Abstract

Angular scanning-based surface plasmon resonance measurement has been utilized in label-free sensing applications. However, the measurement accuracy and precision of the surface plasmon resonance measurements rely on an accurate measurement of the plasmonic angle. Several methods have been proposed and reported in the literature to measure the plasmonic angle, including polynomial curve fitting, image processing, and image averaging. For intensity detection, the precision limit of the SPR is around 10–5 RIU to 10–6 RIU. Here, we propose a deep learning-based method to locate the plasmonic angle to enhance plasmonic angle detection without needing sophisticated post-processing, optical instrumentation, and polynomial curve fitting methods. The proposed deep learning has been developed based on a simple convolutional neural network architecture and trained using simulated reflectance spectra with shot noise and speckle noise added to generalize the training dataset. The proposed network has been validated in an experimental setup measuring air and nitrogen gas refractive indices at different concentrations. The measurement precision recovered from the experimental reflectance images is 4.23 × 10–6 RIU for the proposed artificial intelligence-based method compared to 7.03 × 10–6 RIU for the cubic polynomial curve fitting and 5.59 × 10–6 RIU for 2-dimensional contour fitting using Horner's method.

Highlights

  • Angular scanning-based surface plasmon resonance measurement has been utilized in label-free sensing applications

  • Note that the equivalent reflectance spectra shown on the right side of Fig. 7a–f were simulated the Fresnel equation and the transfer matrix approach explained earlier with the sample refractive indices of 1.000276 for the refractive index of a­ ir[31] and 1.000373 1.001035, 1.002360, 1.002738 and 1.003117 for the other five N­ 2 pressure levels calculated using Eq (1)

  • The designed Convolutional neural network (CNN) network architecture was designed for this purpose

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Summary

Introduction

Angular scanning-based surface plasmon resonance measurement has been utilized in label-free sensing applications. We propose a deep learning-based method to locate the plasmonic angle to enhance plasmonic angle detection without needing sophisticated post-processing, optical instrumentation, and polynomial curve fitting methods. The surface plasmon coupling condition is sensitive to the external environment in contact with the plasmonic metal’s surface It has been widely utilized as a label-free, non-invasive, and real-time sensor and has gained interest in many research fields, such as SPR-based s­ ensing[2–5], SPR-based ­microscopy[6], voltage ­sensing[7,8], biomolecular interaction ­analysis[9,10], environment ­monitoring[11,12] and medical ­diagnosis[13–15]. We propose a deep learning-based method for automatically and accurately locating the plasmonic dip position in real-time to enhance precision in the plasmonic measurement of surface plasmon resonancebased angular scanning detection using the CNN architecture. We demonstrate that simulated data can be Scientific Reports | (2022) 12:2052

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