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Issue 3, 2018
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Probing specific gravity in real-time with graphene oxide plasmonics

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Specific gravity (SG), the ratio of the density of a substance to the density of a reference material, is a standard indicator of the concentration of an analyte in a given solution. SG is routinely used for product quality assessment in the food industry. However, currently available commercial SG meters, such as hand-held refractometers and density meters, are highly sensitive to humidity and temperature, and do not allow real-time measurements. For these reasons, SG detection is often time-consuming which leads to unwanted interruptions in food manufacturing processes. Therefore, highly sensitive, label-free, and real-time sensors for the detection of SG are urgently needed for food quality control. In this context, we develop a graphene oxide (GO)-coated gold (Au) surface plasmon resonance (SPR) sensor, for the first time, to measure the SG of food samples in real-time. The SG values of sample solutions are correlated with the refractive indices (RI) of these solutions, which are captured by the SPR measurements, with a sensitivity of 105 SPR response units. Moreover, the use of a GO coating provides a strong enhancement of plasmonic resonances due to its optoelectronic properties, doubling the sensitivity of SPR response units per RI unit (2 × 105) when compared to conventional Au SPR chips (1 × 105). We also validate our sensor performance by measuring the SG in real food samples. Our results demonstrate a highly sensitive, efficient, high throughput, and reproducible approach for SG measurements in food industry settings, and open new opportunities to utilize improved SPR sensor technology for many other label-free analytical sensing applications.

Graphical abstract: Probing specific gravity in real-time with graphene oxide plasmonics

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Article information

15 Oct 2017
05 Dec 2017
First published
06 Dec 2017

Anal. Methods, 2018,10, 290-297
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Probing specific gravity in real-time with graphene oxide plasmonics

A. Garifullina, N. Bhalla and A. Q. Shen, Anal. Methods, 2018, 10, 290
DOI: 10.1039/C7AY02423A

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