Storage of hydrogen in nano-confined hydrates

This project will explore the feasibility of storing hydrogen in water clathrates stabilized within nanoporous materials. If successful, we envisage this technology to be a game-changing alternative to commonly suggested physical and chemical storage solutions, providing adequate storage in environmentally friendly environments and at modest conditions of temperature and pressure. We will employ data-driven machine-learning methods, thermodynamic modelling and classical molecular simulation techniques to advance the knowledge of chemical relevance, applicability and viability of porous materials as hosts for hydrogen hydrates. The overall process of the formation of clathrates is only partially understood. However, the role of surfaces as promoters of the solid formation is key for the kinetically sustainable formation of hydrates in confinement. Current understanding of the problem [Nguyen, N. N., et al. Critical Review on Gas Hydrate Formation at Solid Surfaces and in Confined Spaces: Why and How Does Interfacial Regime Matter? Energ Fuel 34, 6751-6760 (2020] indicates that surfaces must have an intermediate wettability between the hydrophilic and hydrophobic regimes for clathrates to form. Hence, the initial tasks of the project are aimed at production of computer-generated models which can be deployed in Grand Canonical Monte Carlo simulations for pre-screening of nanoporous materials, e.g. carbons, mesoporous silicas, according to their hydrophilic/hydrophobic nature. Adsorption of H2O will be studied through molecular simulations, populating the regions of phase space inaccessible to experiments (e.g. high pressures, cryogenic temperatures, confinement) and producing a large bank of pseuso-data. The collated results of this research will feed the development of a machine-learned model to correlate volumetric bulk and confined fluid properties including the descriptors for solid materials and the obtained simulation data on the physisorption of water. A key aspect of this task is the feedback loop that links the outputs with the available pseudo-data and the identification of optimal materials and conditions. If time allows, diffusion and transport of H2O and H2 in nanoconfinement will be assessed and large scale simulations of hydrate pre-formation will be attempted with the best models.