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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Framework for assessing land‐management effects on at‐risk species: Example of SE USA wood pellet production and gopher tortoise ( Gopherus polyphemus )

Abstract Land‐management choices made for economic and societal gains intrinsically influence landscapes and species that are dependent upon them. We propose a simple analysis framework to examine critical intersections between land‐management choices and the life‐history conditions of selected species of concern, thereby facilitating the identification of mitigation practices that can reduce negative impacts on species at risk. We test the proposed framework through application to gopher tortoise ( Gopherus polyphemus ), a keystone species that is the focus of conservation efforts across the southeastern region of United States of America, where wood pellets are being produced for bioenergy. Production of these wood pellets for export to Europe and Asia has drastically increased in the past decade, raising concerns about potential harm to biodiversity since many species in the forests sourcing pellet production were already at risk prior to the development of this new commodity. Identifying the mechanisms of potential impacts of wood pellet production on species of concern is essential to establishing meaningful management recommendations that can enhance conservation efforts while supporting sustainable bioenergy. By considering the intersections between life‐history conditions of gopher tortoise and forest‐management practices related to woody biomass extraction for pellet production, we identify several mechanisms by which the wood‐pellet industry might affect this species of concern, both positively and negatively. We then identify mitigation practices that can help offset the potential impacts of logging, thinning, and dead wood removal on gopher tortoise. Our analysis framework may be transferable to other species of concern and land‐management practices across diverse landscapes. This article is categorized under: Bioenergy > Economics and Policy Bioenergy > Climate and Environment

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Li 2 GeS 3 : Lithium Ionic Conductor with an Unprecedented Structural Type

Lithium-ion batteries (LIBs) are widely used in electric vehicles, mobile electronic devices, and large-scale stationary energy storage systems. However, their liquid electrolytes present significant safety concerns due to their inherent flammability. To address this, the focus has shifted toward all-solid-state batteries (ASSBs) utilizing inorganic solid electrolytes that promise enhanced safety. In this work, we report the discovery of a new crystal structural type of Li-ion conductor, Li 2 GeS 3 , with a unique structure, synthesized by a solid-state reaction from Li 2 S and GeS 2 . It was first reported in 2000 with an orthorhombic unit cell, but its detailed crystal structure remains veiled. Here, we have unveiled its structure for the first time, employing an ab initio structure determination technique from powder X-ray and time-of-flight neutron diffraction data. The compound has an unprecedented crystal structural type with a hexagonal $P6_1$ symmetry and a unit cell of α = 6.79364(4) Å and c = 17.90724(14) Å. Its structure is comprised of a distorted hexagonal close-packed arrangement of sulfur anions with three asymmetric metal atoms: Li1, Li2, and Ge are in tetrahedral cavities surrounded by sulfur atoms. The ionic conductivity of Li 2 GeS 3 was measured to be 1.63 × 10 –8 S cm –1 at 303 K and 2.45 × 10 –7 S cm –1 at 383 K. Bond valence energy landscape calculations revealed three-dimensional lithium diffusion pathways within the structure. This novel crystal structure in Li 2 GeS 3 holds the potential for developing high-performance ionic conductors through suitable chemical substitution and offers valuable insights into designing new ionic conductors for ASSBs.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

A Deep Learning-Driven Sampling Technique to Explore the Phase Space of an RNA Stem-Loop

The folding and unfolding of RNA stem-loops are critical biological processes; however, their computational studies are often hampered by the ruggedness of their folding landscape, necessitating long simulation times at the atomistic scale. Here, we adapted DeepDriveMD (DDMD), an advanced deep learning-driven sampling technique originally developed for protein folding, to address the challenges of RNA stem-loop folding. Although tempering- and order parameter-based techniques are commonly used for similar rare-event problems, the computational costs or the need for a priori knowledge about the system often present a challenge in their effective use. DDMD overcomes these challenges by adaptively learning from an ensemble of running MD simulations using generic contact maps as the raw input. DeepDriveMD enables on-the-fly learning of a low-dimensional latent representation and guides the simulation toward the undersampled regions while optimizing the resources to explore the relevant parts of the phase space. We showed that DDMD estimates the free energy landscape of the RNA stem-loop reasonably well at room temperature. Our simulation framework runs at a constant temperature without external biasing potential, hence preserving the information on transition rates, with a computational cost much lower than that of the simulations performed with external biasing potentials. Here, we also introduced a reweighting strategy for obtaining unbiased free energy surfaces and presented a qualitative analysis of the latent space. This analysis showed that the latent space captures the relevant slow degrees of freedom for the RNA folding problem of interest. Finally, throughout the manuscript, we outlined how different parameters are selected and optimized to adapt DDMD for this system. We believe this compendium of decision-making processes will help new users adapt this technique for the rare-event sampling problems of their interest.

