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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 703 records · Page 39
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

An Integrated Modeling Framework for Sediment Dynamics During Urban Flooding: Application to Hurricane Harvey in Houston

Floodwater can mobilize and redistribute large volumes of sediment from upland to downstream urban areas, threatening infrastructure, water quality, and ecosystem health. However, existing modeling approaches often fail to capture sediment dynamics in urban floodplains due to the lack of integration between upland hydrological processes and riverine sediment transport. This study presents the first integrated modeling framework that couples the Energy Exascale Earth System Model (E3SM) land component, which simulates runoff and hillslope erosion, with TELEMAC-GAIA, a two-dimensional hydrodynamic and sediment transport model. This framework enables the fully distributed, process-based simulation of high-resolution (as fine as 30 m) sediment dynamics from hillslopes to floodplains. Applied to a highly urbanized watershed in Houston during Hurricane Harvey, this framework reproduced observed water levels at 16 USGS gauges (median R 2 = 0.83 and KGE = 0.78), key sediment dynamics such as sediment transport and deposition processes, and reproduced spatial deposition patterns consistent with LiDAR-derived data. Based on the simulation, we estimate 8.0 million m 3 of event-scale sediment deposition, including 5.7 million m 3 trapped in the flood-control reservoirs and 2.3 million m 3 deposited along major channels and floodplains. Using a representative unit removal cost, this corresponds to an estimated dredging cost of $581 million for total deposition. These results provide a first-order, physically based quantification of Harvey-scale sediment impacts. This study provides a valuable tool for the holistic analysis of sediment dynamics triggered by extreme urban flooding, supporting flood-resilience planning. More broadly, it highlights the importance of integrating physically based hydrological processes for urban flooding and sediment research.

Hurricane Harvey↗
DOE OSTI2026

Collection of fluorescence from an ion using trap-integrated photonics

Spontaneously emitted photons are entangled with the electronic and nuclear degrees of freedom of the emitting atom, so interference and measurement of these photons can entangle separate matter-based quantum systems as a resource for quantum information processing. Since confinement in a single-mode facilitates the photon interference needed for generating entanglement, the dipole emission patterns relevant in spontaneous emission present a mode-matching challenge. Current demonstrations rely on bulk photon-collection and manipulation optics that suffer from large component size and system-to-system variability—factors that impede scaling to the large numbers of entangled pairs needed for quantum information processing. To address these limitations, we demonstrate a collection method that enables passive phase stability, straightforward photonic manipulation, and intrinsic reproducibility. Specifically, we engineer a waveguide-integrated grating to couple photons emitted from a trapped ion into a single optical mode within a microfabricated ion-trap chip. Using the integrated collection optic, we characterize the collection efficiency, image the ion, and detect the ion’s quantum state. The integrated optic covers 2.18% of the solid angle and collects 1.97 ± 0.3% of the spontaneously emitted light incident on the grating for a total collection efficiency of 0.043% into a single-mode waveguide. This proof-of-principle demonstration lays the foundation for leveraging the inherent stability and reproducibility of integrated photonics to create, manipulate, and measure multipartite quantum states in arrays of quantum emitters.

Optical physics↗
DOE OSTI2019

Charge density wave memory in a cuprate superconductor

Although CDW correlations are a ubiquitous feature of the superconducting cuprates, their disparate properties suggest a crucial role for pinning the CDW to the lattice. Here, we report coherent resonant X-ray speckle correlation analysis, which directly determines the reproducibility of CDW domain patterns in La 1.875 Ba 0.125 CuO 4 (LBCO 1/8) with thermal cycling. While CDW order is only observed below 54 K, where a structural phase transition creates inequivalent Cu-O bonds, we discover remarkably reproducible CDW domain memory upon repeated cycling to far higher temperatures. That memory is only lost on cycling to 240(3) K, which recovers the four-fold symmetry of the CuO 2 planes. We infer that the structural features that develop below 240 K determine the CDW pinning landscape below 54 K. This opens a view into the complex coupling between charge and lattice degrees of freedom in superconducting cuprates.

