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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 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Dangling bonds, the charge neutrality level, and band alignment in semiconductors

We present a systematic study of the electronic properties of dangling bonds (DBs) in a variety of semiconductors and examine the relationship between DBs and the charge neutrality level (CNL) in the context of band alignments of semiconductors. We use first-principles calculations based on density functional theory to assess the energetics of DBs in a set of diamond-structure group-IV and III–V or II–VI zinc-blende-structure semiconductors, considering both cation and anion-derived states. We examine the charge-state transition levels of DBs to assess whether they can serve as a CNL to align band structures, by comparing with offsets calculated from interface calculations. Our results show that this approach for evaluating the CNL yields quantitative results for band offsets and provides useful insights. We discuss the relation with alternative approaches for determination of CNLs based on branch-point energies or transition levels of interstitial hydrogen.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Machine learning for impurity charge-state transition levels in semiconductors from elemental properties using multi-fidelity datasets

Quantifying charge-state transition energy levels of impurities in semiconductors is critical to understanding and engineering their optoelectronic properties for applications ranging from solar photovoltaics to infrared lasers. While these transition levels can be measured and calculated accurately, such efforts are time-consuming and more rapid prediction methods would be beneficial. Here, we significantly reduce the time typically required to predict impurity transition levels using multi-fidelity datasets and a machine learning approach employing features based on elemental properties and impurity positions. We use transition levels obtained from low-fidelity (i.e., local-density approximation or generalized gradient approximation) density functional theory (DFT) calculations, corrected using a recently proposed modified band alignment scheme, which well-approximates transition levels from high-fidelity DFT (i.e., hybrid HSE06). Further, the model fit to the large multi-fidelity database shows improved accuracy compared to the models trained on the more limited high-fidelity values. Crucially, in our approach, when using the multi-fidelity data, high-fidelity values are not required for model training, significantly reducing the computational cost required for training the model. Our machine learning model of transition levels has a root mean squared (mean absolute) error of 0.36 (0.27) eV vs high-fidelity hybrid functional values when averaged over 14 semiconductor systems from the II–VI and III–V families. As a guide for use on other systems, we assessed the model on simulated data to show the expected accuracy level as a function of bandgap for new materials of interest. Finally, we use the model to predict a complete space of impurity charge-state transition levels in all zinc blende III–V and II–VI systems.

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

Design and Realization of Ohmic and Schottky Interfaces for Oxide Electronics

Understanding band alignment and charge transfer at complex oxide interfaces is critical to tailoring and utilizing their diverse functionality. Toward this goal, both Ohmic- and Schottky-like charge transfers at oxide/oxide semiconductor/metal interfaces are designed and experimentally validated. A method for predicting band alignment and charge transfer in ABO 3 perovskites is utilized, where previously established rules for simple semiconductors fail. The prototypical systems chosen are the rare class of oxide metals, SrBO 3 with B = V–Ta, when interfaced with the multifaceted semiconducting oxide, SrTiO 3 . For B = Nb and Ta, it is confirmed that a large accumulation of charge occurs in SrTiO 3 due to the higher energy Nb and Ta states relative to Ti. Furthermore, this gives rise to a high mobility metallic interface, which is an ideal epitaxial oxide/oxide Ohmic contact. On the contrary, for B = V, there is no charge transfer into the SrTiO 3 interface, which serves as a highly conductive epitaxial gate metal. Going beyond these specific cases, this work opens the door to integrating the vast phenomena of ABO 3 perovskites into a wide range of practical devices.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Orienting an Organic Semiconductor into DNA 3D Arrays by Covalent Bonds

Abstract A quasi‐one‐dimensional organic semiconductor, hepta(p‐phenylene vinylene) (HPV), was incorporated into a DNA tensegrity triangle motif using a covalent strategy. 3D arrays were self‐assembled from an HPV‐DNA pseudo‐rhombohedron edge by rational design and characterized by X‐ray diffraction. Templated by the DNA motif, HPV molecules exist as single‐molecule fluorescence emitters at the concentration of 8 mM within the crystal lattice. The anisotropic fluorescence emission from HPV‐DNA crystals indicates HPV molecules are well aligned in the macroscopic 3D DNA lattices. Sophisticated nanodevices and functional materials constructed from DNA can be developed from this strategy by addressing functional components with molecular accuracy.

