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磁性纳米颗粒直流测温模型性能优化的仿真研究

Simulation Study on Performance Optimization of Magnetic Nanoparticles DC Thermometry Model.

作者信息

Zhang Yapeng, Ma Shuangbao, Liu Wenzhong

机构信息

School of Mechanical Engineering and Automation, Wuhan Textile University, Wuhan 430073, China.

Hubei Key Laboratory of Digital Textile Equipment, Wuhan Textile University, Wuhan 430073, China.

出版信息

Sensors (Basel). 2021 Mar 31;21(7):2404. doi: 10.3390/s21072404.

Abstract

Magnetic nanoparticles (MNPs) can work as temperature sensors to realize temperature measurement due to the excellent temperature sensitivity of their magnetization. This paper mainly reports on a performance optimization method of MNPs DC thermometry model. Firstly, by exploring the influencing factors of MNPs magnetization temperature sensitivity, it is found that the optimal excitation of the magnetic field to make the temperature sensitivity of MNPs reach their optimal value is, approximately, inversely proportional to the particle size of MNPs. Then, the temperature sensitivity of MNP magnetization is modulated by adding appropriate DC bias magnetic field in the original triangular wave excitation field, to optimize the original DC thermometry model based on MNP magnetization. The simulation results show that the temperature measurement performance of small-size MNPs can be significantly improved. In short, this paper optimizes the temperature measurement performance of the original DC thermometry model based on MNP magnetization and provides a new application idea for temperature measurement of small-size MNPs.

摘要

磁性纳米颗粒(MNPs)由于其磁化具有出色的温度敏感性,可作为温度传感器来实现温度测量。本文主要报道了一种MNPs直流测温模型的性能优化方法。首先,通过探究影响MNPs磁化温度敏感性的因素,发现使MNPs温度敏感性达到最优值的最佳磁场激励大致与MNPs的粒径成反比。然后,通过在原始三角波激励场中添加适当的直流偏置磁场来调制MNP磁化的温度敏感性,以优化基于MNP磁化的原始直流测温模型。仿真结果表明,小尺寸MNPs的温度测量性能可得到显著提高。简而言之,本文优化了基于MNP磁化的原始直流测温模型的温度测量性能,并为小尺寸MNPs的温度测量提供了新的应用思路。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f1f5/8037848/c37a60ca923c/sensors-21-02404-g001.jpg

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