Unveiling the Complex Magnetization Reversal Process in 3D Nickel Nanowire Networks

Understanding the interactions among magnetic nanostructures is one of the key factors to predict and control the advanced functionalities of 3D integrated magnetic nanostructures. In this work, the focus is on different interconnected Ni nanowires forming an intricate, but controlled, and ordered magnetic system: Ni 3D Nanowire Networks (3DNNs). These self-ordered systems present striking anisotropic magnetic responses, depending on the interconnections’ position between nanowires. To understand their collective magnetic behavior, the magnetization reversal processes are studied within different Ni 3D Nanowire Networks compared to the 1D nanowire 1DNW array counterparts. The systems are characterized at different angles using first magnetization curves, hysteresis loops, and First Order Reversal Curves techniques, which provided information about the key features that enable macroscopic tuning of the magnetic properties of the 3D nanostructures. In addition, micromagnetic simulations endorse the experiments, providing accurate modeling of their magnetic behavior. The results reveal a plethora of magnetic interactions, neither evident nor intuitive, which are the main role players controlling the collective response of the system. The results pave the way for the design and realization of 3D novel metamaterials and devices based on the nucleation and propagation of ferromagnetic domain walls both in 3D self-ordered systems and future nano-lithographed devices.
M.M.G. and O.C.C. acknowledge the financial support from the project PID2020-118430GB-100 (MICINN). J.B.P., and F.P. acknowledge the financial support from PID2019-106165GB-C21 (MICINN) and M. López-Haro and J.J. Calvino from the University of Cádiz for the acquisition of the electron tomography series. The authors also acknowledge the service from the X-SEM Laboratory at IMM, and funding from MINECO under project CSIC13-4E-1794 with support from the EU (FEDER, FSE). The authors acknowledge the support for simulation hardware from J.L. Mesa at INTA. D.N. acknowledges the financial support from the project PID2019-108075RB-C31 and the grant RYC-2017-22820 funded by MICINN/10.13039/501100011033 and by “ESF Investing in your future”.