We have investigated graphene-based three various refractive index sensors (split ring resonator (SRR), split ring resonator with thin wire (SRRTW), and thin wire (TW) refractive index sensors) for the encoding and sensing-based applications. The sensors are designed to detect the presence of hemoglobin biomolecules with high sensitivity. The results are analyzed in the form of transmittance, and electric field and detailed sensitivity analysis is also carried out for the proposed graphene-based refractive index sensors for four various concentrations of hemoglobin biomolecules. We have also investigated the sensor’s performance in terms of quality factor, Q, and figure of merit (FOM). The encoding of ‘0’ and ‘1’ is attained by varying the graphene chemical potential fulfilling the one-digit coding. An array of these sensors can then be used for encoding-based applications. The detailed analysis of reported sensors is also carried out by checking the effect of varying physical parameters such as substrate thickness, split ring gap, and thin wire width on tunability. These sensors can be applied in biomedical or encoding-based applications. Experiments are performed using XGBoost regressor to determine, whether simulation time and resources can be reduced by using regression analysis to predict the transmittance values of intermediate frequency or not. Experimental results prove that regression analysis using XGBoost Regressor can reduce the simulation time and resources by at least 70 percent.

Rapid detection of mycobacterium tuberculosis bacteria is very important in reducing tuberculosis disease. We propose a label-free graphene-based refractive index sensor using a machine learning approach that detects mycobacterium tuberculosis bacteria. The biosensor is designed for higher sensitivity by analyzing different parameters like substrate thickness, resonator thickness, and angle of incidence. Machine learning is applied to predict the values of absorption for different wavelengths. The machine learning model is applied to four different parameters (angle of incidence, substrate thickness, resonator thickness, graphene chemical potential) of the biosensor. The plus shape metasurface is placed above the graphene-SiO2 hybrid layer to improve the sensitivity. The comparative analysis with other published designs is also presented. The proposed sensor with its higher sensitivity and ability to detect mycobacterium tuberculosis bacteria can be used in biomedical devices for diagnostic applications. Experiments are performed to check the K-Nearest Neighbors (KNN)-regressor model’s prediction efficiency for predicting absorption values of intermediate wavelengths. Different values of K and two test cases; R-50, U-50 are used to test the regressor models using the R2 Score as an evaluation metric. It is observed from the experimental results that, high prediction efficiency can be achieved using lower values of K in the KNN-Regressor model.

Machine learning is the latest approach to optimize the performance of absorbers, sensors, etc. A sensor with behavior prediction using polynomial regression is presented. Three different variations of metasurfaces namely double split-ring resonator, single split ring resonator, split ring resonator with thin wire are analyzed. The proposed design aims to achieve the highest sensitivity by observing different designs and different parameter variation. The highest sensitivity is achieved for double split-ring resonator and single split ring resonator designs. The change in thickness of different parameter affect the absorption and the highest sensitivity is calculated based on these variations. The polynomial regression (PR) model is employed to predict the absorption values for assorted combinations of intermediate wavelength values with angle variation, substrate thickness, substrate length, substrate width, graphene potential, and resonator thickness values. Test Cases R-30 and R-50 are evaluated using R² score metric to assess the effectiveness of PR model for predicting the values of absorption. R² score close to 1.0 is achieved for all the experiments at a higher (more than 5) polynomial degree, which proves the prediction efficiency of a regression model. The proposed biosensor designed with a PR model can be applied in biomedical applications for hemoglobin detection.

This work intends for sensitivity analysis of different metasurface array-based biosensors. The array designs in the form of circle array, square array, square circle array, circle square array, five-element square array, plus array, five-element plus square array. The sensitivity of these metasurface biosensors are compared and optimized design is presented. Circle array design shows the best sensitivity performance amongst them. The optimized circle array design is analyzed by varying different physical parameters of the design such as SiO ₂ substrate thickness and metasurface array thickness. The optimized results for this variation are also presented. The sensitivity of the current biosensor design is also compared with the previously published similar designs. The results are also presented for electric field intensity and current field density for the optimized design. The proposed sensitive biosensor can become a building block of future biomedical devices and photovoltaic devices.

This century has introduced very deadly, dangerous, and infectious diseases to humankind such as the influenza virus, Ebola virus, Zika virus, and the most infectious SARS-CoV-2 commonly known as COVID-19 and have caused epidemics and pandemics across the globe. For some of these diseases, proper medications, and vaccinations are missing and the early detection of these viruses will be critical to saving the patients. And even the vaccines are available for COVID-19, the new variants of COVID-19 such as Delta, and Omicron are spreading at large. The available virus detection techniques take a long time, are costly, and complex and some of them generates false negative or false positive that might cost patients their lives. The biosensor technique is one of the best qualified to address this difficult challenge. In this systematic review, we have summarized recent advancements in biosensor-based detection of these pandemic viruses including COVID-19. Biosensors are emerging as efficient and economical analytical diagnostic instruments for early-stage illness detection. They are highly suitable for applications related to healthcare, wearable electronics, safety, environment, military, and agriculture. We strongly believe that these insights will aid in the study and development of a new generation of adaptable virus biosensors for fellow researchers.

In the presented study, a graphene metasurface-based refractive index sensor (RIS) has been developed and built, which is highly beneficial in urine observation. Pregnant and non-pregnant urine samples were simulated and analyzed using effective refractive indices (RIs) of 1.335, 1.34, 1.342, and 1.343. Analysis of the sensor’s sensitivity, resonance frequency, resolution, detection limit, quality factor (QF), figure of merit (FOM), and other parameters has been done. In addition, the numerical investigations employ the Finite Element Method (FEM). In addition to this, a geometrical variation to attain a highly performing sensor performance is also carried out to demonstrate the effect of various structural parameters on sensitivity, and in a process, optimal parameters are also identified. With this investigation, we were able to obtain the greatest possible sensitivity response value of 200 GHz/RIU, as well as the highest QF and a FOM, and the highest resolution and detection limit of 11.97, 5.12 RIU-1, 0.15 THz, and 0.88 RIU, respectively. Along with that, an innovative realization of the NOR gate has been demonstrated by employing graphene with various graphene chemical potential values.