Magnetic iron oxide nanoparticles are relatively advanced nanomaterials, and are widely used in biology, physics and medicine, especially as contrast agents for magnetic resonance imaging. Characterization of the properties of magnetic nanoparticles plays an important role in the application of magnetic particles. As a contrast agent, the relaxation rate directly affects image enhancement. We characterized a series of monodispersed magnetic nanoparticles using different methods and measured their relaxation rates using a 0.47 T low-field Nuclear Magnetic Resonance instrument. Generally speaking, the properties of magnetic nanoparticles are closely related to their particle sizes; however, neither longitudinal relaxation rate $r_1$ nor transverse relaxation rate $r_2$ changes monotonously with the particle size $d$ . Therefore, size can affect the magnetism of magnetic nanoparticles, but it is not the only factor. Then, we defined the relaxation rates $r_i^{\prime }$ (i = 1 or 2) using the induced magnetization of magnetic nanoparticles, and found that the correlation relationship between $r_1^{\prime }$ relaxation rate and $r_1$ relaxation rate is slightly worse, with a correlation coefficient of $R^2$ = 0.8939, while the correlation relationship between $r_2^{\prime }$ relaxation rate and $r_2$ relaxation rate is very obvious, with a correlation coefficient of $R^2$ = 0.9983. The main reason is that $r_2$ relaxation rate is related to the magnetic field inhomogeneity, produced by magnetic nanoparticles; however $r_1$ relaxation rate is mainly a result of the direct interaction of hydrogen nucleus in water molecules and the metal ions in magnetic nanoparticles to shorten the $T_1$ relaxation time, so it is not directly related to magnetic field inhomogeneity.

Weak voltage signals cannot be reliably measured using currently available logging tools when these tools are subject to high-temperature (up to 200 °C) environments for prolonged periods. In this paper, we present a digital lock-in amplifier (DLIA) capable of operating at temperatures of up to 200 °C. The DLIA contains a low-noise instrument amplifier and signal acquisition and the corresponding signal processing electronics. The high-temperature stability of the DLIA is achieved by designing system-in-package (SiP) and multi-chip module (MCM) components with low thermal resistances. An effective look-up-table (LUT) method was developed for the lock-in amplifier algorithm, to decrease the complexity of the calculations and generate less heat than the traditional way. The performance of the design was tested by determining the linearity, gain, Q value, and frequency characteristic of the DLIA between 25 and 200 °C. The maximal nonlinear error in the linearity of the DLIA working at 200 °C was about 1.736% when the equivalent input was a sine wave signal with an amplitude of between 94.8 and 1896.0 nV and a frequency of 800 kHz. The tests showed that the DLIA proposed could work effectively in high-temperature environments up to 200 °C.

The T ₂ spectrum obtained by inversion of the nuclear magnetic resonance logging echo signal can provide various petrophysical parameters to help technicians effectively identify the type of fluid. However, raw logging data with a low signal-to-noise ratio can cause inversion results to deviate from the truth. Therefore, a deep learning denoising method is proposed to eliminate the limitations of traditional mathematical transform methods. A one-dimensional deep convolutional generative adversarial networks model is designed to fit the noise distribution of actual logging data, then generate simulation data. A multi-scale echo denoising network (MsEDNet) is designed to adaptively learn the multi-exponential decay characteristics of the signal and the optimal transform space. The denoising dataset consists of simulation data and logging data, which is applied to train MsEDNet and improve the generalization performance. With effectiveness analysis and various denoising experiments, it is validated that the proposed method has excellent denoising performance on simulation data, logging data, and water tank data.

In this paper, the influences of signal bandwidth and phase delay on the spatial resolution of complex susceptibility based magnetic particle imaging (csMPI) are investigated by modeling the transfer function of the measurement circuit. Based on the simulation and experimental results, it is found that the signal bandwidth and the phase delay will significantly impact on the spatial resolution of csMPI. To avoid the influence of narrow signal bandwidth and phase delay of measurement circuit on csMPI, a resonance measurement circuit with −3 dB bandwidth of 2.3 kHz (much larger than the signal bandwidth of the desired csMPI signal) and resonance frequency of 12.8 kHz is adopted to measure the weak nonlinear magnetization of the magnetic nanoparticles; a phase compensation is used when calculating the real and the imaginary components of ac magnetic susceptibility.