1000 resultados para Chemical trapping
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In this study, chitosan-PEO blend, prepared in a 15 M acetic acid, was electrospun into nanofibers (~ 78 nm diameter) with bead free morphology. While investigating physico-chemical parameters of blend solutions, effect of yield stress on chitosan based nanofiber fabrication was clearly evidenced. Architectural stability of nanofiber mat in aqueous medium was achieved by ionotropic cross-linking of chitosan by tripolyphosphate (TPP) ions. The TPP cross-linked nanofiber mat showed swelling up to ~ 300 % in 1h and ~ 40 % degradation during 30 d study period. 3T3 fibroblast cells showed good attachment, proliferation and viability on TPP treated chitosan based nanofiber mats. The results indicate non-toxic nature of TPP cross-linked chitosan based nanofibers and their potential to be explored as a tissue engineering matrix.
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Following the growing need for adoption of alternative fuels, this project aimed at getting more information on the oxidative potential of biodiesel particulate matter. Within this scope, the physical and chemical characteristics of biodiesel PM were analysed which lead to identification of reactive organic fractions. An in-house developed proflurescent nitroxide probe was used. This project further developed in-depth understanding of the chemical mechanisms following the detection of the oxidative potential of PM. This knowledge made a significant contribution to our understanding of processes behind negative health effects of pollution and enabled us to further develop new techniques to monitor it.
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Camera trapping is a scientific survey technique that involves the placement of heat-and motion-sensing automatic triggered cameras into the ecosystem to record images of animals for the purpose of studying wildlife. As technology continues to advance in sophistication, the use of camera trapping is becoming more widespread and is a crucial tool in the study of, and attempts to preserve, various species of animals, particularly those that are internationally endangered. However, whatever their value as an ecological device, camera traps also create a new risk of incidentally and accidentally capturing images of humans who venture into the area under surveillance. This article examines the current legal position in Australia in relation to such unintended invasions of privacy. It considers the current patchwork of statute and common laws that may provide a remedy in such circumstances. It also discusses the position that may prevail should the recommendations of either the Australian Law Reform Commission and/or New South Wales Law Reform Commission be adopted and a statutory cause of action protecting personal privacy be enacted.
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Liuwei Dihuang Wan (LWD), a classic Chinese medicinal formulae, has been used to improve or restore declined functions related to aging and geriatric diseases, such as impaired mobility, vision, hearing, cognition and memory. It has attracted increasingly much attention as one of the most popular and valuable herbal medicines. However, the systematic analysis of the chemical constituents of LDW is difficult and thus has not been well established. In this paper, a rapid, sensitive and reliable ultra-performance liquid chromatography with electrospray ionization quadrupole time-of-flight high-definition mass spectrometry (UPLC-ESI-Q-TOF-MS) method with automated MetaboLynx analysis in positive and negative ion mode was established to characterize the chemical constituents of LDW. The analysis was performed on a Waters UPLCTM HSS T3 using a gradient elution system. MS/MS fragmentation behavior was proposed for aiding the structural identification of the components. Under the optimized conditions, a total of 50 peaks were tentatively characterized by comparing the retention time and MS data. It is concluded that a rapid and robust platform based on UPLC-ESI-Q-TOF-MS has been successfully developed for globally identifying multiple-constituents of traditional Chinese medicine prescriptions. This is the first report on systematic analysis of the chemical constituents of LDW. This article is protected by copyright. All rights reserved.
