Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “Structural properties”

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 973 records · Page 54

Solution-Processable Copper Halide Based Hybrid Materials Consisting of Cationic Ligands with Different Coordination Modes

Here, using cationic ligands containing both aromatic and aliphatic coordination sites, we have synthesized and structurally characterized five new CuX-based hybrid materials consisting of anionic inorganic motifs that also form coordinate bonds with the cationic organic ligands. As a result of the unique bonding nature at the inorganic/organic interfaces, these compounds demonstrate strong resistance toward heat and can be readily processed in solution. They emit light in the visible region ranging from cyan to yellow color, with the highest photoluminescence quantum yield (PLQY) reaching 71%. The influence of the different coordination modes of the ligands on their emission behavior was investigated employing both experimental and theoretical methods, which have provided insight in understanding structure–property relationships in these materials and guidelines for tuning and enhancing their chemical and physical properties.

77 NANOSCIENCE AND NANOTECHNOLOGY↗

Deep learning-based quasi-continuum theory for structure of confined fluids

Predicting the structural properties of water and simple fluids confined in nanometer scale pores and channels is essential in, for example, energy storage and biomolecular systems. Classical continuum theories fail to accurately capture the interfacial structure of fluids. In this work, we develop a deep learning-based quasi-continuum theory (DL-QT) to predict the concentration and potential profiles of a Lennard-Jones (LJ) fluid and water confined in a nanochannel. The deep learning model is built based on a convolutional encoder–decoder network (CED) and is applied for high-dimensional surrogate modeling to relate the fluid properties to the fluid–fluid potential. The CED model is then combined with the interatomic potential-based continuum theory to determine the concentration profiles of a confined LJ fluid and confined water. Further, we show that the DL-QT model exhibits robust predictive performance for a confined LJ fluid under various thermodynamic states and for water confined in a nanochannel of different widths. The DL-QT model seamlessly connects molecular physics at the nanoscale with continuum theory by using a deep learning model.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

VLA observations of unidentified Leiden-Berkeley Deep-Survey sources - Luminosity and redshift dependence of spectral properties

VLA observations of a complete subset of the Leiden-Berkeley Deep Survey sources that have S(1.4 GHz) greater than 10 mJy and are not optically identified down to F=22 mag are reported. By comparing the spectral and structural properties of the sources with samples from the literature, an attempt was made to disentangle the luminosity and redshift dependence of the spectral indices of extended emission in radio galaxies and of the incidence of compact steep-spectrum sources. It is found that the fraction of compact sources among those with a steep spectrum is related primarily to redshift, being much larger at high redshifts for sources of similar radio luminosity. Only a weak and marginally significant dependence of spectral indices of the extended sources on luminosity and redshift is found in samples selected at 1.4 and 2.7 GHz. It is pointed out that the much stronger correlation of spectral indices with luminosity may be arising partly from spectral curvature, and partly due to the preferential inclusion of very steep-spectrum sources from high redshift in low-frequency surveys.

Kapahi, Vijay K.↗

Generative Adversarial Networks and Mixture Density Networks-Based Inverse Modeling for Microstructural Materials Design

Abstract There are two broad modeling paradigms in scientific applications: forward and inverse. While forward modeling estimates the observations based on known causes, inverse modeling attempts to infer the causes given the observations. Inverse problems are usually more critical as well as difficult in scientific applications as they seek to explore the causes that cannot be directly observed. Inverse problems are used extensively in various scientific fields, such as geophysics, health care and materials science. Exploring the relationships from properties to microstructures is one of the inverse problems in material science. It is challenging to solve the microstructure discovery inverse problem, because it usually needs to learn a one-to-many nonlinear mapping. Given a target property, there are multiple different microstructures that exhibit the target property, and their discovery also requires significant computing time. Further, microstructure discovery becomes even more difficult because the dimension of properties (input) is much lower than that of microstructures (output). In this work, we propose a framework consisting of generative adversarial networks and mixture density networks for inverse modeling of structure–property linkages in materials, i.e., microstructure discovery for a given property. The results demonstrate that compared to baseline methods, the proposed framework can overcome the above-mentioned challenges and discover multiple promising solutions in an efficient manner.

36 MATERIALS SCIENCE↗

Quantum annealing-assisted lattice optimization

High Entropy Alloys (HEAs) have drawn great interest due to their exceptional properties compared to conventional materials. The configuration of HEA system is considered a key to their superior properties, but exhausting all possible configurations of atom coordinates and species to find the ground energy state is extremely challenging. In this work, we proposed a quantum annealing-assisted lattice optimization (QALO) algorithm, which is an active learning framework that integrates the Field-aware Factorization Machine (FFM) as the surrogate model for lattice energy prediction, Quantum Annealing (QA) as an optimizer and Machine Learning Potential (MLP) for ground truth energy calculation. By applying our algorithm to the NbMoTaW alloy, we reproduced the Nb depletion and W enrichment observed in bulk HEA. We found our optimized HEAs to have superior mechanical properties compared to the randomly generated alloy configurations. Our algorithm highlights the potential of quantum computing in materials design and discovery, laying a foundation for further exploring and optimizing structure-property relationships.

