Further Insight into the Conversion of a Ni-Fe Metal-Organic Framework during Water-Oxidation Reaction.
Mahya SalmanionSubhajit NandyKeun-Hwa ChaeMohammad Mahdi NajafpourPublished in: Inorganic chemistry (2022)
Metal-organic frameworks (MOFs) are extensively investigated as catalysts in the oxygen-evolution reaction (OER). A Ni-Fe MOF with 2,5-dihydroxy terephthalate as a linker has been claimed to be among the most efficient catalysts for the oxygen-evolution reaction (OER) under alkaline conditions. Herein, the MOF stability under the OER was reinvestigated by electrochemical methods, X-ray diffraction, X-ray absorption spectroscopy, energy-dispersive spectroscopy, scanning electron microscopy (SEM), transmission electron microscopy, nuclear magnetic resonance, operando visible spectroscopy, electrospray ionization mass spectroscopy, and Raman spectroscopy. The peaks corresponding to the carboxylate group are observed at 1420 and 1520 cm -1 using Raman spectroscopy. The peaks disappear after the reaction, suggesting the removal of the carboxylate group. A drop in carbon content but growth in oxygen content after the OER was detected by energy-dispersive spectra. This shows that after the OER, the surface of MOF is oxidized. SEM images also show deep restructures in the surface morphology of this Ni-Fe MOF after the OER. Nuclear magnetic resonance and electrospray ionization mass spectrometry show the decomposition of the linker in alkaline conditions and even in the absence of potential. These experimental data indicate that during the OER, the synthesized MOF transforms to a Fe-Ni-layered double hydroxide, and the formed metal oxide is a candidate for the OER catalysis. Generalization is not true; however, taken together, these findings suggest that the stability of Ni-Fe MOFs under harsh oxidation conditions should be reconsidered.
Keyphrases
- metal organic framework
- electron microscopy
- raman spectroscopy
- high resolution
- magnetic resonance
- single molecule
- mass spectrometry
- electron transfer
- gas chromatography
- solid state
- magnetic resonance imaging
- deep learning
- liquid chromatography
- visible light
- density functional theory
- atomic force microscopy
- risk assessment
- nitric oxide
- high performance liquid chromatography
- convolutional neural network
- machine learning
- contrast enhanced
- molecular dynamics
- ms ms