Using Synthesized Size-Resolved Lignin Nanoparticles to Investigate the Atmospheric Ice Nucleation of Biomass Burning Organic Aerosols
Bibliographic record
Abstract
Biomass burning organic aerosols (BBOA) released from wildfires impact the formation, lifetime, and optical depth of mixed-phase clouds through heterogeneous ice nucleation. However, the underlying physicochemical mechanism of how organic matter, such as BBOA, promotes ice nucleation remains difficult to predict. Here, we investigated the ice-nucleating ability of lignin, a major component of BBOA, by synthesizing lignin nanoparticles (LNPs) from three different plant sources, namely, a conifer (softwood, sw), an angiosperm (hardwood, hw), and grass (g). First, we used a precipitation technique to make polydispersed LNP suspensions with acetone and water as antisolvents. Transmission electron microscopy (TEM) images indicated that the LNP samples were spherical and, notably, that the surface of grass LNP appeared floccose compared to the other two LNP types. Using our custom-built drop Freezing Ice Nuclei Counter (FINC), we found that LNPs from softwood (LNP sw ) were the most ice-active with a median ice nucleation temperature, T 50, of −15.6 °C at a concentration of 0.2 mg/mL. 31 P NMR suggested that LNP sw had the lowest number of hydroxyl groups, indicating that the functional groups present at the surface of the nanoparticles may be impacting the ice nucleation ability of LNPs. We then separated LNP sw by size with cascade centrifugation to create three distinct size bins of particles with mean diameters of 79, 154, and 279 nm. Nanoparticle tracking analysis (NTA) was used to quantify the size, surface area, and particle number of these size-resolved LNP sw . Despite their different sizes, all size-resolved LNP sw suspensions at 0.2 mg/mL were ice active at the same temperature, with T 50 values ranging from −14.9 to −15.9 °C. Remarkably, solubilized lignin, which did not undergo the nanoprecipitation procedure, froze in the same temperature range. Thus, the conversion of solubilized lignin into nanoparticles did not improve the ice nucleation ability of softwood lignin. We reconcile these results with a proposed role of the aggregation of lignin, as nanoparticles or dissolved, which facilitates the ice nucleation of aqueous droplets of lignin. Overall, the chemical composition and the ability of nonproteinaceous organic matter to aggregate may govern its ice nucleating ability. These findings help us understand how BBOA nucleate ice and impacts the formation and phase of clouds in the atmosphere.
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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 | 0.001 |
| 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".