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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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At least 991 records · Page 55
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Bioenergy Cropping Reduces the Spatiotemporal Scaling of Soil Bacterial Biodiversity

Widespread bioenergy cropping can transform landscapes, strongly affecting biodiversity. However, the impact of bioenergy cropping on the spatiotemporal scaling of soil biodiversity remains virtually unknown, despite its profound implications for the functioning of the ecological community. Here, we investigated how bioenergy cropping influenced the spatiotemporal scaling of soil bacterial biodiversity in marginal soils (sandy loam and clay loam soils) in Oklahoma, USA. We detected strong, significant species-time-area relationships (STARs) and phylogenetic-time-area relationships (PTARs) in bacterial communities and their lineages, suggesting that STARs and PTARs exist in microbial ecology within the studied system. Also, spatiotemporal scaling rates (the slopes of STAR and PTAR models) varied substantially among bacterial lineages and were positively correlated with their 16S rRNA gene copy numbers, a genomic trait indicative of microbial growth potentials. Strikingly, bioenergy cropping significantly reduced spatiotemporal scaling rates by 6.8%-14.1%, with a more pronounced reduction observed in sandy loam soils, where those rates were significantly lower than in clay loam soils. The heterogeneity of soil phosphorus and carbon resulted in variations in bacterial spatiotemporal scaling rates. Collectively, our findings suggest that bioenergy cropping may alleviate rapid shifts in soil biodiversity across space and time, thereby stabilizing soil biodiversity and supporting its role as part of sustainable land management and climate mitigation strategies.

bacterial diversity↗
DOE OSTI2026

Capturing soil moisture and salinity changes in flooded coastal forests using electrical resistivity and induced polarization

Geophysical methods provide high-resolution spatial measurements of physical quantities sensitive to changes in soil moisture and salinity and can be used to monitor soil hydrological responses to flooding. However, extracting quantitative hydrological information from geophysical data remains challenging. In this study, we extended existing petrophysical models to estimate soil moisture and salinity from electrical measurements to address this challenge. We manipulated two hydrologically isolated 2000 m 2 experimental plots by simultaneously inundating them with 265 m 3 of either freshwater or estuarine water. Repeated electrical resistivity and induced polarization measurements were used to image the water and solute infiltration along two transects that are 100 and 42 m in length. Petrophysical models derived from laboratory multi-salinity electrical measurements were used to estimate changes in soil moisture and fluid salinity from field measurements of real and imaginary conductivity during the ecosystem-scale flooding experiment. The real conductivity increased by ∼100% in the freshwater plot and ∼570% in the saltwater plot. The change in imaginary conductivity in the freshwater plot was <1 mS/m, whereas that of the estuarine water plot was ∼5 mS/m. Real conductivity shows dependence on soil moisture content with a coefficient of determination (R 2 ) > 0.7, while the imaginary conductivity shows a dependence on soil salinity with R 2 > 0.6. The results validate the use of electrical resistivity for estimating changes in soil moisture content in response to flooding. Combining electrical resistivity imaging with induced polarization measurements provides the possibility to account for changes in pore fluid conductivity.

Adebayo, Moses B. [Univ. of Toledo, OH (United Sta↗
DOE OSTI2023

Efficient learning of power grid voltage control strategies via model-based deep reinforcement learning

Here this article proposes a model-based deep reinforcement learning (DRL) method to design emergency control strategies for short-term voltage stability problems in power systems. Recent advances show promising results for model-free DRL-based methods in power systems control problems. But in power systems applications, these model-free methods have certain issues related to training time (clock time) and sample efficiency; both are critical for making state-of-the-art DRL algorithms practically applicable. DRL-agent learns an optimal policy via a trial-and-error method while interacting with the real-world environment. It is also desirable to minimize the direct interaction of the DRL agent with the real-world power grid due to its safety-critical nature. Additionally, the state-of-the-art DRL-based policies are mostly trained using a physics-based grid simulator where dynamic simulation is computationally intensive, lowering the training efficiency. We propose a novel model-based DRL framework where a deep neural network (DNN)-based dynamic surrogate model (SM), instead of a real-world power grid or physics-based simulation, is utilized within the policy learning framework, making the process faster and more sample efficient. However, having stable training in model-based DRL is challenging because of the complex system dynamics of large-scale power systems. We addressed these issues by incorporating imitation learning to have a warm start in policy learning, reward-shaping, and multi-step loss in surrogate model training. Finally, we achieved 97.5% reduction in samples and 87.7% reduction in training time for an application to the IEEE 300-bus test system.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Effect of grafting density on the two-dimensional assembly of nanoparticles

