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Abstract
We present a perspective on the molecular modelling of nanoporous silica material synthesis. We focus on two classes of materials: microporous zeolite materials in their all-silica forms, and ordered mesoporous silica materials. Several approaches have provided insight into the synthesis processes. These approaches range from quantum chemistry modelling of silica polymerisation to molecular simulations of ordered mesoporous silica assembly, and consider physical and chemical phenomena over several lengths and time scales. Our article focuses on models of porous silica material formation based on the assembly of corner-sharing tetrahedra, which we illustrate with applications to silica polymerisation, the formation of microporous crystals and the formation of ordered mesoporous materials. This is a research area where theoretical developments must closely align with experimentation. For this reason, we also devote a significant component of the present review to a survey of key developments in the experimental synthesis and characterisation of these materials. In particular, recent experiments have bracketed length scales of zeolite nuclei in the 5–10 nm range. On the other hand, recent molecular modelling work has accomplished the in silico self-assembly of both zeolitic and mesoporous materials within a unified modelling format. Our article serves to demonstrate the substantial progress that has been made in this field, while highlighting the enormous challenges and opportunities for future progress, such as in understanding the interplay of thermodynamics and kinetics in silica nanopore formation.
Acknowledgements
Research by PAM and SMA has been supported by the Department of Energy (Contract No. DEFG02-07ER46466). Research by WF is partially supported by the Catalysis Center for Energy Innovation, an Energy Frontier Research Center funded by the U.S. Department of Energy, Office of Science and Office of Basic Energy Sciences under Award No. DE-SC0001004. PAM and SMA are grateful for contributions to this research by several coworkers: Roope Astale, Matt Ford, Miguel Jorge, Lin Jin, Atteeque Malani, Szu-Chia Chien, Navaid Khan and German Perez-Sanchez.