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Helium recovery from natural gas over porous organic cage membranesAlthough helium is a valuable inert gas available in abundance in the earth's atmosphere, the major source of helium is from natural gas reservoirs. Membrane based separation processes pose many advantages like being cost effective and non-energy intensive. In this current study, we have successfully demonstrated the synthesis of continuous Porous Organic Cage: CC3 membranes to separate equimolar helium methane mixture with permeance of 4.45 x 10−7 mol/ (m2 s Pa) and separation selectivity (α) as high as 8. We also compared the diffusion coefficients of the gases through the membrane to evaluate the dominant mechanism for separation. Lastly, we compared the performance of our membranes to the state-of-the-art membranes with the help of a Robeson plot and found that our membranes outperformed the upper bound.
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Understanding charge carrier mobility in Hg₂GeTe₄High charge carrier mobility in semiconductor materials is desirable across a broad range of fields ranging from light-emitting devices to thermoelectrics. Electronic mobility is driven by both the intrinsic electronic band structure of the material as well as the energy dependent electron scattering mechanisms. Semiconductors with excellent mobility span a large chemical space: transparent conductor CdO, topological insulator HgTe, and Zintl compound KAlSb4. Therefore, engineering high mobility from chemistry alone is difficult if not impossible. Relating chemistry and synthetic processing to their impact on mobility is highly desirable, but experimentally difficult. Adding a fourth thermomagnetic measurement, the Nernst coefficient, to the traditional thermoelectric transport measurement suite (resistivity, Hall coefficient, Seebeck), allows the experimentalist to derive a carrier lifetime/scattering parameter as a function of temperature. We design a custom apparatus to measure the Nernst effect and perform initial model measurements to address the question of what scattering mechanisms limit the mobility of several potential thermoelectric materials. In our design, we test different sample and sample holder geometries to optimize reproducibility. For the model materials we measure the Nernst signal at low magnetic field (µB < 1) in addition to traditional Hall coefficient, Seebeck, and resistivity. We employ the method of four coefficients to determine four electronic parameters: µ, n, m*DOS, and λ (scattering factor). By utilizing the method of four coefficients, we can decouple effects from electronic band structure from energy-dependent scattering effects, and therefore design optimal thermoelectric materials and validate the scattering predictions from computational methods.
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Applications of post-quantum cryptography - survey and application of machine learningQuantum Computing poses a considerable threat in the world of cyber security. Policy makers are largely unprepared for a post-quantum world, significantly due to a lack of understanding and awareness. The goal of this paper is to improve understanding and provide a new and effective way to analyze post-quantum cryptography, for researchers and security engineers alike. This is done by providing a background of quantum computing, a survey of the state of technologies and relevant policies, and a novel application of machine learning to perform analysis of quantum-ready encryption. The machine learning research will provide a Multinominal Naïve Bayes for discrete analysis of the RSA and CRYSTAL-Kyber encryptions.
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Reversible solid oxide electrochemical system as seasonal energy storage in ultra-high renewable energy grid scenariosThe electrochemical production of hydrogen by surplus variable renewable energy (VRE) can reduce the cost of future energy systems. Supported by favorable electric grid conditions and increasing research and development investments, large-scale power-to-gas (P2G) plants are increasingly being deployed worldwide. Techno-economic and energy planning analyses involving hydrogen production and energy storage typically take either "price-taker" or "production cost" modeling approaches, with the "price-taker" approach being predominant. However, given the increasing development and deployment of P2G plants, price-taker models based on the fundamental assumption that the presence of an individual P2G plant will not affect electric grid conditions are no longer valid. To address this issue, the present research uses a production cost model that minimizes the electric grid's total energy generation cost to capture the benefits of operating a utility-scale, grid-connected reversible electrolyzer plant. A generic methodology to analyze seasonal energy storage operating in an ultra-high VRE grid (> 90% integration levels) is developed, and a newly developed seasonal storage modeling methodology is then implemented to analyze the integration of a reversible solid-oxide electrolyzer system with such highly penetrated VRE grid scenarios. This research shows that the reversible solid-oxide system operating in an ultra-high VRE grid can reduce the annual electricity generation cost by 5-15% (subject to grid conditions).
