Microstructural evolution of tungsten oxide thin films

Tungsten oxide thin films are of great interest due to their promising applications in various optoelectronic thin film devices. We have investigated the microstructural evolution of tungsten oxide thin films grown by DC magnetron sputtering on silicon substrate. The structural characterization and surface morphology were carried out using X-ray diffraction and Scanning Electron Microscopy (SEM). The as deposited films were amorphous, where as, thin films annealed above 400 degrees C were crystalline. In order to explain the microstructural changes due to annealing, we have proposed a ``instability wheel'' model for the evolution of the microstructure. This model explains the transformation of mater into various geometries within them selves, followed by external perturbation.

Dynamic pressure modulation for solar desalination system

Obtaining drinking water from seawater is usually done through the process of desalination. The conventional desalination processes at present are centralized, require huge capital cost, and enormous amount of concentrated energy from fossil fuel. Issues like optimal chamber pressure, pressure control and energy savings for desalination are not adequately addressed. This paper proposes a novel pressure control method by means of dynamic pressure modulation within the evaporation chamber. A performance index is proposed that results in a dynamic optimal external pressure and maximum energy saving for a specific flow rate. Experimental results from the laboratory setup that validate the proposed concepts are presented in the paper. (C) 2009 Elsevier B.V. All rights reserved.

Prolonged consumption of sucrose in a binge-like manner, alters the morphology of medium spiny neurons in the nucleus accumbens shell

The modern diet has become highly sweetened, resulting in unprecedented levels of sugar consumption, particularly among adolescents. While chronic long-term sugar intake is known to contribute to the development of metabolic disorders including obesity and type II diabetes, little is known regarding the direct consequences of long-term, binge-like sugar consumption on the brain. Because sugar can cause the release of dopamine in the nucleus accumbens (NAc) similarly to drugs of abuse, we investigated changes in the morphology of neurons in this brain region following short- (4 weeks) and long-term (12 weeks) binge-like sucrose consumption using an intermittent two-bottle choice paradigm. We used Golgi-Cox staining to impregnate medium spiny neurons (MSNs) from the NAc core and shell of short- and long-term sucrose consuming rats and compared these to age-matched water controls. We show that prolonged binge-like sucrose consumption significantly decreased the total dendritic length of NAc shell MSNs compared to age-matched control rats. We also found that the restructuring of these neurons resulted primarily from reduced distal dendritic complexity. Conversely, we observed increased spine densities at the distal branch orders of NAc shell MSNs from long-term sucrose consuming rats. Combined, these results highlight the neuronal effects of prolonged binge-like intake of sucrose on NAc shell MSN morphology.

Single layer graphene film by ethanol chemical vapor deposition: Highly efficient growth and clean transfer method

The choice of ethanol (C2H5OH) as carbon source in the Chemical Vapor Deposition (CVD) of graphene on copper foils can be considered as an attractive alternative among the commonly used hydrocarbons, such as methane (CH4) [1]. Ethanol, a safe, low cost and easy handling liquid precursor, offers fast and efficient growth kinetics with the synthesis of fullyformed graphene films in just few seconds [2]. In previous studies of graphene growth from ethanol, various research groups explored temperature ranges lower than 1000 °C, usually reported for methane-assisted CVD. In particular, the 650–850 °C and 900 °C ranges were investigated, respectively for 5 and 30 min growth time [3, 4]. Recently, our group reported the growth of highly-crystalline, few-layer graphene by ethanol-CVD in hydrogen flow (1– 100 sccm) at high temperatures (1000–1070 °C) using growth times typical of CH4-assisted synthesis (10–30 min) [5]. Furthermore, a synthesis time between 20 and 60 s in the same conditions was explored too. In such fast growth we demonstrated that fully-formed graphene films can be grown by exposing copper foils to a low partial pressure of ethanol (up to 2 Pa) in just 20 s [6] and we proposed that the rapid growth is related to an increase of the Cu catalyst efficiency due weak oxidizing nature of ethanol. Thus, the employment of such liquid precursor, in small concentrations, together with a reduced time of growth and very low pressure leads to highly efficient graphene synthesis. By this way, the complete coverage of a copper catalyst surface with high spatial uniformity can be obtained in a considerably lower time than when using methane.

