No description available.

No description available.

Polymer Light-Emitting Electrochemical Cells (PLECs) are solid state devices based on the in situ electrochemical doping of the luminescent polymer and the formation of a p-n junction where light is emitted upon the application of a bias current or voltage. PLECs answer the drawbacks of polymer light-emitting diodes as they do not require an ultra-thin active layer nor are they reliant on low work function cathode materials that are air unstable. However, because of the dynamic nature of the doping, they suffer from slow response times and poor stability over time. Frozen-junction PLECs offer a solution to these drawbacks, yet they are impractical due to their sub-ambient operation temperature requirement. Our work presented henceforth aims to achieve room temperature frozen-junction PLECS. In order to do that we removed the ion solvating/transporting polymer from the active layer, resulting in a luminescent polymer combined solely with a salt sandwiched between an ITO electrode and an aluminum electrode. The resulting device was not expected to operate like a PLEC due to the absence of an ion-solvating and ion-transporting medium. However, we discovered that the polymer/salt devices could be activated by applying a large voltage bias, resulting in much higher current and luminance. More important, the activated state is quasi static. Devices based on the well-known orange-emitting polymer MEH-PPV displayed a luminance storage half-life of 150 hours when activated by forward bias (ITO biased positively with respect to the aluminum) and 200 hours when activated by reverse bias. More remarkable yet, devices based on a green co-polymer displayed no notable decay in current density or luminance even after being stored for 1200 hours at room temperature! PL imaging under UV excitation demonstrates the presence of doping. These devices are described herein along with an explanation of their operating mechanisms. / Thesis (Master, Physics, Engineering Physics and Astronomy) -- Queen's University, 2013-11-29 17:40:19.152

This thesis focused on the fate of nitrogen and trace metals species from combustion and gasification of biomass. The effect of process parameters on the release of these species during pilot-scale combustion and gasification of biomass was investigated and the information used to identify methods for the reduction of these species. The investigation focused on Miscanthus and Dried distillers’ grains with solubles (DDGS). The pilot-scale test rigs used were a fluidised-bed combustor and a fixed-bed downdraft gasifier. The two fuels were analysed by means of proximate, ultimate and ash analysis. The process parameters monitored were temperatures, gas flow, gas composition and ash composition and the process parameters studied are bed temperature and equivalence ratio. The different nitrogen content of the two fuels plays an important role in the emission. Both bed temperature and air to fuel ratio have demonstrated to have an important influence in the release of nitrogen oxides in combustion and ammonia in gasification, therefore they can be used to mitigate the emission of these species in the flue gas. Both processes are affected by the high alkali metals content of the fuels for the tendency to form low melting composites. Differences have been highlighted in the metal distribution between combustion and gasification. The different nitrogen and ash content of the two fuels make the results of the present thesis applicable to predict the behaviour of other biomass fuels according to the fuel characteristics. The scale of the tests performed allowed highlighting which methods can be used to control the emission of nitrogen and trace metal species. Moreover, the investigation highlighted major drawbacks in the use of biomass fuels in both fluidised bed and fixed bed technology due to ash properties.

The study of brain temperature is important for a number of clinical conditions such as stroke, traumatic brain injury, schizophrenia and birth asphyxia (for neonates). A direct method to estimate brain temperature non-invasively will allow assessment of brain thermoregulation and its variation in clinical conditions. Magnetic resonance imaging is a powerful technique widely used for diagnosis of a range of neurological conditions. All magnetic resonance procedures involve manipulation of the hydrogen nuclei in the water molecules of the human body. The resonance frequency of the water molecules is temperature dependent, thus MR thermometry is a powerful tool for non-invasive temperature measurement. Using internal reference MR spectroscopic imaging (MRSI), absolute brain temperature maps can be estimated. However a number of temperature independent factors influence MRSI data acquisition, thus a thorough validation is necessary and is the focus of this PhD study. In this PhD study using phantom (test object) studies it was shown that optimization of the MRSI pulse sequence is necessary to reduce systematic error in temperature maps and extensive in-vitro validation of MRSI temperature mapping was performed. A custom made temperature-controlled phantom was designed for this purpose and is presented in this thesis. MRSI data acquired from healthy (young and elderly) volunteers was employed to assess regional brain temperature variations and repeatability. Finally, the feasibility of employing fast echo planar spectroscopic imaging for volumetric MRSI temperature mapping will be presented in this thesis.

Daily changes in body temperature as well as physical activeness from mating to pregnancy were illustrated in MF1 mice.  Body temperature and physical activity gradually declined as pregnancy advanced while energy intake and body mass increased in late pregnancy.  Diurnal and nocturnal locomotor activity and body temperature were significantly lower in late pregnancy than in non-reproductive and mating phases. Despite low physical activity, inactive body temperature was relatively high through late pregnancy.  This suggests that pregnant mice tend to increase thermogenesis against a drop of body temperature. Energy intake increased remarkably after parturition and reached a plateau in late lactation suggesting a limit of energy intake.  Litter size and litter mass significantly influenced maternal energy intake and body mass (<i>P</i>&lt;0.05). However, daily pup mass gain declined at the peak lactation when maternal energy intake was limited.  Body temperature rose sharply after parturition.  Body temperature during the day considerably increased.  Consequently, lactating mice faced a constantly high body temperature through the day despite lower activity levels. There were no trends that litter size and litter mass stimulated maternal body temperature and physical activity on average through lactation. Body temperature during suckling inside the nest increased towards the end of suckling.  However, no significant increase in body temperature was found between 20 and 1 minutes before terminating suckling bouts. Dams that raised larger litters encountered higher body temperature while suckling inside the nest, suggesting that suckling offspring considerably contributed to heat retention in mothers.  Suckling offspring appeared to prevent mothers from releasing cumulative heat, although the significance of suckling behaviour on overheating was smaller than that of metabolic heat generation.