36 MATERIALS SCIENCE↗

Nano-engineering the material structure of preferentially oriented nano-graphitic carbon for making high-performance electrochemical micro-sensors

Direct synthesis of thin-film carbon nanomaterials on oxide-coated silicon substrates provides a viable pathway for building a dense array of miniaturized (micron-scale) electrochemical sensors with high performance. However, material synthesis generally involves many parameters, making material engineering based on trial and error highly inefficient. Here, we report a two-pronged strategy for producing engineered thin-film carbon nanomaterials that have a nano-graphitic structure. First, we introduce a variant of the metal-induced graphitization technique that generates micron-scale islands of nano-graphitic carbon materials directly on oxide-coated silicon substrates. A novel feature of our material synthesis is that, through substrate engineering, the orientation of graphitic planes within the film aligns preferentially with the silicon substrate. This feature allows us to use the Raman spectroscopy for quantifying structural properties of the sensor surface, where the electrochemical processes occur. Second, we find phenomenological models for predicting the amplitudes of the redox current and the sensor capacitance from the material structure, quantified by Raman. Our results indicate that the key to achieving high-performance micro-sensors from nano-graphitic carbon is to increase both the density of point defects and the size of the graphitic crystallites. Our study offers a viable strategy for building planar electrochemical micro-sensors with high-performance.

77 NANOSCIENCE AND NANOTECHNOLOGY↗

Effect of low-temperature compression on superconductivity and crystal structure in strontium metal

The superconducting and structural properties of elemental strontium metal were investigated under pressures up to 60 GPa while maintaining cryogenic conditions during pressure application. Applying pressure at low temperatures reveals differences in superconducting and structural phases compared to previous reports obtained at room temperatures. Notably, the superconducting critical temperature exhibits a twofold increase under compression after cryogenic cooling within the pressure range of 35–42 GPa, compared to cryogenic cooling after room-temperature compression. Subsequently, the transition width becomes significantly sharper above 42 GPa. Low-temperature x-ray diffraction measurements under pressure reveal that this change corresponds to the Sr-III to Sr-IV transition, with no evidence of any metastable structure. Furthermore, the monoclinic Sr-IV structure was observed to remain stable to much higher pressures—at least up to 60 GPa, without the appearance of the incommensurate Sr-V phase present at room temperature. As a result, this implies that thermal activation energy plays an important role in overcoming the presence of a kinetic barrier to the Sr-V phase at room temperature.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

Distributed Sensing of Composite Over-wrapped Pressure Vessel Using Fiber-Bragg Gratings at Ambient and Cryogenic Temperatures

Fiber Bragg gratings are use to monitor the structural properties of composite pressure vessels. These gratings optically inscribed into the core of a single mode fiber are used as a tool to monitor the stress strain relation in laminate structure. The fiber Bragg sensors are both embedded within the composite laminates and bonded to the surface of the vessel with varying orientations with respect to the carbon fiber in the epoxy matrix. The response of these fiber-optic sensors is investigated by pressurizing the cylinder up to its burst pressure of around 2800 psi. This is done at both ambient and cryogenic temperatures using water and liquid nitrogen. The recorded response is compared with the response from conventional strain gauge also present on the vessel. Additionally, several vessels were tested that had been damaged to simulate different type of events, such as cut tow, delimitation and impact damage.

Grant, Joseph↗

Distributed sensing of Composite Over-wrapped Pressure Vessel using Fiber-Bragg Gratings at Ambient and Cryogenic Temperatures

Fiber Bragg gratings are use to monitor the structural properties of composite pressure vessels. These gratings optically inscribed into the core of a single mode fiber are used as a tool to monitor the stress strain relation in laminate structure. The fiber Bragg sensors are both embedded within the composite laminates and bonded to the surface of the vessel with varying orientations with respect to the carbon fiber in the epoxy matrix. The response of these fiber-optic sensors is investigated by pressurizing the cylinder up to its burst pressure of around 2800 psi. This is done at both ambient and cryogenic temperatures using water and liquid nitrogen. The recorded response is compared with the response from conventional strain gauge also present on the vessel. Additionally, several vessels were tested that had been damaged to simulate different type of events, such as cut tow, delimitation and impact damage.

