Articles | Volume 22, issue 17
https://doi.org/10.5194/acp-22-11155-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/acp-22-11155-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
01 Sep 2022
Research article |
| 01 Sep 2022
| 01 Sep 2022
Modeling approaches for atmospheric ion–dipole collisions: all-atom trajectory simulations and central field methods
Institute for Atmospheric and Earth System Research/Physics, Faculty of Science, University of Helsinki, P.O. Box 64, 00014 Helsinki, Finland
Center for Joint Quantum Studies and Department of Physics, School of Science, Tianjin University, 92 Weijin Road, Tianjin 300072, China
Hanna Vehkamäki
Institute for Atmospheric and Earth System Research/Physics, Faculty of Science, University of Helsinki, P.O. Box 64, 00014 Helsinki, Finland
Bernhard Reischl
Institute for Atmospheric and Earth System Research/Physics, Faculty of Science, University of Helsinki, P.O. Box 64, 00014 Helsinki, Finland
Related authors
Ivo Neefjes, Yosef Knattrup, Haide Wu, Georg Baadsgaard Trolle, Jonas Elm, and Jakub Kubečka
Aerosol Research Discuss., https://doi.org/10.5194/ar-2025-30, https://doi.org/10.5194/ar-2025-30, 2025
Preprint under review for AR
Short summary
Short summary
We investigated how water vapor affects the earliest steps of atmospheric aerosol formation, a key process influencing clouds and climate. By benchmarking quantum-chemical methods, we identified reliable approaches for modeling hydrated molecular clusters of common atmospheric acids and bases. We show that humidity moderately stabilizes certain clusters but only modestly alters particle formation rates. These findings sharpen our understanding of clusters and their role in aerosol formation.
Yosef Knattrup, Ivo Neefjes, Jakub Kubečka, and Jonas Elm
Aerosol Research, 3, 237–251, https://doi.org/10.5194/ar-3-237-2025, https://doi.org/10.5194/ar-3-237-2025, 2025
Short summary
Short summary
Aerosols, a large uncertainty in climate modeling, can be formed when gas vapors and particles begin sticking together. Traditionally, these particles are assumed to behave like hard spheres that only stick together upon head-on collisions. In reality, particles can attract each other over distances, leading to more frequent sticking events. We found that traditional models significantly undercount these events, with real sticking rates being up to 2.4 times higher.
Huan Yang, Ivo Neefjes, Valtteri Tikkanen, Jakub Kubečka, Theo Kurtén, Hanna Vehkamäki, and Bernhard Reischl
Atmos. Chem. Phys., 23, 5993–6009, https://doi.org/10.5194/acp-23-5993-2023, https://doi.org/10.5194/acp-23-5993-2023, 2023
Short summary
Short summary
We present a new analytical model for collision rates between molecules and clusters of arbitrary sizes, accounting for long-range interactions. The model is verified against atomistic simulations of typical acid–base clusters participating in atmospheric new particle formation (NPF). Compared to non-interacting models, accounting for long-range interactions leads to 2–3 times higher collision rates for small clusters, indicating the necessity of including such interactions in NPF modeling.
Ivo Neefjes, Yosef Knattrup, Haide Wu, Georg Baadsgaard Trolle, Jonas Elm, and Jakub Kubečka
Aerosol Research Discuss., https://doi.org/10.5194/ar-2025-30, https://doi.org/10.5194/ar-2025-30, 2025
Preprint under review for AR
Short summary
Short summary
We investigated how water vapor affects the earliest steps of atmospheric aerosol formation, a key process influencing clouds and climate. By benchmarking quantum-chemical methods, we identified reliable approaches for modeling hydrated molecular clusters of common atmospheric acids and bases. We show that humidity moderately stabilizes certain clusters but only modestly alters particle formation rates. These findings sharpen our understanding of clusters and their role in aerosol formation.
Yosef Knattrup, Ivo Neefjes, Jakub Kubečka, and Jonas Elm
Aerosol Research, 3, 237–251, https://doi.org/10.5194/ar-3-237-2025, https://doi.org/10.5194/ar-3-237-2025, 2025
Short summary
Short summary
Aerosols, a large uncertainty in climate modeling, can be formed when gas vapors and particles begin sticking together. Traditionally, these particles are assumed to behave like hard spheres that only stick together upon head-on collisions. In reality, particles can attract each other over distances, leading to more frequent sticking events. We found that traditional models significantly undercount these events, with real sticking rates being up to 2.4 times higher.
