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Microbial contamination of fuels by fungi and bacteria presents risks of corrosion and fuel system fouling. In this work, a rapid test for the determination of microbial genomic DNA from aqueous fuel extracts is presented. It combines test strips coated with polystyrene core/mesoporous silica shell particles, to the surface of which modified fluorescent molecular beacons are covalently grafted, with a smartphone detection system. In the hairpin loop, the beacons incorporate a target sequence highly conserved in all bacteria, corresponding to a fragment of the 16S ribosomal RNA gene, which is also present to a significant extent in the 18S rRNA gene of fungi, allowing for broadband microbial detection. In the developed assay, the presence of genomic DNA extracts from bacteria and fungi down to ca. 20−50 μg L−1 induced a distinct fluorescence response. The optical read-out was adapted for on-site monitoring by combining a 3D-printed case with a conventional smartphone, taking advantage of the sensitivity of contemporary complementary metal oxide semiconductor (CMOS) detectors. Such an embedded assembly allowed to detect microbial genomic DNA in aqueous extracts down to ca. 0.2−0.7 mg L−1 and presents an important step toward the on-site uncovering of fuel contamination in a rapid and simple fashion.
With the reduction of large oil spills because of stricter regulations and safety measures, the question of how to manage smaller oil spills arises. Few on-site analytical tools are available for first responders or other law enforcement personnel to rapidly test for crude oil in the early management of localized polluted areas. The approach reported here relies on well-described computer-assisted multivariate data analysis of the intrinsic fluorescence fingerprints of crude oils to build a multivariate model for the rapid classification of crude oils and the prediction of their properties. Thanks to a dedicated robust portable reader, the method allowed classification and accurate prediction of various properties of crude oil samples like density (according to API, the American Petroleum Institute and viscosity as well as composition parameters such as volume fractions of paraffins or aromatics. In this way, autonomous operation in on-site or in-the-field applications becomes possible based on the direct (undiluted and untreated) measurement of samples and a rapid, tablet-operated readout system to yield a robust and simple analytical test with superior performance. Testing in real-life scenarios allowed the successful classification and prediction of a number of oil spill samples as well as weathered samples that closely resemble samples collected by first responders.