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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 OSTI2021

Controlled Demolition and Reconstruction of Imidazolate and Carboxylate Metal–Organic Frameworks by Acid Gas Exposure and Linker Treatment

The metal-linker coordination bond in metal–organic frameworks (MOFs) can be unstable in humid and acid gas environments, leading to loss of crystallinity and porosity. This degradation is not necessarily irreversible; solvent-assisted crystal redemption (“SACRed”) has been shown to recover the physical and chemical properties of ZIF-8 exposed to humid SO 2 . This approach can also be useful in creating mixed-linker materials that might be challenging to produce via de novo synthesis. Here, we expand more generally the concept of controlled degradation of a MOF with acid gas, followed by treatment with a fresh linker solution, to the use of different template MOFs (ZIFs, UiO-66, and UiO-67) and acid gases (SO 2 and NO 2 in dry and humid conditions). Significant losses in porosity and crystallinity along with structural changes (acid gas-linker complexes and linker functionalizations) are observed in the acid gas-exposed MOF templates, and SACRed is shown to reconstruct these partially demolished MOFs with a high degree of structural recovery. Detailed structural and spectroscopic characterizations of the controlled degradation and subsequent recovery are presented and analyzed. Furthermore, these findings indicate the generality of controlled degradation and reconstruction as a means for linker replacement in a wider variety of MOFs and also create the potential for linker substitutions (with non-native linkers) to obtain new hybrid MOFs.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS1988

Using data tagging to improve the performance of Kanerva's sparse distributed memory

The standard formulation of Kanerva's sparse distributed memory (SDM) involves the selection of a large number of data storage locations, followed by averaging the data contained in those locations to reconstruct the stored data. A variant of this model is discussed, in which the predominant pattern is the focus of reconstruction. First, one architecture is proposed which returns the predominant pattern rather than the average pattern. However, this model will require too much storage for most uses. Next, a hybrid model is proposed, called tagged SDM, which approximates the results of the predominant pattern machine, but is nearly as efficient as Kanerva's original formulation. Finally, some experimental results are shown which confirm that significant improvements in the recall capability of SDM can be achieved using the tagged architecture.

Rogers, David↗
NASA NTRS1995

Interferometric Tomographic Measurement of an Instataneous Flow Field Under Adverse Environments

Measurement of an instantaneous flow field by interferometric tomography, that is, reconstruction of a three-dimensional refractive-index field from multi-directional projection data, has been conducted. In order to simulate the expected experimental arrangement at a wind tunnel, reconstructions are made from a restricted view angle less than 40 degrees and incomplete projections. In addition, appreciable ambient air and experimental setup disturbances are present. A new phase-stepping technique, based on a generalized phase-stepping approach of a four-bucket model, is applied for expeditious and accurate phase information extraction from projection interferograms under the harsh environments. Phase errors caused by the various disturbances, which can include ambient refractive-index change, optical component disturbance, hologram repositioning error, etc., are partially compensated with a linear corrective model. A new computational tomographic technique based on a series expansion approach was also utilized to efficiently deal with arbitrary boundary shapes and the continuous flow fields in reconstruction. The results of the preliminary investigation are encouraging; however, the technique needs to be further developed in the future through refinement of the approaches reported here and through hybridization with previously developed techniques. Keywords: interferometry, tomography, phase stepping

Yu, En-Xi↗
DOE OSTI2025

Unraveling the Surface Termination and Evolution of Surface States for Electrocatalyst PtSn 4 in Alkaline HER

