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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 1,261 records · Page 70
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

Wide Band Gap Chalcopyrite Photoelectrodes for Direct Water Splitting (Final Technical Report)

The chalcopyrite material class, typically identified by its most popular alloy CuInGaSe 2 , provides exceptionally good candidates for photoelectrochemical (PEC) water splitting and has the potential to meet DoE EERE’s targets in terms of hydrogen production costs (less than $2/gge). Some key advantages of this class include remarkable photon-to-electron conversion efficiency and a high Faradaic efficiency for the hydrogen evolution reaction, two characteristics essential for efficient PEC water splitting. However, the band gaps of commonly used chalcopyrites in the photovoltaic field (1.0-1.6 eV) are too narrow to be compatible with the multi-junction approach for efficient PEC hydrogen production, in which a stack of solar absorbers generates the bias required for water splitting. Nonetheless, previous studies performed by our team demonstrated that chalcopyrites’ narrow band gaps could be effectively widened using simple conversion steps, leading to functional chalcopyrite-based PEC electrodes with optical band gap in the 1.8-2.4 eV range, making this class highly suitable for renewable hydrogen production via water splitting. This multi-disciplinary research program lead by the University of Hawai’i/Hawai’i Natural Energy Institute combined unique analytical techniques (UNLV), state-of-the-art theoretical modeling (LLNL) with advanced thin film materials synthesis (HNEI, NREL and Stanford) to provide deeper understanding of wide band gap chalcopyrite-based PEC materials and engineer high performance/corrosion-resistant photocathodes with tunable energetics and compatible with the multi-junction approach. The goals of this project were to demonstrate photoelectrochemical (PEC) solar-hydrogen production using a dual absorber system with a solar-to-hydrogen conversion efficiency of at least 15% with an operational life up to 2,000 hours and capable of generating at least 3 liters of hydrogen in 8 hours. After a brief introduction (Section 1) and description of our work plan (Section 2), we present in Section 3 our results on the development of new wide bandgap copper chalcopyrites absorbers for PEC waters splitting. We first show through modeling how alloying can be used to tailor the optical properties and/or surface energetics of various material candidates. We also present how specific defects (e.g., GaCu) can lead to recombination centers within the forbidden gap of copper-poor chalcopyrites. Then, we present our efforts to synthesize high efficiency wide band gap CuGa 3 Se 5 , CuGa(S,Se) 2 and Cu(In,Ga)S 2 photocathodes capable of generating photocurrent density over 10 mA/cm 2 . We also demonstrate that Cu-rich absorbers should be avoided as they lead to poor sub-band gap optical transmission. In Section 4, we first report on our efforts to tune the energetics of chalcopyrites toward the hydrogen evolution reaction. First, we quantified the energetics of wide band gap chalcopyrites interfaced with conventional CdS buffer layer, as measured with X-ray photoelectron spectroscopy techniques, and evidence the presence of a large (and undesirable) conduction band offset at this interface. Then, we highlight some of the work performed with alternative buffer layers, including In 2 S 3 . Durability improvements with various ultra-thin protective layers are presented in Section 5. Our data show that TiO 2 combined with MoS 2 can significantly improve chalcopyrites lifetime up to 350 hours under continuous operation at 8 mA/cm 2 . Finally, in Section 6 we report strategies to integrate chalcopyrite photocathodes into standalone tandem PEC devices. Although promising methods were proposed for both mechanical and monolithic stacking with narrow band gap PV drivers, no functional device was fabricated on time to fulfill the program end goals.

