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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 199 records · Page 11
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Spiroligomer‐Based Macrocycles for Atomically Precise Membranes

Abstract Here, we report a new class of peptidomimetic macrocycles with well‐defined three‐dimensional structures and low conformational flexibility. They are assembled from fused‐ring spiro‐ladder oligomers (spiroligomers) by modular solid‐phase synthesis. Two‐dimensional nuclear magnetic resonance confirms their shape persistency. Triangular macrocycles of tunable sizes assemble into membranes with atomically precise pores, which exhibit size and shape‐dependent molecular sieving towards a series of structurally similar compounds. The exceptional structural diversity and stability of spiroligomer‐based macrocycles will be explored for more applications.

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

Investigation of porous polydimethylsiloxane structures with tunable properties induced by the phase separation technique

This article reports the fabrication and characterization of porous polydimethylsiloxane (PDMS) structures developed by the solvent evaporationinduced phase separation technique. Here, ternary systems containing water/ tetrahydrofuran (THF)/PDMS with various concentrations are produced to form a stable solution. The porous PDMS structures are formed by removing the solvent (THF) and nonsolvent (water) phases during the stepping heat treatment procedure. The analytical ternary phase diagram is constructed based on the thermodynamic equilibrium state in the polymer solution to explain the stable/unstable formulations and the possible composition change path. The results show that the isolated pores with the adjustable pore size ranging from 330 to 1900 μm are obtained by tuning the water to the THF ratio. The mechanical properties of the porous PDMS structures are determined by conducting the tensile tests on the prepared dog bone-shaped specimens. A wide range of elastic modulus ranging between 0.49 and 1.05 MPa was achieved without affecting the density of the porous sample by adjusting the solvent and non-solvent content in the solution. It is shown that the flexibility of the porous structures can be improved by reducing the ratio of water to THF and decreasing the PDMS content. The porosity measurements reveal that the PDMS concentration is the major phase controlling the porosity of the structure, while the effect of water/THF is negligible.

42 ENGINEERING↗
DOE OSTI2026

Input specific neural networks

Neural networks have emerged as powerful tools for mapping between inputs and outputs. However, their black-box nature limits the ability to encode or impose specific structural relationships between inputs and outputs. Many scientific and engineering problems, such as constitutive modeling in solid mechanics, require networks that can enforce convexity, monotonicity, or other structural constraints to ensure physical consistency. Here, we introduce the Input Specific Neural Network (ISNN), a new architecture that enables multiple, distinct constraints to be imposed on different input subsets for scalar-valued outputs. This framework unifies convex, monotone–convex, monotone, and arbitrary mappings within a single network for the first time. Two ISNN architectures with analytical first- and second-order derivatives are developed. We demonstrate the performance on synthetic toy problems, inverse problems in isotropic hyperelasticity, and finite element simulations. ISNNs achieve improved extrapolation behavior, require fewer invariant inputs than standard input convex networks for polyconvex potentials, and enable significant computational savings via manual differentiation. We also show how ISNNs can be used to learn structural relationships between inputs and outputs via a binary gating mechanism. Particularly, ISNNs are employed to model a homogenized anisotropic free energy potential in a decoupled multiscale setting. The network learns whether or not the potential should be modeled as polyconvex and retains only the relevant layers while using the minimum number of inputs. ISNNs provide a flexible foundation for embedding structural priors into neural networks, enhancing both interpretability and stability. They are broadly applicable across computational mechanics and other scientific domains requiring constrained functional relationships.

Jadoon, Asghar A. [Univ. of Texas, Austin, TX (Uni↗
DOE OSTI2023

Elucidating Actinide–Pertechnetate and Actinide–Perrhenate Bonding via a Family of Th–TcO 4 and Th–ReO 4 Frameworks and Solutions

