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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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Metal-macrocyclic framework featuring adaptive cavity for precise palladium recognition
Not provided.
Estimating soybean yields from high-temporal-resolution multi-source data using deep learning
Accurate and timely crop yield prediction is crucial for ensuring food security and maintaining stable agricultural markets. In recent years, there has been a surge in interest in leveraging high-temporal-resolution, multi-source data for effective crop growth monitoring and yield estimation. A notable challenge arises from the difficulty in capturing the intricate interactions between variables across different time steps within these high-temporal-resolution time series datasets. This complexity hinders the reliable extraction of yield information from voluminous and often noisy datasets, especially during periods of extreme weather events. Here, in this study, we propose an Attention and Graph Isomorphism Network-enhanced Bi-directional Long Short-Term Memory network (AGB-LSTM) for estimating county-level soybean yield in the United States. This model integrates a diverse set of remote sensing data, including Near-Infrared Reflectance of Vegetation (NIRv), Sun-Induced chlorophyll Fluorescence (SIF), and Gross Primary Productivity (GPP), along with environmental covariates. The AGB-LSTM effectively leverages information related to crop yield from high-temporal-resolution time series data (5-days), achieving an accuracy of R²= 0.67 and rRMSE = 14.46%. This approach significantly outperforms traditional machine learning methods such as Random Forest (RF) (R²= 0.52, rRMSE = 17.36%) and Bi-LSTM (R²= 0.58, rRMSE = 16.17%). Sensitivity experiments with different time steps and ranges demonstrated that our model could accurately and stably predict yields 1 to 2 months before harvest. Moreover, data with a finer temporal resolution consistently improved prediction performance, resulting in an approximately 20% increase in and an approximately 20% decrease in rRMSE compared to using monthly composites. We also evaluated the robustness of the model under extreme climate events and observed strong performance (R²= 0.50, rRMSE = 21.32%). Finally, yield mapping for major soybean-producing regions in North America in 2023 revealed spatial patterns that closely matched USDA yield reports. Our findings suggest that the AGB-LSTM model is a promising and effective method for estimating yield and has notable potential for global crop yield forecasting.
Antibody interfaces revealed through structural mining
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Precise ROCOF estimation algorithm for low inertia power grids
The precise estimation of Rate Of Change Of Frequencies (ROCOFs) in a generation trip event can be helpful on power system inertia estimation, fast system response, and accurate event capturing. However, the ROCOF estimations from the existing Synchronized Measurement Devices (SMDs) are usually simply calculated by the finite difference between two adjacent frequency measurement points. The ineluctable noises, disturbances, and spikes from real-world frequency measurements can bring large dynamics to the ROCOF estimation and thus can result in an inaccurate estimation of the initial ROCOF. This issue becomes more serious when the target power grid has low inertia, and a large amount of distributed energy sources are deployed. To address this issue, a precise ROCOF estimation algorithm is designed based on least square method with flexible window size. In addition, a median filter is also designed and applied on the frequency measurements before using the proposed algorithm. The window size and thresholds in the proposed algorithm are determined with historical event data analysis. The proposed algorithm is deployed in a low cost, flexible, and distribution level universal grid analyzer (UGA) platform. In conclusion, multiple experiments are conducted in both a laboratory and the Hawaiian Islands to verify the effectiveness of the proposed algorithm.
Effects of Al substitution on local structure and morphology of lepidocrocite and its phosphate adso
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Deep Learning-Based Computed Tomography Perfusion Mapping (DL-CTPM) for Pulmonary CT-to-Perfusion Translation
Our purpose was to develop a deep learning–based computed tomography (CT) perfusion mapping (DL-CTPM) method that synthesizes lung perfusion images from CT images.
The Dynamic Alternation of Local and Systemic Tumor Immune Microenvironment During Concurrent Chemoradiotherapy of Cervical Cancer: A Prospective Clinical Trial
This work assessed local and systemic alternations of the tumor immune microenvironment during concurrent chemoradiation therapy (CCRT) of local advanced cervical cancer to estimate the optimal timing for immune therapy in relation to CCRT.