08 HYDROGEN↗
DOE OSTI2024

Characterization of Crystal Properties and Defects in CdZnTe Radiation Detectors

CdZnTe-based detectors are highly valued because of their high spectral resolution, which is an essential feature for nuclear medical imaging. However, this resolution is compromised when there are substantial defects in the CdZnTe crystals. In this study, we present a learning-based approach to determine the spatially dependent bulk properties and defects in semiconductor detectors. This characterization allows us to mitigate and compensate for the undesired effects caused by crystal impurities. We tested our model with computer-generated noise-free input data, where it showed excellent accuracy, achieving an average RMSE of 0.43% between the predicted and the ground truth crystal properties. In addition, a sensitivity analysis was performed to determine the effect of noisy data on the accuracy of the model.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2018

Large Eddy Simulations of Electromagnetic Braking Effects on Argon Bubble Transport and Capture in a Steel Continuous Casting Mold

In continuous casting of steel, argon gas is often injected to prevent clogging of the nozzle, but the bubbles affect the flow pattern, and may become entrapped to form defects in the final product. Further, an electromagnetic field is frequently applied to induce a braking effect on the flow field and modify the inclusion transport. In this study, a previously validated GPU-based in-house code CUFLOW is used to investigate the effect of electromagnetic braking on turbulent flow, bubble transport, and capture. Well-resolved large eddy simulations are combined with two-way coupled Lagrangian computations of the bubbles. The drag coefficient on the bubbles is modified to account for the effects of the magnetic field. The distribution of the argon bubbles, capture, and escape rates, are presented and compared with and without the magnetic field. The bubble capture patterns are also compared with results of a previous RANS model as well as with plant measurements.

36 MATERIALS SCIENCE↗
DOE OSTI2023

From Femtoseconds to Gigaseconds: The SolDeg Project to Analyze and Mitigate the Performance Degradation of a-Si:H/c-Si Heterojunction Solar Cells

We pursued two major projects. In the first, we studied the degradation of a-Si/c-Si Heterojunction cells. We substantially expanded our previously developed SolDeg platform, and incorporated hydrogen into it. We also expanded the capabilities of SolDeg by developing a three channel model for a very efficient way to determine long term junction dynamics. The first main result was that we identified hydrogen drifting away from the interface as a key degradation channel. The second was that we developed and proposed the concept of a “reverse silicon density gradient” in the a-Si layer that had the promise of reducing degradation by up to 80%. In the second project, we studied the degradation of TOPCon solar cells. We again identified a key degradation channel, which in these cells was the formation of pinholes across the oxide layer. We then again were able to develop and to propose a way to dramatically reduce degradation by discovering that tuning the hydrogen concentration forms a sharp and deep minimum of the recombination current.

14 SOLAR ENERGY↗
DOE OSTI2018

Defect Structures of La{sub 1–y}Sr{sub y}F{sub 3–y}, La{sub 1–y}Ba{sub y}F{sub 3–y}, and Nd{sub 1–y}Ca{sub y}F{sub 3–y} (y = 0.05, 0.10) Nonstoichiometric Tysonite Phases

Characteristic features of defect structures of La{sub 1–y}Sr{sub y}F{sub 3–y}, La{sub 1–y}Ba{sub y}F{sub 3–y}, and Nd{sub 1–y}Ca{sub y}F{sub 3–y} (y = 0.05, 0.10) nonstoichiometric phases of different compositions are determined from X-ray diffraction data. Interest in subtle details of their structure is determined by the possibility of ion transport over fluorine vacancies and by a strong compositional dependence of the ionic conductivity. The La{sub 0.95}Sr{sub 0.05}F{sub 2.95}, La{sub 0.95}Ba{sub 0.05}F{sub 2.95}, and Nd{sub 0.95}Ca{sub 0.05}F{sub 2.95} phases, as well as the La{sub 0.9}Ba{sub 0.1}F{sub 2.9} phase, crystallize as β-LaF{sub 3} (sp. gr. P3̅c1, Z = 6). The La{sub 0.9}Sr{sub 0.1}F{sub 2.9} and Nd{sub 0.9}Ca{sub 0.1}F{sub 2.9} phases lose their superstructure and are described by a cell whose volume is three times smaller (sp. gr. P6{sub 3}/mmc, Z = 2). Defects of crystal structure R{sub 1–y}M{sub y}F{sub 3–y} are not exhausted by vacancies in fluorine positions. All crystals with a “large” cell are twinned according to the merohedral twin law. The majority of atomic positions in models with a “small” cell are split by group symmetry elements and are occupied statistically.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2025

