SeParation of Ice Nuclei via Density Layers (SPINDL): A new method for characterizing ice nuclei using density gradient centrifugation
Bibliographic record
Abstract
Atmospheric ice nucleating substances (INSs) play a crucial role in ice cloud formation above -35°C, impacting cloud radiative properties, cloud lifetime, and the hydrological cycle. Characterizing inorganic (e.g., mineral dusts, volcanic ash, metals) and organic (e.g., bacterial cells, fungal spores, pollen, and various biomacromolecules) INSs has typically involved: 1) single-particle analyses, which offer high resolution but require specialized equipment, and 2) bulk sample treatment (e.g., heat, H2O2, (NH₄)₂SO₄) analyses, which are more accessible but may overestimate or underestimate INS concentrations due to non-target effects. There is a need for additional methods to quantify inorganic and organic INSs concentrations in the atmosphere to test and improve climate models.Here we show a new density gradient centrifugation method to differentiate and quantify inorganic (densities ≥ 2.1 g cm-3) and organic INSs (densities ≤ 1.6 g cm-3). Density gradient centrifugation was used to separate the INSs suspension into their respective density isolate. This was followed by a wash procedure consisting of sequential differential centrifugation and ultrafiltration. Lastly, the INSs were quantified using a droplet freezing assay.Our method successfully recovered organic water-soluble INSs (lignin, birch pollen washing water and filtered Fusarium acuminatum) and organic water-insoluble INSs (Snomax and Pseudomonas syringae) in the low-density isolate. We recovered inorganic water-insoluble INSs (K-feldspar) in the high-density isolate. In an INS mixed suspension, we recovered K-feldspar in the high-density isolate and lignin in the low-density isolate both at concentrations similar to the isolated K-feldspar or lignin tests. This work demonstrates the broad applicability of density gradient centrifugation for characterizing a wide range of inorganic and organic atmospheric INSs.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".