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Integrating full-field optical methods, inverse techniques and traditional mechanical testing for damage tolerancing in CFRPs under impact fatigueComposite structures are susceptible to transverse loading due to their inherent layered structure, particularly when under impact. Under low energy repetitive impacts (LERI), little is understood regarding damage mechanisms, damage accumulation, and the post-mortem global response of the composite material under different loading configurations. In this study, we examine carbon fiber-reinforced composite (CFRC) plates subjected to low energy repetitive impacts of 2 J to investigate their behavior under impact fatigue. Post-mortem specimens are then investigated using three main methods: digital image correlation (DIC), the virtual fields method (VFM) and compression-after-impact (CAI) tests. DIC is used to extract surface kinematics under an applied static load to observe the evolution of strain fields under bending as the number of impacts accumulate. This data is then used as input for VFM analysis which is used to reveal local gaps in mechanical equilibrium, allowing it to be used as an indicator of damage. Next, ASTM standard CAI tests are performed on the impacted specimens to compare ultimate compression strength values. X-ray computed tomography (XCT) is also used to corroborate damage detection provided by the equilibrium gap method and identify catastrophic microstructural damage pre-cursors. Using quasi-isotropic 8-ply CFRC plates, XCT results showed that interlaminar cracking appeared in as little as 10 impacts. At 100 impacts, extensive matrix cracking and delaminations were observed. After 300 impacts, severe delaminations were imaged using XCT, while barely visible surface cracks were imaged on the rear face of the specimen.
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Elucidating algal extracellular polymeric substance structures with asymmetrical flow field-flow fractionation and light scatteringExtracellular polymeric substances (EPS) from algae are complex, secreted, aquatic heteropolymers (comprised of carbohydrate and proteins), possibly functioning as carbon sinks. EPS has tremendous potential to be utilized as high-value coproducts, e.g. hydrocolloids or biobased polymers, and playing a significant role in the overall aquatic ecology (feeding a healthy microbiome) during cultivation. Unfortunately, the structural elucidation of these polymers is elusive in literature making the design of custom applications difficult. We must characterize these polymers on a chemical, structural, and physical level to understand their biological significance and industrial potential. The first step is to reduce the complexity of EPS with a size-based separation such as asymmetrical flow field-flow fractionation (AF4). When coupled to multi-angle light scattering (MALS), AF4 can provide the separation and characterization needed to determine the molecular weight and size of different populations in the sample. This work evaluates the different size populations present in the EPS of Chlorella vulgaris using AF4-MALS. Fractions were collected and analyzed to probe differences in compositional analyses between the different size populations. The separation investigates aggregate behavior at different ionic strengths to better understand the interactions of these biopolymers in their native, higher salinity, environments. The EPS of C. vulgaris has demonstrated diverse molecular weight populations ranging from 4x104 – 3x108 Daltons. We observed a reduction of fractogram features at high ionic strength indicating polymer aggregation. This work aims to be the first step in complete structural determination of EPS while probing fundamental separation observations on polymer behavior at different salt concentrations.
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Quantifying channel network morphometrics at Jezero and Eberswalde cratersAncient lakes on Mars and the river deltas which occur along their shorelines offer attractive targets for mission landing sites due to their habitability and high biosignature preservation potential. Deltas are promising targets for finding organic molecules and other signatures of life because on Earth deltas have biodiverse and rich ecosystems. Furthermore, the presence of deltas are used to map paleoshorelines for ancient oceans and lakes on Mars. For instance, Jezero Crater was chosen as the NASA Perseverance landing site because the fan-shaped channel network at the edge of the crater was interpreted as a delta. However, on Earth, fan-shaped channel networks may also form in fluvial fans that are inland terrestrial landforms that can form 1000s of kilometers from shorelines. We demonstrate that morphometric criteria are needed to accurately identify fan-shaped landforms for potential future landing sites. The goal of this research project is to differentiate deltas and fluvial fans on Mars by quantifying fan-shaped paleochannel network morphometrics. To accomplish this, we map Martian fan-shaped paleochannel networks using images from the Mars Reconnaissance Orbiter (MRO) High Resolution Imaging Science Experience (HiRISE) and Contex Camera (CTX) photographs in combination with ArcGIS. Morphometric data is statistically analyzed using python and other open-source data visualization libraries. The outcomes of this project will improve our ability to choose appropriate landing sites in search of life, and to map paleo-shorelines on Mars. Preliminary results suggest the channel network at Jezero resembles a fluvial fan, while the landform at Eberswalde crater resembles a delta. Fluvial fan formation has been linked to large sediment and water discharges, and to fluctuations in discharge as a result of highly seasonal precipitation in climatic settings that promote marked seasonal and interannual hydrological changes, leading to variable discharge regimes and exceptional flood events. Alternative evidence is required to identify paleo-shorelines as fluvial fans may also form along shorelines. On Earth, fluvial fans are less sensitive to sea-level rise and coastal hazards than deltas and react differently from deltas due to changing sea levels.
