In this paper, a highly sensitive graphene-based multiple-layer (BK₇/Au/PtSe₂/Graphene) coated surface plasmon resonance (SPR) biosensor is proposed for the rapid detection of the novel Coronavirus (COVID-19). The proposed sensor was modeled on the basis of the total internal reflection (TIR) technique for real-time detection of ligand-analyte immobilization in the sensing region. The refractive index (RI) of the sensing region is changed due to the interaction of different concentrations of the ligand-analyte, thus impacting surface plasmon polaritons (SPPs) excitation of the multi-layer sensor interface. The performance of the proposed sensor was numerically investigated by using the transfer matrix method (TMM) and the finite-difference time-domain (FDTD) method. The proposed SPR biosensor provides fast and accurate early-stage diagnosis of the COVID-19 virus, which is crucial in limiting the spread of the pandemic. In addition, the performance of the proposed sensor was investigated numerically with different ligand-analytes: (i) the monoclonal antibodies (mAbs) as ligand and the COVID-19 virus spike receptor-binding domain (RBD) as analyte, (ii) the virus spike RBD as ligand and the virus anti-spike protein (IgM, IgG) as analyte and (iii) the specific probe as ligand and the COVID-19 virus single-standard ribonucleic acid (RNA) as analyte. After the investigation, the sensitivity of the proposed sensor was found to provide 183.33°/refractive index unit (RIU) in SPR angle (θ_SPR) and 833.33THz/RIU in SPR frequency (SPRF) for detection of the COVID-19 virus spike RBD; the sensitivity obtained 153.85°/RIU in SPR angle and 726.50THz/RIU in SPRF for detection of the anti-spike protein, and finally, the sensitivity obtained 140.35°/RIU in SPR angle and 500THz/RIU in SPRF for detection of viral RNA. It was observed that whole virus spike RBD detection sensitivity is higher than that of the other two detection processes. Highly sensitive two-dimensional (2D) materials were used to achieve significant enhancement in the Goos-Hänchen (GH) shift detection sensitivity and plasmonic properties of the conventional SPR sensor. The proposed sensor successfully senses the COVID-19 virus and offers additional (1 + 0.55) × L times sensitivity owing to the added graphene layers. Besides, the performance of the proposed sensor was analyzed based on detection accuracy (DA), the figure of merit (FOM), signal-noise ratio (SNR), and quality factor (QF). Based on its performance analysis, it is expected that the proposed sensor may reduce lengthy procedures, false positive results, and clinical costs, compared to traditional sensors. The performance of the proposed sensor model was checked using the TMM algorithm and validated by the FDTD technique.

The detection of microorganisms like Pseudomonas are very important as they trigger an infection in human blood, lungs, and different parts of the body causing various ailments. In this paper, a surface plasmon resonance (SPR) biosensor based on photonic crystal fiber (PCF) has been proposed to detect the presence of Pseudomonas bacteria with attractive performance characteristics. The sensor is designed using a simple circular lattice of PCF, coated with a thin chemically stable gold layer. The performance investigation of the sensor is numerically carried out by using a finite element (FE) based simulation tool where the highest wavelength and amplitude sensitivity are found as 20,000 nm/RIU and 1380 RIU ⁻¹ , respectively. The sensor shows an excellent spectral resolution of the highest value of $5.26\times 10^{-6}$ RIU, ensuring the capability of identifying a very small change in analyte refractive index (RI) within the range of 1.33 to 1.42. The performance investigation is also carried out altering the diameter of air holes, pitch, and gold layer thickness to explore the variation in phase matching conditions due to the change in structural parameters. As the sensor is adept at detecting the sample with high sensitivity and sensing resolution, the proposed sensor can be highly efficient in detecting Pseudomonas bacteria as well as other organic compounds, and biological analytes.

