Quinoid-Based Three-Dimensional Metal-Organic Framework Fe 2 (dhbq) 3 : Porosity, Electrical Conductivity, and Solid-State Redox Properties.
Shraddha GuptaHaruki TanakaKentaro FukuKaiji UchidaHiroaki IguchiRyota SakamotoHiroaki KobayashiYoshiyuki GambeItaru HonmaYutaka HiraiShinya HayamiShinya TakaishiPublished in: Inorganic chemistry (2023)
We report the synthesis, characterization, and electronic properties of the quinoid-based three-dimensional metal-organic framework [Fe 2 (dhbq) 3 ]. The MOF was synthesized without using cations as a template, unlike other reported X 2 dhbq 3 -based coordination polymers, and the crystal structure was determined by using single-crystal X-ray diffraction. The crystal structure was entirely different from the other reported [Fe 2 (X 2 dhbq 3 )] 2- ; three independent 3D polymers were interpenetrated to give the overall structure. The absence of cations led to a microporous structure, investigated by N 2 adsorption isotherms. Temperature dependence of electrical conductivity data revealed that it exhibited a relatively high electrical conductivity of 1.2 × 10 -2 S cm -1 ( E a = 212 meV) due to extended d-π conjugation in a three-dimensional network. Thermoelectromotive force measurement revealed that it is an n-type semiconductor with electrons as the majority of charge carriers. Structural characterization and spectroscopic analyses, including SXRD, Mössbauer, UV-vis-NIR, IR, and XANES measurements, evidenced the occurrence of no mixed valency based on the metal and the ligand. [Fe 2 (dhbq) 3 ] upon incorporating as a cathode material for lithium-ion batteries engendered an initial discharge capacity of 322 mAh/g.
Keyphrases
- metal organic framework
- crystal structure
- solid state
- single cell
- ionic liquid
- risk assessment
- aqueous solution
- electronic health record
- molecular docking
- room temperature
- single molecule
- high resolution
- big data
- gold nanoparticles
- fluorescence imaging
- solar cells
- magnetic resonance
- data analysis
- molecularly imprinted
- deep learning
- liquid chromatography