Search NASA⌕ Search

SEARCH · Search NASA

Results for “1D materials”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 109 records · Page 6

Composite Piezoelectric Resonator 1D Modeling with Loss

Abstract – Maximizing the benefit of applying piezoelectric materials in composite structures, which are a mix of piezoelectric and elastic elements, requires analytical modeling. In this paper we describe two main classes of resonators and how to model using network equivalent including material losses. These include extensional devices where the layer areas are parallel to the direction of wave travel as found in a variety of thickness or length mode transducers (BC. Stress T1 = T2 at boundary) and transverse mode resonators (BC. Strain S1 = S2 at boundary) where the major layer area is perpendicular to the wave direction as is found in radial mode or length thickness mode composite resonators. In order to illustrate these models we will use Mason’s equivalent circuits with elastic, dielectric and piezoelectric loss. It is noted that in order to maintain consistency with the linear equations of piezoelectricity and the wave equation care is required when applying complex loss coefficients to the models. Although these techniques are applicable to other models we use Mason’s network equivalent circuit because it is intuitive for composite modeling due to the frequency independent turns ratio and the independence of the equations describing the elastic layers from the adjoining layer properties which aids in understanding the boundary conditions to impose on the layer boundaries. We will present network models for a variety of applications with loss and show how to calculate their impedance spectra, interface stress, strain velocity and displacement spectra.

Sherrit, Stewart↗

Comparison of tungsten versus molybdenum for double shell capsules using machine learning design optimization

Double shell targets are an alternative ignition platform for inertial confinement fusion. One design consideration for double shell targets is the choice of inner shell material to help trap radiation emitted by the hot fuel to aid ignition. Materials such as molybdenum and tungsten are of interest for the inner shell layer of the targets. While molybdenum has a lower density that could inhibit instability growth and allow for radiography and code benchmarking, tungsten has a higher density that could provide more compression and confinement. These tradeoffs have been explored using optimized designs for each material. Our previous work [Vazirani et al., “Coupling 1D xRAGE simulations with machine learning for graded inner shell design optimization in double shell capsules,” Phys. Plasmas 28, 122709 (2021); Vazirani et al., “Coupling multi-fidelity xRAGE with machine learning for graded inner shell design optimization in double shell capsules,” Phys. Plasmas 30, 062704 (2023); and Vazirani et al., “Bayesian batch optimization for molybdenum versus tungsten inertial confinement fusion double shell target design,” Stat. Anal. Data Min. 17, e11698 (2024)] resulted in a multi-fidelity Bayesian optimization framework to find yield-optimized double shell target geometries. By leveraging simulations of varying fidelities (one-dimensional and two-dimensional) to inform one another, the multi-fidelity optimization was able to optimize a design in the highest fidelity with significantly fewer simulations than would be used in a systematic parameter scan. In this work, we apply the multi-fidelity Bayesian optimization to explore the optimized designs of double shell targets with molybdenum and tungsten inner shells as well as the physics producing the high performing implosions. A physics exploration of all the simulations used in this study shows trends in designs that contribute to high yields, ion temperatures, and fuel areal densities. Comparison of molybdenum and tungsten simulations shows that they can produce similar implosion conditions with different geometries, which would be important to study in experiments. Graded density layers produce varying performances with the two materials but continue to be of interest for future studies along with studies of doped inner shell materials and applied surface roughness.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

Water-Stable 1D Hybrid Tin(II) Iodide Emits Broad Light with 36% Photoluminescence Quantum Efficiency

The optical and light emission properties of tin and lead halide perovskites are remarkable because of the robust room-temperature (RT) performance, broad wavelength tunability, high efficiency, and good quenching resistance to defects. These highly desirable attributes promise to transform current light-emitting devices, phosphors, and lasers. One disadvantage in most of these materials is the sensitivity to moisture. Here, we report a new air-stable one-dimensional (1D) hybrid lead-free halide material (DAO)Sn 2 I 6 (DAO, 1,8-octyldiammonium) that is resistant to water for more than 15 h. The material exhibits a sharp optical absorption edge at 2.70 eV and a strong broad orange light emission centered at 634 nm, with a full width at half-maximum (fwhm) of 142 nm (0.44 eV). The emission has a long photoluminescence (PL) lifetime of 582 ns, while the intensity is constant over a very broad temperature range (145–415 K) with a photoluminescence quantum yield (PLQY) of at least 20.3% at RT. Above 415 K the material undergoes a structural phase transition from monoclinic (C2/c) to orthorhombic (Ibam) accompanied by a red shift in the band gap and a quench in the photoluminescence emission. Density functional theory calculations support the trend in the optical properties and the 1D electronic nature of the structure, where the calculated carrier effective masses along the inorganic chain are significantly lower than those perpendicular to the chain. As a result, thin films of the compound readily fabricated from solutions exhibit the same optical properties, but with improved PLQY of 36%, for a 60 nm thick film, among the highest reported for lead-free low-dimensional 2D and 1D perovskites and metal halides.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Hydrous Transition Metal Oxides for Electrochemical Energy and Environmental Applications