Topological Rigidity and Non-Abelian Defect Junctions in Chiral Nematic Systems with Effective Biaxial Symmetry

We study topologically stable defect structures in systems where the defect line classification in three dimensions and associated algebra of interactions (the fundamental group) are governed by the non-Abelian eight-element group, the quaternions 𝑄 8 . The non-Abelian character of the defect algebra leads to a topological rigidity of bound defect pairs, and trivalent junctions which are the building blocks of multijunction trivalent networks. We realize such structures in laboratory chiral nematics and analyze their behavior analytically, along with numerical modeling.

Liquid crystals↗
DOE OSTI2017

Investigating Alkylsilane Monolayer Tribology at a Single-Asperity Contact with Molecular Dynamics Simulation

Chemisorbed monolayer films are known to possess favorable characteristics for nanoscale lubrication of micro- and nanoelectromechanical systems (MEMS/NEMS). Prior studies have shown that the friction observed for monolayer-coated surfaces features a strong dependence on the geometry of contact. Specifically, tip-like geometries have been shown to penetrate into monolayer films, inducing defects in the monolayer chains and leading to plowing mechanisms during shear, which result in higher coefficients of friction (COF) than those observed for planar geometries. In this work, we use molecular dynamics simulations to examine the tribology of model silica single-asperity contacts under shear with monolayer-coated substrates featuring various film densities. It is observed that lower monolayer densities lead to reduced COFs, in contrast to results for planar systems where COF is found to be nearly independent of monolayer density. This is attributed to a liquid-like response to shear, whereby fewer defects are imparted in monolayer chains from the asperity, and chains are easily displaced by the tip as a result of the higher free volume. This transition in the mechanism of molecular plowing suggests that liquid-like films should provide favorable lubrication at single-asperity contacts.

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

FAIR Data and Interpretable AI Framework for Architectured Metamaterials (Final Report)

This research program established a transformative framework for the discovery and design of mechanical metamaterials, which are architected structures engineered to control physical phenomena like sound and vibration in ways natural materials cannot. To overcome the traditional reliance on trial-and-error, the project developed an interpretable Artificial Intelligence (AI) framework that moves beyond "black box" models to reveal the specific geometric patterns—such as "unit-cell templates"—that govern a material’s performance. A major breakthrough was the development of a hierarchical design method, which allows a single material to block vibrations across multiple frequency ranges simultaneously by layering patterns at different scales without them interfering with one another. This was further expanded to include irregular, graph-based designs that use spanning tree algorithms to ensure structural connectivity while allowing for customized, direction-dependent properties like stiffness and acoustic impedance. Beyond design, the project addressed the practicalities of real-world production by developing uncertainty quantification techniques that account for manufacturing defects and material variability, reducing the need for expensive physical testing by orders of magnitude. To speed up the discovery process, the team implemented Gaussian Process Regression and other surrogate models that provide accurate performance predictions at a fraction of the traditional computational cost. The AI-generated designs were successfully validated through fabrication of physical samples and wave propagation experiments, confirming their ability to accurately guide or reflect waves as predicted. By contributing these tools and high-quality FAIR benchmark datasets to the wider scientific community, this work provides a scalable foundation for advancing technologies in aerospace vibration control, medical imaging, and noise reduction.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Machine learning-enabled multiscale modeling of mechanical deformation of aluminum and Al-SiC nanocomposites

