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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 1,189 records · Page 66
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Understanding the microstructural evolution of hypersaline cemented paste backfill with low-field NMR relaxation

Highlights: • Microstructural evolution of CPB, prepared with tap and hypersaline water, is monitored using low-field NMR relaxometry. • T{sub 1} and T{sub 2} NMR relaxation measurements compared with mechanical strength via UCS tests • Hypersaline mixing water results in slower structural evolution and lower mechanical strength. Cemented paste backfill (CPB) comprising mineral tailings, binders and mixing water is an important potential support material in the mining industry. As the mechanical properties of CPB are significantly influenced by its microstructural characteristics the development of measurement tools to better understand its pore structure evolution is important for its increased utilisation. This study reports the application of low-field nuclear magnetic resonance (NMR) relaxation time measurements to characterise the microstructural evolution of CPB materials over 56 days of hydration, contrasting common tap water and hypersaline water (~22 wt% salt) as mixing water. Distinct NMR relaxation time populations were evidenced within each CPB sample, revealing the presence of both capillary (T{sub 1,2} ≈ 10 ms) and gel pore water (T{sub 1,2} ≈ 300–500 μs), with time-dependent relaxation measurements facilitating characterisation of capillary pore structure evolution over the hydration period assessed. Hypersaline samples demonstrated a time-lag in this measured capillary pore evolution, relative to those hydrated with tap water, while pore structure evolution rates were observed to increase with increased CPB binder content. Further, both T{sub 1} and T{sub 2} NMR relaxation times were found to correlate with the uniaxial compressive strength of the CPB materials investigated, facilitating the formulation of a predictive correlation function between NMR relaxation characteristics and mechanical properties.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Directed energy deposition of radially functionally graded nickel based superalloys IN625 and IN738: Microstructure and mechanical properties

This study investigates the microstructure and mechanical properties of radially functionally graded (RFG) materials fabricated from nickel-based superalloys IN625 and IN738 using directed energy deposition (DED). RFG cylindrical deposits with radial gradients of IN625-IN738 and IN738-IN625 were successfully fabricated and confirmed to be crack-free through tailored composition grading, scan strategy and process parameters that minimized thermal gradients and solidification velocities. Microstructural analysis revealed distinct variations across the gradient, characterized by differences in dendrite arm spacing, microsegregation, and the formation of distinct γ' precipitates and carbide/boride phases (MC, M 6 C, M 2 C, and MB 2 ) in as-deposited (AD) conditions, which evolved into M 23 C 6 and M 2 B upon subsequent heat treatment (HT). EBSD showed predominant columnar grains with a [100] texture, while microhardness profiles demonstrated consistent variation across the gradients. Mechanical testing highlighted the synergy of the graded structure, combining IN738's strength with IN625's ductility, resulting in enhanced properties such as a yield strength of 1131 MPa, ultimate tensile strength of 1382 MPa, and elongation of 12 % for HT IN625-IN738 grading. Numerical simulations employing CALPHAD thermodynamic calculations and finite element thermal modeling are used to establish phase evolution and microstructural feature correlation with thermal profiles, aiding in the optimization of process-structure-property relationship.

Additive manufacturing↗
DOE OSTI2026

Predicting U 3 O 8 powder processing conditions: An AI/ML approach analyzing deep learning embeddings of SEM micrographs

High-resolution SEM images of uranium-oxide powders encode micro- and nanoscale clues to their synthesis route and calcination temperature. We trained a ResNet-50 model on 11 commercial-scale U₃O₈ classes, ammonium diuranate (ADU) or uranyl peroxide (H₂O₂) precursors calcined at temperatures ranging from 400 to 750 °C and added a 256-D projection head before the classifier to analyze the learned representation. The best of eight seeds reached 92.4 % accuracy on reserved testing data, but our focus is the structure of the embedding space rather than the accuracy and labels. We quantify class relatedness in the original 256-D space using centroid similarity and distributional distances, and we use Uniform Manifold Approximation Projection (UMAP) for visualization. ‘Unknown’ images from different preparation methods, SEM operators, and from the literature localized near the expected classes under a nearest-centroid analysis without retraining, as well as clustered in similar UMAP space. In conclusion, this embedding-centered workflow complements black-box classification by providing quantitative, similarity-based comparisons of U₃O₈ morphologies and reduces storage space by up to 98 % for image data used in millisecond vector search comparisons.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Facile polymerization in a bicellar template to produce polymer nano-rings

