On the Role of Starchy Grains in Ice Nucleation Processes
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Little is known about the role of starchy food on climate change processes like ice nucleation. Here, we investigate the ice nucleation efficiency (INE) of eight different starchy food materials, namely, corn (CO), potato (PO), barley (BA), brown rice (BR), white rice (WR), oats (OA), wheat (WH), and sweet potato (SP), in immersion freezing mode under mixed-phase cloud conditions. Notably, among all these food materials, PO and BA exhibit the highest ice nucleation efficiency with ice nucleation temperatures as high as −4.3 °C ( T 50 ∼ −7.0 ± 0.5 °C) and −6.5 °C ( T 50 ∼ −7.2 ± 0.2 °C), respectively. We also explore the effect of environmentally relevant physicochemical conditions on ice nucleation efficiency, including different pH, temperature, UV/O 3 /NO x exposure, and various cocontaminants. The change in shape, size, surface properties, hydrophobicity, and crystallinity of materials accounted for the altered INE. The increase in shape, size, and hydrophobicity of the sample generally reduces the INE, whereas an increase in crystallinity enhances the INE of the sample under our experimental conditions. The results suggest that environmentally relevant concentrations slightly alter INE, indicating their role as catalysts in environmental matrices. The outcome of studies on the ice nucleation properties of these food-containing aerosols might help in the physicochemical understanding of other biomolecule-induced ice nucleation, which is still an underdeveloped research area.
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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) | 0.002 | 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".