Valtteri Tikkanen, Huan Yang, Hanna Vehkamäki, and Bernhard Reischl
EGUsphere, https://doi.org/10.5194/egusphere-2025-507, https://doi.org/10.5194/egusphere-2025-507, 2025
Short summary
Short summary
Collisions of neutral molecules and clusters is the prevalent pathway in atmospheric new particle formation. In heavily polluted urban areas, where clusters are formed rapidly and in large number, cluster-cluster collisions also become relevant. We calculate cluster-cluster collision rates from atomistic molecular dynamics simulations and an interacting hard sphere model. Not accounting for long-range attractive interactions underestimates collision and particle formation rates significantly.
Huan Yang, Ivo Neefjes, Valtteri Tikkanen, Jakub Kubečka, Theo Kurtén, Hanna Vehkamäki, and Bernhard Reischl
Atmos. Chem. Phys., 23, 5993–6009, https://doi.org/10.5194/acp-23-5993-2023, https://doi.org/10.5194/acp-23-5993-2023, 2023
Short summary
Short summary
We present a new analytical model for collision rates between molecules and clusters of arbitrary sizes, accounting for long-range interactions. The model is verified against atomistic simulations of typical acid–base clusters participating in atmospheric new particle formation (NPF). Compared to non-interacting models, accounting for long-range interactions leads to 2–3 times higher collision rates for small clusters, indicating the necessity of including such interactions in NPF modeling.
Golnaz Roudsari, Olli H. Pakarinen, Bernhard Reischl, and Hanna Vehkamäki
Atmos. Chem. Phys., 22, 10099–10114, https://doi.org/10.5194/acp-22-10099-2022, https://doi.org/10.5194/acp-22-10099-2022, 2022
Short summary
Short summary
We use atomistic simulations to study heterogeneous ice nucleation on silver iodide surfaces in slit and wedge geometries at low supercooling which serve as a model of irregularities on real atmospheric aerosol particle surfaces. The revealed microscopic ice nucleation mechanisms in confined geometries strongly support the experimental evidence for the importance of surface features such as cracks or pits functioning as active sites for ice nucleation in the atmosphere.
Dina Alfaouri, Monica Passananti, Tommaso Zanca, Lauri Ahonen, Juha Kangasluoma, Jakub Kubečka, Nanna Myllys, and Hanna Vehkamäki
Atmos. Meas. Tech., 15, 11–19, https://doi.org/10.5194/amt-15-11-2022, https://doi.org/10.5194/amt-15-11-2022, 2022
Short summary
Short summary
To study what is happening in the atmosphere, it is important to be able to measure the molecules and clusters present in it. In our work, we studied an artifact that happens inside a mass spectrometer, in particular the fragmentation of clusters. We were able to quantify the fragmentation and retrieve the correct concentration and composition of the clusters using our dual (experimental and theoretical) approach.
Shahzad Gani, Lukas Kohl, Rima Baalbaki, Federico Bianchi, Taina M. Ruuskanen, Olli-Pekka Siira, Pauli Paasonen, and Hanna Vehkamäki
Geosci. Commun., 4, 507–516, https://doi.org/10.5194/gc-4-507-2021, https://doi.org/10.5194/gc-4-507-2021, 2021
Short summary
Short summary
In this article, we present authorship guidelines which also include a novel authorship form along with the documentation of the formulation process for a multidisciplinary and interdisciplinary center with more than 250 researchers. Our practical approach promotes fair authorship practices and, by focusing on clear, transparent, and timely communication, helps avoid late-stage authorship conflict.
Emma Lumiaro, Milica Todorović, Theo Kurten, Hanna Vehkamäki, and Patrick Rinke
Atmos. Chem. Phys., 21, 13227–13246, https://doi.org/10.5194/acp-21-13227-2021, https://doi.org/10.5194/acp-21-13227-2021, 2021
Short summary
Short summary
The study of climate change relies on climate models, which require an understanding of aerosol formation. We train a machine-learning model to predict the partitioning coefficients of atmospheric molecules, which govern condensation into aerosols. The model can make instant predictions based on molecular structures with accuracy surpassing that of standard computational methods. This will allow the screening of low-volatility molecules that contribute most to aerosol formation.
