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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 181 records · Page 10
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Prediction of exotic magnetic states in the alkali-metal quasi-one-dimensional iron selenide compound Na 2 FeSe 2

The magnetic and electronic phase diagram of a model for the quasi-one-dimensional alkali-metal iron selenide compound Na 2 FeSe 2 is presented. The novelty of this material is that the valence of iron is Fe 2+ , contrary to most other iron-chain compounds with valence Fe 3+ . Using first-principles techniques, we developed a three-orbital tight-binding model that reproduces the ab initio band structure near the Fermi level. Including Hubbard and Hund couplings and studying the model via the density-matrix renormalization group and Lanczos methods, we constructed the ground-state phase diagram. A robust region where the block state ↑↑↓↓↑↑↓↓ is stabilized was unveiled. The analog state in iron ladders, employing 2×2 ferromagnetic blocks, is by now well established, but in chains a block magnetic order has not been observed yet in real materials. The phase diagram also contains a large region of canonical staggered spin order ↑↓↑↓↑↓↑ at very large Hubbard repulsion. At the block-to-staggered transition region, an exotic phase is stabilized with a mixture of both states: an inhomogeneous orbital-selective charge density wave with the exotic spin configuration ↑↑↓↑↓↓↑↓. Our predictions for Na 2 FeSe 2 may guide crystal growers and neutron-scattering experimentalists towards the realization of block states in one-dimensional iron selenide chain materials.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2025

Revealing the electronic structure of van der Waals antiferromagnetic NiPS 3 through synchrotron-based 𝜇-ARPES and alkali metal dosing

Antiferromagnetic NiPS 3 has recently emerged as a quantum material of considerable interest, thanks to the discovery of multiple new couplings involving electrons, spins, orbitals, phonons, and magnons. However, controversies and open questions persist concerning the fundamental origins of these couplings. A critical piece of information required to advance the understanding is the precise electronic band structure of NiPS 3 . Angle-resolved photoemission spectroscopy (ARPES), combined with alkali metal dosing (AMD), can enable us to directly observe the subtle electronic states that appear around the Fermi surface, offering valuable insights into the intriguing quantum properties and interplays of the examined material. Here, in this study, we present a comprehensive characterization and analysis of the band structure of van der Waals layered antiferromagnet NiPS 3 , leveraging state-of-the-art μ-ARPES measurements supported by density functional theory (DFT) calculations. Theoretical DFT results identify the orbital contributions to the observed bands, providing a precise understanding of the experimental ARPES data. Crucially, AMD enables the observation of conduction band and defect-related states above the valence band maximum in NiPS 3 . Furthermore, temperature dependent ARPES results across the Néel transition temperature of NiPS 3 reveal that the paramagnetic and antiferromagnetic phases have nearly identical band structures, underlining the highly localized character of Ni d states. These findings substantially deepen our understanding of the electronic properties of NiPS 3 and lay a vital foundation for exploring the intriguing quantum phenomena it exhibits.

Cao, Yifeng [Boston Univ., MA (United States); Law↗
DOE OSTI2020

Structure and stability of alkali gallates structurally reminiscent of hollandite

Abstract Single crystals of CsGa 7 O 11 , RbGa 7 O 11 , and RbGa 4 In 5 O 14 were grown from alkali halide melts and their structures were characterized by single crystal and powder X‐ray diffraction. CsGa 7 O 11 and RbGa 7 O 11 adopt the same structure type, reminiscent of the hollandite structure type, as it contains nearly rectangular channels made up of two dimers of edge‐sharing GaO 6 octahedra, and two corner‐sharing octahedron/tetrahedron pairs. The structure of RbGa 4 In 5 O 14 is more complex and is comprised of indium octahedra, gallium trigonal bipyramids, and gallium tetrahedra, and contains similar sized tunnels as CsGa 7 O 11 and RbGa 7 O 11 . CsGa 7 O 11 and RbGa 4 In 5 O 14 were further characterized by TGA, ion exchange experiments, and DFT studies revealing that both structures are thermodynamically stable up to 850°C; however, CsGa 7 O 11 decomposes to GaO(OH) xH 2 O when heated in warm aqueous solutions. CsGa 7 O 11 undergoes ion exchange in both an aqueous solution of RbCl and a RbNO 3 melt, as predicted by DFT studies, where the ion exchange is more extensive in the RbNO 3 melt.

