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Natural Biowaste-Cocoon-Derived Granular Activated Carbon-Coated ZnO Nanorods: A Simple Route To Synthesizing a Core-Shell Structure and Its Highly Enhanced UV and Hydrogen Sensing Properties.

Adhimoorthy SaravananBohr-Ran HuangDeepa KathiravanAdhimoorthy Prasannan
Published in: ACS applied materials & interfaces (2017)
Granular activated carbon (GAC) materials were prepared via simple gas activation of silkworm cocoons and were coated on ZnO nanorods (ZNRs) by the facile hydrothermal method. The present combination of GAC and ZNRs shows a core-shell structure (where the GAC is coated on the surface of ZNRs) and is exposed by systematic material analysis. The as-prepared samples were then fabricated as dual-functional sensors and, most fascinatingly, the as-fabricated core-shell structure exhibits better UV and H2 sensing properties than those of as-fabricated ZNRs and GAC. Thus, the present core-shell structure-based H2 sensor exhibits fast responses of 11% (10 ppm) and 23.2% (200 ppm) with ultrafast response and recovery. However, the UV sensor offers an ultrahigh photoresponsivity of 57.9 A W-1, which is superior to that of as-grown ZNRs (0.6 A W-1). Besides this, switching photoresponse of GAC/ZNR core-shell structures exhibits a higher switching ratio (between dark and photocurrent) of 1585, with ultrafast response and recovery, than that of as-grown ZNRs (40). Because of the fast adsorption ability of GAC, it was observed that the finest distribution of GAC on ZNRs results in rapid electron transportation between the conduction bands of GAC and ZNRs while sensing H2 and UV. Furthermore, the present core-shell structure-based UV and H2 sensors also well-retained excellent sensitivity, repeatability, and long-term stability. Thus, the salient feature of this combination is that it provides a dual-functional sensor with biowaste cocoon and ZnO, which is ecological and inexpensive.
Keyphrases
  • reduced graphene oxide
  • quantum dots
  • room temperature
  • aqueous solution
  • visible light
  • machine learning
  • low cost
  • risk assessment
  • deep learning
  • mass spectrometry
  • electron transfer