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Probing Specific Gravity in Real-time With Graphene Oxide Plasmonics


Specific gravity (SG), the ratio of density of a substance to the density of a reference material, is a standard indicator of concentration of an analyte in a given solution. SG is routinely used for product quality assessment in food industries. However, currently available commercial SG meters, such as hand-held refractometers and density meters, are highly sensitive to humidity, 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 process. 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, used to measure SG of food samples in real-time. SG values of sample solutions are correlated with 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 GO coating provides a strong enhancement of plasmonic resonances due to their optoelectronic properties, sometimes doubling the sensitivity of SPR response units per RI unit (2x105) when compared to conventional Au SPR chips (1x105). 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.

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Publication details

The article was received on 15 Oct 2017, accepted on 05 Dec 2017 and first published on 06 Dec 2017

Article type: Paper
DOI: 10.1039/C7AY02423A
Citation: Anal. Methods, 2017, Accepted Manuscript
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    Probing Specific Gravity in Real-time With Graphene Oxide Plasmonics

    A. Garifullina, N. Bhalla and A. Q. Shen, Anal. Methods, 2017, Accepted Manuscript , DOI: 10.1039/C7AY02423A

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