Juillerat, Christian A.↗
DOE OSTI2020

Vibro-acoustic modulation and data fusion for localizing alkali–silica reaction–induced damage in concrete

This article investigates the application of vibro-acoustic modulation testing for diagnosing damage in concrete structures. The vibro-acoustic modulation technique employs two excitation frequencies on a structure. The interaction of these excitations in the measured response indicates damage through the presence of sidebands in the frequency spectra. Past studies using this technique have mostly focused on metals and composites (thin plates or laminates). Our research focuses on concrete, which is a highly heterogeneous material susceptible to a variety of chemical, physical, and mechanical damage processes. In particular, this article investigates diagnosing cracking in concrete from an expansive gel produced by an alkali–silica reaction in the presence of moisture. Past studies have been limited to damage detection using vibro-acoustic modulation testing, whereas this article extends the technique to damage localization. A cement slab with pockets of reactive aggregate is used to investigate the diagnosis technique. The effects of different testing parameters, such as locations, magnitudes, and frequencies of the two excitations, are analyzed and incorporated in the damage localization methodology. A Bayesian probabilistic methodology is developed to fuse the information from multiple test configurations in order to construct damage probability maps for the test specimen. The results of vibro-acoustic modulation–based damage localization are validated by petrographic study of cores taken from the slab.

Karve, Pranav↗
DOE OSTI2021

Evaluations of the Fates of Alkali Metals, Actinides, Mercury, and Iodine During DWPF Recycle Diversion

The fates of alkali metals, actinides, mercury, and iodine in the Defense Waste Processing Facility Recycle Diversion process (as currently conceptualized) have been evaluated through paper studies based on available knowledge of the chemistry, physical properties, solubility, and volatility of the various species involved. The effect of pH in the range from 9 to 13 has been discussed. Recommendations for additional studies to close technology gaps have been provided, many of which are contingent upon the results of pending testing and sample characterization efforts. There is uncertainty in the amounts of soluble actinides passing through the process filter, though the bulk of the actinides should be captured on the filter with the Recycle Collection Tank solids and the total amounts of actinides should be relatively low. The Recycle Collection Tank pH could impact the fraction of actinides reaching the evaporator, but the primary factors determining the actinide fate are expected to be the amount of CO 2 sorption from air sparging or, for certain actinides (such as plutonium), oxidation and/or sorption to MnO 2 solids from permanganate additions to destroy the glycolate anion. Process optimization could minimize the amounts of actinides passing the filter. Depending upon the levels of mercury observed in recycle stream samples and because of the volatility of mercury, the evaporator should be designed with the capability to remove dense mercury phases from the condensate to avoid exceeding ETP WAC limits. The facility design must be adequate to transfer dense mercury phases and testing to confirm mercury transfer is needed. Simulant containing mercury is recommended for both filtration and evaporation testing. OLI Modeling of the various recycle streams is recommended to provide insight on the fate of iodine. Iodine-spiked simulants are recommended for upcoming evaporation tests. The pro) ect should consider the likelihood and impact of NAS scale formation in the evaporators. Process optimization may be needed to minimize the accumulation of NAS scale and possibly the sorption of actinides in the evaporator. Actual waste testing of the Recycle Diversion filtration and evaporation should include the analysis of actinides, mercury, and iodine to determine their partitioning.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2023

Machine Learning for Processing Ultrasonic Data from Long-Term Monitoring of Concrete with Alkali-Silica Reaction (ASR)

