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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 307 records · Page 17
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Bridging length scales in hard materials with ultra-small angle X-ray scattering – a critical review

Owing to their exceptional properties, hard materials such as advanced ceramics, metals and composites have enormous economic and societal value, with applications across numerous industries. Understanding their microstructural characteristics is crucial for enhancing their performance, materials development and unleashing their potential for future innovative applications. However, their microstructures are unambiguously hierarchical and typically span several length scales, from sub-ångstrom to micrometres, posing demanding challenges for their characterization, especially for in situ characterization which is critical to understanding the kinetic processes controlling microstructure formation. This review provides a comprehensive description of the rapidly developing technique of ultra-small angle X-ray scattering (USAXS), a nondestructive method for probing the nano-to-micrometre scale features of hard materials. USAXS and its complementary techniques, when developed for and applied to hard materials, offer valuable insights into their porosity, grain size, phase composition and inhomogeneities. We discuss the fundamental principles, instrumentation, advantages, challenges and global status of USAXS for hard materials. Using selected examples, we demonstrate the potential of this technique for unveiling the microstructural characteristics of hard materials and its relevance to advanced materials development and manufacturing process optimization. We also provide our perspective on the opportunities and challenges for the continued development of USAXS, including multimodal characterization, coherent scattering, time-resolved studies, machine learning and autonomous experiments. Our goal is to stimulate further implementation and exploration of USAXS techniques and inspire their broader adoption across various domains of hard materials science, thereby driving the field toward discoveries and further developments.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Preliminary results of neutron and X-ray diffraction data collection on a lytic polysaccharide monooxygenase under reduced and acidic conditions

Lytic polysaccharide monooxygenases (LPMOs) are copper-center enzymes that are involved in the oxidative cleavage of the glycosidic bond in crystalline cellulose and other polysaccharides. The LPMO reaction is initiated by the addition of a reductant and oxygen to ultimately form an unknown activated copper–oxygen species that is responsible for polysaccharide-substrate H-atom abstraction. Given the sensitivity of metalloproteins to radiation damage, neutron protein crystallo­graphy provides a nondestructive technique for structural characterization while also informing on the positions of H atoms. Neutron cryo-crystallography permits the trapping of catalytic intermediates, thereby providing insight into the protonation states and chemical nature of otherwise short-lived species in the reaction mechanism. Here, to characterize the reaction-mechanism intermediates of LPMO9D from Neurospora crassa, a cryo-neutron diffraction data set was collected from an ascorbate-reduced crystal. A second neutron diffraction data set was collected at room temperature from an LPMO9D crystal exposed to low-pH conditions to probe the protonation states of ionizable groups involved in catalysis under acidic conditions.

Neurospora crassa↗
DOE OSTI2023

Process-dependent anisotropic thermal conductivity of laser powder bed fusion AlSi10Mg: impact of microstructure and aluminum-silicon interfaces

