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Article

Multi-Layer Reflectivity Calculation Based Meta-Modeling of the Phase Mapping Function for Highly Reproducible Surface Plasmon Resonance Biosensing

1
Department of Biomedical Engineering, National Taiwan University, No. 1, Sec. 4, Roosevelt Rd., Taipei 10617, Taiwan
2
Graduate Institute of Bio-Electronics and Bio-Informatics, National Taiwan University, No. 1, Sec. 4, Roosevelt Rd., Taipei 10617, Taiwan
3
Department of Medical Biotechnology and Laboratory Sciences, College of Medicine, Chang Gung University, Taoyuan 333, Taiwan
4
Kidney Research Center, Department of Nephrology, Chang Gung Memorial Hospital, Taoyuan 333, Taiwan
5
Department of Plant Pathology and Microbiology, National Taiwan University, No. 1, Sec. 4, Roosevelt Rd., Taipei 10617, Taiwan
6
Laboratory Light, Nanomaterials & Nanotechnologies (L2n), CNRS ERL 7004, University of Technology of Troyes, 12 rue Marie Curie, 10004 Troyes, France
*
Authors to whom correspondence should be addressed.
Biosensors 2021, 11(3), 95; https://doi.org/10.3390/bios11030095
Submission received: 24 February 2021 / Revised: 15 March 2021 / Accepted: 19 March 2021 / Published: 23 March 2021
(This article belongs to the Special Issue Emerging Nanoplasmonic Technologies in Biosensors)

Abstract

Phase-sensitive surface plasmon resonance biosensors are known for their high sensitivity. One of the technology bottle-necks of such sensors is that the phase sensorgram, when measured at fixed angle set-up, can lead to low reproducibility as the signal conveys multiple data. Leveraging the sensitivity, while securing satisfying reproducibility, is therefore is an underdiscussed key issue. One potential solution is to map the phase sensorgram into refractive index unit by the use of sensor calibration data, via a simple non-linear fit. However, basic fitting functions poorly portray the asymmetric phase curve. On the other hand, multi-layer reflectivity calculation based on the Fresnel coefficient can be employed for a precise mapping function. This numerical approach however lacks the explicit mathematical formulation to be used in an optimization process. To this end, we aim to provide a first methodology for the issue, where mapping functions are constructed from Bayesian optimized multi-layer model of the experimental data. The challenge of using multi-layer model as optimization trial function is addressed by meta-modeling via segmented polynomial approximation. A visualization approach is proposed for assessment of the goodness-of-the-fit on the optimized model. Using metastatic cancer exosome sensing, we demonstrate how the present work paves the way toward better plasmonic sensors.
Keywords: surface plasmon resonance biosensor; phase sensitive detection; exosome; algorithm surface plasmon resonance biosensor; phase sensitive detection; exosome; algorithm

Share and Cite

MDPI and ACS Style

Wu, T.-H.; Yang, C.-H.; Chang, C.-C.; Liu, H.-W.; Yang, C.-Y.; Shen, T.-L.; Lin, C.-W.; Bruyant, A. Multi-Layer Reflectivity Calculation Based Meta-Modeling of the Phase Mapping Function for Highly Reproducible Surface Plasmon Resonance Biosensing. Biosensors 2021, 11, 95. https://doi.org/10.3390/bios11030095

AMA Style

Wu T-H, Yang C-H, Chang C-C, Liu H-W, Yang C-Y, Shen T-L, Lin C-W, Bruyant A. Multi-Layer Reflectivity Calculation Based Meta-Modeling of the Phase Mapping Function for Highly Reproducible Surface Plasmon Resonance Biosensing. Biosensors. 2021; 11(3):95. https://doi.org/10.3390/bios11030095

Chicago/Turabian Style

Wu, Tzu-Heng, Ching-Hsu Yang, Chia-Chen Chang, Hui-Wen Liu, Chia-Yu Yang, Tang-Long Shen, Chii-Wann Lin, and Aurélien Bruyant. 2021. "Multi-Layer Reflectivity Calculation Based Meta-Modeling of the Phase Mapping Function for Highly Reproducible Surface Plasmon Resonance Biosensing" Biosensors 11, no. 3: 95. https://doi.org/10.3390/bios11030095

APA Style

Wu, T.-H., Yang, C.-H., Chang, C.-C., Liu, H.-W., Yang, C.-Y., Shen, T.-L., Lin, C.-W., & Bruyant, A. (2021). Multi-Layer Reflectivity Calculation Based Meta-Modeling of the Phase Mapping Function for Highly Reproducible Surface Plasmon Resonance Biosensing. Biosensors, 11(3), 95. https://doi.org/10.3390/bios11030095

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