Design and simulation of liquid infiltrated photonic crystal fibre in terahertz frequencies

Ethanol, methanol and water are polar solvents with similar physical properties albeit contrasting chemical properties. Therefore, it is essential to provide accurate and reliable methods for detecting these liquids. In this paper, a novel liquid infiltrated photonic crystal fibre for ethanol, methanol and water sensing is introduced. The novel structure is modelled, simulated and analysed in the terahertz (THz) region using a full vectorial finite element method. It is shown that the THz light, which is guided using modified total internal reflection, is confined within the infiltrated analytes with negligible losses. For the detection of infiltrated liquids at 1.6 THz operating frequency, the proposed fibre demonstrates high sensitivities up to 99.73% and confinement losses in the order of 10−4 dB/m. Manufacturing of the proposed fibre is feasible using existing fabrication technologies and it is envisaged that the fibre may provide a solution to existing challenges in detecting common polar solvents.

Enhancing the sensitivity of a BIOFET by varying the oxide thickness and nanowire length for water sensing application

BIOFET is a device which changes the electrostatic potential due to binding of an analyte. Nanotechnology plays a vital role in Biosensor field-effect transistor (BIOFET) by incorporating the Nanowire in the structure. The aim of the project is to improve the sensitivity of a BIOFET by varying the oxide thickness, nanowire length and radius. The sensitivity and Debye length was calculated. The obtained results were analysed and compared by using the SPSS tool.

The pleiotropic functions of Intracellular hydrophobins in aerial hyphae and fungal spores

Higher fungi can rapidly produce large numbers of spores suitable for aerial dispersal. The efficiency of the dispersal and spore resilience to abiotic stresses correlate with their hydrophobicity provided by the unique amphiphilic and superior surface-active proteins–hydrophobins (HFBs)–that self-assemble at hydrophobic/hydrophilic interfaces and thus modulate surface properties. Using the HFB-enriched mold Trichoderma (Hypocreales, Ascomycota) and the HFB-free yeast Pichia pastoris (Saccharomycetales, Ascomycota), we revealed that the rapid release of HFBs by aerial hyphae shortly prior to conidiation is associated with their intracellular accumulation in vacuoles and/or lipid-enriched organelles. The occasional internalization of the latter organelles in vacuoles can provide the hydrophobic/hydrophilic interface for the assembly of HFB layers and thus result in the formation of HFB-enriched vesicles and vacuolar multicisternal structures (VMSs) putatively lined up by HFBs. These HFB-enriched vesicles and VMSs can become fused in large tonoplast-like organelles or move to the periplasm for secretion. The tonoplast-like structures can contribute to the maintenance of turgor pressure in aerial hyphae supporting the erection of sporogenic structures (e.g., conidiophores) and provide intracellular force to squeeze out HFB-enriched vesicles and VMSs from the periplasm through the cell wall. We also show that the secretion of HFBs occurs prior to the conidiation and reveal that the even spore coating of HFBs deposited in the extracellular matrix requires microscopic water droplets that can be either guttated by the hyphae or obtained from the environment. Furthermore, we demonstrate that at least one HFB, HFB4 in T. guizhouense, is produced and secreted by wetted spores. We show that this protein possibly controls spore dormancy and contributes to the water sensing mechanism required for the detection of germination conditions. Thus, intracellular HFBs have a range of pleiotropic functions in aerial hyphae and spores and are essential for fungal development and fitness.

Deep Learning Based Filtering Algorithm for Noise Removal in Underwater Images

Under-water sensing and image processing play major roles in oceanic scientific studies. One of the related challenges is that the absorption and scattering of light in underwater settings degrades the quality of the imaging. The major drawbacks of underwater imaging are color distortion, low contrast, and loss of detail (especially edge information). The paper proposes a method to address these issues by de-noising and increasing the resolution of the image using a model network trained on similar data. The network extracts frames from a video and filters them with a trigonometric–Gaussian filter to eliminate the noise in the image. It then applies contrast limited adaptive histogram equalization (CLAHE) to improvise the image contrast, and finally enhances the image resolution. Experimental results show that the proposed method could effectively produce enhanced images from degraded underwater images.