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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 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Elucidating microstructural evolution and hardness variation across friction self-piercing riveted Al-7055 using synchrotron X-ray scattering and advanced microscopy techniques

Friction self-piercing riveting (FSPR) is a unique hybrid joining technique that combines the advantages of mechanical interlocking, frictional heat, and solid-state joining (if metallurgically compatible) to produce crack free joints in high strength and/or low-ductility alloys at room temperature. Here, in the current study, Al-7055 sheets were joined using FSPR for lightweight automotive applications and significant microhardness variations were observed across the joint cross-section. A detailed microstructural characterization at multiple length scales was carried out using advanced electron microscopy and X-ray scattering techniques to provide a fundamental understanding of the process-structure-property relationships. The relative contributions of microstructural characteristics at various length scales (i.e., grain size, dislocation density, solute concentration, precipitate nature) to strengthening were estimated using existent formulations (i.e., Hall-Petch, Taylor, precipitate bypass/shear equations) and correlated to the observed microhardness values across different regions. Small-angle X-ray scattering and scanning transmission electron microscopy revealed significant changes in the size and volume fraction of precipitate species, i.e., GP-I Zones, η′, and Mg/Zn solute co-clusters, depending on the process region. It was observed that the dissolution of the small η′/GP-I zones (T ∼ 150–200 °C) in the heat-affected zone were the key reason for the hardness drop. Further, it was shown that solid-solution, dislocation, grain size and solute co-cluster strengthening played a key role in the thermo-mechanically affected zone and grain-refined zone (GRZ). Finally, these observations were leveraged along with the Zener-Holloman relationship and grain size in the GRZ to estimate the peak joining temperature of the GRZ (∼ 350 °C) near the steel rivet.

aluminum 7xxx alloy↗
DOE OSTI2026

A generative machine learning model for designing metal hydrides applied to hydrogen storage

Developing new metal hydrides is a critical step toward efficient hydrogen storage in carbon-neutral energy systems. However, existing materials databases, such as the Materials Project, contain a limited number of well-characterized hydrides, which constrains the discovery of optimal candidates. This work presents a framework that integrates causal discovery with a lightweight generative machine learning model to generate novel metal hydride candidates that may not exist in current databases. Using a dataset of 450 samples (270 training, 90 validation, and 90 testing), the model generates 1000 candidates. After ranking and filtering, six previously unreported chemical formulas and crystal structures are identified, four of which are validated by density functional theory simulations and show strong potential for future experimental investigation. Overall, the proposed framework provides a scalable and time-efficient approach for expanding hydrogen storage datasets and accelerating materials discovery.

generative model↗
DOE OSTI2025

Corrosion resistant hot melt adhesive to bind metals

Hot melt adhesives (HMAs) play an important role in many industries, and their demand is expected to grow. HMAs don't require any solvents, and their application results in the formation of strong bonds with the substrate upon cooling within seconds. These properties differentiate them from liquid glues and make them preferable for practical application. Currently, commercial HMAs are used in bonding lightweight materials such as paper, polymers, and cartons, and have limited usage in areas necessitating the bonding of heavier objects like metals. Here, in this study, we report a design and testing of versatile platform comprising an ion-coordinating polymer and ionic fillers for performance optimization and understanding of structure–property relationships, enabling the rational design of HMAs with improved adhesion to metal surfaces. All-atom Molecular dynamics (MD) simulations and various characterization methods are used to elucidate the adhesion mechanism in model composite system containing polyethylene oxide mixed with chemically diverse salt particles. The maximum adhesion strength is found in composites with Al(OH) 3 and FeCl 3 fillers. Interestingly, the presence of Al(OH) 3 also provides a multifunctional anticorrosion property as measured electrochemically using the Tafel method. The discovered path to formulations with improved adhesion to metal surfaces constitutes an important step toward advancing HMAs for use in the structural and semi-structural metal work domain.

