The amorphous phase state of suspended nanoparticles affects their atmospheric lifetimes and environmental impact. Influence of relative humidity and chemical composition on the glass-to-liquid transition is well-known. However, the influence of the particle size on the phase transition remains uncertain. Here we show experimental data that probe the amorphous phase transition of suspended sucrose particles as a function of particle size. The depression in glass-transition temperature follows the Gibbs-Thomson or Keesom-Laplace predicted proportionality of ΔT_g ∝ D^-1 for particles 100-700 nm in diameter, but the proportionality changes to ΔT_g ∝ D^-1/2 for smaller sizes. Literature data for glass-transition temperature depression in thin films and nanoconfined compounds show similar and strong deviations from the expected D^-1 behavior. While the observed proportionalities remain incompletely understood, the results here provide evidence that the deviation from ΔT_g ∝ D^-1 is not attributable to substrate effects.

Quantifying how atmospheric particles interact with water vapor is critical for understanding the effects of aerosols on climate. We present a novel method to measure the mass-based hygroscopicity of particles while characterizing their elemental and carbon functional group compositions. Since mass-based hygroscopicity is insensitive to particle geometry, it is advantageous for probing the hygroscopic behavior of atmospheric particles, which can have irregular morphologies. Combining scanning electron microscopy with energy dispersive X-ray analysis (SEM/EDX), scanning transmission X-ray microscopy (STXM) analysis, and in situ STXM humidification experiments, this method was validated using laboratory-generated, atmospherically relevant particles. Then, the hygroscopicity and elemental composition of 15 complex atmospheric particles were analyzed by leveraging quantification of C, N, and O from STXM, and complementary elemental quantification from SEM/EDX. We found three types of hygroscopic responses, and correlated high hygroscopicity with Na and Cl content. The mixing state of 158 other particles from the sample broadly agreed with those of the humidified particles, indicating the potential to infer atmospheric hygroscopic behavior from a selected subset of particles. These methods offer unique quantitative capabilities to characterize and correlate the hygroscopicity and chemistry of individual submicrometer atmospheric particles.

The importance of organic aerosol particles in the environment has been long established, influencing cloud formation and lifetime, absorbing and scattering sunlight, affecting atmospheric composition and impacting on human health. Conventionally, ambient organic particles were considered to exist as liquids. Recent observations in field measurements and studies in the laboratory suggest that they may instead exist as highly viscous semi-solids or amorphous glassy solids under certain conditions, with important implications for atmospheric chemistry, climate and air quality. This review explores our understanding of aerosol particle phase, particularly as identified by measurements of the viscosity of organic particles, and the atmospheric implications of phase state.

Ice-nucleating particles (INPs) are rare atmospheric aerosols that initiate primary ice formation, but accurately simulating their concentrations and variability in large-scale climate models remains a challenge. Doing so requires both simulating major particle sources and parameterizing their ice nucleation (IN) efficiency. Validating and improving model predictions of INP concentrations requires measuring their concentrations delineated by particle type. We present a method to speciate INP concentrations into contributions from dust, sea spray aerosol (SSA), and bioaerosol. Field campaign data from Bodega Bay, California, showed that bioaerosols were the primary source of INPs between -12° and -20°C, while dust was a minor source and SSA had little impact. We found that recent parameterizations for dust and SSA accurately predicted ambient INP concentrations. However, the model did not skillfully simulate bioaerosol INPs, suggesting a need for further research to identify major factors controlling their emissions and INP efficiency for improved representation in models.