Reconfigurable and scalable 2,4-and 6-channel plasmonics demultiplexer utilizing symmetrical rectangular resonators containing silver nano-rod defects with FDTD method

Machine learning-enhanced surface plasmon resonance based photonic crystal fiber sensor

This study introduces what we believe to be a novel photonic crystal fiber sensor utilizing surface plasmon resonance (SPR), incorporating four gold nanowires to enhance sensing capabilities. The research employs machine learning, specifically artificial neural networks (ANN), to predict confinement loss and sensitivity, achieving high accuracy without needing the imaginary part of the effective refractive index. The machine learning technique is applied in three different scenarios, resulting in mean squared errors of 0.084, 0.002, and 0.003, highlighting the reliability of the ANN models in predicting sensor outputs. Additionally, the sensor demonstrates impressive wavelength sensitivities of 2000-18000 nm/RIU (nanometers per refractive index unit) for refractive indices of 1.31-1.4 within the 720-1280 nm wavelength range, and a notable maximum amplitude sensitivity of 889.89 RIU-1. This integration of SPR, photonic crystal fiber, and machine learning not only optimizes sensor performance but also offers an efficient methodology for prediction, showcasing the potential of machine learning in advancing optical sensor design.

Observation of Plasmonics Talbot effect in graphene nanostructures

We report on the theoretical models of the plasmoincs Talbot effect in graphene nanostructure. The Talbot effect for the plasmonics applications in the IR range is theoretically studied and the respective Talbot effect for the novel advanced plasmonics structures are numerically investigated for the first time. It is shown that the metamaterial structures with periodic grating configuration represents a complex three-dimensional lattice of beamlet-like graphene plasmonics devices. The calculated results agree well with the experimental ones. The results obtained can be used to create and optimize the structures considering diffraction limit for a wide range of application areas. Effective focusing of plasmonic waves with exact focal spots and a subwavelength full width at half maximum can be obtained by using periodic graphene grating.

Optical biosensors using plasmonic and photonic crystal band-gap structures for the detection of basal cell cancer

One of the most interesting topics in bio-optics is measuring the refractive index of tissues. Accordingly, two novel optical biosensor configurations for cancer cell detections have been proposed in this paper. These structures are composed of one-dimensional photonic crystal (PC) lattices coupled to two metal-insulator-metal (MIM) plasmonic waveguides. Also, the tapering method is used to improve the matching between the MIM plasmonic waveguides and PC structure in the second proposed topology. The PC lattices at the central part of the structures generate photonic bandgaps (PBGs) with sharp edges in the transmission spectra of the biosensors. These sharp edges are suitable candidates for sensing applications. On the other hand, the long distance between two PBG edges causes that when the low PBG edge is used for sensing mechanism, it does not have an overlapping with the high PBG edge by changing the refractive index of the analyte. Therefore, the proposed biosensors can be used for a wide wavelength range. The maximum obtained sensitivities and FOM values of the designed biosensors are equal to 718.6, 714.3 nm/RIU, and 156.217, 60.1 RIU^-1, respectively. The metal and insulator materials which are used in the designed structures are silver, air, and GaAs, respectively. The finite-difference time-domain (FDTD) method is used for the numerical investigation of the proposed structures. Furthermore, the initial structure of the proposed biosensors is analyzed using the transmission line method to verify the FDTD simulations. The attractive and simple topologies of the proposed biosensors and their high sensitivities make them suitable candidates for biosensing applications.