Optical Fano resonance sensing of bilayer asymmetric photonic crystal slabs as biosensors

Glucose sensor modeling based on Fano resonance excitation in titania nanotube photonic crystal coated by titanium nitride as a plasmonic material

The brilliant optical properties of plasmonic metal nitrides improve many applications. Modeling of light-confining Fano resonance based on a titanium nitride (TiN)-coated titanium oxide one-dimensional photonic crystal is investigated as a glucose sensor. There is a cavity layer filled with a glucose solution between the TiN thin layer and photonic crystals. The reflection spectrum is calculated numerically by using Bruggeman's effective medium approximation and transfer matrix method. The effect of plasmonic layer thickness, cavity layer thickness, and the thicknesses of the titanium oxide nanotube layers are optimized to achieve a high performance sensor. The result shows that the Fano resonances shift to higher wavelengths with increasing glucose concentration. The best sensitivity of the optimized biosensor is about 3798.32 nm/RIU. Also, the sensor performance parameters such as the limit of detection, figure of merit, and quality factor are discussed. The proposed sensor can be of potential interest due to its easy fabrication and higher performance than many previous reported sensors in the sensing field.

Two-Dimensional Hole-Array Grating-Coupling-Based Excitation of Bloch Surface Waves for Highly Sensitive Biosensing

In this study, a surface diffraction two-dimensional (2D) grating structure was placed on the topmost layer of distributed Bragg reflectors (DBRs) for biosensing. Bloch surface wave (BSW) resonance was realized by coupling a 2D subwavelength hole-array grating and could be excited at different locations: the surface of 2D-grating layer or the inter-face between the DBR and bio-solution. Material losses in the multilayer dielectric were measured to test the robustness of this scheme. Both the surface diffraction-grating BSW (DG-BSW) and the alternative guided grating-coupled BSW (GC-BSW) configuration showed markedly enhanced angular sensitivity compared to conventional prism-coupled schematics. Exciting these modes using a grating-coupling technique appears to yield different extreme sensitivity modes with a maximum of 1190°/RIU for DG-BSW and 2255°/RIU for GC-BSW. Refractive index sensors with a high figure of merit may be realized via such compact configurations.

Pesticide Residues Identification by Optical Spectrum in the Time-Sequence of Enzyme Inhibitors Performed on Microfluidic Paper-Based Analytical Devices (µPADs)

Pesticides vary in the level of poisonousness, while a conventional rapid test card only provides a general “absence or not” solution, which cannot identify the various genera of pesticides. In order to solve this problem, we proposed a seven-layer paper-based microfluidic chip, integrating the enzyme acetylcholinesterase (AChE) and chromogenic reaction. It enables on-chip pesticide identification via a reflected light intensity spectrum in time-sequence according to the different reaction efficiencies of pesticide molecules and assures the optimum temperature for enzyme activity. After pretreatment of figures of reflected light intensity during the 15 min period, the figures mainly focused on the reflected light variations aroused by the enzyme inhibition assay, and thus, the linear discriminant analysis showed satisfying discrimination of imidacloprid (Y = −1.6525X − 139.7500), phorate (Y = −3.9689X − 483.0526), and avermectin (Y = −2.3617X − 28.3082). The correlation coefficients for these linearity curves were 0.9635, 0.8093, and 0.9094, respectively, with a 95% limit of agreement. Then, the avermectin class chemicals and real-world samples (i.e., lettuce and rice) were tested, which all showed feasible graphic results to distinguish all the chemicals. Therefore, it is feasible to distinguish the three tested kinds of pesticides by the changes in the reflected light spectrum in each min (15 min) via the proposed chip with a high level of automation and integration.