52 citations as recorded by crossref.

1. An Atmospheric Cluster Database Consisting of Sulfuric Acid, Bases, Organics, and Water J. Elm 10.1021/acsomega.9b00860
2. Freeze-thaw process boosts penguin-derived NH3 emissions and enhances climate-relevant particles formation in Antarctica R. Tian et al. 10.1038/s41612-024-00873-1
3. Identification of molecular cluster evaporation rates, cluster formation enthalpies and entropies by Monte Carlo method A. Shcherbacheva et al. 10.5194/acp-20-15867-2020
4. Particle number concentrations and size distributions in the stratosphere: implications of nucleation mechanisms and particle microphysics F. Yu et al. 10.5194/acp-23-1863-2023
5. Revised treatment of wet scavenging processes dramatically improves GEOS-Chem 12.0.0 simulations of surface nitric acid, nitrate, and ammonium over the United States G. Luo et al. 10.5194/gmd-12-3439-2019
6. New Particle Formation in the Atmosphere: From Molecular Clusters to Global Climate S. Lee et al. 10.1029/2018JD029356
7. Quantification of Atmospheric Ammonia Concentrations: A Review of Its Measurement and Modeling A. Nair & F. Yu 10.3390/atmos11101092
8. On the Ship Particle Number Emission Index: Size‐Resolved Microphysics and Key Controlling Parameters J. Mao et al. 10.1029/2020JD034427
9. Experiment–theory hybrid method for studying the formation mechanism of atmospheric new particle formation Y. Liu et al. 10.1039/D2CP03551K
10. H2SO4–H2O binary and H2SO4–H2O–NH3 ternary homogeneous and ion-mediated nucleation: lookup tables version 1.0 for 3-D modeling application F. Yu et al. 10.5194/gmd-13-2663-2020
11. Sulfuric Acid Nucleation Potential Model Applied to Complex Reacting Systems in the Atmosphere J. Johnson & C. Jen 10.1029/2023JD039344
12. Using machine learning to derive cloud condensation nuclei number concentrations from commonly available measurements A. Nair & F. Yu 10.5194/acp-20-12853-2020
13. The importance of ammonia for springtime atmospheric new particle formation and aerosol number abundance over the United States A. Nair et al. 10.1016/j.scitotenv.2022.160756
14. Tutorial: The discrete-sectional method to simulate an evolving aerosol C. Li & R. Cai 10.1016/j.jaerosci.2020.105615
15. An indicator for sulfuric acid–amine nucleation in atmospheric environments R. Cai et al. 10.1080/02786826.2021.1922598
16. Reducing chemical complexity in representation of new-particle formation: evaluation of simplification approaches T. Olenius et al. 10.1039/D2EA00174H
17. Revisiting Contrail Ice Formation: Impact of Primary Soot Particle Sizes and Contribution of Volatile Particles F. Yu et al. 10.1021/acs.est.4c04340
18. Environmental exposure disparities in ultrafine particles and PM2.5 by urbanicity and socio-demographics in New York state, 2013–2020 A. Nair et al. 10.1016/j.envres.2023.117246
19. Volatile organic compounds enhancing sulfuric acid-based ternary homogeneous nucleation: The important role of synergistic effect Y. Zhao et al. 10.1016/j.atmosenv.2020.117609
20. Evaluation of global simulations of aerosol particle and cloud condensation nuclei number, with implications for cloud droplet formation G. Fanourgakis et al. 10.5194/acp-19-8591-2019
21. A sulfuric acid nucleation potential model for the atmosphere J. Johnson & C. Jen 10.5194/acp-22-8287-2022
22. New particle formation (NPF) events in China urban clusters given by sever composite pollution background Q. Zhang et al. 10.1016/j.chemosphere.2020.127842
23. Wintertime new particle formation and its contribution to cloud condensation nuclei in the Northeastern United States F. Yu et al. 10.5194/acp-20-2591-2020
24. Global–regional nested simulation of particle number concentration by combing microphysical processes with an evolving organic aerosol module X. Chen et al. 10.5194/acp-21-9343-2021
25. New particle formation from sulfuric acid and ammonia: nucleation and growth model based on thermodynamics derived from CLOUD measurements for a wide range of conditions A. Kürten 10.5194/acp-19-5033-2019
