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.

Magnetoresistive sensor devices can be developed in the form of magnetic multilayer systems or on the basis of magnetic nanoparticles or beads that are immersed in a nonmagnetic matrix. Using a conductive gel matrix with a liquid-solid transition, printable low-cost sensor systems can be developed. While in the liquid state of the gel, the magnetoresistive properties can be adjusted by structuring the magnetic particles by means of an external magnetic field. Then, the optimized properties are preserved in the solid state of the gel. In order to design sensor devices, numerical investigation is an essential tool. Here, we present a hybrid approach that involves classical spin dynamics and molecular dynamics simulations. By this means, detailed information about the structuring process of interacting magnetic particles can be obtained. Furthermore, it can be concluded with the magnetoresistive properties from the resulting magnetization curves.

The development of magnetoresistive sensors based on magnetic nanoparticles which are immersed in conductive gel matrices requires detailed information about the corresponding magnetoresistive properties in order to obtain optimal sensor sensitivities. Here, crucial parameters are the particle concentration, the viscosity of the gel matrix and the particle structure. Experimentally, it is not possible to obtain detailed information about the magnetic microstructure, i.e., orientations of the magnetic moments of the particles that define the magnetoresistive properties, however, by using numerical simulations one can study the magnetic microstructure theoretically, although this requires performing classical spin dynamics and molecular dynamics simulations simultaneously. Here, we present such an approach which allows us to calculate the orientation and the trajectory of every single magnetic nanoparticle. This enables us to study not only the static magnetic microstructure, but also the dynamics of the structuring process in the gel matrix itself. With our hybrid approach, arbitrary sensor configurations can be investigated and their magnetoresistive properties can be optimized.