Land use greenhouse gas emissions from conventional oil production and oil sands

Debates surrounding the greenhouse gas (GHG) emissions from land use of biofuels production have created a need to quantify the relative land use GHG intensity of fossil fuels. When contrasting land use GHG intensity of fossil fuel and biofuel production, it is the energy yield that greatly distinguishes the two. Although emissions released from land disturbed by fossil fuels can be comparable or higher than biofuels, the energy yield of oil production is typically 2-3 orders of magnitude higher, (0.33-2.6, 0.61-1.2, and 2.2 5.1 PJ/ha) for conventional oil production, oil sands surface mining, and in situ production, respectively). We found that land use contributes small portions of GHGs to life cycle emissions of California crude and in situ oil sands production ( <0.4% or < 0.4 gCO?e/MJ crude refinery feedstock) and small to modest portions for Alberta conventional oil (0.1-4% or 0.1-3.4 gCO?e/MJ) and surface mining of oil sands (0.9-11% or 0.8-10.2 gCO?e/MJ).Our estimates are based on assumptions aggregated over large spatial and temporal scales and assuming 100% reclamation. Values on finer spatial and temporal scales that are relevant to policy targets need to account for site-specific information, the baseline natural and anthropogenic disturbance.     

Comparative modeling approaches for personal exposure to particle-associated PAH

Several models for simulation of personal exposure (PE) to particle-associated polycyclic aromatic hydrocarbons (PAH) have been developed and tested. The modeling approaches include linear regression models (Model 1), time activity weighted models (Models 2 and 3), a hybrid model (Model 4), a univariate linear model (Model 5), and machine learning technique models (Model 6 and 7). The hybrid model (Model 4), which utilizes microenvironment data derived from time-activity diaries (TAD) with the implementation of add-on variables to account for external factors that might affect PE, proved to be the best regression model (R(2) for B(a)P = 0.346, p < 0.01; N = 68). This model was compared with results from two machine learning techniques, namely decision trees (Model 6) and neural networks (Model 7), which represent an innovative approach to PE modeling. The neural network model was promising in giving higher correlation coefficient results for all PAH (R(2) for B(a)P = 0.567, p < 0.01; N = 68) and good performance with the smaller test data set (R(2) for B(a)P = 0.640, p < 0.01; N = 23). Decision tree accuracies (Model 6) which assess how precisely the algorithm can determine the correct classification of a PE concentration range indicate good performance, but this is not comparable to the other models through R(2) values. Using neural networks (Model 7) showed significant improvements over the performance of hybrid Model 4 and the univariate general linear Model 5 for test samples (not used in developing the models). The worst performance was given by linear regression Models 1 to 3 based solely on home and workplace concentrations and time-activity data.     

A structure oriented compact thermal model for multiple heat source ASICs

This paper proposes a methodology for the extraction of a compact thermal model for multiple heat source devices, which can be used for estimation of the overall temperature field. This extraction is based on the physical parameters as well as on the layout and the packaging of the device. The model directly represents the regions surrounding a source by a resistance network containing the specific parameters of source and chip in analytical form, so that it is easy to vary parameters like power dissipation and thermal conductivity within a wide range. In order to obtain a good and fast approximation of the continuous case, the shape of the volume elements represented by a node in the network is chosen regarding the direction of the heat flow within these elements. Therefore these volume elements are not rectangular but pyramid- or parallelogram-like structures. The temperature fields of multiple sources add up for the total temperature distribution, by the use of a matrix field representation.     

Quantitative study of shrinkage and warpage behavior for microcellular and conventional injection molding

This research investigated the effects of processing conditions on the shrinkage and warpage (S&W) behavior of a box‐shaped, polypropylene part using conventional and microcellular injection molding. Two sets of 2^6‐1 fractional factorial design of experiments (DOE) were employed to perform the experiments and proper statistical theory was used to analyze the data. After the injection molding process reached steady state, molded samples were collected and measured using an optical coordinate measuring machine (OCMM), which had been evaluated using a proper R&R (repeatability and reproducibility) measurement study. By analyzing the statistically significant main and two‐factor interaction effects, the results show that the supercritical fluid (SCF) content (nitrogen in this case, in terms of SCF dosage time) and the injection speed affect the S&W of microcellular injection molded parts the most, whereas pack/hold pressure and pack/hold time have the most significant effect on the S&W of conventional injection molded parts. Also, this study quantitatively showed that, within the processing range studied, a reduction in the S&W could be achieved with the microcellular injection molding process. POLYM. ENG. SCI., 45:1408–1418, 2005. © 2005 Society of Plastics Engineers     

Optical qubit generation by state truncation using an experimentally feasible scheme

Generation of arbitrary superposition of vacuum and one-photon states using a quantum scissors device (QSD) is studied. The device allows the preparation of states by truncating an input coherent light. Optimum values of the intensity of the coherent light for the generation of any desired state using the experimentally feasible QSD scheme are found.     

Release profile and characteristics of electrosprayed particles for oral delivery of a practically insoluble drug

Poly(lactic-co-glycolic acid) (PLGA) microspheres containing celecoxib were prepared via electrospraying, and the influence of three processing parameters namely flow rate, solute concentration and drug loading, on the physico-chemical properties of the particles and the drug-release profile was studied. Microspheres with diameters between 2 and 8 μm were produced and a near-monodisperse size distribution was achieved (polydispersivity indices of 6–12%). Further, the inner structure of the particles showed that the internal porosity of the particles increased with increasing solvent concentration. X-ray powder diffraction (XRPD) analysis indicated that the drug was amorphous and remained stable after eight months of storage. Drug release was studied in USP 2 (United States Pharmacopeia Dissolution Apparatus 2) dissolution chambers, and differences in release profiles were observed depending on the parametric values. Changes in release rate were found to be directly related to the influence of the studied parameters on particle size and porosity. The results indicate that electrospraying is an attractive technique for producing drug-loaded microspheres that can be tailored towards an intended drug-delivery application. Compared with the more conventional spray-drying process, it provides better control of particle characteristics and less aggregation during particle formation. In particular, this study demonstrated its suitability for preparing capsules in which the drug is molecularly dispersed and released in a sustained manner to facilitate improved bioavailability.