We report a well-defined discoidal bicelle composed of three lipids, specifically zwitterionic long-chain 1,2-dipalmitoyl phosphocholine (DPPC) and short-chain 1,2-dihexanoyl phosphocholine (DHPC) doped with anionic 1,2-dipalmitoyl phosphoglycerol (DPPG) provides a generalized template for the synthesis of hydrophobic polymer nano-rings. The lipid molar ratio of DPPC/DHPC/DPPG is 0.71/0.25/0.04. The detailed investigation and discussion were based on styrene but tested on three other vinyl monomers. The structure of nano-rings is identified through the detailed analysis of small angle X-ray/neutron scattering (SAXS and SANS) data and transmission electron micrographs (TEM), supported by the differential scanning calorimetric (DSC) data before and after polymerization. The investigation covers samples with a styrene-to-lipid ratio ranged varied from 1:50 to 1:10.The styrene monomers are initially located at both the discoidal planar (long-chain lipid rich) and rim (short-chain lipid rich) regions. During polymerization, they migrate to the more fluid rim region-section. The formation mechanism involves the interplay of hydrophobic interaction, mismatched miscibility of polystyrene between the ordered and disordered phases, and crystallinity of the long lipid acyl chains. This facile synthesis is proven applicable for several hydrophobic monomers. The well-defined nano-rings greatly enhance the interfacial area and have the potential to be the building blocks for functional materials, if monomers are incorporated with desirable functions, for future applications. Crosslinked polymer nano-rings are produced in well-defined discoidal bicelles. The enhanced immiscibility in the planar region drives the hydrophobic polymers to the fluidic rim. The platform can be generalized for synthesis of other hydrophobic polymer nano-rings.

36 MATERIALS SCIENCE↗
DOE OSTI2016

Physicochemical and antibacterial characterization of ionocity Ag/Cu powder nanoparticles

Metal ion in bimetallic nanoparticles has shown vast potential in a variety of applications. In this paper we show the results of physical and chemical investigations of powder Ag/Cu nanoparticles obtained by chemical synthesis. Transmission electron microscopy (TEM) experiment indicated the presence of bimetallic nanoparticles in the agglomerated form. The average size of silver and copper nanoparticles is 17.1(4) nm (Ag) and 28.9(2) nm (Cu) basing on the X-ray diffraction (XRD) data. X-ray photoelectron (XPS) and Raman spectroscopies revealed the existence of metallic silver and copper as well as Cu{sub 2}O and CuO being a part of the nanoparticles. Moreover, UV–Vis spectroscopy showed surface alloy of Ag and Cu while Time of Flight Secondary Ion Mass Spectroscopy (ToF-SIMS) and Energy Dispersive X-ray Spectroscopy (EDX) showed heterogeneously distributed Ag structures placed on spherical Cu nanoparticles. The tests of antibacterial activity show promising killing/inhibiting growth behaviour for Gram positive and Gram negative bacteria. - Highlights: • Ag/Cu nanoparticles were obtained in the powder form. • The average size of nanoparticles is 17.1(4) nm (Ag) and 28.9(2) nm (Cu). • Ag/Cu powder nanoparticle shows promising antibacterial properties.

