In this paper; the surface plasmon resonance (SPR) sensor with a porous silica film was studied. The effect of the thickness and porosity of the porous silica film on the performance of the sensor was analyzed. The results indicated that the figure of merit (FOM) of an SPR sensor can be enhanced by using a porous silica film with a low-refractive-index. Particularly; the FOM of an SPR sensor with 40 nm thick 90% porosity porous silica film; whose refractive index is 1.04 was improved by 311% when compared with that of a traditional SPR sensor. Furthermore; it was found that the decrease in the refractive index or the increase in the thickness of the low-refractive-index porous silica film can enlarge the FOM enhancement. It is believed that the proposed SPR sensor with a low-refractive-index porous silica film will be helpful for high-performance SPR sensors development.
This paper demonstrates further research on intelligent algorithms of binary amplitude optimization for wavefront shaping by numerical simulations. A better fitness function of the genetic algorithm (GA) has been presented after a comparative analysis of enhancement. With this new discriminant, we have achieved a relative enhancement of 0.225, which is higher than the theoretical value (0.159). In addition, we have also proposed a kind of modified particle swarm optimization algorithm (PSO), which has a higher enhancement than the unmodified PSO and a faster convergence speed than the GA. These studies provide remarkable insights into future exploration of intelligent algorithms for wavefront shaping.
In this paper, we presented a design method of a surface plasmon resonance (SPR) biosensor with high performance using a genetic algorithm (GA). The constraint conditions of the sensitivity and the reflectivity at the resonance angle were used in the merit function (MF) of GA to achieve simultaneous optimization of the sensitivity and the resolution. By using the proposed method, we designed an Au-Ag-TiO2-graphene based SPR biosensor at first and compared its performance with a traditional Au-graphene based SPR biosensor. The resolution of the designed biosensor was nearly three times that of the traditional one on the premise of the same sensitivity. In addition, a series of SPR biosensors with sensitivities ranging from 50 to 180°/RIU and improved resolutions was designed by using different target sensitivities in MF. A comparison of the designed biosensors with the traditional Au-graphene SPR biosensor was also done, and the biosensors with higher sensitivity and meanwhile higher resolution than the traditional one were demonstrated to be existed. Lastly, the influences of target reflectivity at the resonance angle and the prism on the design of the Au-Ag-TiO2-graphene based SPR biosensor were investigated. It is believed that the proposed design method based on the genetic algorithm could be applied to optimize the performances of a SPR biosensor with an arbitrary multilayer structure.
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