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DOE OSTI2025

Advanced Brine Processing to Enable U.S. Lithium Independence (CRADA Report)

Current production of LiOH, which is needed to make Li-ion battery cathode active materials, utilizes a multistep process including solar evaporation, precipitation with Na 2 CO 3 and then conversion to LiOH using Ca(OH) 2 . This process requires a large amount of land area for solar evaporation, the right weather conditions, and chemicals for the conversion process that result in NaCl and CaCO 3 waste products. The production of Ca(OH) 2 is very energy intensive and evolves significant quantities of CO 2 . An alternative process flow utilizing direct lithium extraction techniques, followed by a chemical free conversion process can have benefits in reducing the needed land requirements and chemicals for traditional brine processing. There are many potential direct lithium extraction technologies that are currently being developed. The direct lithium extraction process from typical brine sources will produce a LiCl solution with some impurities including typically high concentrations of Na. This brine then needs to be converted to LiOH for use in battery cathode production. Ideally this conversion could occur without the use of additional chemicals. Electrochemistry can do this conversion either via electrolysis or bipolar membrane electrodialysis (BPED) to produce LiOH and HCl in solution. BPED utilized bipolar membranes to split water, which has a reduced potential as compared to splitting water at electrodes into hydrogen and oxygen gas. This reduced potential required results in a significant energy savings for BPED over electrolysis methods. This CRADA project aimed to develop such an integrated process using direct lithium extraction followed by BPED to produce a LiOH solution. That solution can then be crystallized into battery grade LiOH. In particular, Albemarle utilized a direct lithium extraction process to produce a concentrated LiCl solution that could be used for the BPED process. The BPED process was first tested using various LiCl solutions with impurity ions added at bench scale to understand the effects of impurities and determine processing parameters. Then testing was performed using the direct lithium extracted brine at the bench scale before scaling the process up. After the process was scaled up a long duration test was carried out to estimate the lifetime of the membranes, which is key to the economics of the BPED process.

25 ENERGY STORAGE↗

DOE OSTI2025

Assessment of Benefits of Solid-State Advanced Manufacturing Processes for Nuclear Energy Products

The Advanced Materials and Manufacturing Technology (AMMT) program develops cross cutting technologies in support of a broad range of nuclear reactor technologies and maintains U.S. leadership in materials and manufacturing technologies for nuclear energy applications. This overall project provides the U.S. Department of Energy a critical feasibility study comparison of three solid-state processes to other AM processes, thereby providing the feasibility of the solid-state processes examined to manufacture 316H SS and ODS steel components: • Fused-filament fabrication (FFF): This work provides an initial evaluation of the impact of powder morphology and sizes on the FFF process, and the feasibility and adaptability for different material systems, to use FFF for ODS steels and 316 SS. • Shear-assisted processing and extrusion (ShAPE): Specifically for this portion of the project on ShAPE tube forming, the objectives will be to determine the feasibility to direct tube forming of high tensile strength steel tubing, specifically for ODS steel to determine the effect of the patented extrusion process on the dispersoids of the ODS material. Additionally, as often ODS powders are mechanically alloyed and therefore more platelike or angular, this feasibility was to explore the impact on the optimization process and initial feasibility of direct tube forming. • Cold spray and friction stir additive manufacturing as a stretch goal: Bulk and near net shape manufacturing processes for high-temperature, high-strength alloys are needed. Additionally, cold spray techniques can be applied in-situ at the operational level for repair and can provide multiple benefits to the nuclear industry. These tasks aim to provide information to show benefits of cold spray during the full life cycle, namely research and development, product manufacturing, and repair to mention a few key points.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2025

Microwave-driven synthesis and modification of nanocarbons and hybrids in liquid and solid phases

