In Silico Design of 2D and 3D Covalent Organic Frameworks for Methane Storage Applications

DOI

Here we present 69,840 covalent organic frameworks (COFs) assembled in silico from a set of 666 distinct organic linkers into 2D-layered and 3D configurations. We investigate the feasibility of using these frameworks for methane storage by using grand-canonical Monte Carlo (GCMC) simulations to calculate their deliverable capacities (DCs). From these calculations, we predict that the best structure in the database is linker91_C_linker91_C_tbd, a structure composed of carbon-carbon bonded triazine linkers in the tbd topology. This structure has a predicted 65-bar DC of 216 v STP/v, greater than that of the best current methane storage material. We also predict other top performing materials, with 305 structures having DCs of over 190 v STP/v, and 34 of these having DCs of over 200 v STP/v. This archive entry contains the database of assembled COF structures (in CIF file format) together with all of their properties. Among the calculated properties for each structure are the framework density, the methane heats of desorption at the storage and depletion pressures, the methane uptakes at the storage and deplation pressures, the supercell volume, and the geometric surface area. Structures are also labeled according to their bond types (amide, amine, imine, carbon-carbon, or mixed) and their dimensionalities (2D or 3D).

Identifier
DOI http://dx.doi.org/doi:10.24435/materialscloud:2018.0003/v1
Source https://archive.materialscloud.org/2018.0003/v1
Metadata Access https://archive.materialscloud.org/xml?verb=GetRecord&metadataPrefix=oai_dc&identifier=oai:materialscloud.org:2018.0003/v1
Provenance
Creator Talirz, Leopold;Smit, Berend;Fu, Rueih-Sheng;Mercado, Rocio;Haranczyk, Maciej;Yakutovich, Aliaksandr V.
Publisher Materials Cloud
Publication Year 2018
Rights Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode;info:eu-repo/semantics/openAccess
OpenAccess true
Contact Materials Cloud
Representation
Language English
Resource Type Dataset
Coverage
Discipline Materials Science And Engineering