Electrospun nanofiber mats may serve as new hardware for neuromorphic computing. To enable data storage and transfer in them, they should be magnetic, possibly electrically conductive and able to respond to further external impulses. Here we report on creating magnetic nanofiber mats, consisting of magnetically doped polymer nanofibers for data transfer and polymer beads containing larger amounts of magnetic nanoparticles for storage purposes. Using magnetite and iron nickel oxide nanoparticles, a broad range of doping ratios could be electrospun with a needleless technique, resulting in magnetic nanofiber mats with varying morphologies and different amounts of magnetically doped beads.

Different types of reversal processes, including either uniform-rotation or domain-wall driven processes, were indentified in magnetic nano-wires of four-fold symmetry using micromagnetic simulations. Iron wires were tested for diameters ranging from 6 nm up to 20 nm, while their lengths were taken from 30 nm to 70 nm range, and for several directions of externally applied magnetic field. Physical parameters of presented low-dimensional structures enabled reversal via intermediate states, which can lead to additional stable states at remanence, contrary to instable vortexes observed in magnetic nano-rings or cylindrical nanodots.

The magnetization reversal dynamics of three-dimensional (3D) ferromagnetic permalloy half-balls has been examined using micromagnetic simulations and finite element methods. The comparison between disturbed and nondisturbed sample shapes enabled observations of the nature of switching, types of oscillations, and times of reversal, triggered by an externally applied magnetic field.

Computers nowadays have different components for data storage and data processing, making data transfer between these units a bottleneck for computing speed. Therefore, so-called cognitive (or neuromorphic) computing approaches try combining both these tasks, as is done in the human brain, to make computing faster and less energy-consuming. One possible method to prepare new hardware solutions for neuromorphic computing is given by nanofiber networks as they can be prepared by diverse methods, from lithography to electrospinning. Here, we show results of micromagnetic simulations of three coupled semicircle fibers in which domain walls are excited by rotating magnetic fields (inputs), leading to different output signals that can be used for stochastic data processing, mimicking biological synaptic activity and thus being suitable as artificial synapses in artificial neural networks.

Magnetic nanofibers belong to the geometries which are intensively investigated in simulations and experiments due to their unique magnetic properties, varying in their lengths, cross-sections, and bending radii. Besides basic research of different magnetization reversal processes and magnetization dynamics in bent nanofibers, these structures are of potential interest for data storage applications, data transport, or other tasks in spintronics devices. While previous simulations concentrated on the domain wall transport through coupled bent nanofibers, creating networks with many in- and outputs to establish nanofiber-based domain wall logics, here we show the influence of the constricted area, in which a rotating magnetic field is applied in the middle of bent or straight magnetic nanofibers, on the magnetization dynamics. Our micromagnetic simulations, performed by Magpar, reveal a strong impact not only of this area, but also of the curvature of the nanofiber as well as of an additional Dzyaloshinskii–Moriya interaction (DMI).