Design optimization of giant magneto resistance–based magnetic nanoparticle detection in liquid samples for biomedical applications

In this paper, the design of a Giant Magneto Resistive (GMR) sensor is optimized using single-objective optimization algorithms, for measuring magnetic nanoparticles stored in an Eppendorf tube. The iron oxide nanoparticle (Fe₃O₄) is employed as a magnetic nanoparticle. The variance-based sensitivity analysis is used to identify the most significant variable affecting the GMR sensitivity which is found to be the magnetic bias. As a result, using single-objective optimization algorithms, the optimal value for GMR sensor magnetic bias value (H) was computed and incorporated in the instrument design. The device thus designed was fabricated using Rapid Prototyping (RPT)-solid works. To identify the sensor response in a linear range, a couple of permanent neodymium magnets were used to provide horizontal and vertical magnetic fields for sensor bias and nanoparticle magnetization. This process gives an idea of a combined hardware-software approach, to reduce the measurement uncertainty and increase the system’s sensitivity. The proposed design achieved an output signal change of 248 mV for a magnetic particle concentration change of 1 µg. The device’s lowest measurable concentration was improved using the appropriate single-objective optimization technique, resulting in 36 ng as the lowest measurable concentration. The performance of the optimal GMR device design was analyzed for hysteresis analysis Fe₃O_4, Distance vs Sensor output for various input voltages, Temperature performance and SEM analysis of Fe₃O₄. The average nanoparticle size range is measured as 97 nm from SEM analysis.