36 MATERIALS SCIENCE↗
DOE OSTI2015

Pyrolytic synthesis and characterization of N-doped carbon nanoflakes for electrochemical applications

Highlights: • Carbon nanoflakes doped with nitrogen were produced by a pyrolytic technique. • Quarternary, pyrrolic and pyridinic types of nitrogen are confirmed by XPS. • Nitrogen content depends on precursor used and temperature processed. • Specific surface area values decrease with increasing of synthesis duration. • N-doped carbon nanoflakes may be suitable for electrochemical applications. - Abstract: Nitrogen doped carbon nanoflakes, which are very important for many electrochemical applications, were synthesized by pyrolysis of nitrogen containing organic compounds over metal oxide template. Acetonitrile, pyridine and butylamine, which are of different volatility were tested as N-containing precursors. Morphology, structure and chemical composition of the as-synthesized materials were investigated by scanning electron microscopy (SEM), high resolution transmission electron microscopy (TEM), Raman spectroscopy and X-ray photoelectron spectroscopy (XPS). It was found that materials are highly defective and consist of a few malformed graphene layers. X-ray photoelectron spectra reflect the dominant graphitic and pyridinic N-bonding configuration. It was also noted that specific surface area depends on the duration and temperature of the reaction. Increase in duration and temperature led to decrease of the specific surface area from 1000 to 160 m{sup 2}/g, 1170 to 210 m{sup 2}/g and 1180 to 480 m{sup 2}/g for acetonitrile, butylamine and pyridine precursors, respectively.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2015

Increasing Sequence Diversity with Flexible Backbone Protein Design: The Complete Redesign of a Protein Hydrophobic Core

Protein design tests our understanding of protein stability and structure. Successful design methods should allow the exploration of sequence space not found in nature. However, when redesigning naturally occurring protein structures, most fixed backbone design algorithms return amino acid sequences that share strong sequence identity with wild-type sequences, especially in the protein core. This behavior places a restriction on functional space that can be explored and is not consistent with observations from nature, where sequences of low identity have similar structures. Here, we allow backbone flexibility during design to mutate every position in the core (38 residues) of a four-helix bundle protein. Only small perturbations to the backbone, 12 {angstrom}, were needed to entirely mutate the core. The redesigned protein, DRNN, is exceptionally stable (melting point >140C). An NMR and X-ray crystal structure show that the side chains and backbone were accurately modeled (all-atom RMSD = 1.3 {angstrom}).

Murphy, Grant S.↗
DOE OSTI2018

Tuning Inner-Ear Tip-Link Affinity Through Alternatively Spliced Variants of Protocadherin-15

Human hearing relies upon the tip-to-tip interaction of two nonclassical cadherins, protocadherin-15 (PCDH15) and cadherin-23 (CDH23). Together, these proteins form a filament called the tip link that connects neighboring stereocilia of mechanosensitive hair cells. As sound waves enter the cochlea, the stereocilia deflect and tension is applied to the tip link, opening nearby transduction channels. Disruption of the tip link by loud sound or calcium chelators eliminates transduction currents and illustrates that tip-link integrity is critical for mechanosensing. Tip-link remodeling after disruption is a dynamic process, which can lead to the formation of atypical complexes that incorporate alternatively spliced variants of PCDH15. These variants are categorized into six groups (N1–N6) based upon differences in the first two extracellular cadherin (EC) repeats. Here, we characterized the two N-terminal EC repeats of all PCDH15 variants (pcdh15(N1) to pcdh15(N6)) and combined these variants to test complex formation. We solved the crystal structure of a new complex composed of CDH23 EC1-2 (cdh23) and pcdh15(N2) at 2.3 Å resolution and compared it to the canonical cdh23–pcdh15(N1) complex. While there were subtle structural differences, the binding affinity between cdh23 and pcdh15(N2) is ~6 times weaker than cdh23 and pcdh15(N1) as determined by surface plasmon resonance analysis. Steered molecular dynamics simulations predict that the unbinding force of the cdh23–pcdh15(N2) complex can be lower than the canonical tip link. Our results demonstrate that alternative heterophilic tip-link structures form stable protein–protein interactions in vitro and suggest that homophilic PCDH15–PCDH15 tip links form through the interaction of additional EC repeats.