Hydrous transition metal oxides (TMOs) are redox-active materials that confine structural water within their bulk, organized in 1D, 2D, or 3D networks. In an electrochemical cell, hydrous TMOs can interact with electrolyte species not only via their outer surface but also via their hydrous inner surface, which can transport electrolyte species to the interior of the material. Many TMOs operating in an aqueous electrochemical environment transform to hydrous TMOs, which then serve as the electrochemically active phase. This review summarizes the physicochemical properties of hydrous TMOs and recent mechanistic insights into their behavior in electrochemical reactions of interest for energy storage, conversion, and environmental applications. Particular focus is placed on first-principles calculations and operando characterization to obtain an atomistic view of their electrochemical mechanisms. Hydrous TMOs represent an important class of energy and environmental materials in aqueous and nonaqueous environments. Further understanding of their interaction with electrolyte species is likely to yield advancements in electrochemical reactivity and kinetics for energy and environmental applications.

36 MATERIALS SCIENCE↗

One-Dimensional Edge Transport in Few-Layer WTe 2

WTe 2 is a layered transitional-metal dichalcogenide (TMD) with a number of intriguing topological properties. Recently, WTe 2 has been predicted to be a higher-order topological insulator (HOTI) with topologically protected hinge states along the edges. The gapless nature of WTe 2 complicates the observation of one-dimensional (1D) topological states in transport due to their small contribution relative to the bulk. Here, we study the behavior of the Josephson effect in magnetic field to distinguish edge from bulk transport. The Josephson effect in few-layer WTe 2 reveals 1D states residing on the edges and steps. Moreover, our data demonstrates a combination of Josephson transport properties observed solely in another HOTI—bismuth, including Josephson transport over micrometer distances, extreme robustness in a magnetic field, and nonsinusoidal current-phase relation (CPR). Our observations strongly suggest the topological origin of the 1D states and that few-layer WTe 2 is a HOTI.

1D edge states↗

Vertically Self-Oriented, Ultrafast 1D ZnO:Li Nanorods as Scintillators for Thermal Neutron Detection

Detection of special nuclear materials (SNMs) is of vital importance in the prevention of nuclear terrorism and to secure states’ national security. Neutron detection is a particularly useful tool to identify SNM, and neutron-sensitive scintillators have many promising properties, such as ease of use, good time resolution, and high detection efficiency. In this work, we develop highly stable, self-oriented, ultrafast 1D ZnO:Li (and codoped with Al, Ga, and In) nanorods (NRs) as thermal neutron-sensitive scintillators. Lithium-6 has high thermal neutron cross section for the (n, α) reaction in ZnO:Li scintillators which have a vertical nano array design greatly increasing the effective surface area and scintillation efficiency. Cost-effective low-temperature (95 °C) hydrothermal growth is used to obtain highly crystalline ZnO:Li nano scintillators by combining nuclear range data and electron transport mechanisms. Among the studies using low-temperature hydrothermal synthesis and a relatively low annealing temperature (≈350 °C) along with optimized NRs (length ≈ 5–8 μm, mean diameter ≈ 700 nm) for thermal neutron detection, this study reports the shortest scintillation decay time (≈ 470 ps) so far to the best of our knowledge. In conclusion, this nano array scintillator combines the advantages of a low-cost growth technique with environmentally friendly and widely available materials.

