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Microdevice with Half-Ring Shaped GMR Sensors for Magnetic Bead Manipulation and Detection

  • C. P. Gooneratne
  • I. Giouroudi
  • J. Kosel
Part of the Smart Sensors, Measurement and Instrumentation book series (SSMI, volume 1)

Abstract

Micro and nanosized superparamagnetic beads (MBs) have been used in several biomedical applications due to their comparable size to biomolecules and their ability to respond to external magnetic fields. The stray fields of magnetized MBs can be detected by a magnetic sensor, which is utilized for quantification of target biomolecules present in immunoassays when MBs are used as biomolecular labels.

In this chapter, we describe the design, fabrication and testing of a microdevice for manipulating, trapping and detecting MBs. Manipulation and trapping is accomplished with a microstructure comprising conducting rings to produce magnetic field gradients, which exert a force on MBs. Controlling the movement of MBs paves the way for their rapid detection, since the beads can be attracted and transported towards a sensing site. In order to ensure that the majority of the MBs trapped at the sensing site are detected, we designed a spin valve type giant magnetoresistance (GMR) sensor with half-ring geometry. Analytical and numerical analysis leading towards the fabrication of the microstructure and the half-ring GMR sensor are presented. Full characterization of a single half-ring sensing element showed a DC magnetoresistance of 5.9 %, a small signal AC sensitivity of 0.53 mV/mT and a noise level of 6 \(nV/\surd\)Hz. An analytical model backed up by experimental results is presented to characterize the behavior of MBs in solution. Experimental results showed that the half-ring GMR sensor detected the presence of 2 μm MBs, thus indicating the feasibility of integrating an MB manipulation microstructure with half-ring GMR sensors to optimize the active sensing site.

Keywords

Magnetic Bead Passivation Layer Spin Valve Stray Field Magnetic Stray Field 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • C. P. Gooneratne
    • 1
  • I. Giouroudi
    • 2
  • J. Kosel
    • 1
  1. 1.King Abdullah University of Science and TechnologyThuwalSaudi Arabia
  2. 2.Vienna University of TechnologyViennaAustria

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