Semimetal PtSn 4 has been experimentally demonstrated as a promising topological electrocatalyst for the hydrogen evolution reaction (HER) under both acidic and alkaline conditions. While two possible mechanisms have been proposed to explain its activity, the role of its surface states in HER remains unclear. It is indeed in question how the surface states of this alloy evolve as HER proceeds. In this study, we investigate the surface termination that sustains conducting surface states on PtSn 4 , and we track their evolution during HER catalysis. We show that a reconstructed surface with a Sn-poor termination reproduces the scanning tunneling microscopy pattern observed in experiments and sustains a conducting surface. Through phase diagram and geometric structure analysis, we outline the HER profile following the Volmer–Heyrovsky mechanism. As hydrogen atoms adsorb onto the surface, the structure undergoes further reconstruction to an equilibrium phase with a coverage of two hydrides per unit cell. Meanwhile, the surface electronic bands evolve in response to interactions with the adsorbed hydrogen atoms. A hybridization diagram is further proposed for understanding the surface state evolution based on wave function and chemical bonding analyses. While the Pt atoms serve as conventional sites for hydrogen binding, the surface states of PtSn 4 are essential for stabilizing the hydrogen antibonding states via in-phase electronic interactions with the Sn components. This stabilization results in frontier surface bands that are responsible for driving the HER catalysis. Here, our findings provide a detailed description for the direct involvement of surface states on PtSn4 when employed as a catalyst for HER.

catalysts↗
DOE OSTI2022

Predicting deviations in ex-core detector response in Krsko NPP during an earthquake with Monte-Carlo neutron transport methods

To study the response of ex-core detectors during an earthquake, an approach using Monte Carlo code MCNP was developed. As a first step to predict the response of the ex-core detectors a detailed MCNP model of the Krsko reactor core was created. A detailed MCNP core model was used to calculate 3D power distributions inside the core. Calculated power distributions were verified by comparison to the CORD-2 calculations, which is currently used for core design calculation verification of the Krsko nuclear power plant. Due to the outside the core position of the neutron detectors, the hybrid code ADVANTG is used to generate weight windows to speed up neutron transport outside the reactor core. To be able to use ADVANTG, fixed neutron source had to be reconstructed from the criticality core calculation. It was found that the power range ex-core detector movement has a negligible effect on the value of the thermal neutron flux at the active part of the detector. However, the 5 cm radial movement of the intermediate-range detector leads to 7 % - 8 % change in thermal neutron flux within the active intermediate-range detector region. The analysis continued with the evaluation of the effect of core barrel movement on the ex-core detector response. It was determined that the 2 mm core barrel radial oscillation can lead to ∼ 4% change in thermal neutron flux within the active detector region. It was evaluated that reactor core movement inside the baffle can lead up to ∼ 1.9% and ∼ 1.3% deviation in power-range and intermediate-range detector signal respectively. Analysis showed that the mechanical movement of ex-core neutron detectors could not explain the fluctuations in the ex-core detector signal. However, combined core barrel and reactor core inside baffle oscillations could be a probable reason for the observed fluctuations in the ex-core detector signal during an earthquake. (authors)

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2025

Introducing a multiscale feature integration network for inpainting with applications to enhanced CMB map reconstruction

We introduce a novel neural network, SkyReconNet, which combines the expanded receptive fields of dilated convolutional layers along with standard convolutions, to capture both the global and local features for reconstructing the missing information in an image. We implement our network to inpaint the masked regions in a full-sky cosmic microwave background (CMB) map. Inpainting CMB maps is a particularly formidable challenge when dealing with extensive and irregular masks, such as galactic masks which can obscure substantial fractions of the sky. The hybrid design of SkyReconNet leverages the strengths of standard and dilated convolutions to accurately predict CMB fluctuations in the masked regions by effectively utilizing the information from surrounding unmasked areas. During training, the network optimizes its weights by minimizing a composite loss function that combines the structural similarity index measure (SSIM) and mean squared error (MSE). SSIM preserves the essential structural features of the CMB, ensuring an accurate and coherent reconstruction of the missing CMB fluctuations, while MSE minimizes the pixelwise deviations, thus enhancing the overall accuracy of the predictions. The predicted CMB maps and their corresponding angular power spectra align closely with the targets, achieving the performance limited only by the fundamental uncertainty of cosmic variance. The network’s generic architecture enables application to other physics-based challenges involving data with missing or defective pixels, systematic artifacts, etc. In conclusion, our results demonstrate its effectiveness in addressing the challenges posed by large irregular masks, offering a significant inpainting tool not only for CMB analyses but also for image-based experiments across disciplines where such data imperfections are prevalent.

