Jump to main content
Jump to site search

Issue 43, 2017
Previous Article Next Article

A silver nanoislands on silica spheres platform: enriching trace amounts of analytes for ultrasensitive and reproducible SERS detection

Author affiliations

Abstract

The performance of surface-enhanced Raman scattering (SERS) for detecting trace amounts of analytes depends highly on the enrichment of the diluted analytes into a small region that can be detected. A super-hydrophobic delivery (SHD) process is an excellent process to enrich even femtomolar analytes for SERS detection. However, it is still challenging to easily fabricate a low detection limit, high sensitivity and reproducible SHD-SERS substrate. In this article, we present a cost-effective and fewer-step method to fabricate a SHD-SERS substrate, named the “silver nanoislands on silica spheres” (SNOSS) platform. It is easily prepared via the thermal evaporation of silver onto a layer of super-hydrophobic paint, which contains single-scale surface-fluorinated silica spheres. The SNOSS platform performs reproducible detection, which brings the relative standard deviation down to 8.85% and 5.63% for detecting 10−8 M R6G in one spot and spot-to-spot set-ups, respectively. The coefficient of determination (R2) is 0.9773 for R6G. The SNOSS platform can be applied to the quantitative detection of analytes whose concentrations range from sub-micromolar to femtomolar levels.

Graphical abstract: A silver nanoislands on silica spheres platform: enriching trace amounts of analytes for ultrasensitive and reproducible SERS detection

Back to tab navigation

Supplementary files

Publication details

The article was received on 19 Sep 2017, accepted on 10 Oct 2017 and first published on 11 Oct 2017


Article type: Paper
DOI: 10.1039/C7NR06987A
Citation: Nanoscale, 2017,9, 16749-16754
  •   Request permissions

    A silver nanoislands on silica spheres platform: enriching trace amounts of analytes for ultrasensitive and reproducible SERS detection

    Z. Wang, L. Feng, D. Xiao, N. Li, Y. Li, D. Cao, Z. Shi, Z. Cui and N. Lu, Nanoscale, 2017, 9, 16749
    DOI: 10.1039/C7NR06987A

Search articles by author

Spotlight

Advertisements