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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

FY25 status report on the addition of candidate materials in Class B Code Case

This report provides the time-dependent allowable stress calculation strategy leveraging the limited creep rupture tests data generated to support the allowable stress for 100,000 hours in American Society of Mechanical Engineers (ASME) Boiler and Pressure Vessel Code (BPVC), Section II, Part D. A variable confidence index procedure to extrapolate material properties to support 500,000 hours design life is discussed. Time-dependent allowable stresses for Class B component design and analysis are presented for Grade 1 and Grade 2 of Alloy 625. The presented data extrapolation and allowable stress calculation method will support new material addition using limited creep rupture data in the new ASME Boiler and BPVC, Section III, Division 5, Class B rules.

Part D↗
DOE OSTI2023

Revealing Variable Dependences in Hexagonal Boron Nitride Synthesis via Machine Learning

Wafer-scale monolayer two-dimensional (2D) materials have been realized by epitaxial chemical vapor deposition (CVD) in recent years. To scale up the synthesis of 2D materials, a systematic analysis of how the growth dynamics depend on the growth parameters is essential to unravel its mechanisms. However, the studies of CVD-grown 2D materials mostly adopted the control variate method and considered each parameter as an independent variable, which is not comprehensive for 2D materials growth optimization. Herein, we synthesized a representative 2D material, monolayer hexagonal boron nitride (hBN), on single-crystalline Cu (111) by epitaxial chemical vapor deposition and varied the growth parameters to regulate the hBN domain sizes. Furthermore, we explored the correlation between two growth parameters and provided the growth windows for large flake sizes by the Gaussian process. Here, this new analysis approach based on machine learning provides a more comprehensive understanding of the growth mechanism for 2D materials.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Tunable negative coefficient thermal expansion materials and composites

The present disclosure is directed to variable composition ceramics. Zr (2−x) Hf (x) WP 2 O 12 and Hf 2 WP 2 O 12 exhibit large negative thermal expansion that is linear over a large temperature range up to at least 900° C., These new ceramic material particles may be mixed with polymers to make a composite suitable for use in backsheets for photovoltaic modules or in other applications. The thermal expansion coefficient of the composite can be tailored to match that of the solar cell in order to reduce stress resulting from daily thermal cycling.

Gordon, Margaret Ellen↗
DOE OSTI2025

Multiscale and Machine Learning Modeling for Process-informed Microstructure Prediction in Additively Manufactured Materials using MALAMUTE

The Advanced Materials and Manufacturing Technologies (AMMT) program under the Department of Energy Office of Nuclear Energy aims to develop and qualify additively manufactured materials for nuclear applications. One key challenge to this is the microstructural variability observed in the additively manufactured products and their impact on the properties and performance of the material in extreme environments. AMMT is using a combination of high-throughput experimental and modeling techniques to accelerate qualification. Conventionally, in-situ and ex-situ characterizations and testing are performed to correlate different aspects of the additive manufacturing process to the final product and its performance. However, adopting a trial-and-error approach to experimentally evaluate the vast range of process parameters required to capture microstructural variability is cost-prohibitive. Modeling and simulation provide a comparatively inexpensive way to understand and correlate the microstructural evolution to the processing conditions. The modeling and simulation work-packages within the AMMT program aims to use physics-based and machine learning models to develop a digital twin for additive manufacturing that can correlate the process conditions to the final product and establish a process-structure-property-performance (PSPP) correlation. The melting and subsequent solidification that occurs during the additive process is a complex phenomenon that requires multiscale multiphysics analysis. This work package focuses on understanding the role of process variabilities on the unique microstructural characteristics of additively manufactured materials. Microstructural features at the subgrain level, such as compositional micro-heterogeneity and dislocation cells, are of particular interest here since they can influence the creep properties and radiation performance. Idaho National Laboratory’s Multiphysics Object-Oriented Simulation Environment (MOOSE), specifically the MOOSE Application Library for Advanced Manufacturing UTilitiEs (MALAMUTE) software, provides an ideal platform for developing the multiphysics multiscale model to explore the intricacies of the microstructural evolution during the AM processes within a single framework. Furthermore, given that such full-fidelity simulations can be computationally intensive, reduced order models are necessary to explore the PSPP space for additively manufactured materials in an efficient, reliable, and cost-effective way. This work focuses on capturing the microstructural variabilities at the subgrain level that are often missing in the part-scale models. In fiscal year 2025, we significantly advanced upon our work in the last fiscal year, in terms of the predictive capabilities of the physics-based and ML models, by adding the capabilities to capture subgrain-level micro-segregation during solidification using phase-field model and to predict the time-dependent dynamics of the AM process through the MOGPAR model. The alloy solidification model in MOOSE incorporates the thermodynamic properties and free energy relevant to 316 stainless steel. The model demonstrates the Cr and Ni segregation that occurs during solidification, including that the rate of solidification. The microstructural evolution model is connected to the process conditions via the surrogate model developed in this work. This enables predictions of the final microstructure in conjunctions with the manufacturing process. This work supports AMMT's rapid qualification goals by laying the foundation for an efficient and cost-effective model establishing the PSPP correlation for AM. The generated microstructures and predicted micro-segregation can be used by other work packages under AMMT to evaluate the properties and environmental response of the material at the mesoscale. Thus, this work helps to identify the key microstructural features at the subgrain level that are significant in property and performance predictions of additively manufactured components. This work will also provide inputs to the large-scale process variability models to reevaluate and validate assumptions and simplifications made in the part-scale models. Furthermore, through active learning this work can help identify the data need from both modeling and experimental sides for development of a robust digital twin for additive manufacturing and accelerate the AMMT's qualification efforts.