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

Surfactant-Specific AI-Driven Molecular Design: Integrating Generative Models, Predictive Modeling, and Reinforcement Learning for Tailored Surfactant Synthesis

Molecular design is a critical aspect of various scientific and industrial fields, where the properties of molecules hold significant importance. In this study, a 3-fold methodology design is presented that leverages the power of generative artificial intelligence (AI), predictive modeling, and reinforcement learning to create tailored molecules with desired properties. This model synergistically combines deep learning techniques with Self-Referencing Embedded Strings (SELFIES) molecular representation to build a generative model that generates valid molecules and a graphical neural network model that accurately forecasts molecular properties. The Variational Autoencoder (VAE) coupled with reinforcement learning helps refine molecule generation based on targeted attributes. Data from an experimental study involving surfactants were used to test the framework. A validation of the structural integrity of the molecules generated was conducted, and Tanimoto similarities were used to quantify the similarity and diversity between the original and generated molecular structures. Also, saliency maps for the generated surfactants were produced to identify the features explaining the property values. Lastly, molecular dynamics simulations were used to validate the stability of the generated molecules. The results showed that the proposed framework can effectively produce valid molecules within the set property threshold value.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Exploring Critical Synthetic Parameters for Nanoscale ε-Fe 2 O 3 and Their Influence on Magnetic Behaviors

An intermediate polymorph of iron oxide, ε-Fe 2 O 3 , has attracted significant attention due to its giant coercive field ( H c ) and potential applications in high-frequency millimeter-wave absorption and high-density magnetic recording. However, the fabrication of ε-Fe 2 O 3 with high phase purity is still a challenge due to complicated synthetic procedures and a large variety of reaction parameters. In this study, we have identified critical reaction parameters to improve the phase purity of ε-Fe 2 O 3 , and the effects of all possible reaction parameters have been tested through systematic studies. A combination of structural and magnetic characterization techniques provides us with an accurate and reliable phase purity analysis of the ε-Fe 2 O 3 phase. Specifically, we observed that (1) the reaction temperature and time and (2) the addition of Ba are critical parameters to improve the phase purity. We identified the optimal conditions that maximize the coercivity and phase purity, giving insight into the effects of each parameter on the γ- to ε- to α-phase-transition pathway. We obtained nearly single-phase ε-Fe 2 O 3 (~87 wt % with ~13 wt % α-phase) with a large coercivity of H c = 20.6 kOe, enabling us to obtain pure ε-Fe 2 O 3 by a simple magnetic separation protocol, and tuned the H c of the ε-Fe 2 O 3 nanoparticles in the range of 4.0–20.6 kOe by controlling the reaction parameters. Furthermore, the structural properties of the resulting ε-Fe 2 O 3 nanoparticles are confirmed by characterizing their chemical and magnetic properties using X-ray absorption spectroscopy and X-ray magnetic circular dichroism measurements.

36 MATERIALS SCIENCE↗
DOE OSTI2016

Undoped and Ni-doped CoO x surface modification of porous BiVO 4 photoelectrodes for water oxidation

Surface modification of photoanodes with oxygen evolution reaction (OER) catalysts is an effective approach to enhance water oxidation kinetics, to reduce external bias, and to improve the energy harvesting efficiency of photoelectrochemical (PEC) water oxidation. Here, the surface of porous BiVO 4 photoanodes was modified by the deposition of undoped and Ni-doped CoO x via nitrogen flow assisted electrostatic spray pyrolysis. This newly developed atmospheric pressure deposition technique allows for surface coverage throughout the porous structure with thickness and composition control. PEC testing of modified BiVO 4 photoanodes shows that after deposition of an undoped CoO x surface layer, the onset potential shifts negatively by ca. 420 mV and the photocurrent density reaches 2.01 mA cm –2 at 1.23 vs V RHE under AM 1.5G illumination. Modification with Ni-doped CoO x produces even more effective OER catalysis and yields a photocurrent density of 2.62 mA cm –2 at 1.23 V RHE under AM 1.5G illumination. Furthermore, the valence band X-ray photoelectron spectroscopy and synchrotron-based X-ray absorption spectroscopy results show the Ni doping reduces the Fermi level of the CoO x layer; the increased surface band bending produced by this effect is partially responsible for the superior PEC performance.

