scholarly journals Zinc Sulfide, Silicon Dioxide, and Black Phosphorus Based Ultra-Sensitive Surface Plasmon Biosensor

2021 ◽  
Author(s):  
Bhishma Karki ◽  
Youssef Trabelsi ◽  
Arun Uniyal ◽  
Amrindra Pal
Keyword(s):  
Silicon Dioxide ◽  
Quality Factor ◽  
Zinc Sulfide ◽  
Metal Layer ◽  
Polarized Light ◽  
Optical Biosensor ◽  
Research Area ◽  
Black Phosphorus ◽  
Detection Accuracy ◽  
Hybrid Configuration

Abstract The optical biosensor is the emerging research area in the field of bio-photonics. The black phosphorus zinc sulfide-based hybrid configuration is suitable for implementing and analyzing ultrasensitive biosensors. Ag/Zinc sulfide/silicon dioxide/black phosphorus-based biosensor has been implemented in the proposed work using the modified Kretschmann configuration. The sensitivity improvement of the designed SPR sensor is analyzed in the different arrangements of the layers. The thickness of the layers of all the materials has been optimized. The thickness of the Ag metal layer is optimized and taken as 45 nm. The sensitivity and quality factor measured here is as high as \(664.6^\circ /\text{R}\text{I}\text{U}\) and 200 at 1.37 refractive index—the P-polarized light source of \(633\text{n}\text{m}\) wavelength. The proposed biosensor confirms tremendous growth in terms of sensitivity, detection accuracy, and quality factor compared with the traditional SPR sensors. Zinc sulfide has multiple applications in the sensing fields, like sensors based on UV rays, lasers, and gas.

scholarly journals Sensitivity Enhancement of a Graphene, Zinc Sulfide-based Surface Plasmon Resonance Biosensor With an Ag Metal Configuration in the Visible Region

2021 ◽  
Author(s):  
Bhishma Karki ◽  
Arun Uniyal ◽  
Amrindra Pal ◽  
Keyword(s):  
Zinc Sulfide ◽  
Graphene Layer ◽  
Metal Layer ◽  
Visible Region ◽  
Sensitivity Enhancement ◽  
Detection Accuracy ◽  
Performance Parameters ◽  
Kretschmann Configuration ◽  
Interrogation Technique ◽  
Analyte Detection

Abstract A biosensor based on the modified Kretschmann configuration is proposed here. The sensitivity of the conventional prism-based sensor using angular interrogation is low. To enhance the sensor's performance, layers of zinc sulfide (ZnS) and graphene have been deposited over the metal layer. The angular interrogation technique is used to analyze the performance of the sensor. The thickness of the Ag metal has been optimized. The thickness of the Ag metal is taken as 50 nm because minimum reflectance has been achieved. With the combinations of the four layers of ZnS and one graphene layer, the maximum sensitivity attained is 305o/RU. Performance parameters such as detection accuracy, FWHM, and quality factor of the sensor have been evaluated as obtained as 0.33 deg-1, 3.05 deg, 100.7 RIU-1, respectively. The proposed sensor has potential application in the field of biochemical and biological analyte detection.

scholarly journals Zinc sulfide, silicon dioxide, and black phosphorus based ultra-sensitive surface plasmon biosensor

2022 ◽  
Vol 54 (2) ◽  
Author(s):  
Bhishma Karki ◽  
Youssef Trabelsi ◽  
Arun Uniyal ◽  
Amrindra Pal
Keyword(s):  
Silicon Dioxide ◽  
Zinc Sulfide ◽  
Surface Plasmon ◽  
Black Phosphorus

Super-Slippery Degraded Black Phosphorus/Silicon Dioxide Interface

2020 ◽  
Vol 12 (6) ◽  
pp. 7717-7726 ◽  
Author(s):  
Shuai Wu ◽  
Feng He ◽  
Guoxin Xie ◽  
Zhengliang Bian ◽  
Yilong Ren ◽  
...  
Keyword(s):  
Silicon Dioxide ◽  
Black Phosphorus

scholarly journals An Advanced Deep Learning Approach for Multi-Object Counting in Urban Vehicular Environments

