Ultra-sensitive electrodes for extracellular recordings were fabricated and electrically characterized. A signal detection limit defined by a noise level of 0.3–0.4 μV for a bandwidth of 12.5 Hz was achieved. To obtain this high sensitivity, large area (4 mm²) electrodes were used. The electrode surface is also micro-structured with an array of gold mushroom-like shapes to further enhance the active area. In comparison with a flat gold surface, the micro-structured surface increases the capacitance of the electrode/electrolyte interface by 54%. The electrode low impedance and low noise enable the detection of weak and low frequency quasi-periodic signals produced by astrocytes populations that thus far had remained inaccessible using conventional extracellular electrodes. Signals with 5 μV in amplitude and lasting for 5–10 s were measured, with a peak-to-peak signal-to-noise ratio of 16. The electrodes and the methodology developed here can be used as an ultrasensitive electrophysiological tool to reveal the synchronization dynamics of ultra-slow ionic signalling between non-electrogenic cells.

Conducting polymer electrodes based on poly (3, 4 ethylenedioxythiophene):polystyrene sulfonate were used to record electrophysiological signals from autonomous cardiac contractile cells present in embryoid bodies. Signal detection was carried out by measuring the displacement current across the polymer/electrolyte double-layer capacitance, and compared with voltage detection. While for relatively low capacitance electrodes, the voltage amplification provides higher signal quality, and for high capacitive electrodes, the displacement current method exhibits a higher signal-to-noise ratio. It is proposed that the displacement current method combined with high capacitive polymer-based electrodes is adequate to measure clusters of cells and whole organs. Our approach has a great potential in fundamental studies of drug discovery and safety pharmacology.

Astrocytes are neural cells that exhibit functional electrical properties sensitive to neuronal activity and capable of modulating neurotransmission. Thus, electrophysiological recordings of astroglial activity are very attractive to study the dynamics of glial signaling. This contribution reports on the use of ultra-sensitive planar electrodes combined with low noise and low frequency amplifiers that enable the detection of extracellular signals produced by primary cultures of astrocytes isolated from rat cerebral cortex. Recorded activity is characterized by spontaneous bursts comprised of discrete signals with pronounced changes on the spike rate and amplitude. Weak and sporadic signals become synchronized and evolve with time to higher amplitude signals with a quasi-periodic behavior, revealing a cooperative signaling mechanism. The methodology presented herewith enables the study of ionic fluctuations of population of cells, complementing the single cells observation by calcium imaging as well as by patch-clamp techniques.

An artificial neural network (ANN) was implemented on an FPGA to predict cetane number in diesel fuel from its chemical compositions, extracted by liquid chromatography (LC) and gas chromatography (GC). An MLP network is used. To train the MLP, two variants of the backpropagation algorithm are utilized: backpropagation with plummeting learning rate factor and backpropagation with declining learning-rate. By adjusting the ANNpsilas parameters the total sum square error in train phase and average error percent in test phase are reduced to 0.085 and 4.4018%, respectively. The number of occupied slices on the FPGA is 5971 which covers 55% of the chip.

Widely used demodulation technique in EIT/EIS systems is the direct conversion (zero-IF) method in which measured single tone current (or voltage) signal is mixed with source waveform at the same frequency, so that the magnitude and phase of the measured current (or voltage) can be determined after a low pass filtering stage. This is especially true for an EIS system which might need to operate in a low frequency range as small as 100 Hz. In the system presented here, the amplitude and phase of the IF signal can be measured after digitizing the signal by an ADC in digital domain with transferring the IF signal to the baseband (zero frequency). In this system, a lowpass FIR filter and a variable frequency digital oscillator have been used. In our designed system there is no need for zero crossing technique which suffers from jitter noise and delay of different path, for signal mixing. We have implemented digital demodulation part of system on a Virtex-4 LX25 FPGA from Xilinx.