The high porosity, extended connectivity, and tunability of metal-organic frameworks (MOFs) makes them ideal candidates for the development of electrocatalysts, charge storage materials, and chemiresistive sensors.[1] However, the highly ionic nature of the inorganic-organic interface inhibits the realization of high electrical conductivity thereby limiting their utility for these applications.[2] Post-synthetic retrofitting strategies enable targetted modification of MOF scaffolds, leading to enhanced desired properties. [3, 4] In this project, we explore a retrofitting scheme to post-synthetically install redox-active interstitials, such as thiophene and poly(p-phenylene vinylene), that covalently interconnect MOF linkers as one avenue towards augmenting the electrical properties of these otherwise insulating frameworks. The formation of retrofitted organic linkages within a given scaffold is governed by a statistical distribution of the orientation of linkable units as demonstrated by an atomic-level simulation examining the kinetics of wire formation and the resulting distribution within an extended unit cell of MOF-5. Periodic density functional theory (DFT) calculations quantify the conductive capacity of the newly formed charge transport pathways. Examining other common scaffolds, we find that the macroscopic topology of post-synthetically installed wires (i.e. extended wires vs. conductive loops) formed through new covalent bonds, and their resultant capacity for electronic conduction, is dictated by the effect of MOF symmetry on the statistical distribution of linker orientations. By combining statistical models of wire formation with electronic structure theory applied to periodic models of the retrofitted MOFs we show that post-synthetic formation of pseudo-polymeric wires is a promising strategy to instill electrical conductivity in MOF scaffolds.

Post-synthetic installation of redox-active interstitials form extended conductivity pathways in metal-organic frameworks: insights from electronic structure theory and statistical models

ACS Spring Meeting 2021

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