Electrical Signal Recording of a Nerve Cell in a Micro-channel

Author(s):  
N. Kumamoto ◽  
N. Shirakawa ◽  
T. Yasuda
Author(s):  
Zhengjie Liu ◽  
Dongxin Xu ◽  
Jiaru Fang ◽  
Qijian Xia ◽  
Wenxi Zhong ◽  
...  

The electrophysiological signal can reflect the basic activity of cardiomyocytes, which is often used to study the working mechanism of heart. Intracellular recording is a powerful technique for studying transmembrane potential, proving a favorable strategy for electrophysiological research. To obtain high-quality and high-throughput intracellular electrical signals, an integrated electrical signal recording and electrical pulse regulating system based on nanopatterned microelectrode array (NPMEA) is developed in this work. Due to the large impedance of the electrode, a high-input impedance preamplifier is required. The high-frequency noise of the circuit and the baseline drift of the sensor are suppressed by a band-pass filter. After amplifying the signal, the data acquisition card (DAQ) is used to collect the signal. Meanwhile, the DAQ is utilized to generate pulses, achieving the electroporation of cells by NPMEA. Each channel uses a voltage follower to improve the pulse driving ability and isolates each electrode. The corresponding recording control software based on LabVIEW is developed to control the DAQ to collect, display and record electrical signals, and generate pulses. This integrated system can achieve high-throughput detection of intracellular electrical signals and provide a reliable recording tool for cell electro-physiological investigation.


2020 ◽  
Vol 64 (1-4) ◽  
pp. 439-446
Author(s):  
Gildas Diguet ◽  
Gael Sebald ◽  
Masami Nakano ◽  
Mickaël Lallart ◽  
Jean-Yves Cavaillé

Magneto Rheological Elastomers (MREs) are composite materials based on an elastomer filled by magnetic particles. Anisotropic MRE can be easily manufactured by curing the material under homogeneous magnetic field which creates column of particles. The magnetic and elastic properties are actually coupled making these MREs suitable for energy conversion. From these remarkable properties, an energy harvesting device is considered through the application of a DC bias magnetic induction on two MREs as a metal piece is applying an AC shear strain on them. Such strain therefore changes the permeabilities of the elastomers, hence generating an AC magnetic induction which can be converted into AC electrical signal with the help of a coil. The device is simulated with a Finite Element Method software to examine the effect of the MRE parameters, the DC bias magnetic induction and applied shear strain (amplitude and frequency) on the resulting electrical signal.


2020 ◽  
Vol 64 (1-4) ◽  
pp. 431-438
Author(s):  
Jian Liu ◽  
Lihui Wang ◽  
Zhengqi Tian

The nonlinearity of the electric vehicle DC charging equipment and the complexity of the charging environment lead to the complex and changeable DC charging signal of the electric vehicle. It is urgent to study the distortion signal recognition method suitable for the electric vehicle DC charging. Focusing on the characteristics of fundamental and ripple in DC charging signal, the Kalman filter algorithm is used to establish the matrix model, and the state variable method is introduced into the filter algorithm to track the parameter state, and the amplitude and phase of the fundamental waves and each secondary ripple are identified; In view of the time-varying characteristics of the unsteady and abrupt signal in the DC charging signal, the stratification and threshold parameters of the wavelet transform are corrected, and a multi-resolution method is established to identify and separate the unsteady and abrupt signals. Identification method of DC charging distortion signal of electric vehicle based on Kalman/modified wavelet transform is used to decompose and identify the signal characteristics of the whole charging process. Experiment results demonstrate that the algorithm can accurately identify ripple, sudden change and unsteady wave during charging. It has higher signal to noise ratio and lower mean root mean square error.


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