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Development and application of inorganic adsorbent materials have been continuously investigated due to their variability and versatility. This Master thesis has expanded the knowledge in the field of adsorption targeting radioactive iodine waste and proteins using modified inorganic materials. Industrial treatment of radioactive waste and safety disposal of nuclear waste is a constant concern around the world with the development of radioactive materials applications. To address the current problems, laminar titanate with large surface area (143 m2 g−1) was synthesized from inorganic titanium compounds by hydrothermal reactions at 433 K. Ag2O nanocrystals of particle size ranging from 5–30 nm were anchored on the titanate lamina surface which has crystallographic similarity to that of Ag2O nanocrystals. Therefore, the deposited Ag2O nanocrystals and titanate substrate could join together at these surfaces between which there forms a coherent interface. Such coherence between the two phases reduces the overall energy by minimizing surface energy and maintains the Ag2O nanocrystals firmly on the outer surface of the titanate structure. The combined adsorbent was then applied as efficient adsorbent to remove radioactive iodine from water (one gram adsorbent can capture up to 3.4 mmol of I- anions) and the composite adsorbent can be recovered easily for safe disposal. The structure changes of the titanate lamina and the composite adsorbent were characterized via various techniques. The isotherm and kinetics of iodine adsorption, competitive adsorption and column adsorption using the adsorbent were studied to determine the iodine removal abilities of the adsorbent. It is shown that the adsorbent exhibited excellent trapping ability towards iodine in the fix-bed column despite the presence of competitive ions. Hence, Ag2O deposited titanate lamina could serve as an effective adsorbent for removing iodine from radioactive waste. Surface hydroxyl group of the inorganic materials is widely applied for modification purposes and modification of inorganic materials for biomolecule adsorption can also be achieved. Specifically, γ-Al2O3 nanofibre material is converted via calcinations from boehmite precursor which is synthesised by hydrothermal chemical reactions under directing of surfactant. These γ-Al2O3 nanofibres possess large surface area (243 m2 g-1), good stability under extreme chemical conditions, good mechanical strength and rich surface hydroxyl groups making it an ideal candidate in industrialized separation column. The fibrous morphology of the adsorbent also guarantees facile recovery from aqueous solution under both centrifuge and sedimentation approaches. By chemically bonding the dyes molecules, the charge property of γ-Al2O3 is changed in the aim of selectively capturing of lysozyme from chicken egg white solution. The highest Lysozyme adsorption amount was obtained at around 600 mg/g and its proportion is elevated from around 5% to 69% in chicken egg white solution. It was found from the adsorption test under different solution pH that electrostatic force played the key role in the good selectivity and high adsorption rate of surface modified γ-Al2O3 nanofibre adsorbents. Overall, surface modified fibrous γ-Al2O3 could be applied potentially as an efficient adsorbent for capturing of various biomolecules.
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Stress corrosion cracking (SCC) is a well known form of environmental attack in low carat gold jewellery. It is desirable to have a quick, easy and cost effective way to detect SCC in alloys and prevent them from being used and later failing in their application. A facile chemical method to investigate SCC of 9 carat gold alloys is demonstrated. It involves a simple application of tensile stress to a wire sample in a corrosive environment such as 1–10 % FeCl3 which induces failure in less than 5 minutes. In this study three quaternary (Au, Ag, Cu and Zn) 9 carat gold alloy compositions were investigated for their resistance to SCC and the relationship between time to failure and processing conditions is studied. It is envisaged that the use of such a rapid and facile screening procedure at the production stage may readily identify alloy treatments that produce jewellery that will be susceptible to SCC in its lifetime.
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Chemical reaction studies of N-methyl-N-propyl-pyrrolidinium-bis(fluorosulfonyl)imide-based ionic liquid with the lithium metal surface were performed using ab initio molecular dynamics (aMD) simulations and X-ray Photoelectron Spectroscopy (XPS). The molecular dynamics simulations showed rapid and spontaneous decomposition of the ionic liquid anion, with subsequent formation of long-lived species such as lithium fluoride. The simulations also revealed the cation to retain its structure by generally moving away from the lithium surface. The XPS experiments showed evidence of decomposition of the anion, consistent with the aMD simulations and also of cation decomposition and it is envisaged that this is due to the longer time scale for the XPS experiment compared to the time scale of the aMD simulation. Overall experimental results confirm the majority of species suggested by the simulation. The rapid chemical decomposition of the ionic liquid was shown to form a solid electrolyte interphase composed of the breakdown products of the ionic liquid components in the absence of an applied voltage.
