330 resultados para Hybrid material


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This article presents mathematical models to simulate coupled heat and mass transfer during convective drying of food materials using three different effective diffusivities: shrinkage dependent, temperature dependent and average of those two. Engineering simulation software COMSOL Multiphysics was utilized to simulate the model in 2D and 3D. The simulation results were compared with experimental data. It is found that the temperature dependent effective diffusivity model predicts the moisture content more accurately at the initial stage of the drying, whereas, the shrinkage dependent effective diffusivity model is better for the final stage of the drying. The model with shrinkage dependent effective diffusivity shows evaporative cooling phenomena at the initial stage of drying. This phenomenon was investigated and explained. Three dimensional temperature and moisture profiles show that even when the surface is dry, inside of the sample may still contain large amount of moisture. Therefore, drying process should be carefully dealt with otherwise microbial spoilage may start from the centre of the ‘dried’ food. A parametric investigation has been conducted after the validation of the model.

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The transfer of chemical vapor deposited graphene is a crucial process, which can affect the quality of the transferred films and compromise their application in devices. Finding a robust and intrinsically clean material capable of easing the transfer of graphene without interfering with its properties remains a challenge. We here propose the use of an organic compound, cyclododecane, as a transfer material. This material can be easily spin coated on graphene and assist the transfer, leaving no residues and requiring no further removal processes. The effectiveness of this transfer method for few-layer graphene on a large area was evaluated and confirmed by microscopy, Raman spectroscopy, x-ray photoemission spectroscopy, and four-point probe measurements. Schottky-barrier solar cells with few-layer graphene were fabricated on silicon wafers by using the cyclododecane transfer method and outperformed reference cells made by standard methods.

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This article outlines the knowledge and skills students develop when they engage in digital media production and analysis in school settings. The metaphor of ‘digital building blocks’ is used to describe the material practices, conceptual understandings and production of knowledge that lead to the development of digital media literacy. The article argues that the two established approaches to media literacy education, critical reading and media production, do not adequately explain how students develop media knowledge. It suggests there has been too little focus on material practices and how these relate to the development of conceptual understanding in media learning. The article explores empirical evidence from a four-year investigation in a primary school in Queensland, Australia using actor–network theory to explore ‘moments of translation’ as students deploy technologies and concepts to materially participate in digital culture. A generative model of media learning is presented with four categories of building blocks that isolate the specific skills and knowledge that can be taught and learnt to promote participation in digital media contexts: digital materials, conceptual understandings, media production and media analysis. The final section of the article makes initial comments on how the model might become the basis for curriculum development in schools and argues that further empirical research needs to occur to confirm the model’s utility.

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This report documents research that was commissioned in order to review the materials used in the Road Ready program for relevancy and acceptability to the target audiences as part of the implementation of the ACT Road Safety Strategy Action Plan. Relevant literature on young driver crash risk and best practice principles in driver education was reviewed as a first step (Section 2). The evidence for effectiveness of driver education programs in Australia was summarised (Section 3) and a separate review of the use of incentives in relation to driver education was performed. The brief called for an expert review of the materials and delivery design for both the Road Ready and Road Ready Plus programs. This is reported in Section 5, along with the overall recommendations for program improvement. More specific comments on individual modules in the Road Ready program are listed at the end of Section 5. Lastly, feedback from stakeholders, specifically the facilitators and teachers of the programs, as well as former students who have completed the Road Ready program, was sought. Interviews and surveys were conducted with these groups. Summaries of the methods and findings are contained in Section 6.

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This thesis studied cadmium sulfide and cadmium selenide quantum dots and their performance as light absorbers in quantum dot-sensitised solar cells. This research has made contributions to the understanding of size dependent photodegradation, passivation and particle growth mechanism of cadmium sulfide quantum dots using SILAR method and the role of ZnSe shell coatings on solar cell performance improvement.

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The MOCVD assisted formation of nested WS2 inorganic fullerenes (IF-WS2) was performed by enhancing surface diffusion with iodine, and fullerene growth was monitored by taking TEM snapshots of intermediate products. The internal structure of the core-shell nanoparticles was studied using scanning electron microscopy (SEM) after cross-cutting with a focused ion beam (FIB). Lamellar reaction intermediates were found occluded in the fullerene particles. In contrast to carbon fullerenes, layered metal chalcogenides prefer the formation of planar, plate-like structures where the dangling bonds at the edges are stabilized by excess S atoms. The effects of the reaction and annealing temperatures on the composition and morphology of the final product were investigated, and the strength of the WS2 shell was measured by intermittent contact-mode AFM. The encapsulated lamellar structures inside the hollow spheres may lead to enhanced tribological activities.

