Design and Development of Ultrabroadband, High-Gain, and High-Isolation THz MIMO Antenna with a Complementary Split-Ring Resonator Metamaterial.

The need for high-speed communication has created a way to design THz antennas that operate at high frequencies, speeds, and data rates. In this manuscript, a THz MIMO antenna is designed using a metamaterial. The two-port antenna design proposed uses a complementary split-ring resonator patch. The design results are also compared with a simple patch antenna to show the improvement. The design shows a better isolation of 50 dB. A broadband width of 8.3 THz is achieved using this complementary split-ring resonator design. The percentage bandwidth is 90%, showing an ultrabroadband response. The highest gain of 10.34 dB is achieved with this design. Structural parametric optimization is applied to the complementary split-ring resonator MIMO antenna design. The designed antenna is also optimized by applying parametric optimization to different geometrical parameters. The optimized design has a 20 µm ground plane, 14 µm outer ring width, 6 µm inner ring width, and 1.6 µm substrate thickness. The proposed antenna with its broadband width, high gain, and high isolation could be applied in high-speed communication devices.

Terahertz Metasurface-Based Refractive Index Sensor for Amino Acid Detection: A Numerical Approach.

A graphene disk metasurface-inspired refractive index sensor (RIS) with a subwavelength structure is numerically investigated to enhance the functionality of flexible metasurface in the biosensor sector. The main aim behind the sensor development is to detect amino acids with high sensitivity. The results in form of transmittance and the electric field intensity are carried out to verify the sensor's performance. The optimal design of the proposed sensor is also obtained by varying several structural parameters such as glass-based substrate thickness, the inner radius of the graphene disk metasurface, and the angle of incidence. The proposed sensor is also wide-angle insensitive for the angle of incidence ranging from 0° to 60°. Furthermore, the sensor's attributes are analyzed based on numerous parameters with an achieved maximum sensitivity of 333.33 GHz/RIU, Figure of Merit (FOM) of 3.11 RIU-1, and Q-factor of 7.3 are achieved. As a result, these insights offered an enhanced direction for designing metasurface biosensors with a high Q-factor and FOM with high sensitivity for the detection of amino acids.

SARS-CoV-2 detecting rapid metasurface-based sensor.

We have proposed a novel approach to detect COVID-19 by detecting the ethyl butanoate which high volume ratio is present in the exhaled breath of a COVID-19 infected person. We have employed a refractive index sensor (RIS) with the help of a metasurface-based slotted T-shape perfect absorber that can detect ethyl butanoate present in exhaled breath of COVID-19 infected person with high sensitivity and in-process SARS-CoV-2. The optimized structure of the sensor is obtained by varying several structure parameters including structure length and thickness, slotted T-shape resonator length, width, and thickness. Sensor's performance is evaluated based on numerous factors comprising of sensitivity, Q factor, detection limit, a figure of merit (FOM), detection accuracy, and other performance defining parameters. The proposed slotted T-shape RIS achieved the largest sensitivity of 2500 nm/RIU, Q factor of 131.06, a FOM of 131.58 RIU-1, detection limit of 0.0224 RIU.

Graphene-Based Refractive Index Sensor Using Machine Learning for Detection of Mycobacterium Tuberculosis Bacteria.

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.

Graphene-Based Metasurface Refractive Index Biosensor for Hemoglobin Detection: Machine Learning Assisted Optimization.

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.

Recent Advances in Biosensors for Detection of COVID-19 and Other Viruses.

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.

Highly efficient, perfect, large angular and ultrawideband solar energy absorber for UV to MIR range.

Although different materials and designs have been tried in search of the ideal as well as ultra-wideband light absorber, achieving ultra-broadband and robust unpolarized light absorption over a wide angular range has proven to be a major issue. Light-field regulation capabilities provided by optical metamaterials are a potential new technique for perfect absorbers. It is our goal to design and demonstrate an ultra-wideband solar absorber for the ultraviolet to a mid-infrared region that has an absorptivity of TE/TM light of 96.2% on average. In the visible, NIR, and MIR bands of the solar spectrum, the absorbed energy is determined to be over 97.9%, above 96.1%, and over 95%, respectively under solar radiation according to the Air Mass Index 1.5 (AM1.5) spectrum investigation. In order to achieve this wideband absorption, the TiN material ground layer is followed by the SiO2 layer, and on top of that, a Cr layer with patterned Ti-based resonators of circular and rectangular multiple patterns. More applications in integrated optoelectronic devices could benefit from the ideal solar absorber's strong absorption, large angular responses, and scalable construction.

Machine learning assisted metamaterial-based reconfigurable antenna for low-cost portable electronic devices.