Gupta, Ayush↗
DOE OSTI2020

Emerging materials intelligence ecosystems propelled by machine learning

We report that the age of cognitive computing and artificial intelligence (AI) is just dawning. Inspired by its successes and promises, several AI ecosystems are blossoming, many of them within the domain of materials science and engineering. These materials intelligence ecosystems are being shaped by several independent developments. Machine learning (ML) algorithms and extant materials data are utilized to create surrogate models of materials properties and performance predictions. Materials data repositories, which fuel such surrogate model development, are mushrooming. Automated data and knowledge capture from the literature (to populate data repositories) using natural language processing approaches is being explored. The design of materials that meet target property requirements and of synthesis steps to create target materials appear to be within reach, either by closed-loop active-learning strategies or by inverting the prediction pipeline using advanced generative algorithms. AI and ML concepts are also transforming the computational and physical laboratory infrastructural landscapes used to create materials data in the first place. Surrogate models that can outstrip physics-based simulations (on which they are trained) by several orders of magnitude in speed while preserving accuracy are being actively developed. Automation, autonomy and guided high-throughput techniques are imparting enormous efficiencies and eliminating redundancies in materials synthesis and characterization. The integration of the various parts of the burgeoning ML landscape may lead to materials-savvy digital assistants and to a human-machine partnership that could enable dramatic efficiencies, accelerated discoveries and increased productivity. Here, we review these emergent materials intelligence ecosystems and discuss the imminent challenges and opportunities. The materials research landscape is being transformed by the infusion of approaches based on machine learning. This Review discusses the emerging materials intelligence ecosystems and the potential of human-machine partnerships for fast and efficient virtual materials screening, development and discovery.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Quantum Computing and Visualization: A Disruptive Technological Change Ahead

The focus of this Visualization Viewpoints article is to provide some background on quantum computing (QC), to explore ideas related to how visualization helps in understanding QC, and examine how QC might be useful for visualization with the growth and maturation of both technologies in the future. In a quickly evolving technology landscape, QC is emerging as a promising pathway to overcome the growth limits in classical computing. In some cases, QC platforms offer the potential to vastly outperform the familiar classical computer by solving problems more quickly or that may be intractable on any known classical platform. As further performance gains for classical computing platforms are limited by diminishing Moore’s Law scaling, QC platforms might be viewed as a potential successor to the current field of exascale-class platforms. Importantly, while present-day QC hardware platforms are still limited in scale, the field of quantum computing is robust and rapidly advancing in terms of hardware capabilities, software environments for developing quantum algorithms, and educational programs for training the next generation of scientists and engineers. After a brief introduction to QC concepts, the focus of this article is to explore the interplay between the fields of visualization and QC. First, visualization has played a role in QC by providing the means to show representations of the quantum state of single-qubits in superposition states and multiple-qubits in entangled states. Second, there are a number of ways in which the field of visual data exploration and analysis may potentially benefit from this disruptive new technology though there are challenges going forward.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

COVID-19 Technical Assistance Program: Agrivoltaic for Rural Economic Development and Electric Grids Resilience