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The effect of storage time on the cyclability of lithium electrodes in an ionic liquid electrolyte, namely 0.5 m LiBF4 in N-methyl-N-propyl pyrrolidinium bis(fluorosulfonyl)imide, [C3mpyr+][FSI–], was investigated. A chemical interaction was observed which is time dependent and results in a morphology change of the Li surface due to build up of passivation products over a 12-day period. The formation of this layer significantly impacts on the Li electrode resistance before cycling and the charging/discharging process for symmetrical Li|0.5 m LiBF4 in [C3mpyr+][FSI–]|Li coin cells. Indeed it was found that introducing a rest period between cycling, and thereby allowing the chemical interaction between the Li electrode and electrolyte to take place, also impacted on the charging/discharging process. For all Li surface treatments the electrode resistance decreased after cycling and was due to significant structural rearrangement of the surface layer. These results suggest that careful electrode pretreatment in a real battery system will be required before operation.
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Globally, obesity and diabetes (particularly type 2 diabetes) represents a major challenge to world health. Despite decades of intense research efforts, the genetic basis involved in diabetes pathogenesis & conditions associated with obesity are still poorly understood. Recent advances have led to exciting new developments implicating epigenetics as an important mechanism underpinning diabetes and obesity related disease. One epigenetic mechanism known as the "histone code" describes the idea that specific patterns of post-translational modifications to histones act like a molecular "code" recognised and used by non-histone proteins to regulate specific chromatin functions. One modification which has received significant attention is that of histone acetylation. The enzymes which regulate this modification are described as lysine acetyltransferases or KATs and histone deacetylases or HDACs. Due to their conserved catalytic domain HDACs have been actively targeted as a therapeutic target. Some of the known inhibitors of HDACs (HDACi) have also been shown to act as "chemical chaperones" to alleviate diabetic symptoms. In this review, we discuss the available evidence concerning the roles of HDACs in regulating chaperone function and how this may have implications in the management of diabetes. © 2009 Bentham Science Publishers Ltd.
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This thesis reports a comprehensive study on the physical and chemical properties of airborne particles in Brisbane, especially around schools. The sources and potential toxicity of the particles were identified, enabling an assessment of the contributing factors to children's exposure at school. The results from this thesis give a quantitative estimate of the range of airborne particles that children are exposed to at urban schools with different traffic conditions.
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Chemical vapor deposition (CVD) is widely utilized to synthesize graphene with controlled properties for many applications, especially when continuous films over large areas are required. Although hydrocarbons such as methane are quite efficient precursors for CVD at high temperature (∼1000 °C), finding less explosive and safer carbon sources is considered beneficial for the transition to large-scale production. In this work, we investigated the CVD growth of graphene using ethanol, which is a harmless and readily processable carbon feedstock that is expected to provide favorable kinetics. We tested a wide range of synthesis conditions (i.e., temperature, time, gas ratios), and on the basis of systematic analysis by Raman spectroscopy, we identified the optimal parameters for producing highly crystalline graphene with different numbers of layers. Our results demonstrate the importance of high temperature (1070 °C) for ethanol CVD and emphasize the significant effects that hydrogen and water vapor, coming from the thermal decomposition of ethanol, have on the crystal quality of the synthesized graphene.
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Near-infrared spectroscopy (NIRS) calibrations were developed for the discrimination of Chinese hawthorn (Crataegus pinnatifida Bge. var. major) fruit from three geographical regions as well as for the estimation of the total sugar, total acid, total phenolic content, and total antioxidant activity. Principal component analysis (PCA) was used for the discrimination of the fruit on the basis of their geographical origin. Three pattern recognition methods, linear discriminant analysis, partial least-squares-discriminant analysis, and back-propagation artificial neural networks, were applied to classify and compare these samples. Furthermore, three multivariate calibration models based on the first derivative NIR spectroscopy, partial least-squares regression, back-propagation artificial neural networks, and least-squares-support vector machines, were constructed for quantitative analysis of the four analytes, total sugar, total acid, total phenolic content, and total antioxidant activity, and validated by prediction data sets.
