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Field-deployed surface plasmon resonance (SPR) sensor for RDX quantification in environmental waters

Abstract

A field-deployable surface plasmon resonance (SPR) sensor is reported for the detection of energetic material (commonly termed explosives) RDX at ppb concentration in environmental samples. The SPR sensor was first validated in laboratory conditions with uncontaminated natural water samples spiked with known concentrations of RDX near the EPA limit of 2 ppb, which was then applied to monitor environmental samples collected in different downgradient wells near a grenade training range. The SPR sensor was finally tested on the field, where environmental samples were analysed on location in less than 90 minutes per well, which included the time to setup the equipment, sample the well and analyse the sample. The SPR analysis time was less than 45 minutes for equilibration, recalibration and measuring the water sample. Results obtained with the SPR sensors were cross-validated with the standard HPLC method (EPA method 8330B), showed good agreement with an accuracy within less than 1.6 ppb for analysis at the sampling sites, and with relative standard deviation (RSD) better than 20 % for field and laboratory measurements. The SPR sensor worked in a range of environmental conditions, including operation from about 0oC to nearly 30oC. The instrument was easily deployed near the sampling site using motor vehicles in summer conditions (Lab-in-a-Jeep) and using a sled in winter conditions (Lab-on-a-sled), showcasing the field deployability of the RDX SPR sensor and the possibility of continuously monitoring RDX in the environment.

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Publication details

The article was received on 06 Feb 2017, accepted on 09 May 2017 and first published on 10 May 2017


Article type: Paper
DOI: 10.1039/C7AN00216E
Citation: Analyst, 2017, Accepted Manuscript
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    Field-deployed surface plasmon resonance (SPR) sensor for RDX quantification in environmental waters

    T. Brule, G. Granger, N. Bukar, C. Deschenes-Rancourt, T. Havard, A. R. Schmitzer, R. Martel and J. Masson, Analyst, 2017, Accepted Manuscript , DOI: 10.1039/C7AN00216E

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