Cosmic microwave background↗
DOE OSTI2020

Hybrid geological modeling: Combining machine learning and multiple-point statistics

Accurately modeling and constructing a geologically realistic subsurface model remains an outstanding problem as the morphology controls the flow behaviors. Particularly, one of the pattern-based methods, namely cross-correlation based simulation, has been proved to be an effective way to reconstruct a realistic model, at both small and large scales. However, conditioning to point data in the large-scale problems is still a crucial issue in these algorithms, since there is always a trade-off between the quality of the realizations and the degree of point data reproduction. Specifically, it is not practical to build a training image (TI) which includes all the possibilities and variabilities. Therefore, finding a pattern that can represent the point data and, at the same time, preserving the connectivities is difficult. This leads to producing highly-connected realizations with a significant mismatch or poor models with a reasonable degree of point data reproduction. To accurately reproduce the densely distributed hard data, pixel-based methods can also produce some unrealistic artifacts around the hard data. In this paper, to overcome this challenge, however, we use pattern-based methods as they often produce more disconnected geobodies when dealing with dense hard data, and proposed a hybrid algorithm using the pattern-based methods and convolutional neural network (CNN). The trained CNN model is utilized to improve the quality of conditioning to point data for the original realizations generated by the pattern-based algorithm. As such, the mismatch locations are identified, and the same regions are used in the training of CNN to mimic the procedure through which a missing region can be filled. To evaluate the performance of the proposed hybrid algorithm, it is tested on cases with different dimensions and different numbers of facies. Then, the newly improved realizations are compared with the initial realizations generated by the pattern-based algorithm. The comparison is also conducted by the flow simulation test. And it indicates that the proposed hybrid algorithm can better reproduce the point data, while the connectivities are better preserved.

58 GEOSCIENCES↗
DOE OSTI2025

Hybrid learning techniques for scientific data reduction with performance guarantees

The research initiatives supported by the U.S. Department of Energy (DOE) Grant DE-SC0022265 are fundamentally aimed at pioneering advanced machine learning (ML) techniques for scientific data compression within high-performance computing (HPC) environments. This comprehensive body of work addresses the critical challenge posed by the exponential growth of data generated by scientific simulations in domains such as fusion energy, climate modeling, and computational fluid dynamics (CFD). A core objective is to develop compression algorithms that achieve substantial data reduction—often by orders of magnitude—while rigorously ensuring the fidelity of both the primary data (PD) and scientifically crucial derived quantities of interest (QoI). The methodologies deployed under this grant integrate sophisticated deep learning architectures, prominently featuring autoencoders, advanced generative models like conditional diffusion, and hybrid learning techniques. Key innovations include the development of Guaranteed Autoencoders (GAE) and the Guaranteed Conditional Diffusion with Tensor Correction (GCDTC) framework, which provide explicit, instance-level error bounds on reconstructed data. Furthermore, specialized strategies such as nonlinear constraint satisfaction are employed to preserve the integrity of QoI, a vital requirement for the trustworthiness of downstream scientific analyses. This research also focuses on the design and implementation of scalable, GPU-accelerated software pipelines that seamlessly integrate into existing HPC workflows, ensuring both computational efficiency and practical applicability. The CAESAR framework, for example, unifies foundation and generative models to create an adaptive and efficient compression solution for spatio-temporal scientific data. Collectively, these efforts represent a significant advancement in mitigating the scientific data deluge, enabling more effective data management, accelerated scientific discovery, and optimized utilization of HPC resources.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Final report- UFL - RAPIDS2: A SciDAC Institute for Computer Science, Data, and Artificial Intelligence

