Quantitatively sensitive and resolution-enhancing strategies in optical signal conversion of SPR images using extensive angle-scanning and image-reconstruction

Surface plasmon resonance imaging (SPRi) is a technology developed from SPR that has emerged in response to the growing need for high-throughput and for discovering diverse applications across multiple domains. Despite its potential, SPRi has encountered limitations in sensitivity when handling low-molecular-mass materials, which emphasizes the need for the quantitative processing of SPRi signals. In this study, an SPRi signal conversion process was developed to enhance the contrast of SPR images and convert them into quantitatively analyzed signals. At first, an optimum incident angle of light was determined as an angle that maximized the difference between sums of the brightness % of functionalized regions and the brightness % of background region in the SPR image, which enabled to obtain a distinct SPR image. In addition, the enhancement of contrast and reduction of noise in the SPR image was achieved using Contrast Limited Adaptive Histogram Equalization (CLAHE) method. Then various pixel domains of the SPR images were simultaneously quantified and visualized the multipixel processing technique. For validating the process, an SiO2/Au thin film was modified with four different functionalities and applied to the detection of gas molecules. The SPR images processed using the proposed process exhibited sensitive and quantitative information about the interactions of the functionalities with the target gas in the distinct multiple regions, thus, were applicable to the quantitative monitoring of various phenomena occurring on the plasmonic interface via the advantages of SPRi.

Automated summary

Surface plasmon resonance imaging (SPRi) is a widely-used technology in various fields, but it faces sensitivity limitations when handling low-molecular-mass materials. This study developed an SPRi signal conversion process to enhance image contrast and convert it into quantitatively analyzed signals.