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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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Presentation to be presented during the Site-Directed Research and Development (SDRD) program FY 2021 review meeting (Webex), September 22–23, 2021.
4th quarter presentation for FY20 Annual SDRD meeting to be held Sept. 23-24 on WebEx. An embedded movie is also attached. Dynamic Measurements of the Structural Evolution of Material Defects at the Mesoscale, NLV-001-19, Year 2
A method and system are provided to detect defects in a material. Waves of known frequency(ies) are mixed at an interaction zone in the material. As a result, at least one of a difference wave and a sum wave are generated in the interaction zone. The difference wave occurs at a difference frequency and the sum wave occurs at a sum frequency. The amplitude of at least one nonlinear signal based on the sum and/or difference waves is then measured. The nonlinear signal is defined as the amplitude of one of the difference wave and sum wave relative to the product of the amplitude of the surface waves. The amplitude of the nonlinear signal is an indication of defects (e.g., dislocation dipole density) in the interaction zone.
One of the critical challenges for large area cadmium zinc telluride (CdZnTe) detector arrays is obtaining material capable of uniform imaging and spectroscopic response. Two complementary nondestructive techniques for characterizing bulk CdZnTe have been developed to identify material with a uniform response. The first technique, infrared transmission imaging, allows for rapid visualization of bulk defects. The second technique, x-ray spectral mapping, provides a map of the material spectroscopic response when it is configured as a planar detector. The two techniques have been used to develop a correlation between bulk defect type and detector performance. The correlation allows for the use of infrared imaging to rapidly develop wafer mining maps. The mining of material free of detrimental defects has the potential to dramatically increase the yield and quality of large area CdZnTe detector arrays.
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We present a general method to produce well-conditioned continuum reaction–drift–diffusion equations directly from master equations on a discrete, periodic state space. We assume the underlying data to be kinetic Monte Carlo models (i.e. continuous-time Markov chains) produced from atomic sampling of point defects in locally periodic environments, such as perfect lattices, ordered surface structures or dislocation cores, possibly under the influence of a slowly varying external field. Our approach also applies to any discrete, periodic Markov chain. Here, the analysis identifies a previously omitted non-equilibrium drift term, present even in the absence of external forces, which can compete in magnitude with the reaction rates, thus being essential to correctly capture the kinetics. To remove fast modes which hinder time integration, we use a generalized Bloch relation to efficiently calculate the eigenspectrum of the master equation. A well conditioned continuum equation then emerges by searching for spectral gaps in the long wavelength limit, using an established kinetic clustering algorithm to define a proper reduced, Markovian state space.
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During the post-flight inspection of SSME engines, several inaccessible regions must be disassembled to inspect for defects such as cracks, scratches, gouges, etc. An improvement to the inspection process would be the design and development of very small robots capable of penetrating these inaccessible regions and detecting the defects. The goal of this research was to utilize an evolutionary design approach for the robotic detection of these types of defects. A simulation and visualization tool was developed prior to receiving the hardware as a development test bed. A small, commercial off-the-shelf (COTS) robot was selected from several candidates as the proof of concept robot. The basic approach to detect the defects was to utilize Cadmium Sulfide (CdS) sensors to detect changes in contrast of an illuminated surface. A neural network, optimally designed utilizing a genetic algorithm, was employed to detect the presence of the defects (cracks). By utilization of the COTS robot and US sensors, the research successfully demonstrated that an evolutionarily designed neural network can detect the presence of surface defects.
This is a technical report to be published as part of the FY2021 annual overview package. We have designed experiments and developed analytical methods using images from x-ray and electron microscopy in order to quantify the behavior of crystal dislocation defects at their native nanometer-length and micro-length scales. The results of these analyses are used to directly inform theoretical physics models and guide interpretation of dark field x-ray microscopy (DFXM) images that offer spatial resolution surpassing the resolution of beamline optics. Additionally, we developed new analytical methods to automate the alignment of optics hardware used in both synchrotron and x-ray free electron laser beamline facilities. The tools and workflows developed for this project are now being deployed as proof-of-concept in reaction history analysis (RHA) for future programmatic integration.