Purpose AlSi10Mg alloy is commonly used in laser powder bed fusion due to its printability, relatively high thermal conductivity, low density and good mechanical properties. However, the thermal conductivity of as-built materials as a function of processing (energy density, laser power, laser scanning speed, support structure) and build orientation, are not well explored in the literature. This study aims to elucidate the relationship between processing, microstructure, and thermal conductivity. Design/methodology/approach The thermal conductivity of laser powder bed fusion (L-PBF) AlSi10Mg samples are investigated by the flash diffusivity and frequency domain thermoreflectance (FDTR) techniques. Thermal conductivities are linked to the microstructure of L-PBF AlSi10Mg, which changes with processing conditions. The through-plane exceeded the in-plane thermal conductivity for all energy densities. A co-located thermal conductivity map by frequency domain thermoreflectance (FDTR) and crystallographic grain orientation map by electron backscattered diffraction (EBSD) was used to investigate the effect of microstructure on thermal conductivity. Findings The highest through-plane thermal conductivity (136 ± 2 W/m-K) was achieved at 59 J/mm 3 and exceeded the values reported previously. The in-plane thermal conductivity peaked at 117 ± 2 W/m-K at 50 J/mm 3 . The trend of thermal conductivity reducing with energy density at similar porosity was primarily due to the reduced grain size producing more Al-Si interfaces that pose thermal resistance. At these interfaces, thermal energy must convert from electrons in the aluminum to phonons in the silicon. The co-located thermal conductivity and crystallographic grain orientation maps confirmed that larger colonies of columnar grains have higher thermal conductivity compared to smaller columnar grains. Practical implications The thermal properties of AlSi10Mg are crucial to heat transfer applications including additively manufactured heatsinks, cold plates, vapor chambers, heat pipes, enclosures and heat exchangers. Additionally, thermal-based nondestructive testing methods require these properties for applications such as defect detection and simulation of L-PBF processes. Industrial standards for L-PBF processes and components can use the data for thermal applications. Originality/value To the best of the authors’ knowledge, this paper is the first to make coupled thermal conductivity maps that were matched to microstructure for L-PBF AlSi10Mg aluminum alloy. This was achieved by a unique in-house thermal conductivity mapping setup and relating the data to local SEM EBSD maps. This provides the first conclusive proof that larger grain sizes can achieve higher thermal conductivity for this processing method and material system. This study also shows that control of the solidification can result in higher thermal conductivity. It was also the first to find that the build substrate (with or without support) has a large effect on thermal conductivity.

Engineering↗
DOE OSTI2021

Application of Principal Component Analysis for the Monitoring of the Aging Process of Nuclear Electrical Cable Insulation

To ensure the safe operation of nuclear power plants (NPPs), it is critical to understand how NPP electrical cable insulation will degrade under different service environments. In this study, various nondestructive examination methods were selected to evaluate degradation of electrical cable insulation after aging. Elongation at break, indenter modulus, relaxation constant, mass change, total color difference, and carbonyl index were collected stepwise on cross-linked polyethylene (XLPE) cable insulations after predetermined exposure intervals. Three different insulation aging scenarios were investigated: 1) simultaneous irradiation and heating at 150°C, 2) heating at 150°C followed by corresponding times of irradiation at ambient temperature, and 3) irradiation at ambient temperature followed by heating at 150°C for the same duration. A constant dose rate of 300 Gy/hr was selected with a total gamma irradiation absorbed dose up to 320 kGy. To gain insight into the long-term performance of NPP electrical cable insulation, principal component analysis (PCA), a data-driven approach, was utilized to identify key indicators of cable insulation degradation. By reducing the dimensionality of the data while retaining degradation information, PCA was used to highlight the changes in the measured properties under gamma irradiation according to total absorbed dose and the different aging scenarios.

Li, Donghui↗
DOE OSTI2020

Neural Learning Based Blind Source Separation for Detection of Material Defects in Pulsed Thermography Images

In this paper, we introduce a neural learning-based approach to blind source separation for detection of material flaws in pulsed thermography (PT) images. This approach can be used to detect internal defects (pores) in metallic Additively Manufactured (AM) materials. Such defects occur in high-strength alloys produced with direct laser sintering AM method for nuclear energy applications. Pulsed thermal imaging system utilizes a high intensity flash lamp to rapidly heat surface of sample, a high sensitivity infrared camera to capture data of surface temperature variations. The data cube obtained with PT (stack of surface temperature images at different times) can be analyzed with image processing algorithms to detect material defects. Compared with conventional nondestructive evaluation (NDE) methods, such as digital radiography and ultrasonic testing, the PT pulsed infrared thermal detection technique has advantages of one-sided non-contact measurements, fast processing of large sample areas captured in one image. Detection of small material defects requires finding features in thermal images, which have signal contrast levels approaching sensitivity limit of IR camera. In this study, an optimized Neural Learning based Blind Source Separation (NLBSS) algorithm, including Principal Component Analysis (PCA), and Independent Component Analysis (ICA) is demonstrated to automatically extract principal temporal and spatial features of thermography frames to enhance flaw detection. By using the NLBSS algorithm, material internal defects can be automatically detected. Furthermore, this processing approach compensates for experimental thermal imaging artifacts, such as noise and uneven heating. By merging artificial intelligence with the photothermic, the NDE system detects internal calibrated defects of various sizes and depths in AM nuclear-grade metallic alloys.