Anna Shcherbacheva, Tracey Balehowsky, Jakub Kubečka, Tinja Olenius, Tapio Helin, Heikki Haario, Marko Laine, Theo Kurtén, and Hanna Vehkamäki
Atmos. Chem. Phys., 20, 15867–15906, https://doi.org/10.5194/acp-20-15867-2020, https://doi.org/10.5194/acp-20-15867-2020, 2020
Short summary
Short summary
Atmospheric new particle formation and cluster growth to aerosol particles is an important field of research, in particular due to the climate change phenomenon. Evaporation rates are very difficult to account for but they are important to explain the formation and growth of particles. Different quantum chemistry (QC) methods produce substantially different values for the evaporation rates. We propose a novel approach for inferring evaporation rates of clusters from available measurements.
Cited articles
Amelynck, C., Schoon, N., Kuppens, T., Bultinck, P., and Arijs, E.: A selected
ion flow tube study of the reactions of H3O+, NO+ and
Barducci, A., Bussi, G., and Parrinello, M.: Well-Tempered Metadynamics: A
Smoothly Converging and Tunable Free-Energy Method, Phys. Rev. Lett., 100,
020603, https://doi.org/10.1103/PhysRevLett.100.020603, 2008. a
Chai, J.-D. and Head-Gordon, M.: Long-range corrected hybrid density
functionals with damped atom–atom dispersion corrections,
Phys. Chem. Chem. Phys., 10, 6615–6620, https://doi.org/10.1039/B810189B, 2008. a, b
Chesnavich, W. J., Su, T., and Bowers, M. T.: Ion-dipole collisions: recent
theoretical advances, in: Kinetics of Ion-Molecule Reactions, edited by:
Ausloos, P. J., 31–53, Springer, https://doi.org/10.1007/978-1-4613-2931-2, 1979. a, b
Chesnavich, W. J., Su, T., and Bowers, M. T.: Collisions in a noncentral field:
a variational and trajectory investigation of ion–dipole capture,
J. Chem. Phys., 72, 2641–2655, https://doi.org/10.1063/1.439409, 1980. a, b
Clary, D. C.: Fast Chemical Reactions: Theory Challenges Experiment,
Ann. Rev. Phys. Chem., 41, 61–90, https://doi.org/10.1146/annurev.pc.41.100190.000425,
1990. a, b
Dugan Jr., J. V. and Magee, J. L.: Capture collisions between ions and polar
molecules, J. Chem. Phys., 47, 3103–3112, https://doi.org/10.1063/1.1712359, 1967. a
Elm, J.: Toward a Holistic Understanding of the Formation and Growth of
Atmospheric Molecular Clusters: A Quantum Machine Learning Perspective,
J. Phys. Chem. A, 125, 895–902, https://doi.org/10.1021/acs.jpca.0c09762, 2020. a
Elm, J., Kubečka, J., Besel, V., Jääskeläinen, M. J.,
Halonen, R., Kurtén, T., and Vehkamäki, H.: Modeling the formation
and growth of atmospheric molecular clusters: A review, J. Aerosol Sci., 149,
105621, https://doi.org/10.1016/j.jaerosci.2020.105621, 2020. a, b, c
Falcon-Rodriguez, C. I., Osornio-Vargas, A. R., Sada-Ovalle, I., and
Segura-Medina, P.: Aeroparticles, Composition, and Lung Diseases, Front.