36 MATERIALS SCIENCE↗
DOE OSTI2021

A model for the fragmentation kinetics of crumpled thin sheets

As a confined thin sheet crumples, it spontaneously segments into flat facets delimited by a network of ridges. Despite the apparent disorder of this process, statistical properties of crumpled sheets exhibit striking reproducibility. Experiments have shown that the total crease length accrues logarithmically when repeatedly compacting and unfolding a sheet of paper. Here, we offer insight to this unexpected result by exploring the correspondence between crumpling and fragmentation processes. We identify a physical model for the evolution of facet area and ridge length distributions of crumpled sheets, and propose a mechanism for re-fragmentation driven by geometric frustration. This mechanism establishes a feedback loop in which the facet size distribution informs the subsequent rate of fragmentation under repeated confinement, thereby producing a new size distribution. We then demonstrate the capacity of this model to reproduce the characteristic logarithmic scaling of total crease length, thereby supplying a missing physical basis for the observed phenomenon.

42 ENGINEERING↗
DOE OSTI2021

Elevating density functional theory to chemical accuracy for water simulations through a density-corrected many-body formalism

Abstract Density functional theory (DFT) has been extensively used to model the properties of water. Albeit maintaining a good balance between accuracy and efficiency, no density functional has so far achieved the degree of accuracy necessary to correctly predict the properties of water across the entire phase diagram. Here, we present density-corrected SCAN (DC-SCAN) calculations for water which, minimizing density-driven errors, elevate the accuracy of the SCAN functional to that of “gold standard” coupled-cluster theory. Building upon the accuracy of DC-SCAN within a many-body formalism, we introduce a data-driven many-body potential energy function, MB-SCAN(DC), that quantitatively reproduces coupled cluster reference values for interaction, binding, and individual many-body energies of water clusters. Importantly, molecular dynamics simulations carried out with MB-SCAN(DC) also reproduce the properties of liquid water, which thus demonstrates that MB-SCAN(DC) is effectively the first DFT-based model that correctly describes water from the gas to the liquid phase.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Microscopic theory, analysis, and interpretation of conductance histograms in molecular junctions

Molecular electronics break-junction experiments are widely used to investigate fundamental physics and chemistry at the nanoscale. Reproducibility in these experiments relies on measuring conductance on thousands of freshly formed molecular junctions, yielding a broad histogram of conductance events. Experiments typically focus on the most probable conductance, while the information content of the conductance histogram has remained unclear. Here we develop a microscopic theory for the conductance histogram by merging the theory of force-spectroscopy with molecular conductance. The procedure yields analytical equations that accurately fit the conductance histogram of a wide range of molecular junctions and augments the information content that can be extracted from them. Our formulation captures contributions to the conductance dispersion due to conductance changes during the mechanical elongation inherent to the experiments. In turn, the histogram shape is determined by the non-equilibrium stochastic features of junction rupture and formation. The microscopic parameters in the theory capture the junction’s electromechanical properties and can be isolated from separate conductance and rupture force (or junction-lifetime) measurements. The predicted behavior can be used to test the range of validity of the theory, understand the conductance histograms, design molecular junction experiments with enhanced resolution and molecular devices with more reproducible conductance properties.

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

Neuromorphic intermediate representation: A unified instruction set for interoperable brain-inspired computing

