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dc.contributor.authorBallard, Zachary S.
dc.contributor.authorShir, Daniel
dc.contributor.authorBhardwaj, Aashish
dc.contributor.authorBazargan, Sarah
dc.contributor.authorSathianathan, Shyama
dc.contributor.authorOzcan, Aydogan
dc.date.accessioned2018-01-04T07:51:39Z
dc.date.available2018-01-04T07:51:39Z
dc.date.issued2017-01-27
dc.identifier.citationBallard ZS, Shir D, Bhardwaj A, Bazargan S, Sathianathan S, et al. (2017) Computational Sensing Using Low-Cost and Mobile Plasmonic Readers Designed by Machine Learning. ACS Nano 11: 2266–2274. Available: http://dx.doi.org/10.1021/acsnano.7b00105.
dc.identifier.issn1936-0851
dc.identifier.issn1936-086X
dc.identifier.doi10.1021/acsnano.7b00105
dc.identifier.urihttp://hdl.handle.net/10754/626685
dc.description.abstractPlasmonic 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 to create a minimum-error refractive index prediction model, which statistically takes into account the varied spectral responses and fabrication-induced variability of a given sensor design. This computational sensing approach was experimentally validated using a modular mobile plasmonic reader. We tested different plasmonic sensors with hexagonal and square periodicity nanohole arrays and revealed that the optimal illumination bands differ from those that are “intuitively” selected based on the spectral features of the sensor, e.g., transmission peaks or valleys. This framework provides a universal tool for the plasmonics community to design low-cost and mobile multispectral readers, helping the translation of nanosensing technologies to various emerging applications such as wearable sensing, personalized medicine, and point-of-care diagnostics. Beyond plasmonics, other types of sensors that operate based on spectral changes can broadly benefit from this approach, including e.g., aptamer-enabled nanoparticle assays and graphene-based sensors, among others.
dc.description.sponsorshipThe Ozcan Research Group at UCLA gratefully acknowledges the support of the Presidential Early Career Award for Scientists and Engineers (PECASE), the Army Research Office (ARO; W911NF-13-1-0419 and W911NF-13-1-0197), the ARO Life Sciences Division, the National Science Foundation (NSF) CBET Division Biophotonics Program, the NSF Emerging Frontiers in Research and Innovation (EFRI) Award, the NSF EAGER Award, NSF INSPIRE Award, NSF Partnerships for Innovation: Building Innovation Capacity (PFI:BIC) Program, Office of Naval Research (ONR), the National Institutes of Health (NIH), the Howard Hughes Medical Institute (HHMI), Vodafone Americas Foundation, the Mary Kay Foundation, Steven and Alexandra Cohen Foundation, and KAUST. This work is based upon research performed in a laboratory renovated by the NSF under grant no. 0963183, which is an award funded under the American Recovery and Reinvestment Act of 2009 (ARRA).
dc.publisherAmerican Chemical Society (ACS)
dc.subjectcomputational sensing
dc.subjectlocalized surface plasmon resonance
dc.subjectmachine learning
dc.subjectmobile sensing
dc.subjectplasmonic sensing
dc.subjectplasmonics
dc.titleComputational Sensing Using Low-Cost and Mobile Plasmonic Readers Designed by Machine Learning
dc.typeArticle
dc.identifier.journalACS Nano
dc.contributor.institutionCalifornia NanoSystems Institute (CNSI), University of California, Los Angeles, California 90095, United States
dc.contributor.institutionBioengineering Department, University of California, Los Angeles, California 90095, United States
dc.contributor.institutionElectrical Engineering Department, University of California, Los Angeles, California 90095, United States


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