Investigating lignin’s ice nucleation mechanisms by applying nano-particle synthesis and high-speed cryo-microscopy
Bibliographic record
Abstract
Due to the changing climate, wildfires globally have been increasing in size and intensity. With the increase of these biomass burning events there is a surge of organic aerosols present in the atmosphere. Recent evidence from our group and the community suggests that organic aerosols can catalyze heterogeneous ice nucleation.1–3 Currently, heterogeneous ice nucleation is the largest source of uncertainty in climate models as it governs the formation of mixed-phase clouds, important climate regulators linked to annual precipitation and global cloud coverage. We are interested in what impact an increase in atmospheric biomass burning aerosols will have on mixed-phase cloud formation.An important component of organic biomass aerosols is lignin, a macromolecule which provides strength and structure to vascular plants. Lignin has been measured as a notably recalcitrant component of organic aerosols following biomass burning events.4,5 To elucidate the role of morphology and size of biomass burning organic aerosols in ice nucleation, we synthesized nanoparticles from commercially available Kraft lignin via a facile nanoprecipitation process.6,7 The nanoparticles were centrifugally separated by size, characterized by dynamic light scattering (DLS) and by transmission electron microscopy (TEM), then tested for their freezing ability in our home-built Freezing Ice Nuclei Counter (FINC).8 Next, the freezing mechanism and location of onset freezing for lignin was investigated using a high-speed camera on a cryo-microscope.9 Cylindrical droplets, between two glass slides, were frozen to localize the onset location of freezing at the air-water interface (AWI) or in the bulk of the droplets. Videos of single freezing events were recorded with a time resolution of over 2000 frames per second.Our preliminary results suggest that lignin nanoparticles ranging in size from 50 – 500 nm in diameter are ice active at -15 ºC, well above the background freezing of the instrument (-25 °C). Normalizing the freezing data to mass and surface area suggests that aggregation facilitates ice nucleation. Moreover, the high-speed videos suggest that lignin’s ice nucleation activity is higher closer to the AWI of a droplet, indicating that hydrophobic interactions could be responsible for the aggregation of lignin and adsorption at the AWI, similar to the behavior of surfactants. These findings help understand how lignin within biomass burning organic aerosols are able to nucleate ice and hence impact the ice crystal concentration in mixed-phase clouds.References:(1)        Bogler, S.; Borduas-Dedekind, N. Atmospheric Chem. Phys. 2020, 20 (23), 14509–14522.(2)        Knopf, D. A. et al., Atmos Chem Phys 2014, 14 (16), 8521–8531.(4)        Shakya, K. M. et al., Environ. Sci. Technol. 2011, 45 (19), 8268–8275.(5)        Myers-Pigg, A. N. et al., Environ. Sci. Technol. 2016, 50 (17), 9308–9314.(6)        Lievonen, M. et al., Green Chem. 2016, 18 (5), 1416–1422.(7)        Zou, T. et al., J. Phys. Chem. B 2021, 125 (44), 12315–12328.(8)        Miller, A. J. et al., Atmospheric Meas. Tech. 2021, 14 (4), 3131–3151.(9)        Bieber, P.; Borduas-Dedekind, N. ChemRxiv 2023. (preprint)
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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; both teacher heads agree on what is shown here.
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".