar samples from different sources.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".