Computational chemistry of cluster: Understanding the mechanism of atmospheric new particle formation at the molecular level

The role of sulfur cycle in new particle formation: Cycloaddition reaction of SO3 to H2S

The chemistry of sulfur cycle contributes significantly to the atmospheric nucleation process, which is the first step of new particle formation (NPF). In the present study, cycloaddition reaction mechanism of sulfur trioxide (SO3) to hydrogen sulfide (H2S) which is a typical air pollutant and toxic gas detrimental to the environment were comprehensively investigate through theoretical calculations and Atmospheric Cluster Dynamic Code simulations. Gas-phase stability and nucleation potential of the product thiosulfuric acid (H2S2O3, TSA) were further analyzed to evaluate its atmospheric impact. Without any catalysts, the H2S + SO3 reaction is infeasible with a barrier of 24.2 kcal/mol. Atmospheric nucleation precursors formic acid (FA), sulfuric acid (SA), and water (H2O) could effectively lower the reaction barriers as catalysts, even to a barrierless reaction with the efficiency of cis-SA > trans-FA > trans-SA > H2O. Subsequently, the gas-phase stability of TSA was investigated. A hydrolysis reaction barrier of up to 61.4 kcal/mol alone with an endothermic isomerization reaction barrier of 5.1 kcal/mol under the catalytic effect of SA demonstrates the sufficient stability of TSA. Furthermore, topological and kinetic analysis were conducted to determine the nucleation potential of TSA. Atmospheric clusters formed by TSA and atmospheric nucleation precursors (SA, ammonia NH3, and dimethylamine DMA) were thermodynamically stable. Moreover, the gradually decreasing evaporation coefficients for TSA-base clusters, particularly for TSA-DMA, suggests that TSA may participate in NPF where the concentration of base molecules are relatively higher. The present new reaction mechanism may contributes to a better understanding of atmospheric sulfur cycle and NPF.

Interfacial quantum chemical characterization of aromatic organic matter adsorption on oxidized microplastic surfaces

Examining the adsorption efficiency of individual contaminants on microplastics (MPs) is resource-intensive and time-consuming. To address this challenge, combined laboratory adsorption experiments with model simulations were performed to investigate the adsorption capacities and mechanisms of MPs before and after aging. Our adsorption experiments revealed that aged polyethylene (PE) and polyvinyl chloride (PVC) MPs exhibited increased adsorption capacity for benzene, phenol, and naphthalene. Additionally, density functional theory (DFT) simulations provided insights into changes in adsorption sites, adsorption energy, and charge density on MPs. The π bond of the benzene ring emerged as a pivotal factor in the adsorption process, with van der Waals forces exerting dominant influence. For instance, the adsorption energy of benzene on pristine PE was -0.01879 eV. When oxidized groups, such as hydroxyl, carbonyl, and carboxyl, on the surface of aged PE became the adsorption sites, the adsorption energy increased to -0.06976, -0.04781, and -0.04903 eV, respectively. Regions with unoxidized functional groups also exhibited higher adsorption energies than pristine PE. These results indicated that aged PE had a stronger affinity for benzene compared to pristine PE, enhancing its adsorption. Charge density difference and energy density of states corroborated this observation, revealing larger π-bond charge accumulation areas for benzene on aged PE, suggesting stronger dipole interactions and enhanced adsorption. Similar trends were observed for phenol and naphthalene. In summary, the DFT calculations aligned with the adsorption experiment findings, confirming the effectiveness of simulation methods in predicting changes in the adsorption performance of aged MPs.

Clusterome: A Comprehensive Data Set of Atmospheric Molecular Clusters for Machine Learning Applications

Formation and growth of atmospheric molecular clusters into aerosol particles impact the global climate and contribute to the high uncertainty in modern climate models. Cluster formation is usually studied using quantum chemical methods, which quickly becomes computationally expensive when system sizes grow. In this work, we present a large database of ∼250k atmospheric relevant cluster structures, which can be applied for developing machine learning (ML) models. The database is used to train the ML model kernel ridge regression (KRR) with the FCHL19 representation. We test the ability of the model to extrapolate from smaller clusters to larger clusters, between different molecules, between equilibrium structures and out-of-equilibrium structures, and the transferability onto systems with new interactions. We show that KRR models can extrapolate to larger sizes and transfer acid and base interactions with mean absolute errors below 1 kcal/mol. We suggest introducing an iterative ML step in configurational sampling processes, which can reduce the computational expense. Such an approach would allow us to study significantly more cluster systems at higher accuracy than previously possible and thereby allow us to cover a much larger part of relevant atmospheric compounds.