Insight into the role of excess hydroxide ions in silicate condensation reactions

The formation of silicate oligomers in the early stages is key to zeolite synthesis. The pH and the presence of hydroxide ions are important in regulating the reaction rate and the dominant species in solutions. This paper describes the formation of silicate species, from dimers to 4-membered rings, using ab initio molecular dynamics simulations in explicit water molecules with an excess hydroxide ion. The thermodynamic integration method was used to calculate the free energy profile of the condensation reactions. The hydroxide group's role is not only to control the pH of the environment, but also to actively participate in the condensation reaction. The results show that the most favorable reactions are linear-tetramer and 4-membered-ring formation, with overall barriers of 71 kJ mol^-1 and 73 kJ mol^-1, respectively. The formation of trimeric silicate, with the largest free-energy barrier of 102 kJ mol^-1, is the rate-limiting step under these conditions. The excess hydroxide ion aids in the stabilization of the 4-membered-ring structure over the 3-membered-ring structure. Due to a relatively high free-energy barrier, the 4-membered ring is the most difficult of the small silicate structures to dissolve in the backward reaction. This study is consistent with the experimental observation that silicate growth in zeolite synthesis is slower in a very-high-pH environment.

Combined Deep Learning and Classical Potential Approach for Modeling Diffusion in UiO-66

Modeling of diffusion of adsorbates through porous materials with atomistic molecular dynamics (MD) can be a challenging task if the flexibility of the adsorbent needs to be included. This is because potentials need to be developed that accurately account for the motion of the adsorbent in response to the presence of adsorbate molecules. In this work, we show that it is possible to use accurate machine learning atomistic potentials for metal-organic frameworks in concert with classical potentials for adsorbates to accurately compute diffusivities though a hybrid potential approach. As a proof-of-concept, we have developed an accurate deep learning potential (DP) for UiO-66, a metal-organic framework, and used this DP to perform hybrid potential simulations, modeling diffusion of neon and xenon through the crystal. The adsorbate-adsorbate interactions were modeled with Lennard-Jones (LJ) potentials, the adsorbent-adsorbent interactions were described by the DP, and the adsorbent-adsorbate interactions used LJ cross-interactions. Thus, our hybrid potential allows for adsorbent-adsorbate interactions with classical potentials but models the response of the adsorbent to the presence of the adsorbate through near-DFT accuracy DPs. This hybrid approach does not require refitting the DP for new adsorbates. We calculated self-diffusion coefficients for Ne in UiO-66 from DFT-MD, our hybrid DP/LJ approach, and from two different classical potentials for UiO-66. Our DP/LJ results are in excellent agreement with DFT-MD. We modeled diffusion of Xe in UiO-66 with DP/LJ and a classical potential. Diffusion of Xe in UiO-66 is about a factor of 30 slower than that of Ne, so it is not computationally feasible to compute Xe diffusion with DFT-MD. Our hybrid DP-classical potential approach can be applied to other MOFs and other adsorbates, making it possible to use an accurate DP generated from DFT simulations of an empty adsorbent in concert with existing classical potentials for adsorbates to model adsorption and diffusion within the porous material, including adsorbate-induced changes to the framework.

Catalytic properties of the ferryl ion in the solid state: a computational review

This review summarises the last findings in the emerging field of heterogeneous catalytic oxidation of light alkanes by ferryl species supported on solid-state systems such as the conversion of methane into methanol by FeO-MOF74.