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

ABSTRACT: Plasmonic sensors have been used for a wide range of biological and chemical sensing applications. Emerging nanofabrication techniques have enabled these sensors to be cost-effectively mass manufactured onto various types of substrates. To accompany these advances, major improvements in sensor read-out devices must also be achieved to fully realize the broad impact of plasmonic nanosensors. Here, we propose a machine learning framework which can be used to design low-cost and mobile multispectral plasmonic readers that do not use traditionally employed bulky and expensive stabilized light sources or high-resolution spectrometers. By training a feature selection model over a large set of fabricated plasmonic nanosensors, we select the optimal set of illumination light-emitting diodes needed

SUBMITTER: Ballard ZS 

PROVIDER: S-EPMC5451292 | biostudies-literature | 2017 Feb

REPOSITORIES: biostudies-literature