Human Systems Integration Report / This study examined the effects of noise, temperature, humidity, motion and light on the sleep patterns of the crew of the HSV-2 SWIFT during Gulf of Mexico Exercise (GOMEX) 05-1. HSV-2 SWIFT was chosen for this study to examine crew sleep on an unconventional hull type manned with a small crew. Noise dosimeters, temperature and humidity monitors, actiwatches and questionnaires were used to quantify the data. With the exception of light, the independent variables did not have significant effect upon participant sleep. This is likely due to the limited range of the independent variables and the small number of participants in this study. There were two findings in this study; the relationship between the demographic variable sea time and participant sleep and the relationship between the independent variable light and participant sleep. Due to the limitations in the current study, it is recommended that further studies be conducted in more extreme operational environments. Additionally, studies such as the one discussed in this thesis, should be completed on different platforms to determine the differences in environmental factors that affect sleep between hull types so that the results can be applied to future vessel design.

Approved for public release, distribution unlimited / The flow patterns of two oil mist nozzles used in rotor blade excitation experiments were characterized using a Laser Doppler Velocimeter (LDV). Both nozzles were operated in a vacuum test chamber and velocity measurements were taken at three axial distances from the nozzle exit, at three or four different pressures, at three different temperatures. The 4 gallon per hour "mini-mist" nozzle produced a "referenced velocity" consistent with a hollow cone at each axial location, pressure, and temperature. The temperature of the oil flowing through the 4 gallon per hour nozzle did not affect the nozzle's performance. The 6 gallon per hour "standard" nozzle produced a "solid" cone structure at each axial location, pressure, and temperature. The temperature of the oil flowing through the 6 gallon per hour nozzle did affect the nozzle's performance. The spray pattern quantification can be used to design blade excitation experiments in high cycle fatigue (HCF) vacuum spin tests.

The three dimensional distribution of clouds is of great interest to the Air Force, and to the aviation community in general. The Stochastic Cloud Forecast Model (SCFM) is a novel, global cloud model currently operated at the Air Force Weather Agency (AFWA) which diagnoses cloud cover statistically using a minimal set of predictors from global numerical forecasts. Currently the four predictors are pressure, temperature, vertical velocity, and relative humidity. In this thesis, 330 sets of predictors are compared in the SCFM-R, a research version of the model programmed for this thesis. There are some differences in the SCFM and the SCFM-R that yield important information. It is found that the SCFM is very sensitive to how cloud cover in the boundary layer is diagnosed. An analysis of the diagnosis method used to initialize the model revealed a bias for over-diagnosing cloud at lower levels and under-diagnosing cloud at upper levels. Also, it is recommended that AFWA consider exchanging temperature for another predictor more related to moisture, such as cloud water, and that relative humidity is included as relative humidity to the fourth power. Other recommendations include improving the method for diagnosing cloud cover in the boundary layer and improving the model initial condition.

Soybean (Glycine max L. Merr.) planting date and plant density are agronomic decisions made simultaneously at the beginning of the growing season that can be used to maximize yield and economic return. Research on these basic soybean agronomic decisions must be conducted to support the expansion of soybean production in northern growing regions of the Northern Great Plains (NGP). The objectives of this study were to evaluate the effects of planting dates based on soil temperature on soybean emergence, maturity, and yield for short and long season varieties in Manitoba, and to determine optimum soybean plant density for early to very late planting dates in northern growing regions of the NGP. In the first experiment, calendar date had a greater influence than soil temperature at planting on soybean yield. Soybean yield declined with later planting rather than increasing soil temperature at planting. The earliest planting dates resulted in the greatest soybean yields. In the second experiment, soybean yield-density relationships were responsive to planting date. Yield-density relationships formed early/mid (May 4 to 26) and late/very late (June 2 to 23) planting date groups for combined site years. Early/mid planting dates resulted in greater maximum yields. According to the yield-density model, true yield maximization did not occur for any planting dates and site years within the range of plant densities tested in this field study. Soybean economic optimum seed densities (EOSDs) were much lower than predicted plant densities that maximized yield. Soybean EOSDs were identified as 492,000 and 314,000 seeds ha-1 by marginal cost analysis for early/mid and late/very late planting, respectfully. These values were sensitive to changes in soybean grain price and seed cost. Thus, growers need to adjust EOSDs for changes in price and cost. A combined analysis of soybean yields from both experiments using similar target plant densities determined that a significant negative linear relationship existed between soybean yield and planting date. The greatest soybean yields resulted from early planting and declined by 16 kg ha-1 for each one-day delay in planting from Apr 27 to June 16. However, yield responses varied among site years. The overall recommendation from this study would be to plant soybeans during the month of May at a profit-maximizing seed density, accounting for fluctuating grain price and seed cost. / May 2017