Immunol., 7, 3, https://doi.org/10.3389/fimmu.2016.00003, 2016. a
Fernández-Ramos, A., Miller, J. A., Klippenstein, S. J., and Truhlar,
D. G.: Modeling the Kinetics of Bimolecular Reactions, Chem. Rev., 106,
4518–4584, https://doi.org/10.1021/cr050205w, 2006. a
Frisch, M. J., Trucks, G. W., Schlegel, H. B., Scuseria, G. E., Robb, M. A.,
Cheeseman, J. R., Scalmani, G., Barone, V., Petersson, G. A., Nakatsuji, H.,
Li, X., Caricato, M., Marenich, A. V., Bloino, J., Janesko, B. G., Gomperts,
R., Mennucci, B., Hratchian, H. P., Ortiz, J. V., Izmaylov, A. F.,
Sonnenberg, J. L., Williams-Young, D., Ding, F., Lipparini, F., Egidi, F.,
Goings, J., Peng, B., Petrone, A., Henderson, T., Ranasinghe, D., Zakrzewski,
V. G., Gao, J., Rega, N., Zheng, G., Liang, W., Hada, M., Ehara, M., Toyota,
K., Fukuda, R., Hasegawa, J., Ishida, M., Nakajima, T., Honda, Y., Kitao, O.,
Nakai, H., Vreven, T., Throssell, K., Montgomery Jr., J. A., Peralta,
J. E., Ogliaro, F., Bearpark, M. J., Heyd, J. J., Brothers, E. N., Kudin,
K. N., Staroverov, V. N., Keith, T. A., Kobayashi, R., Normand, J.,
Raghavachari, K., Rendell, A. P., Burant, J. C., Iyengar, S. S., Tomasi, J.,
Cossi, M., Millam, J. M., Klene, M., Adamo, C., Cammi, R., Ochterski, J. W.,
Martin, R. L., Morokuma, K., Farkas, O., Foresman, J. B., and Fox, D. J.:
Gaussian 16 Revision C.01, gaussian Inc. Wallingford CT, 2016. a, b
Georgievskii, Y. and Klippenstein, S. J.: Long-range transition state theory,
J. Chem. Phys., 122, 194103, https://doi.org/10.1063/1.1899603, 2005. a, b
Gopalakrishnan, R. and Hogan Jr., C. J.: Determination of the Transition Regime
Collision Kernel from Mean First Passage Times, Aerosol Sci. Technol., 45,
1499–1509, https://doi.org/10.1080/02786826.2011.601775, 2011. a
Gordon, H., Kirkby, J., Baltensperger, U., Bianchi, F., Breitenlechner, M.,
Curtius, J., Dias, A., Dommen, J., Donahue, N. M., Dunne, E. M., Duplissy,
J., Ehrhart, S., Flagan, R. C., Frege, C., Fuchs, C., Hansel, A., Hoyle,
C. R., Kulmala, M., Kürten, A., Lehtipalo, K., Makhmutov, V., Molteni,
U., Rissanen, M. P., Stozkhov, Y., Tröstl, J., Tsagkogeorgas, G., Wagner,
R., Williamson, C., Wimmer, D., Winkler, P. M., Yan, C., and Carslaw, K. S.:
Causes and importance of new particle formation in the present-day and
preindustrial atmospheres, J. Geophys. Res.-Atmos., 122, 8739–8760,
https://doi.org/10.1002/2017JD026844, 2017. a, b
Goudeli, E., Lee, J., and Hogan Jr., C. J.: Silica nanocluster binding rate
coefficients from molecular dynamics trajectory calculations, J. Aerosol
Sci., 146, 105558, https://doi.org/10.1016/j.jaerosci.2020.105558, 2020. a
Grimme, S., Bannwarth, C., and Shushkov, P.: A Robust and Accurate
Tight-Binding Quantum Chemical Method for Structures, Vibrational
Frequencies, and Noncovalent Interactions of Large Molecular Systems
Parametrized for All spd-Block Elements (Z=1–86), J. Chem.
Theor. Comput., 13, 1989–2009, https://doi.org/10.1021/acs.jctc.7b00118, 2017. a
Halonen, R., Zapadinsky, E., Kurtén, T., Vehkamäki, H., and Reischl, B.:
Rate enhancement in collisions of sulfuric acid molecules due to long-range
intermolecular forces, Atmos. Chem. Phys., 19, 13355–13366,
https://doi.org/10.5194/acp-19-13355-2019, 2019. a, b, c
Halonen, R., Neefjes, I., and Reischl, B.: Technical note on the efficiency of
different thermostats for equilibrating molecules in the gas phase, in
preparation, 2022. a
He, X.-C., Iyer, S., Sipilä, M., Ylisirniö, A., Peltola, M., Kontkanen,
J., Baalbaki, R., Simon, M., Kürten, A., Tham, Y. J., Pesonen, J.,
Ahonen, L. R., Amanatidis, S., Amorim, A., Baccarini, A., Beck, L., Bianchi,
F., Brilke, S., Chen, D., Chiu, R., Curtius, J., Dada, L., Dias, A., Dommen,
J., Donahue, N. M., Duplissy, J., Haddad, I. E., Finkenzeller, H., Fischer,