Over the past 20 years, nanocarbons have become more significant as nanostructured fillers in composites and, more recently, as functional elements in a brand-new class of hybrid materials. Microwave-assisted synthesis and processing is a burgeoning subject matter in materials research with significant strides in the realm of nanocarbon during the last decade. Here, the review examines recent approaches to producing various nanocarbons using microwaves as energy sources, the characterization of such materials for various applications, and their results. The underlying factors supporting the increased performance of such materials or their composites are analyzed and reaction mechanisms are presented wherever necessary. In particular, the recently developed and verified approaches to produce porous carbon materials, CNTs and fibers, carbon nanospheres, carbon dots, CQDs, reduced graphene oxide, nanocarbon hybrid materials, and the purification and modification of CNTs are discussed. The reduction of graphene oxide and the preparation of graphene derivative hybrids using solid-state and liquid-state routes such as polyopl, mixed solvents, ionic liquids, and microwave-assisted hydrothermal/solvothermal methods are analyzed in detail. In addition, the principles of microwave heating in liquid and solid states, the use of metals particles as arcing agents or catalysts, and carbonaceous materials as internal or external susceptors during synthesis and modifications are presented in detail.

25 ENERGY STORAGE↗

DOE OSTI2025

Advanced electrode processing for lithium-ion battery manufacturing

Lithium-ion batteries (LIBs) need to be manufactured at speed and scale for their use in electric vehicles and devices. However, LIB electrode manufacturing via conventional wet slurry processing is energy-intensive and costly, challenging the goal to achieve sustainable, affordable and facile manufacturing of high-performance LIBs. Here, in this Review, we discuss advanced electrode processing routes (dry processing, radiation curing processing, advanced wet processing and 3D-printing processing) that could reduce energy usage and material waste. Maxwell-type dry processing is a scalable alternative to conventional processing and has relatively low manufacturing cost and energy consumption. Radiation curing processing could enable high-throughput manufacturing, but binder selection is limited to certain radiation curable chemistries. 3D-printing processing can produce electrodes with diverse architectures and improved rate performance, but scalability is yet to be demonstrated. 3D-printing processing is good for special applications where throughput and cost can be compromised for performance.

25 ENERGY STORAGE↗

DOE OSTI2026

Thermal analysis of combustion synthesis of FeAlxOy catalysts for dehydrogenation of fossil fuels

The growing demand for hydrogen as a clean energy carrier highlights the need for its alternative production method with reduced CO₂ emissions. Microwave-assisted thermocatalytic dehydrogenation of fossil fuels offers a promising solution for clean hydrogen production, with FeAlxOy nanocomposites acting as efficient catalysts due to their thermal stability, catalytic activity, and microwave-absorption properties. FeAlxOy nanocomposites, fabricated by solution combustion synthesis (SCS), have shown good microwave-absorption and catalytic properties. However, the effects of synthesis parameters such as fuel type and Fe:Al molar ratio on the SCS process and properties of the final material are not well understood. The SCS process involves vaporization of water, thermal decomposition of the mixture components, and oxidation – reduction reactions. In the present work, thermogravimetric analysis (TGA) and differential scanning calorimetry (DSC) were utilized to study the SCS mechanism, with a focus on understanding decomposition processes. TGA provided insights into the thermal stability and mass loss profiles of the mixtures, while DSC quantified the heat release and identified reaction onset temperatures. Results demonstrated that the choice of fuel significantly influenced the thermal behavior and properties of FeAlxOy nanocomposites. Glycine-based mixtures exhibited superior thermal stability and complete decomposition in a single step with high heat release compared to citric acid-based mixtures, which required higher synthesis temperatures and experienced slower, two-step decomposition processes. These findings emphasize the effectiveness of glycine-fueled SCS in producing thermally stable and catalytically active FeAlxOy nanocomposites.

