Microstrip Patch Antenna as Sensor Based on Metamaterial Integrated Structures for Noninvasive Characterization of Biological Materials

This article presents a novel approach for noncontact dielectric characterization of micrometer -sized samples through the integration of a microwave patch antenna and metamaterial-based structure which is executing metamaterial properties such as negative refractive index. The proposed sensor, operating at resonant frequencies of 12.12 GHz with
$S_{\mathbf {{11}}}$
= −42.01 dB, demonstrates excellent agreement with both Computer Simulation Technology (CST) and Advanced Design System (ADS) simulations. To ensure significant application of the sensor, comprehensive analyses are conducted, including specific absorption rate (SAR) analysis, electric field analysis, efficiency, and radiation pattern assessments of the antenna. In addition, we performed simulations using CST, evaluating 1000 samples with varying dielectric constants and conductivities. Subsequently, a Python-based graphical user interface (GUI) is developed to create a data prediction algorithm for determining the dielectric properties of unknown samples, using a trained linear regression model derived from a diverse dataset of 1000 CST simulations. For the experimental setup, a 3-D-printed device is used, enabling precise antenna movement. The proposed sensor is tested on 15 prior semi-biological samples to validate its accuracy. Subsequently, the sensor’s capabilities are leveraged to predict the dielectric properties of bovine serum albumin (BSA) protein. Notably, we observed significant changes in the measured dielectric properties of BSA protein in the presence of urea, a known denaturing agent. This observation highlights the sensor’s potential in predicting biological phenomena, including protein denaturation. In conclusion, the integration of the microwave patch antenna and square split ring resonator (SSRR) structures offers an efficient, single-port sensor for noncontact dielectric characterization of micrometer -sized samples. The sensor successfully predicts the dielectric properties for various samples, showcasing reliability and versatility in noninvasive measurements.