Ions↗

ArcjetCV: a new machine learning application for extracting time-resolved recession measurements from arc jet test videos

Arc jet Computer Vision (ArcjetCV) is a software application built to automate analysis of arc jet ground test video footage. This includes tracking material recession, sting arm motion, and the shock-material standoff distance. This provides a new capability to resolve and validate new physics associated with non-linear processes. This is an essential step to reduce testing, modeling, and validation uncertainties for heatshield material performance. ArcjetCV uses several types of machine learning (convolutional neural net: CNN, decision tree: DT, k-means unsupervised clustering: KM) to automate the video processing pipeline. These include inferring the start/stop of time segments of interest (1D CNN), measuring the time-dependent 2D recession of the material samples (2D CNN, DT), measuring the time-dependent shock standoff distance (2D CNN, DT), and post-processing cleaning of the recession data (KM). The software also provides a graphical user interface for ease of use. The results of using this tool on arc jet videos show non-linear time-dependent effects can be important for certain materials.

machine learning↗

ArcjetCV: A New Machine Learning Application for Extracting Time-Resolved Recession Measurements From Arc Jet Test Videos

Arc jet Computer Vision (ArcjetCV) is a software application built to automate analysis of arc jet ground test video footage. This includes tracking material recession, sting arm motion, and the shock-material standoff distance. This provides a new capability to resolve and validate new physics associated with non-linear processes. This is an essential step to reduce testing, modeling, and validation uncertainties for heatshield material performance. ArcjetCV uses several types of machine learning (convolutional neural net: CNN, decision tree: DT, k-means unsupervised clustering: KM) to automate the video processing pipeline. These include inferring the start/stop of time segments of interest (1D CNN), measuring the time-dependent 2D recession of the material samples (2D CNN, DT), measuring the time-dependent shock standoff distance (2D CNN, DT), and post-processing cleaning of the recession data (KM). The software also provides a graphical user interface for ease of use. The results of using this tool on arc jet videos show non-linear time-dependent effects can be important for certain materials.

machine learning↗

ArcjetCV: A New Machine Learning Application for Extracting Time-Resolved Recession Measurements From Arc Jet Test Videos

Arc jet Computer Vision (ArcjetCV) is a software application built to automate analysis of arc jet ground test video footage. This includes tracking material recession and the shock-material standoff distance. This provides a new capability to resolve and validate new physics associated with non-linear processes. This is an essential step to reduce testing, modeling, and validation uncertainties for heatshield material performance. ArcjetCV uses several types of machine learning (convolutional neural net: CNN, decision tree: DT, k-means unsupervised clustering: KM) to automate the video processing pipeline. These include inferring the start/stop of time segments of interest (1D CNN), measuring the time-dependent 2D recession of the material samples (2D CNN, DT), measuring the time-dependent shock standoff distance (2D CNN, DT), and post-processing cleaning of the recession data (KM). The software also provides a graphical user interface for ease of use. The results of using this tool on arc jet videos show non-linear time-dependent effects can be important for certain materials and characterizing certain failure modes.

machine learning↗

arcjetCV: A New Machine Learning Application for Extracting Time-Resolved Recession Measurements From Arc Jet Test Videos

Arc jet Computer Vision (ArcjetCV) is a software application built to automate analysis of arc jet ground test video footage. This includes tracking material recession and the shock-material standoff distance. This provides a new capability to resolve and validate new physics associated with non-linear processes. This is an essential step to reduce testing, modeling, and validation uncertainties for heatshield material performance. ArcjetCV uses several types of machine learning (convolutional neural net: CNN, decision tree: DT, k-means unsupervised clustering: KM) to automate the video processing pipeline. These include inferring the start/stop of time segments of interest (1D CNN), measuring the time-dependent 2D recession of the material samples (2D CNN, DT), measuring the time-dependent shock standoff distance (2D CNN, DT), and post-processing cleaning of the recession data (KM). The software also provides a graphical user interface for ease of use. The results of using this tool on arc jet videos show non-linear time-dependent effects can be important for certain materials and characterizing certain failure modes.

machine learning↗

arcjetCV: automating recession extraction from video

Arc jet Computer Vision (arcjetCV)[1][2] is a software application built to automate time-resolved recession tracking of edges in test videos, specifically for tracking material recession and the shock-material standoff. This provides a new capability to resolve and validate new physics associated with non-linear processes and an essential step to reduce testing, modeling, and validation uncertainties for heatshield material performance. ArcticCV uses several types of machine learning (convolutional neural net: CNN, decision tree: DT, k-means unsupervised clustering: KM) to automate the video processing pipeline. These include inferring the start/stop of time segments of interest (1D CNN), measuring the time-dependent 2D recession of the material samples (2D CNN, DT), measuring the time-dependent shock standoff distance (2D CNN, DT), and post-processing cleaning of the recession data (KM). The software also provides a graphical user interface for ease of use. The results of using this tool on arc jet videos show non-linear time-dependent effects can be important for certain materials and characterizing certain failure modes.

