377 resultados para Single layer
Resumo:
The enhanced large-scale model and numerical simulations are used to clarify the growth mechanism and the differences between the plasma- and neutral gas-grown carbon nanotubes, and to reveal the underlying physics and the key growth parameters. The results show that the nanotubes grown by plasma can be longer due to the effects of hydrocarbon ions with velocities aligned with the nanotubes. We show that the low-temperature growth is possible when the hydrocarbon ion flux dominates over fluxes of other species. We have also analysed the dependencies of the nanotube growth rates on nanotube and process parameters. The results are verified by a direct comparison with the experimental data. The model is generic and can be used for other types of carbon nanostructures such as carbon nanowalls, vertical graphenes, etc.
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In this article, natural convection boundary layer flow is investigated over a semi-infinite horizontal wavy surface. Such an irregular (wavy) surface is used to exchange heat with an external radiating fluid which obeys Rosseland diffusion approximation. The boundary layer equations are cast into dimensionless form by introducing appropriate scaling. Primitive variable formulations (PVF) and stream function formulations (SFF) are independently used to transform the boundary layer equations into convenient form. The equations obtained from the former formulations are integrated numerically via implicit finite difference iterative scheme whereas equations obtained from lateral formulations are simulated through Keller-box scheme. To validate the results, solutions produced by above two methods are compared graphically. The main parameters: thermal radiation parameter and amplitude of the wavy surface are discussed categorically in terms of shear stress and rate of heat transfer. It is found that wavy surface increases heat transfer rate compared to the smooth wall. Thus optimum heat transfer is accomplished when irregular surface is considered. It is also established that high amplitude of the wavy surface in the boundary layer leads to separation of fluid from the plate.
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Pattern recognition is a promising approach for the identification of structural damage using measured dynamic data. Much of the research on pattern recognition has employed artificial neural networks (ANNs) and genetic algorithms as systematic ways of matching pattern features. The selection of a damage-sensitive and noise-insensitive pattern feature is important for all structural damage identification methods. Accordingly, a neural networks-based damage detection method using frequency response function (FRF) data is presented in this paper. This method can effectively consider uncertainties of measured data from which training patterns are generated. The proposed method reduces the dimension of the initial FRF data and transforms it into new damage indices and employs an ANN method for the actual damage localization and quantification using recognized damage patterns from the algorithm. In civil engineering applications, the measurement of dynamic response under field conditions always contains noise components from environmental factors. In order to evaluate the performance of the proposed strategy with noise polluted data, noise contaminated measurements are also introduced to the proposed algorithm. ANNs with optimal architecture give minimum training and testing errors and provide precise damage detection results. In order to maximize damage detection results, the optimal architecture of ANN is identified by defining the number of hidden layers and the number of neurons per hidden layer by a trial and error method. In real testing, the number of measurement points and the measurement locations to obtain the structure response are critical for damage detection. Therefore, optimal sensor placement to improve damage identification is also investigated herein. A finite element model of a two storey framed structure is used to train the neural network. It shows accurate performance and gives low error with simulated and noise-contaminated data for single and multiple damage cases. As a result, the proposed method can be used for structural health monitoring and damage detection, particularly for cases where the measurement data is very large. Furthermore, it is suggested that an optimal ANN architecture can detect damage occurrence with good accuracy and can provide damage quantification with reasonable accuracy under varying levels of damage.
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Crashes at any particular transport network location consist of a chain of events arising from a multitude of potential causes and/or contributing factors whose nature is likely to reflect geometric characteristics of the road, spatial effects of the surrounding environment, and human behavioural factors. It is postulated that these potential contributing factors do not arise from the same underlying risk process, and thus should be explicitly modelled and understood. The state of the practice in road safety network management applies a safety performance function that represents a single risk process to explain crash variability across network sites. This study aims to elucidate the importance of differentiating among various underlying risk processes contributing to the observed crash count at any particular network location. To demonstrate the principle of this theoretical and corresponding methodological approach, the study explores engineering (e.g. segment length, speed limit) and unobserved spatial factors (e.g. climatic factors, presence of schools) as two explicit sources of crash contributing factors. A Bayesian Latent Class (BLC) analysis is used to explore these two sources and to incorporate prior information about their contribution to crash occurrence. The methodology is applied to the state controlled roads in Queensland, Australia and the results are compared with the traditional Negative Binomial (NB) model. A comparison of goodness of fit measures indicates that the model with a double risk process outperforms the single risk process NB model, and thus indicating the need for further research to capture all the three crash generation processes into the SPFs.