The research initiatives supported by the U.S. Department of Energy (DOE) Grant DE-SC0022265 are fundamentally aimed at pioneering advanced machine learning (ML) techniques for scientific data compression within high-performance computing (HPC) environments. This comprehensive body of work addresses the critical challenge posed by the exponential growth of data generated by scientific simulations in domains such as fusion energy, climate modeling, and computational fluid dynamics (CFD). A core objective is to develop compression algorithms that achieve substantial data reduction—often by orders of magnitude—while rigorously ensuring the fidelity of both the primary data (PD) and scientifically crucial derived quantities of interest (QoI). The methodologies deployed under this grant integrate sophisticated deep learning architectures, prominently featuring autoencoders, advanced generative models like conditional diffusion, and hybrid learning techniques. Key innovations include the development of Guaranteed Autoencoders (GAE) and the Guaranteed Conditional Diffusion with Tensor Correction (GCDTC) framework, which provide explicit, instance-level error bounds on reconstructed data. Furthermore, specialized strategies such as nonlinear constraint satisfaction are employed to preserve the integrity of QoI, a vital requirement for the trustworthiness of downstream scientific analyses. This research also focuses on the design and implementation of scalable, GPU-accelerated software pipelines that seamlessly integrate into existing HPC workflows, ensuring both computational efficiency and practical applicability. The CAESAR framework, for example, unifies foundation and generative models to create an adaptive and efficient compression solution for spatio-temporal scientific data. Collectively, these efforts represent a significant advancement in mitigating the scientific data deluge, enabling more effective data management, accelerated scientific discovery, and optimized utilization of HPC resources.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2012

Low-Dissipation Advection Schemes Designed for Large Eddy Simulations of Hypersonic Propulsion Systems

The 2nd-order upwind inviscid flux scheme implemented in the multi-block, structured grid, cell centered, finite volume, high-speed reacting flow code VULCAN has been modified to reduce numerical dissipation. This modification was motivated by the desire to improve the codes ability to perform large eddy simulations. The reduction in dissipation was accomplished through a hybridization of non-dissipative and dissipative discontinuity-capturing advection schemes that reduces numerical dissipation while maintaining the ability to capture shocks. A methodology for constructing hybrid-advection schemes that blends nondissipative fluxes consisting of linear combinations of divergence and product rule forms discretized using 4th-order symmetric operators, with dissipative, 3rd or 4th-order reconstruction based upwind flux schemes was developed and implemented. A series of benchmark problems with increasing spatial and fluid dynamical complexity were utilized to examine the ability of the candidate schemes to resolve and propagate structures typical of turbulent flow, their discontinuity capturing capability and their robustness. A realistic geometry typical of a high-speed propulsion system flowpath was computed using the most promising of the examined schemes and was compared with available experimental data to demonstrate simulation fidelity.

White, Jeffrey A.↗
DOE OSTI2023

Surface Sn(IV) Hydrolysis Improves Inorganic Sn–Pb Perovskite Solar Cells

Cesium tin-lead (Sn-Pb) perovskites are exceptional for their combined capabilities in unlocking ideal bandgaps for solar cells and mitigating the stability issue faced by their hybrid organic-inorganic counterparts. But the development of high-performance solar cells using these materials is retarded by their inherent high density of detrimental Sn(IV) defects. Herein, we demonstrate a sequential surface treatment method, which entails a Sn(II) halide treatment to displace the buried Sn(IV) ions underneath the film surface, followed by a H 2 O treatment to hydrolyze the displaced Sn(IV) ions. The surface treatment induces chemical and microstructural reconstructions that significantly improve the optoelectronic properties and stability of perovskites. As a result, a power conversion efficiency of 16.79% and a T 90 stability of 958 h are achieved, topping all previously reported performance parameters for inorganic Sn-Pb PSCs. This achievement further shortens the performance gap between all-inorganic and hybrid organic- inorganic Sn-Pb PSCs with sub-1.4 eV bandgaps.

14 SOLAR ENERGY↗
NASA NTRS2008

Output-Adaptive Tetrahedral Cut-Cell Validation for Sonic Boom Prediction

A cut-cell approach to Computational Fluid Dynamics (CFD) that utilizes the median dual of a tetrahedral background grid is described. The discrete adjoint is also calculated, which permits adaptation based on improving the calculation of a specified output (off-body pressure signature) in supersonic inviscid flow. These predicted signatures are compared to wind tunnel measurements on and off the configuration centerline 10 body lengths below the model to validate the method for sonic boom prediction. Accurate mid-field sonic boom pressure signatures are calculated with the Euler equations without the use of hybrid grid or signature propagation methods. Highly-refined, shock-aligned anisotropic grids were produced by this method from coarse isotropic grids created without prior knowledge of shock locations. A heuristic reconstruction limiter provided stable flow and adjoint solution schemes while producing similar signatures to Barth-Jespersen and Venkatakrishnan limiters. The use of cut-cells with an output-based adaptive scheme completely automated this accurate prediction capability after a triangular mesh is generated for the cut surface. This automation drastically reduces the manual intervention required by existing methods.