Adhesive and cohesive forces↗
DOE OSTI2026

SPRUCE Wood Anatomy of Picea mariana and Larix laricina in SPRUCE Experimental Plots, Marcell Experimental Forest, Minnesota, July 2023

Branch samples were collected in July 2023 to measure wood anatomical traits on two dominant conifer species, Picea mariana and Larix laricina, in a bog forest at the SPRUCE (Spruce and Peatland Responses Under Changing Environments) experiment in northern Minnesota. Anatomical measurements were made on the annual rings of those branches with dates ranging from 2011-2023. Wood anatomical measurements include annual tracheid diameter, tracheid density, cell wall thickness, thickness-to-span-ratio, and conduit lumen fraction in both earlywood and latewood. Wood anatomical samples were prepared using a portable sliding microtome (G.S.L.-1 lightweight microtome, WSL) and a light microscope (Leica DM2500). This dataset spans 2011–2023, with full branch-level coverage for 2020–2023, whereas some thinner branches formed only in recent years and therefore do not contain rings from earlier years. By providing annual, treatment-specific anatomical measurements, this dataset can help quantify structural acclimation to global change, clarify links among phenology, wood formation, and hydraulic traits, and improve predictions of forest growth response under future climates. This dataset contains one data file in comma separate (.csv) format. Additional metadata are provided: one data dictionary and a file-level metadata file in comma separate (.csv) format and a user guide in PDF (*.pdf) format.

annual tracheid diameter↗
DOE OSTI2024

Lifetime Energy Savings Via Advanced Manufacturing of Low Density Steels for Transportation Applications

The purpose of this “Low Density Steels for Transportation Applications” project was to develop an alloy composition and processing parameters that would result in a material suitable for use in automotive structural components at a reduced density over the current advanced high strength steel (AHSS) materials used. The project work successfully developed a robust alloy capable of exceeding project mechanical property targets at each stage of development, with an 8% density reduction over benchmark AHSS materials (7.8 g/cm3). The developed alloy has the potential to offer significant vehicle lightweighting and improved fuel economy, without sacrificing the increased passenger safety of more traditional AHSS. Through the three tasks of the project, (1) Alloy design and small-scale laboratory evaluation, (2) Laboratory development of hot rolled material and (3) Laboratory development of a cold rolled material, the laboratory work utilized advanced characterization and analytical methods on novel alloy compositions subjected to both conventional and non-conventional processing operations.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Thermodynamic Stability and Site‐Specific Distribution of Graphitic and Pyridinic Nitrogen in Graphene Moiré on Ru(0001)

Abstract Graphene‐like materials are of interest for large‐scale hydrogen storage applications due to their lightweight, durable, and scalable properties. Nitrogen‐doping minimizes kinetic limitations in diffusion and recombination on surfaces, however, the role of graphitic nitrogen (GN) and pyridinic nitrogen (PN) is not well understood. Nitrogen‐doped graphene is synthesized on Ru(0001) using chemical vapor deposition (CVD) of pyridine and ion irradiation. Scanning tunneling microscopy (STM), x‐ray photoelectron spectroscopy (XPS), and density functional theory (DFT) are used to identify the structure, location, and thermodynamic stability of nitrogen species within the graphene moiré. CVD of pyridine results in a low nitrogen concentration (<0.1at%), while the post‐growth nitrogen ion irradiation allows us to increase the concentration further. The concentration of GN and PN is controlled by varying the ion dose and annealing temperature. Comparison of measured and simulated STM images of GN and PN yield an excellent agreement, allowing us to confidently establish that GN is preferentially located near the center of the Atop region, while PN is located in the valley region of the graphene moiré. This report explicitly confirms the site assignments and provides a foundation for the site synthesis and analysis of structural and electronic properties that drive the reactivity of N‐doped graphene.