Grant, Joseph↗

The Impact of Catalyst Layer Composition and Structure on Performance and Durability of PEMWE Anodes

A study of various compositions of anodes for proton exchange membrane water electrolysis aimed at reducing precious metal content and system costs without compromising performance and durability is presented. A key challenge in current water electrolysis technologies is the reliance on high iridium loadings to ensure sufficient catalytic activity, electronic conductivity, and durability for the oxygen evolution reaction. To address this, catalyst layers based on the stable but kinetically limited rutile phase of iridium oxide are combined with platinum nanoparticles and carbon-based additives to improve structural properties and ink processability. By systematically varying the volume ratios of carbon to precious metals and ionomer to solids, compositional trends have been identified, and significant performance improvements have been achieved. Structural and elemental analysis confirms improved dispersion of platinum and iridium can be achieved, as well as electronic conductivity improvements within the catalyst layer. Polarization curve analysis has shown the ability of added Pt and C to significantly increase catalytic activity. These results highlight the potential positive impact of Pt and C on anode structure, composition, and cell performance.

36 MATERIALS SCIENCE↗

Large-scale atomistic model construction of subbituminous and bituminous coals for solvent extraction simulations with reactive molecular dynamics

Large-scale atomistic models for complex polycyclic aromatic hydrocarbon systems help understand the chemical properties and behaviors of complex feedstocks such as coal or petroleum. However, the development and utilization of large-scale models remain limited due to the difficulty in achieving the varied structural characteristics necessary to capture stochastic nature of these feedstocks. Here we demonstrate a systematic workflow to construct stochastic molecular systems from a broad analytical suite: high-resolution transmission electron microscopy (HRTEM), carbon-13 nuclear magnetic resonance spectroscopy ( 13 C NMR), laser desorption ionization mass spectroscopy (LDI-MS), and elemental analysis. We present a model construction and analysis utility of a new Python-based module. We selected one subbituminous and three high-volatile bituminous coals to construct large-scale models (~40,000 atoms). The constructed models were utilized to examine the affinity for solvent extraction (naphthalene or tetralin) and the effect of structural properties (e.g., aromatic cluster size, functional groups, and cross-linking) in reactive molecular dynamics simulations. Complex chemical reactions were monitored with bond order transitions, intermediates formation, and mass distributions. Reactive molecular dynamics simulations suggest a plausible chemical extraction process and products for the complex fossil feedstocks. The results indicated that radical formations with bond breaking of bridging oxygens and carbons were required at high temperatures to facilitate hydrogeneration and extraction of gas molecules from radical-free molecules. We observed that aliphatic chains of tetralin were easily decomposed and combined with radicals to form small size of molecules with aryl bonding, mainly increasing molecules in the 500–1000 Da, while naphthalene had little impact on chemical extraction process.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Denoising diffusion algorithm for inverse design of microstructures with fine-tuned nonlinear material properties

Here we introduce a denoising diffusion algorithm to discover microstructures with nonlinear fine-tuned properties. Denoising diffusion probabilistic models are generative models that use diffusion-based dynamics to gradually denoise images and generate realistic synthetic samples. By learning the reverse of a Markov diffusion process, we design an artificial intelligence to efficiently manipulate the topology of microstructures to generate a massive number of prototypes that exhibit constitutive responses sufficiently close to designated nonlinear constitutive behaviors. To identify the subset of microcstructures with sufficiently precise fine-tuned properties, a convolutional neural network surrogate is trained to replace high-fidelity finite element simulations to filter out prototypes outside the admissible range. Results of this study indicate that the denoising diffusion process is capable of creating microstructures of fine-tuned nonlinear material properties within the latent space of the training data. More importantly, this denoising diffusion algorithm can be easily extended to incorporate additional topological and geometric modifications by introducing high-dimensional structures embedded in the latent space. Numerical experiments are conducted on the open-source mechanical MNIST data set (Lejeune, 2020). Consequently, this algorithm is not only capable of performing inverse design of nonlinear effective media, but also learns the nonlinear structure–property map to quantitatively understand the multiscale interplay among the geometry, topology, and their effective macroscopic properties.

42 ENGINEERING↗

Framework Short-Range Order Observed in a Spinel-Type Li Superionic Conductor

Solid-state superionic conductors are characterized by rich structural disorders. Though structural complexities are central to their functionalities, they often give rise to short-range order that eludes detection by conventional diffraction-based techniques and is thus overlooked in establishing precise structure-property relationships. In this work, we synthesized single crystals of a recently discovered lithium (Li) superionic conductor Li16.2(1)In9.00(2)Sn1.10(1)O23.8 (LISO) for in-depth characterizations of structural subtleties. LISO exhibits an unusual spinel-like phase with significant Li overstoichiometry and a face-sharing Li network. Single-crystal neutron diffraction confirms significant Li disorder, as manifested in Li site splitting and partial occupancy. More importantly, synchrotron diffuse scattering combined with 3D-ΔPDF analysis and Monte Carlo simulations reveal short-range order in the nonalkali framework that might contribute to the phase stability and ionic conductivity. This work showcases an example in which subtle local energetics can be directly visualized in structurally disordered ionic conductors.

