Chinese Academy of Sciences

Innovation Academy for Precision Measurement Science and Technology, Cas

Xiamen Tianma Micro-electronics Co., Ltd.

Fang Chen

Chaoyang Liu

Li Chen

Junfei Chen

Zhen Zhang

Jiwen Feng

Zhiqiang Xia

Xin Cheng

Zhi Zhang

Maili Liu

Tao Feng

Zhekai Zhang

Sheng Shen

Xinjie Liu

Le Ta

Jiaxin Wang

Qingjia Bao

A low-cost modular liquid nitrogen low-temperature multi-nuclear magnetic resonance probe includes a Dewar, a pluggable coil and a front-end gain amplifier. The Dewar includes a cylindrical sandwich chamber, the center of the cylindrical sandwich chamber constitutes a room-temperature chamber, a sandwich of the cylindrical sandwich chamber is divided into a vacuum chamber and a liquid nitrogen chamber by a liquid nitrogen vessel wall, the vacuum chamber is located between the room-temperature chamber and the liquid nitrogen chamber, the pluggable coil and the front-end gain amplifier are provided in the vacuum chamber, the pluggable coil comprises a coil portion and a pluggable base, the coil portion is in pluggable connection with the pluggable base, and the pluggable coil is connected with the front-end gain amplifier. The probe realizes the transmission of radio frequency pulses and the reception of magnetic resonance signals, and is applicable to whole-body imaging of a small animal.

Low-cost modular liquid nitrogen low-temperature multi-nuclear magnetic resonance probe

A method for separating oil-water two-phase NMR signals by using dynamic nuclear polarization comprising: using a combination of a non-selective free radical and a selective relaxation reagent to selectively enhance an NMR signal of an oil phase or a water phase, the relaxation reagent being capable of selectively suppressing dynamic polarization enhancement of the water phase or oil phase, thus achieving the polarization enhancement of a single fluid phase in the mixed fluid phases and realizing separation of the two-phase signals; or using a selective free radical to selectively enhance the NMR signal of the oil phase or the water phase, thus achieving the polarization enhancement of a single fluid phase in the mixed fluid phases and realizing separation of the oil-water two-phase NMR signals. The method is simple and easy to operate, has a short test time, and can efficiently separate NMR signals of oil and water phases.

Method for separating oil-water two-phase NMR signals by using dynamic nuclear polarization

A method for measuring oil-water distribution using DNP-MRI, comprising adding a free radical for DNP enhanced NMR signal of a water phase or an oil phase in a sample containing oil and water; performing an MRI experiment on the sample, and collecting an MRI image of the sample without DNP enhancement; applying microwave excitation for DNP-MRI experiment under the same MRI experiment condition as step 2, and collecting an MRI image of the sample after DNP enhancement; and comparing the MRI image after DNP enhancement with the MRI image without DNP enhancement. In the MRI image with DNP enhancement, an area with enhanced MRI signal intensity is a selectively enhanced fluid phase distribution area, and an area without obviously changed MRI signal intensity is a non-selectively enhanced fluid phase distribution area. The method is simple, convenient to operate, short in measurement time, and high in measurement efficiency.

Method for measuring oil-water distribution using dynamic nuclear polarization for magnetic resonance imaging (DNP-MRI)

An NMR relaxation time inversion method based on an unsupervised neural network includes simulating inversion kernel matrix, simulating continuous NMR relaxation time spectrum, simulating noise, calculating NMR relaxation signals as samples, various samples forming a sample set, constructing an unsupervised neural network model, and defining a loss function of the unsupervised neural network model; and taking the samples in the training sample set as an input of the unsupervised neural network model, to obtain an optimal mapping relationship between the NMR relaxation signals and the NMR relaxation time spectrum with a minimum loss function. The present invention provides the possibility of using experimental data as the sample for training since the trading sample does not need to be labeled, can automatically learn the optimal regularization parameters without depending on the initial value and manual experience, and predicts fast.

NMR relaxation time inversion method based on unsupervised neural network

Provided are a display panel, a display device and a method for preparing a display panel. The display panel includes an opening region, a transition region, and a display region. The display region at least partially surrounds the opening region, and the transition region is disposed between the display region and the opening region. The transition region of the display panel is further provided with at least one wall structure, and the at least one wall structure surrounds the opening region. The display panel further includes a substrate and a sub-pixel array disposed on the substrate and disposed in the display region, a first organic encapsulation layer, a second organic encapsulation layer, and a barrier structure. The first organic encapsulation layer is disposed on a side of the at least one wall structure facing the display region.

Display panel, display device and method for preparing a display panel

Growing community of inventors

Wuhan, China

Fang Chen