Zhang, Xin↗
DOE OSTI2024

First Demonstration of Vertical 2T-nC FeRAM Hybrid Cell and its Scalability for High-Density 3D Ferroelectric Capacitor Memory

In this article, we perform a comprehensive experimental and modeling study into the scaling of vertical 2T-nC ferroelectric random-access memory (FeRAM) hybrid cell to demonstrate a high performance and high-density 3D capacitor memory. We demonstrate: i) first time successful integration of the vertical 2T-3C FeRAM cell by stacking the vertical metal-ferroelectricmetal (MFM) stack on top of Si CMOS transistors; ii) successful experimental operation of the memory cell, including the quasi-nondestructive read out (QNRO) of the polarization without write back after 106 read cycles; iii) the write bit line (WBL) heavily screens the coupling between neighboring strings, making it a minor concern; V ) aggressive stacking of the WBLs, i.e., number of MFMs in a string, could facilitate the self-boosting during write operation due to ferroelectric linear capacitance (CFE), which allows self-boosted inhibition for Vw/2 scheme and worsens the Vw/3 scheme as disturb increases to intolerable 2Vw/3; v) aggressive horizontal scaling significantly increases the read disturb to cells on neighboring planes due to capacitance between two WBLs (Cz).

42 ENGINEERING↗
DOE OSTI2021

Multiscale Characterization of Photovoltaic Modules—Case Studies of Contact and Interconnect Degradation

The current popularity of photovoltaic (PV) systems is due in large part to their exceptional reliability and significantly lower cost than other energy sources. Studying cell and module degradation is key to promote further development in the state of the art. Fielded or accelerated aged modules exhibit different failure modes, of which metallization degradation (contacts and interconnections) is prevalent. In this work, we discuss how multiscale characterization methods can be applied to a variety of module technologies that have been field exposed and have undergone accelerated age testing. These methods include performing characterization on the module level, cell level, and finally the materials level. The observed performance losses from the module- and cell-level characterization can be correlated with materials properties to find out the root cause of degradation. We recommend an initial nondestructive characterization suite, including module- and cell-level current-voltage ( I--V ), Suns-V OC , photoluminescence and electroluminescence imaging, quantum efficiency, ultraviolet fluorescence imaging, and thermal infrared imaging. Samples are then extracted from particularly degraded regions of the module and prepared for materials characterization techniques, such as top-down and cross-sectional scanning electron microscopy, energy-dispersive X-ray spectroscopy, X-ray photoelectron spectroscopy, secondary ion mass spectrometry, Raman spectroscopy, and transmission electron microscopy, allowing a deeper look into the mechanism behind the metallization degradation. This article serves as an instructional review to introduce the different multiscale characterization methods and how they can be effectively applied to perform PV degradation studies. Furthermore, we also share some of our examples and discuss the strengths, limitations, and best practices for each of the characterization techniques.

14 SOLAR ENERGY↗
DOE OSTI2024

Photovoltaic Cable Connectors: A Comparative Assessment of the Present State of the Industry

The consequences of failure for balance-of-systems components (such as photovoltaic (PV) cable connectors) include offline module string(s); low system voltage; arc, ground, insulation, and overtemperature faults; triggered fuse(s); system shutdown; and fire. The degradation modes for connectors are studied here through an industry survey and its subsequent examination, which are compared with field-degraded specimens. A total of 117 specimens were obtained from a variety of locations and climates or accelerated tests. A failure analysis for connectors from PV installations was developed (and applied to 54 specimens), including nondestructive examinations (photography, a custom resistance-current scan, and X-ray computed tomography) and destructive examinations (featuring milling of the external plastic, extraction of the internal convolute spring, and potting and polishing in cross section). Surface and through-thickness composition of the metal pins and springs was quantified using scanning electron microscopy with energy-dispersive X-ray spectroscopy. Fourier transform infrared spectroscopy was used to verify the base polymer materials and compare the chemical structure of the connector body, bushing, end nut, and o-ring. Thermogravimetric analysis and differential scanning calorimetry were used to further verify the degradation of the same polymeric components.