A machine learning-enabled multiscale framework is developed for modeling the mechanical response of both pure metal and nanoparticle-reinforced metal matrix nanocomposites (MMNCs). Using aluminum–silicon carbide (Al-SiC) as an example MMNC, atomistic simulations reveal three distinct deformation mechanisms (i.e., defect-free, dislocation-based, and interface separation) governed by the interfaces between the Al matrix and SiC nanoparticles. As compared with single crystal Al, the lattice undergoes a more abrupt failure once the dislocation network becomes extensive and void nucleation initiates, whereas in Al-SiC, nanoparticle interfaces enable a more gradual progression of damage. These mechanisms are captured through a combined classification-regression neural network surrogate model that bridges atomic-scale insights with continuum-scale finite element analysis. Machine learning-enabled multiscale modeling of pure Al accurately predicted strain localization and confirmed by in-situ scanning electron microscopic tensile testing on perforated Al specimens. This study underscores the promise of integrating physics-informed machine learning with hierarchical modeling to capture the interface dominated phenomena and guide the design of advanced MMNCs.

Al-SiC↗
DOE OSTI2026

Role of Wadsley Defects and Cation Disorder to Enhance MoNb 12 O 33 Diffusion

Wadsley-Roth (WR) niobates have emerged as high-rate anode materials that can combine rapid ionic diffusion with good electronic conductivity. WR compounds have been defect-enhanced by limited annealing, however, such materials often contain multiple types of defects. In particular, both Wadsley defects (variable block size) and transition metal disorder have the potential to modify transport rates, however the corresponding effects are not well understood mechanistically. Here, MoNb 12 O 33 (MNO) was calcined at two different temperatures to compare a defect-rich condition (MNO-800) with a proximal order-rich condition (MNO-900) as assessed through XRD, XANES, EXAFS, and STEM characterizations. Galvanostatically cycled lithium half cells of MNO-800 exhibited additional capacity (307 mAhg −1 at 0.1C, 4.66% higher) and improved high-rate capacity of 200 mAhg −1 at 10C. ICI-based overpotential analysis identified solid state diffusion as the dominant rate limiting process where MNO-800 correspondingly exhibited ∼3X faster capacity-weighted diffusivity. A machine-learning interatomic potential was trained to density functional theory and then applied with molecular dynamics (MLIP-MD) to examine the possible roles of Wadsley defects and transition metal disorder. For both defect-types, Li was found to populate and activate fast diffusion paths from window sites at lower extents of lithiation as compared to the order-rich model.

defect↗
DOE OSTI2020

Schizophrenia-related microdeletion causes defective ciliary motility and brain ventricle enlargement via microRNA-dependent mechanisms in mice

Progressive ventricular enlargement, a key feature of several neurologic and psychiatric diseases, is mediated by unknown mechanisms. Here, using murine models of 22q11-deletion syndrome (22q11DS), which is associated with schizophrenia in humans, we found progressive enlargement of lateral and third ventricles and deceleration of ciliary beating on ependymal cells lining the ventricular walls. The cilia-beating deficit observed in brain slices and in vivo is caused by elevated levels of dopamine receptors (Drd1), which are expressed in motile cilia. Haploinsufficiency of the microRNA-processing gene Dgcr8 results in Drd1 elevation, which is brought about by a reduction in Drd1-targeting microRNAs miR-382-3p and miR-674-3p. Replenishing either microRNA in 22q11DS mice normalizes ciliary beating and ventricular size. Knocking down the microRNAs or deleting their seed sites on Drd1 mimicked the cilia-beating and ventricular deficits. These results suggest that the Dgcr8–miR-382-3p/miR-674-3p–Drd1 mechanism contributes to deceleration of ciliary motility and age-dependent ventricular enlargement in 22q11DS.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Electronic conductivity in metal-graphene composites: the role of disordered carbon structures, defects, and impurities