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

OmicsMLMentor: A Web Application for Guided Machine Learning Analysis of Omics Data

Expression-based omics technologies (e.g. proteomics, metabolomics, transcriptomics, etc.) increasingly rely on supervised and unsupervised machine learning (ML) models to find key biomolecules distinguishing conditions, identify natural groupings in biological data, or generate predictions for outcomes of interest. Fitting ML models to omics data presents several challenges, including handling missing data, selecting a normalization method, choosing a valid model, and optimizing hyperparameters, all requiring statistical programming skills to address these challenges. Thus, the open-source web application SLOPE was designed to lower the barrier to ML modeling for omics data. SLOPE supports the fitting of 15 ML models (10 supervised and 5 unsupervised) tailored to omics datasets, such as proteomics, metabolomics, lipidomics, and transcriptomics. SLOPE offers several omics-specific features, including methods for handling missingness (imputation, conversion, removal), normalization tests, ranking of models based on the structure of a user’s data and user input, and optimal hyperparameter selections using cross-validation splits. By streamlining ML workflows for omics analysis, SLOPE address critical gaps in existing online web tools, facilitating a broader adoption of these models for omics research. Here, SLOPE is applied to data from a lignin exposure study to highlight the workflow for fitting both supervised and unsupervised models to data.

lipidomics↗
DOE OSTI2025

Boosting the Oxygen Evolution Reaction by Tuning the Interfacial Iron Adsorption on Layered Double Hydroxide

Understanding the interaction between ions in the electrolyte and electrode materials plays an important role in optimizing the water electrolysis performance for hydrogen production. Herein, the synergistic effect of iron (Fe) in the electrolyte and interlayer anions within the layered structure on the oxygen evolution reaction (OER) has been investigated by combining material synthesis with controlled structure, multiple characterization techniques, and first-principles calculations. Nickel aluminum layered double hydroxides (NiAl-LDHs) with different interlayer anions (CO 3 2– , Cl – , and Br – ) show similar oxygen evolution activity in the absence of Fe species in the electrolyte. The addition of Fe into the electrolyte results in improved performance for all of the NiAl-LDHs, following the rank LDH-Br > LDH-Cl > LDH-CO 3 , under all of the conditions with varied concentration of Fe. X-ray absorption spectroscopy and identical location electron microscopy analyses show that the LDH structure remains unchanged after the OER activity test, while in situ stationary probe rotating disk electrode inductively coupled plasma mass spectrometry (SPRDE-ICP-MS) measurements show partial dissolution of the intercalating halide ions during cycling, with less dissolution for Br-intercalated materials. Insights from theoretical calculations demonstrate the thermodynamic preference of Br – to remain intercalated in the presence of Fe, while the stronger adsorption of Fe(OH) 3 species on the LDH-Br sample promotes the OER activity. In conclusion, these results provide mechanistic insights into the rational design of active layered materials with an enhanced OER performance for efficient water electrolysis.

58 GEOSCIENCES↗
DOE OSTI2025

Warpage-Resistant, Under-Extrusion-Free, High-Surface-Quality Additive Manufacturing Process for Polyethylene-Based Composite Radiation Shielding Material

Polyethylene (PE) is one of the best shielding materials for primary space radiation due to its high hydrogen content. For effective secondary neutron shielding, boron-rich fillers are incorporated to enhance performance. The semicrystalline nature and high thermal expansion coefficient of PE impede its adoption for in situ additive manufacture in space via the fused deposition modeling (FDM) 3D printing. Here, we developed an optimized PE blend to mitigate the effects of under-extrusion and warpage. Guided by studies on extrusion and warpage, we developed an optimal set of printing parameters for the proposed PE blend. The optimum PE blend─both in its pure form and when doped with fillers─has been tested on different FDM printers. The printed structures exhibit high and uniform density, smooth surfaces, no warpage, and competitive mechanical properties. The FDM-printed plates demonstrate efficient shielding from thermal neutrons, predicted via modeling and confirmed experimentally using extended Q-range small-angle neutron scattering.