26. Toward a Holistic Understanding of the Formation and Growth of Atmospheric Molecular Clusters: A Quantum Machine Learning Perspective J. Elm 10.1021/acs.jpca.0c09762
27. New Particle Formation Occurrence in the Urban Atmosphere of Beijing During 2013–2020 D. Shang et al. 10.1029/2022JD038334
28. Discovery of a Potent Source of Gaseous Amines in Urban China Y. Chang et al. 10.1021/acs.estlett.1c00229
29. New particle formation, growth and apparent shrinkage at a rural background site in western Saudi Arabia S. Hakala et al. 10.5194/acp-19-10537-2019
30. Interactions of sulfuric acid with common atmospheric bases and organic acids: Thermodynamics and implications to new particle formation Y. Li et al. 10.1016/j.jes.2020.03.033
31. An examination of the algorithm for estimating light extinction from IMPROVE particle speciation data A. Prenni et al. 10.1016/j.atmosenv.2019.116880
32. Quantum Machine Learning Approach for Studying Atmospheric Cluster Formation J. Kubečka et al. 10.1021/acs.estlett.1c00997
33. Analysis of new particle formation events and comparisons to simulations of particle number concentrations based on GEOS-Chem–advanced particle microphysics in Beijing, China K. Wang et al. 10.5194/acp-23-4091-2023
34. The influence of marine environment on the conservation state of Built Heritage: An overview study H. Morillas et al. 10.1016/j.scitotenv.2020.140899
35. On Nucleation Pathways and Particle Size Distribution Evolutions in Stratospheric Aircraft Exhaust Plumes with H2SO4 Enhancement F. Yu et al. 10.1021/acs.est.3c08408
36. Predictive modeling of atmospheric nuclear fallout microphysics D. McGuffin et al. 10.1016/j.scitotenv.2024.175536
37. Description and evaluation of the community aerosol dynamics model MAFOR v2.0 M. Karl et al. 10.5194/gmd-15-3969-2022
38. Particle surface area, ultrafine particle number concentration, and cardiovascular hospitalizations S. Lin et al. 10.1016/j.envpol.2022.119795
39. Atmospheric new particle formation in India: Current understanding and knowledge gaps V. Kanawade et al. 10.1016/j.atmosenv.2021.118894
40. Nucleation mechanisms of iodic acid in clean and polluted coastal regions H. Rong et al. 10.1016/j.chemosphere.2020.126743
41. Sulfuric acid–amine nucleation in urban Beijing R. Cai et al. 10.5194/acp-21-2457-2021
42. Use of Machine Learning to Reduce Uncertainties in Particle Number Concentration and Aerosol Indirect Radiative Forcing Predicted by Climate Models F. Yu et al. 10.1029/2022GL098551
43. Organic acid-ammonia ion-induced nucleation pathways unveiled by quantum chemical calculation and kinetics modeling: A case study of 3-methyl-1,2,3-butanetricarboxylic acid D. Xia et al. 10.1016/j.chemosphere.2021.131354
44. Modeling the formation and growth of atmospheric molecular clusters: A review J. Elm et al. 10.1016/j.jaerosci.2020.105621
45. Measurement of ammonia, amines and iodine compounds using protonated water cluster chemical ionization mass spectrometry J. Pfeifer et al. 10.5194/amt-13-2501-2020
46. Understanding vapor nucleation on the molecular level: A review C. Li & R. Signorell 10.1016/j.jaerosci.2020.105676
47. Trimethylamine from Subtropical Forests Rival Total Farmland Emissions in China Y. Chang et al. 10.1021/acs.est.4c00622
48. Atmospheric new particle formation from the CERN CLOUD experiment J. Kirkby et al. 10.1038/s41561-023-01305-0
49. The effectiveness of the coagulation sink of 3–10 nm atmospheric particles R. Cai et al. 10.5194/acp-22-11529-2022
50. Gas-phase catalytic hydration of I2O5 in the polluted coastal regions: Reaction mechanisms and atmospheric implications Y. Liang et al. 10.1016/j.jes.2021.09.028
51. A core-shell box model for simulating viscosity dependent secondary organic aerosol (CSVA) and its application L. Jia & Y. Xu 10.1016/j.scitotenv.2021.147954
52. Using machine learning to derive cloud condensation nuclei number concentrations from commonly available measurements A. Nair & F. Yu 10.5194/acp-20-12853-2020