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DOI	10.5194/acp-20-15867-2020
	Identification of molecular cluster evaporation rates, cluster formation enthalpies and entropies by Monte Carlo method
	Shcherbacheva A.; Balehowsky T.; Kubečka J.; Olenius T.; Helin T.; Haario H.; Laine M.; Kurtén T.; Vehkamäki H.
发表日期	2020
ISSN	1680-7316
起始页码	15867
结束页码	15906
卷号	20 期号:24
英文摘要	We address the problem of identifying the evaporation rates for neutral molecular clusters from synthetic (computer-simulated) cluster concentrations. We applied Bayesian parameter estimation using a Markov chain Monte Carlo (MCMC) algorithm to determine cluster evaporation/fragmentation rates from synthetic cluster distributions generated by the Atmospheric Cluster Dynamics Code (ACDC) and based on gas kinetic collision rate coefficients and evaporation rates obtained using quantum chemical calculations and detailed balances. The studied system consisted of electrically neutral sulfuric acid and ammonia clusters with up to five of each type of molecules. We then treated the concentrations generated by ACDC as synthetic experimental data. With the assumption that the collision rates are known, we tested two approaches for estimating the evaporation rates from these data. First, we studied a scenario where time-dependent cluster distributions are measured at a single temperature before the system reaches a steady state. In the second scenario, only steady-state cluster distributions are measured but at several temperatures. Additionally, in the latter case, the evaporation rates were represented in terms of cluster formation enthalpies and entropies. This reparameterization reduced the number of unknown parameters, since several evaporation rates depend on the same cluster formation enthalpy and entropy values. We also estimated the evaporation rates using previously published synthetic steady-state cluster concentration data at one temperature and compared our two cases to this setting. Both the time-dependent and the two-temperature steady-state concentration data allowed us to estimate the evaporation rates with less variance than in the steady-state single-temperature case. We show that temperature-dependent steady-state data outperform single-temperature time-dependent data for parameter estimation, even if only two temperatures are used. We can thus conclude that for experimentally determining evaporation rates, cluster distribution measurements at several temperatures are recommended over time-dependent measurements at one temperature. © 2020 Author(s).
语种	英语
scopus关键词	atmospheric chemistry; cluster analysis; enthalpy; entropy; evaporation; kinetics; molecular analysis; Monte Carlo analysis
来源期刊	ATMOSPHERIC CHEMISTRY AND PHYSICS
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/247261
作者单位	Institute for Atmospheric and Earth System Research, University of Helsinki, P.O. Box 64, Helsinki, 00014, Finland; Department of Mathematics and Statistics Subunit, University of Helsinki, P.O. Box 64, Helsinki, 00014, Finland; Department of Environmental Science and Analytical Chemistry, Bolin Centre for Climate Research, Stockholm University, Svante Arrhenius väg 8, Stockholm, 11418, Sweden; Lut School of Engineering Science, Lappeenranta-Lahti University of Technology, P.O.Box 20, Lappeenranta, 53851, Finland; Finnish Meteorological Institute, P.O. Box 503, Helsinki, 00101, Finland; Department of Chemistry, University of Helsinki, P.O. Box 55, Helsinki, 00014, Finland
推荐引用方式 GB/T 7714	Shcherbacheva A.,Balehowsky T.,Kubečka J.,et al. Identification of molecular cluster evaporation rates, cluster formation enthalpies and entropies by Monte Carlo method[J],2020,20(24).
APA	Shcherbacheva A..,Balehowsky T..,Kubečka J..,Olenius T..,Helin T..,...&Vehkamäki H..(2020).Identification of molecular cluster evaporation rates, cluster formation enthalpies and entropies by Monte Carlo method.ATMOSPHERIC CHEMISTRY AND PHYSICS,20(24).
MLA	Shcherbacheva A.,et al."Identification of molecular cluster evaporation rates, cluster formation enthalpies and entropies by Monte Carlo method".ATMOSPHERIC CHEMISTRY AND PHYSICS 20.24(2020).