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CRADA No. TC02400 Final Report
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SENTRA: A Modular Computational Graph Framework for Critical Mineral and Materials Supply Chains: Part I: Network Construction Latent-Quantity Estimation, and Temporal Graph Forecasting
Global supply chains for critical minerals and materials are complex, evolving networks of countries, products, production stages, and trade relationships. Existing analytical approaches are limited by fragmented data and static network representations that do not capture the dynamic production dependencies linking raw materials, intermediate products, and final goods across multiple countries. Trade and production statistics provide only a partial view of domestic production, inventories, and material flows, making it difficult to identify indirect sourcing pathways, hidden dependencies, and embedded foreign exposures. This paper introduces the Supply Chain Exposure Network Tracking and Risk Assessment (SENTRA) framework, a modular graph-based computational framework for constructing, analyzing, and forecasting dynamic supply chain networks. As the first paper in a three-part methodological series, it establishes the computational foundation of SENTRA by constructing a temporal attributed multi-relational graph whose nodes represent product–country pairs and whose edges encode observed trade and within-country value-chain relationships. Statistical estimation and constrained optimization recover latent production, final demand, and product input dependency coefficients while enforcing economic accounting constraints. Graph-derived exposure measures quantify direct, transshipment, value-chain, and multi-hop supply chain dependencies independently of the forecasting model. A temporal graph forecasting architecture based on a relational graph neural network then forecasts the evolution of the graph under mass-balance constraints with distribution-free conformal uncertainty quantification. Validation on the global aluminum supply chain shows that the learned graph representations recover economically meaningful supply chain structure, accurately forecast out-of-sample trade relationships, and produce well-calibrated prediction intervals. Subsequent papers apply this computational foundation to exposure assessment, disruption analysis, and scenario-based policy analysis, and extend the framework to multimaterial supply chain modeling and decision support.
2D Carbon Phosphide for Trapping Sulfur in Rechargeable Li–S Batteries: Structure Design and Interfacial Chemistry
Not provided.
A novel digital lifecycle for Material‐Process‐Microstructure‐Performance relationships of thermoplastic olefins foams manufactured via supercritical fluid assisted foam injection molding
Abstract This research significantly enhances the applicability of thermoplastic olefins (TPOs) in the automotive industry using supercritical N 2 as a physical foaming agent, effectively addressing the limitations of traditional chemical agents. It merges experimental results with simulations to establish detailed material‐process‐microstructure‐performance (MP2) relationships, targeting 5–20% weight reductions. This innovative approach labeled digital lifecycle (DLC) helps accurately predict tensile, flexural, and impact properties based on the foam microstructure, along with experimentally demonstrating improved paintability. The study combines process simulations with finite element models to develop a comprehensive digital model for accurately predicting mechanical properties. Our findings demonstrate a strong correlation between simulated and experimental data, with about a 5% error across various weight reduction targets, marking significant improvements over existing analytical models. This research highlights the efficacy of physical foaming agents in TPO enhancement and emphasizes the importance of integrating experimental and simulation methods to capture the underlying foaming mechanism to establish material‐process‐microstructure‐performance (MP2) relationships. Highlights Establishes a material‐process‐microstructure‐performance (MP2) for TPO foams Sustainably produces TPO foams using supercritical (ScF) N 2 with 20% lightweighting Shows enhanced paintability for TPO foam improved surface aesthetics Digital lifecycle (DLC) that predicts both foam microstructure and properties DLC maps process effects & microstructure onto FEA mesh for precise prediction
Dynamics of Radiation Damage Buildup in Ultrathin Hexagonal Boron Nitride Films under Ion Bombardment
Two-dimensional hexagonal boron nitride (hBN) is attractive for several emerging applications. Ion bombardment can be used to modify the hBN properties. However, the understanding of radiation damage buildup in hBN remains limited. Here, we investigate the effects of the dose rate and ion mass on radiation damage buildup by studying 40 nm-thick hBN films bombarded at room temperature with 500 keV 4 He, 15 N, 40 Ar, and 129 Xe ions and comparing with results for ion bombardment of polycrystalline hBN ceramics. Raman spectroscopy is used to quantify damage buildup, and transmission electron microscopy is used for microstructural analysis. Experiments are complemented by molecular dynamics simulations of the formation and evolution of point defects. Lighter ions are found to be more efficient at disordering hBN than heavier ions. This observation points to a critical role of intracascade defect processes. In contrast, a negligible dose rate effect observed suggests limited intercascade defect dynamic annealing processes for these irradiation conditions. These findings provide a fundamental basis for hBN defect engineering.
Polarized Resonant Soft X-ray Scattering (P-RSoXS) as a New Technique for Characterizing Amorphous Astromaterials
In the coming years, samples will be returned from several asteroids, the lunar surface, and the first material returned directly from the Martian surface. Previous in-situ and remote studies indicate these samples will contain abundant amorphous or weakly crystalline materials. However, detailed characterization and quantification of these amorphous materials remains challenging. Conventional techniques, including electron microscopy and X-ray diffraction, provide important information on material structure, but are generally limited to crystalline materials. Polarized resonant soft X-ray scattering (P-RSoXS) is a synchrotron-based X-ray scattering technique that has been used to characterize and quantify weakly crystalline systems, including soft materials. This research aims to translate this technique to geologic and extraterrestrial materials, building off the knowledge of using P-RSoXS to interrogate soft materials. P-RSoXS is well-suited to interrogate geologic materials, which are often multiphase, heterogeneous systems with crystalline and amorphous components, and domain sizes on the order of tens of nanometers. It is anticipated that future work will help reveal new chemical and structural information in geologic materials. Maturation of this preliminary work to develop P-RSoXS to characterize astromaterial-relevant samples will provide new understanding of the secondary processes responsible for the development of amorphous astromaterials and further elucidate our knowledge of the geologic history and past alteration processes.