Technetium-99, a β-emitter produced from 235 U fission, poses a challenge for the nuclear industry due to co-extraction of pertechnetate (TcO 4 – ) with the actinides (An) during nuclear fuel reprocessing. Previous studies suggested that direct coordination of pertechnetate with An plays an important role in the coextraction process. However, few studies have provided direct evidence for An–TcO 4 – bonding in the solid state, and even fewer in solution. The present study describes synthesis and structural elucidation of a family of thorium(IV)-pertechnetate/perrhenate (ReO 4 – , nonradioactive surrogate) compounds, which is obtained by dissolution of thorium oxyhydroxide in perrhenic/pertechnic acid followed by crystallization, with or without heating. For reaction ratios of 3:1, 4:1, and 6:1 MO 4 – /Th(IV) (M = Tc, Re), the crystallized compounds reflect the same ratio, suggesting facile and flexible coordination. Furthermore, nine structures reveal 1-dimensional and 2-dimensional frameworks with varying topologies. While a multitude of compounds isolated from 4:1 (and 6:1) reaction solutions feature Th monomers linked by MO 4 – , the 3:1 reaction solution yielded the well-known dihydroxide-bridged thorium dimer, linked, and capped by MO 4 – . Density functional theory calculations on ReO 4 – /TcO 4 – isomorphs suggest similar bonding characteristics in the solid state, but experimental solution characterization noted differences. Specifically, small-angle X-ray scattering studies suggest the bonding of Th–TcO 4 – persists in solution, while Th–ReO 4 – bonding is less apparent.

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

A Lizard-inspired Tube Inspector (LTI) Robot

This final report summarizes the research findings of the current project. This project is a collaboration between New Mexico State University (NMSU) as a lead (Recipient) and Arizona State University (ASU) as a Co-Recipient. Tubular structures are common components of boilers and heat exchangers in power plants. Over time, these components may suffer corrosion, cracks, and stress-corrosion cracks in either the body or the welded connections. A single tube leakage can cause an outage of several weeks. Regular inspection is a key safety factor when ensuring that power plants are maintained in reliable, operational condition. This inspection, however, is challenging, time-consuming, and in many cases impossible due to accessibility issues and safety concerns. Recent developments in robotic-based inspection can offer a great solution. Hard-to-reach places can be inspected without overhauling the unit, saving time and cost. Although several robots have been designed and implemented successfully for inspecting power plant components, in particular tubular structures, their mobility and flexibility are limited. Most of these robots use wheels for mobility which reduces their maneuverability of these robots. Moreover, they usually use magnets to attach to tubes which will not work on tubes with non-ferromagnetic materials. These robots usually carry measurement tools for a particular non-destructive testing (NDT) method such as ultrasound testing (UT) that requires a couplant to perform a point-by-point (scanning) inspection of the tubular structure. In this project, we developed a versatile lizard-inspired tube inspector (LTI) robot with embedded inspection sensing components for tube inspection which removes the need for point-by-point scanning of tube surface for crack and corrosion detection. Inspired by a “lizard”, the novelty of the current project is the integration of couplant-free ultrasound sensing and transmission, advanced data-driven defect detection and imaging, and friction-based mechanical mobility components in a single robot to eliminate a need for smooth surfaces and simple geometry for mobility and scanning. The LTI robot could replace the wheel-based approach with friction-based mobility to significantly increase the flexibility and maneuverability of the robot. The LTI robot can get into a power plant unit, such as a boiler, from a small area allowing it to access a component of interest for inspection (e.g., move on OD, curved and flat surfaces, non-ferromagnetic or ferromagnetic materials, and tubes with rough surfaces and complex geometries). Additionally, an advanced data-driven method using ultrasound data was pioneered to allow the robot to detect defects in the entire area between the robot’s multi-functional mobility system (grippers). Integrating a couplant-free ultrasound sensing in the robot’s grippers as well as using advanced data-driven methods allowed the LTI to detect defects in the entire cross-section of a tube using its grippers when stationary and when mobile.

20 FOSSIL-FUELED POWER PLANTS↗
DOE OSTI2020

An inverse vulcanized conductive polymer for Li–S battery cathodes

Polymers with a broad range of properties, structural diversity, and mechanical flexibility have been adopted in all aspects of Li–S batteries. However, currently explored polymers for Li–S cathodes suffer from low conductivity, low S content, and poor cycling performance. In this first-principles study, we theoretically design a new polymer, poly(2-vinyl,1,4-phenylene sulfide), by modifying the conductive poly(1,4-phenylene sulfide) via the vinyl group to enhance its ability to vulcanize with element S. Furthermore, we compare the properties of the experimentally realized sulfur vulcanized polymers via condensation and our designed sulfur vulcanized polymers via crosslinking as Li–S battery cathodes, in terms of gravimetric and specific capacities, as well as structural stability during the lithiation. Overall, we find that our designed polymer possesses better conductivity, higher specific capacity and gravimetric energy density, and better kinetic stability, and restricts the shuttle effect more efficiently than the current experimentally explored one. Also, the cross-linked sulfur compounds can be activated efficiently due to the short transport lengths rather than dissolution in the electrolyte during battery operation. Therefore, we believe our designed polymer cathode is promising for practical applications.

