Nansen Lin

An ascorbic acid amperometric sensor using over-oxidized polypyrrole and palladium nanoparticles composites

We constructed a highly responsive ascorbic acid (AA) sensor utilizing over-oxidized polypyrrole (OPPy) and Palladium nanoparticles (PdNPs) composites (OPPy-PdNPs). In the presence of PdNPs, polypyrrole (PPy) was coated on a gold (Au) electrode through cyclic voltammetry (CV) and over-oxidized at a fixed potential in NaOH solution. The PdNPs were characterized using ultraviolet-visible (UV-vis) spectrum and transmission electron microscopy (TEM). The surface of OPPy-PdNPs on the Au electrode was investigated using field-emission scanning electron microscopy (FE-SEM). Results revealed that the OPPy-PdNPs-modified Au electrode (OPPy-PdNPs/Au) has the capacity to catalyze the oxidation of AA by lowering its oxidation potential to 0 V. The OPPy-PdNPs/Au electrode exhibited 2 different linear concentration ranges. In the low concentration range (1-520 μM), OPPy-PdNPs/Au exhibited a direct linear relation with current responses and had high sensitivity (570 μA mM(-1)cm(-2)) and a high correlation coefficient (0.995). In contrast, in the higher concentration range (120-1600 μM), the relationship between current responses and concentration of AA can be represented by a two-parameter sigmoidal equation. In addition, the sensor exhibited a short response time (less than 2s) and a very low limit of detection of 1 μM. The electrochemical AA sensor constructed in this study was simple, inexpensive, reproducible, sensitive, and resistant to interference. Thus, the proposed sensor has great potential for detecting AA in complex biosystems and can be applied in various fields, particularly neuroscience.

A novel dual mode microelectrode array for neuroelectrical and neurochemical recording in vitro

The communication between neurons is inherently electrical and chemical in nature. In situ, simultaneous acquisition for the dual mode signals is important for neuroscience research. In this paper, the concept of dual mode neural microelectrode array (MEA) sensor was proposed, and a low cost thin film MEA chip for in vitro test was fabricated using standard lithography technology. The sensor incorporates arrayed microelectrodes, a counter electrode and a reference electrode on one glass slide, which is suitable for electrophysiological and electrochemical recording in vitro. Electrophysiological recordings were carried out on acute hippocampus slice. Local field potentials and three different spike firing patterns with the amplitude ranging from ± 20 μV to ± 60 μV were acquired by the arrayed microelectrodes. Electrochemical current response of the microelectrodes to calibrated dopamine solution was tested. A good linear relationship between the current and dopamine concentration was observed, with the detection sensitivity of 4671 μA mM(-1)cm(-2) and a correlation coefficient of 0.986. The sensor is novel for its capability of detecting in vitro dual mode neural signals on one single chip.

A high sensitivity MEA probe for measuring real time rat brain glucose flux.

The mammalian central nervous system (CNS) relies on a constant supply of external glucose for its undisturbed operation. This article presents an implantable Multi-Electrode Array (MEA) probe for brain glucose measurement. The MEA was implemented on Silicon-On-Insulator (SOI) wafer using Micro-Electro-Mechanical-Systems (MEMS) methods. There were 16 platinum recording sites on the probe and enzyme glucose oxidase (GOx) was immobilized on them. The glucose sensitivity of the MEA probe was as high as 489 µA mM(-1) cm(-2). 1,3-Phenylenediamine (mPD) was electropolymerized onto the Pt recording surfaces to prevent larger molecules such as ascorbic acid (AA), 3,4-dihydroxyphenylacetic acid (DOPAC), serotonin (5-HT), and dopamine (DA) from reaching the recording sites surface. The MEA probe was implanted in the anesthetized rat striatum and responded to glucose levels which were altered by intraperitoneal injection of glucose and insulin. After the in vivo experiment, the MEA probe still kept sensitivity to glucose, these suggested that the MEA probe was reliable for glucose monitoring in brain extracellular fluid (ECF).