Wang, Xiao↗
DOE OSTI2021

Orienting an Organic Semiconductor into DNA 3D Arrays by Covalent Bonds

We report a quasi-one-dimensional organic semiconductor, hepta(p-phenylene vinylene) (HPV), was incorporated into a DNA tensegrity triangle motif using a covalent strategy. 3D arrays were self-assembled from an HPV-DNA pseudo-rhombohedron edge by rational design and characterized by X-ray diffraction. Templated by the DNA motif, HPV molecules exist as single-molecule fluorescence emitters at the concentration of 8 mM within the crystal lattice. The anisotropic fluorescence emission from HPV-DNA crystals indicates HPV molecules are well aligned in the macroscopic 3D DNA lattices. Sophisticated nanodevices and functional materials constructed from DNA can be developed from this strategy by addressing functional components with molecular accuracy.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Exploring drought-responsive crucial genes in Sorghum

Drought severely affects global food production. Sorghum is a typical drought-resistant model crop. Based on RNA-seq data for Sorghum with multiple time points and the gray correlation coefficient, this paper firstly selects candidate genes via mean variance test and constructs weighted gene differential co-expression networks (WGDCNs); then, based on guilt-by-rewiring principle, the WGDCNs and the hidden Markov random field model, drought-responsive crucial genes are identified for five developmental stages respectively. Enrichment and sequence alignment analysis reveal that the screened genes may play critical functional roles in drought responsiveness. A multilayer differential co-expression network for the screened genes reveals that Sorghum is very sensitive to pre-flowering drought. Furthermore, a crucial gene regulatory module is established, which regulates drought responsiveness via plant hormone signal transduction, MAPK cascades, and transcriptional regulations. The proposed method can well excavate crucial genes through RNA-seq data, which have implications in breeding of new varieties with improved drought tolerance.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Benchmark Comparison of HyRAM and ALDEA Software for Hydrogen Release Behavior

There are several different calculation approaches and tools that can be used to evaluate the risk of hydrogen energy applications. A comparative study of Air Liquide’s ALDEA (Air Liquide Dispersion and Explosion Assessment) tools suite and Sandia’s HyRAM (Hydrogen Risk Assessment Models) toolkit has been conducted. The purpose of this study was to understand and evaluate the differences between the two calculation approaches, and identify areas for model improvements. There were several scenarios examined in this effort regarding hydrogen release dynamics. These scenarios include free jet release cases at varying pressures, vessel blowdown, and hydrogen build-up scenarios with and without ventilation. For each scenario, the input and output of the HyRAM calculations are documented, along with a comparison to the ALDEA results. Generally, the results from the two different tools were reasonably aligned. However, there were fundamental differences in evaluation methodology and functional limitations in HyRAM that caused discrepancies in some calculations.

08 HYDROGEN↗
DOE OSTI2023

A National Infrastructure for Artificial Intelligence on the Grid (NI4AI) (Final Scientific/Technical Report)

Electric utilities have traditionally taken a very pragmatic yet myopic approach with grid sensors and the resulting collected data. Sensors are purchased and deployed to solve a specific, known problem that has risen to sufficient awareness as to justify the effort of deploying sensors and the needed capital investment. This sensor data flows into proprietary software packages with limited functionality intended only to address the initial problem. This approach aligns with the financial incentives of the utility to deploy capital into fixed hardware assets for which the corporations earn a rate of return. This mentality stands in stark contrast to the big data revolution that started nearly 25 years ago with the rise of Google. In this worldview, data is a fundamental business asset; successful organizations collect, store, explore, merge, and exploit as much data as possible to not only solve problems well understood today but also to tackle new problems that will inevitably rise tomorrow. The ARPA-E Open Innovation 2018 project entitled A National Infrastructure for Artificial Intelligence on the Grid or NI4AI for short was designed to demonstrate this alternative paradigm for using data. To do this, the project was composed of three key thrust areas. The first major component deployed a variety of high-frequency grid sensors and captured terabytes of both wide-scale and localized grid measurements, generating high-value datasets for grid research and algorithm development. The second aspect made available PingThings’ PredictiveGridTM, a horizontally scalable, cloud-based data management and AI platform built for time series data to explore and exploit the collected data. Finally, the project fostered a diverse and open research community composed of experts from numerous fields through focused educational content, code sharing, and data science competitions. Shifting away from “single use” sensors and closed data silos within electric utilities is a major benefit to the public at large. This legacy approach to data is incredibly (1) capital intensive (new sensors must be deployed for each new problem and problems tend to arise continuously) and (2) painfully slow (new problems must be identified first and then new sensors must be deployed to collect data to begin to address the issue). The transition to a carbon neutral grid requires a massive transformation of the existing grid infrastructure and will continue to challenge the legacy grid in unforeseen ways. The only way to make the energy transition cost effective is for utilities to abandon this dated data paradigm and adopt more contemporary approaches. NI4AI has shown that it is technically possible and economically feasible to ingest, explore, and exploit grid data collected from even very high frequency sensing, such as continuous point on wave sensors collecting measurements 10,000 times a second. In fact, the PredictiveGrid platform used is commercially available and deployed at several utilities in the United States. Project accomplishments were numerous and included (1) making available a state of the art time series platform to the community, (2) collecting over 520 streams of time series data from grid sensors totaling over 1 trillion grid measurements, and (3) developing and nurturing a community within the industry focused on the use of data to create value for utilities and, ultimately, end consumers.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Single-Step Fabrication of Au-Fe-BaTiO3 Nanocomposite Thin Films Embedded with Non-Equilibrium Au-Fe Alloyed Nanostructures