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Purpose: This study investigated the effect of chemical conjugation of the amino acid L-leucine to the polysaccharide chitosan on the dispersibility and drug release pattern of a polymeric nanoparticle (NP)-based controlled release dry powder inhaler (DPI) formulation. Methods: A chemical conjugate of L-leucine with chitosan was synthesized and characterized by Infrared (IR) Spectroscopy, Nuclear Magnetic Resonance (NMR) Spectroscopy, Elemental Analysis and X-ray Photoelectron Spectroscopy (XPS). Nanoparticles of both chitosan and its conjugate were prepared by a water-in-oil emulsification – glutaraldehyde cross-linking method using the antihypertensive agent, diltiazem (Dz) hydrochloride as the model drug. The surface morphology and particle size distribution of the nanoparticles were determined by Scanning Electron Microscopy (SEM) and Dynamic Light Scattering (DLS). The dispersibility of the nanoparticle formulation was analysed by a Twin Stage Impinger (TSI) with a Rotahaler as the DPI device. Deposition of the particles in the different stages was determined by gravimetry and the amount of drug released was analysed by UV spectrophotometry. The release profile of the drug was studied in phosphate buffered saline at 37 ⁰C and analyzed by UV spectrophotometry. Results: The TSI study revealed that the fine particle fractions (FPF), as determined gravimetrically, for empty and drug-loaded conjugate nanoparticles were significantly higher than for the corresponding chitosan nanoparticles (24±1.2% and 21±0.7% vs 19±1.2% and 15±1.5% respectively; n=3, p<0.05). The FPF of drug-loaded chitosan and conjugate nanoparticles, in terms of the amount of drug determined spectrophotometrically, had similar values (21±0.7% vs 16±1.6%). After an initial burst, both chitosan and conjugate nanoparticles showed controlled release that lasted about 8 to 10 days, but conjugate nanoparticles showed twice as much total drug release compared to chitosan nanoparticles (~50% vs ~25%). Conjugate nanoparticles also showed significantly higher dug loading and entrapment efficiency than chitosan nanoparticles (conjugate: 20±1% & 46±1%, chitosan: 16±1% & 38±1%, n=3, p<0.05). Conclusion: Although L-leucine conjugation to chitosan increased dispersibility of formulated nanoparticles, the FPF values are still far from optimum. The particles showed a high level of initial burst release (chitosan, 16% and conjugate, 31%) that also will need further optimization.
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An Artificial Neural Network (ANN) is a computational modeling tool which has found extensive acceptance in many disciplines for modeling complex real world problems. An ANN can model problems through learning by example, rather than by fully understanding the detailed characteristics and physics of the system. In the present study, the accuracy and predictive power of an ANN was evaluated in predicting kinetic viscosity of biodiesels over a wide range of temperatures typically encountered in diesel engine operation. In this model, temperature and chemical composition of biodiesel were used as input variables. In order to obtain the necessary data for model development, the chemical composition and temperature dependent fuel properties of ten different types of biodiesels were measured experimentally using laboratory standard testing equipments following internationally recognized testing procedures. The Neural Networks Toolbox of MatLab R2012a software was used to train, validate and simulate the ANN model on a personal computer. The network architecture was optimised following a trial and error method to obtain the best prediction of the kinematic viscosity. The predictive performance of the model was determined by calculating the absolute fraction of variance (R2), root mean squared (RMS) and maximum average error percentage (MAEP) between predicted and experimental results. This study found that ANN is highly accurate in predicting the viscosity of biodiesel and demonstrates the ability of the ANN model to find a meaningful relationship between biodiesel chemical composition and fuel properties at different temperature levels. Therefore the model developed in this study can be a useful tool in accurately predict biodiesel fuel properties instead of undertaking costly and time consuming experimental tests.