additive manufacturing↗
DOE OSTI2021

Efficient calculation of carrier scattering rates from first principles

Abstract The electronic transport behaviour of materials determines their suitability for technological applications. We develop a computationally efficient method for calculating carrier scattering rates of solid-state semiconductors and insulators from first principles inputs. The present method extends existing polar and non-polar electron-phonon coupling, ionized impurity, and piezoelectric scattering mechanisms formulated for isotropic band structures to support highly anisotropic materials. We test the formalism by calculating the electronic transport properties of 23 semiconductors, including the large 48 atom CH 3 NH 3 PbI 3 hybrid perovskite, and comparing the results against experimental measurements and more detailed scattering simulations. The Spearman rank coefficient of mobility against experiment ( r s = 0.93) improves significantly on results obtained using a constant relaxation time approximation ( r s = 0.52). We find our approach offers similar accuracy to state-of-the art methods at approximately 1/500th the computational cost, thus enabling its use in high-throughput computational workflows for the accurate screening of carrier mobilities, lifetimes, and thermoelectric power.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Modified structure of protons and neutrons in correlated pairs

The atomic nucleus is made of protons and neutrons (nucleons), which are themselves composed of quarks and gluons. Understanding how the quark-gluon structure of a nucleon bound in an atomic nucleus is modified by the surrounding nucleons is an outstanding challenge. Although evidence for such modification-known as the EMC effect-was first observed over 35 years ago, there is still no generally accepted explanation for its cause. Recent observations suggest that the EMC effect is related to close-proximity short-range correlated (SRC) nucleon pairs in nuclei. Here we report simultaneous, high-precision measurements of the EMC effect and SRC abundances. We show that EMC data can be explained by a universal modification of the structure of nucleons in neutron-proton SRC pairs and present a data-driven extraction of the corresponding universal modification function. This implies that in heavier nuclei with many more neutrons than protons, each proton is more likely than each neutron to belong to an SRC pair and hence to have distorted quark structure. This universal modification function will be useful for determining the structure of the free neutron and thereby testing quantum chromodynamics symmetry-breaking mechanisms and may help to discriminate between nuclear physics effects and beyond-the-standard-model effects in neutrino experiments.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2016

Gravitational mass of positron from LEP synchrotron losses

General relativity(GR) is the current description of gravity in modern physics. One of the cornerstones of GR, as well as Newton’s theory of gravity, is the weak equivalence principle (WEP), stating that the trajectory of a freely falling test body is independent of its internal structure and composition. WEP is known to be valid for the normal matter with a high precision. However, due to the rarity of antimatter and weakness of the gravitational forces, the WEP has never been confirmed for antimatter. The current direct bounds on the ratio between the gravitational and inertial masses of the antihydrogen do not rule out a repulsive nature for the antimatter gravity. Here we establish an indirect bound of 0.13% on the difference between the gravitational and inertial masses of the positron (antielectron) from the analysis of synchrotron losses at the Large Electron-Positron collider (LEP). As a result, this serves as a confirmation of the conventional gravitational properties of antimatter without common assumptions such as, e.g., coupling of gravity to virtual particles, dynamics of distant astrophysical sources and the nature of absolute gravitational potentials.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2021

Graphical Gaussian Process Regression Model for Aqueous Solvation Free Energy Prediction of Organic Molecules in Redox Flow Battery

The solvation free energy of organic molecules is a critical parameter in determining emergent properties such as solubility, liquid-phase equilibrium constants, and pKa and redox potentials in an organic redox flow battery. In this work, we present a machine learning (ML) model that can learn and predict the aqueous solvation free energy of an organic molecule using Gaussian process regression method based on a new molecular graph kernel. To investigate the performance of the ML model on electrostatic interaction, the nonpolar interaction contribution of solvent and the conformational entropy of solute in solvation free energy, three data sets with implicit or explicit water solvent models, and contribution of conformational entropy of solute are tested. We demonstrate that our ML model can predict the solvation free energy of molecules at chemical accuracy with a mean absolute error of less than 1 kcal/mol for subsets of the QM9 dataset and the Freesolv database. To solve the general data scarcity problem for a graph-based ML model, we propose a dimension reduction algorithm based on the distance between molecular graphs, which can be used to examine the diversity of the molecular data set. It provides a promising way to build a minimum training set to improve prediction for certain test sets where the space of molecular structures is predetermined.

25 ENERGY STORAGE↗