Doctor of Philosophy

dissertationThis dissertation is focused on the radiolysis of plutonium tetrafluoride under long-term storage conditions. Amorphous plutonium tetrafluoride samples were subjected to thermogravimetric/differential thermal analyses, X-ray diffraction analyses, and muffle furnace annealing experiments in argon gas to investigate the totality of this radiolysis, and the effect that thermal annealing has on its reordering. There are three main areas of focus presented in this work that were used to investigate these phenomena. First, thermogravimetric/differential thermal analyses and X-ray diffraction analyses were used to uncover the possible mechanisms responsible for the amorphization in plutonium tetrafluoride through pre-annealing and post-annealing analyses on milligram samples. Second, gram samples of the plutonium tetrafluoride were annealed within flowing argon gas for short time durations. These samples were analyzed with X-ray diffraction to determine the rapidity of recrystallization in plutonium tetrafluoride. Third, gram samples of the plutonium tetrafluoride were annealed within flowing argon gas for long time durations. These samples were analyzed with X-ray diffraction to determine the effect of time at temperature on the recrystallization in plutonium tetrafluoride. The results of these three investigations are that plutonium tetrafluoride that has been stored near 50 years is amorphous. Its amorphization appears to be a result of self-induced alpha radiolysis from the decay of the plutonium isotopes. The alpha particle and the recoil nucleus of this decay look to be the primary driver of this radiolysis through Frenkel type defects and F-center formation. Radiolysis in plutonium tetrafluoride does not follow the crystal lattice parameter expansion as seen in plutonium dioxide. The crystallite size in amorphous plutonium tetrafluoride has been shown to increase under annealing conditions, and this recrystallization begins near 400°C under short and long time scales (minutes to hours) in argon gas

Master of Science

thesisThere are significant challenges to successfully monitoring multiple processes within a nuclear reactor facility. The evidence for this observation can be seen in the historical civilian nuclear incidents that have occurred with similar initiating conditions and sequences of events. Because there is a current lack within the nuclear industry, with regards to the monitoring of internal sensors across multiple processes for patterns of failure, this study has developed a program that is directed at accomplishing that charge through an innovation that monitors these systems simultaneously. The inclusion of digital sensor technology within the nuclear industry has appreciably increased many computer systems' capabilities to manipulate sensor signals, thus making the satisfaction of these monitoring challenges possible. One such manipulation to signal data has been explored in this study. The Nuclear Reactor Condition Analyzer (NRCA) program that has been developed for this research, with the assistance of the Nuclear Regulatory Commission's Graduate Fellowship, utilizes one-norm distance and kernel weighting equations to normalize all nuclear reactor parameters under the program's analysis. This normalization allows the program to set more consistent parameter value thresholds for a more simplified approach to analyzing the condition of the nuclear reactor under its scrutiny. The product of this research provides a means for the nuclear industry to implement a safety and monitoring program that can oversee the system parameters of a nuclear power reactor facility, like that of a nuclear power plant

Advancing the integration of spatial data to map human and natural drivers on coral reefs

<div><p>A major challenge for coral reef conservation and management is understanding how a wide range of interacting human and natural drivers cumulatively impact and shape these ecosystems. Despite the importance of understanding these interactions, a methodological framework to synthesize spatially explicit data of such drivers is lacking. To fill this gap, we established a transferable data synthesis methodology to integrate spatial data on environmental and anthropogenic drivers of coral reefs, and applied this methodology to a case study location–the Main Hawaiian Islands (MHI). Environmental drivers were derived from time series (2002–2013) of climatological ranges and anomalies of remotely sensed sea surface temperature, chlorophyll-<i>a</i>, irradiance, and wave power. Anthropogenic drivers were characterized using empirically derived and modeled datasets of spatial fisheries catch, sedimentation, nutrient input, new development, habitat modification, and invasive species. Within our case study system, resulting driver maps showed high spatial heterogeneity across the MHI, with anthropogenic drivers generally greatest and most widespread on O‘ahu, where 70% of the state’s population resides, while sedimentation and nutrients were dominant in less populated islands. Together, the spatial integration of environmental and anthropogenic driver data described here provides a first-ever synthetic approach to visualize how the drivers of coral reef state vary in space and demonstrates a methodological framework for implementation of this approach in other regions of the world. By quantifying and synthesizing spatial drivers of change on coral reefs, we provide an avenue for further research to understand how drivers determine reef diversity and resilience, which can ultimately inform policies to protect coral reefs.</p></div