36 - MATERIALS SCIENCE↗
DOE OSTI2025

A TOpographic Mapping (ATOM) Method to Design Magnetic Cores

Wireless power transfer offers safe, convenient, and efficient way of charging electric vehicles. Ongoing research is targeting wireless charging pad design optimization; designing the magnetic component is the most important part of the coupler design because the magnetic part determines the coupling factor and efficiency. Optimizing the coil layout and geometry as well as ferrite design requires finite elements analysis based modeling and simulation for minimized core losses, maximized magnetic coupling, and minimized material use for cost-effectiveness. Although parametric finite element analysis or emerging artificial intelligence methods can generate very accurate results, simulation times are extremely long. To address this issue, this study proposes a simple, effective core design called A TOpographic Mapping (ATOM). The proposed design is based on the design of magnetic core by using the magnetic flux distribution. The thickness of the core increases with increasing magnetic flux density, forming a variable thickness core design with less material and minimized core losses compared to conventional designs. A superimposing method is used to create an optimal design for a rotational magnetic field-based system. According to simulation results, the ATOM design reduces the required material volume by 13.19% and yields the lowest core loss and highest mutual inductance compared to other designs. In addition, misalignment, electromagnetic interference, and thermal performance were evaluated for the proposed design.

Aydin, Emrullah [Oak Ridge National Laboratory (OR↗
DOE OSTI2024

Experimental Examination of Additively Manufactured Patterns on Structural Nuclear Materials for Digital Image Correlation Strain Measurements

Abstract Background There are a limited number of commercially available sensors for monitoring the deformation of materials in-situ during harsh environment applications, such as those found in the nuclear and aerospace industries. Such sensing devices, including weldable strain gauges, extensometers, and linear variable differential transformers, can be destructive to material surfaces being investigated and typically require relatively large surface areas to attach (> 10 mm in length). Digital image correlation (DIC) is a viable, non-contact alternative to in-situ strain deformation. However, it often requires implementing artificial patterns using splattering techniques, which are difficult to reproduce. Objective Additive manufacturing capabilities offer consistent patterns using programmable fabrication methods. Methods In this work, a variety of small-scale periodic patterns with different geometries were printed directly on structural nuclear materials (i.e., stainless steel and aluminum tensile specimens) using an aerosol jet printer (AJP). Unlike other additive manufacturing techniques, AJP offers the advantage of materials selection. DIC was used to track and correlate strain to alternative measurement methods during cyclic loading, and tensile tests (up to 1100 µɛ) at room temperature. Results The results confirmed AJP has better control of pattern parameters for small fields of view and facilitate the ability of DIC algorithms to adequately process patterns with periodicity. More specifically, the printed 100 μm spaced dot and 150 μm spaced line patterns provided accurate measurements with a maximum error of less than 2% and 4% on aluminum samples when compared to an extensometer and commercially available strain gauges. Conclusion Our results highlight a new pattern fabrication technique that is form factor friendly for digital image correlation in nuclear applications.

