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Hexagonally Ordered Gold Semishells as Tunable Sers Substrates
| Content Provider | Semantic Scholar |
|---|---|
| Author | Štolcová, Lucie Proška, Jan Procházka, Marek |
| Copyright Year | 2012 |
| Abstract | The spectroscopic technique based on surface-enhanced Raman scattering (SERS) has attracted considerable scientific interest due to the combination of its extreme sensitivity, in certain cases reaching single-molecule detection limits, and the specificity of Raman spectra. For molecules adsorbed on noble metal nanostructures, Raman signal enhancement of several orders of magnitude can arise as a result of localized surface plasmon resonance (LSPR). This effect can be observed when the frequency of light irradiating the metal surface matches the resonant frequency of conduction electrons in the nanostructure. Since this frequency is determined by the geometry and size of the nanostructure, it may be intentionally shifted to the spectral range suitable for specific applications. However, the use of SERS in many applications is limited by the properties of the nanostructure surfaces, so-called SERS substrates, especially by the insufficient homogeneity of the enhancement. Using self-assembled dielectric spheres as templates, we prepared periodic SERS substrates, composed of hexagonally ordered gold semishells, which provide both high and homogenous enhancement over the whole surface. The functionality of our substrates was verified by measuring SERS spectra of methylene blue at very low concentrations. The LSPR frequency of the semishells may be tuned by several parameters, such as its height, thickness of the gold shell etc. We propose a method based on template-assisted self-assembly which enables the precise control over the semishell geometry and paves the way to the preparation of rationally designed semishell arrays with optimal parameters. |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | http://konsys-t.tanger.cz/files/proceedings/04/reports/1030.pdf |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |