Gas-to-Particle Partitioning of Cyclohexene- and α-Pinene-Derived Highly Oxygenated Dimers Evaluated Using COSMOtherm

New mechanism for the participation of aromatic oxidation products in atmospheric nucleation

Oxygenated organic molecules (OOMs) are recognized as important precursors for new particle formation (NPF) in the urban atmosphere. The paper theoretically studied the formation of OOMs by styrene oxidation processes initiated by OH radicals, focusing on the OOMs nucleation mechanism. The results found that in the presence of an H2SO4 molecule, lowly oxygenated organic molecules containing a benzene ring (LOMBs) can form stable clusters and grow to the scale of a critical nucleus through pi-pi stacking and OH hydrogen bonding. In addition, LOMBs are more readily generated in a styrene-oxidized system in the presence/absence of NOx than highly oxygenated organic molecules (HOMs). The reaction of OH radicals with other aromatics containing a branched chain on the benzene ring produces LOMBs to varying degrees, with pi-pi stacking playing an essential role. This result suggests that, in the presence of H2SO4 molecules, LOMBs may play a more significant role in promoting nucleation than HOMs. Our findings serve as a pivotal foundation for future investigations into the oxidation and nucleation processes of diverse aromatics in urban environments.

Formation of Highly Oxidized Organic Compounds and Secondary Organic Aerosol from α-Thujene Ozonolysis

We conducted laboratory chamber experiments to probe the gas- and particle-phase composition of oxidized organics and secondary organic aerosol (SOA) formed from α-thujene ozonolysis under different chemical regimes. The formation of low-volatility compounds was observed using chemical ionization mass spectrometry with nitrate (NO3-) and iodide (I-) reagent ions. The contribution of measured low-volatility compounds to particle growth was predicted using a simple condensational growth model and found to underpredict the measured growth rates in our chamber (on the order of several nm min-1). The yields of low-volatility compounds and SOA mass were similar to those of other monoterpene ozonolysis systems. While semivolatile compounds C10H14-16O3-7 were measured most abundantly with I- reagent ion, a large fraction of products measured with NO3- were C5-7 fragments with predicted intermediate volatility. Additionally, particle composition was measured with ultrahigh-performance liquid chromatography with high-resolution mass spectrometry and compared to particle composition from α-pinene ozonolysis. Structural isomers were identified from tandem mass spectrometry analysis of two abundant product ions (C8H13O5-, C19H27O7-). Our results indicate that although this system efficiently generates low-volatility organics and SOA under the conditions studied, fragmentation pathways that produce more highly volatile products effectively compete with these processes.

Atomic structures, conformers and thermodynamic properties of 32k atmospheric molecules

Low-volatile organic compounds (LVOCs) drive key atmospheric processes, such as new particle formation (NPF) and growth. Machine learning tools can accelerate studies of these phenomena, but extensive and versatile LVOC datasets relevant for the atmospheric research community are lacking. We present the GeckoQ dataset with atomic structures of 31,637 atmospherically relevant molecules resulting from the oxidation of α-pinene, toluene and decane. For each molecule, we performed comprehensive conformer sampling with the COSMOconf program and calculated thermodynamic properties with density functional theory (DFT) using the Conductor-like Screening Model (COSMO). Our dataset contains the geometries of the 7 Mio. conformers we found and their corresponding structural and thermodynamic properties, including saturation vapor pressures (p_Sat), chemical potentials and free energies. The p_Sat were compared to values calculated with the group contribution method SIMPOL. To validate the dataset, we explored the relationship between structural and thermodynamic properties, and then demonstrated a first machine-learning application with Gaussian process regression.

Molecular Dynamics Simulation Prediction of the Partitioning Constants (KH, Kiw, Kia) of 82 Legacy and Emerging Organic Contaminants at the Water-Air Interface

The tendency of organic contaminants (OCs) to partition between different phases is a key set of properties that underlie their human and ecological health impacts and the success of remediation efforts. A significant challenge associated with these efforts is the need for accurate partitioning data for an ever-expanding list of OCs and breakdown products. All-atom molecular dynamics (MD) simulations have the potential to help generate these data, but existing studies have applied these techniques only to a limited variety of OCs. Here, we use established MD simulation approaches to examine the partitioning of 82 OCs, including many compounds of critical concern, at the water-air interface. Our predictions of the Henry's law constant (K_H) and interfacial adsorption coefficients (K_iw, K_ia) correlate strongly with experimental results, indicating that MD simulations can be used to predict K_H, K_iw, and K_ia values with mean absolute deviations of 1.1, 0.3, and 0.3 logarithmic units after correcting for systematic bias, respectively. A library of MD simulation input files for the examined OCs is provided to facilitate future investigations of the partitioning of these compounds in the presence of other phases.

Machine Learning for Predicting Chemical Potentials of Multifunctional Organic Compounds in Atmospherically Relevant Solutions

We have trained the Extreme Minimum Learning Machine (EMLM) machine learning model to predict chemical potentials of individual conformers of multifunctional organic compounds containing carbon, hydrogen, and oxygen. The model is able to predict chemical potentials of molecules that are in the size range of the training data with a root-mean-square error (RMSE) of 0.5 kcal/mol. There is also a linear correlation between calculated and predicted chemical potentials of molecules that are larger than those included in the training set. Finding the lowest chemical potential conformers is useful in condensed phase thermodynamic property calculations, in order to reduce the number of computationally demanding density functional theory calculations.