Novich, K. A. (ORCID:0000000204466022)↗
DOE OSTI2023

A Spectroscopic Study of Mars-analog Materials with Amorphous Sulfate and Chloride Phases: Implications for Detecting Amorphous Materials on the Martian Surface

The Chemistry and Mineralogy X-ray diffraction (XRD) instrument aboard the Curiosity rover consistently identifies amorphous material at Gale Crater, which is compositionally variable, but often includes elevated sulfur and iron, suggesting that amorphous ferric sulfate (AFS) may be present. Understanding how desiccating ferric sulfate brines affect the spectra of Martian material analogs is necessary for interpreting complex/realistic reaction assemblages. Visible and near-infrared reflectance (VNIR), mid-infrared attenuated total reflectance (MIR, FTIR-ATR), and Raman spectra, along with XRD data are presented for basaltic glass, hematite, gypsum, nontronite, and magnesite, each at three grain sizes (<25, 25–63, and 63–180 μm), mixed with ferric sulfate (+/–NaCl), deliquesced, then rapidly desiccated in 11% relative humidity or via vacuum. All desiccated products are partially or completely XRD amorphous; crystalline phases include starting materials and trace precipitates, leaving the bulk of the ferric sulfate in the amorphous fraction. Due to considerable spectral masking, AFS detectability is highly dependent on spectroscopic technique and minerals present. This has strong implications for remote and in situ observations of Martian samples that include an amorphous component. AFS is only identifiable in VNIR spectra for magnesite, nontronite, and gypsum samples; hematite and basaltic glass samples appear similar to pure materials. Sulfate features dominate Raman spectra for nontronite and basaltic glass samples; the analog material dominates Raman spectra of hematite and gypsum samples. MIR data are least affected by masking, but basaltic glass is almost undetectable in MIR spectra of those mixtures. NaCl produces similar FTIR-ATR and Raman features, regardless of analog material.

58 GEOSCIENCES↗
DOE OSTI2025

Computational materials reliability assessment of hydrogen fueled gas turbine power generation engines

The use of blended fuel sources in land based gas turbine engines drives variations in the resulting operational profile (temperatures and pressures) which can impact engine reliability. Furthermore, variability in the manufacture of components affects the resulting microstructure which directly impacts material performance and reliability. Currently, data-driven models are typically used for maintaining and inspecting fleets of engines. Without explicitly capturing material and operational sources of variability conservatism must be used in developing component-level reliability models. Therefore, there exists an opportunity to use information from materials-scale physics models to better inform reliability modeling and reduce conservatism; the impact is more cost-efficient operation and maintenance of current and future fleets. Specifically, this work establishes a computational framework for evaluating the probabilistic high temperature creep performance of hot-section Ni-based superalloys where uncertainty comes from both microstructural and operational variability. A novel high-fidelity physics model which phenomenologically captures grain-boundary sensitive phenomena has been established. A probabilistic calibration procedure was used to calibrate the model and capture uncertainty in the parameterized model coefficients. A design of experiments methodology was established for identifying informative microstructural digital representations for suitable for forward model evaluation. Results show that training a machine-learning surrogate using this design criteria outperforms random selection of microstructural representations. Finally, two surrogate models were developed: (1) a deterministic surrogate model which predicts the local field response given microstructure, constitutive model parameters, and operating conditions (stress, temperature) and (2) a probabilistic model, where uncertainty comes from constitutive law uncertainty, built using denoising diffusion probabilistic models which samples responses given (1) microstructure and (2) operating conditions. These surrogate models enable partner Siemens Energy to rapidly perform UQ analysis specific to creep deformation across a range of microstructures and operating conditions. The impact is that these ML and physics codes can be used to establish more advanced reliability models for the inspection, servicing, and maintenance of land based gas turbine engines.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Liquid Alkaline Water Electrolyzers: Comparing Performance across Design, Operation, and End-of-Life Scenarios

Liquid alkaline water electrolysis (LAWE) is a demonstrated technology for hydrogen production, yet a comprehensive life cycle assessment (LCA) of their deployment is lacking. Research leading to improvements to the core component, the electrochemical stack, along with auxiliary system materials and dynamic operation of stacks from variable electricity supply offers new data that allows for detailed modeling and evaluation. Here, we present an LCA of two facility designs based on the current state-of-the-art stack and an advanced stack with zero-gap between electrodes, and capture dynamic electricity use from solar, wind, and hybrid sources and stack recycling strategies. We present life cycle impact factors characterizing the production of 1 kg of hydrogen across 12 environmental, human health, and resource impact categories (TRACI and ReCiPe) in the contiguous United States. As expected, the source of electricity will drive impacts (e.g., 83-94% of carbon intensity); however, we find that operating using wind electricity can lower hydrogen leakage and the overall carbon intensity (1.03 kgCO 2 e/kgH 2 ) relative to solar electricity (2.57 kgCO 2 e/kgH 2 ) at matched 1:1 capacity between LAWE and the electricity source. The deployment of the advanced design and stack recycling lowers impacts across all life cycle stages. We highlight opportunities to further reduce potential impacts, including the balance of plant materials and operation cycles associated with the use of variable wind and solar electricity that result in hydrogen leakage.

