How the pH of aqueous droplets and its size dependence are controlled by the air-water interface acidity Miguel de la Puente and Damien Laage PASTEUR, Department of Chemistry, cole Normale Supérieure, PSL University, France The air-water interface exhibits a unique chemical reactivity that is completely different from that in the bulk and that is central to fields ranging from “on-droplet” catalysis to atmospheric chemistry. 1 One of the most fundamental properties altered by the air-water interface is acidity. However, defining and measuring acidity in micro-droplets is extremely challenging, since factors ranging from system size to spatial resolution can critically impact such measurements. 2 Recent innovative experiments 3,4 have reported a mapping of acidity within droplets, but the results remain contrasted and a molecular understanding of the interface impact on acidity is needed. Molecular dynamics (MD) simulations are a precious tool to obtain a molecular-level picture of acidity in interfacial systems. However, the computational cost of typical reactive simulations traditionally imposes a compromise either on the accuracy of the electronic structure descriptions or on the statistical sampling, which are both required to provide a quantitative measure of acidity. Here, we overcome these limitations by employing deep neural network potentials 5 trained to reproduce potential energy surfaces of hybrid DFT quality at a fraction of the computational cost, which we combine with path-integral MD to account for nuclear quantum effects. 6 We performed reactive simulations of the water self-dissociation equilibrium and calculated the hydronium and hydroxide self-ion stabilities near the air-water interface. We then combined these results with an analytical model to determine the pH and self-ion concentration profiles within nano- and micro-droplets and to assess the impact of system size and interfacial depth on these key quantities. References 1. M.F. Ruiz-López et al. , Nat. Rev. Chem. , 2020, 4, 459-475 2. M. de la Puente et al. , J. Am. Chem. Soc. , 2022, 144, 10524-10529 3. M. Li et al. , Chem. , 2023, 9, 1-11 4. K. Gong et al. , Proc. Nat. Acad. Sci. U.S.A ., 2023, 120, 20, e2219588120 5. H. Wang et al. , Comput. Phys. Commun. , 2018, 228, 178-184 6. M. Ceriotti et al. , Phys. Rev. Lett. , 2012, 109, 100604
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