Over the past 50 years, the Renewable Energy Program at Sandia has advanced research in the field with a focus on three key goals; 1) reduce the cost, 2) improve resilience and reliability and, 3) decrease the regulatory burden of renewable energy. Sandia’s expertise, coupled with the Village of Questa’s expanding renewable energy portfolio, presents the opportunity to deploy the Labs’ deep science and engineering capabilities towards the energy goals of KCEC and the Village of Questa. Preliminary research efforts by Sandia technical staff has broadly identified early opportunities for further research, development, and demonstration in the emerging renewable energy segment of agrivoltaics. Agrivoltaics is an emerging and promising area of photovoltaics which entails land use considerations as well as concerns regarding landscape transformation, biodiversity, and ecosystem well-being. In recent years, agrivoltaics systems have been the subject of numerous studies due to their potential in the food-energy (and water) nexus. This document is a preliminary evaluation of the projects performance opportunities of agrivoltaics as a renewable energy technology strategy in the region of Questa, NM.

14 SOLAR ENERGY↗
DOE OSTI2022

Defining the S-Glutathionylation Proteome by Biochemical and Mass Spectrometric Approaches

Protein S-glutathionylation (SSG) is a reversible post-translational modification (PTM) featuring the conjugation of glutathione to a protein cysteine thiol. SSG can alter protein structure, activity, subcellular localization, and interaction with small molecules and other proteins. Thus, it plays a critical role in redox signaling and regulation in various physiological activities and pathological events. In this review, we summarize current biochemical and analytical approaches for characterizing SSG at both the proteome level and at individual protein levels. To illustrate the mechanism underlying SSG-mediated redox regulation, we highlight recent examples of functional and structural consequences of SSG modifications. Finally, we discuss the analytical challenges in characterizing SSG and the thiol PTM landscape, future directions for understanding of the role of SSG in redox signaling and regulation and its interplay with other PTMs, and the potential role of computational approaches to accelerate functional discovery.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

A Comprehensive Review on Finite Element Analysis of Laser Shock Peening

Laser shock peening (LSP) is a formidable cold working surface treatment that provides high-energy precision to enhance the mechanical properties of materials. This paper delves into the intricacies of the LSP process, offering insights into its methodology and the simulation thereof through the finite element method. This review critically examines various points, such as laser energy, overlapping of shots, effect of LSP on residual stress, effect of LSP on grain refinement, and algorithms for simulation extrapolated from finite element analyses conducted by researchers, shedding light on the nuanced considerations integral to this technique. As the significance of LSP continues to grow, the collective findings underscore its potential as a transformative technology for fortifying materials against mechanical stress and improving their overall performance and longevity. The discourse encapsulates the evolving landscape of the LSP, emphasizing the pivotal role played by finite element analysis in advancing our understanding and application of this innovative surface treatment.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Observed impacts of large wind farms on grassland carbon cycling

Deployment of wind energy is an essential renewable energy source that mitigates climate change and reduces air pollution. Over the last several decades, wind energy development has increased worldwide, expanding from ~20 to ~900 GW (gigawatt) during 2001-2022. Nonetheless, researchers have identified unintended consequences of wind energy on microclimate via turbine-altered surface-atmosphere exchanges of energy, momentum, mass, and trace gases. Based on multi-source observations and models, researchers also have drawn some conclusions that wind farms could warm the land surface, especially at night, at regional and continental scales. Consequently, altered microclimates at wind farms may affect vegetation productivity and carbon sequestration, two critically important ecosystem services related to carbon dynamics; however, such potential impacts and driving mechanisms remain poorly understood. Wind energy deployment is increasing globally to meet carbon neutrality goals, with upscaling of onshore wind power capacity projected to grow from 542 GW in 2018 to 1787 and 5044 GW by 2030 and 2050, respectively. Furthermore, increased demand for wind energy deployment may lead to much larger wind farms in open, expansive landscapes. In turn, a large array of geographically clustered wind turbines could collectively modify local microclimate and amplify turbine-atmosphere interactions, which, if large enough, may produce detectable impacts on ecosystem dynamics. Thus, identifying and quantifying the potential impacts of wind farms on carbon-related ecosystem services may facilitate sustainable wind energy development globally.

