Investigations of the physical and chemical properties of ambient fine particles in urban schools

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.

High-temperature growth of graphene films on copper foils by ethanol chemical vapour deposition

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.

A Near-Infrared Reflectance Spectroscopy Method for Direct Analysis of Several Chemical Components and Properties of Fruit, for Example, Chinese Hawthorn

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.

Effect of chemical conjugation of L-leucine to chitosan on the dispersibility and drug release of a dry powder inhaler nanoparticle formulation

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.

Effects of cold weather on durability of CFRP strengthened circular hollow steel members

Tubular members have become progressively more popular due to excellent structural properties, aesthetic appearance, corrosion and fire protection capability. However, a large number of such structures are found structurally deficient due to reduction of strength when they expose to severe environmental conditions such as marine environment, cold and hot weather. Hence strengthening and retrofitting of structural members are in high demands. In recent times Carbon Fibre Reinforced Polymers (CFRP) composites appears to be an excellent solution to enhance the load carrying capacity and serviceability of steel structures because of its superior physical and mechanical properties. However, the durability of such strengthening system under cold environmental condition has not yet been well documented to guide the engineers. This paper presents the findings of a study conducted to enhance the bond durability of CFRP strengthened steel tubular members by treating steel surface using epoxy based adhesion promoter under cold weather subjected to bending. The experimental program consisted of six number of CFRP strengthened specimens and one bare specimen. The sand blasted surface of the three specimens to be strengthened was pre-treated with MBrace primer and other three were remained untreated and then cured under ambient temperature and cold weather (3oC) for three and six months period of time. The beams were then loaded to failure under four point bending. The structural response of each specimen was predicted in terms of failure mode, failure load and mid-span deflection. The research findings show that the cold weather immersion had an adverse effect on durability of CFRP strengthened structures. Moreover, the epoxy based adhesion promoter was found to enhance the bond durability in elastic range.

An Artificial Neutral Network (ANN) model for predicting biodiesel kinetic viscosity as a function of temperature and chemical compositions

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.

Strain engineering of selective chemical adsorption on monolayer MoS2

Nanomaterials are prone to influence by chemical adsorption because of their large surface to volume ratios. This enables sensitive detection of adsorbed chemical species which, in turn, can tune the property of the host material. Recent studies discovered that single and multi-layer molybdenum disulfide (MoS2) films are ultra-sensitive to several important environmental molecules. Here we report new findings from ab inito calculations that reveal substantially enhanced adsorption of NO and NH3 on strained monolayer MoS2 with significant impact on the properties of the adsorbates and the MoS2 layer. The magnetic moment of adsorbed NO can be tuned between 0 and 1 μB; strain also induces an electronic phase transition between half-metal and metal. Adsorption of NH3 weakens the MoS2 layer considerably, which explains the large discrepancy between the experimentally measured strength and breaking strain of MoS2 films and previous theoretical predictions. On the other hand, adsorption of NO2, CO, and CO2 is insensitive to the strain condition in the MoS2 layer. This contrasting behavior allows sensitive strain engineering of selective chemical adsorption on MoS2 with effective tuning of mechanical, electronic, and magnetic properties. These results suggest new design strategies for constructing MoS2-based ultrahigh-sensitivity nanoscale sensors and electromechanical devices.

Toxic state–corporate crimes, Neo-liberalism and green criminology : the hazards and legacies of the oil, chemical and mineral industries

This paper uses examples from the history and practices of multi-national and large companies in the oil, chemical and asbestos industries to examine their legal and illegal despoiling and destruction of the environment and impact on human and non-human life. The discussion draws on the literature on green criminology and state-corporate crime and considers measures and arrangements that might mitigate or prevent such damaging acts. This paper is part of ongoing work on green criminology and crimes of the economy. It places these actions and crimes in the context of a global neo-liberal economic system and considers and critiques the distorting impact of the GDP model of ‘economic health’ and its consequences for the environment.

