From Graphene Oxide to Graphene: Changes in Interfacial Water Structure and Reactivity Using Deep Neural Network Force Fields

Abstract

Graphene oxides (GO) are thin graphene sheets containing oxygen-bearing defects. These sheets have a complex structure with sp 3 carbons interspersed among sp 2 carbons, which results in competition between aromatic and hydrophilic domains at the GO−water interface. The GO−water as well as neat graphene−water interfacial regions play a crucial role in various applications. While ab initio molecular dynamics simulations provide high accuracy in studying this complex region, they require significant computational resources, which limits the investigation of the interface at both time and length scales. To tackle this issue, a deep neural network force field (DNNF), trained and validated on AIMD data, was developed. It achieves DFT-level accuracy using only a fraction of the computational cost. This DNNF has been successfully used for simulating graphene oxide to reduced graphene oxide right up to fully reduced graphene−water interfaces. The ordering of water near the interface was studied as a function of oxidation level from fully oxidized graphene oxide to graphene. The vibrational sum frequency generation spectrum of the graphene−water interface was determined and compared to experimental data as well as spectra from graphene oxide−water sheets at different oxidation levels. Connections between different spectral signatures and the orientation of different waters were determined. The reactivity and buckling of the different sheets were examined. The analyses of the trajectories revealed the formation of multiple hydronium formation events with sustained proton hopping over more than a 100 ps in the fully oxidized GO−water systems.