Gedara, Buddhika S. A. [Physical and Computational↗
DOE OSTI2025

Prediction of carbon nanostructure mechanical properties and the role of defects using machine learning

Graphene-based nanostructures hold immense potential as strong and lightweight materials, however, their mechanical properties such as modulus and strength are difficult to fully exploit due to challenges in atomic-scale engineering. This study presents a database of over 2,000 pristine and defective nanoscale CNT bundles and other graphitic assemblies, inspired by microscopy, with associated stress–strain curves from reactive molecular dynamics (MD) simulations using the reactive INTERFACE force field (IFF-R). These 3D structures, containing up to 80,000 atoms, enable detailed analyses of structure-stiffness-failure relationships. By leveraging the database and physics- and chemistry-informed machine learning (ML), accurate predictions of elastic moduli and tensile strength are demonstrated at speeds 1,000 to 10,000 times faster than efficient MD simulations. Hierarchical Graph Neural Networks with Spatial Information (HS-GNNs) are introduced, which integrate chemistry knowledge. HS-GNNs as well as extreme gradient boosted trees (XGBoost) achieve forecasts of mechanical properties of arbitrary carbon nanostructures with only 3 to 6% mean relative error. The reliability equals experimental accuracy and is up to 20 times higher than other ML methods. Predictions maintain 8 to 18% accuracy for large CNT bundles, CNT junctions, and carbon fiber cross-sections outside the training distribution. The physics- and chemistry-informed HS-GNN works remarkably well for data outside the training range while XGBoost works well with limited training data inside the training range. The carbon nanostructure database is designed for integration with multimodal experimental and simulation data, scalable beyond 100 nm size, and extendable to chemically similar compounds and broader property ranges. The ML approaches have potential for applications in structural materials, nanoelectronics, and carbon-based catalysts.

Winetrout, Jordan J.↗
DOE OSTI2025

DOE Repository Metadata Profile (DRMP): A Metadata Framework for Advancing Interoperability and AI Readiness Across Scientific Repositories

The Department of Energy (DOE) funds a diverse and distributed ecosystem of repositories that steward scientific data, publications, and software across its research programs, user facilities, and national laboratories. While significant progress has been made in standardizing dataset-level metadata, the metadata describing repositories themselves (their identity, governance, access interfaces, policies, and technical capabilities) remains inconsistent and fragmented across DOE-funded systems. This variability limits discoverability, interoperability, automated validation, and AI-driven analysis, all of which are increasingly essential for modern scientific workflows. To address this gap, the DOE Data Curation Working Group (DCWG) developed the DOE Repository Metadata Profile (DRMP). The DRMP is a practical, community-driven framework that defines how repositories can describe themselves in a consistent, machine-actionable, and scalable manner. The DRMP is not a new metadata schema. Instead, it is a mapping profile and structured element set capturing the essential characteristics of DOE repositories. It harmonizes repository-level metadata across six widely adopted community schemas: RE3Data; DCAT-US v3; Schema.org; Dublin Core; DataCite 4.6; and PREMIS 3.0. This harmonization eliminates reinvention and enables interoperability within DOE and across the broader scientific ecosystem. A core objective of the DRMP is to reduce burden on repositories by allowing them to reuse their existing metadata through a Rosetta-style crosswalk rather than redesigning local implementations. The profile introduces a three-level conformance model that supports incremental adoption: • Level 1 – Minimum Viable Record (MVR): foundational identification elements required for workflows, project registration, and basic repository presence. • Level 2 – Interoperable: structured metadata enabling alignment with national and international discovery systems. • Level 3 – AI-Ready: enhanced provenance, policy transparency, fixity, semantic context, and capabilities that support automated reasoning, model training governance, and machine-assisted curation. To support implementation, the DRMP includes JSON Schema definitions, OpenAPI patterns, and MCP templates that allow repositories to publish machine-readable metadata directly within existing platforms. These resources are modular and lightweight, enabling adoption without major architectural change. Adopting the DRMP enables repositories to: • Enhance discoverability and interoperability by aligning identifiers, classifications, and descriptive elements across widely used schema standards. • Support federated discovery and cross-registration across DOE systems, Data.gov, and international catalogs. • Enable AI agents and workflow orchestration systems to interpret repository-level metadata within the American Science Cloud (AmSC) through Model Context Protocol (MCP)-based context publication. • Demonstrate alignment with DOE’s open science, stewardship, and FAIR data priorities. This guidance represents a community-driven step forward. Through voluntary adoption and continued feedback, the DRMP advances a cohesive, machine-actionable description of DOE repositories that supports FAIR data practices, preparing the infrastructure for AI-enabled research, and strengthening the discoverability and reuse of DOE’s scientific outputs.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2026

