(185a) MOF-Derived Nanoparticles Coupled with N-Doped Porous Carbon Polyhedrons for Electrochemical Sensing

Authors: 
Wang, K., Department of Chemical Engineering, Key Lab of Industrial Biocatalysis, Ministry of Education, Tsinghua University
Jiang, G., Key Lab of Industrial Biocatalysis, Ministry of Education, Tsinghua University
MOF-derived nanostructured materials through a facile synthesis not only have a variety of properties of MOF, such as high surface area, uniform cavity, and tailorable chemical properties, but also acquire unique characteristics such as high conductivity, abundant catalytic sites. In the present work, a facile and scalable strategy was proposed to prepare MOF-derived CoPx nanoparticles in hairy nitrogen-doped porous carbon (CoPx@NCNTs) polyhedrons for electrochemical sensing of p-nitrophenol (p-NP). After pyrolysis of ZIF-67 with a slow heating rate and subsequent phosphidation treatment, the obtained CoPx@NCNTs had excellent electrical conductivity, ultrahigh electrochemically active surface area (ECSA), favorable interface properties, preferable internal diffusion properties, unique pore structures, and CoPx nanoparticles for molecular recognition as well as nitrogen doping and confined CoPx nanoparticles for synergistic catalytic effects. As a result, the prepared sensor based on CoPx@NCNTs exhibited superior electrochemical performances for p-NP detection with a low limit of detection (LOD) of 0.79 nM, a high sensitivity of 802 μA μM-1 cm-2, a broad linear range from 0.0025 μM to 1 μM, and a broad logarithmic range from 1 μM to 1000 μM. Moreover, the prepared sensor displayed excellent anti-interference ability toward a variety of benzene derivatives, prominent reproducibility, superior stability, and achieved practical applications for p-NP detection in tap water and pond water. Considering the diversity of cobalt-based materials and adjustable pyrolysis strategy, synthesizing functional nanomaterials with superior performances for electrochemical sensing, catalysis, and energy conversion is expected.