2021 ◽  
Vol 13 (12) ◽  
pp. 306
Author(s):  
Ahmed Dirir ◽  
Henry Ignatious ◽  
Hesham Elsayed ◽  
Manzoor Khan ◽  
Mohammed Adib ◽  
...  
Keyword(s):  
Deep Learning ◽  
Intelligent Transportation Systems ◽  
Smart Cities ◽  
Weather Conditions ◽  
Research Area ◽  
Transportation Systems ◽  
Detection Accuracy ◽  
Adverse Weather ◽  
Object Counting ◽  
The Impact

Object counting is an active research area that gained more attention in the past few years. In smart cities, vehicle counting plays a crucial role in urban planning and management of the Intelligent Transportation Systems (ITS). Several approaches have been proposed in the literature to address this problem. However, the resulting detection accuracy is still not adequate. This paper proposes an efficient approach that uses deep learning concepts and correlation filters for multi-object counting and tracking. The performance of the proposed system is evaluated using a dataset consisting of 16 videos with different features to examine the impact of object density, image quality, angle of view, and speed of motion towards system accuracy. Performance evaluation exhibits promising results in normal traffic scenarios and adverse weather conditions. Moreover, the proposed approach outperforms the performance of two recent approaches from the literature.

Thermoelastic Dissipation in Composite Silicon MEMS Resonators with Thin Film Silicon Dioxide Coating

2012 ◽  
Vol 1426 ◽  
pp. 193-198 ◽  
Author(s):  
Shirin Ghaffari ◽  
Thomas W. Kenny
Keyword(s):  
Thin Film ◽  
Silicon Dioxide ◽  
Quality Factor ◽  
Thermal Relaxation ◽  
Oxide Coating ◽  
Thermal Mode ◽  
Silicon Dioxide Film ◽  
Thermal Dynamics ◽  
Mems Resonators ◽  
Composite Silicon

ABSTRACTWe analyze thermoelastic dissipation in composite silicon MEMS resonators that exhibit multiple mechanical and thermal modes with complex dynamics. Silicon resonators that are coated with thin films of silicon dioxide can have near-zero temperature coefficients of frequency, making them attractive for use as precision time references. The quality factor of MEMS resonators can be dominated by thermoelastic dissipation (TED), which is triggered by the relaxation of mechanically induced temperature gradients. Recently, Chandorkar et al. (2009) have shown an expression of TED based on entropy generation as a weighted sum of the modal solutions of the three-dimensional heat transfer equation. This expression was obtained for weak coupling between mechanical and thermal dynamics. Applying this same technique to a fully coupled solution to the dynamics, we show that the TED contribution of the dominant thermal modes can be inhibited in the presence of a thin silicon dioxide film. Reduction of the contribution from the dominant thermal mode is shown with increasing oxide. We studied the effects of varying oxide film thickness and beam length. The quality factor was simulated for each unique case and compared to multimode energy dissipation. Our results suggest with some variability, thin film oxide coating affects the thermal relaxation of the composite resonator in the direction of lower TED and increased quality factor.

scholarly journals Design of Hybrid Blind Detection Based Spectrum Sensing Technique

2020 ◽  
Vol 12 (4) ◽  
pp. 575-583
Author(s):  
V. Sharma ◽  
S. Joshi
Keyword(s):  
Cognitive Radio ◽  
Spectrum Sensing ◽  
Cooperative Spectrum Sensing ◽  
Primary User ◽  
Research Area ◽  
Detection Accuracy ◽  
Blind Detection ◽  
Spectrum Utilization ◽  
Detection Techniques ◽  
Generation Spectrum