CAHS: Context-Aware Homology Search

Protein homology search is foundational to bioinformatics: it supports annotation transfer, structure/function inference, and evolutionary analysis over rapidly expanding sequence repositories (e.g., UniProtKB). Profile hidden Markov models (pHMMs), as implemented in HMMER, remain the most widely trusted approach because they provide statistically calibrated E-values; however, their gap behavior is fixed once a profile is trained, despite biological evidence that insertion/deletion tolerance varies across flexible loops and intrinsically disordered regions. We present CAHS (Context-Aware Homology Search), a lightweight query-time adapter for pHMM search that incorporates learned and biologically motivated signals without changing HMMER's downstream search pipeline or its calibrated E-value reporting. Given a query sequence, CAHS computes per-residue representations from a protein language model and a disorder predictor, maps these to profile coordinates, and modulates only match-state transition rows (gap-open and gap-extension probabilities) while preserving Plan7 constraints. We comprehensively evaluate CAHS across six structurally diverse protein families and multi-domain architectures against a 570k-sequence target corpus. CAHS expands detection capability, retrieving thousands of additional remote homologs at relaxed thresholds by maintaining alignment quality through flexible regions. For multi-domain proteins, context-aware modulation resolves 94% of fragmented alignments. Crucially, CAHS preserves hit-set invariance at stringent operating points (E<10-10), demonstrating increased statistical confidence without inflating false positives. Furthermore, sharper statistical distinction between homologs and background noise during early filter stages yields up to a 3.87× acceleration in end-to-end wall-clock time on high-performance computing clusters. Overall, CAHS illustrates a practical AI-for-science design pattern: augmenting a trusted probabilistic model with query-specific learned signals to improve interpretable, reproducible inference in data-rich biology.

Bhattaram, Swethasree [Georgia Institute of Techno↗
DOE OSTI2024

Epoxy resin reinforced with carbonized chicken feathers: An innovative composite material with sustainable potentials

Here, this article explores a novel method for enhancing the mechanical properties of epoxy resin composites by incorporating carbonized chicken feathers as a filler material. The fabrication process involves carbonizing chicken feathers at 600°C and incorporating 5-10 wt% of the fillers into an epoxy matrix. The composites showed enhanced mechanical properties and samples containing 10 wt% filler exhibit the best properties. The performance corresponds to 49% increase in tensile strength, 16% rise in Young’s modulus, 40% improvement in flexural modulus, and 57% in flexural strength. X-ray diffraction and scanning electron microscopy with energy dispersive spectroscopy were employed to characterize the filler. This characterization provides valuable insights into the structure and chemical composition of the pulverized carbonized chicken feathers that contributed to the attained improvement in composites’ properties. Microstructural examination of the developed composite under scanning electron microscope also provides insights into matrix-filler interface and dispersion of the fillers within the composite matrix. The study not only highlights the unique combination of carbonized feathers’ inherent strength and compatibility with the epoxy matrix but also underscores the eco-friendly nature of utilizing agricultural waste. The findings suggest promising applications in industries demanding lightweight, high-strength materials, which can contribute to sustainable engineering solutions.