L., Heinritzi, M., Hofbauer, V., Kangasluoma, J., Kim, C., Koenig, T. K.,
Kubečka, J., Kvashnin, A., Lamkaddam, H., Lee, C. P., Leiminger, M., Li,
Z., Makhmutov, V., Xiao, M., Marten, R., Nie, W., Onnela, A., Partoll, E.,
Petäjä, T., Salo, V.-T., Schuchmann, S., Steiner, G., Stolzenburg,
D., Stozhkov, Y., Tauber, C., Tomé, A., Väisänen, O.,
Vazquez-Pufleau, M., Volkamer, R., Wagner, A. C., Wang, M., Wang, Y., Wimmer,
D., Winkler, P. M., Worsnop, D. R., Wu, Y., Yan, C., Ye, Q., Lehtinen, K.,
Nieminen, T., Manninen, H. E., Rissanen, M., Schobesberger, S., Lehtipalo,
K., Baltensperger, U., Hansel, A., Kerminen, V.-M., Flagan, R. C., Kirkby,
J., Kurtén, T., and Kulmala, M.: Determination of the collision rate
coefficient between charged iodic acid clusters and iodic acid using the
appearance time method, Aerosol Sci. Technol., 55, 1–12,
https://doi.org/10.1080/02786826.2020.1839013, 2020. a, b
Jiang, S., Liu, Y.-R., Huang, T., Feng, Y.-J., Wang, C.-Y., Wang, Z.-Q., Ge,
B.-J., Liu, Q.-S., Guang, W.-R., and Huang, W.: A universal deep
learning-based framework towards fully ab initio simulation of atmospheric
aerosol nucleation, Research Square [preprint],
https://doi.org/10.21203/rs.3.rs-1191188/v1, 2022. a
Jorgensen, W. L., Maxwell, D. S., and Tirado-Rives, J.: Development and Testing
of the OPLS All-Atom Force Field on Conformational Energetics and Properties
of Organic Liquids, J. Am. Chem. Soc., 118, 11225–11236,
https://doi.org/10.1021/ja9621760, 1996. a
Kirkby, J., Duplissy, J., Sengupta, K., Frege, C., Gordon, H., Williamson, C.,
Heinritzi, M., Simon, M., Yan, C., Almeida, J., Tröstl, J., Nieminen, T.,
Ortega, I. K., Wagner, R., Adamov, A., Amorim, A., Bernhammer, A.-K.,
Bianchi, F., Breitenlechner, M., Brilke, S., Chen, X., Craven, J., Dias, A.,
Ehrhart, S., Flagan, R. C., Franchin, A., Fuchs, C., Guida, R., Hakala, J.,
Hoyle, C. R., Jokinen, T., Junninen, H., Kangasluoma, J., Kim, J., Krapf, M.,
Kürten, A., Laaksonen, A., Lehtipalo, K., Makhmutov, V., Mathot, S.,
Molteni, U., Onnela, A., Peräkylä, O., Piel, F., Petäjä, T.,
Praplan, A. P., Pringle, K., Rap, A., Richards, N. A. D., Riipinen, I.,
Rissanen, M. P., Rondo, L., Sarnela, N., Schobesberger, S., Scott, C. E.,
Seinfeld, J. H., Sipilä, M., Steiner, G., Stozhkov, Y., Stratmann, F.,
Tomé, A., Virtanen, A., Vogel, A. L., Wagner, A. C., Wagner, P. E.,
Weingartner, E., Wimmer, D., Winkler, P. M., Ye, P., Zhang, X., Hansel, A.,
Dommen, J., Donahue, N. M., Worsnop, D. R., Baltensperger, U., Kulmala, M.,
Carslaw, K. S., and Curtius, J.: Ion-induced nucleation of pure biogenic
particles, Nature, 533, 521–526, https://doi.org/10.1038/nature17953, 2016. a
Kubečka, J., Besel, V., Kurtén, T., Myllys, N., and Vehkamäki, H.:
Configurational Sampling of Noncovalent (Atmospheric) Molecular Clusters:
Sulfuric Acid and Guanidine, J. Phys. Chem. A, 123, 6022–6033,
https://doi.org/10.1021/acs.jpca.9b03853, 2019. a
Kürten, A., Jokinen, T., Simon, M., Sipilä, M., Sarnela, N., Junninen,
H., Adamov, A., Almeida, J., Amorim, A., Bianchi, F., Breitenlechner, M.,
Dommen, J., Donahue, N. M., Duplissy, J., Ehrhart, S., Flagan, R. C.,
Franchin, A., Hakala, J., Hansel, A., Heinritzi, M., Hutterli, M.,
Kangasluoma, J., Kirkby, J., Laaksonen, A., Lehtipalo, K., Leiminger, M.,
Makhmutov, V., Mathot, S., Onnela, A., Petäjä, T., Praplan, A. P.,
Riccobono, F., Rissanen, M. P., Rondo, L., Schobesberger, S., Seinfeld,
J. H., Steiner, G., Tomé, A., Tröstl, J., Winkler, P. M., Williamson,
C., Wimmer, D., Ye, P., Baltensperger, U., Carslaw, K. S., Kulmala, M.,
Worsnop, D. R., and Curtius, J.: Neutral molecular cluster formation of
sulfuric acid–dimethylamine observed in real time under atmospheric
conditions, P. Natl. Acad. Sci. USA, 111, 15019–15024,
