The role of surface-active macromolecules in the ice-nucleating ability of lignin, Snomax, and agricultural soil extracts
Bibliographic record
Abstract
Abstract. Organic matter in agricultural soil dust can enhance dust's ice-nucleating ability relative to mineral dust and thus impact local cloud formation. But how is this organic matter able to nucleate ice? We hypothesised that hydrophobic interfaces, such as the air–water interface, influence how organic matter nucleates ice, which can be quantified by measuring surface tension. Here, we investigated correlations between surfactant properties and ice-nucleating activities of amphiphilic macromolecules common in agricultural soils and known ice nucleators, namely lignin and macromolecules from Snomax. Lignin and Snomax solutions were analysed using our droplet freezing technique, FINC, and an optical contact angle tensiometer. Results showed that lignin and Snomax solutions of increasing concentrations had increasing ice-nucleating activity and decreasing surface tension. In addition, high-speed cryo-microscopy of the same solutions revealed a preference for freezing at the air–water interface, consistent with these proxies being ice-active surfactants preferentially residing at the air–water interface, and thus hydrophobic surfaces. We then tested this relationship in field-collected agricultural soil extracts from the UK and Canada. Despite the trend observed for lignin and Snomax, there was no clear correlation between surface tension and freezing temperature of the soil extracts. This discrepancy may arise from the high complexity of the soil solutions, where hydrophobic interfaces in the bulk potentially compete with the air–water interface. Overall, we present further evidence of the role of hydrophobic interfaces in the heterogeneous ice nucleation of organic aerosols with implications for aerosol–cloud interactions.
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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".