A fluctuation-based characterization of athermal phase transitions:Application to shape memory alloys

We employ a fluctuation-based technique to investigate the athermal component associated with martensite phase transition, which is a prototype of temperature-driven structural transformation. Statistically, when the phase transition is purely athermal, we find that the temporal sequence of avalanches under constant drive is insensitive to the drive rate. We have used fluctuations in electrical resistivity or noise in nickel titanium shape memory alloys in three different forms: a thin film exhibiting well-defined transition temperatures,a highly disordered film, and a bulk wire of rectangular cross-section. Noise is studied in the realm of dynamic transition,viz.while the temperature is being ramped, which probes into the kinetics of the transformation at real time scales,and could probably stand out as a promising tool for material testing in various other systems, including nanoscale devices.

Singular value decomposition based computationally efficient algorithm for rapid dynamic near-infrared diffuse optical tomography

Purpose: A computationally efficient algorithm (linear iterative type) based on singular value decomposition (SVD) of the Jacobian has been developed that can be used in rapid dynamic near-infrared (NIR) diffuse optical tomography. Methods: Numerical and experimental studies have been conducted to prove the computational efficacy of this SVD-based algorithm over conventional optical image reconstruction algorithms. Results: These studies indicate that the performance of linear iterative algorithms in terms of contrast recovery (quantitation of optical images) is better compared to nonlinear iterative (conventional) algorithms, provided the initial guess is close to the actual solution. The nonlinear algorithms can provide better quality images compared to the linear iterative type algorithms. Moreover, the analytical and numerical equivalence of the SVD-based algorithm to linear iterative algorithms was also established as a part of this work. It is also demonstrated that the SVD-based image reconstruction typically requires O(NN2) operations per iteration, as contrasted with linear and nonlinear iterative methods that, respectively, requir O(NN3) and O(NN6) operations, with ``NN'' being the number of unknown parameters in the optical image reconstruction procedure. Conclusions: This SVD-based computationally efficient algorithm can make the integration of image reconstruction procedure with the data acquisition feasible, in turn making the rapid dynamic NIR tomography viable in the clinic to continuously monitor hemodynamic changes in the tissue pathophysiology.

Predicting the temporal response of seagrass meadows to dredging using Dynamic Bayesian Networks

Predicting temporal responses of ecosystems to disturbances associated with industrial activities is critical for their management and conservation. However, prediction of ecosystem responses is challenging due to the complexity and potential non-linearities stemming from interactions between system components and multiple environmental drivers. Prediction is particularly difficult for marine ecosystems due to their often highly variable and complex natures and large uncertainties surrounding their dynamic responses. Consequently, current management of such systems often rely on expert judgement and/or complex quantitative models that consider only a subset of the relevant ecological processes. Hence there exists an urgent need for the development of whole-of-systems predictive models to support decision and policy makers in managing complex marine systems in the context of industry based disturbances. This paper presents Dynamic Bayesian Networks (DBNs) for predicting the temporal response of a marine ecosystem to anthropogenic disturbances. The DBN provides a visual representation of the problem domain in terms of factors (parts of the ecosystem) and their relationships. These relationships are quantified via Conditional Probability Tables (CPTs), which estimate the variability and uncertainty in the distribution of each factor. The combination of qualitative visual and quantitative elements in a DBN facilitates the integration of a wide array of data, published and expert knowledge and other models. Such multiple sources are often essential as one single source of information is rarely sufficient to cover the diverse range of factors relevant to a management task. Here, a DBN model is developed for tropical, annual Halophila and temperate, persistent Amphibolis seagrass meadows to inform dredging management and help meet environmental guidelines. Specifically, the impacts of capital (e.g. new port development) and maintenance (e.g. maintaining channel depths in established ports) dredging is evaluated with respect to the risk of permanent loss, defined as no recovery within 5 years (Environmental Protection Agency guidelines). The model is developed using expert knowledge, existing literature, statistical models of environmental light, and experimental data. The model is then demonstrated in a case study through the analysis of a variety of dredging, environmental and seagrass ecosystem recovery scenarios. In spatial zones significantly affected by dredging, such as the zone of moderate impact, shoot density has a very high probability of being driven to zero by capital dredging due to the duration of such dredging. Here, fast growing Halophila species can recover, however, the probability of recovery depends on the presence of seed banks. On the other hand, slow growing Amphibolis meadows have a high probability of suffering permanent loss. However, in the maintenance dredging scenario, due to the shorter duration of dredging, Amphibolis is better able to resist the impacts of dredging. For both types of seagrass meadows, the probability of loss was strongly dependent on the biological and ecological status of the meadow, as well as environmental conditions post-dredging. The ability to predict the ecosystem response under cumulative, non-linear interactions across a complex ecosystem highlights the utility of DBNs for decision support and environmental management.

