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Light-addressable potentiometric sensors (LAPS) are semiconductor-based potentiometric sensors, with the advantage to detect the concentration of a chemical species in a liquid solution above the sensor surface in a spatially resolved manner. The addressing is achieved by a modulated and focused light source illuminating the semiconductor and generating a concentration-depending photocurrent. This work introduces a LAPS set-up that is able to monitor the electrical impedance in addition to the photocurrent. The impedance spectra of a LAPS structure, with and without illumination, as well as the frequency behaviour of the LAPS measurement are investigated. The measurements are supported by electrical equivalent circuits to explain the impedance and the LAPS-frequency behaviour. The work investigates the influence of different parameters on the frequency behaviour of the LAPS. Furthermore, the phase shift of the photocurrent, the influence of the surface potential as well as the changes of the sensor impedance will be discussed.
The chemical imaging sensor is a device to visualize the spatial distribution of chemical species based on the principle of LAPS (light-addressable potentiometric sensor), which is a field-effect chemical sensor based on semiconductor. In this study, the chemical imaging sensor has been applied to investigate the ion profile of laminar flows in a microfluidic channel. The chemical images (pH maps) were collected in a Y-shaped microfluidic channel while injecting HCl and NaCl solutions into two branches. From the chemical images, it was clearly observed that the injected solutions formed laminar flows in the channel. In addition, ion diffusion across the laminar flows was observed, and the diffusion coefficient could be derived by fitting the pH profiles to the Fick's equation.
Learning- and memory-related processes are thought to result from dynamic interactions in large-scale brain networks that include lateral and mesial structures of the temporal lobes. We investigate the impact of incidental and intentional learning of verbal episodic material on functional brain networks that we derive from scalp-EEG recorded continuously from 33 subjects during a neuropsychological test schedule. Analyzing the networks' global statistical properties we observe that intentional but not incidental learning leads to a significantly increased clustering coefficient, and the average shortest path length remains unaffected. Moreover, network modifications correlate with subsequent recall performance: the more pronounced the modifications of the clustering coefficient, the higher the recall performance. Our findings provide novel insights into the relationship between topological aspects of functional brain networks and higher cognitive functions.