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Secondary organic aerosols contribute a large fraction to atmospheric aerosols. The phase states of secondary organic aerosols influence heterogeneous and multiphase chemistry in the atmosphere and thus climate. In previous studies we have used the dual tandem differential mobility analyzer technique to characterize the temperature- and humidity-dependent viscosity and glass transition temperature of suspended particles. However, the technique requires high particle number concentrations, is a complex setup, is expensive, and measurements are time consuming. Here we demonstrate a new simplified and more cost-effective method to obtain similar data. The technique was used to measure the temperature where the viscosity is ∼107 Pa s for submicron particles composed of binary and ternary mixtures of the sucrose/tartaric acid/citric acid system. Sucrose, tartaric acid and citric acid are taken as proxies for viscous organic aerosol components in the atmosphere. A subset of data were compared to measurements with the dual-tandem differential mobility analyzer method. Results show good agreement between the two techniques. The same mixed chemical systems were modeled using an updated version of the parametric phase diagram model described in Kasparoglu et al. (2021, https://doi.org/10.5194/acp-21-1127-2021) as well as the predictions with the viscosity module of the Aerosol Inorganic–Organic Mixtures Functional groups Activity Coefficients model (AIOMFAC-VISC). Results show that appropriately parameterized mixing rules are suitable to describe these mixtures. We anticipate that the new technique will accelerate discovery of aerosol phase transitions in aerosol research.
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Making thermodynamic models of mixtures predictive by machine learning: matrix completion of pair interactions
Predictive models of thermodynamic properties of mixtures are paramount in chemical engineering and chemistry. Classical thermodynamic models are successful in generalizing over (continuous) conditions like temperature and concentration. On the other hand, matrix completion methods (MCMs) from machine learning successfully generalize over (discrete) binary systems; these MCMs can make predictions without any data for a given binary system by implicitly learning commonalities across systems. In the present work, we combine the strengths from both worlds in a hybrid approach. The underlying idea is to predict the pair-interaction energies , as they are used in basically all physical models of liquid mixtures, by an MCM. As an example, we embed an MCM into UNIQUAC, a widely-used physical model for the Gibbs excess energy. We train the resulting hybrid model in a Bayesian machine-learning framework on experimental data for activity coefficients in binary systems of 1146 components from the Dortmund Data Bank. We thereby obtain, for the first time, a complete set of UNIQUAC parameters for all binary systems of these components, which allows us to predict, in principle, activity coefficients at arbitrary temperature and composition for any combination of these components, not only for binary but also for multicomponent systems. The hybrid model even outperforms the best available physical model for predicting activity coefficients, the modified UNIFAC (Dortmund) model.
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- PAR ID:
- 10329936
- Date Published:
- Journal Name:
- Chemical Science
- Volume:
- 13
- Issue:
- 17
- ISSN:
- 2041-6520
- Page Range / eLocation ID:
- 4854 to 4862
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1039/D1SC07210B
