Presentation + Paper
6 March 2023 Optical tastebuds for water quality testing
Author Affiliations +
Abstract
To achieve the UN Sustainable Development Goal of universal access to clean water and sanitation, we need to rethink centralized water systems with global net-zero carbon and sustainability in mind. One approach is to develop scalable off-grid systems that are reliable and easy to use and maintain. A major challenge for such systems is translating the standard laboratory-based monitoring of centralized systems to a more sustainable and scalable model for regularly and routinely monitoring system outputs, which consist of complex mixtures with varying concentrations of molecules and ions in water. Here, we demonstrate a preliminary sensor that, once fully developed, could allow for point-of-use measurements with a single output to monitor. Rather than developing multiple sensors to monitor the levels of each individual component in the water, our label-free, array-based design mimics the biological system of taste. The sensor is comprised of an array of nano-tastebuds made of tailored plasmonic metasurfaces. The combination of different signals from each nano-tastebud to the same sample yields a unique fingerprint for that sample. Through training, these fingerprints build an identification model. By integrating a fully developed sensor into decentralized water systems, we seek to provide non-expert end-users with an easy-to-read output capable of warning of imminent system failures.
Conference Presentation
© (2023) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Justin R. Sperling, Laurie Savage, Liam T. Wilson, Calum Cuthill, Katie McGuire, Jill Robbie, William D. Sloan, Caroline Gauchotte-Lindsay, William J. Peveler, and Alasdair W. Clark "Optical tastebuds for water quality testing", Proc. SPIE 12372, Optical Fibers and Sensors for Medical Diagnostics, Treatment and Environmental Applications XXIII, 123720M (6 March 2023); https://doi.org/10.1117/12.2647396
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KEYWORDS
Sensors

Principal component analysis

Plasmonics

Environmental sensing

Mixtures

Statistical analysis

Water quality

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