Land use and land use change strongly influence the carbon (C) dynamics within ecosystems. This study quantified four aspects of land use and land use change effects: 1) ecosystem C stocks and distribution; 2) soil respiration; 3) soil C mineralization; and 4) net ecosystem productivity. Land use systems studied include agriculture (AG), 2-yr- and 9-yr-old hybrid poplar plantations (2HP and 9HP, respectively), grassland (GRA), and native forest stand (NAT). Ecosystem C stock in NAT (223 Mg C ha-1) was similar to 9HP (174 Mg C ha-1) and both were significantly greater than AG (122 Mg C ha-1), GRA (121 Mg C ha-1), and 2HP (110 Mg C ha-1). Cumulative soil C loss via soil respiration averaged over two growing seasons was in the order of: NAT (7.810.40 Mg C ha-1) > 9HP (5.510.31 Mg C ha-1) > GRA (5.230.30 Mg C ha-1) > AG (5.020.24 Mg C ha 1) > 2HP (4.280.20 Mg C ha-1). Depending on land use, seasonal heterotrophic and autotrophic respiration had respective contributions to soil respiration of up to 35 and 83%. Soil C mineralization of bulk soil across the land uses ranged between 2 to 5% of initial total organic C (Ci), with mineralization rates ranging from 0.06 to 0.12 g C mg-1 Ci d-1 and mean residence times ranging from 30 to 51 yrs. Across particle size fractions, soil C mineralization was in the order of: AG > HPs > GRA > NAT of which the coarse fractions, representing labile C, were the main source of mineralized C (79%). Mineralization increased when NAT was converted to AG; and decreased when AG was converted to HP or GRA. Net ecosystem productivity across land uses, expressed in terms of C, ranged between 2 (AG) and 11 Mg C ha-1 yr-1 (older HP). Conversion from AG to GRA increased net ecosystem productivity three-fold. When AG was converted to HP, the plantation was a C source in the first four years and became a C sink by year five. Results obtained from this study are relevant to modeling efforts designed at determining the impact of future climate change on a variety of land uses. / Soil Science

Graduation date: 1983

Thesis (M.S.)--University of Hawaii at Manoa, 2007. / Includes bibliographical references (leaves 145-146). / vi, 146 leaves, bound ill., maps 29 cm

A computational investigation of some optoelectronic applications of carbon nanotubes (CNT) is presented, including CNT-based solar cells and biosensors. The results could be used to evaluate the performance of CNT devices and clarify the necessity of further experimental research in this area. A coaxially-gated CNT field-effect transistor (CNFET) forms the basic structure of the devices modeled in this thesis. Diffusive transport is present in long-channel devices, as in our case, while the quantum mechanical effects are mainly present in the form of tunneling from Schottky-barrier contacts at the metal-CNT interfaces. Band-to-band recombination of electron-hole pairs (EHP) is assumed to be the source of electroluminescence. In a first-order approximation, protein-CNT interactions are modeled as the modification of the potential profile along the longitudinal axis of CNTs due to electrostatic coupling between partial charges, in the oxide layer of the CNFET, and the nanotube. The possibility of electronic detection is evaluated. The electroluminescence of the CNT is proposed as an optical detection scheme due to its sensitivity to the magnitude and the polarity of the charge in the oxide. The validity of the model is argued for the given models. A value for the minimum required size of a computational window in a detailed simulation is derived. The structure of an electrostatically gated p-i-n diode is simulated and investigated for photovoltaic purposes. The absorbed power from the incident light and the interaction between the nanotubes is modeled with COMSOL. The results are interpreted as a generation term and introduced to the Drift-Diffusion Equation (DDE). We have observed behavior similar to that in an experimentally-realized device. The performance of CNT-based solar cells under standard AM 1.5 sunlight conditions is evaluated in the form of an individual solar cell and also in an array of such devices.

