On Chip Protein Pre-Concentration for Enhancing the Sensitivity of Porous Silicon Biosensors

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Arshavsky-Graham, S. et al.: On Chip Protein Pre-Concentration for Enhancing the Sensitivity of Porous Silicon Biosensors. In: ACS Sensors 2 (2017), S. 1767-1773. DOI: https://doi.org/10.1021/acssensors.7b00692

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Sum total of downloads: 151




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Abstract: 
Porous silicon (PSi) nanomaterials have been widely studied as label-free optical biosensors for protein detection. However, these biosensors’ performance, specifically in terms of their sensitivity (which is typically in the micromolar range), is insufficient for many applications. Herein, we present a proof-of-concept application of the electrokinetic isotachophoresis (ITP) technique for real-time preconcentration of a target protein on a PSi biosensor. With ITP, a highly concentrated target zone is delivered to the sensing area, where the protein target is captured by immobilized aptamers. The detection of the binding events is conducted in a label-free manner by reflective interferometric Fourier transformation spectroscopy (RIFTS). Up to 1000-fold enhancement in local concentration of the protein target and the biosensor’s sensitivity are achieved, with a measured limit of detection of 7.5 nM. Furthermore, the assay is successfully performed in complex media, such as bacteria lysate samples, while the selectivity of the biosensor is retained. The presented assay could be further utilized for other protein targets, and to promote the development of clinically useful PSi biosensors.
License of this version: ACS AuthorChoice License
Document Type: Article
Publishing status: publishedVersion
Issue Date: 2017
Appears in Collections:Naturwissenschaftliche Fakultät

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pos. country downloads
total perc.
1 image of flag of Germany Germany 95 62.91%
2 image of flag of United States United States 24 15.89%
3 image of flag of Netherlands Netherlands 8 5.30%
4 image of flag of Spain Spain 5 3.31%
5 image of flag of India India 3 1.99%
6 image of flag of China China 3 1.99%
7 image of flag of Russian Federation Russian Federation 2 1.32%
8 image of flag of Ireland Ireland 2 1.32%
9 image of flag of Vietnam Vietnam 1 0.66%
10 image of flag of Argentina Argentina 1 0.66%
    other countries 7 4.64%

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