Plasmonic sensor for blood type detection: optimizing resolution in blood type differentiation

Abstract

In this paper, we analyze and review the performance of refractive index (RI) sensors for detecting human blood groups (BGs). Additionally, we introduce the standard deviation (SD) as a means to identify the optimal highest resolution sensor. We design and simulate the behavior of 14 different nano-RI sensors based on Metal-Insulator-Metal (MIM) plasmonic waveguides using the FDTD method. Previously, it was believed that the most effective sensor for sensing BGs would be the one with the highest sensitivity. However, our research has revealed otherwise, showing that the best sensor is not necessarily the one with the highest sensitivity. Significant observations indicate that the sensor with the highest sensitivity does not necessarily provide the best blood type resolution. Instead, it is the sensor that can generate the maximum spectral distance between the resonance peaks. Our conclusion was reached through an analysis of the spectrum of blood groups A, O, and B (BGAOB). To evaluate the resolving power between different BG resonance peaks, we introduced SD parameter as a key metric for assessing blood type differentiation, clarifying that a larger SD correlates with improved resolution. By comparing SD, we identified the sensor that creates the greatest spectral distance between the peaks. This finding is highly valuable and effective in optimizing the design and comparison of BG sensors, ultimately resulting in sensors with enhanced sensing capabilities. With the proposed sensor configurations and the defined mathematical model, we achieved optimal performance in BG sensing. This nanoscale structure and high-resolution approach offer a promising option for designing non-invasive sensors on a single chip.