Development and Application of 16-Channel Two-Mode Recording System for Neurochemical and Neuroelectrical Signals

Abstract A 16-channel two-mode recording system used for detection of neurochemical and neuroeletrical signals was developed. The instrument system consists of hardware and software components whose current and voltage resolutions are 10 pA and 0.6 μV, respectively. The software adopted multithreading, multicache, and other techniques to achieve real-time detection, spike separation, IIR filter, chronoamperometry, cyclic voltammetry, etc. The performance of the system was demonstrated in electrophysiological experiment and dopamine concentration measurement. The signal-to-noise ratio ( S/N ) recorded from VTA of SD rat was 9.7. The current response increased linearly with the concentration of dopamine in the range of 0.1–378 μM with a correlation coefficient of 0.9958. The results indicated that the recording system had high resolution and was suitable for electrophysiological and the electrochemical signal detection.

A novel method to directionally stabilize enzymes together with redox mediators by electrodeposition

This paper depicts a novel method to directionally stabilize enzymes together with redox mediators by electrodeposition. Chitosan was used as a stabilizing matrix. By electrochemical removal of local H(+), chitosan close to working electrode became locally insoluble, and enzymes and redox mediators in chitosan were stabilized. The microelectrode on home-made microelectrode array (MEA) served as the working electrode. Three model enzymes--horseradish peroxidase (HRP), glucose oxidase (GOD), and glutamate oxidase (GlOD)--were used to fabricate different biosensors, and the redox mediator model was a poly(vinylpyridine) complex of Os(bpy)2Cl and a diepoxide (PVP-Os). Biosensors fabricated by the method exhibited very high performance. For HRP biosensor fabricated by this method, the sensitivity was 5.274 nA μM(-1) mm(-2), with linear detection range (LDR) of 2-220 μM and limit of detection (LOD) of 1 μM (S/N=3); for GOD biosensor, the sensitivity was 2.65 nA μM(-1) mm(-2), with LDR of 4-500 μM and LOD of 2 μM (S/N=3); for GlOD biosensor, the sensitivity was 0.33 nA μM(-1)mm(-2), with LDR of 4-500 μM and LOD of 2 μM (S/N=3). Since this method is very simple and especially suitable for directionally introducing enzymes and redox mediators onto microelectrode without contaminating other sites in the same microenvironment, it could be used for fabricating in vivo or in vitro 2nd generation biosensors in μm-scale, especially in neuroscience.

Power spectral differences of electrophysiological signals detected at acupuncture points and non-acupuncture points.

In this study, we chose 10 acupoints and non-acupuncture point control groups to see if there are electrical differences between acupoints and non-acupoints. 4 adjacent non-acupoints around each acupoint were chosen as a control group in 400 trials on 10 volunteers aged 23-30 years to characterize the Power Spectral Density of acupoint electrophysiological signals, which means the differences of power and its distribution in frequency. The electrophysiological signals of acupoints and control groups were recorded simultaneously. The results show that acupoint electrophysiological signals have higher Power Spectral Density and power than nearby non-acupoint areas. Integrating the entire data, power of acupoint electrical signals are about 14.7% higher than nearby non-acupoint electrical signals, and most of the higher power is distributed from 0 to 10 Hz and 0-2 Hz is the highest. The maximum power difference between acupoints and non-acupoint is 61.5% appeared in LI 11(see text for symbol). From physiological view, the percentage is high enough to show the electrical specificity of acupoint, which is strong proof of Traditional Chinese Medicine theory and one of the bases for further research. As acupoint electrophysiological signals are driven by internal organs, they can reflect the health condition of internal organs effectively, and so analysis of acupoint electrophysiological signals may be a new way to diagnose organ diseases instead of with the experience of doctor of Traditional Chinese Medicine.

Research on neural information detecting system measuring neuroelectricity in hippocampus in vivo and dopamine in vitro based on microelectrode array

