Optimization for temperature estimation using magnetic nanoparticle: A set of equations solving solution investigation

Zhong, Jing; Liu, Wenzhong; Pi, Shiqiang; Zhang, Pu

doi:10.1109/i2mtc.2013.6555629

Cited by 3 publications

(5 citation statements)

References 7 publications

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“…According to the results reported by Frank Ludwig, the relaxation effects on the MNPT are negligible when the average particle size is smaller than 30nm, the particle size distribution is narrow and the excitation field has frequencies lower than 1 kHz [ 25 , 26 ]. As such, the first-order Langevin function describing the superparamagnetism of the MNPs is specified by [ 19 , 20 , 21 , 22 , 23 , 24 ]:

where ϕ is the volume fraction of MNPs, M s is the saturation magnetization, V is the particle’s volume, k B is the Boltzmann constant, T is the absolute temperature and H is the external excitation magnetic field.…”

Section: System Constitution and Temperature Solutionmentioning

confidence: 99%

“…Solving Equation (4) allows temperature measurement by using the Levenberg–Marquardt algorithm (L–M) [ 20 , 21 , 22 , 23 , 24 ].…”

Section: System Constitution and Temperature Solutionmentioning

confidence: 99%

“…As the first harmonic includes abundant temperature information, the first and third harmonic amplitudes were substituted for the temperature in order to improve the precision of MNPT. The first and third harmonic amplitude model are described by the first order Langevin function when the working frequency is very low (<1 kHz), so that the temperature of the MNPs can be estimated [ 5 , 18 , 19 , 20 , 21 , 22 , 23 , 24 ].…”

Section: Introductionmentioning

confidence: 99%

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Magnetic Nanoparticle Thermometer: An Investigation of Minimum Error Transmission Path and AC Bias Error

Liu

et al. 2015

Sensors

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The signal transmission module of a magnetic nanoparticle thermometer (MNPT) was established in this study to analyze the error sources introduced during the signal flow in the hardware system. The underlying error sources that significantly affected the precision of the MNPT were determined through mathematical modeling and simulation. A transfer module path with the minimum error in the hardware system was then proposed through the analysis of the variations of the system error caused by the significant error sources when the signal flew through the signal transmission module. In addition, a system parameter, named the signal-to-AC bias ratio (i.e., the ratio between the signal and AC bias), was identified as a direct determinant of the precision of the measured temperature. The temperature error was below 0.1 K when the signal-to-AC bias ratio was higher than 80 dB, and other system errors were not considered. The temperature error was below 0.1 K in the experiments with a commercial magnetic fluid (Sample SOR-10, Ocean Nanotechnology, Springdale, AR, USA) when the hardware system of the MNPT was designed with the aforementioned method.

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Section: System Constitution and Temperature Solutionmentioning

confidence: 99%

“…Solving Equation (4) allows temperature measurement by using the Levenberg–Marquardt algorithm (L–M) [ 20 , 21 , 22 , 23 , 24 ].…”

Section: System Constitution and Temperature Solutionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Magnetic Nanoparticle Thermometer: An Investigation of Minimum Error Transmission Path and AC Bias Error

Liu

et al. 2015

Sensors

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“…In 2012, Zhong Jing et al established a theoretical model based on the first-order Langevin's equation [3][4], and used SQUID VSM to measure the magnetization curve under different DC excitation magnetic fields, and realized the temperature measurement by inversion calculation method [5]. At the same time, the theoretical model of temperature measurement was optimized and achieved higher precision [6].…”

Section: Introductionmentioning

confidence: 99%

Simulation and experimental research on model parameter in a magnetic nanoparticle thermometer

Zhang¹,

He²

2018

Proceedings of the 2018 3rd Joint International Information Technology，Mechanical and Electronic Engineering Conference (JIMEC

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Magnetic nano-temperature measurement is a new type of non-contact temperature measurement method, which is expected to overcome the technical bottleneck of the current internal temperature of the tumor in the tumor hyperthermia. In view of the current low measurement accuracy of magnetic nanometer temperature measurement, this paper studies the system and model parameters, and uses the low frequency triangular wave as the excitation magnetic field to analyze the influence of model and system parameters (particle size and excitation magnetic field strength) on temperature measurement accuracy. And optimize and improve the related system and model parameters. The results show that as the effective particle size of the magnetic nanoparticles increases, the average effective magnetic moment of the sample increases, and the magnetic nanometer temperature measurement accuracy increases. For the smaller diameter magnetic nanoparticles, as the applied excitation magnetic field increases, the accuracy gradually increases. After selecting a certain magnetic nanoparticles, the optimal excitation magnetic field size can be found to maximize the temperature measurement accuracy. The magnetic nanometer temperature measurement method achieves higher temperature measurement accuracy, enabling it to be applied to higher temperature measurement accuracy requirements.

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“…Weaver et al found that temper ature could be determined from the ratio of the third and fifth magnetization harmonics of MNPs in an AC magn etic field [13][14][15]. Liu et al developed methods of magnetic nanothermometry using Langevin function and DC magnetization or AC magnetization harmonics [5,6,[16][17][18][19][20][21][22][23][24].…”

Section: Introductionmentioning

confidence: 99%

Design and use of a very stable magnetic nanothermometer

Wei

et al. 2016

Meas. Sci. Technol.

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The effect of the nonlinear time-drift gain in the power amplifier on the instability in a magnetic nanothermometer was studied to allow a very stable magnetic nanotermometer to be designed, which was then used to measure the temperature of a light-emitting diode pn-junction. Experiments showed that the system response fluctuated by less than 0.04% and the temperature measurement error was less than 0.1 K when real-time measurements were made at 1 s intervals. The temperature of a pn-junction of a light-emitting diode cooled using a heat sink or circulating water was measured successfully using the improved magnetic nanothermometer.

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Optimization for temperature estimation using magnetic nanoparticle: A set of equations solving solution investigation

Cited by 3 publications

References 7 publications

Magnetic Nanoparticle Thermometer: An Investigation of Minimum Error Transmission Path and AC Bias Error

Magnetic Nanoparticle Thermometer: An Investigation of Minimum Error Transmission Path and AC Bias Error

Simulation and experimental research on model parameter in a magnetic nanoparticle thermometer

Design and use of a very stable magnetic nanothermometer

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