Extraordinary Tibetan Plateau early-winter heating drove record-breaking winter precipitation in California and adjacent regions
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Environmental sciences↗
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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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TRACE Input Modernization
This work presents a Tom’s Obvious Minimal Language (TOML)-based representation of input for the US Nuclear Regulatory Commission’s TRAC/RELAP Advanced Computational Engine (TRACE) thermal hydraulics code. Implemented using the Workbench Analysis Sequence Processor (WASP), the approach maps traditional TRACE input structures to a hierarchical format composed of named parameters, typed values, and native data collections. The resulting representation preserves TRACE’s existing modeling capabilities while providing a modern, structured interface for model development and management. WASP further extends TOML through a file import directive that supports modular model composition and reusable input organization. In addition, WASP provides extended array data entry convenience with various data repeat and interpolation capabilities. Examples of the new TOML syntax are provided for major TRACE input categories, including hydraulic components, heat structures, control systems, and trip logic. The TOML representation establishes a foundation for improved validation, tooling, automation, and model maintainability while remaining compatible with existing TRACE workflows. To facilitate migration to the TOML-based input format, the TRACE executable now supports conversion of native TRACE input into an intermediate JSON representation. A Python utility subsequently transforms the JSON data into an equivalent TOML model. Lastly, the TRACE executable now supports execution using TOML-formatted input.
Lefebvre, Robert A. [Oak Ridge National Laboratory↗
Tree tensor network hierarchical equations of motion based on time-dependent variational principle for efficient open quantum dynamics in structured thermal environments
In this work, we introduce an efficient method, TTN-HEOM, for exactly calculating the open quantum dynamics for driven quantum systems interacting with highly structured bosonic baths by combining the tree tensor network (TTN) decomposition scheme with the bexcitonic generalization of the numerically exact hierarchical equations of motion (HEOM). The method yields a series of quantum master equations for all core tensors in the TTN that efficiently and accurately capture the open quantum dynamics for non-Markovian environments to all orders in the system–bath interaction. These master equations are constructed based on the time-dependent Dirac–Frenkel variational principle, which isolates the optimal dynamics for the core tensors given the TTN ansatz. The dynamics converges to the HEOM when increasing the rank of the core tensors, a limit in which the TTN ansatz becomes exact. We introduce TENSO, tensor equations for non-Markovian structured open systems, as a general-purpose Python code to propagate the TTN-HEOM dynamics. We implement three general propagators for the coupled master equations: two fixed-rank methods that require a constant memory footprint during the dynamics and one adaptive-rank method with a variable memory footprint controlled by the target level of computational error. We exemplify the utility of these methods by simulating a two-level system coupled to a structured bath containing one Drude–Lorentz component and eight Brownian oscillators, which is beyond what can presently be computed using the standard HEOM. Our results show that the TTN-HEOM is capable of simulating both dephasing and relaxation dynamics of driven quantum systems interacting with structured baths, even those of chemical complexity, with an affordable computational cost.
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Evaluating Fracture Behavior of Bioinspired Alumina-YSZ Composites through Static and Dynamic Mechanical Testing
Ceramic materials are known for their high hardness and strength but are limited by low toughness and sudden failure. Inspired by the microstructure of dental enamel, which features undulating rods that promote crack deflection and energy absorption, architected specimens were designed and manufactured. A bespoke ball-on-ring (BoR) testing apparatus measured the static biaxial rupture strength of these bioinspired materials. Additionally, impact testing with spherical steel projectiles assessed their behavior under dynamic conditions. Monolithic alumina specimens were compared to architected alumina reinforced with yttria-stabilized zirconia (YSZ) rods produced via direct ink write 3D printing. BoR tests revealed that monolithic alumina had a Weibull modulus of 10.53 and a characteristic strength of 372.3 MPa, while the composite specimens showed a Weibull modulus of 5.46 and a characteristic strength of 213.3 MPa. Although the composite failed at lower stresses and projectile velocities, it exhibited notable crack deflection and fracture resistance, with cracks being effectively interrupted by the rod inclusions. The composite specimens also resulted in fewer and larger fragments upon impact. Finite element simulations confirmed the effectiveness of the rod structures in enhancing fracture resistance.
Bioinspired materials↗
CFD Simulation of the Dosing Behavior within the Atomic Layer Deposition Feeding System
The effective operation of atomic layer deposition (ALD) feeding system is the premise of realizing specific ALD processes. In the present work, a detailed computational fluid dynamics (CFD) model of the feeding system has been developed and validated, which accounts for the roles of ALD valves and manifolds. A numerical simulation of the compressible fluid flow and heat/mass transfer within the feeding system was conducted. The dosing amounts and the spatiotemporal distributions of the precursors can be accurately predicted using the CFD model, as validated by experimental results. Different precursors, operating conditions, and structures of the feeding system were simulated and analyzed to examine the operating flexibility of the feeding system. The simulation results can be adopted as the upstream boundary conditions for simulations of the ALD process in the reaction chamber. The substrate-scale simulation indicates that the effect of the feeding system on the film deposition is highly related to the surface kinetics of ALD. The present work can serve as a guide for the development and optimization of different ALD-based processes via proper operation and even the design of the feeding system.
