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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 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Materials Characterization Techniques Lecture Series: X-Ray Diffraction, A Summary of LANL's Materials Capabilities in MPA-11 [Slides]

This presentation provides an overview on X-Ray Diffraction (XRD) to cover background, fundamentals, limitations, best practices, applications, analysis, software, and resources. To summarize: XRD is a great tool for characterizing solid crystalline samples. X-Rays are E-Mag radiation and generate diffraction profiles based on the phase of the sample. Diffraction patterns are unique to the phase of the sample. Powder diffraction gives us better diffraction profiles. We can obtain both qualitative and quantitative data. Analysis requires us to know a little about our sample and a lot about our diffractometer.

36 MATERIALS SCIENCE↗
NASA NTRS1991

Characterizing mode I fatigue delamination of composite materials

Two techniques for characterizing fatigue delamination in double cantilever beams, the delamination growth onset method and the delamination growth and arrest method, are evaluated. Two glass/epoxy systems, S2/SP250 and S2/CE9000, were tested at R = 0.1 and R = 0.5. The values of the strain energy release rate threshold Delta G(Ith) for no onset of delamination growth until one million cycles were compared with the values of Delta G(I) at delamination arrest. The values at arrest were consistently higher because fiber bridging occurred in the tests. Normalizing Delta G(I) by the mode I value of interlaminar fracture toughness G(Ir) at the corresponding delta A gave values of Delta G(I)/G(Ir) at delamination arrest which were closer to the threshold values obtained from the Delta G-N technique. However, this normalization did not represent a generic materials characterization technique and did not allow for delamination onset predictions.

Martin, Roderick H.↗
NASA NTRS1989

Signal processing applied to photothermal techniques for materials characterization

There is a need to make noncontact measurements of material characteristics in the microgravity environment. Photothermal and photoacoustics techniques offer one approach for attaining this capability since lasers can be used to generate the required thermal or acoustic signals. The perturbations in the materials that can be used for characterization can be detected by optical reflectance, infrared detection or laser detection of photoacoustics. However, some of these laser techniques have disadvantages of either high energy pulsed excitation or low signal-to-noise ratio. Alternative signal processing techniques that have been developed can be applied to photothermal or photoacoustic instrumentation. One fully coherent spread spectrum signal processing technique is called time delay spectrometry (TDS). With TDS the system is excited using a combined frequency-time domain by employing a linear frequency sweep excitation function. The processed received signal can provide either frequency, phase or improved time resolution. This signal processing technique was shown to outperform other time selective techniques with respect to noise rejection and was recently applied to photothermal instrumentation. The technique yields the mathematical equivalent of pulses yet the input irradiances are orders of magnitude less than pulses with the concomitant reduction in perturbation of the sample and can increase the capability of photothermal methods for materials characterization.

Rooney, James A.↗
DOE OSTI2020

Combined Spatially Resolved Characterization of Antireflection and Antisoiling Coatings for PV Module Glass

Characterization of photovoltaic (PV) module materials throughout different stages of service life is crucial to understanding and improving the durability of these materials. Currently the large-scale of PV modules (>1 m 2 ) is imbalanced with the small-scale of most materials characterization tools (= 1 cm 2 ). Furthermore, understanding degradation mechanisms often requires a combination of multiple characterization techniques. Here, we present adaptations of three standard materials characterization techniques to enable mapping characterization over moderate sample areas (=25 cm 2 ). Contact angle, ellipsometry, and UV-Vis spectroscopy are each adapted and demonstrated on two representative samples: a commercial multi-functional coating for PV glass and an oxide combinatorial sample library. Best practices are discussed for adapting characterization techniques for large area mapping and combining mapping information from multiple techniques.

14 SOLAR ENERGY↗
DOE OSTI2021

Machine learning on neutron and x-ray scattering and spectroscopies

Neutron and x-ray scattering represent two classes of state-of-the-art materials characterization techniques that measure materials structural and dynamical properties with high precision. These techniques play critical roles in understanding a wide variety of materials systems from catalysts to polymers, nanomaterials to macromolecules, and energy materials to quantum materials. In recent years, neutron and x-ray scattering have received a significant boost due to the development and increased application of machine learning to materials problems. This article reviews the recent progress in applying machine learning techniques to augment various neutron and x-ray techniques, including neutron scattering, x-ray absorption, x-ray scattering, and photoemission. We highlight the integration of machine learning methods into the typical workflow of scattering experiments, focusing on problems that challenge traditional analysis approaches but are addressable through machine learning, including leveraging the knowledge of simple materials to model more complicated systems, learning with limited data or incomplete labels, identifying meaningful spectra and materials representations, mitigating spectral noise, and others. We present an outlook on a few emerging roles machine learning may play in broad types of scattering and spectroscopic problems in the foreseeable future.