https://doi.org/10.1073/pnas.1404853111, 2014. a, b
Kurtén, T., Kulmala, M., Dal Maso, M., Suni, T., Reissell, A.,
Vehkamäki, H., Hari, P., Laaksonen, A., Viisanen, Y., and Vesala, T.:
Estimation of different forest-related contributions to the radiative balance
using observations in southern Finland, Boreal Environ. Res., 8, 275–285,
2003. a
Liakos, D. G., Sparta, M., Kesharwani, M. K., Martin, J. M. L., and Neese, F.:
Exploring the Accuracy Limits of Local Pair Natural Orbital Coupled-Cluster
Theory, J. Chem. Theory Comput., 11, 1525–1539, https://doi.org/10.1021/ct501129s,
2015. a
Loukonen, V., Bork, N., and Vehkamäki, H.: From collisions to clusters:
first steps of sulphuric acid nanocluster formation dynamics, Mol. Phys.,
112, 1979–1986, https://doi.org/10.1080/00268976.2013.877167, 2014. a
Maergoiz, A., Nikitin, E., Troe, J., and Ushakov, V.: Classical trajectory and
adiabatic channel study of the transition from adiabatic to sudden capture
dynamics, I. Ion–dipole capture, J. Chem. Phys., 105, 6263–6269,
https://doi.org/10.1063/1.472480, 1996a. a, b
Maergoiz, A., Nikitin, E., Troe, J., and Ushakov, V.: Classical trajectory and
adiabatic channel study of the transition from adiabatic to sudden capture
dynamics, II. Ion–quadrupole capture, J. Chem. Phys., 105, 6270–6276,
https://doi.org/10.1063/1.472468, 1996b. a
Maergoiz, A., Nikitin, E., Troe, J., and Ushakov, V.: Classical trajectory and
adiabatic channel study of the transition from adiabatic to sudden capture
dynamics. III. Dipole–dipole capture, J. Chem. Phys., 105, 6277–6284,
https://doi.org/10.1063/1.472481, 1996c. a
McGrath, M. J., Olenius, T., Ortega, I. K., Loukonen, V., Paasonen, P.,
Kurtén, T., Kulmala, M., and Vehkamäki, H.: Atmospheric Cluster
Dynamics Code: a flexible method for solution of the birth-death equations,
Atmos. Chem. Phys., 12, 2345–2355, https://doi.org/10.5194/acp-12-2345-2012, 2012. a
Midey, A. J., Williams, S., and Viggiano, A. A.: Reactions of NO+ with
Isomeric Butenes from 225 to 500 K, J. Phys. Chem. A, 105, 1574–1578,
https://doi.org/10.1021/jp0019005, 2001. a
Moran, T. F. and Hamill, W. H.: Cross Sections of Ion–Permanent-Dipole
Reactions by Mass Spectrometry, J. Chem. Phys., 39, 1413–1422,
https://doi.org/10.1063/1.1734457, 1963. a
Mosallanejad, S., Oluwoye, I., Altarawneh, M., Gore, J., and Dlugogorski,
B. Z.: Interfacial and bulk properties of concentrated solutions of ammonium
nitrate, Phys. Chem. Chem. Phys., 22, 27698–27712,
https://doi.org/10.1039/D0CP04874G, 2020. a
Plimpton, S.: Fast Parallel Algorithms for Short-Range Molecular Dynamics,
J. Comp. Phys., 117, 1–19, https://doi.org/10.1006/jcph.1995.1039, 1995. a, b, c
Riplinger, C. and Neese, F.: An efficient and near linear scaling pair natural
orbital based local coupled cluster method, J. Chem. Phys., 138, 034 106,
https://doi.org/10.1063/1.4773581, 2013. a
Riplinger, C., Sandhoefer, B., Hansen, A., and Neese, F.: Natural triple
excitations in local coupled cluster calculations with pair natural orbitals,
J. Chem. Phys., 139, 134101, https://doi.org/10.1063/1.4821834, 2013. a
Strekowski, R. S., Alvarez, C., Petrov-Stojanović, J., Hagebaum-Reignier, D.,
and Wortham, H.: Theoretical chemical ionization rate constants of the
concurrent reactions of hydronium ions (H3O+) and oxygen ions (O) with
selected organic iodides, J. Mass Spectrom., 54, 422–428,
https://doi.org/10.1002/jms.4349, 2019. a
Su, T. and Bowers, M. T.: Theory of ion-polar molecule collisions. Comparison
with experimental charge transfer reactions of rare gas ions to geometric
isomers of difluorobenzene and dichloroethylene, J. Chem. Phys., 58,
3027–3037, https://doi.org/10.1063/1.1679615, 1973. a, b
Su, T., Su, E. C., and Bowers, M. T.: Ion–polar molecule collisions.
