Use of an Entropy Indicator to Predict the Quality of a Multi-Component Mixture of Granular Materials

The feasibility of using a quality criterion for a multi-component mixture, which can be obtained on the basis of informational entropy to predict the composition of a batch produced in newly developed mixer models is examined. It is proposed that a system of kinetic Kolomogorov equations be used to determine the quantities defining the entropy.__________Translated from Khimicheskoe i Neftegazovoe Mashinostroenie, No. 5, pp. 8–9, May, 2005.     

Two step deposition method with a high growth rate for ZnO nanowire arrays and its application in photovoltaics

We present an approach for the growth of ZnO nanowire arrays with a length of up to 30 μm on fluorine doped tin oxide substrates with a growth rate of 20 μm per hour. The growth was carried out in a vapor phase transport setup at a temperature of 600 °C. To achieve an aligned growth on fluorine doped tin oxide substrates we used wet-chemically grown nanowire arrays with typical length of 2 to 3 μm as a seeding layer. Additionally the nanowire arrays were used as electrodes for the manufacturing of dye sensitized solar cells and the best achieved efficiencies were 1.83% at 10 mW/cm² and 1.47% at 100 mW/cm².     

Digital Camera Imaging System Simulation

A digital camera is a complex system including a lens, a sensor (physics and circuits), and a digital image processor, where each component is a sophisticated system on its own. Since prototyping a digital camera is very expensive, it is highly desirable to have the capability to explore the system design tradeoffs and preview the system output ahead of time. An empirical digital imaging system simulation that aims to achieve such a goal is presented. It traces the photons reflected by the objects in a scene through the optics and color filter array, converts photons into electrons with consideration of noise introduced by the system, quantizes the accumulated voltage to digital counts by an analog-to-digital converter, and generates a Bayer raw image just as a real camera does. The simulated images are validated against real system outputs and show a close resemblance to the images captured under similar condition at all illumination levels.     

Gallium nitride heterostructures on 3D structured silicon

We investigated GaN-based heterostructures grown on three-dimensionally patterned Si(111) substrates by metal organic vapour phase epitaxy, with the goal of fabricating well controlled high quality, defect reduced GaN-based nanoLEDs. The high aspect ratios of such pillars minimize the influence of the lattice mismatched substrate and improve the material quality. In contrast to other approaches, we employed deep etched silicon substrates to achieve a controlled pillar growth. For that a special low temperature inductively coupled plasma etching process has been developed. InGaN/GaN multi-quantum-well structures have been incorporated into the pillars. We found a pronounced dependence of the morphology of the GaN structures on the size and pitch of the pillars. Spatially resolved optical properties of the structures are analysed by cathodoluminescence.     

Learning-induced physiological memory in adult primary auditory cortex: receptive fields plasticity, model, and mechanisms

It is well established that the functional organization of adult sensory cortices, including the auditory cortex, can be modified by deafferentation, sensory deprivation, or selective sensory stimulation. This paper reviews evidence establishing that the adult primary auditory cortex develops physiological plasticity during learning. Determination of frequency receptive fields before and at various times following aversive classical conditioning and instrumental avoidance learning in the guinea pig reveals increased neuronal responses to the pure tone frequency used as a conditioned stimulus (CS). In contrast, responses to the pretraining best frequency and other non-CS frequencies are decreased. These opposite changes are often sufficient to shift cellular tuning toward or even to the frequency of the CS. Learning-induced receptive field (RF) plasticity (i) is associative (requires pairing tone and shock), (ii) highly specific to the CS frequency (e.g., limited to this frequency +/- a small fraction of an octave), (iii) discriminative (specific increased response to a reinforced CS+ frequency but decreased response to a nonreinforced CS- frequency), (iv) develops extremely rapidly (within 5 trials, the fewest trials tested), and (v) is retained indefinitely (tested to 8 weeks). Moreover, RF plasticity is robust and not due to arousal, but can be expressed in the deeply anesthetized subject. Because learning- induced RF plasticity has the major characteristics of associative memory, it is therefore referred to as "physiological memory". We developed a model of RF plasticity based on convergence in the auditory cortex of nucleus basalis cholinergic effects acting at muscarinic receptors, with lemniscal and nonlemniscal frequency information from the ventral and magnocellular divisions of the medial geniculate nucleus, respectively. In the model, the specificity of RF plasticity is dependent on Hebbian rules of covariance. This aspect was confirmed in vivo using microstimulation techniques. Further, the model predicts that pairing a tone with activation of the nucleus basalis is sufficient to induce RF plasticity similar to that obtained in behavioral learning. This prediction has been confirmed. Additional tests of the model are described. RF plasticity is thought to translate the acquired significance of sound into an increased frequency representation of behaviorally important stimuli.     

Electrical conductivity of NaF-AlF<Subscript>3</Subscript>-CaF<Subscript>2</Subscript>-Al<Subscript>2</Subscript>O<Subscript>3</Subscript> melts

The electrical conductivity of NaF-AlF₃-Al₂O₃ melts with a CaF₂ concentration of 5 wt % is measured at a continuously varying cell constant when the molar cryolitic ratio CR = [NaF]/[AlF₃] changes from 1.2 to 2.0 [1, 2]. The experimental data are used to obtain a regression equation to describe the dependence of the electrical conductivity of the melts under study on CR, the alumina content, and temperature {χ] = f(CR, [Al₂O₃], T)}.