journal article·2019-09

Pd-Fe dual-metal nanoparticles confined in the interface of carbon nanotubes/N-doped carbon for excellent catalytic performance

Deng Wang, Jin Liu, Jiangbo Xi, Jizhou Jiang, Zhengwu Bai

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Abstract

Abstract Efficient catalyst with enhanced activity and high stability is important for chemical industry. Active species, support as well as their interaction play key roles on catalytic performance for supported heterogeneous catalysts. In this work, Pd Fe dual-metal nanoparticles (NPs) were anchored and confined in an interface of double-layered carbon nanotubes/nitrogen-doped carbon (CNT/NC) support to obtain a dual-metal CNT/PdFe/NC catalyst. The microstructure and composition of CNT/PdFe/NC catalyst were systematically characterized by transmission electron microscopy (TEM), X-ray photoelectron spectrometry (XPS), inductively coupled plasma mass spectrometry (ICP-MS), X-ray diffraction (XRD) and surface area analysis. Benefiting from the catalytically active tubular CNT/NC support, uniformly dispersed Pd Fe dual-metal NPs and unique confined structure, the CNT/PdFe/NC catalyst exhibited excellent catalytic activity for nitroarenes reduction reaction. The catalytic activity of the Pd Fe dual-metal NPs was much better than that of monometal counterparts (CNT/Pd/NC and CNT/Fe/NC), showing an enhanced synergetic effect. The catalytic turnover frequency (TOF) of the CNT/PdFe/NC catalyst amounted to 70.36 min−1 toward 4-nitrophenol (4-NP) reduction reaction, being superior to previously reported noblemetal-based catalysts. Furthermore, this catalyst also exhibited an excellent stability and reusability due to the intrinsic confined structure. This work provides valuable insights to rationally design and develop an efficient dual-metal catalyst.
Source: openaire · Quality: candidate unverified · Source record ↗

Research context

OpenAlex topics: Nanomaterials for catalytic reactions · Electrocatalysts for Energy Conversion · Catalytic Processes in Materials Science

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

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