Jobst, Simon and Recum, Patrick and Ecija-Arenas, Aïngela and Moser, Elisabeth and Bierl, Rudolf and Hirsch, Thomas (2023) Semi-Selective Array for the Classification of Purines with Surface Plasmon Resonance Imaging and Deep Learning Data Analysis. ACS SENSORS, 8 (9). pp. 3530-3537. ISSN 2379-3694,
Full text not available from this repository. (Request a copy)Abstract
In process analytics or environmental monitoring, thereal-timerecording of the composition of complex samples over a long periodof time presents a great challenge. Promising solutions are label-freetechniques such as surface plasmon resonance (SPR) spectroscopy. Theyare, however, often limited due to poor reversibility of analyte binding.In this work, we introduce how SPR imaging in combination with a semi-selectivefunctional surface and smart data analysis can identify small andchemically similar molecules. Our sensor uses individual functionalspots made from different ratios of graphene oxide and reduced grapheneoxide, which generate a unique signal pattern depending on the analytedue to different binding affinities. These patterns allow four purinebases to be distinguished after classification using a convolutionalneural network (CNN) at concentrations as low as 50 & mu;M. Thevalidation and test set classification accuracies were constant acrossmultiple measurements on multiple sensors using a standard CNN, whichpromises to serve as a future method for developing online sensorsin complex mixtures.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | GRAPHENE-OXIDE; MONOLAYER GRAPHENE; ELECTRONIC TONGUE; surface plasmon resonance imaging; functional surface; graphene oxide; reduced graphene oxide; 2Dmaterials; pattern recognition; small-molecule sensing |
| Subjects: | 500 Science > 540 Chemistry & allied sciences |
| Divisions: | Chemistry and Pharmacy > Institut für Analytische Chemie, Chemo- und Biosensorik |
| Depositing User: | Dr. Gernot Deinzer |
| Date Deposited: | 28 Feb 2024 06:55 |
| Last Modified: | 28 Feb 2024 06:55 |
| URI: | https://pred.uni-regensburg.de/id/eprint/59136 |
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