Plant-derived biopolymers initiate heterogeneous ice nucleation via particulate interfaces immersed in supercooled droplets
Bibliographic record
Abstract
Organic matter can initiate heterogeneous ice nucleation in supercooled water droplets, thereby influencing atmospheric cloud glaciation. Atmospheric organic matter includes biopolymers, which are emitted as primary bioaerosols, biomass burning aerosols, soil dust and sea spray aerosols and can nucleate ice in the absence of a solid surface like mineral dust. There is evidence that biopolymers could form aggregates in solution, thereby creating ice-nucleating sites. However, the submicron size and heterogeneity of these aggregates creates challenges in studying and predicting their ice-nucleating ability. Here, we characterized self-assembled nanoparticles of cellulose and lignin and of two newly identified ice-nucleating biopolymers, namely xylan and laminarin. Our freezing ice nuclei counter (FINC) instrument measured the median ice nucleation temperatures of aqueous cellulose, lignin, xylan and laminarin samples to be –22.9 °C, –22.0 °C, –14.2 °C, and –20.0 °C, respectively. Furthermore, a nanoparticle tracking technique detected and quantified particles in the biopolymer solutions, with mean diameters between 132 nm and 267 nm. A positive trend between size and nucleation temperatures suggests that the biopolymers initiate freezing via particulate interfaces immersed in the supercooled droplets. Finally, we determined ice-active site densities normalized to quantitatively measured surface area and mass of the nanoparticles, to demonstrate how the biopolymers self-assemble in solution and subsequently nucleate ice in supercooled droplets. The mechanism by which biopolymers nucleate ice leads to improved predictive capabilities to estimate the impact of organic aerosols on climate.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".