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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 505 records · Page 28
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Quantum chaos, integrability, and late times in the Krylov basis

Quantum chaotic systems are conjectured to display a spectrum whose fine-grained features (gaps and correlations) are well described by random matrix theory (RMT). We propose and develop a complementary version of this conjecture: quantum chaotic systems display a Lanczos spectrum whose local means and covariances are well described by RMT. To support this proposal, we first demonstrate its validity in examples of chaotic and integrable systems. We then show that for Haar-random initial states in RMTs the mean and covariance of the Lanczos spectrum suffice to produce the full long-time behavior of general survival probabilities including the spectral form factor, as well as the spread complexity. In addition, for initial states with continuous overlap with energy eigenstates, we analytically find the long-time averages of the probabilities of Krylov basis elements in terms of the mean Lanczos spectrum. This analysis suggests a notion of eigenstate complexity, the statistics of which differentiate integrable systems and classes of quantum chaos. Lastly, we clarify the relation between spread complexity and the universality classes of RMT by exploring various values of the Dyson index and Poisson distributed spectra.

Combinatorics↗
DOE OSTI2022

Reaction processes at step edges on S-decorated Cu(111) and Ag(111) surfaces: MD analysis utilizing machine learning derived potentials

We report a variety of complexation, reconstruction, and sulfide formation processes can occur at step edges on the {111} surfaces of coinage metals (M) in the presence of adsorbed S under ultra-high vacuum conditions. Given the cooperative many-atom nature of these reaction processes, Molecular Dynamics (MD) simulation of the associated dynamics is instructive. However, only quite restricted Density Functional Theory (DFT)-level ab initio MD is viable. Thus, for M = Ag and Cu, we instead utilize the DeePMD framework to develop machine-learning derived potentials, retaining near-DFT accuracy for the M–S systems, which should have broad applicability. These potentials are validated by comparison with DFT predictions for various key quantities related to the energetics of S on M(111) surfaces. The potentials are then utilized to perform extensive MD simulations elucidating the above diverse restructuring and reaction processes at step edges. Key observations from MD simulations include the formation of small metal–sulfur complexes, especially MS 2 ; development of a local reconstruction at A-steps featuring an S-decorated {100} motif; and 3D sulfide formation. Additional analysis yields further information on the kinetics for metal–sulfur complex formation, where these complexes can strongly enhance surface mass transport, and on the propensity for sulfide formation.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Bimetallic Copper/Ruthenium/Osmium Complexes: Observation of Conformational Differences Between the Solution Phase and Solid State by Atomic Pair Distribution Function Analysis

Abstract High‐energy X‐ray scattering and pair distribution function analysis (HEXS/PDF) is a powerful method to reveal the structure of materials lacking long‐range order, but is underutilized for molecular complexes in solution. We demonstrate the application of HEXS/PDF with 0.26 Å resolution to uncover the solution structure of five bimetallic Cu I /Ru II /Os II complexes. HEXS/PDF of each complex in acetonitrile solution confirms the pairwise distances in the local coordination sphere of each metal center as well as the metal⋅⋅⋅metal distances separated by over 12 Å. The metal⋅⋅⋅metal distance detected in solution is compared with that from the crystal structure and molecular models to confirm that distortions to the metal bridging ligand are unique to the solid state. This work presents the first example of observing sub‐Ångström conformational differences by direct comparison of solution phase and solid‐state structures and shows the potential for HEXS/PDF in the determination of solution structure of single molecules.

