Laboratory-generated mixtures of mineral dust particles with biological substances: Characterization of the particle mixing state and immersion freezing behavior
Bibliographic record
Abstract
Biological particles such as bacteria, fungal spores or pollen are known to be efficient ice nucleating particles. Their ability to nucleate ice is due to ice nucleation active macromolecules (INMs). It has been suggested that these INMs maintain their nucleating ability even when they are separated from their original carriers. This opens the possibility of an accumulation of such INMs in soils, resulting in an internal mixture of mineral dust and INMs. If particles from such soils which contain biological INMs are then dispersed into the atmosphere due to wind erosion or agricultural processes, they could induce ice nucleation at temperatures typical for biological substances, i.e., above −20 up to almost 0 °C, while they might be characterized as mineral dust particles due to a possibly low content of biological material. We conducted a study within the research unit INUIT (Ice Nucleation research UnIT), where we investigated the ice nucleation behavior of mineral dust particles internally mixed with INM. Specifically, we mixed a pure mineral dust sample (illite-NX) with ice active biological material (birch pollen washing water) and quantified the immersion freezing behavior of the resulting particles utilizing the Leipzig Aerosol Cloud Interaction Simulator (LACIS). A very important topic concerning the investigations presented here as well as for atmospheric application is the characterization of the mixing state of aerosol particles. In the present study we used different methods like single-particle aerosol mass spectrometry, Scanning Electron Microscopy (SEM), Energy Dispersive X-ray analysis (EDX), and a Volatility–Hygroscopicity Tandem Differential Mobility Analyser (VH-TDMA) to investigate the mixing state of our generated aerosol. Not all applied methods performed similarly well in detecting small amounts of biological material on the mineral dust particles. Measuring the hygroscopicity/volatility of the mixed particles with the VH-TDMA was the most sensitive method. We found that internally mixed particles, containing ice active biological material, follow the ice nucleation behavior observed for the pure biological particles. We verified this by modeling the freezing behavior of the mixed particles with the Soccerball model (SBM). It can be concluded that a single INM located on a mineral dust particle determines the freezing behavior of that particle with the result that freezing occurs at temperatures at which pure mineral dust particles are not yet ice active.
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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".