Park, Michael A.↗
DOE OSTI2024

Multicellular magnetotactic bacteria are genetically heterogeneous consortia with metabolically differentiated cells

Consortia of multicellular magnetotactic bacteria (MMB) are currently the only known example of bacteria without a unicellular stage in their life cycle. Because of their recalcitrance to cultivation, most previous studies of MMB have been limited to microscopic observations. To study the biology of these unique organisms in more detail, we use multiple culture-independent approaches to analyze the genomics and physiology of MMB consortia at single-cell resolution. We separately sequenced the metagenomes of 22 individual MMB consortia, representing 8 new species, and quantified the genetic diversity within each MMB consortium. This revealed that, counter to conventional views, cells within MMB consortia are not clonal. Single consortia metagenomes were then used to reconstruct the species-specific metabolic potential and infer the physiological capabilities of MMB. To validate genomic predictions, we performed stable isotope probing (SIP) experiments and interrogated MMB consortia using fluorescence in situ hybridization (FISH) combined with nanoscale secondary ion mass spectrometry (NanoSIMS). By coupling FISH with bioorthogonal noncanonical amino acid tagging (BONCAT), we explored their in situ activity as well as variation of protein synthesis within cells. We demonstrate that MMB consortia are mixotrophic sulfate reducers and that they exhibit metabolic differentiation between individual cells, suggesting that MMB consortia are more complex than previously thought. These findings expand our understanding of MMB diversity, ecology, genomics, and physiology, as well as offer insights into the mechanisms underpinning the multicellular nature of their unique lifestyle.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Elliptic anisotropy measurement of the f 0 (980) hadron in proton-lead collisions and evidence for its quark-antiquark composition

Despite the f 0 (980) hadron having been discovered half a century ago, the question about its quark content has not been settled: it might be an ordinary quark-antiquark ($q\bar{q}$) meson, a tetraquark ($q\bar{q}$$q\bar{q}$) exotic state, a kaon-antikaon ($K\bar{K}$) molecule, or a quark-antiquark-gluon ($q\bar{q}$g) hybrid. This paper reports strong evidence that the f 0 (980) state is an ordinary $q\bar{q}$ meson, inferred from the scaling of elliptic anisotropies (v 2 ) with the number of constituent quarks (n q ), as empirically established using conventional hadrons in relativistic heavy ion collisions. The f 0 (980) state is reconstructed via its dominant decay channel f 0 (980) → π + π − , in proton-lead collisions recorded by the CMS experiment at the LHC, and its v 2 is measured as a function of transverse momentum (p T ). It is found that the n q = 2 ($q\bar{q}$ state) hypothesis is favored over n q = 4 ($q\bar{q}$$q\bar{q}$ or $K\bar{K}$ states) by 7.7, 6.3, or 3.1 standard deviations in the p T < 10, 8, or 6 GeV/c ranges, respectively, and over n q = 3 ($q\bar{q}$g hybrid state) by 3.5 standard deviations in the p T < 8 GeV/c range. This result represents the first determination of the quark content of the f 0 (980) state, made possible by using a novel approach, and paves the way for similar studies of other exotic hadron candidates.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

Scattering Calorimeter FY24 Deliverable Report

A simulation-based method has been developed to prototype new detector designs for nuclear data measurements utilizing neutron scattering. This method uses representative physics inputs for signal and background generation, full detector resolution smearing benchmarked by experimental data, and a neutron beam timing simulation to produce analyzable output like a physical measurement. A test case has been studied using a hybrid time-of-flight calorimeter detector for scattering cross-section measurements on 239 Pu with 1-5 MeV incident monoenergetic neutrons. Data analysis methods have been developed to perform event-level particle reconstruction and reaction channel discrimination. This analysis has been used to estimate the capability of the test detector to perform simultaneous scattering and fission cross section measurements, as well as its ability to provide neutron spectra and particle angular information.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
NASA NTRS2015