The alkali–silica reaction (ASR) is a phenomenon that leads to material degradation in concrete, resulting in the formation of microcracks and cracks. This deterioration causes a loss of mechanical properties, concrete damage, and even corrosion. To address this issue, ultrasonic nondestructive evaluation can be employed as a technique for long-term monitoring of ASR development and condition assessment of concrete subjected to ASR. However, traditional approaches typically utilize only a few wave parameters, such as wave velocity or amplitude, to characterize ASR-induced concrete damage while disregarding the majority of information present in the ultrasonic signals.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Assessment of Machine Learning for Ultrasonic Nondestructive Evaluation of Alkali–Silica Reaction in Concrete

Alkali–silica reaction (ASR) is a type of material degradation in concrete structures that leads to concrete cracking and rebar corrosion, thereby reducing the material’s structural integrity and the overall structure’s lifetime and raising safety concerns. Ultrasonic nondestructive evaluation (NDE) has been proven to be a valuable technique for assessing concrete properties and monitoring ASR progression in concrete. However, the deployment and analysis of ultrasonic NDE and its data requires specialized expertise, often relying on the engineer’s subjective interpretation. With the surge in computational power, artificial intelligence (AI) and machine learning (ML) algorithms have become popular in automating NDE data analysis. Various industrial sectors are increasingly adopting ML algorithms for NDE data analysis with a growing emphasis on AI–assisted automation. Regulatory agencies are also preparing for this technological shift, anticipating corresponding revisions in standards. Thus, there is an urgent need to identify the capabilities and limitations of current ML technologies for the evaluation of concrete material properties and damage status. Furthermore, the effects of various factors on ML model performance must be thoroughly investigated. The study summarized herein evaluated the effectiveness of two ML models (i.e., support vector regression (SVR) and deep neural network (DNN)) in predicting concrete material damage induced by ASR based on the long-term ultrasonic monitoring data. Four distinct concrete specimens were cast with artificially induced ASR, and over a period exceeding 500 days, ultrasonic signals and expansion data were continuously collected. For the SVR model, wave velocity and 12 other wave features were extracted from the ultrasonic signals, with 6 out of 13 features selected as input for the model. Different combinations of training and testing datasets were designed to explore factors influencing prediction performance, including the range of data within training and testing sets, in addition to various signal preprocessing methodologies. These findings suggest the importance of using a training dataset with a broader data range compared with testing datasets for improved model performance alongside consistent signal preprocessing across datasets.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Performance Comparison of Machine Learning Models for Ultrasonic Nondestructive Evaluation of Alkali-Silica Reaction in Concrete

Alkali-silica reaction (ASR) causes concrete degradation, leading to cracking, rebar corrosion, and reduced structural integrity, which raises safety concerns. Ultrasonic nondestructive evaluation (NDE) effectively assesses concrete properties and monitors ASR progression. However, its deployment and analysis require specialized expertise and subjective interpretation. As computational power increases, artificial intelligence (AI) and machine learning (ML) algorithms are increasingly being used to automate NDE data analysis across various industries for AI-assisted automation. Regulatory agencies are adapting to this technological shift, prompting a need to evaluate current ML technologies’ capabilities and limitations in assessing concrete material properties and damage. This report presents a comparative analysis of four ML regression models for predicting concrete material damage induced by ASR expansion using long-term ultrasonic data monitoring. The models investigated include linear regression (LR), support vector regression (SVR), shallow neural networks (NN), and deep neural networks (DNN). LR, SVR, and shallow NN models use features extracted from ultrasonic signals, whereas the DNN model processes time-domain ultrasonic signals and frequency spectra directly. The study systematically compared the models’ performance from various perspectives, including model input, prediction performance, and generalization ability. The findings indicate significant variability in model performance, with some ML algorithms achieving very high or very low prediction accuracy depending on the preprocessing and feature engineering (extraction and selection) applied. Key insights include the observation that shallow ML models (LR, SVR, and shallow NNs) require meticulous preprocessing and feature extraction to achieve high accuracy. In contrast, the DNN model, although it bypasses the need for feature engineering, necessitates extensive preprocessing to mitigate noise and computational demands. The SVR model emerged as the top performer among the shallow models, and the DNN model exhibited superior performance on specific datasets but struggled with generalization across specimens from different batches. Additionally, the SVR model is sensitive to temperature variations, whereas the DNN model is robust in this regard. Using recurrent neural networks is recommended for future ASR expansion prediction studies. Recurrent neural networks’ inherent ability to capture temporal dependencies and long-term patterns makes them well suited for analyzing sequential ultrasonic monitoring data. Overall, the results and conclusions of this study could provide insights into the capabilities and effectiveness of ML when applied to ultrasonic NDE data and help identify best practices for using ML for ultrasonic NDE of concrete material properties.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Synthesis and Crystal Structures of Alkali (Na, Rb, Cs) Iron Phosphate Crystals