Martinez Espinoza, Laura Alejandra↗

DOE OSTI2025

MPEX AI Digital Twins

All magnetically confined plasma fusion power plant concepts (Tokamak, Spherical Tokamak, Stellarator, Mirror, ...) must exhaust the heat and plasma from the core confinement region to the material walls. The primary channel for this exhaust is through a plasma divertor which directs plasma along open magnetic field lines to a material target. The Material Plasma Exposure eXperiment (MPEX) illustrated in Figure 1, is a high-power, steady-state linear plasma device designed to produce the plasma material interaction (PMI) conditions of the divertor of future magnetic confinement fusion power plants: energy flux 20MW/m 2 , ion fluence 1031/m 2 , pulse duration 106 sec. These goals of plasma exposure in MPEX are well beyond those achieved in magnetic fusion experimental devices. Successfully achieving these high power steady state conditions for long pulses requires operational control of the heating and particle sources and the plasma flux to the walls and target. The MPEX AI Hot Spot Controller, proposed in this project, will help achieve the operational milestones of MPEX. The MPEX device will begin commissioning at the end of FY26. A smaller proto-MPEX was operated for 14,666 plasma discharges and will resume operation in September of 2025 as proto-MPEX-lite, with reduced capability, to test a new window for the Helicon plasma source. The proto-MPEX data has undergone surrogate modeling with machine learning methods (R. Archibald, 2022 IEEE International Conference on Big Data). This proto-MPEX data will be used to begin development of the AI digital twins described in this white paper. The scientific mission of MPEX is to qualify materials of different composition for use in the high energy and plasma flux conditions of a fusion power plant. The materials exposed in MPEX will in some cases be exposed to high neutron fluxes at other ORNL facilities to measure the changes to their PMI properties. The targets exposed in MPEX will be transported under vacuum to a Surface Analysis Station (SAS). The SAS will be equipped with the following diagnostics: Focused Ion Beam (FIB) for trench milling, 100-400 angstrom resolution scanning electron microscope (SEM), surface mapping x-ray spectrometer, high resolution camera, and a future upgrade to a laser induced breakdown spectroscopy quadruple mass spectrometer (LIBS-QMS). The MPEX experiments will generate diverse pre- and post-exposure measurement data of detailed material properties down to the crystal grain level in 3D for post-exposure assessment of PMI damage (e.g. cracking, melting, erosion and redeposition of the material). Physics models for the PMI, and how the material composition and manufacturing impact its performance under high energy plasma exposure, need to be validated with MPEX data to guide the selection of new candidate materials. Our vision for the MPEX AI Digital Twins project is to supply experimental and physics model simulation data to train Artificial Intelligence (AI) models for data processing, analysis, operational control, PMI and materials simulation to maximize the scientific output of the MPEX device. Ultimately, an AI digital twin of MPEX material assessment metrics for tested and synthetic material types with simulated PMI will be trained by the AI Modeling Teams on the experimental and physics simulation data submitted to the American Science Cloud by this project. A purely empirical search for the best material is inefficient given the finite number of samples that can be tested on MPEX. In order to expand the material properties database for training the MPEX Material Assessment AI Digital Twin, and to gain physics understanding of the PMI processes, physics models of the material properties and PMI processes are required. The physics simulations provide detailed simulation data, like impact angles for plasma ions, sputtering yields, transport of the ionized sputtered target material in the plasma, and redeposition locations. This simulation data expands the measurement data for deeper physics understanding. The experimental data is essential to validate the PMI and material structure simulation models. The validated models can then be used to generate new simulation data of MPEX material assessments for synthetic material compositions that have not been exposed in MPEX. These predictive simulations, plus the whole experimental dataset, will be used to train the MPEX Material Assessment AI Digital Twin allowing a rapid generative AI search for new materials with reduced PMI damage by interpolating the domain of the training set. These new optimum materials can be simulated with the physics codes and/or tested in MPEX. The ability of AI neural networks to interpolate multi-dimensional parameter spaces and generate virtual data is exploited for a more efficient search for optimum materials. The advent of the Transformational AI Models Consortium (TAIMC) is an opportunity to engage with state of the art private and public AI developers to achieve the goals of the AI digital twins and AI accelerated physics models proposed in this project. Our partners at ORNL from the Advance Scientific Computing Research (ASCR) organization will collaborate in accelerating the integrated plasma material interaction simulation framework. This simulation framework will provide a platform for generating simulation data across a range of physical fidelities, including hybrid methods that produce multi-fidelity results. This data will be leveraged for AI model development, both for generation of surrogates and the automation of simulation campaigns. A part of the research below will include collaborative efforts with the TAIMC to (i) adapt data storage approaches to ensure AI-readiness, (ii) provide a protypical exemplar to inform and exercise constructed workflows, and (iii) generate and share data, using the TAIMC unified AI data standard, for foundational models that will be trained from multiple sources across the DOE complex. We will also collaborate with the TAIMC, as well as the planned AI modeling teams, to develop approaches for reducing the cost of data generation. These include tailored multi-fidelity approaches as well as fine-tuning strategies to augment general, large-scale foundational models.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2025