Conservation of angular momentum in the average dipole orientation theory.
The AADO theory, J. Chem. Phys., 69, 2243–2250, https://doi.org/10.1063/1.436783,
1978. a
Thajudeen, T., Gopalakrishnan, R., and Hogan Jr., C. J.: The Collision Rate of
Nonspherical Particles and Aggregates for all Diffusive Knudsen Numbers,
Aerosol Sci. Technol., 46, 1174–1186, https://doi.org/10.1080/02786826.2012.701353,
2012. a
Tribello, G. A., Bonomi, M., Branduardi, D., Camilloni, C., and Bussi, G.:
Plumed 2: New feathers for an old bird, Comput. Phys. Commun., 185,
604–613, https://doi.org/10.1016/j.cpc.2013.09.018, 2014. a
Troe, J.: Statistical adiabatic channel model of ion-neutral dipole capture
rate constants, Chem. Phys. Lett., 122, 425–430,
https://doi.org/10.1016/0009-2614(85)87240-7, 1985. a
Troe, J.: Statistical adiabatic channel model for ion–molecule capture
processes, J. Chem. Phys., 87, 2773–2780, https://doi.org/10.1063/1.453701, 1987. a
Wagner, R., Yan, C., Lehtipalo, K., Duplissy, J., Nieminen, T., Kangasluoma,
J., Ahonen, L. R., Dada, L., Kontkanen, J., Manninen, H. E., Dias, A.,
Amorim, A., Bauer, P. S., Bergen, A., Bernhammer, A.-K., Bianchi, F., Brilke,
S., Mazon, S. B., Chen, X., Draper, D. C., Fischer, L., Frege, C., Fuchs, C.,
Garmash, O., Gordon, H., Hakala, J., Heikkinen, L., Heinritzi, M., Hofbauer,
V., Hoyle, C. R., Kirkby, J., Kürten, A., Kvashnin, A. N., Laurila, T.,
Lawler, M. J., Mai, H., Makhmutov, V., Mauldin III, R. L., Molteni, U.,
Nichman, L., Nie, W., Ojdanic, A., Onnela, A., Piel, F., Quéléver, L.
L. J., Rissanen, M. P., Sarnela, N., Schallhart, S., Sengupta, K., Simon, M.,
Stolzenburg, D., Stozhkov, Y., Tröstl, J., Viisanen, Y., Vogel, A. L.,
Wagner, A. C., Xiao, M., Ye, P., Baltensperger, U., Curtius, J., Donahue,
N. M., Flagan, R. C., Gallagher, M., Hansel, A., Smith, J. N., Tomé, A.,
Winkler, P. M., Worsnop, D., Ehn, M., Sipilä, M., Kerminen, V.-M.,
Petäjä, T., and Kulmala, M.: The role of ions in new particle formation
in the CLOUD chamber, Atmos. Chem. Phys., 17, 15181–15197,
https://doi.org/10.5194/acp-17-15181-2017, 2017. a
Williams, S., Knighton, W. B., Midey, A. J., Viggiano, A. A., Irle, S., Wang,
Q., and Morokuma, K.: Oxidation of Alkyl Ions in Reactions with O2 and
O3 in the Gas Phase, J. Phys. Chem. A, 108, 1980–1989,
https://doi.org/10.1021/jp031099+, 2004. a
Woon, D. and Herbst, E.: Quantum chemical predictions of the properties of
known and postulated neutral interstellar molecules, Astrophys, J. Suppl.
