Characterization of SNL identified BBB-penetrating moieties (BBB PMs) for delivery of therapeutic cargo to the CNS.
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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
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This presentation focuses on renewable energy project development and planning and highlights common project costs at each stage of a project's lifecycle. The presentation was produced as part of a series of trainings for awardees of the Energizing Rural Communities prize.
With efficiencies of halide perovskite solar cells stagnating over the last few years at values above 25% and approaching thermodynamic limits, focus must shift to ensuring stability of these devices over the long term. The overarching goal of this project was to understand the root causes of instability in halide perovskite solar cells. Our approach was rooted in fundamental scientific pursuits, determining at the molecular level those processes responsible for degradation of the metal halide perovskite system, either in the precursor ink, or in the solid state. We then used this understanding to show that degradation leads to midgap states in halide perovskite semiconductors, explaining loss in performance; also, this in-depth understanding allowed us to develop specifically targeted solutions to prevent these degradation mechanisms. These include guidance about amines in perovskite inks, solvents to use (and not to use), and impacts of Cs incorporation to perovskite inks and solids. Finally, we achieved our end-of-project goal and demonstrated an effective lifetime of a halide perovskite solar cell of 5000 h, including measurement of devices over 3000 h, amongst the longest in the field. Overall, this project has considerably pushed forward the science of perovskite degradation and sets examples and groundwork for future studies to enable halide perovskite solar cells to be a successful solar energy conversion technology.
MicroBooNE, a Liquid Argon Time Projection Chamber (LArTPC) located in the $\nu_{\mu}$-dominated Booster Neutrino Beam at Fermilab, has been studying $\nu_{e}$ charged-current (CC) interaction rates to shed light on the MiniBooNE low energy excess. The LArTPC technology employed by MicroBooNE provides the capability to image neutrino interactions with mm-scale precision. Computer vision and other machine learning techniques are promising tools for image processing that could boost efficiencies for selecting $\nu_{e}$-CC and other rare signals, reduce cosmic and beam-induced backgrounds, and improve the reconstruction of neutrino energies. The MicroBooNE experiment has been at the forefront of developing and testing such techniques for use in physics analyses. In this poster we overview deep-learning based reconstruction methods. We will showcase the use of a recurrent neural network to estimate neutrino energies and present a new reconstruction framework that uses convolutional neural networks to locate neutrino interaction vertices, tag pixels with track and shower labels, and perform particle identification on reconstructed clusters. We will present studies characterizing the performance of these new tools and demonstrate their effectiveness through their use in an inclusive $\nu_{e}$-CC event selection.
This report details the development and evaluation of a Mid-Infrared (Mid-IR) Unmanned Aerial Vehicle (UAV)-based sensing platform integrated with deep learning algorithms for the identification and quantification of gaseous emissions in gas flares. The project, spearheaded by Omega Optics, Inc., aimed to address environmental monitoring challenges by leveraging advanced photonic technologies and autonomous UAV operations. The research focused on designing, optimizing, and fabricating photonic crystal waveguides and grating couplers to enhance the sensitivity and accuracy of gas detection. A comprehensive drone-based system was developed, featuring a miniaturized sensor, GPS module, and microcontroller communication network for real-time gas concentration monitoring. The system's adaptive sampling algorithm, implemented using the Robot Operating System (ROS), enables autonomous detection and localization of gas emission sources. Preliminary results demonstrate the platform's capability to detect and monitor gas emissions with high precision, cost-effectiveness, and scalability. Future work will expand upon this foundation by introducing 3D wind model-based learning for dynamic environmental conditions and further enhancing the user interface and data processing algorithms to support broader environmental monitoring applications. Overall, this project represents a significant step forward in UAV-based environmental sensing technologies, offering robust solutions for detecting and mitigating the impacts of gaseous emissions on public health and safety.