Three alkali-containing diphosphate crystals of A FeP 2 O 7 (A = Na, Rb, Cs) were synthesized through melt-crystallization. The crystal structures were determined using single-crystal X-ray diffraction (XRD) data. The synthesized diphosphates crystallized within the P 2 1 /c space group, and the structural parameters were compared to literature data of other A FeP 2 O 7 crystals within the P 2 1 /c space group.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Time-Evolved Hetero-Alkali Interphases Enable Long-Life Sulfide-Based Anode-Free Solid-State Batteries

Sulfide-based anode-free solid-state batteries (AFSSBs) offer compelling advantages in terms of energy density and safety, yet their practical implementation is severely hindered by undesirable interfacial reactions between sulfide solid electrolytes (SEs) and freshly plated lithium (Li), as well as non-uniform Li plating/stripping behavior. Herein, an effective interfacial stabilization strategy by incorporating sodium bis(fluorosulfonyl)imide (NaFSI) additive into the Li5.4PS4.4Cl1.6 (LPSC) is investigated. Unlike conventional Li-based additives that form static passivation layers, NaFSI introduces a transient hetero-alkali chemistry that kinetically governs interphase evolution during fresh Li plating. NaFSI induces a timesequenced interphase evolution: an initial NaF/LiF-rich layer that suppresses early sulfide reduction, followed by a LiF/Li3N-rich layer that optimizes Li⁺ transport during repeated anode-free cycling. This evolved robust and fast ion conducting layer mitigates interfacial impedance growth, enhances Li + transport kinetics, and suppresses localized Li growth and filamentary shorting. As a result, the anode-free full cell with NaFSI modified LPSC as the interlayer exhibits an excellent cycling stability over 500 cycles at 0.2 C with a capacity retention of 77.6%, whereas the cell with bare LPSC suffers from rapid capacity decay after 100 cycles, retaining only 32.1% of its initial capacity. This work establishes dynamic heteroalkali additive chemistry as a general strategy to kinetically program solid-solid interphases, guiding the interface design in anode-free solid-state batteries.

25 ENERGY STORAGE↗
DOE OSTI2025

Synthesis and characterization of uranium trichloride in alkali-metal chloride media

Given a growing interest in uranium salts for pyrochemical processing of used fuel and uranium-fueled molten salt reactors, the synthesis of uranium trichloride in alkali-metal chloride media was investigated in a series of four experiments. Specifically, uranium metal powder and uranium hydride powder were prepared and separately blended with ammonium chloride and lithium chloride – potassium chloride eutectic in two runs, while the same powders were separately blended with ammonium chloride and sodium chloride in two additional runs. Each of the lithium chloride – potassium chloride containing blends was slowly heated to 923 K, while those containing sodium chloride were heated to 1123 K. During each heat up, the ammonium chloride sublimed into gaseous ammonia and hydrogen chloride, leading to the chlorination of uranium metal or uranium hydride and the formation of molten salt solutions of the respective chlorides. Experimental conditions were incorporated in the runs to promote formation of uranium trichloride over uranium tetrachloride in the respective media. Molten samples of each run product were taken and characterized via chemical analyses, diffractometry, and microscopy. The final products from each run were dark dense ingots of the respective salt systems with uranium concentrations ranging from 44 to 51 wt%. Chemical analyses and diffractometry identified the predominant presence of uranium trichloride in these systems; however, a possible minor presence of uranium tetrachloride could not be conclusively dismissed.

