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Jonathan Leliaert

Publications and source records attributed to Jonathan Leliaert.

At least 19 recordsLinked to original sources

Engineering micro-disorder for macro-performance in magnetic nanoparticles

Spin disorder, inherent to magnetic nanoparticles, has traditionally been regarded as a detrimental feature, with materials-engineering efforts largely focused on producing ''perfect particles'' containing as few defects as possible. Alongside this pursuit of perfection, however, an alternative framework has emerged in recent years that reframes intra-particle disorder as an ''ugly duckling'' whose functional potential remains to be unlocked. In this Perspective, we review the emerging concept of disorder engineering in magnetic nanoparticles, identify its current challenges, and outline promising future directions. From a theoretical standpoint, progress requires moving beyond the widely used macrospin approximation, which severely restricts the description of intra-particle degrees of freedom. Micromagnetic modelling, in contrast, treats magnetisation as a continuous vector field and thereby enables (i) the explicit representation of intra-particle degrees of freedom, linking microstructural features to internal magnetisation textures, and (ii) direct correspondence with polarized small-angle neutron scattering, an experimental technique that provides quantitative access to ensemble-averaged magnetic correlations on nanometre length scales. The field must now advance towards falsifiable and uncertainty-aware models with structurally motivated parameters and predictions that can be tested against independent experimental observables. The overarching goal is to establish quantitative relationships between particle structure, intra-particle magnetisation textures, and macroscopic functionality, thereby transforming spin disorder from an elusive hidden variable into an engineerable design parameter.

cond-mat.mes-hall

Polarized neutron scattering as a probe for vortex-type spin correlations in iron oxide multicore assemblies

We report an experimental investigation of the magnetic microstructure of iron oxide multicore assemblies by means of polarized small-angle neutron scattering (SANS). Guided by a recently developed analytical theory for vortex-state magnetic nanoparticles, we provide a quantitative comparison between the measured and calculated cross sections, revealing signatures that are consistent with vortex-type magnetization configurations at low applied magnetic fields. In particular, the field evolution and the characteristic isotropic ring-type feature of the spin-flip scattering intensity at intermediate momentum transfers are in line with the formation of flux-closure states. The latter are stabilized by the interplay of exchange, Zeeman, and magnetostatic energies. The methodology allows for a statistically significant characterization of vortex states in densely packed nanoparticle systems, thereby complementing surface-sensitive techniques that are commonly limited to the observation of spin structures in individual particles.

cond-mat.mes-hall

Simulating altermagnets using mumax+

In this paper, we demonstrate how altermagnets can be simulated in the recently released micromagnetic simulation package mumax+. We have added a new magnet class for d-wave altermagnets and demonstrate how mumax+ is able to reproduce the analytical solutions for line profiles of the N\'eel vector and net magnetization for a Bloch domain wall. Next, we show simulation results of the magnon dispersion relation and its dependence on the anisotropic nature of the exchange interaction. Finally, we study the motion of a N\'eel skyrmion by applying a spin transfer torque. This new feature was implemented by extending the pre-existing code base for antiferromagnetic simulations. The object-oriented design of mumax+ allows for a correct calculation of the magnetostatic field in multi-sublattice systems, a feature that many other micromagnetic simulators lack.

cond-mat.mes-hall

Nanoscale mapping of internal magnetization dynamics reveals how disorder shapes heat generation in magnetic particle hyperthermia