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Partial shading and rapidly changing irradiance conditions significantly impact on the performance of photovoltaic (PV) systems. These impacts are particularly severe in tropical regions where the climatic conditions result in very large and rapid changes in irradiance. In this paper, a hybrid maximum power point (MPP) tracking (MPPT) technique for PV systems operating under partially shaded conditions witapid irradiance change is proposed. It combines a conventional MPPT and an artificial neural network (ANN)-based MPPT. A low cost method is proposed to predict the global MPP region when expensive irradiance sensors are not available or are not justifiable for cost reasons. It samples the operating point on the stairs of I–V curve and uses a combination of the measured current value at each stair to predict the global MPP region. The conventional MPPT is then used to search within the classified region to get the global MPP. The effectiveness of the proposed MPPT is demonstrated using both simulations and an experimental setup. Experimental comparisons with four existing MPPTs are performed. The results show that the proposed MPPT produces more energy than the other techniques and can effectively track the global MPP with a fast tracking speed under various shading patterns.

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This study reports a hybrid of two metal-organic semiconductors that are based on organic charge transfer complexes of 7,7,8,8-tetracyanoquinodimethane (TCNQ). It is shown that the spontaneous reaction between semiconducting microrods of CuTCNQ with Ag+ ions leads to the formation of a CuTCNQ/AgTCNQ hybrid, both in aqueous solution and acetonitrile, albeit with completely different reaction mechanisms. In an aqueous environment, the reaction proceeds by a complex galvanic replacement (GR) mechanism, wherein in addition to AgTCNQ nanowires, Ag0 nanoparticles and Cu(OH)2 crystals decorate the surface of CuTCNQ microrods. Conversely, in acetonitrile, a GR mechanism is found to be thermodynamically unfavorable and instead a corrosion-recrystallization mechanism leads to the decoration of CuTCNQ microrods with AgTCNQ nanoplates, resulting in a pure CuTCNQ/AgTCNQ hybrid metal-organic charge transfer complex. While hybrids of two different inorganic semiconductors are regularly reported, this report pioneers the formation of a hybrid involving two metal-organic semiconductors that will expand the scope of TCNQ-based charge transfer complexes for improved catalysis, sensing, electronics and biological applications.

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Identifying appropriate decision criteria and making optimal decisions in a structured way is a complex process. This paper presents an approach for doing this in the form of a hybrid Quality Function Deployment (QFD) and Cybernetic Analytic Network Process (CANP) model for project manager selection. This involves the use of QFD to translate the owner's project management expectations into selection criteria and the CANP to weight the expectations and selection criteria. The supermatrix approach then prioritises the candidates with respect to the overall decision-making goal. A case study is used to demonstrate the use of the model in selecting a renovation project manager. This involves the development of 18 selection criteria in response to the owner's three main expectations of time, cost and quality.

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This paper presents a novel framework for the modelling of passenger facilitation in a complex environment. The research is motivated by the challenges in the airport complex system, where there are multiple stakeholders, differing operational objectives and complex interactions and interdependencies between different parts of the airport system. Traditional methods for airport terminal modelling do not explicitly address the need for understanding causal relationships in a dynamic environment. Additionally, existing Bayesian Network (BN) models, which provide a means for capturing causal relationships, only present a static snapshot of a system. A method to integrate a BN complex systems model with stochastic queuing theory is developed based on the properties of the Poisson and exponential distributions. The resultant Hybrid Queue-based Bayesian Network (HQBN) framework enables the simulation of arbitrary factors, their relationships, and their effects on passenger flow and vice versa. A case study implementation of the framework is demonstrated on the inbound passenger facilitation process at Brisbane International Airport. The predicted outputs of the model, in terms of cumulative passenger flow at intermediary and end points in the inbound process, are found to have an R2 goodness of fit of 0.9994 and 0.9982 respectively over a 10 h test period. The utility of the framework is demonstrated on a number of usage scenarios including causal analysis and ‘what-if’ analysis. This framework provides the ability to analyse and simulate a dynamic complex system, and can be applied to other socio-technical systems such as hospitals.

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Phenols are well known noxious compounds, which are often found in various water sources. A novel analytical method has been researched and developed based on the properties of hemin–graphene hybrid nanosheets (H–GNs). These nanosheets were synthesized using a wet-chemical method, and they have peroxidase-like activity. Also, in the presence of H2O2, the nanosheets are efficient catalysts for the oxidation of the substrate, 4-aminoantipine (4-AP), and the phenols. The products of such an oxidation reaction are the colored quinone-imines (benzodiazepines). Importantly, these products enabled the differentiation of the three common phenols – pyrocatechol, resorcin and hydroquinone, with the use of a novel, spectroscopic method, which was developed for the simultaneous determination of the above three analytes. This spectroscopic method produced linear calibrations for the pyrocatechol (0.4–4.0 mg L−1), resorcin (0.2–2.0 mg L−1) and hydroquinone (0.8–8.0 mg L−1) analytes. In addition, kinetic and spectral data, obtained from the formation of the colored benzodiazepines, were used to establish multi-variate calibrations for the prediction of the three phenol analytes found in various kinds of water; partial least squares (PLS), principal component regression (PCR) and artificial neural network (ANN) models were used and the PLS model performed best.