The presence of nanoparticles in aqueous droplets containing plant-derived biopolymers plays a role in heterogeneous ice nucleation
Bibliographic record
Abstract
Organic matter can initiate heterogeneous ice nucleation in supercooled water droplets, thereby influencing atmospheric cloud glaciation. Predicting the ice nucleation ability of organic matter-containing cloud droplets is challenging due to the unknown mechanism for templating ice. Here, we observed the presence of nanoparticles in aqueous samples of known ice-nucleating biopolymers cellulose and lignin, as well as in newly identified ice-nucleating biopolymers xylan and laminarin. Using our drop Freezing Ice Nuclei Counter (FINC), we measured the median ice nucleation temperature (T50) of xylan and of laminarin droplets of 2 μl to be -14.2 and -20.0 °C, respectively. Next, we characterized these samples using nanoparticle tracking analysis, and we detected and quantified nanoparticles with mean diameters between 132 and 267 nm. Xylan contained the largest nanoparticles and froze at higher temperatures. Xylan also dictated the freezing in a 1:1:1:1 mixture with cellulose, lignin, laminarin, and xylan. Filtration experiments down to 300 kDa with the xylan sample indicated that the presence of nanoparticles triggered freezing. Overall, only samples with mean diameters above 150 nm froze above -20 °C. Furthermore, we determined the ice-active site densities normalized to particle concentrations, surface area, and mass of the nanoparticles to show that the samples' nucleation site densities are similar to sea spray aerosols and nanometer-sized dust. The identification and characterization of xylan and laminarin as nanometer-sized ice-nucleating substances expands the growing list of organic matter capable of impacting cloud formation and thus climate.
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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) | 0.000 | 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 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".