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Record W4409333250 · doi:10.1021/acsestair.4c00223

Using Synthesized Size-Resolved Lignin Nanoparticles to Investigate the Atmospheric Ice Nucleation of Biomass Burning Organic Aerosols

2025· article· en· W4409333250 on OpenAlexafffund
A Zelený, Jingqian Chen, Paul Bieber, Orlando J. Rojas, Nadine Borduas‐Dedekind

Bibliographic record

VenueACS ES&T Air · 2025
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicAtmospheric chemistry and aerosols
Canadian institutionsUniversity of British Columbia
FundersUniversity of British ColumbiaCanada Excellence Research Chairs, Government of CanadaCanada Foundation for Innovation
KeywordsLigninNucleationIce nucleusBiomass (ecology)Biomass burningAerosolNanoparticleEnvironmental scienceChemical engineeringMaterials scienceEnvironmental chemistryChemistryNanotechnologyOrganic chemistryGeologyOceanography

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.565

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.017
GPT teacher head0.231
Teacher spread0.214 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2025
Admission routes2
Has abstractyes

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