08 HYDROGEN↗
DOE OSTI2024

Uncertainty quantification in multivariable regression for material property prediction with Bayesian neural networks

With the increased use of data-driven approaches and machine learning-based methods in material science, the importance of reliable uncertainty quantification (UQ) of the predicted variables for informed decision-making cannot be overstated. UQ in material property prediction poses unique challenges, including multi-scale and multi-physics nature of materials, intricate interactions between numerous factors, limited availability of large curated datasets, etc. In this work, we introduce a physics-informed Bayesian Neural Networks (BNNs) approach for UQ, which integrates knowledge from governing laws in materials to guide the models toward physically consistent predictions. To evaluate the approach, we present case studies for predicting the creep rupture life of steel alloys. Experimental validation with three datasets of creep tests demonstrates that this method produces point predictions and uncertainty estimations that are competitive or exceed the performance of conventional UQ methods such as Gaussian Process Regression. Additionally, we evaluate the suitability of employing UQ in an active learning scenario and report competitive performance. The most promising framework for creep life prediction is BNNs based on Markov Chain Monte Carlo approximation of the posterior distribution of network parameters, as it provided more reliable results in comparison to BNNs based on variational inference approximation or related NNs with probabilistic outputs.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Enhanced superconductivity and ferroelectric quantum criticality in plastically deformed strontium titanate

The properties of quantum materials are commonly tuned using experimental variables such as pressure, magnetic field and doping. Here we explore a different approach using irreversible, plastic deformation of single crystals. We show that compressive plastic deformation induces low-dimensional superconductivity well above the superconducting transition temperature (Tc) of undeformed SrTiO3, with evidence of possible superconducting correlations at temperatures two orders of magnitude above the bulk Tc. The enhanced superconductivity is correlated with the appearance of self-organized dislocation structures, as revealed by diffuse neutron and X-ray scattering. We also observe deformation-induced signatures of quantum-critical ferroelectric fluctuations and inhomogeneous ferroelectric order using Raman scattering. Our results suggest that strain surrounding the self-organized dislocation structures induces local ferroelectricity and quantum-critical dynamics that strongly influence Tc, consistent with a theory of superconductivity enhanced by soft polar fluctuations. Our results demonstrate the potential of plastic deformation and dislocation engineering for the manipulation of electronic properties of quantum materials.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2022

Comparing Calibration Algorithms for the Rapid Characterization of Pretreated Corn Stover Using Near-Infrared Spectroscopy

Rapid characterization of biomass composition is a key enabling technology for biorefineries—the ability to measure the chemical composition of biomass materials entering the biorefinery as well as the composition of key process intermediate streams would allow real-time process control and the development of robust models to predict process performance. The utility of near-infrared (NIR) spectroscopy for rapid characterization requires multivariate algorithms for building calibration models. The most prevalent algorithm used for building calibration models using NIR spectra is the linear modeling algorithm Partial Least Squares Regression (PLS). Nonlinear regression algorithms (which are typically more computationally intensive than linear modeling approaches) have gained popularity in recent years due to their ability to solve a wide variety of classification and regression problems and the dramatic increase in available computational resources. In this work, we demonstrate that a calibration model can predict the composition of corn stover process intermediate samples pretreated with three different treatments—hot water (HW), dilute acid (DA), and deacetylation followed by dilute acid (DDA). We quantitatively compare three different algorithms for building prediction models based on near-infrared spectroscopy—partial least squares (PLS), support vector machines (SVM), and random forests (RF). We demonstrate the utility of improving model performance by accounting for instrument performance variability using repeated measurements of standard materials (e.g., the “repeatability file” strategy) and investigate its performance with nonlinear regression techniques, and we discuss methods for quantifying the uncertainties of specific predictions among the three methods.