Estimation of biodiesel properties from chemical composition – An artificial neural network (ANN) approach

Biodiesel, produced from renewable feedstock represents a more sustainable source of energy and will therefore play a significant role in providing the energy requirements for transportation in the near future. Chemically, all biodiesels are fatty acid methyl esters (FAME), produced from raw vegetable oil and animal fat. However, clear differences in chemical structure are apparent from one feedstock to the next in terms of chain length, degree of unsaturation, number of double bonds and double bond configuration-which all determine the fuel properties of biodiesel. In this study, prediction models were developed to estimate kinematic viscosity of biodiesel using an Artificial Neural Network (ANN) modelling technique. While developing the model, 27 parameters based on chemical composition commonly found in biodiesel were used as the input variables and kinematic viscosity of biodiesel was used as output variable. Necessary data to develop and simulate the network were collected from more than 120 published peer reviewed papers. 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 and learning algorithm were 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 coefficient of determination (R2), root mean squared (RMS) and maximum average error percentage (MAEP) between predicted and experimental results. This study found high predictive accuracy of the ANN in predicting fuel properties of biodiesel and has demonstrated the ability of the ANN model to find a meaningful relationship between biodiesel chemical composition and fuel properties. Therefore the model developed in this study can be a useful tool to accurately predict biodiesel fuel properties instead of undertaking costly and time consuming experimental tests.

Sensitive methods for studying the environmental performance of protective coatings

The safe working lifetime of a structure in a corrosive or other harsh environment is frequently not limited by the material itself but rather by the integrity of the coating material. Advanced surface coatings are usually crosslinked organic polymers such as epoxies and polyurethanes which must not shrink, crack or degrade when exposed to environmental extremes. While standard test methods for environmental durability of coatings have been devised, the tests are structured more towards determining the end of life rather than in anticipation of degradation. We have been developing prognostic tools to anticipate coating failure by using a fundamental understanding of their degradation behaviour which, depending on the polymer structure, is mediated through hydrolytic or oxidation processes. Fourier transform infrared spectroscopy (FTIR) is a widely-used laboratory technique for the analysis of polymer degradation and with the development of portable FTIR spectrometers, new opportunities have arisen to measure polymer degradation non-destructively in the field. For IR reflectance sampling, both diffuse (scattered) and specular (direct) reflections can occur. The complexity in these spectra has provided interesting opportunities to study surface chemical and physical changes during paint curing, service abrasion and weathering, but has often required the use of advanced statistical analysis methods such as chemometrics to discern these changes. Results from our studies using this and related techniques and the technical challenges that have arisen will be presented.

The fragmentation pathways of protonated Amiton in the gas phase : towards the structural characterisation of organophosphorus chemical warfare agents by electrospray ionisation tandem mass spectrometry

Amiton (O,O-diethyl-S-[2-(diethylamino)ethyl]phosphorothiolate), otherwise known as VG, is listed in schedule 2 of the Chemical Weapons Convention (CWC) and has a structure closely related to VX (O-ethyl-S-(2-diisopropylamino)ethylmethylphosphonothiolate). Fragmentation of protonated VG in the gas phase was performed using electrospray ionisation ion trap mass spectrometry (ESI-ITMS) and revealed several characteristic product ions. Quantum chemical calculations provide the most probable structures for these ions as well as the likely unimolecular mechanisms by which they are formed. The decomposition pathways predicted by computation are consistent with deuterium-labeling studies. The combination of experimental and theoretical data suggests that the fragmentation pathways of VG and analogous organophosphorus nerve agents, such as VX and Russian VX, are predictable and thus ESI tandem mass spectrometry is a powerful tool for the verification of unknown compounds listed in the CWC. Copyright (c) 2006 Commonwealth of Australia. Published by John Wiley & Sons, Ltd.