Xie, Zhu‐Lin↗
DOE OSTI2024

Deep learning-driven super-resolution in Raman hyperspectral imaging: Efficient high-resolution reconstruction from low-resolution data

Deep learning (DL) has become an indispensable tool in hyperspectral data analysis, automatically extracting valuable features from complex, high-dimensional datasets. Super-resolution reconstruction, an essential aspect of hyperspectral data, involves enhancing spatial resolution, particularly relevant to low-resolution hyperspectral data. Yet, the pursuit of super-resolution in hyperspectral analysis is fraught with challenges, including acquiring ground truth high-resolution data for training, generalization, and scalability. The pressing issue of extended spectral acquisition times, notably for high-resolution scans, is a significant roadblock in hyperspectral imaging. Super-resolution methods offer a promising solution by providing higher spatial resolution data to expedite data collection and yield more efficient outcomes. This paper delves into a practical application of these concepts using Raman imaging, where spectral acquisition times can be prohibitively long. In this context, DL-based super-resolution models demonstrate their efficacy by predicting and reconstructing high-resolution Raman data from low-resolution input, eliminating the need for resource-intensive high-resolution scans. While previous work often relied on substantial high-resolution datasets, this study showcases the ability to achieve similar outcomes even with limited data, presenting a more practical and cost-effective approach. In conclusion, the results offer a glimpse into the transformative potential of this technology to streamline hyperspectral imaging applications by saving valuable time and resources through the successful generation of high-resolution data from low-resolution inputs.

42 ENGINEERING↗
DOE OSTI2025

Digital Droplet PCR and Mesocosm-Based Methods to Evaluate Biocontainment Strategies in a Native Soil Ecosystem

Genetically modified industrial production microbes and their associated bioproducts have emerged as an integral component of a sustainable bioeconomy. However, the rapid development of these innovative technologies raises biosecurity concerns, namely, the risk of environmental escape. Thus, the realization of a bioeconomy hinges not only on the development and deployment of microbial production hosts, but also on the development of secure biosystems and biocontainment designs. Current laboratory-based biocontainment testing systems do not accurately reflect the complexities found in natural environments, necessitating an environmentally relevant analysis pipeline that allows for the detection of rare escapees within a complex soil microbiome and differentiation between closely related strains. To this end, we have developed an approach that utilizes soil mesocosms and integrated digital droplet PCR (ddPCR) system to evaluate the efficacy of novel biocontainment strategies. We demonstrate the utility of this approach by modeling contamination with industrial microbial chasses versus their biocontained counterparts. Here we demonstrate the broad utility of this system by highlighting findings from strains of Saccharomyces cerevisiae that are contained with an inducible toxin anti-toxin system, strains of Synechocystis sp. PCC 6803 contained via gene knockout or toxin anti-toxin system, and strains of Escherichia coli that are contained via genomic recoding. We also show that ddPCR can be used to detect gene copies from E. coli equal to those counted by traditional spot plating assays. The resultant data demonstrates that this system has broad utility across diverse microbial chassis and biocontainment strategies and enables researchers to track the fate of our contaminating microbe with high sensitivity in the soil. The findings presented here support the use of this mesocosm-based approach to assess the environmental impact of industrial microbes and to validate biocontainment strategies.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Bimetallic Copper/Ruthenium/Osmium Complexes: Observation of Conformational Differences Between the Solution Phase and Solid State by Atomic Pair Distribution Function Analysis

High-energy X-ray scattering and pair distribution function analysis (HEXS/PDF) is a powerful method to reveal the structure of materials lacking long-range order, but is underutilized for molecular complexes in solution. Here we demonstrate the application of HEXS/PDF with 0.26 Å resolution to uncover the solution structure of five bimetallic Cu I /Ru II /Os II complexes. HEXS/PDF of each complex in acetonitrile solution confirms the pairwise distances in the local coordination sphere of each metal center as well as the metal...metal distances separated by over 12 Å. The metal…metal distance detected in solution is compared with that from the crystal structure and molecular models to confirm that distortions to the metal bridging ligand are unique to the solid state. This work presents the first example of observing sub-Ångström conformational differences by direct comparison of solution phase and solid-state structures and shows the potential for HEXS/PDF in the determination of solution structure of single molecules.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Trans-Influence in Dinuclear Pt(III) Complexes: Electronic Structure, σ-Donation, and Pt–Pt Spin–Spin Coupling