Interphase Chromosome Conformation and Chromatin-Chromatin Interactions in Human Epithelial Cells Cultured Under Different Gravity Conditions

Microgravity has been shown to alter global gene expression patterns and protein levels both in cultured cells and animal models. It has been suggested that the packaging of chromatin fibers in the interphase nucleus is closely related to genome function, and the changes in transcriptional activity are tightly correlated with changes in chromatin folding. This study explores the changes of chromatin conformation and chromatin-chromatin interactions in the simulated microgravity environment, and investigates their correlation to the expression of genes located at different regions of the chromosome. To investigate the folding of chromatin in interphase under various culture conditions, human epithelial cells, fibroblasts, and lymphocytes were fixed in the G1 phase. Interphase chromosomes were hybridized with a multicolor banding in situ hybridization (mBAND) probe for chromosome 3 which distinguishes six regions of the chromosome as separate colors. After images were captured with a laser scanning confocal microscope, the 3-dimensional structure of interphase chromosome 3 was reconstructed at multi-mega base pair scale. In order to determine the effects of microgravity on chromosome conformation and orientation, measures such as distance between homologous pairs, relative orientation of chromosome arms about a shared midpoint, and orientation of arms within individual chromosomes were all considered as potentially impacted by simulated microgravity conditions. The studies revealed non-random folding of chromatin in interphase, and suggested an association of interphase chromatin folding with radiation-induced chromosome aberration hotspots. Interestingly, the distributions of genes with expression changes over chromosome 3 in cells cultured under microgravity environment are apparently clustered on specific loci and chromosomes. This data provides important insights into how mammalian cells respond to microgravity at molecular level.

International Space Station↗
DOE OSTI2021

DNA origami single crystals with Wulff shapes

DNA origami technology has proven to be an excellent tool for precisely manipulating molecules and colloidal elements in a three-dimensional manner. However, fabrication of single crystals with well-defined facets from highly programmable, complex DNA origami units is a great challenge. Here, we report the successful fabrication of DNA origami single crystals with Wulff shapes and high yield. By regulating the symmetries and binding modes of the DNA origami building blocks, the crystalline shapes can be designed and well-controlled. The single crystals are then used to induce precise growth of an ultrathin layer of silica on the edges, resulting in mechanically reinforced silica-DNA hybrid structures that preserve the details of the single crystals without distortion. The silica-infused microcrystals can be directly observed in the dry state, which allows meticulous analysis of the crystal facets and tomographic 3D reconstruction of the single crystals by high-resolution electron microscopy.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS2013

Chromatin Folding, Fragile Sites, and Chromosome Aberrations Induced by Low- and High- LET Radiation

We previously demonstrated non-random distributions of breaks involved in chromosome aberrations induced by low- and high-LET radiation. To investigate the factors contributing to the break point distribution in radiation-induced chromosome aberrations, human epithelial cells were fixed in G1 phase. Interphase chromosomes were hybridized with a multicolor banding in situ hybridization (mBAND) probe for chromosome 3 which distinguishes six regions of the chromosome in separate colors. After the images were captured with a laser scanning confocal microscope, the 3-dimensional structure of interphase chromosome 3 was reconstructed at multimega base pair scale. Specific locations of the chromosome, in interphase, were also analyzed with bacterial artificial chromosome (BAC) probes. Both mBAND and BAC studies revealed non-random folding of chromatin in interphase, and suggested association of interphase chromatin folding to the radiation-induced chromosome aberration hotspots. We further investigated the distribution of genes, as well as the distribution of breaks found in tumor cells. Comparisons of these distributions to the radiation hotspots showed that some of the radiation hotspots coincide with the frequent breaks found in solid tumors and with the fragile sites for other environmental toxins. Our results suggest that multiple factors, including the chromatin structure and the gene distribution, can contribute to radiation-induced chromosome aberrations.

Zhang, Ye↗