17 WIND ENERGY↗
DOE OSTI2022

Learning in continuous action space for developing high dimensional potential energy models

Reinforcement learning (RL) approaches that combine a tree search with deep learning have found remarkable success in searching exorbitantly large, albeit discrete action spaces, as in chess, Shogi and Go. Many real-world materials discovery and design applications, however, involve multi-dimensional search problems and learning domains that have continuous action spaces. Exploring high-dimensional potential energy models of materials is an example. Traditionally, these searches are time consuming (often several years for a single bulk system) and driven by human intuition and/or expertise and more recently by global/local optimization searches that have issues with convergence and/or do not scale well with the search dimensionality. Here, in a departure from discrete action and other gradient-based approaches, we introduce a RL strategy based on decision trees that incorporates modified rewards for improved exploration, efficient sampling during playouts and a “window scaling scheme" for enhanced exploitation, to enable efficient and scalable search for continuous action space problems. Using high-dimensional artificial landscapes and control RL problems, we successfully benchmark our approach against popular global optimization schemes and state of the art policy gradient methods, respectively. We demonstrate its efficacy to parameterize potential models (physics based and high-dimensional neural networks) for 54 different elemental systems across the periodic table as well as alloys. We analyze error trends across different elements in the latent space and trace their origin to elemental structural diversity and the smoothness of the element energy surface. Broadly, our RL strategy will be applicable to many other physical science problems involving search over continuous action spaces.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Advancing wildlife connectivity in land use planning: a case study with four-toed salamanders

Stable habitat connections that wildlife can safely traverse are essential to biodiversity conservation and healthy ecosystems. We developed high-resolution landscape connectivity models to predict resistance to movement by a threatened wetland-obligate amphibian, the four-toed salamander ( Hemidactylium scutatum ), and identified priority management areas on the 13,000-ha Department of Energy Oak Ridge Reservation (ORR) from 2019 to 2022. We developed a resistance surface based on aerial light detection and ranging data (LiDAR), >30 years of field-based mapping of forest, hydrologic, and geologic features, and contemporary population surveys, alongside derived predictors at <1-m resolution. We then modeled predicted movement corridors using a circuit theory-based modeling approach. We worked closely with land management and natural resources personnel to integrate ecological modeling with broader land use priorities, monetary costs, and feasibility. We identified important terrestrial and aquatic areas on ORR and simulated management scenarios to promote stable connections for four-toed salamanders. This approach allowed us to narrow down a list of 438 potential habitat manipulation sites to 10 sites where open-bottomed culverts and buffers could be implemented. This smaller-scale restoration approach produced a similar increase in landscape connectivity while costing <20% of a larger-scale approach based on barrier removal. We successfully identified feasible, cost-effective management strategies that integrated knowledge from a variety of sources. In conclusion, we offer a strategy that permitted integration of wildlife management goals into infrastructure upgrades wherein wildlife was not an initial consideration.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

The Landscape of Thermal Transients from Supernovae Interacting with a Circumstellar Medium

The interaction of supernova ejecta with a surrounding circumstellar medium (CSM) generates a strong shock, which can convert ejecta kinetic energy into observable radiation. Given the diversity of potential CSM structures (arising from diverse mass-loss processes such as late-stage stellar outbursts, binary interaction, and winds), the resulting transients can display a wide range of light-curve morphologies. We provide a framework for classifying the transients arising from interaction with a spherical CSM shell. The light curves are decomposed into five consecutive phases, starting from the onset of interaction and extending through shock breakout and subsequent shock cooling. The relative prominence of each phase in the light curve is determined by two dimensionless quantities representing the CSM-to-ejecta mass ratio η, and the breakout parameter ξ. These two parameters define four light-curve morphology classes, where each class is characterized by the location of the shock breakout and the degree of deceleration as the shock sweeps up the CSM. We compile analytic scaling relations connecting the luminosity and duration of each light-curve phase to the physical parameters. We then run a grid of radiation hydrodynamics simulations for a wide range of ejecta and CSM parameters to numerically explore the landscape of interaction light curves, and to calibrate and confirm the analytic scalings. We connect our theoretical framework to several case studies of observed transients, highlighting the relevance in explaining slow-rising and superluminous supernovae, fast blue optical transients, and double-peaked light curves.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