25 ENERGY STORAGE↗
DOE OSTI2026

Relaxor Ferroelectric-Like Spatiotemporal Memory in Field-Driven Lipid Bilayers

Lipid membranes are often regarded as passive barriers, yet their nonlinear dielectric response remains poorly understood. Using all-atom molecular dynamics, we show that fully hydrated dipalmitoylphosphatidylcholine bilayers exhibit relaxor ferroelectric-like behavior under time-dependent electric fields. Unlike crystalline relaxors, which are bipolar and display little remanent polarization, lipid bilayers exhibit a unipolar polarization response: even an alternating current field produces persistent, asymmetric polarization. Furthermore, the underlying free-energy landscape contains two distinct minima, a nonpolarized state and a unipolarly polarized state, between which stochastic thermally activated transitions occur. Directionally resolved Van Hove analysis reveals pronounced anisotropy arising from out-of-plane electric dipole alignment, interleaflet coupling, and lateral polarization domains. Each field cycle nucleates polarization at distinct sites and monitors their relaxation, marking a crossover from thermal fluctuations to field-sustained polarization. Remarkably, these polarized domains persist after field removal, generating long-lived, spatially coherent dipolar patterns that encode nanoscale polarization memory. Potassium chloride amplifies these effects via dielectric screening and a modified hydration structure, enhancing electric dipole flexibility and cooperativity. Together, these results establish protein-free bilayers as nonlinear, history-dependent dielectrics capable of sustaining field-tunable electromechanical coupling, providing an emergent physical foundation for nanoscale information storage and memory phenomena reminiscent of short- and long-term plasticity in soft neuromorphic systems.

Insulators↗
DOE OSTI2022

The temperature-dependent conformational ensemble of SARS-CoV-2 main protease (M pro )

The COVID-19 pandemic, instigated by the SARS-CoV-2 coronavirus, continues to plague the globe. The SARS-CoV-2 main protease, or M pro , is a promising target for the development of novel antiviral therapeutics. Previous X-ray crystal structures of M pro were obtained at cryogenic temperature or room temperature only. Here we report a series of high-resolution crystal structures of unliganded M pro across multiple temperatures from cryogenic to physiological, and another at high humidity. We interrogate these data sets with parsimonious multiconformer models, multi-copy ensemble models, and isomorphous difference density maps. Our analysis reveals a perturbation-dependent conformational landscape for M pro , including a mobile zinc ion interleaved between the catalytic dyad, mercurial conformational heterogeneity at various sites including a key substrate-binding loop, and a far-reaching intramolecular network bridging the active site and dimer interface. Our results may inspire new strategies for antiviral drug development to aid preparation for future coronavirus pandemics.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

N-terminal domain swapping: A new paradigm for spermidine/spermine N -acetyltransferase (SSAT) protein structures?

Enterococcus faecalis is a multi-drug-resistant human pathogen that is found in a variety of environments and is challenging to treat. Under stress conditions, some bacteria regulate intracellular polyamine concentrations via polyamine acetyltransferases to reduce their toxicity. The E. faecalis genome encodes two polyamine acetyltransferases: PmvE and BltD. Both of these proteins belong to the Gcn5-related N-acetyltransferase (GNAT) superfamily. It is unclear why there are two enzymes with similar substrate specificities in this organism. To better understand the structure/function relationship of the E. faecalis BltD enzyme, we determined its crystal structure and performed additional assays to explore its oligomeric state and enzymatic activity. The goal was to determine whether there were structural or catalytic differences between this enzyme and other polyamine acetyltransferases that could explain this redundancy and be exploited for future development of targeted inhibitors for this important human pathogen. We found the BltD enzyme was structurally unique due to its N-terminal domain swapped dimer. However, this enzyme adopts a catalytically active monomer rather than dimer in solution. This indicates the crystal structure we obtained may represent a state that forms at high protein and salt concentrations and at low pH used during crystallization. The BltD dimer found in the crystal may represent a unique view of how an inhibitory peptide or molecule could be designed to occupy its active site. Additionally, this structure shows the extensive flexibility of the N-terminal portion of the E. faecalis BltD enzyme.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