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Low thermal budget dopant activation in shallow junctions of implanted Si via Tunable plasma enhanced annealing
Low thermal budget (LTB) annealing has become increasingly critical for shallow junctions as semiconductor devices are scaled down. Plasma Enhanced Annealing (PEA) has emerged as a promising LTB annealing process, however its mechanisms remain unclear. In this study, arsenic and boron implanted silicon wafers were subjected to helium or argon ion bombardment generated by annealing plasmas under controlled ion energies and doses in a home-built annealing reactor. The structural and electrical changes were characterized by four-point probe measurements, secondary ion mass spectrometry, Raman spectroscopy and spectroscopic ellipsometry. A pronounced reduction in sheet resistance, accompanied by a decrease in the damage layer thickness demonstrates the surface selective annealing capability of PEA process. Comparative studies using He and Ar plasmas and related ion bombardment reveal a dependence of activation efficacy on both ion species and the dopant implantation profile. Furthermore, a multi-step annealing mechanism is proposed, consisting of an initial (low dose) structural recovery stage followed by the generation of “extended” defects and concluded at large doses by an enhanced dopant activation. The proposed annealing kinetics are thought to be controlled by ion flux, dose and energy. Finally, these results highlight PEA as an effective, surface-selective, LTB approach for shallow-dopant activation and near surface annealing, and provide mechanistic insights into PEA processes.
dopant activation↗
Challenge of Fielding Experiments at the Annular Core Research Reactor
Conference is the annual Test, Research &Training Reactors (TRTR) meeting. Typically attended by dozens of research reactor operators, educators, trainers, maintenance & service contractors Also, members of the Nuclear Regulatory Commission (NRC) attend. Presentation highlights ACRRs capability and the process used to submit and approve experimental activities. This presentation is adapted from my previous presentation to the Material Test and Research conference, SAND2025-1775526.
Martin, Lonnie E. [Sandia National Laboratories (S↗
Entropy-defect synergy for dual luminescence mechanism in spinel: Time-resolved anti-counterfeiting and fingerprint visualization
Multimodal luminescent materials, while promising for anti-counterfeiting, often lack dynamic time-dependent responses and controllable spatial distribution, limiting their encryption capabilities in the spatiotemporal dimension. Here, this work presents a coordinated control strategy based on entropy and defect engineering, and uses a backpropagation (BP) neural network for material screening to successfully prepare spinel Mg 0.8 (Fe 0.04 Co 0.04 Ni 0.04 Cu 0.04 Zn 0.04 )Cr 2 O 4 (MgA 5 CO) phosphors with time-dependent dynamic luminescence behavior. This phosphor simultaneously activated the d-d transition luminescence (∼618 nm) derived from Co 2+ /Cr 3+ and the defect luminescence (∼398 nm) related to zinc vacancies (V Zn ) in a single-phase solid solution. The phosphor exhibits a time-dependent color evolution from pink to purple under fixed-wavelength excitation, due to the different excited-state dynamics and decay lifetimes associated with the d-d transition and defect luminescence. Structural characterization and spectral analysis confirmed the existence of V Zn and its significant role in defect luminescence process. The fluorescent and dynamic luminescent properties of entropy-based spinel oxide enable its use in advanced anti-counterfeiting applications like fingerprint recognition and color-changing dedicated anti-counterfeiting mark, showing promise in high-end and time-dynamic anti-counterfeiting fields. This research not only developed a new type of fluorescent dynamic anti-counterfeiting material, but also provided a new idea for constructing advanced optical functional materials with multiple luminescence mechanisms.
Defect project↗
DEPRECATED AI-Batt-OS (Autonomous Identification of Battery Life Models - Open Source) [SWR 21-17]
DEPRECATED. This repository was archived by the owner on Jun 30, 2026. It is now read-only. Open source implementation of some of the methods utilized by AI-Batt, a battery lifetime modeling and analysis toolkit provided by the National Laboratory of the Rockies (NLR). This software demonstrates the use of bi-level optimization and symbolic regression techniques to semi-autonomously identify algebraic models predicting the capacity fade of lithium-ion batteries during calendar aging. Modeling the degradation of batteries is a complex task, due to the difficulty in separating the time-dependent and time-independent factors impacting cell level degradation, across multiple data series with different numbers of measurements and/or data quality. Bi-level optimization enables model parameters to be optimized to either the entire data set or to individual data series, allowing statistical disambiguation of global behaviors (data series independent) and local behaviors (data series dependent). Symbolic regression is used to automatically search for optimal low-dimesional models predicting the variation of locally optimized parameters versus time-independent experimental variables from millions of possible models, resulting in a more accurate and repeatable model identification process than is possible by a manual search. The provided tools also implement cross-validation and bootstrap resampling schemes, empowering statistical model comparison/selection and quantification of model uncertainties. An example script replicates the results from the manuscript "Challenging Practices of Algebraic Battery Life Models through Statistical Validation and Model Identification via Machine-Learning", submitted to ECS. All code is written in MATLAB. Requires the Statistics and Machine Learning Toolbox. Contact Dr. Paul Gasper at Paul.Gasper@nlr.gov for any questions.