cable connectors↗
DOE OSTI2025

High-Intensity UV Exposure for the Rapid Screening of Silicon Photovoltaic Architectures

Advanced Si photovoltaic architectures incorporate different materials and processing pathways that influence degradation modes. Ultraviolet-induced degradation (UVID) is an understudied degradation mode for advanced cell architectures and is of increasing concern to industry due to growing adoption of UV-transparent encapsulation and bifacial technologies. In order to adopt new and evolving technologies confidently, novel component materials and processing techniques must be evaluated and designed for long-term stability, in addition to the conventional design focus on efficiency. In this work, a study protocol framework is presented for the rapid screening of unencapsulated devices against UVID. Unencapsulated passivated emitter rear contact (PERC) and tunnel oxide passivated contact (TOPCon) devices were aged under different UV irradiance intensities and measured via conventional nondestructive electrical characterization methods to assess performance degradation. Based on the results, protocol efficacy and recommendations for further study are discussed. As a result, this work is part of a broader effort to develop rapid screening processes that cut across architectures and exposure conditions to aid module manufacturers in vetting new materials choices for long-term stability.

Accelerated exposure↗
DOE OSTI2026

Surface Stress Characterization and Trends in Thermally Strengthened Photovoltaic Module Glass

Simultaneous changes in photovoltaic (PV) module architecture-such as increased area, replacing the polymeric backsheet with a glass backsheet, and reducing glass thickness from 3.2 to 2.0 mm-have resulted in a novel failure called spontaneous glass breakage. Spontaneous glass breakage results in front and/or rear glass failure without any obvious cause. The resulting low-energy fracture patterns have raised concerns about the thermal strengthening in 2.0-mm glass. We present validation of a nondestructive method for measuring the glass surface stress in PV modules. We use a scattered light polariscope to examine the glass properties of 11 modules from 6 solar fields, with and without spontaneous glass breakage. Based on a Mann-Whitney test p-value of 1.4 x 10-6, we conclude that there is a correlation between lower surface stress and susceptibility to spontaneous breakage. A Kendall's Tau test p-value of 2.0 x 10-4 indicates a correlation between increased module area and lower surface stress. We find that 2.0-mm glass can be, and often is, fully tempered (surface stress ≥69 MPa), but inspecting the fracture pattern is not a reliable way to assess the amount of thermal strengthening. A combination of factors, not just surface stress, influences glass breakage.

14 SOLAR ENERGY↗
DOE OSTI2021

Algorithm-Driven Advances for Scientific CT Instruments: From model-based to deep learning-based approaches

Multiscale 3D characterization is widely used by materials scientists to further their understanding of the relationships between microscopic structure and macroscopic function. Scientific computed tomography (SCT) instruments are one of the most popular choices for 3D nondestructive characterization of materials at length scales ranging from the angstrom scale to the micron scale. These instruments typically have a source of radiation (such as electrons, X-rays, or neutrons) that interacts with the sample to be studied and a detector assembly to capture the result of this interaction (see Figure 1 ). A collection of such high-resolution measurements is made by reorienting the sample, which is mounted on a specially designed stage/holder after which reconstruction algorithms are used to produce the final 3D volume of interest. The specific choice of which instrument to use depends on the desired resolution and properties of the materials being imaged. Additionally, the end goal of SCT scans includes determining the morphology, chemical composition, or dynamic behavior of materials when subjected to external stimuli. In summary, SCT instruments are powerful tools that enable 3D characterization across multiple length scales and play a critical role in furthering the understanding of the structure–function relationships of different materials.

42 ENGINEERING↗
DOE OSTI2020

Luminescence from Poly-Si Films and its Application to Study Passivating-Contact Solar Cells

In recent years, polycrystalline silicon (poly-Si) based passivating-contact solar cells have received tremendous attention from the solar research community due to its excellent surface passivation and high carrier conductivity. However, the poly-Si films are not transparent to all wavelengths of the solar spectrum. There is often some parasitic absorption in these films. From a different standpoint, as they absorb some light, they can luminesce. This phenomenon provides us with unique opportunities to investigate optoelectronic properties of the films in a fast, contactless, and nondestructive manner. In this work, we report the luminescence phenomenon from poly-Si films used in passivating-contact solar cells. We then utilize this phenomenon to report a range of applications for solar cells including studies of carrier transport behaviors and hydrogenation inside the films.