A large-scale comparison of Artificial Intelligence and Data Mining (AI&DM) techniques in simulating reservoir releases over the Upper Colorado Region

In recent years, the Artificial Intelligence and Data Mining (AI&DM) models have become popular tools in assisting various aspects of reservoir operation. However, the practical uses are still rarely reported. Comparison experiment of many AI&DM models over a large number of reservoir cases is particularly valuable to help reservoir operators first examine the usefulness and transferability of different AI&DM models, and then identify the most stable and reliable AI&DM model in assist of various decision-making processes. In this study, a total of 12 AI&DM models with different parameterizations and simulation scenarios are comprehensively tested out and compared in simulating the controlled reservoir outflows of 33 reservoir cases over the Upper Colorado Region, United States. Results show that the Random Forecast and the Long-Short-Term-Memory model could consistently derive the best statistical performance than other models under the baseline simulation scenario. The employed AI&DM models could obtain satisfactory statistical interquartile ranges (25–75%) between [0.6–0.9], [0.3–0.8], and [0.2–0.8], for CORR, NSE, and KGE measurements, respectively, and [1.5–6.5], [–15 to 20], and [0.5–8.5] for the normalized RMSE, PBIAS and RSR measurements, respectively. Results also show Multi-Layer Perceptron model and Extreme Gradient Boosting Tree Algorithm produced more stable and superior performance than other models under more complex input scenarios. We also found that the performance of different AI&DM models are closely relevant to the reservoir elevations, sizes, and functionalities. Discussions were made about the sensitivity of AI&DM models’ parameterizations and the key advantages of AI&DM models over the rule-based reservoir models. We further identify that the main advantage of AI&DM models is the flexibility in designing input structures, whereas the rule-based simulation model is rather limited. Future studies were suggested regarding the best way reservoir operators and researchers could use, select, and apply different AI&DM models in simulating reservoir releases under different natural and modeling environments. Finally, this comparison study also serves as a reference and a piece of groundwork for further promoting the practical uses of AI&DM models in assisting reservoir operation.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Versatile Graphitized Carbon Nanofibers in Energy Applications

Graphitized carbon nanofibers (CNFs) are the allotropic forms of carbon that offer exceptional electrical conductivity, high aspect ratio, large surface-to-volume ratio, flexibility, mechanical robustness, and structural stability. These properties render CNFs as useful materials in energy applications, including water electrocatalysis and electrochemical energy storage devices (EESDs). Graphitized CNFs manifest huge versatility in EESDs and have been employed as electrode materials, as supports for other electrode materials, and conductive additives. This perspective summarizes the production methods, structural aspects, and energy applications of graphitized CNFs. Different design strategies have been employed to produce CNFs with diverse and distinct morphologies and structures. Their fabrication methods, along with precursors and dopants or cocatalysts, impact the overall structural, functional, and topographical properties. Recent developments on the state-of-the-art interests of graphitic CNFs in lithium-ion batteries (LIBs), lithium–sulfur (Li–S) batteries, vanadium redox flow batteries (VRFBs), supercapacitors, and water splitting have explored the many possibilities in energy sectors. Finally, the assembly and functioning of CNFs in water splitting and EESDs as well as primary challenges are discussed in this perspective.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Giant uniaxial negative thermal expansion in FeZr 2 alloy over a wide temperature range

