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Growth of Graphitic Carbon Nitride-Incorporated ZnO Nanorods on Silicon Pyramidal Substrates for Enhanced Hydrogen Sensing Applications.

Bohr-Ran HuangAdhimoorthy SaravananDeepa KathiravanTing-Yen ChiangWen-Luh Yang
Published in: ACS applied materials & interfaces (2022)
Monitoring the hydrogen gas (H 2 ) level is highly important in a wide range of applications. Oxide-carbon hybrids have emerged as a promising material for the fabrication of gas sensors for this purpose. Here, for the first time, graphitic carbon nitride (g-C 3 N 4 )-doped zinc oxide nanorods (ZNRs) have been grown on silicon (Si) pyramid-shaped surfaces by the facile hydrothermal reaction method. The systematic material analyses have revealed that the g-C 3 N 4 nanostructures (NS) have been consistently incorporated into the ZNRs on the pyramidal silicon (Py-Si) surface (g-C 3 N 4 -ZNRs/Py-Si). The combined properties of the present structure exhibit an excellent sensitivity (∼53%) under H 2 gas exposure, better than that of bare ZNRs (12%). The results revealed that the fine incorporation of g-C 3 N 4 into ZNRs on the Py-Si surface improves the H 2 gas sensing properties when compared to that of the planar silicon (Pl-Si) surface. The doping of g-C 3 N 4 into ZNRs increases the electrical conductivity through its graphene-like edges (due to the formation of delocalized bonds in g-C 3 N 4 during carbon self-doping), as revealed by FESEM images. In addition, the presence of defects in g-C 3 N 4 induces the gas adsorption properties of ZnO through its active sites. Moreover, the integration of the 1D structure (g-C 3 N 4 -ZNRs) into a 3D pyramidal structure opens up new opportunities for low-cost H 2 gas sensing at room temperature. It is an easy way to enhance the gas sensing properties of ZNRs at room temperature, which is desirable for practical H 2 sensor applications.
Keyphrases
  • room temperature
  • ionic liquid
  • visible light
  • low cost
  • quantum dots
  • reduced graphene oxide
  • optical coherence tomography
  • air pollution
  • machine learning
  • staphylococcus aureus
  • carbon dioxide