Recession tracking↗

ArcjetCV: Automating Recession Tracking

Arc jet Computer Vision (arcjetCV) is a software application built to automate time-resolved recession tracking of edges in test videos, specifically for tracking material recession and the shock-material standoff. This provides a new capability to resolve and validate new physics associated with non-linear processes and an essential step to reduce testing, modeling, and validation uncertainties for heatshield material performance. ArcticCV uses several types of machine learning (convolutional neural net: CNN, decision tree: DT, k-means unsupervised clustering: KM) to automate the video processing pipeline. These include inferring the start/stop of time segments of interest (1D CNN), measuring the time-dependent 2D recession of the material samples (2D CNN, DT), measuring the time-dependent shock standoff distance (2D CNN, DT), and post-processing cleaning of the recession data (KM). The software also provides a graphical user interface for ease of use. The results of using this tool on arc jet videos show non-linear time-dependent effects can be important for certain materials and characterizing certain failure modes.

Recession tracking↗

ArcjetCV: Automating Arc Jet Analysis

Arc jet Computer Vision (arcjetCV) is a software application built to automate time-resolved recession tracking of edges in test videos, specifically for tracking material recession and the shock-material standoff. This provides a new capability to resolve and validate new physics associated with non-linear processes and an essential step to reduce testing, modeling, and validation uncertainties for heatshield material performance. ArcticCV uses several types of machine learning (convolutional neural net: CNN, decision tree: DT, k-means unsupervised clustering: KM) to automate the video processing pipeline. These include inferring the start/stop of time segments of interest (1D CNN), measuring the time-dependent 2D recession of the material samples (2D CNN, DT), measuring the time-dependent shock standoff distance (2D CNN, DT), and post-processing cleaning of the recession data (KM). The software also provides a graphical user interface for ease of use. The results of using this tool on arc jet videos show non-linear time-dependent effects can be important for certain materials and characterizing certain failure modes.

Recession tracking↗

K 3 Ir 2 O 6 and K 16.3 Ir 8 O 30 , Low-Dimensional Iridates with Infinite IrO 6 Chains

A previously unreported 1D iridate, K 3 Ir 2 O 6 , has been grown by a flux method in O 2 -rich environment and its crystal structure determined via single crystal structural analysis. It exhibits straight chains of face-sharing [IrO 6 ] octahedra, which are arranged along the crystallographic c axis, separated by nonmagnetic K ions. No magnetic transitions are observed during measured range, and the material is electrically insulating. Potentially interesting electronic behavior for K 3 Ir 2 O 6 is supported by electronic structure calculations. A structurally related material, K 16.3 Ir 8 O 30 , which displays similar fundamental geometric units but in a different spatial arrangement – zigzag chains based on edge and face sharing [IrO 6 ] octahedra, is also reported. Both materials are of interest for probing the properties of a 1D system with strong spin-orbit coupling

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

Iron-Based Chalcogenide Spin Ladder BaFe2 X 3 (X = Se,S)

The relevance of magnetic, structural, orbital, and charge degrees of freedom in the iron-based superconductors (FeSCs) and related materials occupies a central focus in condensed matter physics. While the majority of iron-based materials exhibit the same two-dimensional iron square lattice structural motif, a family of AFe2X3 (X = Se,S) compounds introduces a quasi-one-dimensional (1D) ladder motif, which resembles the two-legged spin ladder copper oxide materials. Furthermore, unlike most parent compounds of FeSCs, the members of this spin ladder family are insulators. Recently, a superconducting transition has been observed under pressure with Tc up to 24 K, similar to the pressure-induced superconductivity in the copper oxide ladder Sr14-xCaxCu24O41 material, stimulating much interest. Here, we review the magnetic, structural, and electronic properties in this family, particularly in the BaFe2X3 series tuned by pressure and by chemical substitution. The established pressure-temperature (P-T) and carrier concentration-temperature (x-T) phase diagrams in related materials provide useful information to extend the variety of high-temperature superconductors and compare with other FeSCs. We also review some essential information about analogous square lattice FeSCs.

