Federica Celegato

Rapid progress in information technologies has spurred the need for innovative memory concepts, for which advanced data-processing methods and tailor-made materials are required. Here we introduce a previously unexplored nanoscale magnetic object: an analog magnetic vortex controlled by electric-field-induced ion motion, termed magneto-ionic vortex or “vortion”. This state arises from paramagnetic FeCoN through voltage gating and gradual N^3– ion extraction within patterned nanodots. Unlike traditional vortex states, vortions offer comprehensive analog adjustment of key properties such as magnetization amplitude, nucleation/annihilation fields, or coercivity using voltage as an energy-efficient tuning knob. This manipulation occurs post-synthesis, obviating the need for energy-demanding methods like laser pulses or spin-torque currents. By leveraging an overlooked aspect of N^3– magneto-ionics—planar ion migration within nanodots—precise control of the magnetic layer’s thickness is achieved, which enables reversible transitions among paramagnetic, single-domain, and vortion states, offering future prospects for analog computing, multi-state data storage, or brain-inspired devices.

The detection of magnetic nanoparticles in a liquid medium and the quantification of their concentration have the potential to improve the efficiency of several relevant applications in different fields, including medicine, environmental remediation, and mechanical engineering. To this end, sensors based on the magneto-impedance effect have attracted much attention due to their high sensitivity to the stray magnetic field generated by magnetic nanoparticles, their simple fabrication process, and their relatively low cost. To improve the sensitivity of these sensors, a multidisciplinary approach is required to study a wide range of soft magnetic materials as sensing elements and to customize the magnetic properties of nanoparticles. The combination of magneto-impedance sensors with ad hoc microfluidic systems favors the design of integrated portable devices with high specificity towards magnetic ferrofluids, allowing the use of very small sample volumes and making measurements faster and more reliable. In this work, a magneto-impedance sensor based on an amorphous Fe_73.5Nb₃Cu₁Si_13.5B₉ wire as the sensing element is integrated into a customized millifluidic chip. The sensor detects the presence of magnetic nanoparticles in the ferrofluid and distinguishes the different stray fields generated by single-domain superparamagnetic iron oxide nanoparticles or magnetically blocked Co-ferrite nanoparticles.

The connection of multidisciplinary and versatile techniques capable of depositing and modeling thin films in multistep complex fabrication processes offers different perspectives and additional degrees of freedom in the realization of patterned magnetic materials whose peculiar physical properties meet the specific needs of several applications. In this work, a fast and cost-effective dealloying process is combined with a fast, low-cost, scalable electroless deposition technique to realize hybrid magnetic heterostructures. The gold nanoporous surface obtained by the dealloying of an Au₄₀Si₂₀Cu₂₈Ag₇Pd₅ ribbon is used as a nanostructured substrate for the electrodeposition of cobalt. In the first steps of the deposition, the Co atoms fill the gold pores and arrange themselves into a patterned thin film with harder magnetic properties; then they continue their growth into an upper layer with softer magnetic properties. The structural characterization of the hybrid magnetic heterostructures is performed using an X-ray diffraction technique and energy-dispersive X-ray spectroscopy, while the morphology of the samples as a function of the electrodeposition time is characterized by images taken in top and cross-section view using scanning electron microscopy. Then, the structural and morphologic features are correlated with the room-temperature magnetic properties deduced from an alternating-gradient magnetometer’s measurements of the hysteresis loop and first order reversal curves.

Dense and mesoporous FePd nanowires (NWs) with 45 to 60 at.% Pd content were successfully fabricated by template- and micelle-assisted pulsed potentiostatic electrodeposition using nanoporous anodic alumina and polycarbonate templates of varying pore sizes. An FePd electrolyte was utilized for obtaining dense NWs while a block copolymer, P-123, was added to this electrolyte as the micelle-forming surfactant to produce mesoporous NWs. The structural and magnetic properties of the NWs were investigated by electron microscopy, X-ray diffraction, and vibrating sample magnetometry. The as-prepared NWs were single phase with a face-centered cubic structure exhibiting 3.1 µm to 7.1 µm of length. Mesoporous NWs revealed a core-shell structure where the porosity was only witnessed in the internal volume of the NW while the outer surface remained non-porous. Magnetic measurements revealed that the samples displayed a soft ferromagnetic behavior that depended on the shape anisotropy and the interwire dipolar interactions. The mesoporous core and dense shell structure of the NWs were seen to be slightly affecting the magnetic properties. Moreover, mesoporous NWs performed excellently as SERS substrates for the detection of 4,4′-bipyridine, showing a low detection limit of 10⁻¹² M. The signal enhancement can be attributed to the mesoporous morphology as well as the close proximity of the embedded NWs being conducive to localized surface plasmon resonance.

In this work, dewetting process has been investigated in shape-memory Heuslers. To this aim, series of high-temperature annealing (1100–1150 K) have been performed at high vacuum (time is varied in the range of 55–165 min) in Ni-Mn-Ga epitaxial thin films grown on MgO(001). The process kinetics have been followed by studying the evolution of morphology and composition. In particular, we report the initiation of the dewetting process by the formation of symmetric holes in the films. The holes propagate and integrate, leaving micrometric and submicron islands of the material, increasing the average roughness of the films by a factor of up to around 30. The dewetting process is accompanied by severe Ga and Mn sublimation, and Ni-Ga segregation, which significantly modify the magnetic properties of the films measured at each stage. The annealed samples show a relatively weak magnetic signal at room temperature with respect to the pristine sample.

A machine learning approach has been applied to the prediction of magnetic hysteresis properties (coercive field, magnetic remanence, and hysteresis loop area) of magnetic nanoparticles for hyperthermia applications. Trained on a dataset compiled from numerical simulations, a neural network and a random forest were used to predict power losses of nanoparticles as a function of their intrinsic properties (saturation, anisotropy, and size) and mutual magnetic interactions, as well as of application conditions (temperature, frequency, and applied field magnitude), for values of the parameters not represented in the database. The predictive ability of the studied machine learning approaches can provide a valuable tool toward the application of magnetic hyperthermia as a precision medicine therapy tailored to the patient’s needs.