Abstract Spiking neural networks and neuromorphic hardware platforms that simulate neuronal dynamics are getting wide attention and are being applied to many relevant problems using Machine Learning. Despite a well-established mathematical foundation for neural dynamics, there exists numerous software and hardware solutions and stacks whose variability makes it difficult to reproduce findings. Here, we establish a common reference frame for computations in digital neuromorphic systems, titled Neuromorphic Intermediate Representation (NIR). NIR defines a set of computational and composable model primitives as hybrid systems combining continuous-time dynamics and discrete events. By abstracting away assumptions around discretization and hardware constraints, NIR faithfully captures the computational model, while bridging differences between the evaluated implementation and the underlying mathematical formalism. NIR supports an unprecedented number of neuromorphic systems, which we demonstrate by reproducing three spiking neural network models of different complexity across 7 neuromorphic simulators and 4 digital hardware platforms. NIR decouples the development of neuromorphic hardware and software, enabling interoperability between platforms and improving accessibility to multiple neuromorphic technologies. We believe that NIR is a key next step in brain-inspired hardware-software co-evolution, enabling research towards the implementation of energy efficient computational principles of nervous systems. NIR is available atneuroir.org

Science & Technology - Other Topics↗
DOE OSTI2024

Revisiting the activity of two poly(vinyl chloride)- and polyethylene-degrading enzymes

Biocatalytic degradation of non-hydrolyzable plastics is a rapidly growing field of research, driven by the global accumulation of waste. Enzymes capable of cleaving the carbon-carbon bonds in synthetic polymers are highly sought-after as they may provide tools for environmentally friendly plastic recycling. Despite some reports of oxidative enzymes acting on non-hydrolyzable plastics, including polyethylene or poly(vinyl chloride), the notion that these materials are susceptible to efficient enzymatic degradation remains controversial, partly driven by a general lack of studies independently reproducing previous observations. Here, we attempt to replicate two recent studies reporting that deconstruction of polyethylene and poly(vinyl chloride) can be achieved using an insect hexamerin from Galleria mellonella (so-called “Ceres”) or a bacterial catalase-peroxidase from Klebsiella sp., respectively. Reproducing previously described experiments, we do not observe any activity on plastics using multiple reaction conditions and multiple substrate types. Digging deeper into the discrepancies between the previous data and our observations, we show how and why the original experimental results may have been misinterpreted.

36 MATERIALS SCIENCE↗
DOE OSTI2026

High-throughput methods leveraging robotics and computer vision for the development of therapeutic phage cocktails

We present the high-throughput automated screening techniques that are being used to develop bacteriophage-based therapeutic products currently under investigation in human clinical trials to combat urinary tract infections. By integrating modern liquid handling robotics, standardized phenotypic assays, and computer vision-based enumeration, we established a platform capable of reproducibly screening large collections of phages against clinically derived bacterial strain panels. This approach enabled systematic assessment of phage-bacteria interactions at scale, facilitating the identification and optimization of phage cocktails with broad in vitro activity. Although bacteriophage therapy has long been investigated as a strategy for treating bacterial infections, few frameworks exist for developing phage combinations in a reproducible and scalable manner. The methods outlined here address this gap and aim to support the broader development of therapeutic assets available to combat antibiotic resistance.

Penke, Taylor J. R. [Locus Biosciences, Morrisvill↗
DOE OSTI2020

Machine learning the Hubbard U parameter in DFT+U using Bayesian optimization

Abstract Within density functional theory (DFT), adding a Hubbard U correction can mitigate some of the deficiencies of local and semi-local exchange-correlation functionals, while maintaining computational efficiency. However, the accuracy of DFT+U largely depends on the chosen Hubbard U values. We propose an approach to determining the optimal U parameters for a given material by machine learning. The Bayesian optimization (BO) algorithm is used with an objective function formulated to reproduce the band structures produced by more accurate hybrid functionals. This approach is demonstrated for transition metal oxides, europium chalcogenides, and narrow-gap semiconductors. The band structures obtained using the BO U values are in agreement with hybrid functional results. Additionally, comparison to the linear response (LR) approach to determining U demonstrates that the BO method is superior.

