Papers
* Corresponding author.
In preparation
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Machine learning-derived surface tension in climate models and its implications for CCN activationin prep, 2026 -
Investigating the competition in cloud droplets activation by different aerosol systems using cloud chamber experiment datasets and modellingin prep, 2026 -
Diagnosing the Role of Mixing State in Biomass-Burning Aerosol Optical Properties Using Particle-Resolved Modelingin prep, 2026 -
A diagnostic scaling framework for connecting surfactant-induced CCN perturbations to global aerosol–cloud forcing patternsin prep, 2026
2026
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Quantifying Black Carbon Mixing State Heterogeneity Using a Machine Learning ModelAccepted at Journal of Geophysical Research: Atmospheres, 2026 -
Compensating biases in CCN predictions from composition averaging and neglected surfactant effectsAtmospheric Chemistry and Physics, 2026 [link] -
Role of liquid-liquid phase separation-induced surface tension changes in cloud droplet activationAerosol Science and Technology, 2026 [link]
Before PhD
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Modeling the high-mercury wet deposition in the southeastern US with WRF-GC-Hg v1. 0Geoscientific Model Development, 2022 [link] -
Substantial changes in nitrogen dioxide and ozone after excluding meteorological impacts during the COVID-19 outbreak in mainland ChinaEnvironmental Science & Technology Letters, 2020 [link] -
Characterization of size-resolved urban haze particles collected in summer and winter at Taiyuan City, China using quantitative electron probe X-ray microanalysisAtmospheric Research, 2017 [link] -
Influences of Biomass and Coal Burning on Ambient Fine Particulate Matter over Taiyuan CityJournal of Anhui Agriculture Science, 2016 [link]