Chen, Zhantao↗
NASA NTRS2016

Composite Characterization Using Ultrasonic Wavefield Techniques

The large-scale use of composite components in aerospace applications is expected to continue due to the benefits of composite materials, such as reduced weight, increased strength, and tailorability. NASA's Advanced Composites Project (ACP) has the goals of reducing the timeline for certification of composite materials and enabling the expanded use of advanced composite materials. A key technical challenge area for accomplishing these goals is the need for nondestructive evaluation and materials characterization techniques that are optimized for rapid inspection and detailed defect/damage characterization in composite materials. This presentation will discuss ongoing research investigating the use of ultrasonic wavefield techniques for the characterization of defects such as fiber waviness and delamination damage. Ongoing work includes the development of realistic ultrasonic simulation tools for use in predicting the inspectability of composites and optimizing inspection methodologies. Recent studies on detecting/characterizing delamination damage and fiber waviness via wavefield methods will be described.

Leckey, Cara A. C.↗
DOE OSTI2023

Report Outlining Computed Tomography Strategy and Microscopy Approach to Qualifying AM 316 Materials

This report is part of work package CR-22OR0406012, Automated, High-Throughput Materials Characterization Techniques , under the Advanced Materials and Manufacturing Technologies program (AMMT). The project’s primary objective is to leverage our AI-based rapid and high-throughput automated characterization framework to qualify additively manufactured 316 materials comprehensively, focusing on optimizing the additive manufacturing process and evaluating the performance of 3D-printed stainless steel components. This report outlines our strategy for leveraging the automated characterization process for qualifying 316H materials.

36 MATERIALS SCIENCE↗
DOE OSTI2023

High-Throughput Electric-Field-Assisted Sintering and Characterization Techniques for Materials Discovery

Despite improvements in computing and modeling capabilities, the performance of new materials, particularly those which deviate greatly in composition from well-studied materials (e.g., high-entropy alloys), can be difficult to simulate given the lack of available experimental property data. While some modeling techniques may attempt to predict the properties of these exotic materials, most are forced to make extrapolations from more traditional materials. To fulfill the need for accelerated material synthesis and property measurement, a high-throughput methodology has been developed. Utilizing electric-field-assisted sintering (EFAS), also known as spark plasma sintering (SPS), equipped with custom tooling, samples of differing alloy compositions can be produced simultaneously as a single alloy array. Several arrays have been produced with compositions spanning the Co-Cr-Fe-Mn-Ni alloy family, including many high-entropy alloys, while the novel array geometry has enabled the samples to be polished and characterized in parallel, using X-ray diffraction, scanning-electron microscopy, and laser-based thermal diffusivity measurements.

36 MATERIALS SCIENCE↗
DOE OSTI2023

The Materials Provenance Store

Abstract We present a database resulting from high throughput experimentation, primarily on metal oxide solid state materials. The central relational database, the Materials Provenance Store (MPS), manages the metadata and experimental provenance from acquisition of raw materials, through synthesis, to a broad range of materials characterization techniques. Given the primary research goal of materials discovery of solar fuels materials, many of the characterization experiments involve electrochemistry, along with optical, structural, and compositional characterizations. The MPS is populated with all information required for executing common data queries, which typically do not involve direct query of raw data. The result is a database file that can be distributed to users so that they can independently execute queries and subsequently download the data of interest. We propose this strategy as an approach to manage the highly heterogeneous and distributed data that arises from materials science experiments, as demonstrated by the management of over 30 million experiments run on over 12 million samples in the present MPS release.

36 MATERIALS SCIENCE↗
NASA NTRS2015

Ultrasonic Characterization of Aerospace Composites

Composite materials have seen an increased use in aerospace in recent years and it is expected that this trend will continue due to the benefits of reduced weight, increased strength, and other factors. Ongoing work at NASA involves the investigation of the large-scale use of composites for spacecraft structures (SLS components, Orion Composite Crew Module, etc). NASA is also involved in work to enable the use of composites in advanced aircraft structures through the Advanced Composites Project (ACP). In both areas (space and aeronautics) there is a need for new nondestructive evaluation and materials characterization techniques that are appropriate for characterizing composite materials. This paper will present an overview of NASA's needs for characterizing aerospace composites, including a description of planned and ongoing work under ACP for the detection of composite defects such as fiber waviness, reduced bond strength, delamination damage, and microcracking. The research approaches include investigation of angle array, guided wave, and phase sensitive ultrasonic methods. The use of ultrasonic simulation tools for optimizing and developing methods will also be discussed.