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Single nucleotide polymorphisms (SNPs) have been classically used for dissecting various human complex disorders using candidate gene studies. During the last decade, large scale SNP analysis i.e. genome-wide association studies (GWAS) have provided an agnostic approach to identify possible genetic loci associated with heterogeneous disease such as cancer susceptibility, prognosis of survival or drug response. Further, the advent of new technologies, including microarray based genotyping as well as high throughput next generation sequencing has opened new avenues for SNPs to be used in clinical practice. It is speculated that the utility of SNPs to understand the mechanisms, biology of variable drug response and ultimately treatment individualization based on the individual’s genome composition will be indispensable in the near future. In the current review, we discuss the advantages and disadvantages of the clinical utility of genetic variants in disease risk-prediction, prognosis, clinical outcome and pharmacogenomics. The lessons and challenges for the utility of SNP based biomarkers are also discussed, including the need for additional functional validation studies.
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Circulating tumor cells (CTCs) are found in the blood of patients with cancer. Although these cells are rare, they can provide useful information for chemotherapy. However, isolation of these rare cells from blood is technically challenging because they are small in numbers. An integrated microfluidic chip, dubbed as CTC chip, was designed and fabricated for conducting tumor cell isolation. As CTCs usually show multidrug resistance (MDR), the effect of MDR inhibitors on chemotherapeutic drug accumulation in the isolated single tumor cell is measured. As a model of CTC isolation, human prostate tumor cells were mixed with mouse blood cells and the labelfree isolation of the tumor cells was conducted based on cell size difference. The major advantages of the CTC chip are the ability for fast cell isolation, followed by multiple rounds of single-cell measurements, suggesting a potential assay for detecting the drug responses based on the liquid biopsy of cancer patients.
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Motorcyclists were involved in 6.4% of all police-reported crashes and 12.5% of all fatal crashes in Queensland during 2004-2011. Of these crashes, 43% were single-vehicle (SV) and 57% were multi-vehicle (MV). The overall reduction in motorcycle crashes in this period masked different trends: single-vehicle crashes increased while MV motorcycle crashes decreased. However, little research has been undertaken to understand the similarities and differences between SV and MV motorcycle crashes in Queensland and the factors underlying these diverging trends. The descriptive analyses and regression model developed here confirm international research findings regarding the greater role of road infrastructure factors in SV crashes. In particular, road geometric factors such as horizontal and vertical alignment and road surface factors such as sealed/unsealed and wet/dry were more important in SV than MV crashes.
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Studies of Bi heteroepitaxy on Si(001) have shown that lines grow to lengths of up to 500nm if the substrate is heated to above the Bi desorption temperature (500°C) during or after Bi deposition. Unlike many other nanoline systems, the lines formed by this nonequilibrium growth process have no detectable width dispersion. Although much attention has been given to the atomic geometery of the line, in this paper, we focus on how the lines can be used to create a majority 2×1 domain orientation. It is demonstrated that the Bi lines can be used to produce a single-domain orientation on Si(001) if the lines are grown on Si(001) surfaces with a regular distribution of single height steps. This is a compelling example of how a nanoscale motif can be used to modify mesoscopic surface structure on Si(001).
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The adsorption of In on the Si(111)−Ge(5×5) surface reconstruction has been studied with scanning tunneling microscopy and ab initio calculations to investigate the possibility of using this reconstruction as a template for cluster formation. As with In adsorption on Si(111)−7×7 at low substrate temperatures and low In fluences, the In adatoms are found to preferentially adsorb on the faulted half-unit cell. However, in contrast to In adsorption on Si(111)−7×7, the In adatoms are also frequently found in the unfaulted half-unit cell at low coverages. The filling of unfaulted unit cell halves is primarily due to the formation of large clusters that span multiple substrate half-unit cells. Moreover, many of the faulted half-unit cells have a streaked appearance that indicates that surface atoms within them are mobile.
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Many software applications extend their functionality by dynamically loading libraries into their allocated address space. However, shared libraries are also often of unknown provenance and quality and may contain accidental bugs or, in some cases, deliberately malicious code. Most sandboxing techniques which address these issues require recompilation of the libraries using custom tool chains, require significant modifications to the libraries, do not retain the benefits of single address-space programming, do not completely isolate guest code, or incur substantial performance overheads. In this paper we present LibVM, a sandboxing architecture for isolating libraries within a host application without requiring any modifications to the shared libraries themselves, while still retaining the benefits of a single address space and also introducing a system call inter-positioning layer that allows complete arbitration over a shared library’s functionality. We show how to utilize contemporary hardware virtualization support towards this end with reasonable performance overheads and, in the absence of such hardware support, our model can also be implemented using a software-based mechanism. We ensure that our implementation conforms as closely as possible to existing shared library manipulation functions, minimizing the amount of effort needed to apply such isolation to existing programs. Our experimental results show that it is easy to gain immediate benefits in scenarios where the goal is to guard the host application against unintentional programming errors when using shared libraries, as well as in more complex scenarios, where a shared library is suspected of being actively hostile. In both cases, no changes are required to the shared libraries themselves.