09 BIOMASS FUELS↗
DOE OSTI2021

Tailoring Cementitious Materials Towards Value-Added Use of Large CO 2 Volumes

Hydraulic cements with alternative chemistries were developed for large-volume and value-added use of carbon dioxide. The hydraulic cements were processed using the energy-efficient mechanochemical technique at room temperature and atmospheric pressure. Carbon dioxide was captured directly from combustion emissions during processing. For this purpose, mechanochemical processing of hydraulic cements was accomplished under a flow of combustion emissions prior to the release of emissions to the atmosphere. The process removed a significant fraction of carbon dioxide from combustion emissions. The hydraulic cements captured carbon dioxide at about 10% of their weight. Two hydraulic cement chemistries were developed, and their mechanochemical processing was successfully scaled-up. The characteristic feature of one chemistry was its relatively low (near-neutral) pH where the integration of carbon dioxide yielded clear value. The second cement chemistry was based on alkali activation of industrial wastes. This chemistry could make value-added use of carbon dioxide, but it had to be refined to control the pH drop caused by CO 2 integration. These two cements render binding effects upon hydration by forming a combination of stable carbonates and aluminosilicates or phosphates. Efforts to develop cement chemistries based solely on carbonates were not successful. The mechanochemical process was found to integrate carbon dioxide into the alternative cement chemistries in the form of disordered and metastable carbonates. During hydration reactions, the disordered/metastable carbonates are either transformed into stable carbonate phases with desired binding efforts, or carbonates get integrated into the primary inorganic binders (hydrates). When compared with mechanochemical processing in pure carbon dioxide, mechanochemical processing in combustion emissions produced hydraulic cements with improved engineering properties. The mechanochemical process was scaled-up, and its variables as well as the raw materials formulations were optimized for implementation at pilot scale. Scale-up was found to enhance the carbon capture potential and the engineering qualities of the resulting hydraulic cements. This was because scale-up raises the intensity of mechanical energy input to raw materials. Certain mechanochemical phenomena cannot be induced, irrespective of the cumulative mechanical energy input, unless the intensity of impact is raised above a minimum level that cannot be achieved in laboratory-scale implementation of the process. Due to this effect, the duration of the mechanochemical process as well as its energy demand could be reduced significantly (by an order of magnitude) upon transition from laboratory to pilot scale, with the hydraulic cements produced at pilot scale offering engineering properties that were superior to those realized in laboratory-scale mechanochemical processing. The hydraulic cements produced at pilot scale via mechanochemical processing under a flow of combustion emissions were thoroughly characterized. They were found to meet standard requirements for ‘General Use’ hydraulic cements. They were compatible with the industrial-scale concrete production and construction practices that are used with the currently prevalent hydraulic cements. The new hydraulic cements with integrated carbon dioxide were found to offer distinct advantages over the currently prevalent Portland cement in terms of net carbon footprint and energy content. The combined raw materials and energy costs of the new hydraulic cements are competitive, and major cost savings can be realized because of the simplified production process that significantly lowers the capital investment in cement production plants. Mechanochemical processing of the new hydraulic cements under a flow of combustion emissions can be implemented using some existing components of cement manufacturing plants; this facilitates adoption of the technology by the cement industry.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Multi-material direct ink writing and co-sintering of gadolinium oxide – zirconium oxide components

Methods for fabrication of multi-material or functionally graded ceramic composite architectures are of interest for numerous applications. However, conventional co-sintering of multi-material ceramic parts is a challenge because differences in the sintering behavior of the two materials leads to interfacial strain and, ultimately, component failure. Direct ink writing (DIW) is an extrusion-based additive manufacturing process that excels at multi-material printing because multiple extrusion nozzles can be installed on the same gantry system. Furthermore, the use of DIW as a method to fabricate multi-material ceramic green bodies offers an additional variable for controlling and potentially matching sintering kinetics in the slurry formulation used for two dissimilar feedstocks. In the work documented in this manuscript, we explored two strategies to successfully co-sinter multi-material ceramic oxides: slurry optimization to match sintering kinetics and material gradients to step from one material to another. This manuscript also quantifies the allowable mismatch that avoids part cracking in solid solution forming multi-material systems and discusses best strategies to reduce mismatch during co-sintering. Inks composed of gadolinium oxide (Gd 2 O 3 ) and zirconium oxide (ZrO 2 ), a surrogate for uranium oxide (UO 2 ), were thermally matched, which resulted in a sintering mismatch reduction of over 10%. It was found that ~1% mismatch is tolerable during debind cycles and that ~5% mismatch is manageable during sintering cycles after slurry formulations are optimized to match the sintering behavior. Use of continuous gradients is shown to reduce sintering mismatch, although geometric resolution may be lost due to solid solution formation.