Chen, Yu↗

Decoupling the metal insulator transition and crystal field effects of VO 2

VO 2 is a highly correlated electron system which has a metal-to-insulator transition (MIT) with a dramatic change of conductivity accompanied by a first-order structural phase transition (SPT) near room temperature. The origin of the MIT is still controversial and there is ongoing debate over whether an SPT induces the MIT and whether the T c can be engineered using artificial parameters. We examined the electrical and local structural properties of Cr- and Co-ion implanted VO 2 (Cr-VO 2 and Co-VO 2 ) films using temperature-dependent resistance and X-ray absorption fine structure (XAFS) measurements at the V K edge. The temperature-dependent electrical resistance measurements of both Cr-VO 2 and Co-VO 2 films showed sharp MIT features. The T c values of the Cr-VO 2 and Co-VO 2 films first decreased and then increased relative to that of pristine VO 2 as the ion flux was increased. The pre-edge peak of the V K edge from the Cr-VO 2 films with a Cr ion flux ≥ 10 13 ions/cm 2 showed no temperature-dependent behavior, implying no changes in the local density of states of V 3d t 2g and e g orbitals during MIT. Extended XAFS (EXAFS) revealed that implanted Cr and Co ions and their tracks caused a substantial amount of structural disorder and distortion at both vanadium and oxygen sites. The resistance and XAFS measurements revealed that VO 2 experiences a sharp MIT when the distance of V–V pairs undergoes an SPT without any transitions in either the VO 6 octahedrons or the V 3d t 2g and eg states. This indicates that the MIT of VO 2 occurs with no changes of the crystal fields.

36 MATERIALS SCIENCE↗

Innovative fabrication processing of advanced composite materials concepts for primary aircraft structures

The autoclave based THERM-X(sub R) process was evaluated by cocuring complex curved panels with frames and stiffeners. The process was shown to result in composite parts of high quality with good compaction at sharp radius regions and corners of intersecting parts. The structural properties of the postbuckled panels fabricated were found to be equivalent to those of conventionally tooled hand laid-up parts. Significant savings in bagging time over conventional tooling were documented. Structural details such as cocured shear ties and embedded stiffener flanges in the skin were found to suppress failure modes such as failure at corners of intersecting members and skin stiffeners separation.

Kassapoglou, Christos↗

Design of Multifunctional Hierarchical Space Structures

We describe a system for the design of space structures with tunable structural properties based on the discrete assembly of modular lattice elements. These lattice elements can be constructed into larger beam-like elements, which can then be assembled into large scale truss structures. These discrete lattice elements are reversibly assembled with mechanical fasteners, which allows them to be arbitrarily reconfigured into various application-specific designs. In order to assess the validity of this approach, we design two space structures with similar geometry but widely different structural requirements: an aerobrake, driven by strength requirements, and a precision segmented reflector, driven by stiffness and accuracy requirements. We will show agreement between simplified numerical models based on hierarchical assembly and analytical solutions. We will also present an assessment of the error budget resulting from the assembly of discrete structures. Lastly, we will address launch vehicle packing efficiency issues for transporting these structures to lower earth orbit.

Aeronautics↗

Quantum Size Effects, Multiple Dirac Cones, and Edge States in Ultrathin Bi(110) Films

The presence of inherently strong spin–orbit coupling in bismuth, its unique layer-dependent band topology and high carrier mobility make it an interesting system for both fundamental studies and applications. Theoretically, it has been suggested that strong quantum size effects should be present in the Bi(110) films, with the possibility of Dirac Fermion states in the odd-bilayer (BL) films, originating from dangling p z orbitals and quantum-spin hall (QSH) states in the even-bilayer films. However, the experimental verification of these claims has been lacking. Here, we study the electronic structure of Bi(110) films grown on a high-T c superconductor, Bi 2 Sr 2 CaCu 2 O 8+δ (Bi2212) using angle-resolved photoemission spectroscopy (ARPES). We observe an oscillatory behavior of electronic structure with the film thickness and identify the Dirac-states in the odd-bilayer films, consistent with the theoretical predictions. In the even-bilayer films, we find another Dirac state that was predicted to play a crucial role in the QSH effect. In the low thickness limit, we observe several extremely one-dimensional states, probably originating from the edge-states of Bi(110) islands. Our results provide a much needed experimental insight into the electronic and structural properties of Bi(110) films.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