Gasper, Paul [National Renewable Energy Lab. (NREL↗
Theoretical design and performance of three-dimensional, pillared FeS 2 cathodes
Three-dimensional (3D) electrode design can provide improved capacities and rate capabilities over conventional two-dimensional electrodes by enhancing electrical and ionic transport. Here, expanding upon our previous modeling efforts for conversion chemistry lithium-ion batteries, we develop a pseudo-four-dimensional (P4D) approach that is subsequently used to investigate the design of a pillared FeS 2 electrode. The model considers transport in three dimensions with an additional “fourth” dimension corresponding to the solid-state lithium transport within the active material particles. Additionally, we allow for expansion of the active material during the conversion reaction to understand how internal stresses impact the electrochemical performance of the cell. By optimizing the model with respect to areal capacity, we are able to predict areal capacities up to 16.8 mAh/cm 2 for an areal current density of 1.78 mA/cm 2 and a 103% improvement for the three-dimensional electrodes over planar electrodes of equal volume. Despite the promising results, our simulations suggest that 3D design may be difficult for conversion cathode materials due to the large internal stresses that arise during conversion. Nevertheless, the model is robust and adaptable to other materials that may be more suitable for 3D electrodes due to a lesser change in volume during discharge.
Conversion cathode materials↗
A Membraneless Electrochemically Mediated Amine Regeneration for Carbon Capture
Not provided.
Science & Technology - Other Topics↗
Role of Amorphous Chains in Nanoplastic Formation from Semicrystalline Polymers
Semicrystalline polymers, e.g., polyethylene terephthalate (PET), release micro- (100 nm to 1 mm) and nanoplastics (up to 100 nm) [MNPL] when they are degraded under quiescent conditions. However, the exact molecular mechanisms leading to material fragmentation into MNPL are unknown. Here, we monitor the evolution of chain molecular weight and the MNPL production kinetics during hydrolysis of PET pellets. We find that only ~0.6% of the amorphous phase ester bonds are hydrolyzed at the onset of MNPL release. Then, by combining a random scission model with measured amorphous spacings, we estimate that only ~15% of the stress transmitters in the amorphous phase, namely, bridges and entangled loops, have failed by this point. Thus, spontaneous fragmentation of the semicrystalline nanostructure occurs despite significant intercrystalline connectivity. We resolve this apparent contradiction by proposing that fragmentation can only occur when progressive tie-chain scission causes the material to undergo the ductile/brittle transition. Mechanistically, we speculate that the internal stresses responsible for material fragmentation are caused by processing (i.e., residual stresses) and/or sample densification induced by chemi-crystallization. We discuss additional factors that may affect our analysis and thus require further investigation, such as skin-core effects, preferential degradation of stress transmitters and/or recrystallization processes.
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Experimental investigation of thermal stratification development in boiling water reactor suppression pools during reactor core isolation cooling system operation
Not provided.
Nuclear Science & Technology↗
On the Physical Origins of Reduced Ionic Conductivity in Nanoconfined Electrolytes
Not provided.
Chemistry↗
A machine learning based classifier for topological quantum materials
Not provided.
Science & Technology - Other Topics↗
Nonlocal Effect of Percolated Particle Networks on Viscoelasticity of Polymer–Filler Nanocomposites: A Mesoscale Simulation Study
With nanoparticles (NPs) as fillers, polymer nanocomposites (PNCs) usually exhibit enhanced mechanical properties. However, a direct connection between the microscopic structural relaxation and macroscopic mechanical properties of PNCs remains to be established. To investigate the micro-to-macro connection, we develop a mesoscale model, in which the NPbridging polymer chains are represented by a dynamic bonded interaction between NPs, and the bulk polymer matrix is implicitly modeled by overdamped Langevin dynamics. Extensive equilibrium simulations are performed to quantify the microscopic dynamics of model PNCs. Systematic analyses of modified Rouse dynamics, dynamic structure factor, and relaxation modulus uncover that the microscopic relaxation dynamics of PNCs are significantly decelerated across different length scales because of nonlocal effects of percolated particle networks a phenomenon that has not been adequately captured in prior simulation studies. We find that NPvolume- fraction and NP-bonding-energy barrier are the two critical variables that affect bulk viscoelasticity the most. The proposed mesoscale model is versatile and provides a powerful framework for studying structure−property relations of different PNCs.
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