Defect dynamics in materials are of central importance to a broad range of technologies from catalysis to energy storage systems to microelectronics. Material functionality depends strongly on the nature and organization of defects-their arrangements often involve intermediate or transient states that present a high barrier for transformation. The lack of knowledge of these intermediate states and the presence of this energy barrier presents a serious challenge for inverse defect design, especially for gradient-based approaches. Here, we present a reinforcement learning (RL) [Monte Carlo Tree Search (MCTS)] based on delayed rewards that allow for efficient search of the defect configurational space and allows us to identify optimal defect arrangements in low-dimensional materials. Using a representative case of two-dimensional MoS 2 , we demonstrate that the use of delayed rewards allows us to efficiently sample the defect configurational space and overcome the energy barrier for a wide range of defect concentrations (from 1.5 to 8% S vacancies)-the system evolves from an initial randomly distributed S vacancies to one with extended S line defects consistent with previous experimental studies. Detailed analysis in the feature space allows us to identify the optimal pathways for this defect transformation and arrangement. Comparison with other global optimization schemes like genetic algorithms suggests that the MCTS with delayed rewards takes fewer evaluations and arrives at a better quality of the solution. The implications of the various sampled defect configurations on the 2H to 1T phase transitions in MoS 2 are discussed. In this study, we introduce a RL strategy employing delayed rewards that can accelerate the inverse design of defects in materials for achieving targeted functionality.
Here, plane-wave density functional theory has been used to study oxygen adsorption on graphene, graphite, and (12,0) zigzag single-walled carbon nanotubes with and without Stone–Wales (SW) and single-vacancy (SV) defects to understand the role of defects on carbonaceous material reactivity. Atomic oxygen adsorption leads to the formation of an epoxide on defect-free graphene and graphite and an ether on the exterior wall of carbon nanotubes and SW-defected materials. O 2 chemisorption is endothermic on defect-free graphene and graphite and slightly exothermic on defect-free nanotubes. O 2 chemisorption energies are predicted to be –1.1 to –1.4 eV on an SW defect and –6.0 to –8.0 eV on an SV defect. An SW defect lowers the energy barriers by 0.90 and 0.50 eV for O 2 chemisorption on graphene and nanotubes, respectively. The formation of a C–O–O–C group is important for O 2 dissociation on defect-free and SW-defected materials. The energy barrier is less than 0.30 eV on an SV defect. The more reactive SW defect toward O adsorption on graphene is mostly due to the strained defective carbon atoms being able to donate more electrons to an O to form an ether. The larger 2s character in the hybrid orbitals in an ether than in an epoxide makes the ether C–O bond stronger. Stronger C–O binding on an SW-defective carbon nanotube than on a defect-free nanotube is in part due to more flexibility of the defect to release the epoxide ring strain to form an ether.
Point defects can often determine the properties of semiconductor and optoelectronic materials. Due to the large simulation cell and the higher-cost density functionals required for defect simulations, the computational cost of defect calculations is often orders of magnitude higher than that of bulk calculations. As such, managing and curating the results of the defect calculations generated by a single user has the potential to save a significant amount of computational resources. Moreover, eventually building a high-quality, persistent defects database will significantly reduce the computational cost of defect calculations for the entire community.