Compact fiber Bragg grating dynamic strain sensor cum broadband thermometer for thermally unstable ambience

An instrument for simultaneous measurement of dynamic strain and temperature in a thermally unstable ambience has been proposed, based on fiber Bragg grating technology. The instrument can function as a compact and stand-alone broadband thermometer and a dynamic strain gauge. It employs a source wavelength tracking procedure for linear dependence of the output on the measurand, offering high dynamic range. Two schemes have been demonstrated with their relative merits. As a thermometer, the present instrumental configuration can offer a linear response in excess of 500 degrees C that can be easily extended by adding a suitable grating and source without any alteration in the procedure. Temperature sensitivity is about 0.06 degrees C for a bandwidth of 1 Hz. For the current grating, the upper limit of strain measurement is about 150 mu epsilon with a sensitivity of about 80 n epsilon Hz(-1/2). The major source of uncertainty associated with dynamic strain measurement is the laser source intensity noise, which is of broad spectral band. A low noise source device or the use of optical power regulators can offer improved performance. The total harmonic distortion is less than 0.5% up to about 50 mu epsilon, 1.2% at 100 mu epsilon and about 2.3% at 150 mu epsilon. Calibrated results of temperature and strain measurement with the instrument have been presented. Traces of ultrasound signals recorded by the system at 200 kHz, in an ambience of 100-200 degrees C temperature fluctuation, have been included. Also, the vibration spectrum and engine temperature of a running internal combustion engine has been recorded as a realistic application of the system.

On-line Dynamic Security Assessment: Determination of Critical Transmission Lines

The main objective of on-line dynamic security assessment is to take preventive action if required or decide remedial action if a contingency actually occurs. Stability limits are obtained for different contingencies. The mode of instability is one of the outputs of dynamic security analysis. When a power system becomes unstable, it splits initially into two groups of generators, and there is a unique cutset in the transmission network known as critical cutset across which the angles become unbounded. The knowledge of critical cutset is additional information obtained from dynamic security assessment, which can be used for initiating preventive control actions, deciding emergency control actions, and adaptive out-of-step relaying. In this article, an analytical technique for the fast prediction of the critical cutset by system simulation for a short duration is presented. Case studies on the New England ten-generator system are presented. The article also suggests the applications of the identification of critical cutsets.

Integrated CMOS Gas Sensors

We present a low power gas sensor system on CMOS platform consisting of micromachined polysilicon microheater, temperature controller circuit, resistance readout circuit and SnO2 transducer film. The design criteria for different building blocks of the system is elaborated The microheaters are optimized for temperature uniformity as well as static and dynamic response. The electrical equivalent model for the microheater is derived by extracting thermal and mechanical poles through extensive laser doppler vibrometer measurements. The temperature controller and readout circuit are realized on 130nm CMOS technology The temperature controller re-uses the heater as a temperature sensor and controls the duty cycle of the waveform driving the gate of the power MOSFET which supplies heater current. The readout circuit, with subthreshold operation of the MOSFETs, is based oil resistance to time period conversion followed by frequency to digital converter Subthreshold operatin of MOSFETs coupled with sub-ranging technique, achieves ultra low power consumption with more than five orders of magnitude dynamic range RF sputtered SnO2 film is optimized for its microstructure to achive high sensitivity to sense LPG gas.