14 SOLAR ENERGY↗
DOE OSTI2025

Single-Cell Universal Logic-in-Memory Using 2T-nC FeRAM: An Area and Energy-Efficient Approach for Bulk Bitwise Computation

This work presents a novel approach to configure 2T-nC ferroelectric RAM (FeRAM) for performing single cell logic-in-memory operations, highlighting its advantages in energy-efficient computation over conventional DRAM-based approaches. Unlike conventional 1T-1C dynamic RAM (DRAM), which incurs refresh overhead, 2T-nC FeRAM offers a promising alternative as a non-volatile memory solution with low energy consumption. Our key findings include the potential of quasi-nondestructive readout (QNRO) sensing in 2T-nC FeRAM for logic-in-memory (LiM) applications, demonstrating its inherent capability to perform inverting logic without requiring external modifications, a feature absent in traditional 1T-1C DRAM. We successfully implement the MINORITY function within a single cell of 2T-nC FeRAM, enabling universal NAND and NOR logic, validated through SPICE simulations and experimental data. Additionally, the research investigates the feasibility of 3D integration with 2T-nC FeRAM, showing substantial improvements in storage and computational density, facilitating bulk-bitwise computation. Our evaluation of eight real-world, data-intensive applications reveals that 2T-nC FeRAM achieves 2× higher performance and 2.5× lower energy consumption compared to DRAM. Furthermore, the thermal stability of stacked 2T-nC FeRAM is validated, confirming its reliable operation when integrated on a compute die. These findings emphasize the advantages of 2T-nC FeRAM for LiM, offering superior performance and energy efficiency over conventional DRAM.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Material Identification From Radiographs Without Energy Resolution

We propose a method for performing material identification from radiographs without energy-resolved measurements. Material identification has a wide variety of applications, including in biomedical imaging, nondestructive testing, and security. While existing techniques for radiographic material identification make use of dual energy sources, energy-resolving detectors, or additional (e.g., neutron) measurements, such setups are not always practical— requiring additional hardware and complicating imaging. We tackle material identification without energy resolution, allowing standard X-ray systems to provide material identification information without requiring additional hardware. Assuming a setting where the geometry of each object in the scene is known and the materials come from a known set of possible materials, we pose the problem as a combinatorial optimization with a loss function that accounts for the presence of scatter and an unknown gain and propose a branch and bound algorithm to efficiently solve it. We present experiments on both synthetic data and real, experimental data with relevance to security applications— thick, dense objects imaged with MeV X-rays. We show that material identification can be efficient and accurate, for example, in a scene with three shells (two copper, one aluminum), our algorithm ran in six minutes on a consumer-level laptop and identified the correct materials as being among the top 10 best matches out of 8,000 possibilities.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Spatiotemporal Learning in Power Modules: Wavelet-Enhanced Forecasting of Thermomechanical Degradation

Detecting internal defects in power electronics packages is critical for their performance and reliability, especially under extreme operating conditions, as these defects can lead to catastrophic failure if not properly addressed. Confocal scanning acoustic microscopy (C-SAM) plays a key role in the nondestructive evaluation of bond layer degradation within a power electronics package by detecting defects such as delamination, voids, and cracks. However, accurately quantifying and predicting these defects from C-SAM images remains a significant challenge due to the low noise-to-signal ratio, which typically arises from both imaging process and bond patterns itself. In this paper, we explore machine learning strategies for processing C-SAM images and providing predictive models of defect growth. We use C-SAM images of sintered copper and sintered silver samples, which are obtained under accelerated thermal experiments, as the representative dataset for our study. We investigate the effect of Fourier transforms and wavelet transforms on these datasets to remove high-frequency noise and address noise across multiple scales with histogram equalization to enhance the contrast and improve the visibility of defects. As a result, defect boundaries can be clearly distinguished, enabling more accurate tracking of their growth over time. We then employ different time-series forecasting algorithms on the denoised images to formulate an image-based lifetime prediction model. Statistical models and deep-learning techniques are trained on images obtained in the early stages of thermal shock, and defect growth in the later stages is predicted. Our work serves as a preliminary attempt to improve the accuracy of lifetime prediction models of power electronics packages, which is critical under extreme operating environments.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