Estimating Nearshore Fisheries Catch for the Main Hawaiian Islands

M.S. University of Hawaii at Manoa 2015.Includes bibliographical references.Nearshore fisheries in the main Hawaiian Islands (MHI) have great economic, recreational, and cultural value. Currently, information on these fisheries is disparate and incomplete, creating a challenge for effective management. This study combines and synthesizes several commercial and small-scale non-commercial datasets to estimate the total catch of nearshore fisheries in the MHI. Data used came from catch reports submitted by commercial fishers, a statewide recreational fisheries survey, and 12 small-scale, non-commercial creel surveys conducted at sites in Kauai, Oahu, Lanai, Maui, and Hawaii. Results include an estimated range for total nearshore catch between 1,441,407 and 7,739,548 kg/yr., with the non-commercial catch between 9 and 53 times the reported commercial reef fish catch. Additionally this study provides a comprehensive overview of the MHI nearshore fishery, including best-available estimates of fishery data such as catch-per-unit-effort (CPUE), gear-preference and participation rates, with data broken out at island-scale. This is likely more appropriate for management purposes than the statewide level at which nearshore catch data is currently reported in the MHI

Estimating nearshore coral reef-associated fisheries production from the main Hawaiian Islands

<div><p>Currently, information on nearshore reef-associated fisheries is frequently disparate or incomplete, creating a challenge for effective management. This study utilized an existing non-commercial fishery dataset from Hawaiʻi, covering the period 2004–13, to estimate a variety of fundamental fishery parameters, including participation, effort, gear use, and catch per unit effort. We then used those data to reconstruct total catches per island. Non-commercial fisheries in this case comprise recreational, subsistence, and cultural harvest, which may be exchanged, but are not sold. By combining those data with reported commercial catch data, we estimated annual catch of nearshore reef-associated fisheries in the main Hawaiian Islands over the study period to be 1,167,758 ± 43,059 kg year^-1 (mean ± standard error). Average annual commercial reef fish catch over the same time period—184,911 kg year^-1—was 16% of the total catch, but that proportion varied greatly among islands, ranging from 23% on Oʻahu to 5% on Molokaʻi. These results emphasize the importance of reef fishing in Hawaiʻi for reasons beyond commerce, such as food security and cultural practice, and highlight the large differences in fishing practices across the Hawaiian Islands.</p></div

Recreational CPUE per island, platform, and gear.

<p>Data shown are mean and standard error (SE) of CPUE by island for each combination of island, platform, and gear in intercept surveys by MRIP between 2004–13. Eqs <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0195840#pone.0195840.e003" target="_blank">3</a> and <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0195840#pone.0195840.e006" target="_blank">4</a> were used to calculate mean and variance of CPUE values. Reef fishes are taxa as defined in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0195840#pone.0195840.s001" target="_blank">S1 Table</a>. Note that as there were no intercept surveys on Lānaʻi, CPUE data for there are assumed to be the average from other islands. Lānaʻi CPUE SE was generated using the average precision (SE/mean) from all other islands. The number of catch interviews per combination of island, platform, and gear is given in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0195840#pone.0195840.s003" target="_blank">S3 Table</a>.</p

Recreational fishing effort.

<p>Data come from MRIP telephone surveys in 2004–2013. Data are summarized as mean and standard deviation of values per wave (i.e., for the 60 2-month periods in that 10-year period). Participation rate is proportion of households in which someone fished in the preceding wave; trips per household represents the number of fishing trips per fishing household in that period. Total # trips was calculated using <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0195840#pone.0195840.e001" target="_blank">Eq 1</a>. Number of households ranged from 63,209 to 64,909 for Hawaiʻi Island, 21,968 to 22,390 for Kauaʻi, 1,068 to 1,074 for Lānaʻi, 43,505 to 49,080 for Maui, 2,525 to 2,561 for Molokaʻi, and 303,794 to 309,803 for Oʻahu.</p

Recreational trip types.

<p>Data come from MRIP telephone surveys in 2004–2013. Data are summarized as mean and standard deviation per island and wave of the percentages of fishing trips per combination of platform (boat or shore) and gear as defined by <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0195840#pone.0195840.e002" target="_blank">Eq 2</a>. Gears have been pooled into line, net, spear, or not-reef (offshore, pelagic or bottomfish fishing), as described in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0195840#pone.0195840.s002" target="_blank">S2 Table</a>.</p

Total catch calculations from MRIP surveys.

<p>Information came from telephone effort surveys (outlined in green) or intercept surveys (outlined in orange), and correspond with the variables in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0195840#pone.0195840.e007" target="_blank">Eq 5</a>.</p

Total catch by island in kg year^-1.

<p>Values show estimates of commercial and non-commercial catch for each island, as well as a statewide total and combined catch. Yearly estimates are from the time frame of 2004–2013. ‘Other’ for CML comes largely from Niʻihau, which has a resident population of ~100 people and is ~30km from Kauaʻi; but also includes catch from offshore banks and pinnacles.</p