Discipline: Technology and Engineering
Subcategory: Electrical Engineering
Zachary S. Ballard - University of California, Los Angeles
Co-Author(s): Daniel Shir, Aashish Bhardwaj, Shyama Sathianathan, Sarah Bazargan, and Aydogan Ozcan, University of California, Los Angeles, CA
Localized Surface Plasmon Resonance (LSPR) based sensing is one of the current gold-standard label-free methods for detecting low concentrations of analytes in a given sample. These sensors rely on collective electron oscillations generated by the interaction of light with noble-metal nanostructures. The LSPR, manifested as a significantly enhanced electric field, is sensitive to changes in the local near-field refractive index and is therefore modulated by surface adsorbed molecules such as proteins, DNA, and whole viruses . Additionally, the advent of new nano-fabrication techniques such as soft-lithography, has enabled scalable production of flexible, one-time-use LSPR sensors which can support resonances in the visible part of the spectrum under normal illumination . However, current sensor read-out devices largely rely on expensive and bulky high-resolution spectrometers to track the LSPR peak-shift in response to an adsorbed analyte. In this work, we demonstrate a framework which utilizes machine learning techniques to design low-cost, field-portable and sensitive read-out devices for any given plasmonic nanostructure. First, a training dataset is generated by measuring the full spectral response of a fabricated set of LSPR sensors due to bulk refractive index modulation. Then a comprehensive LED library, composed of commercially available LEDs, is used to model all the possible center wavelengths and bandwidths which could be used to probe the response of the LSPR based sensor. These simulated LED transmission values are subsequently inputted as features into an L1-norm regularization algorithm which outputs the optimal LEDs with which to sub-sample the LSPR peak shift as well as a corresponding linear model to predict the refractive index. This optimal set of LEDs can then be integrated with a conventional CMOS imager, photodetector, or mobile phone to create a low-cost, field-portable device entirely eliminating the need for an optical spectrum analyzer. Our experimental results show that for two different LSPR nanostructure designs, the optimal LEDs were counter-intuitively distanced from the prominent spectral features of the plasmonic sensor, and yielded, in some cases, up to 70% error reduction in blind testing using bulk refractive index when compared to other choices of LEDs. In conclusion we proposed and validated a mathematical framework which can be used to select the optimal bands to quantify any arbitrary LSPR based sensor design using low-cost and field-portable readers, having the potential to impact the fields of point-of-care diagnostics, wearable sensing, and global health.
References:  M. E. Stewart et al., ‘Nanostructured Plasmonic Sensors,’ Chem. Rev., vol. 108, no. 2, pp. 494-521, Feb. 2008.
 Y. Chuo, D. Hohertz, C. Landrock, B. Omrane, K. L. Kavanagh, and B. Kaminska, ‘Large-Area Low-Cost Flexible Plastic Nanohole Arrays for Integrated Bio-Chemical Sensing,’ IEEE Sens. J., vol. 13, no. 10, pp. 3982-3990, Oct. 2013.
Funder Acknowledgement(s): The primary author, Zachary S. Ballard acknowledges his support from the NSF Graduate Research Fellowship Program. The 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 & Alexandra Cohen Foundation, and KAUST. This work is based upon research performed in a laboratory renovated by the National Science Foundation under Grant No. 0963183, which is an award funded under the American Recovery and Reinvestment Act of 2009 (ARRA).
Faculty Advisor: Aydogan Ozcan, email@example.com
Role: As the primary author, I did the majority of the work in this conference submission including the conception of the research question, the sensor fabrication, experimental design and execution, as well as data processing and analysis.