Negative thermal expansion (NTE) alloys possess great practical merit as thermal offsets for positive thermal expansion due to its metallic properties. However, achieving a large NTE with a wide temperature range remains a great challenge. Herein, a metallic framework-like material FeZr 2 is found to exhibit a giant uniaxial (1D) NTE with a wide temperature range (93-1078 K, $\overline{α}_{l}$=–34.01×10 –6 K –1 ). Such uniaxial NTE is the strongest in all metal-based NTE materials. The direct experimental evidence and DFT calculations reveal that the origin of giant NTE is the couple with phonons, flexible framework-like structure, and soft bonds. Interestingly, the present metallic FeZr 2 excites giant 1D NTE mainly driven by high-frequency optical branches. It is unlike the NTE in traditional framework materials, which are generally dominated by low energy acoustic branches. In the present study, a giant uniaxial NTE alloy is reported, and the complex mechanism has been revealed. It is of great significance for understanding the nature of thermal expansion and guiding the regulation of thermal expansion.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Tailoring plasticity mechanisms in compositionally graded hierarchical steels fabricated using additive manufacturing

Abstract While there exists in nature abundant examples of materials with site-specific gradients in microstructures and properties, engineers and designers have traditionally used monolithic materials with discrete properties. Now, however, additive manufacturing (AM) offers the possibility of creating structures that mimic some aspects of nature. One example that has attracted attention in the recent years is the hierarchical structure in bamboo. The hierarchical architecture in bamboo is characterized by spatial gradients in properties and microstructures and is well suited to accommodate and survive complex stress states, severe mechanical forces, and large deformations. While AM has been used routinely to fabricate functionally graded materials, this study distinguishes itself by leveraging AM and physical metallurgy concepts to trigger cascading deformation in a single sample. Specifically, we have been successful in using AM to fabricate steel with unique spatial hierarchies in structure and property to emulate the structure and deformation mechanisms in natural materials. This study shows an improvement in the strength and ductility of the nature-inspired “hierarchical steel” compared with conventional cast stainless steels. In situ characterization proves that this improvement is due to the sequential activation of multiple deformation mechanisms namely twinning, transformation-induced plasticity, and dislocation-based plasticity. While significantly higher strengths can be achieved by refining the chemical and processing technique, this study sets the stage to achieve the paradigm of using AM to fabricate structures which emulate the flexibility in mechanical properties of natural materials and are able to adapt to in-service conditions.

36 MATERIALS SCIENCE↗
DOE OSTI2021

The solution structures of higher-order human telomere G-quadruplex multimers

Human telomeres contain the repeat DNA sequence 5'-d(TTAGGG), with duplex regions that are several kilobases long terminating in a 3' single-stranded overhang. The structure of the single-stranded overhang is not known with certainty, with disparate models proposed in the literature. We report here the results of an integrated structural biology approach that combines small-angle X-ray scattering, circular dichroism (CD), analytical ultracentrifugation, size-exclusion column chromatography and molecular dynamics simulations that provide the most detailed characterization to date of the structure of the telomeric overhang. We find that the single-stranded sequences 5'-d(TTAGGG)n, with n = 8, 12 and 16, fold into multimeric structures containing the maximal number (2, 3 and 4, respectively) of contiguous G4 units with no long gaps between units. The G4 units are a mixture of hybrid-1 and hybrid-2 conformers. In the multimeric structures, G4 units interact, at least transiently, at the interfaces between units to produce distinctive CD signatures. Global fitting of our hydrodynamic and scattering data to a worm-like chain (WLC) model indicates that these multimeric G4 structures are semi-flexible, with a persistence length of ~34 Å. Investigations of its flexibility using MD simulations reveal stacking, unstacking, and coiling movements, which yield unique sites for drug targeting.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Wassersplines for Neural Vector Field-Controlled Animation

Much of computer-generated animation is created by manipulating meshes with rigs. While this approach works well for animating articulated objects like animals, it has limited flexibility for animating less structured free-form objects. Here we introduce Wassersplines, a novel trajectory inference method for animating unstructured densities based on recent advances in continuous normalizing flows and optimal transport. The key idea is to train a neurally-parameterized velocity field that represents the motion between keyframes. Trajectories are then computed by advecting keyframes through the velocity field. We solve an additional Wasserstein barycenter interpolation problem to guarantee strict adherence to keyframes. Our tool can stylize trajectories through a variety of PDE-based regularizers to create different visual effects. We demonstrate our tool on various keyframe interpolation problems to produce temporally-coherent animations without meshing or rigging.

97 MATHEMATICS AND COMPUTING↗