Non-destructive testing is essential in many fields of manufacturing and research in order to perform reliable examination of potentially damaged materials and parts without destroying the inherent structure of the materials. Thus, the Non-Destructive Evaluation (NDE) Team at NASA Glenn Research Center partakes in various projects to improve materials testing equipment as well as analyze materials, material defects, and material deficiencies. Due to the array of projects within the NDE Team at this time, five research aims were supplemental to some current projects. A literature survey of "DE and testing methodologies as related to rocks was performed. Also, Mars Expedition Rover technology was assessed to understand the requirements for instrumentation in harsh space environments (e.g. temperature). Potential instrumentation and technologies were also considered and documented. The literature survey provided background and potential sources for a proposal to acquire funding for ultrasonic instrumentation on board a future Mars expedition. The laboratory uses a Santec Systems AcousticScope AS200 acoustography system. Labview code was written within the current program in order to improve the current performance of the acoustography system. A sample of Reinforced Carbon/Carbon (RCC) material from the leading edge of the space shuttle underwent various non-destructive tests (guided wave scanning, thermography, computed tomography, real time x-ray, etc.) in order to characterize its structure and examine possible defects. Guided wave scan data of a ceramic matrix composite (CMC) panel was reanalyzed utilizing image correlations and signal processing variables. Additional guided wave scans and thermography were also performed on the CMC panel. These reevaluated data and images will be used in future presentations and publications. An additional axis for the guided wave scanner was designed, constructed, and implemented. This additional axis allowed incremental spacing of the previously fixed transducers for ultrasonic velocity measurements.
Characterizing internal structures and defects in materials is a challenging task, often requiring solutions to inverse problems with unknown topology, geometry, material properties, and nonlinear deformation. Here, we present a general framework based on physics-informed neural networks for identifying unknown geometric and material parameters. By using a mesh-free method, we parameterize the geometry of the material using a differentiable and trainable method that can identify multiple structural features. We validate this approach for materials with internal voids/inclusions using constitutive models that encompass the spectrum of linear elasticity, hyperelasticity, and plasticity. We predict the size, shape, and location of the internal void/inclusion as well as the elastic modulus of the inclusion. Our general framework can be applied to other inverse problems in different applications that involve unknown material properties and highly deformable geometries, targeting material characterization, quality assurance, and structural design.
Abstract A 3D Convolutional Variational Autoencoder (3D‐CVAE) is introduced for automated anomaly detection in electron energy‐loss spectroscopy spectrum imaging (EELS‐SI) data. This approach leverages the full 3D structure of EELS‐SI data to detect subtle spectral anomalies while preserving both spatial and spectral correlations across the datacube. By employing cross‐entropy loss and training on bulk spectra, the model learns to reconstruct bulk features characteristic of the defect‐free material. In exploring methods for anomaly detection, both the 3D‐CVAE approach and principal component analysis (PCA) are evaluated, testing their performance using FeL‐edge ΔEpeak shifts designed to simulate material defects. These results show that 3D‐CVAE achieves superior anomaly detection and maintains consistent performance across various shift magnitudes. The method demonstrates clear bimodal separation between bulk and anomalous spectra, enabling reliable classification. Further analysis verifies that lower‐dimensional representations are robust to anomalies in the data. While performance advantages over PCA diminish with decreasing anomaly concentration, our method maintains high reconstruction quality even in challenging, noise‐dominated spectral regions. This approach provides a robust framework for unsupervised automated detection of spectral anomalies in EELS‐SI data, particularly valuable for analyzing complex material systems.
Atomistic defects play a pivotal role in functional and structural materials’ performance across a myriad of technology applications. Quantitative prediction of the thermodynamics and kinetics of defect formation and migration, respectively, typically requires accurate but expensive first-principles approaches, such as density functional theory (DFT). Their computational expense limits the throughput needed to perform high-throughput materials discovery/screening exercises or to perform materials modeling tasks relying on extensive sampling techniques. Therefore, in this Sandia National Laboratories Laboratory Directed Research and Development (LDRD) project (Project #229366), we developed a variety of machine learning techniques, trained on density functional theory calculations, to accelerate the discovery and modeling of materials in which vacancy and interstitial defects primarily dictate material performance. These include applications such as metal oxides for water-splitting or mixed ionic-electronic conduction, metal hydrides for hydrogen storage, and transition metal dichalcogenides for electronics, and the approaches developed herein can further be applied to many other domains that similarly depend on materials’ thermodynamic and kinetic defect properties for their desired functionality.