• Skip to main content
  • Skip to after header navigation
  • Skip to site footer
ERN: Emerging Researchers National Conference in STEM

ERN: Emerging Researchers National Conference in STEM

  • About
    • About AAAS
    • About the NSF
    • About the Conference
    • Partners/Supporters
    • Project Team
  • Conference
  • Abstracts
    • Undergraduate Abstract Locator
    • Graduate Abstract Locator
    • Abstract Submission Process
    • Presentation Schedules
    • Abstract Submission Guidelines
    • Presentation Guidelines
  • Travel Awards
  • Resources
    • Award Winners
    • Code of Conduct-AAAS Meetings
    • Code of Conduct-ERN Conference
    • Conference Agenda
    • Conference Materials
    • Conference Program Books
    • ERN Photo Galleries
    • Events | Opportunities
    • Exhibitor Info
    • HBCU-UP/CREST PI/PD Meeting
    • In the News
    • NSF Harassment Policy
    • Plenary Session Videos
    • Professional Development
    • Science Careers Handbook
    • Additional Resources
    • Archives
  • Engage
    • Webinars
    • ERN 10-Year Anniversary Videos
    • Plenary Session Videos
  • Contact Us
  • Login

Optimization of Spectral Sensor Performance Using Machine Learning

Graduate #119
Discipline: Technology and Engineering
Subcategory: Electrical Engineering

Zachary S. Ballard - University of California, Los Angeles
Co-Author(s): Ming Lei, Department of Precision Instrument, Tsinghua University, Beijing, China; Aydogan Ozcan, University of California, Los Angeles, CA



Optical sensors which operate via a spectral response, i.e., a spectral shift or change in transmission or reflection intensity, are of intense interest in the bio-sensing field. Specifically, sensors which are compact and low-cost can have wide impact in areas related to distributed sensor networks, wearable sensing, personalized medicine, and point-of-care diagnostics [1]. Plasmonic sensors, which support resonant phenomenon induced by photon interaction with a given nanostructured metal and dielectric interfaces, have recently been demonstrated in such a capacity [2]. To realize their full potential, recent focus has been placed on engineering ‘stable’ spectral features through added design complexities, such as novel nano-structures or metal/dielectric/metal multilayer structures [3]. Such ‘stable’ spectral features, for instance, do not undergo spectral shifts in response to near-field changes induced by e.g., biomolecules, but instead increase or decrease their overall transmission, resulting in plasmonic sensors which can intuitively be read with a single interrogation band. In this work, we investigated the efficacy of engineering spectral features through statistical analysis of the sensor response from a large set of independently manufactured plasmonic sensors, produced through scalable fabrication techniques. Through our experimental analysis, we found that, on the contrary, added sensor design complexity for the sake of supporting ?stable? spectral features is detrimental to sensor performance. Instead, we propose a computational framework which employs machine learning techniques to best understand performance trade-offs and guide spectral sensor design. Through cross-validation analysis of the spectral response from a statically significant set of fabricated sensors, we demonstrated a machine learning based framework to optimize the design of plasmonic sensors for low-cost mobile reader platforms, and to optimally select interrogation bands (using e.g., LEDs, spectral filters, or laser diodes) to minimize error. In conclusion, we demonstrated a computational framework that can learn inherent trade off in design complexities of specifically tailored spectral sensor designs to create optimized biosensors achieving minimum error. This statistical learning framework can broadly be applied to any sensor which operates via a spectral shift, such as nano-particle assays, enzymatic or colorimetric reactions, among others, especially if their subsequent low-cost readers must choose a sparse set of interrogation bands in lieu of bulky and expensive spectrometers and broadband light sources.
[1] A. G. Brolo, ‘Plasmonics for future biosensors,’ Nat. Photonics, vol. 6, no. 11, pp. 709-713, Nov. 2012.
[2] Z. S. Ballard, D. Shir, A. Bhardwaj, S. Bazargan, S. Sathianathan, and A. Ozcan, ‘Computational Sensing Using Low-Cost and Mobile Plasmonic Readers Designed by Machine Learning,’ ACS Nano, vol. 11, no. 2, pp. 2266?2274, Feb. 2017.
[3] A. Ameen et al., ‘Plasmonic Sensing of Oncoproteins without Resonance Shift Using 3D Periodic Nanocavity in Nanocup Arrays,’ Adv. Opt. Mater., vol. 5, no. 11, p. n/a-n/a, Jun. 2017.

Not Submitted

Funder Acknowledgement(s): The primary author, Zachary S. Ballard acknowledges his support from the NSF Graduate Research Fellowship Program. Ming Lei would like to acknowledge the Undergraduate Overseas Research Training Program of Tsinghua University for their support. Ozcan research group also acknowledges the support of NSF EFRI.

Faculty Advisor: Aydogan Ozcan, ozcan@ucla.edu

Role: I conceived of the project, fabricated the spectral sensor samples, designed and constructed the experimental set-up, and wrote all of the data processing code for analyzing the experimental results.

Sidebar

Abstract Locators

  • Undergraduate Abstract Locator
  • Graduate Abstract Locator

This material is based upon work supported by the National Science Foundation (NSF) under Grant No. DUE-1930047. Any opinions, findings, interpretations, conclusions or recommendations expressed in this material are those of its authors and do not represent the views of the AAAS Board of Directors, the Council of AAAS, AAAS’ membership or the National Science Foundation.

AAAS

1200 New York Ave, NW
Washington,DC 20005
202-326-6400
Contact Us
About Us

  • LinkedIn
  • Facebook
  • Instagram
  • Twitter
  • YouTube

The World’s Largest General Scientific Society

Useful Links

  • Membership
  • Careers at AAAS
  • Privacy Policy
  • Terms of Use

Focus Areas

  • Science Education
  • Science Diplomacy
  • Public Engagement
  • Careers in STEM

Focus Areas

  • Shaping Science Policy
  • Advocacy for Evidence
  • R&D Budget Analysis
  • Human Rights, Ethics & Law

© 2023 American Association for the Advancement of Science