This study investigates the trans influence in dinuclear platinum(III) complexes using a combined approach of ab initio molecular dynamics and natural localized molecular orbital (NLMO) analysis. Focusing on pivalamidate-bridged Pt III complexes with axial ligands of varying σ-donation strength, it is quantified how ligand−metal interactions propagate through the Pt−Pt bond, and how they affect bond polarization, axial water coordination, and 1 J PtPt spin−spin coupling constants. NLMO analysis reveals quantitatively that strong σ-donating ligands polarize the Pt−Pt bond, shifting the electron density toward the opposite platinum center. The polarization mechanism is identified as the primary reason for the observed reduction of 1 J PtPt , because the bond polarization diminishes the transmission of the nuclear magnetic spin-induced electron spin density through the Pt−Pt bond. Additionally, the destabilization of axial water coordination at the opposite Pt site can be rationalized through a polarizationinduced Pt IV − Pt II -like mixed-valence character.

Ab initio molecular dynamics↗
DOE OSTI2024

Multi-year analysis of physical interactions between solar PV arrays and underlying soil-plant complex in vegetated utility-scale systems

Concerns over the land use changes impacts of solar photovoltaic (PV) development are increasing as PV energy development expands. Co-locating utility-scale solar energy with vegetation may maintain or rehabilitate the land's ability to provide ecosystem services. Previous studies have shown that vegetation under and around the panels may improve the performance of the co-located PV and that PV may create a favorable environment for the growth of vegetation. While there have been some pilot-scale experiments, the existence and magnitude of these benefits of vegetation has not been confirmed in a utility-scale PV facility over multiple years. In this study we use power output data coupled with microclimatic measurements in temperate climates to assess these potential benefits. Here this study combines multi-year microclimatic measurements to analyze the physical interactions between PV arrays and the underlying soil-vegetation system in three utility-scale PV facilities in Minnesota, USA. No significant cooling of PV panels or increased power production was observed in PV arrays with underlying vegetation. Fine soil particle fraction was the highest in soils within PV arrays with the vegetation which was attributable to the lowest wind speeds from the compounding suppression of wind by vegetation and PV arrays. Soil moisture and soil nutrient response to re-vegetation varied between PV facilities, which could be attributed to differing soil texture. No statistically significant vegetation-driven panel cooling was observed in this climate. This finding prompts a need for site-specific studies to identify contributing factors for environmental co-benefits in co-located systems.

14 SOLAR ENERGY↗
DOE OSTI2024

The Average Spectrum Norm and Near-Optimal Tensor Completion

We propose the average spectrum norm to study the minimum number of measurements required to approximate a multidimensional array (i.e., sample complexity) via low-rank tensor recovery. Our focus is on the tensor completion problem, where the aim is to estimate a multiway array using a subset of tensor entries corrupted by noise. Our average spectrum norm-based analysis provides near-optimal sample complexities, exhibiting dependence on the ambient dimensions and rank that do not suffer from exponential scaling as the order increases.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Evaluation of global teleconnections in CMIP6 climate projections using complex networks

In climatological research, the evaluation of climate models is one of the central research subjects. As an expression of large-scale dynamical processes, global teleconnections play a major role in interannual to decadal climate variability. Their realistic representation is an indispensable requirement for the simulation of climate change, both natural and anthropogenic. Therefore, the evaluation of global teleconnections is of utmost importance when assessing the physical plausibility of climate projections. We present an application of the graph-theoretical analysis tool δ-MAPS, which constructs complex networks on the basis of spatio-temporal gridded data sets, here sea surface temperature and geopotential height at 500 hPa. Complex networks complement more traditional methods in the analysis of climate variability, like the classification of circulation regimes or empirical orthogonal functions, assuming a new non-linear perspective. While doing so, a number of technical tools and metrics, borrowed from different fields of data science, are implemented into the δ-MAPS framework in order to overcome specific challenges posed by our target problem. Those are trend empirical orthogonal functions (EOFs), distance correlation and distance multicorrelation, and the structural similarity index. δ-MAPS is a two-stage algorithm. In the first place, it assembles grid cells with highly coherent temporal evolution into so-called domains. In a second step, the teleconnections between the domains are inferred by means of the non-linear distance correlation. We construct 2 unipartite and 1 bipartite network for 22 historical CMIP6 climate projections and 2 century-long coupled reanalyses (CERA-20C and 20CRv3). Potential non-stationarity is taken into account by the use of moving time windows. The networks derived from projection data are compared to those from reanalyses. Our results indicate that no single climate projection outperforms all others in every aspect of the evaluation. But there are indeed models which tend to perform better/worse in many aspects. Differences in model performance are generally low within the geopotential height unipartite networks but higher in sea surface temperature and most pronounced in the bipartite network representing the interaction between ocean and atmosphere.