A Plutonium Mass Uncertainty Assessment Using a Cherenkov-Based Neutron Multiplicity Water Detector

We present a new analysis of the sensitivity of a water Cherenkov-based neutron multiplicity counter for nondestructive assay of trace quantities of 240 Pu. The multiplicity counter [water-based neutron detector (WaND)] contains 1.0 m 3 of water doped with 0.2% gadolinium in the form of a gadolinium salt (0.4% GdCl 3 ). It can be used for testing the presence of trace quantities of 240 Pu within a sample of unknown material by observing the correlated timing behavior of neutron capture candidates in the detector. The new analysis takes advantage of the fact that the detector is comprised almost entirely of a homogeneous medium, which permits the use of a simple model to reproduce the observed neutron capture timing behavior. Finally, the results of this analysis indicate that the detector has an absolute neutron detection efficiency of 23.4 ± 1.1% and is sensitive to a 240 Pu mass as small as 2.3 mg (at 1σ) after a 1-h data acquisition.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
DOE OSTI2026

Optimization of an Energy Tuning Assembly for High Explosives Detection

The Portable Isotopic Neutron Spectroscopy (PINS) system, employs neutron-induced gamma-ray spectroscopy and provides a nondestructive method for high explosives detection. In standard operation it uses Californium-252 as a neutron source. Operating PINS with a deuterium-tritium (DT) neutron generator has some advantages over Cf-252, including lifetime and ability to produce high-energy inelastic scattering gamma rays. However, current systems using DT neutron generators suffer from a high environmental background and reduced ability to induce neutron capture, reducing spectral quality and limiting nitrogen sensitivity. Here, this study presents the development of an energy-tuning assembly (ETA) designed to optimize the DT neutron energy spectrum to increase nitrogen reaction rates in a target, thereby improving high explosive detection capabilities. A metaheuristic optimization framework, MultiGNOWEE, coupled with MCNP, was employed to generate two ETA configurations: a single-objective ETA, which maximizes nitrogen capture reactions, and a multi-objective ETA, which balances neutron capture and inelastic scattering. Simulations demonstrated the optimized configurations achieved up to a 10-fold improvement in nitrogen capture rates compared to the bare configuration. Experimental validation was conducted using a DT neutron generator and a high-purity germanium (HPGe) detector. Two prototype ETAs were constructed and assessed on a melamine simulant. Measurements demonstrated improved nitrogen detection for both prototype ETA configurations when compared to the standard system.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Photovoltaic Inverter Failure Mechanism Estimation Using Unsupervised Machine Learning and Reliability Assessment

This article introduces a data-driven approach to assessing failure mechanisms and reliability degradation in outdoor photovoltaic (PV) string inverters. The manufacturer's stated PV inverter lifetime can vary due to the impact of operating site conditions. To address limitations in degradation estimation through accelerated testing, condition monitoring, or degradation modeling, we propose a machine learning (ML) oriented approach. Utilizing data from a 1.4 MW PV power plant operational since 2016, with 46 string PV inverters tied to the grid, we employ the unsupervised one-class support vector machine ML technique to analyze inverter and sensor data, capable of classifying humidity cycling and temperature fluctuations as dominant failure mechanisms. Utilizing the anomaly alert relationship and alert details specific to the inverter, the level of PV inverter output is considered as its availability or available reliability. Subsequently, a continuous Markov model is applied to six-month alert data, revealing an average stated reliability of 20% after 20 years of continuous operation. These results support recommendations for time-bound preventive measures to enhance PV inverter reliability under diverse outdoor conditions. Furthermore, the approach provides a nondestructive, top–down, and generalized method for analyzing any commercial PV inverter exposed to outdoor conditions, contingent on the availability of relevant data.

14 SOLAR ENERGY↗