Dataset for hierarchical tetramodal-porous architecture of zinc oxide nanoparticles microfluidically synthesized via dual-step nanofabrication

Su Eon Jin, Sung Joo Hwang, Hyo Eon Jin

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Abstract

Zinc oxide (ZnO) nanoparticles (NPs) have been applied as high-performance intelligent materials to create a hierarchical multimodal-porous architectures for application in biomedical research fields [1]. They were microfluidically synthesized via dual-step nanofabrication compared to the conventional particles including ZnO NPs synthesized at single-pot macroscale, nanosized ZnO, and hybrid ZnO. The physicochemical properties were characterized, including morphology, particle size distribution, atomic composition, crystallinity, purity, reactant viscosity, surface charge, photocatalysis, photoluminescence, and porosity. A hierarchical multimodal-porous three-dimensional (3D) architecture of ZnO NPs was generated and optimized on the solid plate substrate of cellulose paper sheet after solvent evaporation. The dataset provides the nanomaterial design and architecture generation of ZnO NPs, explaining multi-physics phenomena in association with performance optimization processes.

Original languageEnglish
Article number108137
JournalData in Brief
Volume42
DOIs
Publication statusPublished - 2022 Jun

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