Chemistry↗
DOE OSTI2021

A systematic approach to generating accurate neural network potentials: the case of carbon

Abstract Availability of affordable and widely applicable interatomic potentials is the key needed to unlock the riches of modern materials modeling. Artificial neural network-based approaches for generating potentials are promising; however, neural network training requires large amounts of data, sampled adequately from an often unknown potential energy surface. Here we propose a self-consistent approach that is based on crystal structure prediction formalism and is guided by unsupervised data analysis, to construct an accurate, inexpensive, and transferable artificial neural network potential. Using this approach, we construct an interatomic potential for carbon and demonstrate its ability to reproduce first principles results on elastic and vibrational properties for diamond, graphite, and graphene, as well as energy ordering and structural properties of a wide range of crystalline and amorphous phases.

Chemistry↗
DOE OSTI2024

JARVIS-Leaderboard: a large scale benchmark of materials design methods

Abstract Lack of rigorous reproducibility and validation are significant hurdles for scientific development across many fields. Materials science, in particular, encompasses a variety of experimental and theoretical approaches that require careful benchmarking. Leaderboard efforts have been developed previously to mitigate these issues. However, a comprehensive comparison and benchmarking on an integrated platform with multiple data modalities with perfect and defect materials data is still lacking. This work introduces JARVIS-Leaderboard, an open-source and community-driven platform that facilitates benchmarking and enhances reproducibility. The platform allows users to set up benchmarks with custom tasks and enables contributions in the form of dataset, code, and meta-data submissions. We cover the following materials design categories: Artificial Intelligence (AI), Electronic Structure (ES), Force-fields (FF), Quantum Computation (QC), and Experiments (EXP). For AI, we cover several types of input data, including atomic structures, atomistic images, spectra, and text. For ES, we consider multiple ES approaches, software packages, pseudopotentials, materials, and properties, comparing results to experiment. For FF, we compare multiple approaches for material property predictions. For QC, we benchmark Hamiltonian simulations using various quantum algorithms and circuits. Finally, for experiments, we use the inter-laboratory approach to establish benchmarks. There are 1281 contributions to 274 benchmarks using 152 methods with more than 8 million data points, and the leaderboard is continuously expanding. The JARVIS-Leaderboard is available at the website: https://pages.nist.gov/jarvis_leaderboard/

36 MATERIALS SCIENCE↗
DOE OSTI2020

Consensus statement for stability assessment and reporting for perovskite photovoltaics based on ISOS procedures

Improving the long-term stability of perovskite solar cells is critical to the deployment of this technology. Despite the great emphasis laid on stability-related investigations, publications lack consistency in experimental procedures and parameters reported. It is therefore challenging to reproduce and compare results and thereby develop a deep understanding of degradation mechanisms. Here, we report a consensus between researchers in the field on procedures for testing perovskite solar cell stability, which are based on the International Summit on Organic Photovoltaic Stability (ISOS) protocols. We propose additional procedures to account for properties specific to PSCs such as ion redistribution under electric fields, reversible degradation and to distinguish ambient-induced degradation from other stress factors. These protocols are not intended as a replacement of the existing qualification standards, but rather they aim to unify the stability assessment and to understand failure modes. Finally, we identify key procedural information which we suggest reporting in publications to improve reproducibility and enable large data set analysis.

14 SOLAR ENERGY↗
DOE OSTI2020

Bandgap tuning of two-dimensional materials by sphere diameter engineering

Developing a precise and reproducible bandgap tuning method that enables tailored design of materials is of crucial importance for optoelectronic devices. Towards this end, we report a sphere diameter engineering (SDE) technique to manipulate the bandgap of two-dimensional (2D) materials. A one-to-one correspondence with an ideal linear working curve is established between the bandgap of MoS 2 and the sphere diameter in a continuous range as large as 360 meV. Fully uniform bandgap tuning of all the as-grown MoS 2 crystals is realized due to the isotropic characteristic of the sphere. More intriguingly, both a decrease and an increase of the bandgap can be achieved by constructing a positive or negative curvature. By fusing individual spheres in the melted state, post-synthesis bandgap adjustment of the supported 2D materials can be realized. In conclusion, this SDE technique, showing good precision, uniformity and reproducibility with high efficiency, may further accelerate the potential applications of 2D materials.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Attosecond pulse shaping using a seeded free-electron laser

