Abstract
The formation of magnetic bead or nanoparticle superstructures due to magnetic dipole dipole interactions can be used as configurable matter in order to realize low-cost magnetoresistive sensors with very high GMR-effect amplitudes. Experimentally, this can be realized by immersing magnetic beads or nanoparticles in conductive liquid gels and rearranging them by applying suitable external magnetic fields. After gelatinization of the gel matrix the bead or nanoparticle positions are fixed and the resulting system can be used as a magnetoresistive sensor. In order to optimize such sensor structures we have developed a simulation tool chain that allows us not only to study the structuring process in the liquid state but also to rigorously calculate the magnetoresistive characteristic curves for arbitrary nanoparticle arrangements. As an application, we discuss the role of magnetoresistive sensors in finding answers to molecular recognition.
Highlights
The giant magnetoresistance (GMR) effect was originally discovered in magnetic multilayer systems in 1988/89 [1,2] but it was soon extended to granular systems [3,4], i.e., samples based on magnetic nanoparticles in metallic matrices
For the equilibrium state, where both, on and off rates are the same, Equation (8) may be rewritten to the equation for the one-site binding model used in [17,18]
The concentration of the proteins may be derived from the surface concentration utilizing the equilibrium dissociation constant KD
Summary
The giant magnetoresistance (GMR) effect was originally discovered in magnetic multilayer systems in 1988/89 [1,2] but it was soon extended to granular systems [3,4], i.e., samples based on magnetic nanoparticles in metallic matrices. This situation corresponds to a “freezing” with respect to the positions and the orientations of the magnetic moments as well. In our demagnetization routine the sample is exposed to a sinusoidally varying, rotating, and damped magnetic field Using this technique one can overcome the energy barriers which occur due to strong inter-particle interactions with the result that lowest energy configurations can be found much more efficiently as compared to the standard relaxation algorithm. 30 sinusoidal oscillations, 7 azimuthal, and 3 polar turns have been applied during a simulation run of 1 × 10−7 s while damping the magnetic field from 0.05 T to 0 T
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