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During the past few decades, developing efficient methods to solve dynamic facility layout problems has been focused on significantly by practitioners and researchers. More specifically meta-heuristic algorithms, especially genetic algorithm, have been proven to be increasingly helpful to generate sub-optimal solutions for large-scale dynamic facility layout problems. Nevertheless, the uncertainty of the manufacturing factors in addition to the scale of the layout problem calls for a mixed genetic algorithm–robust approach that could provide a single unlimited layout design. The present research aims to devise a customized permutation-based robust genetic algorithm in dynamic manufacturing environments that is expected to be generating a unique robust layout for all the manufacturing periods. The numerical outcomes of the proposed robust genetic algorithm indicate significant cost improvements compared to the conventional genetic algorithm methods and a selective number of other heuristic and meta-heuristic techniques.
Resumo:
Rail track undergoes complex loading patterns under moving traffic conditions compared to roads due to its continued and discontinued multi-layered structure, including rail, sleepers, ballast layer, sub-ballast layer, and subgrade. Particle size distributions (PSDs) of ballast, subballast, and subgrade layers can be critical in cyclic plastic deformation of rail track under moving traffic on frequent track degradation of rail tracks, especially at bridge transition zones. Conventional test approaches: static shear and cyclic single-point load tests are however unable to replicate actual loading patterns of moving train. Multi-ring shear apparatus; a new type of torsional simple shear apparatus, which can reproduce moving traffic conditions, was used in this study to investigate influence of particle size distribution of rail track layers on cyclic plastic deformation. Three particle size distributions, using glass beads were examined under different loading patterns: cyclic sin-gle-point load, and cyclic moving wheel load to evaluate cyclic plastic deformation of rail track under different loading methods. The results of these tests suggest that particle size distributions of rail track structural layers have significant impacts on cyclic plastic deformation under moving train load. Further, the limitations in con-ventional test methods used in laboratories to estimate the plastic deformation of rail track materials lead to underestimate the plastic deformation of rail tracks.
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By using electric-field-induced optical second-harmonic generation (EFISHG) measurement, we analyzed hysteresis behavior of capacitance-voltage (C-V) characteristics of IZO/polyterpenol (PT)/C₆₀/pentacene/Au diodes, where PT layer is actively working as a hole-transport electron-blocking layer. The EFISHG measurement verified the presence of interface accumulated charges in the diodes, and showed that a space charge electric field from accumulated excess electrons (holes) that remain at the PT/C₆₀ (C₆₀/pentacene) interface is responsible for the hysteresis loop observed in the C-V characteristics.
Resumo:
The effect of material properties of an environmentally friendly, optically transparent dielectric material, polyterpenol, on the carrier transients within the pentacene-based double-layer MTM device was investigated. Polyterpenol films were RF plasma polymerised under varied process conditions, with resultant films differing in surface chemistry and morphology. Independent of type of polyterpenol, time-resolved EFISHG study of IZO/polyterpenol/pentacene/Au structures showed similar transient behaviour with carriers injected into pentacene from Au electrode only, confirming polyterpenol to be a suitable blocking layer for visualisation of single-species carrier transportation during charging and discharging under different bias conditions. Polyterpenol fabricated under higher input power show better promise due to higher chemical and thermal stability, improved uniformity, and absence of defects.
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A non-synthetic polymer material, polyterpenol, was fabricated using a dry polymerization process namely RF plasma polymerization from an environmentally friendly monomer and its surface, optical and electrical properties investigated. Polyterpenol films were found to be transparent over the visible wavelength range, with a smooth surface with an average roughness of less than 0.4 nm and hardness of 0.4 GPa. The dielectric constant of 3.4 for polyterpenol was higher than that of the conventional polymer materials used in the organic electronic devices. The non-synthetic polymer material was then implemented as a surface modification of the gate insulator in field effect transistor (OFET) and the properties of the device were examined. In comparison to the similar device without the polymer insulating layer, the polyterpenol based OFET device showed significant improvements. The addition of the polyterpenol interlayer in the OFET shifted the threshold voltage significantly; + 20 V to -3 V. The presence of trapped charge was not observed in the polyterpenol interlayer. This assisted in the improvement of effective mobility from 0.012 to 0.021 cm 2/Vs. The switching property of the polyterpenol based OFET was also improved; 107 compared to 104. The results showed that the non-synthetic polyterpenol polymer film is a promising candidate of insulators in electronic devices.