58 GEOSCIENCES↗
DOE OSTI2022

Structural and functional analysis of the human cone‐rod homeobox transcription factor

Abstract The cone‐rod homeobox (CRX) protein is a critical K50 homeodomain transcription factor responsible for the differentiation and maintenance of photoreceptor neurons in the vertebrate retina. Mutant alleles in the human gene encoding CRX result in a variety of distinct blinding retinopathies, including retinitis pigmentosa, cone‐rod dystrophy, and Leber congenital amaurosis. Despite the success of using in vitro biochemistry, animal models, and genomics approaches to study this clinically relevant transcription factor over the past 25 years since its initial characterization, there are no high‐resolution structures in the published literature for the CRX protein. In this study, we use bioinformatic approaches and small‐angle X‐ray scattering (SAXS) structural analysis to further understand the biochemical complexity of the human CRX homeodomain (CRX‐HD). We find that the CRX‐HD is a compact, globular monomer in solution that can specifically bind functional cis‐regulatory elements encoded upstream of retina‐specific genes. This study presents the first structural analysis of CRX, paving the way for a new approach to studying the biochemistry of this protein and its disease‐causing mutant protein variants.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Symmetry in Seeded Metal Nanocrystal Growth

Symmetry underpins the structure and function of the world around us and is also captured in modern nanomaterials, where changing the symmetry of a nanocrystal or the interparticle spacing and orientation of nanocrystal building blocks in a superlattice can give new function. However, the synthesis and assembly of nanocrystals have been limited largely to simple compositions and structures. It remains a grand challenge to achieve nanocrystals with compositional and structural complexity while maintaining the monodispersity required for their use. This Account will illustrate through recent examples that seeded methods enable the synthesis of compositionally and structurally complex multimetallic crystals with defined and predictable symmetries for applications in plasmonics and catalysis. This outcome arises because the barrier for heterogeneous nucleation (i.e., seeded) is lower than that of homogeneous nucleation, where seeds can serve as preferential sites for the growth of complex structures and crystal phases. Our analysis begins by considering metal overgrowth from single-crystalline seeds of different shapes and symmetries, where the kinetics of adatom addition to seeds relative to their diffusion across seeds accounts for the expressed nanocrystal shapes. These results are then compared to overgrowth from seeds with different internal structures (i.e., planar defects), where the relationships between nanocrystal size and volumetric strain energy and surface energy are discussed. A major finding from this analysis is that often the underlying symmetry of seeds can be predictably transferred to the final crystals during overgrowth processes. Consequences of this finding are the predictable syntheses of crystals with different hierarchies akin to snowcrystals as well as nanocrystals with complex compositions (e.g., quaternary nanoparticles). Yet, there are subtle aspects to seeded growth that pave a path toward examples where nanocrystal symmetry has been reduced compared to the original seeds in a controlled manner. As we found, both the concentrations of metal precursors and capping agents can impact whether symmetry is transferred or reduced during overgrowth. Examples from our laboratory will be placed in context to other reported strategies for symmetry breaking. As will be argued, understanding what conditions favor symmetry preservation versus symmetry reduction during seeded crystal growth is central to accessing next-generation crystal forms. The Account concludes by outlining synthetic challenges associated with forming nanoscale heterostructures with precise 3-D placement of different materials within a given nanocrystal as well as facet control within different material domain and interface engineering. Furthermore, we envision meeting these challenges through regioselective and chemoselective seeded syntheses for which a foundation is outlined herein.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Influence of Rare-Earth Ion Radius on Metal–Metal Charge Transfer in Trinuclear Mixed-Valent Complexes