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Temporal downscaling for solar radiationGlobal and regional climate model projections are useful for gauging future patterns of climate variables, including solar radiation, but data from these models is often too spatio-temporally course for local use. Within the context of solar radiation, the changing climate may have an effect on photo-voltaic (PV) production, especially as the PV industry moves to extend plant lifetimes to 50 years. Predicting PV production while taking into account a changing climate requires data at a resolution that is useful for building PV plants. Temporal and spatial downscaling of solar radiation data is widely studied. We present a novel method to downscale global horizontal irradiance (GHI) data from daily averages to hourly profiles, while maintaining spatial correlation of parameters characterizing the diurnal profile of GHI. The method focuses on the use of a diurnal template which can be shifted and scaled according to the time or year and location. Variability in the profile is later added to account for clouds if the daily average value indicates a cloudy day. This analysis is applied to data from the National Solar Radiation Database housed at the National Renewable Energy Lab and a case study of the mentioned methods over California is presented. This method will later be applied to future projections of solar radiation from bias-corrected regional climate models to create a massive dataset that projects solar radiation for future years across the United States.
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Failure conditions and triggers of the Achoma landslide, central Andes region, Arequipa PeruThe Colca River valley in southern Peru is the longest waterway of the Pacific Peruvian hydrologic basins, starting in the Perú Altiplano, and crossing the western Andean Cordillera to the Pacific Ocean. The area has been regularly impacted by large landslides of the valley slopes, and geologic evidence documents intense, recurrent, and catastrophic events, like landslides, debris avalanches, and floods. On June 18, 2020, more than 5,400,000 m3 of soil and weak rock slid into the Colca River valley near the town of Achoma. The rotational slide involved 40 hectares of land that was displaced 500 meters. The event destroyed the agricultural land, impacting the economy of many families, and the displaced material over the Colca River created a dam that increased the risk of flooding for the towns upstream. The exact factors that led to the landslide in Achoma, including triggering factors, are uncertain. The activity of farms, most of which are currently irrigated or have been irrigated in the past, and the presence of a large water transportation canal upslope of the landslide are the most likely causes of the increase in ground-water levels leading to failure. The purpose of this work is test various groundwater and infiltration scenarios to estimate the amount of water involved in the destabilization and triggering of the Achoma landslide, using numerical simulation of changing groundwater conditions. While not definitive, our early work indicates that, even though the landslide occurred during the dry season and before the irrigation began for the year, we cannot yet rule out irrigation as a contributing factor. On the other hand, increasing ground-water levels from leakage from the water conveyance canal appears to be a necessary component to cause slope failure.
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Power harvesting using FDTD for 5G biomedical applicationsThe finite difference time domain (FDTD) method is well characterized for various simulations of 5G applications. Biomedical applications are one notable area of research, particularly wearable devices for remotely monitoring patients. The current simulations are conducted at 28 and 29 GHz within the n257 and n261 bands of the 5G allocation spectrum [1]. Higher frequencies allow for compact designs, higher data rates, and more capacity, however, they also have higher path loss, increased sensitivity to fabrication, potential health effects, and require efficiency of power transfer for wirelessly charging without the need for batteries [1]. Despite these challenges, there is a need for research and demonstrated performance of 5G wearable devices. The FDTD is used to enhance the transfer of power for wearable antenna on human wrist using combination of dielectric cylinders at the surface of the skin [2]. A 2D model of the wrist tissues is used with the traditional FDTD algorithm to solve for the E-field distribution within the first tissue layer. A 3D wrist model is used with the dispersive FDTD formulation Debye permittivity model to accurately simulate more practical layouts [3]. Our 2D optimized model results show a potential gain of over 8 dB for 28 and 29 GHz which is higher by at least 2 dB over existing similar analysis showing 6 dB of gain [2]. The more practical 3D simulations show a potential gain of 6 dB and 3 dB for 28 and 29 GHz, respectively.