This paper presents a performance comparison of heterostructure surface plasmon resonance (SPR) biosensors for the application of Novel Coronavirus SARS-CoV-2 diagnosis. The comparison is performed and compared with the existing literature based on the performance parameters in terms of several prisms such as BaF₂, BK₇, CaF₂, CsF, SF₆, and SiO₂, several adhesion layers such as TiO₂, Chromium, plasmonic metals such as Ag, Au, and two-dimensional (2D) transition metal dichalcogenides materials such as BP, Graphene, PtSe₂ MoS₂, MoSe₂, WS₂, WSe₂. To study the performance of the heterostructure SPR sensor, the transfer matrix method is applied, and to analyses, the electric field intensity near the graphene-sensing layer contact, the finite-difference time-domain approach is utilized. Numerical results show that the heterostructure comprised of CaF₂/TiO₂/Ag/BP/Graphene/Sensing-layer has the best sensitivity and detection accuracy. The proposed sensor has an angle shift sensitivity of 390°/refractive index unit (RIU). Furthermore, the sensor achieved a detection accuracy of 0.464, a quality factor of 92.86/RIU, a figure of merit of 87.95, and a combined sensitive factor of 85.28. Furthermore, varied concentrations (0–1000 nM) of biomolecule binding interactions between ligands and analytes have been observed for the prospects of diagnosis of the SARS-CoV-2 virus. Results demonstrate that the proposed sensor is well suited for real-time and label-free detection particularly SARS-CoV-2 virus detection.

Electric vehicles (EVs) are commonly recognized as environmentally friendly modes of transportation. They function by converting electrical energy into mechanical energy using different types of motors, which aligns with the sustainable principles embraced by smart cities. The motors of EVs store and consume electrical power from renewable energy (RE) sources through interfacing connections using power electronics technology to provide mechanical power through rotation. The reliable operation of an EV mainly relies on the condition of interfacing connections in the EV, particularly the connection between the 3- $\phi $ inverter output and the brushless DC (BLDC) motor. In this paper, machine learning (ML) tools are deployed for detecting and classifying the faults in the connecting lines from 3- $\phi $ inverter output to the BLDC motor during operational mode in the EV platform, considering double-line and three-phase faults. Several machine learning-based fault identification and classification tools, namely the Decision Tree, Logistic Regression, Stochastic Gradient Descent, AdaBoost, XGBoost, K-Nearest Neighbour, and Voting Classifier, were tuned for identifying and categorizing faults to ensure robustness and reliability. The ML classifications were developed based on the datasets of healthy and faulty conditions considering the combination of six critical parameters that have significance in reliable EV operation, namely the current supplied to the BLDC motor from the inverter, the modulated DC voltage, output speed, and measured speed, as well as the output of the Hall-effect sensor. In addition, the superiority of the proposed fault detection and classification approaches using ML tools was assessed by comparing the detection and classification efficiency through some statistical performance parameter comparisons among the classifiers.

<!-- *** Custom HTML *** --> We present a MoS2–graphene bilayer-based high-performance refractive index surface plasmon resonance (SPR) sensor applying an angular interrogation technique. We used it for inspecting the reflected optical signal from the sensing medium. For enhancing the sensor angular sensitivity (S), signal-to-noise ratio (SNR), and quality factor (QF), we undertook some steps sequentially. First, the impact of gold layer thickness was investigated and optimized to 40 nm. Second, the MoS2 and graphene coating layers were optimized to four and three, respectively. Finally, the minimum reflectance and SPR angle were identified with this optimum structure. It is seen that the angular sensitivity of this optimum structure improved to an excellent value of 130 deg-RIU^−  1 along with an improved angular SNR of 1.37 and QF of 17.02 RIU^−  1. Then we compared the proposed sensor with the existing works in terms of angular sensitivity, SNR, and QF to prove its effective application. Finally, an analysis of DNA hybridization is presented as an application of the proposed structure. Our offered configuration is capable of recognizing absorption of DNAs by means of the attenuated total reflection method. The effective refractive index of the sensing medium alters because of the absorption of various concentrated DNAs. Computational investigation demonstrates that the change of SPR angle and minimum reflectance for mismatched DNA strands is inconsiderable, though it is significant for complementary DNA strands, which is crucial for the sensing of accurate DNA hybridization.