Thesis (PhD)--University of South Australia, 2000

Thesis (PhD) -- University of South Australia, 1999

University of Technology, Sydney. Faculty of Engineering. / This research has analysed the economy-wide impacts of carbon tax as a policy option to reduce the rate of growth of carbon-dioxide emissions from the electricity sector in Australia. These impacts are analysed for energy and non energy sectors of the economy. An energy-oriented Input–Output framework, with ‘flexible’ production functions, based on Translog and Cobb-Douglas formulations, is employed for the analysis of various impacts. Further, two alternative conceptions of carbon tax are considered in this research, namely, based on Polluter Pays Principle (PPP) and Shared Responsibility Principle (SRP). In the first instance, the impacts are analysed, for the period 2005–2020, for tax levels of $10 and $20 per tonne of CO2, in a situation of no a-priori limit on CO2 emissions. The analysis shows that CO2 emissions from the electricity sector, when carbon tax is based on PPP, would be 211 and 152 Mt, for tax levels of $10 and $20, respectively (as compared to 250 Mt in the Base Case scenario, that is, the business-as-usual-case). The net economic costs, corresponding with these tax levels, expressed in present value terms, would be $27 and $49 billion, respectively, over the period 2005-2020. These economic costs are equivalent to 0.43 and 0.78 per cent of the estimated GDP of Australia. Further, most of the economic burden, in this instance, would fall on the electricity sector, particularly coal-fired electricity generators – large consumers of direct fossil fuel. On the other hand, in the case of a carbon tax based on SRP, CO2 emissions would be 172 and 116 Mt, for tax levels of $10 and $20, respectively. The corresponding net economic costs would be $47 (0.74 per cent of GDP) and $84 (1.34 per cent of GDP) billion, respectively, with significant burden felt by the commercial sector – large consumers of indirect energy and materials whose production would contribute to CO2 emissions. Next, the impacts are analysed by placing an a-priori limit on CO2 emissions from the electricity sector – equivalent to 108 per cent of the 1990 level (that is, 138 Mt), by the year 2020. Two cases are analysed, namely, early action (carbon tax introduced in 2005) and deferred action (carbon tax introduced in 2010). In the case of early action, the analysis suggests, carbon tax of $25 and $15, based on PPP and SRP, respectively, would be required to achieve the above noted emissions target. The corresponding tax levels in the case of deferred action are $51 and $26, respectively. This research also shows that the net economic costs, in the case of early action, would be $32 billion (for PPP) and $18 billion (for SRP) higher than those in the case of deferred action. However, this research has demonstrated, that this inference is largely due to the selection of particular indicator (that is, present value) and the relatively short time frame (that is, 2005–2020) for analysis. By extending the time frame of the analysis to the year 2040, the case for an early introduction of carbon tax strengthens. Overall, the analysis in this research suggests that an immediate introduction of carbon tax, based on SRP, is the most attractive approach to reduce the rate of growth of CO2 emissions from the electricity sector and to simultaneously meet economic and social objectives. If the decision to introduce such a tax is deferred, it would be rather difficult to achieve not only environmental objectives but economic and social objectives as well.

The aim of this project was to investigate methods for purification and modification of Single Wall and Multi Wall Carbon Nanotubes. Covalent and noncovalent approaches to functionalization were studied. The dispersibility, structure and electronic properties of modified tubes were characterized by Raman, UV-vis-NIR and XPS. Fluorescence, NMR and TEM were further employed to characterize the interaction between nanotubes and non-covalent modifiers. The effects of five different purification methods on the dispersibility, and degree of carboxylic acid functionality of SWCNTs, along with the level of defects on the tube side walls, and the resulting electronic properties of SWCNTs have been investigated. It was found that all oxidation treatments successfully removed metallic oxides and amorphous carbon impurities, while different oxidation treatments introduced different levels of oxidized sites on the SWCNTs. Heat treatment after oxidation eliminated some of the carboxylic groups introduced by oxidation. SWCNTs covalently functionalized by aromatic diazonium salts containing nitro, carboxylate and fluoro groups on the aromatic ring were prepared. Heating of these tubes in vacuum at 350_C for 5 h partially reversed the effects of functionalization. However, due to the low degree of functionalization achieved in the preliminary studies, the dispersibility/solubility of functionalized tubes did not greatly improve. The interaction in stable suspensions of CNTs with positively or negatively charged pyrene derivatives via noncovalent functionalization, was extensively studied. 1-pyrene methylamine hydrochloride gave most stable dispersions. 1H and 2H NMR spectroscopy of MWCNTs/1-pyrene methylamine hydrochloride dispersion in DMF-d7 showed that the broadened signals are associated with weakly or unbound pyrene, while strongly bound pyrene is not observable in solution-state NMR. The strong pyrene attachment on MWCNTs by π-π stacking can be reversed by dialysis and/or extensive washing. Biological molecules such as polypeptides and amino acids also dispersed MWCNTs into solvents by noncovalent modification. It is found that polytryptophan demonstrated the greatest ability to disperse MWCNTs. Digestion with chymotrypsin enabled polytryptophan binding to be reversed. A combination of tube cutting and non-covalent functionalization by pyrenes or peptides enables tubes to be suspended/dissolved in solvents such as DMF and ethanol, and significantly allows tubes to be manipulated for practical device applications.

