Related papers: Magnetically Multiplexed Heating of Single Domain …
We present a general study of frequency and magnetic field dependence of the specific heat power produced during field-driven hysteresis cycles in magnetic nanoparticles with relevance to hyperthermia applications in biomedicine. Employing…
Magnetic fluid hyperthermia (MFH), the procedure of raising the temperature of tumor cells using magnetic nanoparticles (MNPs) as heating agents, has proven successful in treating some types of cancer. However, the low heating power…
The main aim of the present work is to analyse the effect of dipolar interaction strength $\lambda$, particle size $D$ and temperature $T$ on the hysteresis mechanism in ordered arrays of magnetic nanoparticles (MNPs) using computer…
Magnetic vortices existing in soft magnetic nanoparticles with sizes larger than the single-domain diameter can be efficient nano-heaters in biomedical applications. Using micromagnetic numerical simulation we prove that in the optimal…
Magnetic nanoparticles constitute potential nanomedicine tools based on the possibility to obtain different responses triggered by safe remote stimulus. However, such richness can be detrimental if the different performances are not…
When magnetic nanoparticles (MNPs) are single-domain and magnetically independent, their magnetic properties and the conditions to optimize their efficiency in magnetic hyperthermia applications are now well-understood. However, the…
Magnetic nanoparticle-based hyperthermia has emerged as a promising therapeutic modality for treating malignant solid tumors that exhibit resistance to conventional cancer treatments, including chemotherapy and radiation. Despite the…
This chapter provides an introduction to the fundamental physical ideas and models relevant to the phenomenon of magnetic hysteresis in nanoparticle assemblies. The concepts of single-domain particles and superparamagnetism are discussed.…
To optimize the heating properties of magnetic nanoparticles (MNPs) in magnetic hyperthermia applications, it is necessary to calculate the area of their hysteresis loops in an alternating magnetic field. The three types of theories…
In this work, we theoretically investigate how shape-induced anisotropy dispersion and magnetic field amplitude jointly control both the magnitude and heterogeneity of heating in magnetite nanoparticle assemblies under AC magnetic fields.…
Machine learning potentials have emerged as a powerful tool to extend the time and length scales of first principles-quality simulations. Still, most machine learning potentials cannot distinguish different electronic spin orientations and…
Space-division multiplexing is a promising technology in optical fibre communication to improve the transmission capacity of a single optical fibre. However, the number of channels that can be multiplexed is limited by the crosstalks…
Understanding the influence of dipolar interactions in magnetic hyperthermia (MH) experiments is of crucial importance for a fine optimization of nanoparticle (NP) heating power. In this study, we use a kinetic Monte-Carlo algorithm to…
Heating magnetic nanoparticles with high frequency magnetic fields is a topic of interest for biological applications (magnetic hyperthermia) as well as for heterogeneous catalysis. This study shows why FeC NPs of similar structures and…
We study hysteresis in a micron-sized bead: a non-magnetic matrix embedded with super- paramagnetic nanoparticles. These hold tremendous promise in therapeutic applications as heat generating machines. The theoretical formulation uses a…
We consider the thermodynamic behavior of a disordered interacting electron system in two dimensions. We show that the corrections to the thermodynamic potential in the weakly localized regime give rise to a non monotonic behavior of the…
As carriers slowly move through a disordered energy landscape in organic semiconductors, tiny spatial variations in spin dynamics relieve spin blocking at transport bottlenecks or in the electron-hole recombination process that produces…
We propose a novel approach called Self-Learning Hybrid Monte Carlo (SLHMC) which is a general method to make use of machine learning potentials to accelerate the statistical sampling of first-principles density-functional-theory (DFT)…
Magnetic nanoparticle hyperthermia is an attractive emerging cancer treatment, but the acting microscopic energy deposition mechanisms are not well understood and optimization suffers. We describe several approximate forms for the…
Slowly rotating magnetic massive stars develop "dynamical magnetospheres" (DM's), characterized by trapping of stellar wind outflow in closed magnetic loops, shock heating from collision of the upflow from opposite loop footpoints, and…