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The ideal combination among biomolecules and nanomaterials is the key for reaching biosensing units with high sensitivity. The challenge, however, is to find out a stable and sensitive film architecture that can be incorporated on the sensor’s surface. In this paper, we report on the benefits of incorporating a layer-by-layer (LbL) nanofilm of polyamidoamine (PAMAM) dendrimer and carbon nanotubes (CNTs) on capacitive electrolyte-insulator-semiconductor (EIS) field-effect sensors for detecting urea. Three sensor arrangements were studied in order to investigate the adequate film architecture, involving the LbL film with the enzyme urease: (i) urease immobilized directly onto a bare EIS [EIS-urease] sensor; (ii) urease atop the LbL film over the EIS [EIS-(PAMAM/CNT)-urease] sensor; and (iii) urease sandwiched between the LbL film and another CNT layer [EIS-(PAMAM/CNT)-urease-CNT]. The surface morphology of all three urea-based EIS biosensors was investigated by atomic force microscopy (AFM), while the biosensing abilities were studied by means of capacitance–voltage (C/V) and dynamic constant-capacitance (ConCap) measureaments at urea concentrations ranging from 0.1 mM to 100 mM. The EIS-urease and EIS-(PAMAM/CNT)-urease sensors showed similar sensitivity (∼18 mV/decade) and a nonregular signal behavior as the urea concentration increased. On the other hand, the EIS-(PAMAM/CNT)-urease-CNT sensor exhibited a superior output signal performance and higher sensitivity of about 33 mV/decade. The presence of the additional CNT layer was decisive to achieve a urea based EIS sensor with enhanced properties. Such sensitive architecture demonstrates that the incorporation of an adequate hybrid enzyme-nanofilm as sensing unit opens new prospects for biosensing applications using the field-effect sensor platform.
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.
In the present work an optical sensor in combination with a spectrally resolved detection device for in-line particle-size-monitoring for quality control in beer production is presented. The principle relies on the size and wavelength dependent backscatter of growing particles in fluids. Measured interference structures of backscattered light are compared with calculated theoretical values, based on Mie-Theory, and fitted with a linear least square method to obtain particle size distributions. For this purpose, a broadband light source in combination with a process-CCD-spectrometer (charge ? coupled device spectrometer) and process adapted fiber optics are used. The goal is the development of an easy and flexible measurement device for in-line-monitoring of particle size. The presented device can be directly installed in product fill tubes or vessels, follows CIP- (cleaning in place) and removes the need of sample taking. A proof of concept and preliminary results, measuring protein precipitation, are presented.
In-beam study of ¹⁴⁵ Gd
(1979)
In-beam study of ¹⁴⁴ Gd
(1978)