Magnetic particle hyperthermia relies on the efficient conversion of magnetic field energy into heat in biomedical applications, yet the microscopic mechanisms governing heat generation within individual particles remain poorly understood. In this study, AC magnetometry experiments are combined with dynamic micromagnetic simulations to connect microstructural features, magnetization dynamics, and macroscopic heat dissipation. Beyond macroscopic heating metrics, the heat generation is resolved at the intra-particle level, uncovering a heterogeneous landscape of localized ''hot spots'' with nanometer spatial and nanosecond temporal resolution. The results demonstrate that grain size acts as a key experimentally tunable parameter, balancing anisotropy disorder and pinning strength, thereby controlling both the magnitude and spatio-temporal distribution of heat release within the particle. In particular, nanoflower architectures composed by larger grains deliver larger heat generation, while the smaller grains offer a deeper intra-particle pinning landscape, which effectively redistributes the heat generation over extended time windows. Together, our results provide a mechanistic framework linking nanoparticle microstructure to magnetic heating and establish design principles for optimizing nanoflowers as magnetic hyperthermia transducers.

cond-mat.mes-hall

Coercivity-size map of magnetic nanoflowers: spin disorder tunes the vortex reversal mechanism and tailors the hyperthermia sweet spot

Iron-oxide nanoflowers (NFs) are one of the most efficient nanoheaters for magnetic hyperthermia therapy (MHT). However, the physics underlying the spin texture of disordered iron-oxide nanoparticles beyond the single-domain limit remains still poorly understood. Using large-scale micromagnetic simulations we completely map the magnetization of NFs over an unprecedented size range, from 10 to 400 nm in diameter, connecting their microstructure to their macroscopic magnetic response. Above the single domain (d > 50 nm), the magnetization folds into a vortex state, within which the coercivity describes a secondary maximum, not present for non-disordered nanoparticles. We have extended our understanding by resolving also the NF magnetization dynamics, capturing the physics of the magnetization reversal. Within the vortex regime, two distinct reversal modes exist: i) A core-dominated one, in which the core immediately switches along the direction of the applied field, resulting in an increasing coercivity for larger sizes; and ii) a flux-closure dominated reversal mode, going through the perpendicular alignment of the vortex core to the field, resulting in a decreasing coercivity-size dependence. The coercivity maximum is located at the transition between both reversal modes, and results from the combination of grain anisotropy and grain-boundary pinning: weak (but non-negligible) inter-grain exchange keeps the vortex profile coherent, yet allows the core to be pinned by the random anisotropy easy axes of the single grains, maximizing magnetic losses. Our results provide the first full description of spin textures in iron oxide NFs beyond the macrospin framework, and clarify the role of internal spin disorder in magnetic hyperthermia heating. By adjusting the grain size, the coercivity "sweet spot" can be tailored, offering a practical route to next-generation, high-efficiency nanoheaters.

cond-mat.mes-hall

Micromagnetic structure of oxidized magnetite nanoparticles: sharp structural versus diffuse magnetic interface

The oxidation of magnetite to maghemite is a naturally occurring process that leads to the degradation of the magnetic properties of magnetite nanoparticles. Despite being systematically observed with traditional macroscopic magnetization measurement techniques, a detailed understanding of this process at the microscale is still missing. In this study, we track the evolution of the magnetic structure of magnetite nanoparticles during their oxidation to maghemite through numerical micromagnetic simulations. To capture realistic interparticle effects, we incorporate dipolar interactions by modeling the nanoparticles arranged in chains. Our computational results are benchmarked against experimental data from magnetotactic bacteria, studied over a time scale of years. To resolve the magnetization at the interface between both oxide phases, we propose spin-polarized small-angle neutron scattering (SANS), an experimental technique capable of probing magnetization textures at nanometer length scales. By analyzing the pair-distance distribution function extracted from SANS, we identify distinct signatures of magnetic disorder. Specifically, our findings suggest that the magnetization from the non-oxidized core region varies smoothly across the (structurally sharp) interface into the oxidized shell. The existence of such a diffuse magnetic interface may account for the superior magnetic properties of partially oxidized magnetite nanoparticles compared to fully converted maghemite samples.

