Determining Optical Band Gaps of MOFs

Abstract

Assigning optical band gaps to MOFs is paramount for understanding their optical, electronic, and reactivity properties, but literature reports have produced a wide range of values for even the same MOF. Despite the molecular nature of MOF electronic structures, experimental assignments employ Tauc analysis, a method applied to semiconductors. Here, we report optical band gaps of common MOFs and demonstrate that Gaussian fitting is more appropriate for assigning accurate gap energies. We further support this claim with DFT simulation, providing a reliable method for estimating optical band gaps from ground-state hybrid-GGA. MOFs that require Tauc analysis exhibit overlapping optical transitions uncommon for typical carboxylate-based MOFs and more akin to narrow-gap semiconductors. Taken together, these results provide a simple roadmap for assigning MOF optical band gaps.

Efficient Photodegradation of Eriochrome Black-T by a Trimetallic Magnetic Self-Synthesized Nanophotocatalyst Based on Zn/Au/Fe-Embedded Poly(vinyl alcohol)

“…According to Figure c­(i–iii), the diffuse reflectance spectra (DRS) were applied to specify the band gap energy. The band gap energy can be precisely calculated based on computational routes, such as Kubelka Munk or Tauc plot approaches . The highest energy gap was attained in the order of ZnO NRs, Fe 3 O 4 NPs, and the Fe 3 O 4 @PVA–Au/ZnO magnetic photocatalytic system.…”

“…The band gap energy can be precisely calculated based on computational routes, such as Kubelka Munk or Tauc plot approaches. 66 The highest energy gap was attained in the order of ZnO NRs, Fe 3 O 4 NPs, and the Fe 3 O 4 @PVA−Au/ZnO magnetic photocatalytic system. The electron transition from the valence band (VB) to the conduction band (CB) of Au with a band gap value of 3.4 eV provides exciton (electron−hole pairs).…”

Connecting the dots for fundamental understanding of structure–photophysics–property relationships of COFs, MOFs, and perovskites using a Multiparticle Holstein Formalism

“…Skillful exploration of data through data-mining and big data approaches as well as electronic structure modeling have greatly benefited the fields of MOFs and perovskites. 103,[110][111][112][113][114][115][116][117][118][119] Machine learning and high throughput techniques have been routinely em-ployed to screen the chemical space of hybrid materials with the goal of proposing new MOF and perovskite structures that are suitable for industrial applications, including but not limited to catalysis, [120][121][122][123] gas storage and separation, 124 and optoelectronics. [125][126][127][128] While materials modeling based on data-driven approaches has achieved some remarkable success in predicting electronic properties (e.g., band gaps, charge densities, electronic couplings, oscillator strengths, etc.…”

Connecting the Dots for Fundamental Understanding of Structure-Photophysics-Property Relationships of COFs, MOFs, and Perovskites using a Multiparticle Holstein Formalism

“…Skillful exploration of data through data-mining and big data approaches as well as electronic structure modeling have greatly benefited the fields of MOFs and perovskites. 103,[110][111][112][113][114][115][116][117][118][119] Machine learning and high throughput techniques have been routinely employed to screen the chemical space of hybrid materials with the goal of proposing new MOF and perovskite structures that are suitable for different applications, including but not limited to catalysis, [120][121][122][123] gas storage and separation, 124 and optoelectronics. [125][126][127][128] While materials modeling based on data-driven approaches has achieved some remarkable success in predicting electronic properties (e.g., band gaps, charge densities, electronic couplings, oscillator strengths, etc.…”