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      Nanoparticle cluster gas sensor: controlled clustering of SnO₂ nanoparticles for highly sensitive toluene detection.

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          Abstract

          Gas sensing with nanosized oxide materials is attracting much attention because of its promising capability of detecting various toxic gases at very low concentrations. In this study, using clustered SnO2 nanoparticles formed by controlled particle aggregation, we fabricated highly sensitive gas sensing films to detect large gas molecules such as toluene. A hydrothermal method using stanic acid (SnO2·nH2O) gel as a precursor produced monodispersed SnO2 nanoparticles of ca. 5 nm at pH 10.6. Decreasing the solution pH to 9.3 formed SnO2 clusters of ca. 45 nm that were assemblies of the monodispersed nanoparticles, as determined by dynamic light scattering, X-ray diffraction, and transmission electron microscopy analyses. Porous gas sensing films were successfully fabricated by a spin-coating method using the clustered nanoparticles due to the loose packing of the larger aggregated particles. The sensor devices using the porous films showed improved sensor responses (sensitivities) to H2 and CO at 300 °C. The enhanced sensitivity resulted from an increase in the film's porosity, which promoted the gas diffusivity of the sensing films. Pd loading onto the clustered nanoparticles further upgraded the sensor response due to catalytic and electrical sensitization effects of Pd. In particular, the Pd-loaded SnO2 nanoparticle clusters showed excellent sensitivity to toluene, able to detect it at down to low ppb levels.

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          Author and article information

          Journal
          ACS Appl Mater Interfaces
          ACS applied materials & interfaces
          1944-8252
          1944-8244
          Apr 9 2014
          : 6
          : 7
          Affiliations
          [1 ] Department of Energy and Material Sciences, Faculty of Engineering Science, Kyushu University , Kasuga, Fukuoka 816-8580, Japan.
          Article
          10.1021/am500944a
          24635838
          f9289fe9-957c-4fde-92a6-c5af5c220889
          History

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