Cognitive Radio is a boon to efficient utilization of spectrum to meet the demand of next generation. Spectrum Sensing (SS) is an active research area, essential to meet the requirement of efficient spectrum utilization as it detects the vacant bands. This paper develops a Hybrid Blind Detection (HBD) technique for cooperative spectrum sensing which combines the Energy Detector (ED) and the Anti-Eigen Value Detection (AVD) techniques together to enhance the detection accuracy of a cognitive radio. Collaboration among the cognitive users is achieved to reduce the error and hard fusion based detection is implemented to detect the existence of primary user. The detection accuracy of the design is evaluated with respect to detection probabilities and the results are examined for improvements with the traditional two stage detection techniques. Fusion rules for the cooperative environment are implemented and compared to detect majority rule suitable for the proposed design.

scholarly journals An Efficient Classifier for U2R, R2L, DoS Attack

2020 ◽  
Vol 9 (1) ◽  
pp. 644-647
Keyword(s):  
Machine Learning ◽  
Network Security ◽  
Learning Algorithms ◽  
Research Area ◽  
Attack Detection ◽  
Machine Learning Algorithms ◽  
The Internet ◽  
Detection Accuracy ◽  
Cyber Attack ◽  
Detection Systems

The internet has become an irreplaceable communicating and informative tool in the current world. With the ever-growing importance and massive use of the internet today, there has been interesting from researchers to find the perfect Cyber Attack Detection Systems (CADSs) or rather referred to as Intrusion Detection Systems (IDSs) to protect against the vulnerabilities of network security. CADS presently exist in various variants but can be largely categorized into two broad classifications; signature-based detection and anomaly detection CADSs, based on their approaches to recognize attack packets.The signature-based CADS use the well-known signatures or fingerprints of the attack packets to signal the entry across the gateways of secured networks. Signature-based CADS can only recognize threats that use the known signature, new attacks with unknown signatures can, therefore, strike without notice. Alternatively, anomaly-based CADS are enabled to detect any abnormal traffic within the network and report. There are so many ways of identifying anomalies and different machine learning algorithms are introduced to counter such threats. Most systems, however, fall short of complete attack prevention in the real world due system administration and configuration, system complexity and abuse of authorized access. Several scholars and researchers have achieved a significant milestone in the development of CADS owing to the importance of computer and network security. This paper reviews the current trends of CADS analyzing the efficiency or level of detection accuracy of the machine learning algorithms for cyber-attack detection with an aim to point out to the best. CADS is a developing research area that continues to attract several researchers due to its critical objective.

scholarly journals High-Sensitivity Biosensor Based on Glass Resonance PhC Cavities for Detection of Blood Component and Glucose Concentration in Human Urine

Coatings ◽  
2021 ◽  
Vol 11 (12) ◽  
pp. 1555
Author(s):  
Abduladheem Turki Jalil ◽  
Shameen Ashfaq ◽  
Dmitry Olegovich Bokov ◽  
Amer M. Alanazi ◽  
Kadda Hachem ◽  
...  
Keyword(s):  
Human Urine ◽  
Glucose Concentration ◽  
Free Spectral Range ◽  
High Sensitivity ◽  
Optical Biosensor ◽  
Optical Signal ◽  
Blood Component ◽  
Detection Accuracy ◽  
Worst Case ◽  
Detection Range

In this work, a novel structure of an all-optical biosensor based on glass resonance cavities with high detection accuracy and sensitivity in two-dimensional photon crystal is designed and simulated. The free spectral range in which the structure performs well is about FSR = 630 nm. This sensor measures the concentration of glucose in human urine. Analyses to determine the glucose concentration in urine for a normal range (0~15 mg/dL) and urine despite glucose concentrations of 0.625, 1.25, 2.5, 5 and 10 g/dL in the wavelength range 1.326404~1.326426 μm have been conducted. The detection range is RIU = 0.2 × 10−7. The average bandwidth of the output resonance wavelengths is 0.34 nm in the lowest case. In the worst case, the percentage of optical signal power transmission is 77% with an amplitude of 1.303241 and, in the best case, 100% with an amplitude of 1.326404. The overall dimensions of the biosensor are 102.6 µm2 and the sensitivity is equal to S = 1360.02 nm/RIU and the important parameter of the Figure of Merit (FOM) for the proposed biosensor structure is equal to FOM = 1320.23 RIU−1.

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