Wu, S↗

Stabilization of NbTe 3 , VTe 3 , and TiTe 3 via Nanotube Encapsulation

The structure of MX 3 transition metal trichalcogenides (TMTs, with M a transition metal and X a chalcogen) is typified by one-dimensional (1D) chains weakly bound together via van der Waals interactions. This structural motif is common across a range of M and X atoms (e.g. NbSe 3 , HfTe 3 , TaS 3 ), but not all M and X combinations are stable. We report here that three new members of the MX 3 family which are not stable in bulk, specifically NbTe 3 , VTe 3 , and TiTe 3 , can be synthesized in the few- to single-chain limit via nano-confined growth within the stabilizing cavity of multi-walled carbon nanotubes. Transmission electron microscopy (TEM) and atomic-resolution scanning transmission electron microscopy (STEM) reveal the chain-like nature and the detailed atomic structure. The synthesized materials exhibit behavior unique to few-chain quasi-1D structures, such as multi-chain spiraling and a trigonal anti-prismatic rocking distortion in the single-chain limit. Density functional theory (DFT) calculations provide insight into the crystal structure and stability of the materials, as well as their electronic structure.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Efficient Measurement of Length Distribution of 1D Nanoparticles in Solution via Optical Polarimetry

The efficient measurement of the length distribution of nanotubes, nanowires, and other one-dimensional (1D) nanoparticles in solution is important to enable their incorporation into materials and devices and to optimize their processing for properties of interest, such as thermal/electrical conductivity or mechanical strength, in suspensions and composites. We report an electric-field (E-field)-assisted optical-polarimetry technique to measure the length distribution of ensembles of high-aspect-ratio particles in dilute suspension. The degree of alignment of polarizable 1D particles suspended in a fluid under Brownian motion explicitly depends on the E-field strength and the particle length. We show that it is possible to extract the length distribution of 1D nanoparticles suspended in an insulating fluid by applying a range of E-fields and using optical polarimetry to measure the corresponding alignment order parameter. Notably, the method is relatively insensitive to the diameter of the 1D particles, which can be poorly known or vary within a sample. The technique is validated with silver nanowires and carbon nanotubes of known lengths, as well as polymer-depletion-length-separated single-wall carbon nanotube samples with length distributions independently measured with analytical ultracentrifugation. Finally, we demonstrate the ability of the optical-polarimetry technique to quantify changes in the length distribution of ultranarrow, sub-nanometer-diameter single-wall carbon nanotubes under different types and durations of ultrasonication. Within its range of applicability (polarizable 1D nanoparticles in the 0.5 to 15 μm length range, constrained by the voltage stability of the media and the suspended particles), the E-field-assisted optical-polarimetry method is a particularly efficient and accurate method to measure the length distribution of nanowires and nanotubes in suspension.

1D nanoparticles↗

Intergranular Hotspots: A Molecular Dynamics Study on the Influence of Compressive and Shear Work

Numerous crystal- and microstructural-level mechanisms are at play in the formation of hotspots, which are known to govern high explosives initiation behavior. Most of these mechanisms, including pore collapse, interfacial friction, and shear banding, involve both compressive and shear work done within the material and have thus far remained difficult to separate. Here, we assess hotspots formed at shocked crystal–crystal interfaces using quasi-1D molecular dynamics simulations that isolate effects due to compression and shear. Two high explosive materials are considered (TATB and PETN) that exhibit distinctly different levels of molecular conformational flexibility and crystal packing anisotropy. Temperature and intramolecular strain energy localization in the hotspot are assessed through parametric variation of the crystal orientation and two velocity components that respectively modulate compression and shear work. The resulting hotspots are found to be highly localized to a region within 5–20 nm of the crystal–crystal interface. Compressive work plays a considerably larger role in localizing temperature and intramolecular strain energy for both materials and all crystal orientations considered. Shear induces a moderate increase in energy localization relative to unsheared cases only for relatively weak compressive shock pressures of approximately 10 GPa. These results help isolate and rank the relative importance of hotspot generation mechanisms and are anticipated to guide the treatment of crystal–crystal interfaces in coarse-grained models of polycrystalline high-explosive materials.

36 MATERIALS SCIENCE↗