36 MATERIALS SCIENCE↗
DOE OSTI2025

WRF-ELM v1.0: a regional climate model to study land–atmosphere interactions over heterogeneous land use regions

Abstract. The Energy Exascale Earth System Model (E3SM) Land Model (ELM) is a state-of-the-art land surface model that simulates the intricate interactions between the terrestrial land surface and other components of the Earth system. Originating from the Community Land Model (CLM) version 4.5, ELM has been under active development, with added new features and functionality, including plant hydraulics, radiation–topography interaction, subsurface multiphase flow, and more explicit land use and management practices. This study integrates ELM v2.1 with the Weather Research and Forecasting (WRF; WRF-ELM) model through a modified Lightweight Infrastructure for Land Atmosphere Coupling (LILAC) framework, enabling affordable high-resolution regional modeling by leveraging ELM's innovative features alongside WRF's diverse atmospheric parameterization options. This framework includes a top-level driver for variable communication between WRF and ELM and Earth System Modeling Framework (ESMF) caps for the WRF atmospheric component and ELM workflow control, encompassing initialization, execution, and finalization. Importantly, this LILAC–ESMF framework demonstrates a more modular approach compared to previous coupling efforts between WRF and land surface models. It maintains the integrity of ELM's source code structure and facilitates the transfer of future developments in ELM to WRF-ELM. To test the ability of the coupled model to capture land–atmosphere interactions over regions with a variety of land uses and land covers, we conducted high-resolution (4 km) WRF-ELM ensemble simulations over the Great Lakes region (GLR) in the summer of 2018 and systematically compared the results against observations, reanalysis data, and WRF-CTSM (WRF coupled with the Community Terrestrial Systems Model). In general, the coupled WRF-ELM model has reasonably captured the spatial distribution of surface state variables and fluxes across the GLR, particularly over the natural vegetation areas. The evaluation results provide a baseline reference for further improvements in ELM in the regional application of high-resolution weather and climate predictions. Our work serves as an example to the model development community for expanding an advanced land surface model's capability to represent fully-coupled land–atmosphere interactions at fine spatial scales. The development and release of WRF-ELM marks a significant advancement for the ELM user community, providing opportunities for fine-scale regional representation, parameter calibration in coupled mode, and examination of new schemes with atmospheric feedback.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Controlling homogenization length scales and microstructure in additively manufactured Ti-Ta functionally graded materials

Materials with smooth compositional gradients or functionally grade materials (FGMs) produced via additive manufacturing (AM), enables joining dissimilar materials and optimizing multiple properties in advanced engineering applications. However, as-printed AM microstructures exhibit micro-segregation and solidification defects which, when combined with controlling macroscale gradient properties, complicates necessary post-processing. Here, we use CALPHAD-informed diffusion modelling to design post-processing heat treatments for lightweight to refractory FGMs. Ti-Ta (0 to 85 at. % Ta) FGMs were fabricated using laser-based directed energy deposition AM. Post-processing heat treatments at 1000° C and 1500° C were designed to promote homogenization across specific length scales and experimentally validated. Investigation of chemical segregation and microstructures demonstrated that the length scale of homogenization is controlled as a function of time, temperature, and local composition. Ta-rich regions exhibited incomplete homogenization compared to Ti-rich layers. Unmelted Ta particles were found to completely dissolve at 1500 °C. By controlling cooling rate (200 °C/min), martensitic structures were produced between 14–36 at. % Ta, consistent with martensite-start temperatures calculations, while furnace cooling (2 °C/min) produced α+β morphologies. This work establishes a validated predictive framework for designing post-processing to tailor microstructure and chemical architecture in AM FGMs, facilitating their deployment in demanding environments.