AB5 hydrogen storage alloys have been intensively studied due to its superior ability to store hydrogen and release at ambient conditions. It is also a major component in the negative electrode of Ni-MH batteries. However, it has poor high rate capability and cycle life stability. Carbon nanotubes (CNTs) were found to store a tremendous amount of hydrogen, owing to the fact that they possess very large surface areas. It is because the hydrogen storage capacity is in general highly dependent on the surface area of the storing materials. The aim of this project has been to investigate the effect on electrochemical behaviours of Ab5 negative electrode in Ni-MH batteries by adding carbon nanotubes. The research also studied the influence of the ball milling treatments applied to both the Ab5 and CNTs. La0.59Ce0.27Nd0.08Pr0.06 (Ni0.76Mn0.08Al0.01Co0.15)5 AB5 alloy powder was used as active material in the negative electrode in the Ni-MH batteries, CNTs were used as additive, nickel powers as conductor in a three-electrode cell. Electrodes with compositions of AB5 + x wt.% CNTs (x=0, 5, 10) were studied. Activation, high rate capability and cycle life stability were investigated. The three-electrode cell in an open container with 6 M of KOH as electrolyte was connected to charge/discharge machine where galvanostatically charging and discharging took place. Hydrogenation of ball milled and as-received AB5 alloy powders were examined by conventional volumetric method. Morphology of AB5 and CNTs was examined by scanning electron microscopy (SEM) and transition electron microscopy (TEM), respectively. The phase identification and crystal lattice parameters were analysed by multi-purpose X-ray diffraction before and after ball milling treatments for both materials. The chemical composition of Ab5 alloy powders was tested using ICP chemical method. The results show the addition of CNTs in negative electrode in a Ni-MH battery enhanced the specific discharge capacity remarkably. A maximum discharge capacity of 252 mAh/g was observed for electrode with low energy ball-milled (LEBM) Ab5 with 5 wt.% of CNTs. This was due to the superior properties and great surface area of CNTs which allow more hydrogen to be stored and diffused onto the surface. Not only CNTs could act as a hydrogen reservoir in the negative electrode, it also acted as a conductor by building a conductive network between active material and nickel powders, and hence an increase in discharge capacity. However, the milling on CNTs alone will not improve the electrochemical properties of the electrode. In contrary, the activation profiles, high rate capability and cycle stability have been enhanced significantly when Ab5 alloy powders were ball-milled. The possible explanation is the smaller particle size and rough surface (and hence large surface area) obtained after ball milling induces a better hydrogen diffusion between the particles, as a result of shorter distance between particles after ball milling. Ball milling treatments on AB5 alloy powders did not improve the hydrogen absorption capacity. A highest value of 1.27 wt.% was observed for LEBM alloy powders. Ball milled samples have a slightly lower plateau pressure as compared with that of as-received alloy powders. In addition, only 4% of the maximum absorption capacity was lost after 10 repeated absorption and desorption cycles due to pulverisation of the particle over cycling. It can be concluded that LEBM Ab5 with addition of 5 wt.% CNTs, can significantly improve the electrochemical properties of negative electrode in Ni-MH batteries.

This thesis presents the development and application of a dynamic model of gold adsorption onto activated carbon in gold processing. The primary aim of the model is to investigate different carbon management strategies of the Carbon in Pulp (CIP) process. This model is based on simple film-diffusion mass transfer and the Freundlich isotherm to describe the equilibrium between the gold in solution and gold adsorbed onto carbon. A major limitation in the development of a dynamic model is the availability of accurate plant data that tracks the dynamic behaviour of the plant. This limitation is overcome by using a pilot scale CIP gold processing plant to obtain such data. All operating parameters of this pilot plant can be manipulated and controlled to a greater degree than that of a full scale plant. This enables a greater amount of operating data to be obtained and utilised. Two independent experiments were performed to build the model. A series of equilibrium tests were performed to obtain parameter values for the Freundlich isotherm, and results from an experimental run of the CIP pilot plant were used to obtain other model parameter values. The model was then verified via another independent experiment. The results show that for a given set of operating conditions, the simulated predictions were in good agreement with the CIP pilot plant experimental data. The model was then used to optimise the operations of the pilot plant. The evaluation of the plant optimisation simulations was based on an objective function developed to quantitatively compare different simulated conditions. This objective function was derived from the revenue and costs of the CIP plant. The objective function costings developed for this work were compared with published data and were found to be within the published range. This objective function can be used to evaluate the performance of any CIP plant from a small scale laboratory plant to a full scale gold plant. The model, along with its objective function, was used to investigate different carbon management strategies and to determine the most cost effective approach. A total of 17 different carbon management strategies were investigated. An additional two experimental runs were performed on the CIP pilot plant to verify the simulation model and objective function developed. Finally an application of the simulation model is discussed. The model was used to generate plant data to develop an operational classification model of the CIP process using machine learning algorithms. This application can then be used as part of an online diagnosis tool.