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SPR Sensor Chip for Detection of Small Molecules Using Molecularly Imprinted Polymer with Embedded Gold Nanoparticles

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Matsui, Jun, Akamatsu, Kensuke, Hara, Noriaki, Miyoshi, Daisuke, Nawafune, Hidemi, Tamaki, Katsuyuki, Sugimoto, Naoki·2005·Analytical Chemistry

Abstract

Molecularly imprinted polymer gel with embedded gold nanoparticle was prepared on a gold substrate of a chip for a surface plasmon resonance (SPR) sensor for fabricating an SPR sensor sensitive to a low molecular weight analyte. The sensing is based on swelling of the imprinted polymer gel that is triggered by an analyte binding event within the polymer gel. The swelling causes greater distance between the gold nanoparticles and substrate, shifting a dip of an SPR curve to a higher SPR angle. The polymer synthesis was conducted by radical polymerization of a mixture of acrylic acid, N-isopropylacrylamide, N,N'-methylenebisacrylamide, and gold nanoparticles in the presence of dopamine as model template species on a sensor chip coated with allyl mercaptan. The modified sensor chip showed an increasing SPR angle in response to dopamine concentration, which agrees with the expected sensing mechanism. Furthermore, the gold nanoparticles were shown to be effective for enhancing the signal intensity (the change of SPR angle) by comparison with a sensor chip immobilizing no gold nanoparticles. The analyte binding process and the consequent swelling appeared to be reversible, allowing one the repeated use of the presented sensor chip.

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Metadata

Authors
Matsui, J., Akamatsu, K., Hara, N., Miyoshi, D., Nawafune, H., Tamaki, K., & Sugimoto, N.
Venue
Analytical Chemistry
Published
2005-05-25
Volume
77
Issue
13
Pages
4282-4285
ISSN
  • 0003-2700 (ISSN-L)
  • 1520-6882
OpenAlex
W2037252212
Cited by
292
References
0
FWCI
14.15
Type
journal article
Language
en
Open access
bronze

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.