Leckey, Cara↗
DOE OSTI2021

Multifunctional application of nanoscratch technique to characterize cementitious materials

This study aims to further explore the multifunctional applicability of nanoscratch technique to cementitious materials for comprehensive characterization. Based on the investigation of deformation, applied force, and fracture toughness of hardened cement paste during scratching, the methods of phase identification, fracture toughness of individual phase, and the interfacial transition zone (ITZ) thickness quantification were proposed and discussed. After redefining the vertical loading rate of scratching, the slope of the square scratching depth vs. scratching length is recognized as a reliable parameter to distinguish the individual phases for the first time. A fracture toughness-based method is proposed to quantify the thickness of ITZ between clinker and hydrates. Meanwhile, the applicability of phase identification by the scratch method is discussed for different transverse scratching speeds and the vertical loading rates. The findings will benefit the investigation and mechanism understanding of cementitious materials at the microscale by utilizing nanoscratch technique.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Failure Analysis of a New Polyamide-Based Fluoropolymer-Free Backsheet After Combined-Accelerated Stress Testing

The viability of novel coextruded, fluoropolymer-free backsheets for photovoltaic (PV) modules has been questioned as a result of a large number of early-life backsheet failures in PV installations containing one of the earliest co-extruded polyamide (PA)-based backsheet to reach the market, “AAA.” New PV reliability testing protocols have been recently developed and applied to backsheets to reproduce failures observed in the field and evaluate the durability of novel backsheet materials and designs prior to commercialization. A new co-extruded PA-based backsheet was tested using combined-accelerated stress testing (C-AST) and demonstrated a greater lifetime than AAA, and some other fluoropolymer-based backsheets such as polyvinylidene fluoride. The improved PA-based backsheet also eventually failed by through-thickness cracking. Using surface and bulk material characterization techniques, we performed a comprehensive study of material properties before and after the stress testing. Aging of the backsheet resulted in an increase of surface roughness by erosion of the outer PA layer. However the failure is more likely related to an increase in crystallinity of the polyolefin core layer reducing the backsheet tearing energy. The analysis can ultimately inform on the specific weaknesses of the materials so that the manufacturer can improve the backsheet design to extend its lifetime.

14 SOLAR ENERGY↗
DOE OSTI2023

Materials characterization: Can artificial intelligence be used to address reproducibility challenges?

Material characterization techniques are widely used to characterize the physical and chemical properties of materials at the nanoscale and, thus, play central roles in material scientific discoveries. However, the large and complex datasets generated by these techniques often require significant human effort to interpret and extract meaningful physicochemical insights. Artificial intelligence (AI) techniques such as machine learning (ML) have the potential to improve the efficiency and accuracy of surface analysis by automating data analysis and interpretation. In this perspective paper, we review the current role of AI in surface analysis and discuss its future potential to accelerate discoveries in surface science, materials science, and interface science. We highlight several applications where AI has already been used to analyze surface analysis data, including the identification of crystal structures from XRD data, analysis of XPS spectra for surface composition, and the interpretation of TEM and SEM images for particle morphology and size. We also discuss the challenges and opportunities associated with the integration of AI into surface analysis workflows. These include the need for large and diverse datasets for training ML models, the importance of feature selection and representation, and the potential for ML to enable new insights and discoveries by identifying patterns and relationships in complex datasets. Most importantly, AI analyzed data must not just find the best mathematical description of the data, but it must find the most physical and chemically meaningful results. In addition, the need for reproducibility in scientific research has become increasingly important in recent years. The advancement of AI, including both conventional and the increasing popular deep learning, is showing promise in addressing those challenges by enabling the execution and verification of scientific progress. By training models on large experimental datasets and providing automated analysis and data interpretation, AI can help to ensure that scientific results are reproducible and reliable. Although integration of knowledge and AI models must be considered for the transparency and interpretability of models, the incorporation of AI into the data collection and processing workflow will significantly enhance the efficiency and accuracy of various surface analysis techniques and deepen our understanding at an accelerated pace.