High Pressure Effect on Structural and Electrochemical Properties of Anionic Redox-Based Lithium Transition Metal Oxides
Abstract not provided
Inhibit the strain accumulation for 5V spinel cathode by mitigating the phase separation during high voltage stage
LiNi 0.5 Mn 1.5 O 4 (LNMO) spinel cathode is a potential cathode material for high-power, low-cost lithium-ion batteries (LIBs) due to its low raw material cost, high operating voltage and efficient lithium-ion transport channel. However, LNMO suffers from structural stability due to the two-phase-transformation during charge/discharge process, leading to poor cycling performance. Here, to address the issues, we have constructed a homogeneous aggregated LNMO cathode with a multifaceted primary particle, namely the LNMO with single-crystal secondary particles (SSP LNMO). Ex situ and in situ characterizations including the synchrotron X-ray diffraction, the neutron diffraction and full pouch cell validated that the SSP LNMO exhibit a single solid-solution reactions with a restrained lattice evolution during the delithiation/lithiation process. This is in contrast to the normal LNMO cathode, which demonstrates a significant two-phase transition from spinel Li 1-x Ni 0.5 Mn 1.5 O 4 to rock-salt MnO 2 at high voltage. Therefore, the SSP LNMO has greatly improved cycle life compared to the normal LNMO cathode. Overall, this study demonstrates the possibility of constructing LNMO based on primary-grain morphology modulation to improve the intrinsic stability of LIBs.
Regenerable solvents mediate accelerated low temperature CO 2 capture and carbon mineralization of ash and nano-scale calcium carbonate formation
The dual need to remove CO 2 from our emissions and treat alkaline industrial residues such as ash materials motivate the design of innovative pathways to simultaneously capture and convert CO 2 into mineralized carbonates. Direct carbon mineralization is one approach that addresses the need to simultaneously treat alkaline industrial residues and mineralize CO 2 emissions. Low CO 2 solubility in water and slow kinetics at ambient temperature have challenged the direct carbon mineralization of alkaline industrial residues. Here, to address these challenges, the use of CO 2 capture solvents that enhance CO 2 solubility and facilitate accelerated carbon mineralization of fly ash at temperatures below 90 °C is investigated. Calcium carbonate formation results in the inherent regeneration of the solvent. The carbon mineralization extents of non-calcium carbonate content in fly ash were 50% and 51% and in waste ash were 58% and 62% in 2.5 M sodium glycinate and 30 wt% MEA solutions, respectively. The experiments were performed at 50 °C for 3 hours with CO 2 partial pressure of 1 atm in a continuously stirred slurry environment with 15 wt.% solid. Furthermore, nanoscale CaCO 3 is successfully synthesized from dissolved calcium using CO 2 -loaded sodium glycinate and surfactants such as CTAB (Cetyl Trimethyl Ammonium Bromide). Surfactants such as CTAB bind to the calcium carbonate surface and regulate the growth of calcium carbonate particles. These innovative approaches demonstrate the feasibility of directly storing CO 2 in fly ash and waste ash as calcium carbonate and producing nanoscale calcium carbonate using regenerable CO 2 capture solvents.
DFAT: A web-based toolkit for estimating demand flexibility in building-to-grid integration
Demand Flexibility Assessment Tool (DFAT) is an open source web-based tool that estimates the demand flexibility potential of common control strategies in commercial buildings. The toolkit features a demand flexibility estimation tool that contains two calculators, basic and advanced, based on the level of input of customer data. The basic version calculates demand shed metrics for the control strategy “global temperature adjustment” and “cycle on/off compressors” using customer building information, local weather data, and electrical meter data. The advanced version, which uses detailed HVAC equipment data, calculates demand flexibility metrics for control strategies such as static pressure reset, global temperature adjustment, and cycle on/off compressors. In addition to the demand flexibility estimation tool, this toolkit offers a benchmarking tool that helps facility operators, aggregators, and utility resource managers assess demand flexibility opportunities, quantify/verify performance, and compare their performance against that of their peers.