Antenna design has evolved from bulkier to small portable designs but there is a need for smarter antenna design using machine learning algorithms that can meet today's high growing demand for smart and fast devices. Here in this research, main focus is on developing smart antenna design using machine learning applicable in 5G mobile applications and portable Wi-Fi, Wi-MAX, and WLAN applications. Our design is based on the metamaterial concept where the patch is truncated and etched with a split ring resonator (SRR). The high gain requirement is met by adding metamaterial superstrates having thin wires (TW) and SRRs. The reconfigurability is achieved by adding three PIN diode switches. Multiple designs have been observed by adding superstrate layers ranging from one layer to four layers with interchanging TWs and SRRs. The TW metamaterial superstrate design with two layers is giving the best performance in gain, bandwidth, and the number of bands. The design is optimized by changing the path's physical parameters. To shrink simulation time, Extra Tree Regression based machine learning model is used to learn the behavior of the antenna and predict the reflectance value for a wide range of frequencies. Experimental results prove that the use of the Extra Tree Regression based model for simulation of antenna design can cut the simulation time, resource requirements by 80%.

Ultra-broadband, wide-angle plus-shape slotted metamaterial solar absorber design with absorption forecasting using machine learning.

Energy utilization is increasing day by day and there is a need for highly efficient renewable energy sources. Solar absorbers with high efficiency can be used to meet these growing energy demands by transforming solar energy into thermal energy. Solar absorber design with highly efficient and Ultra-broadband response covering visible, ultraviolet, and near-infrared spectrum is proposed in this paper. The absorption response is observed for three metamaterial designs (plus-shape slotted design, plus-shape design, and square-shape design) and one optimized design is used for solar absorber design based on its high efficiency. The design results are compared with AM 1.5 spectral irradiance response. The electric field response of the plus-shape slotted metamaterial design is also presented which matches well with the absorption results of different solar spectrum regions. The results proved that the attained absorption response showing wide angle of incidence. Machine learning is also used to examine the design data in order to forecast absorption for various substrate thickness, metasurface thickness, and incidence angles. Regression and forecasting simulations based on machine learning are used to try to anticipate absorber behaviour at forthcoming and intermediate wavelengths. Simulation results prove that Machine Learning based methods can lessen the obligatory simulation resources, time and can be used as an effective tool while designing the absorber. The proposed highly efficient, wide-angle, ultra-broadband solar absorber design with its behavior prediction capability using machine learning can be utilized for solar thermal energy harvesting applications.

Graphene-based metasurface solar absorber design with absorption prediction using machine learning.

Solar absorber is required to absorb most of the energy of the solar spectral irradiance. We propose a graphene-based solar absorber design with two different metasurfaces to improve this absorption and increase the efficiency of the solar absorber. The metasurfaces are selected based on their symmetrical/asymmetrical nature (O-shape and L-shape). The O-shape metasurface design is showing better performance over the L-shape metasurface design. The absorption performance is also compared with AM 1.5 solar spectral irradiance to show the effectiveness of the solar absorber. The absorption values are also enhanced by varying the parameters like resonator thickness and substrate thickness. The proposed solar absorber design gives maximum absorption in the ultraviolet and visible range. Furthermore, the design is also showing a high and similar absorption rate over a wide angle of incidence. The absorption of O-shape metasurface design is also predicted using machine learning. 1D-Convolutional Neural Network Regression is used to develop a Machine Learning model to determine absorption values of intermediate wavelength for assorted values of angle of incidence, resonator thickness, and substrate thickness. The results of experiments reveal that absorption values may be predicted with a high degree of accuracy. The proposed absorber with its high absorbing capacity can be applied for green energy applications.

Tunable infrared metamaterial-based biosensor for detection of hemoglobin and urine using phase change material.

This paper reports about the outcomes from an investigation carried out on tunable biosensor for detection using infrared in the range of 1.5 µm and 1.65 µm. The biosensor is made of phase change material formed by different alloy combinations, Ge2Sb2Te5 (GST). The nature of GST allows for the material to change phase with changes in temperature, giving the tunable sensing property for biosensing application. Sensor built with amorphous GST (aGST) and crystalline GST (cGST) in different design structures were tested on different concentrations of biomolecules: hemoglobin (10 g/l, 20 g/l, 30 g/l and 40 g/l); and urine (0-1.5 mg/dL, 2.5 mg/dL, 5 mg/dL and 10 mg/dL). The tunable response observed from the tests demonstrates the potential application of the materials in the design of switching and sensing systems.

Numerical simulation of a highly directional optical leaky wave antenna using diamond-shaped graphene perturbations.

We present a graphene-based optical leaky wave antenna (OLWA) with diamond-shaped perturbations. The leaky wave antenna is created by applying diamond-shaped graphene perturbations to a Si3N4 waveguide. The leaky wave behavior is observed by changing the graphene chemical potential. Results in the form of leakage power, normalized directivity, and reflectance, transmittance, leakage power, normalized directivity, and normalized E-field are presented. The half power beamwidth (HPBW) of 1.2° is achieved by this antenna. The reflectance and transmittance are in a very low wavelength range between 1.4 and 1.6 µm throughout. The leakage of power is more for the lower graphene chemical potential. The graphene-based design is also compared to a gold-based design and silicon-based design to show the leakage comparison. The designed graphene-based OLWA can be used in medical sensing devices.