Nanocomposite thin film materials present great opportunities in coupling materials and functionalities in unique nanostructures including nanoparticles-in-matrix, vertically aligned nanocomposites (VANs), and nanolayers. Interestingly the nanocomposites processed through a non-equilibrium processing method, e.g., pulsed laser deposition (PLD), often possess unique metastable phases and microstructures that could not achieve using equilibrium techniques, and thus lead to novel physical properties. In this work, a unique three-phase system composed of BaTiO3 (BTO), with two immiscible metals, Au and Fe, is demonstrated. By adjusting the deposition laser frequency from 2 Hz to 10 Hz, the phase and morphology of Au and Fe nanoparticles in BTO matrix vary from separated Au and Fe nanoparticles to well-mixed Au-Fe alloy pillars. This is attributed to the non-equilibrium process of PLD and the limited diffusion under high laser frequency (e.g., 10 Hz). The magnetic and optical properties are effectively tuned based on the morphology variation. This work demonstrates the stabilization of non-equilibrium alloy structures in the VAN form and allows for the exploration of new non-equilibrium materials systems and their properties that could not be easily achieved through traditional equilibrium methods.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Monolithically Integrated $\varepsilon$-Ge/In x Ga 1-x As Quantum Well Laser Design: Experimental and Theoretical Investigation

Here, we have analyzed the electrical and optical phenomenon occurring in a $\varepsilon$-Ge/In x Ga 1-x -Ge/In x Ga 1-x As quantum well (QW) laser through self-consistent physical solvers calibrated using in-house experimental results. A separate confinement heterostructure QW design is proposed to enable lasing from tensile strained germanium ($\varepsilon$-Ge/In x Ga 1-x -Ge) in the range of 1.55 um to 4 um wavelengths as a function of QW thickness and indium (In) composition. Different recombination mechanisms were analyzed as a function of tensile strain in $\varepsilon$-Ge/In x Ga 1-x -Ge QW. Minority carrier lifetime and band alignment are key attributes of a QW laser, which were measured using microwave photoconductive decay and x-ray photoelectron spectroscopy (as a function of In composition), respectively. The transition point of Ge to a direct bandgap material is re-affirmed to be at $\varepsilon$-Ge/In x Ga 1-x = 1.6% (In ~24%) and the transition from type I to type II for $\varepsilon$-Ge/In x Ga 1-x -Ge/In x Ga 1-x As QW is found to be at In ~55%. Also, the transition to a TM mode dominant laser is identified at In ~15%. Using a tunable waveguide design to optimize confinement as a function of In composition, strain, wavelength, QW thickness, refractive index, and geometry, the $\varepsilon$-Ge/In x Ga 1-x -Ge QW laser design provided a net material gain of ~2000 cm -1 and a threshold current density of ~5 kA/cm 2 , which is an improvement over existing Ge based lasers. In conclusion, the impact of In composition and QW thickness on the band structure, polarized gain spectra, and various lasing metrics were analyzed to show $\varepsilon$-Ge/In x Ga 1-x -Ge/InGaAs QW lasers as promising for integrated photonics.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2026

Mesoporous peptide frameworks engineered from crystallizable collagen-mimetic peptide amphiphiles

The rational design of porous frameworks with tunable pore dimensions and chemical functionalities is a critical step toward their implementation in diverse applications. While traditional porous materials are typically constructed from abiotic components, there is increasing interest in employing biologically derived building blocks (e.g., peptides and proteins) that offer unmatched structural and functional diversity. Here, we report the construction of crystalline mesoporous frameworks that are self-assembled from amphiphilic collagen-mimetic peptides. Comprehensive structural characterization via microscopy, spectroscopy, and computational techniques provides insights into the assembly packing model, in which hexagonally packed channels are interconnected by antiparallel-aligned collagen triple helices via hydrophobic and electrostatic interactions. Lastly, we demonstrate the functional potential of aCMP frameworks through the encapsulation of various molecular guests, including doxorubicin, an anti-cancer drug. Overall, this work establishes a class of mesoporous frameworks, derived from synthetically engineerable peptide conjugates, marking a significant step forward in broadening the architectural scope and application potential of peptide-based materials.

