Matching Items (2)
Description
Large-scale cultivation of perennial bioenergy crops (e.g., miscanthus and switch-
grass) offers unique opportunities to mitigate climate change through avoided fossil fuel use and associated greenhouse gas reduction. Although conversion of existing agriculturally intensive lands (e.g., maize and soy) to perennial bioenergy cropping systems has been shown to reduce near-surface temperatures, unintended consequences on natural water resources via depletion of soil moisture may offset these benefits. In the effort of the cross-fertilization across the disciplines of physics-based modeling and spatio-temporal statistics, three topics are investigated in this dissertation aiming to provide a novel quantification and robust justifications of the hydroclimate impacts associated with bioenergy crop expansion. Topic 1 quantifies the hydroclimatic impacts associated with perennial bioenergy crop expansion over the contiguous United States using the Weather Research and Forecasting Model (WRF) dynamically coupled to a land surface model (LSM). A suite of continuous (2000–09) medium-range resolution (20-km grid spacing) ensemble-based simulations is conducted. Hovmöller and Taylor diagrams are utilized to evaluate simulated temperature and precipitation. In addition, Mann-Kendall modified trend tests and Sieve-bootstrap trend tests are performed to evaluate the statistical significance of trends in soil moisture differences. Finally, this research reveals potential hot spots of suitable deployment and regions to avoid. Topic 2 presents spatio-temporal Bayesian models which quantify the robustness of control simulation bias, as well as biofuel impacts, using three spatio-temporal correlation structures. A hierarchical model with spatially varying intercepts and slopes display satisfactory performance in capturing spatio-temporal associations. Simulated temperature impacts due to perennial bioenergy crop expansion are robust to physics parameterization schemes. Topic 3 further focuses on the accuracy and efficiency of spatial-temporal statistical modeling for large datasets. An ensemble of spatio-temporal eigenvector filtering algorithms (hereafter: STEF) is proposed to account for the spatio-temporal autocorrelation structure of the data while taking into account spatial confounding. Monte Carlo experiments are conducted. This method is then used to quantify the robustness of simulated hydroclimatic impacts associated with bioenergy crops to alternative physics parameterizations. Results are evaluated against those obtained from three alternative Bayesian spatio-temporal specifications.
grass) offers unique opportunities to mitigate climate change through avoided fossil fuel use and associated greenhouse gas reduction. Although conversion of existing agriculturally intensive lands (e.g., maize and soy) to perennial bioenergy cropping systems has been shown to reduce near-surface temperatures, unintended consequences on natural water resources via depletion of soil moisture may offset these benefits. In the effort of the cross-fertilization across the disciplines of physics-based modeling and spatio-temporal statistics, three topics are investigated in this dissertation aiming to provide a novel quantification and robust justifications of the hydroclimate impacts associated with bioenergy crop expansion. Topic 1 quantifies the hydroclimatic impacts associated with perennial bioenergy crop expansion over the contiguous United States using the Weather Research and Forecasting Model (WRF) dynamically coupled to a land surface model (LSM). A suite of continuous (2000–09) medium-range resolution (20-km grid spacing) ensemble-based simulations is conducted. Hovmöller and Taylor diagrams are utilized to evaluate simulated temperature and precipitation. In addition, Mann-Kendall modified trend tests and Sieve-bootstrap trend tests are performed to evaluate the statistical significance of trends in soil moisture differences. Finally, this research reveals potential hot spots of suitable deployment and regions to avoid. Topic 2 presents spatio-temporal Bayesian models which quantify the robustness of control simulation bias, as well as biofuel impacts, using three spatio-temporal correlation structures. A hierarchical model with spatially varying intercepts and slopes display satisfactory performance in capturing spatio-temporal associations. Simulated temperature impacts due to perennial bioenergy crop expansion are robust to physics parameterization schemes. Topic 3 further focuses on the accuracy and efficiency of spatial-temporal statistical modeling for large datasets. An ensemble of spatio-temporal eigenvector filtering algorithms (hereafter: STEF) is proposed to account for the spatio-temporal autocorrelation structure of the data while taking into account spatial confounding. Monte Carlo experiments are conducted. This method is then used to quantify the robustness of simulated hydroclimatic impacts associated with bioenergy crops to alternative physics parameterizations. Results are evaluated against those obtained from three alternative Bayesian spatio-temporal specifications.