We report the synthesis and characterization of a highly conjugated bisferrocenyl pyrrolediimine ligand, Fc 2 PyrDIH (1), and its trinuclear complexes with rare earth ions—(Fc 2 PyrDI)M(N(TMS) 2 ) 2 (2-M, M = Sc, Y, Lu, La). Crystal structures, NMR spectra, and UV/Vis-NIR data are presented. The latter are in good agreement with DFT calculations, illuminating the impact of the rare earth ionic radius on NIR charge transfer excitations. For [2-Sc] + , the charge transfer is at 11500 cm -1 , while for [2-Y] + only a d-d transition at 8000 cm -1 is observed. Lu has an ionic radius in between Sc and Y, and the [2-Lu] + complex exhibits both transitions. From TDDFT analysis we assign the 11500 cm -1 transition as a mixture of MLCT and MMCT, rather than pure metal-to-metal CT, because it has significant ligand character. Typically, the ferrocenes have high rotational freedom in bis-ferrocenyl mixed valent complexes. However, in the present (Fc 2 PyrDI)M(N(TMS) 2 ) 2 complexes, ligand-ligand repulsions lock the rotational freedom, so that rare earth ionic radius-dependent geometric differences increasingly influence orbital overlap as the ionic radius falls. The Marcus-Hush coupling constant HAB trends as: [2-Sc] + >[2-Lu] + >[2-Y] +

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Comparative Analysis of Model Predictive Control and MPC-Informed Rule-Based Control for Thermal Storage Operation in Ultra-Low Temperature 4th Generation District Heating Networks

The integration of thermal storage and heat pumps in district heating networks (DHNs) can significantly enhance operational flexibility and energy efficiency; however, the practical deployment of advanced control strategies is often hindered by forecasting requirements and computational complexity. This study presents a comparative analysis of thermal storage control strategies in an ultra-low-temperature fourth-generation DHN, focusing on the development of a simplified rule-based control (RBC) explicitly informed by Model Predictive Control (MPC) behavior. The proposed methodology systematically analyzes the charging and discharging decisions of an MPC-controlled system under ideal forecasting conditions and extracts recurrent control patterns as a function of key system variables, including outdoor temperature, thermal demand, and electricity price. These patterns are translated into a set of structured time- and condition-based rules, resulting in an MPC-informed RBC that embeds predictive insights while preserving implementation simplicity and operational transparency. The approach is validated on a realistic mixed-use urban district in Denver, Colorado, USA, equipped with a centralized air-source heat pump, distributed water-to-water heat pumps, and a central thermal storage unit. Results show that the tuned RBC attains approximately 96% of ideal MPC economic performance (-27% of costs), preserves values of technical and environmental indicators (reduction only of 2-3%), and substantially reduces complexity. Sensitivity analyses further demonstrate the robustness of the RBC under varying operational conditions (i.e., ambient temperature, electricity price). Overall, the study demonstrates that MPC-informed rule-based control represents an effective trade-off between control performance and real-world applicability, enabling the integration of additional system components while maintaining simplicity, robustness, and ease of implementation.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

Artificial intelligence in cryo-EM protein particle picking: recent advances and remaining challenges

Abstract Cryo-electron microscopy (cryo-EM) has revolutionized structural biology by enabling the determination of high-resolution 3-Dimensional (3D) structures of large biological macromolecules. Protein particle picking, the process of identifying individual protein particles in cryo-EM micrographs for building protein structures, has progressed from manual and template-based methods to sophisticated artificial intelligence (AI)-driven approaches in recent years. This review critically examines the evolution and current state of cryo-EM particle picking methods, with an emphasis on the impact of AI. We conducted a comparative evaluation of popular AI-based particle picking methods, using both general machine learning metrics and specific cryo-EM structure determination metrics. This analysis involved constructing the 3D density map from the picked protein particles and assessing the obtained resolution and particle orientation diversity, underscoring the significant impact of AI on cryo-EM particle picking. Despite the advancements, we also identified key obstacles, such as handling complex micrographs with small proteins. The analysis provides insights into the future development of more sophisticated and fully automated AI methods in cryo-EM particle recognition.