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In this study, micro-X-ray Fluorescence and micro/bulk X-ray Adsorption Near Edge Spectroscopy were collected at Stanford Synchrotron Radiation Lightsource and the Advanced Photon Source to: (1) gain a detailed characterization of critical minerals in Fe/Mn (hydr)oxide host phases; and (2) investigate the Co/Ni coordination and redox speciation associated with Fe/Mn (hydr)oxides phases in AMD solids with diverse composition (e.g., Al-rich, Mn-rich, Al/Mn/Fe-rich). Preliminary results show co-location of Co/Ni within Mn-rich hotspots, while Fe-rich hotspots were associated with heavy REEs. In Al-rich solids, Mn speciation was mostly comprised of Mn+3/+4 oxides, while the Mn-rich solids contain predominately Mn+4. These results suggest the AMD matrix plays an important role in how Co/Ni are coordinated, which aids in evaluating the efficacy to utilize AMD solids as a resource, and informs other novel sorbent and recovery technologies.
The U.S. Department of Energy's blueprints for the transportation, buildings, and electricity sectors call for substantial reductions in greenhouse gas (GHG) emissions by 2050. These plans focus on zero-emission vehicles, investments in transit, energy-efficient buildings, and the widespread adoption and deployment of renewable energy technologies like solar photovoltaics (PV), energy storage and energy-efficient appliances. However, these sectors are often studied and modeled in isolation, overlooking how household decisions to adopt clean technologies in one sector influence others. This study, led by an interdisciplinary team at the National Renewable Energy Laboratory (NREL), explores opportunities for cross-sector collaboration to drive more effective and equitable decarbonization. Through discussions with 22 NREL researchers across transportation, building, solar, and grid sectors, the study highlights the need for integrated tools and models that capture interactions between these sectors. Key insights include the need for data standardization and interoperability to enable cross-sector analysis and decision-making. Strengthening utility partnerships is also critical to align energy policies with decarbonization goals and manage the increased demand for renewable energy. The study also emphasizes the importance of equity in the clean energy transition, calling for targeted incentives and support to ensure that low-income and underserved communities benefit from clean technologies like electric vehicles and energy-efficient appliances. To support these efforts, innovative funding mechanisms must be expanded to facilitate interdisciplinary research, such as city-specific decarbonization plans and federal projects like DOE"s Standard Scenarios. By encouraging collaboration and integrating cross-sector insights, this study aims to provide a roadmap to accelerate the clean energy transition and ensure it is both sustainable and inclusive.
We exhibit the operational energy storage density (W-h L-1) of various redox flow battery technologies based on consideration from experimental parameters and results from literature as key inputs.
Latent heat based thermal energy storage is of interest as a method to mitigate and time-shift thermal load peaks and hence reduce energy demands for heating and cooling buildings. Notably, Glauber’s salt (GS) presents a good choice as a phase change material (PCM) for building applications because it has a melting point near room temperature (i.e., 32.4 °C), a large heat of fusion and energy density, and is low cost (~ $100/ton). However, there are several known limitations to using GS as a PCM, such as incongruent melting, high degree of supercooling, and formation of other hydration states, which renders GS unsuitable for practical use. Inorganic crystals acting as nucleating agents can avoid some of these issues with GS [1]. Understanding nucleation behavior through typical calorimetry experiments offers limited insight into the molecular-scale mechanisms. Here, we report isothermal titration calorimetry [2] to investigate the interactions between sodium sulfate salt and an organic nucleating agent - glycine. Our experiments provide qualitative and quantitative understanding of how glycine interacts with sodium sulfate across various temperatures. The findings offer insights into the nucleation onset temperature (24°C) and underscore the pivotal role of glycine's zwitterionic structure in facilitating nucleation. This preliminary work provides a foundation for subsequent exploration of nucleating agents that would be incorporated in different salt hydrate systems. 1. Purohit, B. K., and V. S. Sistla. "Inorganic salt hydrate for thermal energy storage application: A review." Energy Storage 3, no. 2 (2021): e212. 2. Velázquez-Campoy, Adrián, Hiroyasu Ohtaka, Azin Nezami, Salman Muzammil, and Ernesto Freire. "Isothermal titration calorimetry." Current protocols in cell biology 23, no. 1 (2004): 17-8.
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