cond-mat.mes-hall

mumax+: extensible GPU-accelerated micromagnetics and beyond

We present mumax+, an extensible GPU-accelerated micromagnetic simulator with a Python user interface, to address the challenges posed by current magnetism research into systems with complex magnetic ordering and interfaces. It is a general solver for the space- and time-dependent evolution of the magnetization and related vector quantities, using finite difference discretization. Here, we present its application and design and discuss features not available in \mumaxthree{}, such as the modeling of antiferromagnets with magnetoelastic coupling. As an illustration of its capabilities, we use \mumaxp{} to simulate state of the art magnetic systems. Specifically, we demonstrate the current induced domain wall motion in a polycrystalline antiferromagnet, we simulate the working principle of a strain-driven antiferromagnetic racetrack memory and we reproduce experimentally observed domain structures in a non-collinear antiferromagnet.

cond-mat.mes-hall

Numerical Calculation of the Hopf Index for 3D Magnetic Textures

To gain deeper insight into the complex, stable, and robust configurations of magnetic textures, topological characterisation has proven essential. In particular, while the skyrmion number is a well-established topological invariant for 2D magnetic textures, the Hopf index serves as a key topological descriptor for 3D magnetic structures. In this work, we present and compare various methods for numerically calculating the Hopf index, provide implementations, and offer a detailed analysis of their accuracy and computational efficiency. Additionally, we identify and address common pitfalls and challenges associated with the numerical computation of the Hopf index, offering insights for improving the robustness of these techniques.

cond-mat.mes-hall

The design, verification, and applications of Hotspice: a Monte Carlo simulator for artificial spin ice

We present Hotspice, a Monte Carlo simulation software designed to capture the dynamics and equilibrium states of Artificial Spin Ice (ASI) systems with both in-plane (IP) and out-of-plane (OOP) geometries. An Ising-like model is used where each nanomagnet is represented as a macrospin, with switching events driven by thermal fluctuations, magnetostatic interactions, and external fields. To improve simulation accuracy, we explore the impact of several corrections to this model, concerning for example the calculation of the dipole interaction in IP and OOP ASI, as well as the impact of allowing asymmetric rather than symmetric energy barriers between stable states. We validate these enhancements by comparing simulation results with experimental data for pinwheel and kagome ASI lattices, demonstrating how these corrections enable a more accurate simulation of the behavior of these systems. We finish with a demonstration of `clocking' in pinwheel and OOP square ASI as an example of reservoir computing.

physics.comp-ph

A tabletop Optically Pumped Magnetometer setup for the monitoring of magnetic nanoparticle clustering and immobilization using Thermal Noise Magnetometry

Many characterization techniques for magnetic nanoparticles depend on the usage of external fields. This is not the case in Thermal Noise Magnetometry (TNM), where thermal fluctuations in the magnetic signal of magnetic nanoparticle ensembles are measured without any external excitation. This can provide valuable information about the fundamental dynamical properties of the particles, due to the purely observative experiments of this relatively new technique. Until now, TNM signals have been detected only by a superconducting quantum interference device (SQUID) sensor. We present a tabletop setup using Optically Pumped Magnetometers (OPMs) in a small magnetic shield, offering a flexible and accessible alternative and show the agreement between both measurement systems for two different commercially available nanoparticle samples. We argue that the OPM setup with high accessibility complements the SQUID setup with high sensitivity and bandwidth. Furthermore, because of its excellent sensitivity in the lower frequencies, the OPM tabletop setup is well suited to monitor aggregation processes where the magnetization dynamics of the particles tend to slow down, e.g. in biological processes. As a proof of concept, we show for three different immobilization and clustering processes the changes in the noise spectrum measured in the tabletop setup: 1) the aggregation of particles due to the addition of ethanol, 2) the formation of polymer structures in the sample due to UV exposure, and 3) the cellular uptake of the particles by THP-1 cells. From our results we conclude that the tabletop setup offers a flexible and widely adoptable sensor measurement unit to monitor the immobilization and clustering of magnetic nanoparticles over time for different applications.