Atomistic Simulation of Glasses and Amorphous Materials: Challenges and Opportunities for the Next Decade
Atomistic simulations have become indispensable tools for understanding glass structure, dynamics, and properties, yet persistent challenges limit their predictive power. This perspective examines three interconnected issues, namely glass formation procedures, interatomic potential development, and machine learning applications, which emerged from the 5th International Workshop on Challenges of Atomistic Simulations of Glasses and Amorphous Materials. We identify convergent community priorities for (i) standardized validation protocols, (ii) curated benchmark datasets with complete metadata, and (iii) open repositories for glasses. A systematic was forward is provided by a hierarchical validation framework for assessing the structural fidelity, property prediction, and behavioral realism of simulation techniques. Looking ahead, transformative advances are promised by the fusion of classical techniques with machine learning based approaches, for instance, by integrating swap Monte Carlo with machine-learning (ML) potentials, leveraging foundation models through transfer learning, and finetuning ML potentials with experimental data. Progress depends on the community committing to validated models, reproducible protocols, and sustained data sharing.
Oxidative Electrochemical Coupling Between Bi and Mn in Rechargeable Alkaline MnO2 Cathodes
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Isotope engineering for spin defects in van der Waals materials
Abstract Spin defects in van der Waals materials offer a promising platform for advancing quantum technologies. Here, we propose and demonstrate a powerful technique based on isotope engineering of host materials to significantly enhance the coherence properties of embedded spin defects. Focusing on the recently-discovered negatively charged boron vacancy center ($${{{{{{{{\rm{V}}}}}}}}}_{{{{{{{{\rm{B}}}}}}}}}^{-}$$ V B − ) in hexagonal boron nitride (hBN), we grow isotopically purified h 10 B 15 N crystals. Compared to$${{{{{{{{\rm{V}}}}}}}}}_{{{{{{{{\rm{B}}}}}}}}}^{-}$$ V B − in hBN with the natural distribution of isotopes, we observe substantially narrower and less crowded$${{{{{{{{\rm{V}}}}}}}}}_{{{{{{{{\rm{B}}}}}}}}}^{-}$$ V B − spin transitions as well as extended coherence timeT 2 and relaxation timeT 1 . For quantum sensing,$${{{{{{{{\rm{V}}}}}}}}}_{{{{{{{{\rm{B}}}}}}}}}^{-}$$ V B − centers in our h 10 B 15 N samples exhibit a factor of 4 (2) enhancement in DC (AC) magnetic field sensitivity. For additional quantum resources, the individual addressability of the$${{{{{{{{\rm{V}}}}}}}}}_{{{{{{{{\rm{B}}}}}}}}}^{-}$$ V B − hyperfine levels enables the dynamical polarization and coherent control of the three nearest-neighbor 15 N nuclear spins. Our results demonstrate the power of isotope engineering for enhancing the properties of quantum spin defects in hBN, and can be readily extended to improving spin qubits in a broad family of van der Waals materials.
Li 2 FeCl 4 as a Cost-Effective and Durable Cathode for Solid-State Li-Ion Batteries
Not provided.
Auto-oxidation of redox electrodes for the selective recovery of platinum group metals
Redox-electrodes are designed to selectively bind platinum group metals by auto-oxidation, and release them electrochemically. The platform can efficiently recover PGMs from catalytic converter leachates, and contribute to energy-efficient technologies for materials recycling.
The Use of Immersion Rigs for High Temperature Hydrogen Exposure Testing Within the Nuclear Thermal Rocket Element Environmental Simulator (NTREES): NTREES-5 Hydrogen Heat Exchanger (N5HHX-01)
The Nuclear Thermal Rocket Element Environmental Simulator (NTREES) facility at NASA’s Marshall Space Flight Center (MSFC) subjects materials and components typically found on nuclear thermal propulsion (NTP) systems to non-nuclear simulations of prototypic NTP reactor thermohydraulic environments. With 1.2 MW of inductive power available, the currently employed methods to simulate nuclear heating in NTREES consist of 1) inductively heating a test specimen, which typically cannot be performed on ceramic materials, or 2) heating gas that envelops the test specimen. The latter of these has provided a basis for developing so-called “immersion rigs” to be used in NTREES. Furthermore, immersion rigs are especially useful in the case of smaller, technically challenging test specimens. One such device, the “NTREES-5 Hydrogen Heat Exchanger” (N5HHX-01), designed and deployed to test small material coupons to ~0.5 g/s of 2800 K, 500 psi Hydrogen, is discussed herein. To achieve this desired delivery temperature level, the most suitable material for the N5HHX’s construction was pure tungsten, which imposed budgetary and manufacturing challenges. Also discussed in this paper is the design of the N5HHX rig’s sample container, which became the first iteration of improving NTREES small material coupon test operations by permitting rapid changeout of different material samples.
A Bayesian method for selecting data points for thermodynamic modeling of off-stoichiometric metal oxides
A novel Bayesian approach significantly accelerates data collection for metal oxide reduction/re-oxidation thermodynamic fitting.
Role of Fe Impurity Reactions in the Electrochemical Properties of MgFeB 2 O 5
Not provided.