A rapid transcriptional activation is induced by the dormancy-breaking chemical hydrogen cyanamide in kiwifruit (Actinidia deliciosa) buds

Budbreak in kiwifruit (Actinidia deliciosa) can be poor in locations that have warm winters with insufficient winter chilling. Kiwifruit vines are often treated with the dormancy-breaking chemical hydrogen cyanamide (HC) to increase and synchronize budbreak. This treatment also offers a tool to understand the processes involved in budbreak. A genomics approach is presented here to increase our understanding of budbreak in kiwifruit. Most genes identified following HC application appear to be associated with responses to stress, but a number of genes appear to be associated with the reactivation of growth. Three patterns of gene expression were identified: Profile 1, an HC-induced transient activation; Profile 2, an HC-induced transient activation followed by a growth-related activation; and Profile 3, HC- and growth-repressed. One group of genes that was rapidly up-regulated in response to HC was the glutathione S-transferase (GST) class of genes, which have been associated with stress and signalling. Previous budbreak studies, in three other species, also report up-regulated GST expression. Phylogenetic analysis of these GSTs showed that they clustered into two sub-clades, suggesting a strong correlation between their expression and budbreak across species.

Impact of a change in tillage and crop residue management practice on soil chemical and microbiological properties in a cereal-producing red duplex soil in NSW, Australia

The effect of a change of tillage and crop residue management practice on the chemical and micro-biological properties of a cereal-producing red duplex soil was investigated by superimposing each of three management practices (CC: conventional cultivation, stubble burnt, crop conventionally sown; DD: direct-drilling, stubble retained, no cultivation, crop direct-drilled; SI: stubble incorporated with a single cultivation, crop conventionally sown), for a 3-year period on plots previously managed with each of the same three practices for 14 years. A change from DD to CC or SI practice resulted in a significant decline, in the top 0-5 cm of soil, in organic C, total N, electrical conductivity, NH4-N, NO3-N, soil moisture holding capacity, microbial biomass and CO2 respiration as well as a decline in the microbial quotient (the ratio of microbial biomass C to organic C; P <0.05). In contrast, a change from SI to DD or CC practice or a change from CC to DD or SI practice had only negligible impact on soil chemical properties (P >0.05). However, there was a significant increase in microbial biomass and the microbial quotient in the top 0-5 cm of soil following the change from CC to DD or SI practice and with the change from SI to DD practice (P <0.05). Analysis of ester-linked fatty acid methyl esters (EL-FAMEs) extracted from the 0- to 5-cm and 5- to 10-cm layers of the soils of the various treatments detected changes in the FAME profiles following a change in tillage practice. A change from DD practice to SI or CC practice was associated with a significant decline in the ratio of fungal to bacterial fatty acids in the 0- to 5-cm soil (P <0.05). The results show that a change in tillage practice, particularly the cultivation of a previously minimum-tilled (direct-drilled) soil, will result in significant changes in soil chemical and microbiological properties within a 3-year period. They also show that soil microbiological properties are sensitive indicators of a change in tillage practice.

Formation and electron field emission of graphene films grown by hot filament chemical vapor deposition

Graphene films with different structures were catalytically grown on the silicon substrate pre-deposited with a gold film by hot filament chemical vapor deposition under different conditions, where methane, hydrogen and nitrogen were used as the reactive gases. The morphological and compositional properties of graphene films were studied using advanced instruments including field emission scanning electron microscopy, micro-Raman spectroscopy and X-ray photoelectron spectroscopy. The results indicate that the structure and composition of graphene films are changed with the variation of the growth conditions. According to the theory related to thermodynamics, the formation of graphene films was theoretically analyzed and the results indicate that the formation of graphene films is related to the fast incorporation and precipitation of carbon. The electron field emission (EFE) properties of graphene films were studied in a high vacuum system of ∼10-6 Pa and the EFE results show that the turn-on field is in a range of 5.2-5.64 V μm-1 and the maximum current density is about 63 μ A cm-2 at the field of 7.7 V μm-1. These results are important to control the structure of graphene films and have the potential applications of graphene in various nanodevices.

Microplasma-chemical synthesis and tunable real-time plasmonic responses of alloyed Aux Ag1−x nanoparticles

Tunable synthesis of bimetallic AuxAg1-x alloyed nanoparticles and in situ monitoring of their plasmonic responses is presented. This is a new conceptual approach based on green and energy efficient, reactive, and highly-non-equilibrium microplasma chemistry.