A Kalman filter based strategy for linear structural system identification based on multiple static and dynamic test data

The problem of identification of stiffness, mass and damping properties of linear structural systems, based on multiple sets of measurement data originating from static and dynamic tests is considered. A strategy, within the framework of Kalman filter based dynamic state estimation, is proposed to tackle this problem. The static tests consists of measurement of response of the structure to slowly moving loads, and to static loads whose magnitude are varied incrementally; the dynamic tests involve measurement of a few elements of the frequency response function (FRF) matrix. These measurements are taken to be contaminated by additive Gaussian noise. An artificial independent variable τ, that simultaneously parameterizes the point of application of the moving load, the magnitude of the incrementally varied static load and the driving frequency in the FRFs, is introduced. The state vector is taken to consist of system parameters to be identified. The fact that these parameters are independent of the variable τ is taken to constitute the set of ‘process’ equations. The measurement equations are derived based on the mechanics of the problem and, quantities, such as displacements and/or strains, are taken to be measured. A recursive algorithm that employs a linearization strategy based on Neumann’s expansion of structural static and dynamic stiffness matrices, and, which provides posterior estimates of the mean and covariance of the unknown system parameters, is developed. The satisfactory performance of the proposed approach is illustrated by considering the problem of the identification of the dynamic properties of an inhomogeneous beam and the axial rigidities of members of a truss structure.

Morphology-controllable 1D–3D nanostructured TiO2 bilayer photoanodes for dye-sensitized solar cells

Morphology-controlled bilayer TiO2 nanostructures consisting of one-dimensional (1D) nanowire bottom arrays and a three-dimensional (3D) dendritic microsphere top layer were synthesized via a one-step hydrothermal method. These novel 1D-3D bilayer photoanodes demonstrated the highest energy conversion efficiency of 7.2% for rutile TiO2 dye-sensitized solar cells to date, with TiCl4 post-treatment.

Continually adjustable oriented 1D TiO2 nanostructure arrays with controlled growth of morphology and their application in dye-sensitized solar cells

Oriented, single-crystalline, one-dimensional (1D) TiO2 nanostructures would be most desirable for providing fascinating properties and features, such as high electron mobility or quantum confinement effects, high specific surface area, and even high mechanical strength, but achieving these structures has been limited by the availability of synthetic techniques. In this study, a concept for precisely controlling the morphology of 1D TiO2 nanostructures by tuning the hydrolysis rate of titanium precursors is proposed. Based on this innovation, oriented 1D rutile TiO2 nanostructure arrays with continually adjustable morphologies, from nanorods (NRODs) to nanoribbons (NRIBs), and then nanowires (NWs), as well as the transient state morphologies, were successfully synthesized. The proposed method is a significant finding in terms of controlling the morphology of the 1D TiO2 nano-architectures, which leads to significant changes in their band structures. It is worth noting that the synthesized rutile NRIBs and NWs have a comparable bandgap and conduction band edge height to those of the anatase phase, which in turn enhances their photochemical activity. In photovoltaic performance tests, the photoanode constructed from the oriented NRIB arrays possesses not only a high surface area for sufficient dye loading and better light scattering in the visible light range than for the other morphologies, but also a wider bandgap and higher conduction band edge, with more than 200% improvement in power conversion efficiency in dye-sensitized solar cells (DSCs) compared with NROD morphology.

Preparation of reticulated MAX-phase support with morphology-controllable nanostructured ceria coating for gas exhaust catalyst devices

Reticulated porous Ti3AlC2 ceramic, a member of the MAX-phase family (Mn+1AXn phases, where M is an early transition metal, A is an A-group element, and X is carbon and/or nitrogen), was prepared from the highly dispersed aqueous suspension by a replica template method. Through a cathodic electrogeneration method, nanocrystalline catalytic CeO2 coatings were deposited on the conductive porous Ti 3AlC2 supports. By adjusting the pH value and cathodic deposition current, coatings exhibiting nanocellar, nanosheets-like, or bubble-free morphologies can be obtained. This work expects to introduce a novel practically feasible material system and a catalytic coating preparation technique for gas exhaust catalyst devices.