journal article·2017-09

A facile precursor route to highly loaded metal/ceramic nanofibers as a robust surface-enhanced Raman template

Jay Hoon Park, Yong Lak Joo

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Abstract

Abstract We report silver (Ag)/ceramic nanofibers with highly robust and sensitive optical sensory capabilities that can withstand harsh conditions. These nanofibers are fabricated by first electrospinning solutions of poly vinyl alcohol (PVA) and metal precursor polymers, followed by subsequent series of heat treatment. The reported fabrication method demonstrate the effects of (i) the location of Ag crystals, (ii) crystal size and shape, and (iii) constituents of the ceramic matrix as surface-enhanced Raman spectroscopy (SERS) templates with 10−6 M 4-mercaptobenzoic acid (4-MBA). Notably, these silver/ceramic nanofibers preserved most of their highly sensitive localized surface plasmon resonance (LSPR) even under high temperature of 400 °C, in contrast to preformed Ag nanoparticles (NPs) in PVA nanofibers which lost most of its optical property presumably due to (i) Ag oxidation and (ii) loss of the matrix material. Among the ceramic substrates of ZrO2, Al2O3, and ZnO with silver crystals, we discovered that the ZnO substrate showed the most consistent and the strongest signal strength owing to the synergistic chemical and optical properties of the ZnO substrate. Moreover, the pure Ag nanofiber proved to be the best heat-resistant SERS template, owing to its (i) anisotropic morphology and (ii) thicker diameter when compared with other conventional Ag nanomaterials. These results demonstrated simple yet highly controllable fabrication of robust SERS templates, with potential applications in a catalytic sensory which is often exposed to harsh conditions.
Source: openaire · Quality: candidate unverified · Source record ↗

Research context

OpenAlex topics: Gold and Silver Nanoparticles Synthesis and Applications · Advanced Nanomaterials in Catalysis · Biosensors and Analytical Detection

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

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