Active {1 1 1}-faceted ultra-thin NiO single-crystalline porous nanosheets supported highly dispersed Pt nanoparticles for synergetic enhancement of gas sensing and photocatalytic performance
Source abstract
Abstract
Abstract Proper morphology, surface and interface structure designing are required to obtain efficient gas sensing and photocatalytic materials. In the present work, ultra-thin NiO single-crystalline porous nanosheets with dominant {1 1 1} crystal facets (denoted as SP-NiO) and hierarchical NiO porous microspheres (denoted as HP-NiO) supported highly dispersed Pt nanoparticles with controllable sizes were designed and synthesized. Their gas sensing and photocatalytic performance were investigated. It was found that both the formaldehyde sensing and methyl orange photocatalytic degradation performance were greatly enhanced by decorating Pt nanoparticles on SP-NiO, while Pt nanoparticles decoration contributed little to the improved photocatalytic performance of HP-NiO. The results indicated that surface structure of the NiO support could also produce significant impact on the activity of Pt/NiO heterojunctions. Moreover, Pt decorated SP-NiO with stable structure showed a marked long-term stability with negligible attenuation of gas sensitivity (less than 5%) for 45 days, while Pt decorated HP-NiO exhibited obvious attenuation of gas sensitivity (more than 30%) due to the structural collapse. The work not only offers promising materials for gas sensing and photocatalytic application, but also brings new dawn for the designing of efficient p-type metal oxides gas sensing and photocatalytic materials through the synergistic effect of single-crystalline porous structures modulation, crystal facets engineering and facet-selective deposition of highly-dispersed Pt nanoparticles.
Source: openaire · Quality: candidate unverified · Source record ↗
Research context
OpenAlex topics: Gas Sensing Nanomaterials and Sensors · ZnO doping and properties · Transition Metal Oxide Nanomaterials
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
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