Employing grazing-incidence small-angle X-ray scattering (GISAXS) and X-ray reflectivity (XRR), we demonstrate that films composed of polyethylene glycol (PEG)-grafted silver nanoparticles (AgNPs) and gold nanoparticles (AuNPs), as well as their binary mixtures, form highly stable hexagonal structures at the vapor–liquid interface. These nanoparticles exhibit remarkable stability under varying environmental conditions, including changes in pH, mixing concentration, and PEG chain length. Short-chain PEG grafting produces dense, well-ordered films, while longer chains produce more complex, less dense quasi-bilayer structures. AuNPs exhibit higher grafting densities than AgNPs, leading to more ordered in-plane arrangements. In binary mixtures, AuNPs dominate the population at the surface, while AgNPs integrate into the system, expanding the lattice without forming a distinct binary superstructure. In conclusion, these results offer valuable insights into the structural behavior of PEG-grafted nanoparticles and provide a foundation for optimizing binary nanoparticle assemblies for advanced nanotechnology applications.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Predicting the evolution of biomass bulk density through feedstock preprocessing: Discrete element modeling, regression analysis, and pilot-scale validation

Bulk density is an important material property of biomass feedstocks, influencing handling, storage, transport costs, and conversion efficiency. In this study, predictive regression models for loose and tapped bulk densities of Alamo and Cave-in-Rock switchgrass are developed using a comprehensive dataset generated via calibrated bonded-sphere discrete element method (DEM) simulations. Here, a key contribution of this study is the use of a DEM-based approach, which correlates density with moisture content and particle size distribution parameters and enables analysis across a continuous particle size range, overcoming limitations of purely experimental data. For comparison, regression models are also developed using only experimental data from pilot-scale runs at the Biomass Feedstock National User Facility at Idaho National Laboratory. Validation against pilot-scale data showed reasonable prediction accuracy for both model types, particularly for smaller particle sizes (post-secondary grinding). While the experimental model showed slightly better performance matching the validation data in some cases, the DEM-based model benefits from a much larger dataset, reduced predictor multicollinearity, and continuous parameter coverage, highlighting the utility of validated simulation models for developing robust predictive tools for biomass preprocessing applications.

09 - BIOMASS FUELS↗
DOE OSTI2025

The genomic footprints of wild Saccharum species trace domestication, diversification, and modern breeding of sugarcane

Sugarcane is a major crop of unclear origins due to its complex polyploid interspecific genome. We analyzed genome ancestries using whole-genome sequence data from 390 representative accessions based on repeated k-mers and chloroplast phylogeny. The results provided evidence that Saccharum officinarum was domesticated in the New Guinea region from the S. robustum wild species and revealed that its genome is a mosaic involving different S. robustum subgroups. We discovered a wild Saccharum contributor to most modern cultivars, likely originating from East Melanesia. We highlighted two early centers of sugarcane diversification associated with human transport, one in continental Asia through hybridization with different S. spontaneum subgroups and one in the Melanesian and Polynesian islands via hybridization with the discovered ancestor and Miscanthus. Finally, we revealed the genome ancestry of modern cultivars, highlighting untapped wild Saccharum diversity as a source of alleles for breeding programs.