Perez, Anthony R↗
DOE OSTI2024

High-strength 3D printed poly(lactic acid) composites reinforced by shear-aligned polymer-grafted cellulose nanofibrils

This work demonstrates the application of pilot-scale surface functionalization of cellulose nanofibrils (CNFs) by aqueous grafting-through polymerization and subsequent spray drying in 3D printed poly(lactic acid) (PLA) composites. Grafted-CNF composites attain an ultimate tensile strength of 88 ± 3 MPa and a tensile modulus of elasticity of 7.8 ± 1.3 GPa in the printing direction at 20 wt% reinforcement loading. These increases, 42% and 139% over neat PLA, respectively, represent the strongest reported 3D printed CNF/PLA composite to date in the literature. The mechanisms behind these improvements are investigated by comparisons to neat PLA and unmodified spray-dried CNF/PLA controls using melt rheology, dynamic mechanical analysis, and assessment of the reinforcement dispersion. These experiments reveal that improved network formation and shear-induced alignment of the grafted CNFs facilitate the remarkable tensile properties of the printed composites.

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

A DFT-based kinetic Monte Carlo simulation of multiphase oxide-metal thin film growth

Functional thin films of nanoscale metal pillars in oxide or nitride matrices known as vertically aligned nanocomposite (VAN) have gained much interest owing to their unique strain-coupled and highly anisotropic properties. So far, the deposition of these films has been explored mostly experimentally. In this work, a density functional theory (DFT)-based kinetic Monte Carlo simulation model using Bortz–Kalos–Lebowitz algorithm was developed to understand the growth of VAN films deposited by pulsed laser technique on mismatching substrates. The model has been parameterized and applied to understand the kinetics of growth thin films consisting of Au pillars in CeO2 matrix deposited on SrTiO3 substrates. The effects of pulsed laser deposition (PLD) conditions including the pulse frequency, deposition flux, and substrate temperature were explored. The simulations indicate that the Au pillar size and shape exhibit significant dependence on the PLD conditions. Namely, increasing the temperature increases the average pillar size and lowers the pillar density, and vice versa. In addition, the simulations revealed that increasing the deposition rate results in lowering the average pillar size and increasing the density. Particularly, the DFT results suggest that Au pillar size can be tuned during the initial growth of the first monolayer due to the significantly low activation barrier. Our analysis showed that the relationship between the average pillar size and pillar density is influenced by the kinetics. Furthermore, autocorrelation analysis showed that pillars self-organize in quasi-ordered patterns at certain windows of the deposition conditions, which is attributed to the complex nature of the chemical interactions in the system, the kinetics, and the deposition parameters.

Physics↗
DOE OSTI2023

Surface termination control of charge transfer and band alignment across a semiconductor–crystalline-oxide heterojunction

Charge redistribution across heterojunctions has long been utilized to induce functional response in materials systems. Here we examine how the composition of the terminating surface affects charge transfer across a heterojunction consisting of Si and the crystalline complex oxide SrTiO 3 . Itinerant electrons in Si migrate across the interface toward the surface of SrTiO 3 due to surface depletion. The electron transfer in turn creates an electric field across the interface that modifies the interfacial dipole associated with bonding between SrTiO 3 and Si. The modification in the dipole leads to a change in band alignment, in which the conduction band of SrTiO 3 moves from being above the valence band of Si in energy, to below it. By capping the SrTiO 3 surface with ultrathin (≤ 1 nm) layers of BaO, SrO or TiO 2 , charge transfer across the interface can be weakened or inhibited. Ab initio modeling implicates the adsorption of oxygen associated with exposure to ambient conditions as driving the surface depletion in SrTiO 3 . In conclusion, the electronic coupling between the surface and buried interface expands the functionality of semiconductor-crystalline oxide heterojunctions.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Helical Complex Ladder Polymer with Amplification of Asymmetry