Hurricane-Induced Rainfall is a Stronger Predictor of Tropical Forest Damage in Puerto Rico Than Maximum Wind Speeds

Projected increases in cyclonic storm intensity under a warming climate will have profound effects on forests, potentially changing these ecosystems from carbon sinks to sources. Forecasting storm impacts on these ecosystems requires consideration of risk factors associated with storm meteorology, landscape structure, and forest attributes. Here we evaluate risk factors associated with damage severity caused by Hurricanes María and Irma across Puerto Rican forests. Using field and remote sensing data, total forest aboveground biomass (AGB) lost to the storms was estimated at 10.44 (±2.33) Tg, ca. 23% of island-wide pre-hurricane forest AGB. Storm-related rainfall was a stronger predictor of forest damage than maximum wind speeds. Soil water storage capacity was also an important risk factor, corroborating the influence of rainfall on forest damage. Expected increases of 20% in hurricane-associated rainfall in the North Atlantic highlight the need to consider how such shifts, together with high speed winds, will affect terrestrial ecosystems.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Stability Constants of Lanthanide-nitrate Complexes in Aqueous Solutions: A Theoretical Study

Calculating stability constants for lanthanide--nitrate complexes in aqueous solution is challenging due to the complex free-energy landscapes of the participating species. In this work, we compare cluster-continuum solvation and condensed-phase approaches using universal machine learning interatomic potentials (MLIPs) for determining the stability constants of lanthanide--nitrate complexes in aqueous solutions. Within the cluster--continuum solvation framework at the B3LYP level of theory, reactions involving lanthanide coordination numbers of both 8 and 9 are found to be relevant. After an empirical linear free-energy correction, the cluster--continuum results fall on the same order-of-magnitude scale as the experimental stability constants. By contrast, MACE-MP0 MLIP underestimates lanthanide hydration numbers and gives PMF-derived stability constants with large deviations from experiment, whereas MACE-MATPES-R2SCAN improves both hydration structure and the stability-constant scale but still does not quantitatively reproduce the detailed lanthanide trend. Across both approaches, nitrate binding is best viewed as a labile coordination motif rather than a fixed mono- or bidentate structure, with hydration-shell structure influencing which configurations are favored. Overall, the cluster--continuum calculations provide a practical semi-quantitative baseline for the experimental stability-constant scale, while the explicit-solvent MLIP benchmarks show clear progress from MACE-MP0 to MACE-MATPES-r2SCAN but also highlight the need for lanthanide-targeted training, fine-tuning, or improved long-range and polarization treatments to obtain predictive thermodynamics for complex aqueous lanthanide chemistry.

Dinpajooh, Mohammadhasan↗
DOE OSTI2024

A multiverse outside of the swampland

A multiverse can arise from landscapes without de Sitter minima. It can be populated during a period of eternal inflation without trans-Planckian field excursions and without flat potentials. This multiverse can explain the values of the cosmological constant and of the weak scale. In the process of proving these statements, we derive a few simple, but counterintuitive results. We show that it is easy to write models of eternal inflation compatible with the distance and refined de Sitter conjectures. Secondly, tunneling transitions that move fields from a lower-energy vacuum to a higher-energy vacuum and generate baby universes are possible, and occur during eternal inflation. Finally, we relax the assumption of no de Sitter minima and show that this more standard multiverse can be populated by Coleman-de Luccia transitions in about 100 e-folds of inflation.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Novel CHI3L1 ‐Associated Angiogenic Phenotypes Define Glioma Microenvironments: Insights From Multi‐Omics Integration