Garsmeur, Olivier [CIRAD, Montpellier (France). Ag↗
DOE OSTI2021

Structural basis and mode of action for two broadly neutralizing antibodies against SARS-CoV-2 emerging variants of concern

Emerging variants of concern for the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) can transmit more efficiently and partially evade protective immune responses, thus necessitating continued refinement of antibody therapies and immunogen design. Here, we elucidate the structural basis and mode of action for two potent SARS-CoV-2 spike (S)-neutralizing monoclonal antibodies, CV3-1 and CV3-25, which remain effective against emerging variants of concern in vitro and in vivo. CV3-1 binds to the (485-GFN-487) loop within the receptor-binding domain (RBD) in the “RBD-up” position and triggers potent shedding of the S1 subunit. In contrast, CV3-25 inhibits membrane fusion by binding to an epitope in the stem helix region of the S2 subunit that is highly conserved among β-coronaviruses. Thus, vaccine immunogen designs that incorporate the conserved regions in the RBD and stem helix region are candidates to elicit pan-coronavirus protective immune responses.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Comparison of structurally diverse simulation models for prediction of epidemic outcomes caused by a long-distance dispersed pathogen

Long-distance dispersal (LDD) pathogens pose substantial challenges for epidemic control due to their ability to generate new infection foci at great distances. While various modeling approaches have been developed to understand and manage such outbreaks, little work has compared how models of different structures behave under shared conditions. Here, in this study, we compare four structurally distinct epidemiological models — EPIMUL, GEMF, PoPS, and Warwick — each adapted to simulate the spread of wheat stripe rust (WSR), a wind-dispersed LDD pathogen, under identical epidemiological parameters and dispersal kernel. Using data from a controlled field experiment, we evaluate the ability of each model to replicate disease prevalence under nine intervention scenarios that vary in timing and culling area. While the models differ substantially in design — ranging from spatial grid-based to network-based and raster-based frameworks — the shared dispersal kernel allowed for close alignment in their predictions. All models accurately captured general epidemic trends, particularly the strong effect of early intervention on disease suppression. We qualitatively compared their behavioral responses across scenarios and also evaluated an ensemble prediction by averaging across model outputs. Our findings highlight how integrating shared epidemiological components into distinct modeling frameworks can improve consistency and accuracy, while reinforcing the importance of early culling in managing LDD pathogen outbreaks.

Dispersal kernel↗
DOE OSTI2025

Soil porous microstructure control over soil organic matter mobility: A multimethod workflow for understanding chemistry-dependent organic matter binding in soil

Soil organic matter (SOM) has attracted a great deal of interest; particularly for its potential to mitigate human derived CO 2 emissions. Studies have demonstrated that SOM plays a critical role in carbon storage and CO 2 sequestration. However, the sorption properties of SOM, which influence its transport in pore water and stabilization within the soil, remain poorly understood. This study develops a workflow to: (1) examine compound-specific advective and diffusive transport and desorption behaviors, (2) quantify desorption rates through stop-flow and continuous-flow column experiments, and (3) evaluate the impact of soil microporosity on SOM mobility using high-resolution imaging and extractions. Intact core column experiments were conducted on Uncultivated (Natural) and Cultivated soil samples, both were arid soils, collected in Washington State. X-ray computed tomography was employed to measure porosity and pore connectivity, while Fourier-transform ion cyclotron resonance mass spectrometry was used to analyze SOM composition. The findings revealed that cultivation increased total carbon and nitrogen levels due to irrigation and fertilization, enhancing carbon capture potential in arid soils. In contrast, the Natural soil, characterized by higher porosity and connectivity, contained more oxidized carbon. Pore network analysis indicated that soil compaction in the Cultivated soil may lead to longer diffusion pathways, significantly influencing SOM transport and stability.

Hydraulic Properties↗
DOE OSTI2025

The detrimental ratio ( ρ ): A critical metric complementing coulombic loss for long calendar-life silicon-based lithium-ion batteries

