Computational Sensing Using Low-Cost and Mobile Plasmonic Readers Designed by Machine Learning

Selects the optimal sub‐set of LEDs from a predefined libraryAims to produce the minimum error refractive index prediction model for a given plasmonic nano‐structure design and nano‐fabrication methodReduction in mean errorImproved scalability and cost-effectivenessIdentifies the spectral regions with a consistent response

Wearable sensing  Personalized medicinePoint-of-care diagnosticsMedical equipment integration: intravenous tubes, syringes, blood bags, bandages, or medical garments

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Background

Plasmonic sensors have been used for a wide-range of biological and chemical sensing applications. Localized Surface Plasmon Resonance (LSPR) sensors are particularly sensitive to surface binding events, making them excellent probing tools for a range of applications such as measuring DNA hybridization, heavy metal ion concentration, cancer bio-marker detection, quantification of protein concentration, and even viral load measurement in unprocessed blood samples.

With the proliferation of these low-cost and flexible LSPR-based sensors, new and innovative designs for the corresponding read-out devices must also be considered. Field portability, low-cost, ease-of-use, and network connectivity are all desired design features for ensuring widespread adoption of these sensing systems. There is a need for a machine learning framework which can be used to design low-cost and mobile multi-spectral plasmonic readers that do not use traditionally employed bulky and expensive stabilized light-sources or high-resolution spectrometers. These design methods are aimed to facilitate the translation of nano-sensing technologies to various emerging applications such as wearable sensing, personalized medicine, and point-of-care diagnostics.

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Tech ID/UC Case

28828/2017-520-0

Related Cases

2017-520-0

美国

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