A cross-reactive plasmonic sensing array for drinking water assessment
The continuous monitoring of remote drinking water purification systems is a global challenge with direct consequences for human and environmental health. Here, we utilise a "nano-tastebud" sensor comprised of eight chemically-tailored plasmonic metasurfaces, for testing the composition of...
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Published in | Environmental science. Nano Vol. 1; no. 12; pp. 35 - 358 |
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Main Authors | , , , , , , , , , , , , |
Format | Journal Article |
Language | English |
Published |
England
Royal Society of Chemistry
07.12.2023
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Subjects | |
Online Access | Get full text |
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Summary: | The continuous monitoring of remote drinking water purification systems is a global challenge with direct consequences for human and environmental health. Here, we utilise a "nano-tastebud" sensor comprised of eight chemically-tailored plasmonic metasurfaces, for testing the composition of drinking water. Through undertaking a full chemometric analysis of the water samples and likely contaminants we were able to optimise the sensor specification to create an array of suitable tastebuds. By generating a unique set of optical responses for each water sample, we show that the array-based sensor can differentiate between untreated influent and treated effluent water with over 95% accuracy in flow and can detect compositional changes in distributed modified tap water. Once fully developed, this system could be integrated into water treatment facilities and distribution systems to monitor for changes in water composition.
The continuous monitoring of remote drinking water purification systems is a global challenge with direct consequences for human and environmental health. Nanoplasmonic sensors can monitor treatment systems and warn of failures. |
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Bibliography: | Electronic supplementary information (ESI) available: Further details on sample collection, full analytical chemistry assessment, synthesis of custom nitrilotriacetic acid thiol, sensor fabrication, sensor thiolation, transmission microscopy methodology, further detail on the nano-tastebud sensing of samples, principal component analysis, and linear discriminant analysis. See DOI All data relating to the work outlined in this article can be found here https://doi.org/10.5525/gla.researchdata.1482 https://doi.org/10.1039/d3en00565h ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 |
ISSN: | 2051-8153 2051-8161 |
DOI: | 10.1039/d3en00565h |