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تسجيل مستخدم جديدMagnetic hyperthermia properties of nanoparticles inside lysosomes using kinetic Monte-Carlo simulations : influence of key parameters, of dipolar interactions and spatial variation of heating power
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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 calculate hysteresis loops, so both time and temperature are correctly taken into account. It is demonstrated that this algorithm correctly reproduces the high-frequency hysteresis loop of both superparamagnetic NPs and ferromagnetic ones without any ad-hoc parameters. The algorithm is easily parallelizable so calculation on several processors decreases considerably calculation time. The specific absorption rate (SAR) of NPs dispersed inside spherical lysosomes is studied as a function of several key parameters: volume concentration, applied magnetic field, lysosome size, NP diameter and anisotropy. The influence of these parameters is illustrated and comprehensively explained. In summary, the effect of magnetic interactions is to increase the coercive field, saturation field and hysteresis area of major loops. However, for small amplitude magnetic field such as the ones used in MH, the heating power as function of concentration can increase, decrease or display a bell shape, depending of the relationship between the applied magnetic field and the coercive/saturation fields of the NPs. The hysteresis area is found to be well correlated to the parallel or antiparallel nature of the dipolar field acting on each NP. It is shown that the heating power increases or decreases sharply in the vicinity of the lysosome membrane. The amplitude of variation reaches more than one order of magnitude in certain conditions. Finally, implications of these various findings are discussed in the framework of MH optimization. It is concluded that feedbacks on specific points from biology experiments are required for further advance on the optimization of NPs for MH.
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