Abstract This paper explores the transport properties of aluminum-carbon composite material via ab initio methods. Interfacial and electronic dynamics of the aluminum-graphene interface structure were investigated using models of amorphous graphene added to an aluminum matrix. We examine the impact on electronic conduction caused by the presence of nitrogen impurities within the interfacial amorphous graphene layer. We elucidate the conduction mechanisms by using a projection of the electronic conductivity into space.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Small uranium and oxygen interstitial clusters in UO 2 : An empirical potential study

The aggregation of defect clusters in UO 2 under irradiations creates dislocation loops and cavities, which in turn contribute to swelling of the fuels, leading to detrimental pellet-clad mechanical interactions. The kinetics of the defect clusters, however, have received less attention and are not well understood. As such, using two interatomic potentials, the Morelon potential and a many-body model by Cooper, Rushton, and Grimes (CRG), the energetics and kinetics of small uranium interstitial clusters, with and without bound oxygen interstitials, are investigated with molecular dynamics and accelerated molecular dynamics simulations. For the single bound anti-Schottky interstitial cluster (U i 2O i ), both potentials find the interstitialcy migration mechanism to be a low barrier migration mechanism. However, for the CRG potential, an oxygen Frenkel pair assisted interstitial cluster migration mechanism with a lower migration barrier is identified during accelerated molecular dynamics simulations. For the Morelon potential, the interstitial clusters have geometrically compact structures, and small interstitial clusters (two and three anti-Schottky clusters) are more mobile than the single bound anti-Schottky interstitial cluster; for the CRG potential, the interstitial clusters have geometrically non-compact structures, yielding a more complex energy landscape. The rapid migration of interstitials and interstitial clusters are important for the irradiation response of UO 2 and correlates well with the very low barriers reported in a few historic experimental studies.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗
DOE OSTI2020

Development of neural network force fields for corrosion studies

To fully understand the chemistry and physics of corrosion, novel methods of simulation must be developed. One approach is designing machine learning (ML) algorithms integrated with density functional theory to develop adaptive force fields to gain insight into corrosion behavior namely at the surface of metal oxides. Current methods of modeling corrosion are slow due to the computational cost of resolving both reaction mechanics and mass transport processes. Machine learning methods can be implemented to obtain structure-activity relationships at both the molecular and bulk scale while still retaining the accuracy of density functional theory (DFT) and significantly decreasing the time needed for simulations of complex chemical processes in the various environments of corrosion. Multiscale models are needed for corrosion studies to fully understand its processes not only at the atomic length scale (chemical bonding, energies, and forces), but also at the nano and meso length scales (solid-state physics and material science processes). Current methods of study include DFT, molecular dynamics, and Monte Carlo. The limitation of DFT is that only a small number of atoms or molecules can be simulated at that level of theory. Density functional theory is used to study the electronic structure of atoms and molecules, and calculate the force component of each atom. However, these calculations are limited to about 1000 atoms. Custom periodic boundary conditions (PBC) can be used to describe the various environments and defects that affect the atomic forces to produce a large data set from which a training set can be derived. Machine learning can be utilized to overcome the barrier of modeling macroscopic and multi-scale processes from ab initio calculations through the development of adaptive force fields. Local environments determine the atomic forces of a given system, therefore adaptive force fields must be created to produce reliable quantum mechanical calculations. This can be achieved by developing a learning algorithm that uses the mapped atomic forces or fingerprint as an input to produce energies and magnetic moments as output. A systematic approach was used to begin to build a data set in order to accurately describe the atomic forces in various environments. In Figure 4 below, a simple PBC cell of Fe{sub 2}O{sub 3} was first optimized. A surface optimization was performed next, followed by a hydroxylated surface optimization. Once this calculation has converged, the adsorption of halide species to the hydroxylated surface will be investigated. TensorFlow is an open source platform for machine learning developed by Google. Using a high level application program interface (API) such as Keras allows for building and training ML models easily in a number of different environments and languages. For this project, a neural network was developed within Anaconda in Python. Future Work: Further development of reference data set; Refining neural network and learning algorithm; Fingerprinting atomic environment to enable mapping of atomic force components; Choosing appropriate training set from reference data; Learning from training set and enabling non-linear mapping of training set fingerprints and the atomic forces; Estimation of uncertainty to identify ranges of outside applicability; Testing and analysis of molecular dynamic simulations.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Radiation-Enhanced Anion Transport in Hematite

