Highly sensitive biosensor based on a microstructured photonic crystal fibre for alcohol sensing

This research article was published by Elsevier 2023A microstructure alcohol biosensor has been proposed to operate in the wavelength range of 0.8 to 2.0 μm for the sensing of propanol, butanol, and pentanol, unveiling impressive results of relative sensitivity and confinement loss. The results are achieved by implementing closely arranged cladding air holes of 3 rings with a single elliptical core hole for analyte infiltration. Performance evaluation of the sensor was conducted using COMSOL Multiphysics software and yields relative sensitivity of 96.75%, 89.60%, and 82.02% for propanol, butanol, and pentanol, respectively, and confinement losses of 5.49 × 10 12 dB/m for propanol, 1.98 × 10 12 dB/m for butanol, and 9.36 × 10 13 dB/m for pentanol. Other optical parameters have also been analysed that recorded effective refractive index, high power fraction, low birefringence, small effective area, and large nonlinear co- efficients. The proposed biosensor is eligible for practical application in alcohol sensing with these results. Moreover, this proposed biosensor is suitable as a supercontinuum source in optical communication systems because of the high nonlinear coefficients

Refining Network Lifetime of Wireless Sensor Network Using Energy-Efficient Clustering and DRL-Based Sleep Scheduling.

This research article published by MDPI, 2020Over the recent era, Wireless Sensor Network (WSN) has attracted much attention among industrialists and researchers owing to its contribution to numerous applications including military, environmental monitoring and so on. However, reducing the network delay and improving the network lifetime are always big issues in the domain of WSN. To resolve these downsides, we propose an Energy-Efficient Scheduling using the Deep Reinforcement Learning (DRL) (ES-DRL) algorithm in WSN. ES-DRL contributes three phases to prolong network lifetime and to reduce network delay that is: the clustering phase, duty-cycling phase and routing phase. ES-DRL starts with the clustering phase where we reduce the energy consumption incurred during data aggregation. It is achieved through the Zone-based Clustering (ZbC) scheme. In the ZbC scheme, hybrid Particle Swarm Optimization (PSO) and Affinity Propagation (AP) algorithms are utilized. Duty cycling is adopted in the second phase by executing the DRL algorithm, from which, ES-DRL reduces the energy consumption of individual sensor nodes effectually. The transmission delay is mitigated in the third (routing) phase using Ant Colony Optimization (ACO) and the Firefly Algorithm (FFA). Our work is modeled in Network Simulator 3.26 (NS3). The results are valuable in provisions of upcoming metrics including network lifetime, energy consumption, throughput and delay. From this evaluation, it is proved that our ES-DRL reduces energy consumption, reduces delays by up to 40% and enhances throughput and network lifetime up to 35% compared to the existing cTDMA, DRA, LDC and iABC methods

Lifetime improved WSN using enhanced-LEACH and angle sector-based energy-aware TDMA scheduling

This research article published by Cogent Engineering, 2020Network lifetime remains as a significant requirement in Wireless Sensor Network (WSN) exploited to prolong network processing. Deployment of low power sensor nodes in WSN is essential to utilize the energy efficiently. Clustering and sleep scheduling are the two major processes involved in improving network lifetime. However, abrupt and energy unaware selection of cluster head (CH) is nonoptimal in WSN which reflects in the drop of energy among sensor nodes. This paper addresses the twofold as utilization of sensor nodes to prolong the node’s energy and network lifetime by LEACH-based cluster formation and Time Division Multiple Access scheduling (TDMA). Clusters are constructed by the design of an EnhancedLow-Energy adaptive Clustering Hierarchy protocol (E-LEACH) that uses parallel operating optimization (Grey Wolf Optimization (GWO) and Discrete Particle Swarm Optimization (D-PSO)) for selecting an optimal CH and helper CH. The fitness values estimation from GWO and D-PSO is concatenated to prefer the best optimal CH. E-LEACH also manages the cluster size which is one of the conventional disadvantages in LEACH. CHs are responsible to perform energy-aware TDMA scheduling which segregates the coverage area into 24 sectors. Alternate sectors are assigne

Characteristics of Ultrasensitive Hexagonal-Cored Photonic Crystal Fiber for Hazardous Chemical Sensing

This research article was published by MDPI 2022A highly sensitive non-complex cored photonic crystal fiber sensor for hazardous chemical sensing with water, ethanol, and benzene analytes has been proposed and is numerically analyzed using a full-vector finite element method. The proposed fiber consists of a hexagonal core hole and two cladding air hole rings, operating in the lower operating wavelength of 0.8 to 2.6 μm. It has been shown that the structure has high relative sensitivity of 94.47% for water, 96.32% for ethanol and 99.63% for benzene, and low confinement losses of 7.31 × 10−9 dB/m for water, 3.70 × 10−10 dB/m ethanol and 1.76 × 10−13 dB/m benzene. It also displays a high power fraction and almost flattened chromatic dispersion. The results demonstrate the applicability of the proposed fiber design for chemical sensing applications

Design and Simulation of Photonic Crystal Fiber for Liquid Sensing

This research article was published by MDPI 2021A simple hexagonal lattice photonic crystal fiber model with liquid-infiltrated core for different liquids: water, ethanol and benzene, has been proposed. In the proposed structure, three air hole rings are present in the cladding and three equal sized air holes are present in the core. Numerical investigation of the proposed fiber has been performed using full vector finite element method with anisotropic perfectly match layers, to show that the proposed simple structure exhibits high relative sensitivity, high power fraction, relatively high birefringence, low chromatic dispersion, low confinement loss, small effective area, and high nonlinear coefficient. All these properties have been numerically investigated at a wider wavelength regime 0.6–1.8 μm within mostly the IR region. Relative sensitivities of water, ethanol and benzene are obtained at 62.60%, 65.34% and 74.50%, respectively, and the nonlinear coefficients are 69.4 W−1 km−1 for water, 73.8 W−1 km−1 for ethanol and 95.4 W−1 km−1 for benzene, at 1.3 μm operating wavelength. The simple structure can be easily fabricated for practical use, and assessment of its multiple waveguide properties has justified its usage in real liquid detection

Repurposing Nonnucleoside Antivirals Against SARS-CoV2 NSP12 (RNA Dependent RNA Polymerase) and Identification of Domain Specific Interactions

The pandemic of SARS-CoV-2 has necessitated expedited research efforts towards finding potential antiviral targets and drug development measures. While new drug discovery is time consuming, drug repurposing has been a promising area for elaborate virtual screening and identification of existing FDA approved drugs that could possibly be used for targeting against functions of various proteins of SARS-CoV-2 virus. RNA dependent RNA polymerase (RdRp) is an important enzyme for the virus that mediates replication of the viral RNA. Inhibition of RdRp could inhibit viral RNA replication and thus new virus particle production. Here, we screened non-nucleoside antivirals and found three out of them to be strongest in binding to RdRp. We propose these three drugs as potential RdRp inhibitors based on the site of binding. </p