Tailored Porous Ferrocene-Based Metal-Organic Frameworks as High-Performance Proton Conductors.
Yong-Jie SongSi-Yuan RenShuaiwu ZuoZhi Qiang ShiZifeng LiGang LiPublished in: Inorganic chemistry (2024)
Although crystalline metal-organic frameworks (MOFs) have gained a great deal of interest in the field of proton conduction in recent years, the low stability and poor proton conductivity (σ) of some MOFs have hindered their future applications. As a result, resolving the issues listed above must be prioritized. Due to their exceptional structural stability, MOFs with ferrocene groups that exhibit particular physical and chemical properties have drawn a lot of attention. This study describes the effective preparation of a set of three-dimensional ferrocene-based MOFs, MIL-53-FcDC-Al/Ga and CAU-43 , containing both main group metals and 1,1'-ferrocene dicarboxylic acid (H 2 FcDC). Multiple measurements, including powder X-ray diffraction (PXRD), infrared (IR), and scanning electron microscopy (SEM), confirmed that the addition of ferrocene groups enhanced the thermal, water, and acid-base stabilities of the three MOFs. Consequently, their proton-conductive behaviors were meticulously measured utilizing the AC impedance approach, and their best proton conductivities are 5.20 × 10 -3 , 2.31 × 10 -3 , and 1.72 × 10 -4 S/cm at 100 °C/98% relative humidity (RH), respectively. Excitingly, MIL-53-FcDC-Al/Ga demonstrated an extraordinarily ultrahigh σ of above 10 -4 S·cm -1 under 30 °C/98% RH. Using data from structural analysis, PXRD, SEM, thermogravimetry (TG), and activation energy, their proton transport mechanisms were thoroughly examined. The fact that these MOFs are notably easy to assemble, inexpensive, toxin-free, and stable will increase the range of practical uses for them.
Keyphrases
- metal organic framework
- electron microscopy
- pet ct
- high resolution
- electron transfer
- working memory
- mental health
- electronic health record
- computed tomography
- magnetic resonance imaging
- machine learning
- gold nanoparticles
- climate change
- deep learning
- data analysis
- solid phase extraction
- health risk assessment
- simultaneous determination