Sensitivity is an important performance index for evaluating surface plasmon resonance (SPR) biosensors. Sensitivity enhancement has always been a hot topic. It is found that the different refractive indices of samples require different combinations of prism and metal film for better sensitivity. Furthermore, the sensitivity can be enhanced by coating two-dimensional (2D) materials with appropriate layers on the metal film. At this time, it is necessary to choose the best film configuration to enhance sensitivity. With the emergence of more and more 2D materials, selecting the best configuration manually is becoming more complicated. Compared with the traditional manual method of selecting materials and layers, this paper proposes an optimization method based on a genetic algorithm to quickly and effectively find the optimal film configuration that enhances sensitivity. By using this method, not only can the optimal number of layers of 2D materials be determined quickly, but also the optimal configuration can be conveniently found when many materials are available. The maximum sensitivity can reach 400°/RIU after optimization. The method provided application value for the relevant researchers seeking to enhance sensitivity.

For wavelength interrogation based surface plasmon resonance (SPR) sensors, refractive index (RI) resolution is an important parameter to evaluate the performance of the system. In this paper, we explore the influence of spectral power distribution on the refractive index (RI) resolution of the SPR system by simulating the reflectivity curve corresponding to different incident angles of the classical Kretschmann structure and several different spectral power distribution curves. A wavelength interrogation based SPR system is built, and commercial micro-spectrometers (USB2000 and USB4000) are used as the detection components, respectively. The RI resolutions of the SPR system in these two cases are measured, respectively. Both theoretical and experimental results show that the spectral power distribution has a significant effect on the RI resolution of the SPR system.

In this paper, three fatigue models are employed and proposed for modeling the fatigue life of different fiber-reinforced composite material systems. In order to identify the unknown parameters in these models, Genetic Algorithm (GA) is used for estimating its values. This technique is a stochastic process that leads straight to different S-N curves that predicts the trend of the experimental data without the need for any assumptions. The calculation results show that these three models, especially the nonlinear regression model, whose parameters are assigned by using GA are all satisfied with the experimental data, and the average value of RMSE is below 0.1. The method of fatigue damage simulation presented should have a very good application prospect.