D Herrmann, Steven↗
DOE OSTI2022

The role of alkali metal exchange in zeolite-templated carbon synthesis

Despite great efforts to achieve ideal atomistic packing of carbon in the pore networks of even the largest pore zeolites, templating inaccuracies are ubiquitous in all presently synthesized zeolite-templated carbon (ZTC) materials. Idealized models suggest that the long-sought schwarzite-like structures could in principle be obtained by zeolite templating if the appropriate zeolite template were chosen (e.g., faujasite for D surface schwarzites) and if perfect template fidelity (insertion of a pristine layer of pure carbon directly on the surface of the zeolite) could be achieved. A requirement to achieve such structures is increased carbon density within the zeolite. We report the investigation of a series of alkali metal cation-exchanged zeolites to determine how the periodic trends in the group 1 elements influence zeolite templating, with a specific focus on the metric of structural packing density (SPD) as resolved by ex situ thermogravimetry. In a survey based on controlled synthesis temperature, time, and flow conditions, an increasing SPD was observed with decreasing cation size, an effect that is consistent with the increasing strength of cation-π interactions. Furthermore, this effect could be promising for future work to increase the SPD of ZTCs for the synthesis of closed-tube, schwarzite-like carbonaceous solids.

03 NATURAL GAS↗
DOE OSTI2021

Alkali metal-sulfur secondary battery containing cathode material particulates

Provided is a rechargeable alkali metal-sulfur cell comprising an anode active material layer, an electrolyte, and a cathode active material layer comprising multiple particulates, wherein at least one of the particulates comprises one or a plurality of sulfur-containing material particles being embraced or encapsulated by a thin layer of a conductive sulfonated elastomer composite having from 0.01% to 50% by weight of a conductive reinforcement material dispersed in a sulfonated elastomeric matrix material, wherein the conductive reinforcement material is selected from graphene sheets, carbon nanotubes, carbon nanofibers, metal nanowires, conductive polymer fibers, or a combination thereof and the composite has a recoverable tensile strain from 2% to 500%, a lithium ion conductivity from 10−7 S/cm to 5×10−2 S/cm, and a thickness from 0.5 nm to 10 μm. This battery exhibits an excellent combination of high sulfur content, high sulfur utilization efficiency, high energy density, and long cycle life.

Zhamu, Aruna↗
DOE OSTI2020

Influence of mineralogical and chemical compositions on alkali-silica-reaction of Tennessee limestones

The paper aims to identify potential correlations between mineralogical and chemical compositions of AAR reactive dolomitic limestone aggregates from Tennessee and their expansion on ASTM C1293 for quick evaluation of reactivity. The chemical compositions of 26 limestones, namely the silica content (SiO 2 ), showed a strong correlation with the expansion in the concrete prism test. The early and highly expansive limestones are characterized by a high clay/ mica content (>5%). The reactivity of Tennessee limestones is mainly caused by crypto/microcrystalline quartz, chert, and finely disseminated silica in carbonate and clay/mica matrices, and it can be reasonably evaluated using X-ray diffraction and chemical analysis coupled with petrographic examinations.

36 MATERIALS SCIENCE↗
DOE OSTI2021

The role of environmental conditions on the carbonation of an alkali-activated cementitious waste form

Cast Stone is a cementitious waste form being considered for the solidification of low activity waste at the DOE Hanford Site. Under near-surface disposal conditions in an arid environment, Cast Stone is subject to drying and carbonation which may impact retention of waste constituents. Here this study investigates the effects of environmental CO 2 concentration and relative humidity (RH) on the carbonation of Cast Stone. The rate of carbonation front ingress and the extent of carbonation reaction were characterized for samples aged up to 48 weeks at three RH levels and two CO 2 concentrations. While the 68% RH environment allowed the greatest reaction extent, the 15% RH environment yielded the deepest carbonation front ingress. At 68% RH, there was a linear relationship between the ingress rate and CO 2 concentration. Carbonation reactions increased the drying rate of Cast Stone. Redistribution of sodium toward the wetting front was observed under drying and carbonation conditions.

36 MATERIALS SCIENCE↗