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Monte Carlo Entropic sampling algorithm applied to 3D spin crossover nanoparticles: role of the environment on the thermal hysteresis

Abstract : This contribution deals with the development of a 3D Monte Carlo (MC) entropic sampling algorithm to evaluate the density of states of a SCO nanoparticles using three parameters related to the "magnetization", the "spin-spin correlation" and the number of molecules at the surface. This information is then used to analyze the role of the interaction parameter, L, between the external environment of the nanoparticle and the surface's molecules. We show that increasing "L" shifts downward the system's transition temperature, generating a thermal hysteresis whose width increases linearly with the strength of this parameter "L". These behaviors are also studied as function of the SCO nanoparticle size. 1. Introduction Spin crossover (SCO) compounds [1-6] exhibit a thermal transition between two states: High-spin (HS) and Low-spin (LS) states. The LS to HS transition temperature, Tup, on heating mode is higher than the HS to LS transition temperature, Tdown, on cooling mode. The width ΔT=Tup-Tdown characterizes the thermal hysteresis and is usually related to strength of the interactions in the system. To simulate thermal or pressure behavior of nanoparticles using all the spin configurations, an entropic sampling technique has been developed and applied to 2D SCO nanoparticles configuration. In this contribution we have extended our previous entropic sampling method [7,8] to the 3D cases.
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Submitted on : Wednesday, September 30, 2020 - 4:29:44 PM
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  • HAL Id : insu-02954015, version 1

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Jorge Linares, C. Cazelles, S. Gaci, Pierre-Richard Dahoo, K. Boukheddaden. Monte Carlo Entropic sampling algorithm applied to 3D spin crossover nanoparticles: role of the environment on the thermal hysteresis. 9th International Conference on Mathematical Modeling in the Physical Sciences, Sep 2020, Tinos island, Greece. ⟨insu-02954015⟩

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