Ser., 185, 273–288, https://doi.org/10.1021/jp031099+, 2009.
a
Xiao, M., Hoyle, C. R., Dada, L., Stolzenburg, D., Kürten, A., Wang, M.,
Lamkaddam, H., Garmash, O., Mentler, B., Molteni, U., Baccarini, A., Simon,
M., He, X.-C., Lehtipalo, K., Ahonen, L. R., Baalbaki, R., Bauer, P. S.,
Beck, L., Bell, D., Bianchi, F., Brilke, S., Chen, D., Chiu, R., Dias, A.,
Duplissy, J., Finkenzeller, H., Gordon, H., Hofbauer, V., Kim, C., Koenig,
T. K., Lampilahti, J., Lee, C. P., Li, Z., Mai, H., Makhmutov, V., Manninen,
H. E., Marten, R., Mathot, S., Mauldin, R. L., Nie, W., Onnela, A., Partoll,
E., Petäjä, T., Pfeifer, J., Pospisilova, V., Quéléver, L. L. J.,
Rissanen, M., Schobesberger, S., Schuchmann, S., Stozhkov, Y., Tauber, C.,
Tham, Y. J., Tomé, A., Vazquez-Pufleau, M., Wagner, A. C., Wagner, R.,
Wang, Y., Weitz, L., Wimmer, D., Wu, Y., Yan, C., Ye, P., Ye, Q., Zha, Q.,
Zhou, X., Amorim, A., Carslaw, K., Curtius, J., Hansel, A., Volkamer, R.,
Winkler, P. M., Flagan, R. C., Kulmala, M., Worsnop, D. R., Kirkby, J.,
Donahue, N. M., Baltensperger, U., El Haddad, I., and Dommen, J.: The driving
factors of new particle formation and growth in the polluted boundary layer,
Atmos. Chem. Phys., 21, 14275–14291, https://doi.org/10.5194/acp-21-14275-2021,
2021. a
Yang, H., Goudeli, E., and Hogan Jr., C. J.: Condensation and dissociation rates
for gas phase metal clusters from molecular dynamics trajectory calculations,
J. Chem. Phys., 148, 164304, https://doi.org/10.1063/1.5026689, 2018. a
Yu, F. and Luo, G.: Simulation of particle size distribution with a global
aerosol model: contribution of nucleation to aerosol and CCN number
concentrations, Atmos. Chem. Phys., 9, 7691–7710,
https://doi.org/10.5194/acp-9-7691-2009, 2009. a
Zapadinsky, E., Passananti, M., Myllys, N., Kurtén, T., and Vehkamäki, H.:
Modeling on fragmentation of clusters inside a mass spectrometer,
J. Phys. Chem. A, 123, 611–624, https://doi.org/10.1021/acs.jpca.8b10744, 2018. a
Zhang, J. and Dolg, M.: ABCluster: the artificial bee colony algorithm for
cluster global optimization, Phys. Chem. Chem. Phys., 17, 24173–24181,
https://doi.org/10.1039/C5CP04060D, 2015. a
Zhang, K., Feichter, J., Kazil, J., Wan, H., Zhuo, W., Griffiths, A. D.,
Sartorius, H., Zahorowski, W., Ramonet, M., Schmidt, M., Yver, C., Neubert,
R. E. M., and Brunke, E.-G.: Radon activity in the lower troposphere and its
impact on ionization rate: a global estimate using different radon emissions,
Atmos. Chem. Phys., 11, 7817–7838, https://doi.org/10.5194/acp-11-7817-2011, 2011. a
Zhao, J., Eisele, F. L., Titcombe, M., Kuang, C., and McMurry, P. H.: Chemical
ionization mass spectrometric measurements of atmospheric neutral clusters
using the cluster-CIMS, J. Geophys. Res.-Atmos., 115,
D08205,
https://doi.org/10.1029/2009JD012606, 2010. a
Short summary
Collisions between ionic and dipolar molecules and clusters facilitate the formation of atmospheric aerosol particles, which affect global climate and air quality. We compared often-used classical approaches for calculating ion–dipole collision rates with robust atomistic computer simulations. While classical approaches work for simple ions and dipoles only, our modeling approach can also efficiently calculate reasonable collision properties for more complex systems.
Collisions between ionic and dipolar molecules and clusters facilitate the formation of...
Altmetrics
Final-revised paper
Preprint