Silicon (Si) is a promising high-capacity anode in lithium-ion batteries but suffers from chronic chemical degradation and capacity fading during calendar aging, greatly hindering its automobile applications. Electrolyte engineering currently relies on conventional evaluation criteria of reducing coulombic consumption, which implicitly presume its equivalence to irreversible capacity loss and complicates battery development. Here, we introduce the detrimental ratio p to quantify the fraction of parasitic species that permanently degrades active material. This metric is independent and crucially complements total coulombic consumption for accurate performance evaluation. We systematically investigate multiple electrolyte formulations using high-precision leakage current measurements, open-circuit-voltage experiments, and post-mortem characterizations. Although some electrolytes exhibit similarly low coulombic consumption, they diverge significantly incapacity retention and p. Especially, dimethyl-carbonate-based localized-high concentration electrolyte can synergically achieve low coulombic consumption and detrimental ratio p during calendar aging, owing to its chemically inert and structurally resilient solidelectrolyte interface with minimal isolated Si material. By contrast, increasing fluoroethylene carbonate (FEC) additive content suppresses electrolyte breakdown but suffers aggravated chemical degradation of more LixSi isolation for irreversible capacity loss with arising p. This study critically reveals that the chemistry-characteristic detrimental ratio p establishes physically informed performance evaluation to pave the way for accelerating battery development.

Calendar aging↗
DOE OSTI2026

Beta-delayed gamma spectra compilation and analysis following the thermal-neutron induced fission of 235 U, 239,241 Pu

The integral gamma and electron spectra emitted by fission products, also known as delayed gamma and electron spectra, were measured at Oak Ridge National Laboratory in the 1970s for the thermal-neutron induced fission of 235 U and 239,241 Pu. Scintillator detectors were used to measure these spectra, data used later on to obtain decay heat values - that is, the spectra mean values per unit time as function of time - work that was published in the Nuclear Science and Technology journal; the spectral data, however, was only published in laboratory reports. Here, in this work, we analyze the gamma spectra data using modern methods and nuclear databases to reveal the signature of individual fission products as well as to gauge the performance of the ENDF/B-VIII.0 decay data sub-library, concluding about possible future enhancements in predictive capabilities.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2025

Exploring the Potential of Rainwater Harvesting for Cooling Towers: A Systematic Review and Regional Feasibility Assessment

Water scarcity has driven interest in rainwater harvesting, especially for U.S. industries like power and manufacturing, which dedicate a large percentage of their water needs to cooling towers. This paper provides a comprehensive systematic review of studies on the use of rainwater in cooling tower applications, along with an assessment of the regional feasibility of integrating RWH in U.S. manufacturing cooling towers. The systematic review examines the technical, economic, environmental, and policy feasibility of utilizing harvested rainwater, and the regional feasibility analysis evaluates the practical implementation of RWH in manufacturing cooling towers, considering factors such as regional RWH potential, water costs, state policies, and the industrial water use of manufacturing facilities per state. This study supports the hypothesis that RWH for cooling towers is technically feasible, economically viable, and environmentally beneficial. Harvested rainwater is naturally less conductive and soft, and rainwater reuse minimizes the ecological footprint. Supportive state policies, regional RWH potential, and rising water costs across the U.S. are important variables that may impact RWH adoption. The review highlights rooftop RWH as the most studied method and notes that implementing RWH requires infrastructure changes and filtration techniques. While initial investment costs may be high, operational and maintenance costs are low, making RWHS economically feasible over time. Regions with higher water costs and supportive policies are more likely to benefit from RWH adoption. The study provides a foundation for understanding the potential for using RWH in industrial cooling towers.

Cooling towers↗
DOE OSTI2026

Enzymatic Biocontrol of Fire Blight ( Erwinia amylovora ) Using an Engineered Glycosyl Hydrolase

Current management of fire blight, caused by Erwinia amylovora , relies heavily on streptomycin a practice that has contributed to the emergence of antibiotic-resistant strains and raised environmental and regulatory concerns. Enzyme-based biocontrol agents offer a promising antibiotic-free approach that combines target specificity with environmental compatibility. This study evaluates CAase, a bacteriophage-derived glycosyl hydrolase, for its ability to disrupt E. amylovora biofilms and reduce disease severity. Biochemical assays and scanning electron microscopy confirmed that CAase efficiently degraded the extracellular polysaccharide (EPS) matrix, releasing cells from biofilms. Gas chromatography–mass spectrometry (GC–MS) linkage analysis of EPS isolated from two Erwinia amylovora strains demonstrated that CAase preferentially cleaves galactan-rich amylovoran produced by strain EA273, while exhibiting only limited activity toward the levan-rich EPS from strain EA1430. Functional assays revealed that CAase reduced bacterial viability by nearly 2 orders of magnitude at higher enzyme concentrations, strongly suppressed surface motility, and induced ultrastructural damage visible by transmission electron microscopy. Importantly, field trials showed that CAase significantly lowered blossom and shoot blight incidence under orchard conditions. These results highlight CAase as a potent enzyme-based strategy for reducing the virulence of E. amylovora and demonstrate its potential as a sustainable alternative to antibiotics in fire blight management.