Materials science↗
DOE OSTI2026

Regularizing INR with Diffusion Prior for Self-Supervised 3D Reconstruction OF Neutron Computed Tomography Data

Recently, generative diffusion priors have made huge strides as inverse problem solvers, including the ability to be adapted for inference on out-of-distribution data. Concurrently, implicit neural representations (INRs) have emerged as fast and lightweight inverse imaging solvers that are amenable to hybrid approaches that combine learned priors with traditional inverse problem formulations. In this paper, we present a diffusive computed tomography (CT) inversion framework for regularizing INRs called Diffusive INR (DINR), designed to enable high-quality reconstruction from sparse-view neutron CT. Pretrained purely on synthetic data, DINR is evaluated on simulated and experimentally obtained observations of concrete microstructures, where traditional reconstruction methods suffer substantial degradation when the number of views is reduced. Our approach delivers superior performance, reduces reconstruction artifacts, and achieves gains in PSNR and SSIM, enabling accurate micro-structural characterization even under extreme data limitations compared to state-of-the-art sparse-view reconstruction techniques.

Hossain, Maliha [ORNL]↗
DOE OSTI2025

Flexible transparent conducting electrodes unexpectedly influence MAPbI 3 film morphology and perovskite solar cell performance

Fabricating perovskite solar cells (PSCs) on plastic substrates will enable cost-effective manufacturing of lightweight and portable energy systems. Here, we study the influence of flexible transparent conductive electrodes (TCEs) on the methylammonium lead iodide (MAPbI3) film morphology and PSC performance. Three types of TCEs on polyethylene terephthalate (PET) are compared: a custom-made AgNWs/indium zinc oxide hybrid TCE and two commercial substrates with indium tin oxide (ITO) or metal/insulator/metal (MIM) as the TCE. Using the same p-i-n architecture and hole transport layer (HTL), we find that PSCs fabricated on the hybrid TCE show better performance and stability compared to those made on commercial TCEs. The PSC performance enhancement is attributed to the superior structural and optical properties of MAPbI3 film deposited on the hybrid TCE, which is explained by the surface energy difference of the HTL. While it is anticipated that solar cell performance can be affected by TCEs’ transmittance and sheet resistance as they determine light absorption and carrier transport, respectively, our finding of TCE influencing the morphology and crystallinity of MAPbI3 film, and hence PSC performance, is unexpected.

14 SOLAR ENERGY↗
DOE OSTI2025

ATcT — Active Thermochemical Tables Python Interface

SF-25-140 atct is a lightweight, Python client for the ATcT v1 API that enables programmatic access to high-accuracy thermochemical data and turnkey reaction-enthalpy analysis. The package implements full v1 endpoint coverage (species lookup by ATcT ID, name, formula, SMILES, InChI, CAS RN; covariance queries; health checks) with robust error handling, retries, and environment-based configuration for local/production endpoints. Beyond data retrieval, atct provides rigorously implemented reaction calculators that propagate uncertainties via either (i) a conventional independent-errors method (0 K or 298.15 K) or (ii) covariance-aware propagation using provided covariances at 298.15 K. Typed data classes ensure transparent, reproducible data structures and carry ATcT Thermochemical Network (TN) version identifiers for provenance. Dual import paths and comprehensive examples facilitate integration into research pipelines, enabling reproducible thermochemical calculations, automated validation, and downstream method development.

Bross, DavidHamilton [Argonne National Laboratory ↗
DOE OSTI2024

Enabling Ultra-Compact, Lightweight, Efficient, and Reliable 6.6 kW On-Board Bi-Directional Electric Vehicle Charger with Advanced Topology and Control

