A Linear Slope Analyzing Strategy of GMR Sensor Transfer Curve for Static Detection of Magnetic Nanoparticles

Abstract

For giant magnetoresistance (GMR) sensors, the in-plane DC detection mode is commonly applied because it offers higher tolerance to the misorientation of the excitation magnetic field as compared to vertical detection mode, and it does not suffer the undesired coupling due to induction by the AC excitation field. However, the quantification result was typically obtained by subtraction method whereby the concentration of the magnetic nanoparticles (MNPs) is determined from the difference between the sensor output voltage signals with and without DC excitation field respectively. While this method is simple, it leads to a high noise level, especially when the concentration of the MNPs is low and the sensor output signal is weak and spiky, resulting in unreliable quantification results. This work aimed at developing a new signal analyzing strategy to obtain quantification results with a higher signal-to-noise ratio (SNR). A linear slope analyzing strategy (slope method) was proposed. The slope of the linear region of the output transfer curve measured with a sweeping excitation field is compared before and after depositing MNPs resulting in $\Delta $ Slope, which indicates the quantity of the MNPs presented on the sensor surface. The slope being the trendline of a transfer curve is more stable and statistically less susceptible to random behavior. Experimental implementation was performed using an MNPs detection platform constructed with a commercial GMR sensor. Results showed that the $\Delta $ Slope increased linearly with the concentration of biotinylated superparamagnetic iron oxide nanoparticles (biotin-IONPs). In comparison with the traditional subtraction method, the SNR was improved from 17.3 dB to 25.3 dB.