Structure of the water/magnetite interface from sum frequency generation experiments and neural network based molecular dynamics simulations

Salvatore Romano; Harsharan Kaur; Moritz Zelenka; Pablo Montero De Hijes; Moritz Eder; Gareth S. Parkinson; Ellen H. G. Backus; Christoph Dellago

arXiv · 2410.12717

Structure of the water/magnetite interface from sum frequency generation experiments and neural network based molecular dynamics simulations

Salvatore Romano·Harsharan Kaur·Moritz Zelenka·Pablo Montero De Hijes·Moritz Eder·Gareth S. Parkinson·Ellen H. G. Backus·Christoph Dellago

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Abstract

Magnetite, a naturally abundant mineral, frequently interacts with water in both natural settings and various technical applications, making the study of its surface chemistry highly relevant. In this work, we investigate the hydrogen bonding dynamics and the presence of hydroxyl species at the magnetite-water interface using a combination of neural network potential-based molecular dynamics simulations and sum frequency generation vibrational spectroscopy. Our simulations, which involved large water systems, allowed us to identify distinct interfacial species, such as dissociated hydrogen and hydroxide ions formed by water dissociation. Notably, water molecules near the interface exhibited a preference for dipole orientation towards the surface, with bulk-like water behavior only re-emerging beyond 60 {\AA} from the surface. The vibrational spectroscopy results aligned well with the simulations, confirming the presence of a hydrogen bond network in the surface ad-layers. The analysis revealed that surface-adsorbed hydroxyl groups orient their hydrogen atoms towards the water bulk. In contrast, hydrogen-bonded water molecules align with their hydrogen atoms pointing towards the magnetite surface.

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Salvatore Romano, Harsharan Kaur, Moritz Zelenka, Pablo Montero De Hijes, Moritz Eder, Gareth S. Parkinson, Ellen H. G. Backus, Christoph Dellago. 2024-10-16. Structure of the water/magnetite interface from sum frequency generation experiments and neural network based molecular dynamics simulations. https://arxiv.org/abs/2410.12717

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