Given that greater than 15% of the world’s population currently relies on snow melt for drinking water, hydrological modeling of landscapes subject to routine snowfall is becoming increasingly important. Up to 60% of snowfall in forested terrain is intercepted by the forest canopy. Therefore, tree interception parameters that quantify how much water or snow a tree collects during a precipitation event are critical for understanding temporal and spatial water fluxes, which most notably determine when and in what volume water is delivered to communities. This project is evaluating whether a new video processing technique can be used to reliably and accurately identify snow interception parameters for coniferous trees. In particular, this project seeks to learn whether the resonant frequency of swaying snow-loaded trees, as determined by processing time lapse videos, can be used to compute the mass of snow in a tree. Having shown that video processing can correctly infer the sway frequency of a tree, we are now deploying cameras on Snoqualmie Pass to observe tree sway events concurrent with snow loading events. To date, we have four cameras monitoring a tree and the snow levels around it. Pending further video data collection, we will begin applying the tree sway processing algorithm, which uses FFT processing to compute changes in tree sway frequency and corresponding changes in tree mass. If successful, this method may improve tree interception parameterization and therefore hydrological forecasting. 

Taking advantages of advances in controlled drug delivery, modern medicine now has the ability to access and treat disease within many parts of the human body not previously possible. Where current medicine lacks, however, is the ability to treat complex disorders that require specific control over the timing, order, and sequential dosing of active therapeutics. To access these systems, we have attempted to create a library of light-sensitive compounds that will release protein therapeutics orthogonally in the presence of different wavelengths of tissue-penetrating light. These compounds are based on ruthenium polypyridyl linker complexes and can be structurally tuned to respond to visible and NIR light irradiation, leading to exchange of a ligand with water and rapid cleavage. We have modified the complexes with a reactive azide handle for site-specific incorporation into hydrogel biomaterials that can be transplanted to or formulated within specific bodily locations. Varying the ligands in the complex gives rise to different photocleavable crosslinkers that cleave in response to up to 715 nm light, well into the therapeutic window for in vivo applications. In this poster, I will describe the synthesis and characterization of one model Ru-based linker (RuPhen), including its photolysis, stability, and applications of the complex in the development of dynamic biomaterials.

 Photodetectors are among the most widespread of sensing devices, with use in biological, electronic, and environmental systems. With greater miniaturization brought about by improvements in fabricating nanoscale structures, state of the art detectors can be implemented in new contexts and applications. They also offer advantages such as greater tunability, less noise interference, and higher speed compared to larger counterparts. Optimizing these fabrication techniques, however, still presents a challenge for manufacture in industry as well as for best practice in lab settings. Two methods, nanoimprint lithography and drop coat deposition, were compared for their performance at creating a nanopatterned Al electrode layer in a photodetecting device. The effect of O2 plasma etching during the fabrication process was also investigated. Scanning electron microscope imaging was used to evaluate the imprint layer prior to deposition of Al for comparison between methods. Results showed that after optimized etching conditions, drop coat deposition provided greater coverage and pattern spacing, but with less uniformity in the pattern. Further study should be directed towards the effect of nanoparticle type used in drop coat deposition, along with the use of other gas species in plasma etching.

Conjugated polymers (CPs) are used in a wide variety of organic electronics such as photovoltaics, organic thin-film transistors (OFET), and flexible displays because of the increased flexibility of polymer-based electronics. However, CPs often have significant downsides such as being prone to environmental degradation, lacking mechanical robustness, and being overall very expensive to create and use. To combat these limitations, CPs can be blended with lower-cost commodity engineering plastics (CEP), such as polystyrene, to create a blended composite that forms nanoscale structures of CP in a CEP matrix. To characterize the blends, we use small-angle neutron scattering (SANS) and wide-angle x-ray scattering (WAXS), which are techniques that provide information in the form of a scattering pattern. After data reduction and background removal, SANS data can then be modeled to extract information about the structures that develop from 1 nm – 1000 nm. We have decided to focus on using the sphere, fractal, and parallelepiped models since those geometries often form from self-assembled CPs. The scattering pattern from WAXS can be used to determine the preferential growth direction of CP self-assembly, either along the pi-pi stacking or lamellar directions. Through the combined use of these techniques, we are able to characterize structural dependence on the choice of solvent including both moderate and good solvents for the CPs. We also tested different side-chain lengths on the CP which will affect solubility and the ability to self-assemble. From these experiments, we found that a moderate solvent (such as toluene) will encourage nanofiber formation growth at lower concentrations of CP. Control over nanofiber formation could potentially lead to more favorable electrical performance for these materials. Conductivity and rheology tests will be conducted which will allow us to determine how much of an effect solvent choice has on the mechanical and conductive properties of these blends.

Planning for water resources management (WRM) requires the best available predictions of streamflow. We want to provide actionable predictions that improve WRM as climate change alters streamflows. However, the computer models of these systems result in imperfect predictions despite our best efforts to match observations. These systematic errors reduce the usefulness of model outputs. To improve our ability to plan for WRM, we apply statistical correction techniques to model outputs so that they agree better with observations. The Columbia River Basin, a major river basin in the Northwestern United States, is heavily regulated for a large number of competing uses. In this project, we focus on the Yakima river basin in central Washington, a subbasin of the Columbia River, and use it as a case study for evaluating multiple statistical correction techniques. By comparing streamflow observations to simulations for the same periods we can develop statistical corrections. The application of these statistical corrections is often referred to as “post-processing”. Post-processing adjusts predictions based on previous knowledge of the region as well as the historical observations.These post-processing techniques can then be applied at locations and times without observations. As part of this project, we developed a toolkit for the evaluation of different post-processing techniques. We explore which measures and visualization techniques are adequate at describing key aspects of the streamflow simulations. Our toolkit builds on open source technologies that will allow researchers to reliably measure the statistical performance of these post-processing techniques. Evaluation is performed through exploring different statistical metrics and building a suite of summarizing plotting capabilities in a standalone, open source Python package.

Every year 26,000 pounds of human fecal waste left by Mt. Everest climbers is disposed in pits near Gorak Shep (elevation 16,942 ft.), a village close to Everest Base Camp in Nepal. This waste degrades the environment and pollutes local water resources. Left untreated, Mount Everest faces environmental collapse. The Mount Everest Biogas Project (MEBP) intends to turn this waste into renewable biogas energy by adapting anaerobic digester technology to the extreme climate of Mount Everest. All MEBP digester materials must be carried by foot, including the biologic seed for starting the digester. Thus, identification of a near-by seed is a critical step in the project’s success. In this project, I compare digester seeds from cold-adapted environments and pre-existing anaerobic digesters through bench-scale mesocosm incubation at 10℃, the temperature at Gorak Shep, to help inform seed selection for the MEBP. This project addresses the question of how low temperatures impact anaerobic digestion, a form of biological wastewater treatment traditionally conducted at 30℃. My project explores the hypothesis that methanogenic microbial communities pre-adapted to cold temperatures will allow a more stable start-up of a cold-temperature anaerobic digester than a warm-temperature digester seed. I apply standardized reactor monitoring techniques to measure digester health and performance during start-up. Genetic sequencing data is used to assess microbial community composition and evolution throughout the incubation period to compare each seed. This information will contribute to understanding if one seed source is more favorable for MEBP digester. Developing a finer understanding of anaerobic digestion is a significant field of research in environmental engineering. My project helps elucidate questions regarding microbial community function and evolution during digester start-up and how these parameters relate to digester performance in extreme climates.