ABSTRACT The CHI3L1 signaling pathway significantly influences glioma angiogenesis, but its role in the tumor microenvironment (TME) remains elusive. We propose a novelCHI3L1‐associated vascular phenotype classification for glioma through integrative analyses of multiple datasets with bulk and single‐cell transcriptome, genomics, digital pathology, and clinical data. We investigated the biological characteristics, genomic alterations, therapeutic vulnerabilities, and immune profiles within these phenotypes through a comprehensive multi‐omics approach. We constructed the vascular‐related risk (VR) score based onCHI3L1‐associated vascular signatures (CAVS) identified by machine learning algorithms. Utilizing unsupervised consensus clustering, gliomas were stratified into three distinct vascular phenotypes: Cluster A, marked by high vascularization and stromal activation with a relatively low levels of tumor‐infiltrating lymphocytes (TILs); Cluster B, characterized by moderate vascularization and stromal activity, coupled with a high density of TILs; and Cluster C, defined by low vascularization and sparse immune cell infiltration. We observed that the CAVS effectively indicated glioma‐associated angiogenesis and immune suppression by single‐cell RNA‐seq analysis. Moreover, the high‐VR‐score group exhibited enhanced angiogenic activity, reduced immune response, resistance to immunotherapy, and poorer clinical outcomes. The VR score independently predicted glioma prognosis and, combined with a nomogram, provided a robust clinical decision‐making tool. Potential drug prediction based on transcription factors for high‐risk patients was also performed. Our study reveals thatCHI3L1‐associated vascular phenotypes shape distinct immune landscapes in gliomas, offering insights for optimizing therapeutic strategies to improve patient outcomes.

Oncology↗
DOE OSTI2022

Innovative Technologies for a Low Carbon Electricity System

The electricity sector represents the centerpiece of decarbonization pathways for the state. Decrease in the cost of renewable electricity generation, combined with ample solar energy, wind and other renewable resources, presents a realistic way to achieve electricity generation that is nearly free of CO 2 emissions by mid-century. Expansion of renewable electricity supply could allow replacement of many CO 2 -emitting technologies with ones that use electricity—in transportation, buildings, and possibly industry. Key elements of the path for California’s electricity sector are: restrain electricity demand through higher efficiency, rapidly expand renewable electricity generation, develop electricity storage to complement renewable electricity, manage flexible electricity loads for a low-carbon electricity system, electrify where appropriate to reduce CO 2 emissions, and maintain reliable and resilient electricity supply. This report provides an overview of a multitude of innovative technologies in each of the above areas that have the potential to help the state meet its decarbonization goals, while lowering costs and promoting greater reliability. The information presented provides a portrait of the landscape of technology innovation that can help policymakers, state agencies, and interested parties develop strategies to meet the state’s goals and to target efforts to support and nurture technology innovation.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2026

Electrolyte Organization Leads to Potential-Dependence in Thermochemical Catalysis of Nonpolar Reactions

Electrochemical polarization is now known to play a key role in thermochemical catalysis at solid–liquid interfaces. However, existing frameworks cannot account for why even nonpolar, nonfaradaic reactions are sensitive to interfacial polarization. In order to uncover the molecular basis of this phenomenon, we herein study the potential-dependent reaction kinetics of ethylene and trans-2-butene hydrogenation at Pt–liquid interfaces. Measurements were performed in aqueous and ortho-difluorobenzene (o-DFB) solutions, spontaneously polarizing the Pt–liquid interfaces by, respectively, varying the pH or dissolving distinct metallocene redox buffers into solution. Here, we find that at comparable mechanistic regimes, the rates of both ethylene and trans-2-butene hydrogenation are maximized near the same electrochemical potential, E. Moreover, the potential-dependence, defined as $\frac{∂ln 𝑟}{∂𝐸}$, of trans-2-butene hydrogenation is approximately 2.2× greater than that of ethylene hydrogenation across the full potential range studied. These observations are all consistent with a model in which polarization of the Pt surface away from the local potential of zero free charge (E PZFC ) induces electrostatic organization of the polar solvent and charged ions near the interface, which impedes olefin adsorption and surface reaction because these surface reactions induce electrolyte displacement. Accordingly, interfacial polarization alters the free energy landscape and thus the rate of nonpolar heterogeneous catalysis by controlling the degree of electrostatic organization of polar and charged spectators at the interface, which do not in general need to be specifically chemisorbed onto the surface but could simply be close enough to the surface to be perturbed by the olefin adsorption. These results point toward electrochemical design handles, namely, the electrolyte, catalyst potential, and local E PZFC of the catalyst, with which to tune interfacial catalysis of thermochemical organic transformations.

adsorption↗