ContributorsWang, Meng, Ph.D (Author) / Kamarianakis, Yiannis (Thesis advisor) / Georgescu, Matei (Thesis advisor) / Fotheringham, A. Stewart (Committee member) / Moustaoui, Mohamed (Committee member) / Reiser, Mark R. (Committee member) / Arizona State University (Publisher)
Created2018
Description
An evolving understanding of elastomeric polymer nanocomposites continues to expand commercial, defense, and industrial products and applications. This work explores the thermomechanical properties of elastomeric nanocomposites prepared from bisphenol A diglycidyl ether (BADGE) and three amine-terminated poly(propylene oxides) (Jeffamines). The Jeffamines investigated include difunctional crosslinkers with molecular weights of 2,000 and 4,000 g/mol and a trifunctional crosslinker with a molecular weight of 3,000 g/mol. Additionally, carbon nanotubes (CNTs) were added, up to 1.25 wt%, to each thermoset. The findings indicate that the Tg and storage modulus of the polymer nanocomposites can be controlled independently within narrow concentration windows, and that effects observed following CNT incorporation are dependent on the crosslinker molecular weight.
Polymer matrix composites (PMCs) offer design solutions to produce smart sensing, conductive, or high performance composites for a number of critical applications. Nanoparticle additives, in particular, carbon nanotubes and metallic quantum dots, have been investigated for their ability to improve the conductivity, thermal stability, and mechanical strength of traditional composites. Herein we report the use of quantum dots (QDs) and fluorescently labeled carbon nanotubes (CNTs) to modify the thermomechanical properties of PMCs. Additionally, we find that pronounced changes in fluorescence emerge following plastic deformation, indicating that in these polymeric materials the transduction of mechanical force into the fluorescence occurs in response to mechanical activation.
Segmented ionenes are a class of thermoplastic elastomers that contain a permanent charged group within the polymer backbone and a spacer segment with a low glass transition temperature (Tg) to provide flexibility. Ionenes are of interest because of their synthetic versatility, unique morphologies, and ionic nature. Using phase changing ionene-based nanocomposites could be extended to create reversible mechanically, electrically, optically, and/or thermally responsive materials depending on constituent nanoparticles and polymers. This talk will discuss recent efforts to utilize the synthetic versatility of ionenes (e.g., spacer composition of PTMO or PEG) to prepare percolated ionic domains in microphase separated polymers that display a range of thermomechanical properties. Furthermore, by synthesizing two series of ionene copolymers with either PEG or PTMO spacers at various ratios with 1,12-dibromododecane will yield a range of ion contents (hard contents) and will impact nanoparticle dispersion.
Polymer matrix composites (PMCs) offer design solutions to produce smart sensing, conductive, or high performance composites for a number of critical applications. Nanoparticle additives, in particular, carbon nanotubes and metallic quantum dots, have been investigated for their ability to improve the conductivity, thermal stability, and mechanical strength of traditional composites. Herein we report the use of quantum dots (QDs) and fluorescently labeled carbon nanotubes (CNTs) to modify the thermomechanical properties of PMCs. Additionally, we find that pronounced changes in fluorescence emerge following plastic deformation, indicating that in these polymeric materials the transduction of mechanical force into the fluorescence occurs in response to mechanical activation.
Segmented ionenes are a class of thermoplastic elastomers that contain a permanent charged group within the polymer backbone and a spacer segment with a low glass transition temperature (Tg) to provide flexibility. Ionenes are of interest because of their synthetic versatility, unique morphologies, and ionic nature. Using phase changing ionene-based nanocomposites could be extended to create reversible mechanically, electrically, optically, and/or thermally responsive materials depending on constituent nanoparticles and polymers. This talk will discuss recent efforts to utilize the synthetic versatility of ionenes (e.g., spacer composition of PTMO or PEG) to prepare percolated ionic domains in microphase separated polymers that display a range of thermomechanical properties. Furthermore, by synthesizing two series of ionene copolymers with either PEG or PTMO spacers at various ratios with 1,12-dibromododecane will yield a range of ion contents (hard contents) and will impact nanoparticle dispersion.
ContributorsWang, Meng, Ph.D (Author) / Green, Matthew D (Thesis advisor) / Green, Alexander (Committee member) / Yarger, Jeffery (Committee member) / Arizona State University (Publisher)
Created2019