Biochemistry & Molecular Biology↗
DOE OSTI2020

Experimental determination of electron attenuation lengths in complex materials by means of epitaxial film growth: advantages and challenges

Accurate electron attenuation lengths are of critical importance in using electron spectroscopic methods to quantitatively characterize complex materials. Here we show that analysis of core-level and valence-band x-ray photoelectron spectra excited with monochromatic AlK? x-rays from the substrate and measured as a function of film thickness can be used to determine electron attenuation lengths in epitaxial SrTiO3 films on Ge(001). Closely lattice-matched epitaxial heterojunctions are ideal systems for determining attenuation lengths provided the films grow in a layer-by-layer fashion, leading to atomically flat surfaces, and the buried interfaces are atomically abrupt. In principle, either the rate of attenuation of substrate peak intensities or the rate of increase of film peak intensities can be used for this purpose. However, we find that structural nonuniformities in the films reduce the accuracy of electron attenuation lengths determined from photoelectrons that originate within the films. A more reliable source of information is found in photoelectrons from the substrate which traverse the film. By using the energy dependence of calculated electron attenuation lengths from the NIST database in combination with Ge 3d core and Ge-derived valence band intensities, we determine electron attenuation length as a function of kinetic energy for SrTiO3.

Chambers, Scott A.↗
DOE OSTI2026

Advancing Concentrating Solar Thermal Modeling Using System Advisor Model (SAM)

Concentrating solar thermal (CST) technologies play a critical role in enabling dispatchable power and high-temperature industrial heat applications. Accurate and flexible modeling tools are essential for evaluating system performance, guiding technology research and development, and informing investment decisions. The National Laboratory of the Rockies's System Advisor Model (SAM) is a widely used techno-economic simulation platform for CST systems, providing detailed performance and financial modeling capabilities for multiple CST system configurations. SAM integrates physics-based performance models with financial analysis to simulate the behavior of complex energy systems under realistic operating conditions. For CST technologies (including tower, parabolic trough, and linear Fresnel), SAM enables hourly simulations using site-specific weather data that ensure feasible operating conditions and convergence of mass and energy between core system components (i.e., solar field, receiver, thermal energy storage, and power cycle). These capabilities allow researchers and developers to evaluate annual energy production, capacity factors, levelized cost of energy (LCOE), and system dispatch strategies. A key advantage of SAM lies in its flexibility for parametric analysis and large-scale computational studies. Users can vary system design parameters such as heliostat field layout, receiver dimensions, thermal energy storage capacity, power block sizing, and installation cost assumptions to investigate their impact on system performance and financial metrics. When combined with automated scripting through LK, SDKTool, or Python interfaces, SAM enables high-throughput simulation workflows that support sensitivity analysis, technology benchmarking, and optimization studies. These approaches are particularly valuable for next-generation CST concepts, where design spaces are large and system interactions are complex. Another important capability of SAM is its support for dispatch optimization and thermal energy storage modeling, which are central to the value proposition of CST technologies. The ability to simulate integrated storage and flexible power generation allows researchers to explore strategies that maximize grid value, improve capacity utilization, and enhance integration with variable resources such as photovoltaic and wind generation. This poster will present an overview of SAM's thermal system modeling capabilities including concentrating solar. Additionally, we will highlight new feature developments including: 1) implementing Google's OR-Tools optimization platform for faster and more robust dispatch optimization, 2) developing a new power load following controller for modeling behind-the-meter applications, 3) enabling direct modeling of CSP-PV hybrid systems with the inclusion of battery storage, and 4) developing a multi-receiver falling particle Gen3 system model.

14 SOLAR ENERGY↗