Photonic crystal fibers (PCFs) are the specialized optical waveguides that led to many interesting applications ranging from nonlinear optical signal processing to high-power fiber amplifiers. In this paper, machine learning techniques are used to compute various optical properties including effective index, effective mode area, dispersion and confinement loss for a solid-core PCF. These machine learning algorithms based on artificial neural networks are able to make accurate predictions of above-mentioned optical properties for usual parameter space of wavelength ranging from 0.5-1.8 µm, pitch from 0.8-2.0 µm, diameter by pitch from 0.6-0.9 and number of rings as 4 or 5 in a silica solid-core PCF. We demonstrate the use of simple and fast-training feed-forward artificial neural networks that predicts the output for unknown device parameters faster than conventional numerical simulation techniques. Computation runtimes required with neural networks (for training and testing) and Lumerical MODE solutions are also compared.

The increasing use of nanomaterials and scalable, high-yield nanofabrication process are revolutionizing the development of novel biosensors. Over the past decades, researches on nanotechnology-mediated biosensing have been on the forefront due to their potential application in healthcare, pharmaceutical, cell diagnosis, drug delivery, and water and air quality monitoring. The advancement of nanoscale science relies on a better understanding of theory, manufacturing and fabrication practices, and the application specific methods. The topology and tunable properties of nanoparticles, a part of nanoscale science, can be changed by different manufacturing processes, which separate them from their bulk counterparts. In the recent past, different nanostructures, such as nanosphere, nanorods, nanofiber, core–shell nanoparticles, nanotubes, and thin films, have been exploited to enhance the detectability of labelled or label-free biological molecules with a high accuracy. Furthermore, these engineered-materials-associated transducing devices, e.g., optical waveguides and metasurface-based scattering media, widened the horizon of biosensors over a broad wavelength range from deep-ultraviolet to far-infrared. This review provides a comprehensive overview of the major scientific achievements in nano-biosensors based on optical fiber, nanomaterials and terahertz-domain metasurface-based refractometric, labelled and label-free nano-biosensors.

In this work, a highly sensitive surface plasmon resonance (SPR) sensor based on a single mode fiber (SMF) incorporating a large microfluidic channel (MFC) for refractive index (RI) sensing is designed and optimized using a full-vectorial finite element method (FEM). The fluidic channel size can be varied according to the requirement due to the availability of the large cladding diameter of SMF, which makes it simple and easy to fabricate. The proposed novel sensor is favourable to both analytes and metallic strips. The D-shaped hollow section above the core is filled with the measurand analytes and a gold (Au) strip is deposited on the base of the MFC, as it is known as the most attractive metal for SPR. Our numerical simulations illustrate that the confinement loss of the designed sensor is highly influenced by the distance of the MFC from the core along with the width and thickness of the Au strip. The designed sensor shows an average sensitivity of 1350 nm/RIU and maximum sensitivity of 8250 nm/RIU in the sensing range of 1.33-1.35 and 1.41-1.43, respectively. However, for a small variation of n_a at a step of 0.005, within ranges like 1.415, 1.420, and 1.425, we have achieved a maximum sensitivity of 7000 nm/RIU, 9000 nm/RIU and 11000 nm/RIU, respectively. This novel SPR sensor with MFC can open up a new opportunity in the application of chemical and biological sensing.