cond-mat.mes-hall

Audio Classification with Skyrmion Reservoirs

Physical reservoir computing is a computational paradigm that enables spatio-temporal pattern recognition to be performed directly in matter. The use of physical matter leads the way towards energy-efficient devices capable of solving machine learning problems without having to build a system of millions of interconnected neurons. We propose a high performance "skyrmion mixture reservoir" that implements the reservoir computing model with multi-dimensional inputs. We show that our implementation solves spoken digit classification tasks at the nanosecond timescale, with an overall model accuracy of 97.4% and a less that 1% word error rate; the best performance ever reported for in-materio reservoir computers. Due to the quality of the results and the low power properties of magnetic texture reservoirs, we argue that skyrmion fabrics are a compelling candidate for reservoir computing.

cond-mat.mes-hall

Estimating the heating of complex nanoparticle aggregates for magnetic hyperthermia

Understanding and predicting the heat released by magnetic nanoparticles is central to magnetic hyperthermia treatment planning. These nanoparticles tend to form aggregates when injected in living tissues, which alters their response to the applied alternating magnetic field and prevents predicting the released heat accurately. We performed an in silico analysis to investigate the heat released by nanoparticle aggregates featuring different size and fractal geometry factors. By digitally mirroring aggregates seen in biological tissues, we found that the average heat released per particle stabilizes starting from moderately small aggregates, facilitating the estimates for their larger counterparts. Additionally, we studied the heating performance of particle aggregates over a wide range of fractal parameters. We compared this result with the heat released by non-interacting nanoparticles to quantify the reduction of heating power after being instilled into tissues. This set of results can be used to estimate the expected heating in vivo based on the experimentally determined nanoparticle properties.

cond-mat.mes-hall

Micromagnetic simulation of neutron scattering from spherical nanoparticles: Effect of pore-type defects

We employ micromagnetic simulations to model the effect of pore-type microstructural defects on the magnetic small-angle neutron scattering cross section and the related pair-distance distribution function of spherical magnetic nanoparticles. Our expression for the magnetic energy takes into account the isotropic exchange interaction, the magnetocrystalline anisotropy, the dipolar interaction, and an externally applied magnetic field. The signatures of the defects and the role of the dipolar energy are highlighted and the effect of a particle-size distribution is studied. The results serve as a guideline to the experimentalist.

cond-mat.mes-hall

Perspective: magnetic nanoparticles in theranostic applications

Nanomedicine research started exploring the combination of therapy and diagnostics, so-called theranostics, to offer a more flexible care with improved outcome. As magnetic nanoparticles show great potential in many diagnostic and therapeutic applications, they are prime candidates to be used in a theranostic setting. This perspective gives an overview of state-of-the-art magnetic nanoparticle-based imaging techniques and theranostic applications and discusses their opportunities and challenges. To address these challenges and exploit these opportunities to the fullest, we provide three promising research directions. The first considers novel magnetic field sequences, utilizing the rich magnetic dynamics of the particles, which boost the capabilities of many nanoparticle-based applications. Secondly ,we introduce the concept of smart theranostics based on feedback mechanisms between the particle applications and their supporting imaging procedure to enhance the performance of both and allow real-time monitoring of treatment efficiency. Finally, we show how data-driven models could enhance therapy and diagnostics, and handle the platform's large amount of data and decision support algorithms. The latter research track also includes hybrid models in which physics-based and data-driven models are combined to overcome challenges of applications with limited data, as well as to uncover unknown nanoparticle dynamics. Contrasting other literature, which mainly focuses on developing magnetic nanoparticles with the right characteristics, we put forward advances in magnetic nanoparticle imaging techniques and applications to enable the use of a broader range of magnetic nanoparticles in theranostics. We encourage researchers to also investigate these aspects to advance theranostic applications of magnetic nanoparticles to clinical environments.

physics.med-ph

Commensurate vortex core switching in magnetic nanodisks at Gigahertz frequencies

