Synthesis of Methane Hydrate from Ice Powder Accelerated by Doping Ethanol into Methane Gas

Computational investigations of stable multiple-cage-occupancy He clathrate-like hydrostructures

One of the several possibilities offered by the interesting clathrate hydrates is the opportunity to encapsulate several atoms or molecules, in such a way that more efficient storage materials could be explored or new molecules that otherwise do not exist could be created. These types of applications are receiving growing attention from technologists and chemists, given the future positive implications that they entail. In this context, we investigated the multiple cage occupancy of helium clathrate hydrates, to establish stable novel hydrate structures or ones similar to those predicted previously by experimental and theoretical studies. To this purpose, we analyzed the feasibility of including an increased number of He atoms inside the small (D) and large (H) cages of the sII structure through first-principles properly assessed density functional approaches. On the one hand, we have computed energetic and structural properties, in which we examined the guest-host and guest-guest interactions in both individual and two-adjacent clathrate-like sII cages by means of binding and evaporation energies. On the other hand, we have carried out a thermodynamical analysis on the stability of such He-containing hydrostructures in terms of changes in enthalpy, ΔH, Gibbs free energy, ΔG, and entropy, ΔS, during their formation process at various temperature and pressure values. In this way, we have been able to make a comparison with experiments, reaffirming the ability of computational DFT approaches to describe such weak guest-host interactions. In principle, the most stable structure involves the encapsulation of one and four He atoms inside the D and H sII cages, respectively; however, more He atoms could be entrapped under lower temperature and/or higher pressure thermodynamic conditions. We foresee such accurate computational quantum chemistry approaches contributing to the current emerging machine-learning model development.