antibiotic resistance↗
DOE OSTI2025

Photoabsorption and Stability in Triple-Cation Perovskites Influenced by Interfacial Engineering of the Collector

Charge carrier dynamics in three-dimensional (3D) perovskites is critical to understand for enhancing device performance since the photoabsorption mechanism in perovskites influences key functional devices such as photodetectors and solar cells. Temperature-dependent optoelectronic transport measurements were conducted on our triple cation formulation for the first time from 4 K to 300 K to investigate the role of an interfacial Ti underlayer, beneath the conventional Au collector electrode. The photocurrent was 10X larger with the use of a Ti interfacial layer compared to only Au, where device measurements were made using a broadband white light source at room temperature. As temperature increased from 4 K, the photocurrent increased in both cases, consistent with the semiconducting nature of the triple-cation absorber. Besides computing the responsivity as a function of power and temperature, time-domain measurements with ON/OFF pulses of incident white light, showed the switching time constants to be in the tens to few hundred milliseconds range, and largely temperature-invariant for the two contacts examined. Finally, we constructed solar cells with the same triple cation absorber, in an n-i-p architecture with a Spiro-OMeTAD hole transport layer, but the collector was composed of both types of contacts. Exposing our devices to moisture-rich conditions of up to 70% relative humidity showed the Au/Ti contacted devices to be more robust. Here, our experimental results demonstrate that the addition of a Ti interlayer improves collector efficiency through the photoabsorption process while also potentially stabilizing the solar cells, compared to the bare Au, in moisture-rich environments

optoelectronics↗
DOE OSTI2026

pH-Driven Restructuring of Hydration Layers and Cation Adsorption at the Alumina–Water Interface

Oxide−water interfaces underpin ion separations, catalysis, and electrochemical energy technologies, where the electrical double layer (EDL) controls adsorption, transport, and reactivity. However, the molecular-scale links between pHdependent surface protonation, hydration-layer structure, and counterion adsorption remain poorly defined. Here, we combine in situ crystal truncation rod (CTR) and resonant anomalous X-ray reflectivity (RAXR) with streaming potential measurements and ab initio molecular dynamics (AIMD) simulations to resolve the chemical and structural evolution of the EDL at the single-crystal alumina (012)−water interface in 10 mM Rb + over pH 3−12. CTR measurements reveal two distinct adsorbed water layers at ∼2.2 and ∼3.5 Å above the surface. Each water layer shifts toward the substrate at transition pHs near 6.5 and 10.6, respectively, reflecting changes in primary hydration layer structure in response to the deprotonation of bridging and terminal aluminol groups. RAXR shows a 10-fold increase in Rb + coverage and a decrease in mean adsorption height from ∼3.5 to ∼2.7 Å with increasing pH, indicating enhanced counterion binding accompanied by Stern layer contraction. Streaming potential measurements demonstrate that the zeta potential, i.e., the potential at the hydrodynamic shear plane, is positive at pH 3 and becomes negative at pH ≥ 3.5. The negative charge magnitude increases with increasing pH, consistent with progressive surface deprotonation at higher pH. AIMD identifies inner- and outer-sphere Rb+ complexes whose adsorption heights and coordination geometries depend sensitively on the protonation state of surface oxygens, providing atomistic support for the experimentally inferred trends. These measurements establish two discrete, site-specific pH transitions in hydration-layer structure that track aluminol (de)protonation and quantitatively link them to a pH-driven contraction of the Stern layer (increasing Rb + coverage and decreasing adsorption height). This provides a direct structural basis for connecting surface acid−base chemistry to ion binding distances at an oxide−water interface.

Adsorption↗