The development of future spintronic applications requires a thorough and fundamental understanding of the magnetisation dynamics. Of particular interest are magnetic nanodisks, in which the vortex state emerges as a stable spin configuration. Here, we focus on how the vortex core polarisation can be reversed periodically by an oscillating magnetic field, applied perpendicularly to the disk's surface. By means of micromagnetic simulations, we demonstrate the presence of several subharmonic switching modes, i.e., the commensurate ratio between the switching frequency of the core and the driving frequency. The underlying mechanism of this periodic behaviour depends on the disk thickness. For thin disks, the core switches periodically due to resonant excitation of radial spin wave modes, while it is due to the breathing mode in the case of thick disks. However, overlap of both modes impedes periodic vortex core switching. For thin disks, the threshold field amplitude required for periodic switching can be lowered to about 30~mT by increasing the disk diameter. For thick disks, in contrast, the minimal field is largely unaffected by the disk diameter, as only the energy density of a central region around the vortex core is relevant to excite the breathing mode. Our results contribute to the understanding of the switching mechanisms in magnetic nanodisks, which are of technological interest due to their potential in non-volatile memory devices.

cond-mat.mes-hall

Thermoplasmonic Nanomagnetic Logic Gates

Nanomagnetic logic, in which the outcome of a computation is embedded into the energy hierarchy of magnetostatically coupled nanomagnets, offers an attractive pathway to implement in-memory computation. This computational paradigm avoids separate energy costs associated with transporting and storing the outcome of a computational operation. Thermally-driven nanomagnetic logic gates, which are driven solely by the ambient thermal energy, hold great promise for energy-efficient operation, but have the disadvantage of slow operating speeds due to the lack of spatial selectivity of currently-employed global heating methods. As has been shown recently, this disadvantage can be removed by employing local plasmon-assisted photo-heating. Here, we show by means of micromagnetic and finite-elements simulations how such local heating can be exploited to design reconfigurable nanomagnetic Boolean logic gates. The reconfigurability of operation is achieved either by modifying the initialising field protocol or optically, by changing the order in which horizontally and vertically polarised laser pulses are applied. Our results thus demonstrate that nanomagnetic logic offers itself as a fast (up to GHz), energy-efficient and reconfigurable platform for in-memory computation that can be controlled via optical means.

physics.app-ph

Nonzero Skyrmion Hall Effect in Topologically Trivial Structures

It is widely believed that the skyrmion Hall effect, often disruptive for device applications, vanishes for overall topologically trivial structures such as (synthetic) antiferromagnetic skyrmions and skyrmioniums due to a compensation of Magnus forces. In this manuscript, however, we report that in contrast to the case of spin-transfer torque driven skyrmion motion, this notion is generally false for spin-orbit torque driven objects. We show that the skyrmion Hall angle is directly related to their helicity and imposes an unexpected roadblock for developing faster and lower input racetrack memories based on spin-orbit torques.

cond-mat.mes-hall

Spatial Analysis of Physical Reservoir Computers

Physical reservoir computing is a computational framework that implements spatiotemporal information processing directly within physical systems. By exciting nonlinear dynamical systems and creating linear models from their state, we can create highly energy-efficient devices capable of solving machine learning tasks without building a modular system consisting of millions of neurons interconnected by synapses. To act as an effective reservoir, the chosen dynamical system must have two desirable properties: nonlinearity and memory. We present task agnostic spatial measures to locally measure both of these properties and exemplify them for a specific physical reservoir based upon magnetic skyrmion textures. In contrast to typical reservoir computing metrics, these metrics can be resolved spatially and in parallel from a single input signal, allowing for efficient parameter search to design efficient and high-performance reservoirs. Additionally, we show the natural trade-off between memory capacity and nonlinearity in our reservoir's behaviour, both locally and globally. Finally, by balancing the memory and nonlinearity in a reservoir, we can improve its performance for specific tasks.

cs.LG