Blocking temperature in magnetic nano-clusters

TL;DR

This paper models the blocking temperature in magnetic nano-clusters using a Markov process to analyze the probability density function of magnetization, revealing that initial entropy influences the transition temperature.

Contribution

It introduces a Markov process-based probability model for magnetic nano-clusters to study the superparamagnetic transition and blocking temperature.

Findings

High initial entropy correlates with higher blocking temperature.

The high-temperature density of magnetization influences the transition.

The model emphasizes the importance of initial probability density over Hamiltonian evolution.

Abstract

A recent study of nonextensive phase transitions in nuclei and nuclear clusters needs a probability model compatible with the appropriate Hamiltonian. For magnetic molecules a representation of the evolution by a Markov process achieves the required probability model that is used to study the probability density function (PDF) of the order parameter, i.e. the magnetization. The existence of one or more modes in this PDF is an indication for the superparamagnetic transition of the cluster. This allows us to determine the factors that influence the blocking temperature, i.e. the temperature related to the change of the number of modes in the density. It turns out that for our model, rather than the evolution of the system implied by the Hamiltonian, the high temperature density of the magnetization is the important factor for the temperature of the transition. We find that an initial probability density function with a high entropy leads to a magnetic cluster with a high blocking temperature.