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Smaller ankle muscle forces during rising reduce ankle joint contact forces after injuryBalance control and pain regulation require modulating muscle activity during movement. For example, adults with chronic ankle instability (CAI) had smaller ankle plantarflexor muscle activity and ankle joint moments during walking when compared to healthy participants. Additionally, greater peroneus longus activity has been hypothesized to modulate ankle loading at heel strike during walking for individuals with CAI. Dynamic muscle forces cannot be directly measured non-invasively, and few studies have investigated the effect of muscle recruitment on joint contact forces during dynamic rising tasks. The purpose of this study was to develop movement simulations to determine how ankle injury affects biomechanics during rising. Two participants, one healthy and one recovering from an ankle fracture, completed several rising trials. A musculoskeletal model was scaled to each participant and used to compare peak muscle and ankle joint contact forces between participants. The non-injured participant’s soleus produced 87% greater force and their peroneus longus produced 3.5 times greater force compared to the injured individual. Consistent with greater muscle forces, the healthy participant had 2.3 times greater resultant ankle joint contact force compared to the injured individual. An altered subtalar angle may have been chosen to reduce muscle force requirements and associated joint contact forces, protecting the injured joint, and mitigating pain. Offloading an injured ankle joint was achieved by an altered subtalar angle associated with larger muscle moment arms, which required smaller ankle muscle forces to successfully rise.
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Exploring fundamentals of CoO and 2D rocksalt metal oxidesDue to a high surface area to volume ratio, nanomaterials have a wide variety of applications in catalysis, renewable energy, and more. Aside from controlling the composition to achieve desired properties, a critical aspect of synthesis to control is the morphology of the nanomaterial. Different particle shapes will expose different crystal facets, resulting in different surface energies and chemical reactivities. Therefore, controlling the final shape and structure of a nanomaterial is just as important as the composition. Cobalt oxide is a material of interest for its catalytic properties in decomposition of compounds, lithium battery anodes, and CO oxidation. Nickel oxide, geometrically very similar to cobalt oxide, retains unknown differences between the resulting compounds from synthesis methods used for the two oxides. The synthesis method presented on here successfully makes NiO (111) facet by using an autoclave with supercritical drying for easy sample retrieval; cobalt precursors were substituted for nickel precursors to make various cobalt oxides. This project explores how adjusting temperatures affects the resulting morphology of cobalt oxide. The autoclave and post-synthesis heat treatment temperatures were varied to investigate the resulting cobalt oxide compound and the morphology. The results demonstrating the relationship between temperature and morphology will be presented here. Future work will focus on performing preliminary catalytic studies of the different nanostructures for CO2 capture and electrolysis. This work contributes to furthering knowledge about the relationships between synthesis method, nanomaterial morphology, temperature, and material properties of cobalt oxides, a promising material for a more sustainable society.
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Use of geopolymers for mine tailings managementAs the extent of mining activities increases, more and more waste is produce which has to be managed and stored for many years. Traditional storage of waste in tailings ponds takes up large areas of arable land, allows toxins to leach into the surrounding environment, and the tailings dams can often fail, endangering human lives. In light of these issues, reuse and reclamation of mine tailings is a more attractive solution that can not only stabilize the waste to reduce its toxicity, but also produce a beneficial product such as construction materials. One method for reuse of mining waste is geopolymerization, where the mine tailings are used as an aluminosilicate source material and activated by a strongly alkaline solution (such as sodium hydroxide). Although mine tailings based geopolymers show promising mechanical and chemical properties, the tailings themselves generally have low reactivity, which requires a very strong alkali activator. These concentrated solutions are user-hazardous, expensive, and difficult to store on an industrial scale. An alternative process, called one-part geopolymerization, shows promise in reducing these problems. In a one-part geopolymer, the alkali activator is added to the tailings in solid form to create a ready-to-use mixture. The user must add only water to this mixture, removing the hazards associated with storage and handling of a strongly alkaline solution. One-part geopolymerization is a promising strategy for mine tailings management that immobilizes toxins, produces beneficial material, and is feasible on an industrial scale.
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Quantum reservoir computing for time series predictionQuantum reservoir computing (QRC) is an innovative framework for processing sequential data that has numerous potential advantages over traditional approaches. Unlike conventional neural networks, QRC employs a reservoir for mapping inputs into a high-dimensional space and a readout for pattern analysis, resulting in fast learning, low training costs, and a variety of hardware implementations. The unique feature of QRC is that instead of back-propagation, data structures are passed through non-linear components of the reservoir, resulting in a reduced number of computations. QRC has been shown to outperform classical neural networks for large data sets and is efficient in modeling atomic system dynamics, producing highly accurate representations of complex quantum dynamics. One of the applications of QRC is in time series prediction, which includes finance, weather prediction, and other fields. With QRC, it is possible to develop highly accurate models that can accurately predict future values based on past observations. QRC can handle large, complex data sets and can adapt to changing conditions over time, making it an ideal tool for time series analysis. By using QRC for time series prediction, it is possible to obtain insights into complex phenomena that would be difficult or impossible to obtain using traditional methods. Overall, QRC represents a powerful new approach to sequential data processing that has the potential to revolutionize a wide range of fields.