The influence of radiation-induced (1 MeV energy H+ to ~0.1 dpa at 450°C), non-equilibrium point defect populations on mass transport is studied with an integrated campaign of experimental and theoretical methods. Using epitaxial thin films of hematite with embedded 18O tracer layers and nanoscale atom probe tomography measurements, it is shown that anion self-diffusion is enhanced by at least 2 orders of magnitude under irradiation compared to thermal diffusion alone. Complementary scanning transmission electron microscopy of vacuum annealed specimens, reveals associated microstructural changes in the oxide films, including local phase transformation to Fe3O4 and the development of nanoscale voids from vacancy coalescence. Point defect formation and migration energies were computed from density functional theory and applied within the context of chemical rate theory to analyze contributions from both interstitial and vacancy mechanisms to self-diffusion in thermal and irradiation conditions. Comparisons are made between calculated, literature and newly measured self-diffusion values, revealing good agreement on the magnitude of radiation-enhanced anion diffusion. Further, the model suggests a transition from vacancy to interstitialcy mechanisms at low temperatures and high oxygen activity, consistent with the varied activation energies reported from prior studies.

iron oxides, isotopic tracers, atom probe tomograp↗
DOE OSTI2022

Origin, parameters, and underlying deformation mechanisms of propagating deformation bands in irradiated 316L stainless steel

Lüders-type propagating deformation bands were observed in specimens of irradiated 316L stainless steel samples removed from Spallation Neutron Source target vessels after service. Mechanical testing with digital image correlation (DIC) and in-situ tensile testing with scanning electron microscopy electron backscatter diffraction showed that the observed Lüders-type behavior was not related to the known transformation-induced plasticity or twinning-induced plasticity behavior. Instead, the phenomenon occurs at small local strain values before a significant amount of martensite or deformation twins appear in the microstructure. Microstructural analysis and in-situ mechanical test results suggest Lüders-type band formation and propagation were related to the appearance and evolution of defect-free channels—analogous to slip bands. A modified Swift equation with a Ludwigson-like component was offered to rationalize the phenomenon and model the strain-softening processes at small strain values. Finally, the results indicate complex microstructural processes and deformation mechanisms were active at small strain values and underline the benefits of advanced mechanical test approaches such as DIC.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Influence of carrier trapping on radiation detection properties in $\mathrm{CVD}$ grown 4H-SiC epitaxial layers with varying thickness up to 250 µm

We report low doped n-type 4H-SiC epitaxial layers of thickness 50, 150, and 250 µm grown by hot wall chemical vapor deposition were used to fabricate Ni/4H-SiC Schottky radiation detectors. The epitaxial layers were grown on the (0001) face of highly conductive bulk 4H-SiC substrates 8° offcut towards the $\langle 11\bar{2}1\rangle$ direction. The 50, 150, and 250 µm thick epilayer detectors, under optimized settings, showed energy resolutions of 2.0%, 0.78%, and 0.63%, respectively for 5.48 MeV alpha particles. Deep level transient spectroscopy studies showed that the observed variation in the detector resolution is linked to the defect parameters in the devices. Least-squares fitting of the bias dependence of the charge collection efficiency according to a drift-diffusion model, revealed minority carrier diffusion lengths of 16, 10, and 9.2 µm, respectively, implying that the detectors are not limited by minority carrier trapping. The detector performance was observed to be primarily dependent on the concentration and capture cross-sections of the lifetime killing electron traps Z 1/2 and EH 6/7 .

36 MATERIALS SCIENCE↗