Concurrently detecting the electrical activity of neurons and neurotransmitter release signals, will have a great significance in understanding the working mechanism of the brain. This paper describes a neural information detecting system based on microelectrode array(MEA) measuring neuroelectricity in hippocampus in vivo and dopamine(DA) in vitro. The detecting system contains of electrophysiological headstage, electrochemical headstage, microprocessor, electrophysiological signal amplifier, data acquisition module and neural signal analysis software. In electrophysiological test, the neural information detecting system was applied to detect neuroelectricity in hippocampus of SD rat with 16-channel microelectrode array in vivo. Active potentials were captured. The amplitude of the recorded neural spikes reached 182.90 μV, and signal to noise ratio was 7:1. For measure dopamine as neurotransmitter, there was a good linear relationship between response current and concentration of dopamine from 10nM to 18.88μ with correlation coefficient of 0.9974. Electrophysiological experiment and electrochemical experiment demonstrate the capability of the neural information detecting system to capture dual mode neural signal, which provides a convenient way to study dual mode operating mechanism of neural system.Concurrently detecting the electrical activity of neurons and neurotransmitter release signals, will have a great significance in understanding the working mechanism of the brain. This paper describes a neural information detecting system based on microelectrode array(MEA) measuring neuroelectricity in hippocampus in vivo and dopamine(DA) in vitro. The detecting system contains of electrophysiological headstage, electrochemical headstage, microprocessor, electrophysiological signal amplifier, data acquisition module and neural signal analysis software. In electrophysiological test, the neural information detecting system was applied to detect neuroelectricity in hippocampus of SD rat with 16-channel microelectrode array in vivo. Active potentials were captured. The amplitude of the recorded neural spikes reached 182.90 μV, and signal to noise ratio was 7:1. For measure dopamine as neurotransmitter, there was a good linear relationship between response current and concentration of dopamine from 10nM to 18.88μ with correlation coefficient of 0.9974. Electrophysiological experiment and electrochemical experiment demonstrate the capability of the neural information detecting system to capture dual mode neural signal, which provides a convenient way to study dual mode operating mechanism of neural system.

Characterization and classification of pyramidal cells and interneurons in vitro cell network using extracellular recordings

The goal of the research was to explore the electrophysiological features of the different neurons by cell network cultured on multi-microelectrode arrays (MEA). We used 16-channel detection meter for extracellular signal recording in vitro. We found that the neurons recorded contain two kinds: the principal pyramidal cells and interneurons. The principal cells with excitatory activity and interneurons with suppressed activity contribute differently to nerve signal transduction and integration, the identification and classification of extracellular recorded neurons is significant for neural information studies. The combination of several extracellular features, such as firing rate, action potential duration, pattern and spike waveforms and bursting propensity can be effectively separate the neurons into classes of interneurons and pyramidal cells. The firing rate of pyramidal cells was less than 5Hz and the firing rate of interneurons was higher than 5Hz. The half action potential duration of pyramidal cells was between 0.6~1.8ms and that of interneurons was less than 0.6ms. The principal cells had bursting propensity and interneurons had no bursting. The local field potential (LFP) of adjacent channels often affected by the firing of the action potential (AP), and a certain level of LFP fluctuations can be observed, and the decline and fall of interneurons experienced a process of gradual reduction in the firing rate. The identification and separation of neurons is of great value for neural information studies. First, different AP firing characteristics can distinguish neuron types to avoid missing some information. Second, different kinds of neurons corresponding to different types of neurotransmitters, the identification of neurons combined neurotransmitter detection can promote nerve dual studies to further explore the neural information mechanism.

An improved high-accuracy compressed sensing method using a novel constructed dictionary for neural signal detection

This paper constructs a redundant dictionary using neural spike signals and uses a compressed sensing method to compress and reconstruct neural signals, which are cut into several segments of same length. By analyzing neural signals with different signal to noise ratios (SNRs), different types of spikes and different spike widths, we verify the performance of the method. Results show that, when the Compression Ratio (CR) is less than 5, our method can accurately compress and reconstruct high SNR neural signals, which contain several types of spikes. Compared with the spike width used in the redundant dictionary, the width of detected spikes can range from 0.8 to 1.6 times of it. We can also compress and reconstruct low SNR neural signals with the CR less than 2.

A Novel Feature Extractor Based on Wavelet and Kernel PCA for Spike Sorting Neural Signals

Spike sorting is often required for analyzing neural recordings to isolate the activity of single neurons. In this paper, a new feature extractor based on Wavelet and kernel PCA for spike sorting was proposed. Electrophysiology recordings were made in Sprague-Dawley (SD) rats to provide neural signals. Here, an adaptive threshold based on the duty-cycle keeping method was used to detect spike and a new spike alignment technique was used to decrease sampling skew error. After spikes were detected and alimented, to extract spike features, their wavelet transform was calculated, the first 10 coefficients with the largest deviation from normality provided a compressed representation of the spike features that serves as the input to KPCA algorithm. Once the features have been extracted, k-means clustering was utilised to separate the features and differentiate the spikes. Test results with simulated data files and data obtained from SD rats in vivo showed an excellent classification result, indicating the good performance of the described algorithm approach.