Materials Science↗
DOE OSTI2025

Multiscale Characterization of Additive Manufacturing Components with Computed Tomography, 3D X-ray Microscopy, and Deep Learning

Additive manufacturing (AM) facilitates the creation of complex-geometry parts, driving advancements in lightweight aerospace components, high-efficiency engine cooling channels, and customized medical implants. However, ensuring the quality and reliability of AM parts remains challenging due to internal defects, surface irregularities, porosity, and residual trapped powder, which are often inaccessible to traditional inspection methods. Recent developments in X-ray computed tomography (XCT) and 3D X-ray microscopy (XRM), particularly systems equipped with resolution-at-a-distance (RaaD™) capabilities, enable high-resolution, non-destructive evaluation of AM components across multiple scales, from sub-micrometer to macroscopic levels. This paper explores modern XCT and XRM techniques for multiscale characterization of AM parts, focusing on their ability to detect and analyze defects such as porosity, cracks, inclusions, and surface roughness, while offering insights into defect formation mechanisms, material properties, and process-induced variations. The integration of deep learning (DL) frameworks, including Simurgh, DeepRecon, and DeepScout, enhances XCT/XRM workflows by reducing scan times, improving resolution recovery, and enabling accurate defect detection even with limited projection data. These DL-based methods overcome limitations of traditional reconstruction techniques, enabling faster, more reliable characterization of dense materials like Inconel 718 and novel alloys such as AlCe. Applications include process parameter optimization, high-throughput quality control, and multistage AM process evaluation, with DL-enhanced workflows accelerating analysis times from weeks to days. Correlative imaging approaches further validate XCT and XRM data against scanning electron microscopy (SEM) images of physically sectioned samples, confirming the accuracy of DL-based reconstructions and enabling comprehensive defect analysis. While challenges remain in generalizing DL models to diverse materials and imaging conditions, improvements in resolution, noise reduction, and defect detection highlight the transformative potential of these methods. This multiscale and correlative approach enables precise identification and correlation of microstructural features with the overall performance of AM components. By integrating advanced XCT, XRM, and DL techniques, this paper demonstrates a significant leap forward in AM characterization, offering valuable insights into the relationships between processing parameters, microstructure, and part performance, and driving innovations that enhance the quality and reliability of AM products for demanding industrial applications.

Additive manufacturing↗
DOE OSTI2022

Material Changes in Electrocatalysis: An In Situ/Operando Focus on the Dynamics of Cobalt‐Based Oxygen Reduction and Evolution Catalysts

Abstract The shift towards cheaper, non‐platinum group metal electrocatalyst materials for clean energy technologies is coupled with challenges in maintaining long‐term performance. For practical purposes, electrocatalytic stability typically focuses on catalyst electrochemical performance over time. However, a deeper understanding of catalyst material property changes during operation is needed to enable material‐specific design strategies for long‐term stabilization. In the last several decades, improvements in material characterization techniques have made it possible to probe the composition, structure, and degradation products of catalysts in situ/operando. Herein we review the current understanding of in situ/operando material stability of Co‐based electrocatalysts for the oxygen evolution (OER) and reduction reactions (ORR) in acidic and alkaline environments. We focus on in situ/operando materials characterization of three categories of Co‐based OER/ORR catalysts: oxides and (oxy)hydroxides, mixed‐metal catalysts, and non‐oxide materials. This review aims to compile and compare the results from multiple studies and techniques to provide insight into the role that the starting material, pH, and applied potential have on the active surface and stability of Co‐based materials. We conclude by highlighting directions that have been underexplored with opportunities for continued research including improving in situ/operando characterization, methods to probe long‐term material changes, and the development of operando studies on full‐scale devices. Using Co‐based materials as a case study, this review shows the vital role that in situ/operando characterization must play in the future of improving electrocatalyst stability.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS2023

Short-Transverse Tensile Fracture Behavior of Thick AA2219 Plate - Effects of Theta (Al2Cu) Particle Size and Distribution

An investigation was conducted to obtain a better understanding on the microstructural characteristics of 5-inch-thick AA2219-T87 plate material and the mechanism that led to the unexpected low ductility in the short-transverse (ST) orientation. Materials characterization techniques were utilized to observe the microstructure of fractured tensile specimens and remnant plate material. The mechanism and cause of low ST ductility in the thick plate material is presented and discussed in this report.

Short-Transverse Orientation↗