Here, we report a Cu–salen helical complex ladder polymer (HLP-Cu) built from covalently fused Cu(Salen) units. Point chirality from enantiopure diamines directs the formation of a rigid, π-conjugated helix. Compared with a planar analogue (LLP-Cu) and the Cu(Salen) monomer, HLP-Cu exhibits up to 5-fold stronger circular dichroism and a maximum dissymmetry factor of 7.4 × 10 –3 . Titration experiments suggest a cooperative amplification of asymmetry arising from extended conjugation and helical geometry during helix formation. Time-dependent density functional theory calculations are employed to support this hypothesis, showing that increasing conjugation length and helical order enhance the rotational strength of contributing excited states via elevated magnetic transition moments and improved alignment of electric and magnetic transition moments. Our findings provide a new polymer platform for designing functional chiral materials with tunable chiroptical and spin-dependent properties.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Bootstrap estimation of the effect of instrument response function uncertainty on the reconstruction of fusion neutron sources

Neutron imagers are important diagnostics for the inertial confinement fusion implosions at the National Ignition Facility. They provide two- and three-dimensional reconstructions of the neutron source shape that are key indicators of the overall performance. To interpret the shape results properly, it is critical to estimate the uncertainty in those reconstructions. There are two main sources of uncertainties: limited neutron statistics, leading to random errors in the reconstructed images, and incomplete knowledge of the instrument response function (the pinhole-dependent point spread function). While the statistical errors dominate the uncertainty for lower yield deuterium-tritium (DT) shots, errors due to the instrument response function dominate the uncertainty for DT yields on the order of 10 16 neutrons or higher. In this work, a bootstrapping method estimates the uncertainty in a reconstructed image due to the incomplete knowledge of the instrument response function. The main reconstruction is created from the fixed collection of pinhole images that are best aligned with the neutron source. Additional reconstructions are then built using subsets of that collection of images. Variations in the shapes of these additional reconstructions originate solely from uncertainties in the instrument response function, allowing us to use them to provide an additional systematic uncertainty estimate.

47 OTHER INSTRUMENTATION↗
DOE OSTI2023

An Edge Alignment-Based Orientation Selection Method for Neutron Tomography

Neutron computed tomography (nCT) is a 3D char-acterization technique used to image the internal morphology or chemical composition of samples in biology and materials sciences. A typical workflow involves placing the sample in the path of a neutron beam, acquiring projection data at a predefined set of orientations, and processing the resulting data using an analytic reconstruction algorithm. Typical nCT scans require hours to days to complete and are then processed using conventional filtered back-projection (FBP), which performs poorly with sparse views or noisy data. Hence, the main methods in order to reduce overall acquisition time are the use of an improved sampling strategy combined with the use of advanced reconstruction methods such as model-based iterative reconstruction (MBIR). In this paper, we propose an adaptive orientation selection method in which an MBIR reconstruction on previously-acquired measurements is used to define an objective function on orientations that balances a data-fitting term promoting edge alignment and a regularization term promoting orientation diversity. Using simulated and experimental data, we demonstrate that our method produces high-quality reconstructions using significantly fewer total measurements than the conventional approach.

Yang, Diyu↗
DOE OSTI2025

Surface Phase Stability of Fe 2 O 3 (001) in Hydrogen Reducing Environments: A DFT and XPS Analysis

Here, this study combines density functional theory (DFT) and ab initio thermodynamics calculations with X-ray photoelectron spectroscopy (XPS) investigations to identify the reduction properties of the Fe 2 O 3 (001) surface with implications for corrosion resistance, hydrogen transport, and energy safety. Ab initio thermodynamics modeling predicts fully hydroxylated surface stability across a broad range of pressures (1 × 10 –23 to 1 × 10 5 mbar) and temperatures below 700 K, consistent with previous experimental studies. Above 800 K, exposures to 1 × 10 –4 mbar H 2 , 1 × 10 –4 mbar O 2 , or 1 × 10 –4 mbar H 2 + 1 × 10 –4 mbar O 2 each yield unique XPS signals indicating a loss of −OH coverage, aligning with DFT predictions. Insight into the mechanism of reduction as a function of H 2 exposure is provided, as well as conditions that promote further reduction toward Fe 3 O 4 . Theoretical and experimental investigations indicate the ability to maintain the Fe 2 O 3 protective layer of iron oxides that have been exposed to H 2 environments by including trace amounts of aqueous O2.

77 NANOSCIENCE AND NANOTECHNOLOGY↗