The research explored new topologies, control methods, mechanical integration, and thermal management methods for electric vehicle (EV) on-board chargers. The team investigated capacitor-based power conversion, leveraging the high energy densities inherent to capacitive energy storage compared to inductive methods. The proposed topologies simultaneously enabled high power density and high efficiency of the design. The proposed architecture was demonstrated in a 6.6 kW bi-directional charger prototype. The research pursued several directions to improve system performance. Innovative topologies were studied for both the main power conversion stage as well as the single-phase twice-line-frequency energy buffer. To ensure robust and efficient operation, new control methods were developed to integrate these two subsystems. To achieve high power density in the full system solution, the mechanical structure of the charger is highly optimized to maximally fill the converter box volume. In parallel with the mechanical design effort, the converter was packaged with high-performance cooling methods which removed heat from key areas of power dissipation in the converter. The thermal management system was optimized to minimize its weight and volume, ultimately motivating the design of a custom additively manufactured cold-plate. The full system achieves a peak power of 7 kW with less than 0.3% total harmonic distortion (THD) and greater than 0.994 power factor in power factor correction (PFC) operation, corresponding to a total box-volume power density of 47.9 kW/L and gravimetric power density of 24.6 W/g. The system achieves a peak efficiency of 98.9%, with 97.9% efficiency at maximum power.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2024

High Velocity Joining of Multi-material Stacks Containing 3rd Generation Advanced High-Strength Stainless Steel and Aluminum Extrusion and Casting (Abstract)

Advanced lightweight materials, including multi-phase steels, aluminum extrusions and aluminum castings, have increasingly replaced mild steel to meet original equipment manufacturer (OEM) demand for increased fuel economy and crash-safety requirements. Third Generation (Gen 3) AHSS provides a superior combination of strength and ductility for crash energy management components in the vehicle body-in-white (BIW). Aluminum extrusions are widely used for bumper, door rocker panel, cradle and battery frame applications. Aluminum castings are increasingly being used in electric vehicles to provide part consolidation, weight saving and increased driving range. Rocker panels are, for example, an important component of the battery electric vehicle (BEV) body structure. The rocker panel serves several purposes, including structural support, crash resistance, protection for the vehicle's underbody and battery tray, and contributing to the overall aesthetics. In order to reduce weight, rocker panels are usually constructed using outer panels consisting of 3rd generation advanced high strength steels (AHSS) and an inner multicell aluminum extrusion structure for energy absorption which protects the batteries in a crash event. It is well known, however, that these material combinations provide significant challenges for joining and are susceptible to liquid metal embrittlement and loss of strength. The overall goal is to find a solution that addresses these challenges in order to produce multi-material vehicle components with reduced weight and minimal galvanic and recyclability issues. The approach being proposed here is to utilize a novel high velocity (HiVe) riveting and clinching technique recently demonstrated by Pacific Northwest National Laboratory (PNNL) to join (2T/3T) sheets of aluminum alloys and steel. This process will not only address the aforementioned challenges but also help significantly reduce the joining cycle time. This work will also demonstrate use of HiVe assemblies to be retrofitted to existing body-shop robots thus having minimal impact to existing body shop operations.

36 MATERIALS SCIENCE↗
DOE OSTI2025

An Open-source Llm Enhanced-tool Specialized In Helping Moose Related Problems And Tasks

MOOSEenger is an open-source, terminal-first chat application for the MOOSE ecosystem that couples specialized parsing of MOOSE documentation and “.i” input files with retrieval-augmented generation to deliver grounded answers about multiphysics modeling and workflows. It includes dedicated readers for MOOSE-style HTML and a pyhit-based parser that uses the MOOSE syntax tree to preserve block structure and attach retrieval metadata. A data-ingestion pipeline performs semantic chunking into atomic facts and stores them hierarchically in a local Chroma vector database that maintains parent–child relationships across documents; the system can ingest directories, individual files, and single-page web content, and it provides CRUD operations (insert, update, delete) to manage the corpus. At query time, relevant chunks are embedded, retrieved, and fused into the model context, with interactive features such as token streaming, persistent chat history, and dynamic RAG (retrieval triggered by user input or intermediate model output). Deployment is flexible: MOOSEenger runs with local Ollama models or remote Hugging Face/OpenAI backends—typically coordinating generation, lightweight tagging/summarization, and embeddings across three models—and it also supports a server mode and integration with the VS Code Continue interface.

Li, Mengnan [Idaho National Laboratory (INL), Idah↗