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.

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.

Design and Performance Analysis of Reconfigurable 1D Photonic Crystal Biosensor Employing Ge₂Sb₂Te₅ (GST) for Detection of Women Reproductive Hormones.

The present research demonstrates a novel 1D photonic crystal (PhC) based reconfigurable biosensor pertaining to label-free detection of different concentrations of progesterone and estradiol, which play a vital role in developing reproductive hormones in women. The proposed sensor is designed by an alternative arrangement of Na AlF and CeO, with a central defect layer. A thin layer of novel phase change chalcogenide material (Ge Sb Te) is deposited along the two sides of the defect layer to improve the sensing performance.