Attosecond pulses are central to the investigation of valence- and core-electron dynamics on their natural timescales. The reproducible generation and characterization of attosecond waveforms has been demonstrated so far only through the process of high-order harmonic generation. Several methods for shaping attosecond waveforms have been proposed, including the use of metallic filters, multilayer mirrors and manipulation of the driving field. However, none of these approaches allows the flexible manipulation of the temporal characteristics of the attosecond waveforms, and they suffer from the low conversion efficiency of the high-order harmonic generation process. Free-electron lasers, by contrast, deliver femtosecond, extreme-ultraviolet and X-ray pulses with energies ranging from tens of microjoules to a few millijoules. Recent experiments have shown that they can generate subfemtosecond spikes, but with temporal characteristics that change shot-to-shot. In this paper we report reproducible generation of high-energy (microjoule level) attosecond waveforms using a seeded free-electron laser. We demonstrate amplitude and phase manipulation of the harmonic components of an attosecond pulse train in combination with an approach for its temporal reconstruction. The results presented here open the way to performing attosecond time-resolved experiments with free-electron lasers.

47 OTHER INSTRUMENTATION↗
DOE OSTI2019

EcoFAB: Advancing microbiome science through standardized fabricated ecosystems

The vital role microbiomes play in ecosystems and for human health is now apparent. Great progress has been made by using model systems to gain insight into host-microbe interactions, yet lack of standardized reproducible experimental systems and methods greatly limits microbiome science. Recent successes highlight the importance of developing these types of systems to deconstruct mechanisms mediating host-microbe interactions. Standardization of ‘fabricated ecosystems’ would enable detailed mechanistic interrogation and more effective knowledge transfer between researchers – analogous to the significant impact model organisms and cell systems have had in molecular and cell biology. To advance these efforts we advocate for strategies and overarching themes to enable communities of scientists to work together to develop standardized laboratory microbial consortia for studying host-associated microbiomes.

Zengler, Karsten↗
DOE OSTI2021

Cryo-EM model validation recommendations based on outcomes of the 2019 EMDataResource challenge

This paper describes outcomes of the 2019 Cryo-EM Model Challenge. The goals were to (1) assess the quality of models that can be produced from cryogenic electron microscopy (cryo-EM) maps using current modeling software, (2) evaluate reproducibility of modeling results from different software developers and users and (3) compare performance of current metrics used for model evaluation, particularly Fit-to-Map metrics, with focus on near-atomic resolution. Our findings demonstrate the relatively high accuracy and reproducibility of cryo-EM models derived by 13 participating teams from four benchmark maps, including three forming a resolution series (1.8 to 3.1 Å). The results permit specific recommendations to be made about validating near-atomic cryo-EM structures both in the context of individual experiments and structure data archives such as the Protein Data Bank. We recommend the adoption of multiple scoring parameters to provide full and objective annotation and assessment of the model, reflective of the observed cryo-EM map density.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

National-scale remotely sensed lake trophic state from 1984 through 2020

Lake trophic state is a key ecosystem property that integrates a lake’s physical, chemical, and biological processes. Despite the importance of trophic state as a gauge of lake water quality, standardized and machine-readable observations are uncommon. Remote sensing presents an opportunity to detect and analyze lake trophic state with reproducible, robust methods across time and space. We used Landsat surface reflectance data to create the first compendium of annual lake trophic state for 55,662 lakes of at least 10 ha in area throughout the contiguous United States from 1984 through 2020. The dataset was constructed with FAIR data principles (Findable, Accessible, Interoperable, and Reproducible) in mind, where data are publicly available, relational keys from parent datasets are retained, and all data wrangling and modeling routines are scripted for future reuse. Together, this resource offers critical data to address basic and applied research questions about lake water quality at a suite of spatial and temporal scales.

54 ENVIRONMENTAL SCIENCES↗