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ACS Nano. 2012 Mar 27;6(3):2056-70. doi: 10.1021/nn203661n. Epub 2012 Mar 12.

Nanoparticle cluster arrays for high-performance SERS through directed self-assembly on flat substrates and on optical fibers.

Author information

1
Institute of Materials Research and Engineering (IMRE), Agency for Science, Technology and Research (A*STAR), 3, Research Link, 117602, Singapore.

Abstract

We demonstrate template-guided self-assembly of gold nanoparticles into ordered arrays of uniform clusters suitable for high-performance SERS on both flat (silicon or glass) chips and an optical fiber faucet. Cluster formation is driven by electrostatic self-assembly of anionic citrate-stabilized gold nanoparticles (~11.6 nm diameter) onto two-dimensionally ordered polyelectrolyte templates realized by self-assembly of polystyrene-block-poly(2-vinylpyridine). A systematic variation is demonstrated for the number of particles (N ≈ 5, 8, 13, or 18) per cluster as well as intercluster separations (S(c) ≈ 37-10 nm). Minimum interparticle separations of <5 nm, intercluster separations of ~10 nm, and nanoparticle densities on surfaces as high as ~7 × 10(11)/in.(2) are demonstrated. Geometric modeling is used to support experimental data toward estimation of interparticle and intercluster separations in cluster arrays. Optical modeling and simulations using the finite difference time domain method are used to establish the influence of cluster size, shape, and intercluster separations on the optical properties of the cluster arrays in relation to their SERS performance. Excellent SERS performance, as evidenced by a high enhancement factor, >10(8) on flat chips and >10(7) for remote sensing, using SERS-enabled optical fibers is demonstrated. The best performing cluster arrays in both cases are achievable without the use of any expensive equipment or clean room processing. The demonstrated approach paves the way to significantly low-cost and high-throughput production of sensor chips or 3D-configured surfaces for remote sensing applications.

PMID:
22332718
DOI:
10.1021/nn203661n
[Indexed for MEDLINE]

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