Snarr, Patrick L. [Oak Ridge National Laboratory (↗
DOE OSTI2022

Clean quartz matters for cosmogenic nuclide analyses: An exploration of the importance of sample purity using the CRONUS-N reference material

We report reference materials are key for assessing inter-laboratory variability and measurement quality, and for placing analytical uncertainty bounds on sample analyses. Here, we investigate four years of data resulting from repeated processing of the CRONUS-N reference material for cosmogenic 10 Be and 26 Al analyses. At University of Vermont, we prepared a CRONUS-N aliquot with most of our sample batches from 2013 to 2017; these reference material samples were then distributed to four different accelerator mass spectrometry facilities, yielding 73 10 Be analyses and 58 26 Al analyses. We determine CRONUS-N 10 Be concentrations of (2.26 ± 0.14) x 10 5 atoms g -1 (n = 73, mean, 1 SD) and 26 Al concentrations of (1.00 ± 0.08) x 10 6 atoms g -1 (n = 58, mean, 1 SD). We find a reproducibility of 6.3% for 10 Be and 7.7% for 26 Al (relative standard deviations). We also document highly variable 27 Al and Mg concentrations and a 10 Be dispersion twice as large as the mean AMS analytic uncertainty. Analyses of the CRONUS-N material with and without density separation demonstrate that non-quartz minerals are present in the material and have a large impact on measured concentrations of 27 Al, 10 Be, and impurities; these non-quartz minerals represent only a very small portion of the total mass (0.6–0.8%) but have a disproportionally large effect on the resulting data. Our results highlight the importance of completely removing all non-quartz mineral phases from samples prior to Be/Al extraction for the determination of in situ cosmogenic 10 Be and 26 Al concentrations.

58 GEOSCIENCES↗
DOE OSTI2021

In Situ X-ray Diffraction of High Density Polyethylene During Dynamic Compression: Polymer Chain Compression and Phase Transformations

Polymers are widely implemented materials often used for safety and security in our daily lives. However, we do not have a thorough understanding of the phase transformations of these materials when exposed to extreme condition, high strain rate, events. To study polymers in these severe environments, we performed plate impact experiments that impart a one-dimensional shock wave, at high pressures and temperatures, to observe the materials response. Generally, velocimetry is the experimental diagnostic used to capture the movement of high velocity interfaces to then calculate the mechanical variables associated with shock compression. However, when a material undergoes a phase transition, such as between two crystalline phases or melting to decomposition during shock compression, velocimetry does not explicitly indicate what structural change occurred. At the Dynamic Compression Sector at the Advanced Photon Source gas gun driven compression has been paired with x-ray diffraction, providing the ability to study both the shock parameters and structural changes concurrently. We explored high density polyethylene (HDPE) both on its reactants Hugoniot (<10.12 GPa) and within its transition region (24.3 GPa) where HDPE is hypothesized to decompose from reactants to products. With x-ray diffraction we observed both polymer chain compression and crystal structure reorientation from orthorhombic to monoclinic above 6 GPa, as well as either a possible phase change or chemical decomposition at 24.3 GPa. With these experiments we were able to determine the crystalline and amorphous Hugoniot loci at pressures below the polymer transition region.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Heterogeneity of the Dominant Causes of Performance Loss in End-of-Life Cathodes and Their Consequences for Direct Recycling

Recycling Li-ion batteries from electric vehicles is critical for reducing costs and supporting the development of a domestic battery supply chain. Direct recycling of cathodes, like LiNixMnyCozO2 (NMC), is attractive due to its low cost, energy use, and emissions compared to traditional recycling techniques. However, a comprehensive understanding of the active material properties at end-of-life is needed to guide direct recycling processes and the performance-dependent reuse applications. Here, NMC material from an end-of-life commercial pouch cell is characterized and bench-marked against pristine non-cycled counterparts with respect to capacity, impedance, crystallography, morphology, and microstructure to identify major degradation modes and understand variability in the end-of-life material. The spatial heterogeneity of each property throughout the cell is also quantified. While the degraded material demonstrated similar capacity as the pristine, its impedance and rate capability are severely diminished. Furthermore, samples from the periphery of the electrode layers showed more severe performance loss compared to samples extracted from central regions. The dominant culprit of performance loss is the material microstructure, where the magnitude of particle cracking showed the strongest correlation to the impedance components that are most unfavorably impacted. This work suggests severe cracks in cathode active materials are the primary challenge that direct recycling methods must overcome.

25 ENERGY STORAGE↗