Computational Sensing Using Low-Cost and Mobile Plasmonic Readers Designed by Machine Learning-Reference-Cited by-同舟云学术

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

Published:2017-02-01 Issue:2 Volume:11 Page:2266-2274
ISSN:1936-0851
Container-title:ACS Nano
language:en
Short-container-title:ACS Nano

Author:

Ballard Zachary S.¹,Shir Daniel¹,Bhardwaj Aashish¹,Bazargan Sarah¹,Sathianathan Shyama¹,Ozcan Aydogan¹^ORCID

Affiliation:

1. Electrical Engineering Department, ‡Bioengineering Department, and §California NanoSystems Institute (CNSI), University of California, Los Angeles, California 90095, United States

Funder

Howard Hughes Medical Institute

Division of Industrial Innovation and Partnerships

Division of Emerging Frontiers in Research and Innovation

Army Research Office

King Abdullah University of Science and Technology

Office of Naval Research

Mary Kay Foundation

Division of Chemical, Bioengineering, Environmental, and Transport Systems

National Institutes of Health

National Science Foundation

Vodafone Group Plc

Steven & Alexandra Cohen Foundation

Publisher

American Chemical Society (ACS)

Subject

General Physics and Astronomy,General Engineering,General Materials Science

Link

https://pubs.acs.org/doi/pdf/10.1021/acsnano.7b00105

Reference61 articles.

1. Localized surface plasmon resonance biosensors

2. Localized Surface Plasmon Resonance Biosensing: Current Challenges and Approaches

3. Nanostructured Plasmonic Sensors

4. Quantitative multispectral biosensing and 1D imaging using quasi-3D plasmonic crystals

5. Multi-resonant plasmonic nanodome arrays for label-free biosensing applications

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