Influence of Process Parameter and Build Rate Variations on Defect Formation in Laser Powder Bed Fusion SS316L

Laser powder bed fusion (LPBF) is an additive manufacturing process that has gained interest for its material fabrication due to multiple advantages, such as the ability to print parts with small feature sizes, good mechanical properties, reduced material waste, etc. However, variations in the key process parameters in LPBF may result in the instantiation of porosity defects and variation in build rate. Particularly, volumetric energy density (VED) is a variable that encapsulates a number of those parameters and represents the amount of energy input from the laser source to the feedstock. VED has been traditionally used to inform the quality of the printed part but different values of VED are presented as optimal values for certain material systems. An optimal VED value can be maintained by changing the key process parameters so that various combinations yield a constant value. In this study, an optimal constant VED value is maintained while printing SS316L with variable key processing parameters. Porosity analysis is performed using optical microscopy, as well as X-ray computed tomography, to reveal the volume density and distribution of those pores. Two primary defect categories are identified, namely lack of fusion and porosity induced by balling defects. The findings indicate that, even at optimal VED, variations in process parameters can significantly influence defect type, underscoring the sensitivity of defect formation to the variation of these parameters. Furthermore, a minor change in the build rate, driven by adjustments in process parameters, was found to influence defect categories. These findings emphasize that fine tuning the process parameters and build rate is essential to minimize defects. Finally, fiducial marks have been identified as a source of unintentional porosity defects. These results enable the refinement of process parameters, ultimately optimizing LPBF to achieve enhanced material density and expedite the printing.

36 MATERIALS SCIENCE↗

DOE OSTI2025

PROCESS-STRUCTURE-PROPERTY RELATIONSHIPS IN LASER POWDER BED FUSION PRODUCED 17-4 PH STEEL

Laser powder bed fusion (LPBF) is a metal additive manufacturing method that produces non-traditional microstructures as a result of the rapid solidification and thermal cycling inherent to the process. When using LPBF-produced material in application, these unique microstructures challenge the applicability of well developed mechanical property databases achieved by conventional heat treatments. For wider adoption of this technology, a more holistic understanding is necessary on how process attributes develop material structure, which dictate mechanical properties. This dissertation explores the process– structure–property relationships in LPBF 17-4 PH steel through systematic evaluation of atmospheric processing and heat treatment effects on microstructure and mechanical performance. Specimens were fabricated under controlled build environments, subjected to a range of solutionizing, homogenizing, and aging treatments, and characterized using optical microscopy, electron back scatter diffraction (EBSD), and X-ray diffraction (XRD) to quantify phase evolution. Tensile testing was performed to directly link heat treatment pathway and nitrogen absorption to mechanical performance. This work demonstrates where conventional heat treatment standards are applicable to LPBF 17-4 PH steel and where modifications are required. By directly correlating phase stability, nitrogen effects, and tensile response, this work provides practical guidelines for tailoring post-processing strategies. These findings underscore that successful application of LPBF 17-4 PH steel requires explicit consideration of both build environment and post-processing. By linking processing conditions to microstructure and performance, this work advances understanding of critical variables that govern reliability of additively manufactured precipitation-hardened stainless steels in demanding applications.

Brown, Benjamin [Kansas City National Security Cam↗

DOE OSTI2025

Autonomous Aerosol and Plasma Co‐Jet Printing of Metallic Devices at Ambient Temperature

Abstract Additive manufacturing of metallic materials holds the potential to revolutionize the fabrication of functional devices unattainable via traditional methods. Despite recent advancements, printing metallic materials typically requires thermal processing at elevated temperatures to form dense structures with desired properties, which presents a major challenge for direct printing and integration with temperature‐sensitive materials. Herein, a unique co‐jet printing (CJP) method is reported integrating an aerosol jet and a non‐thermal, atmospheric pressure plasma jet to enable concurrent aerosol deposition of metal nanoparticle inks and in situ sintering at ambient temperature. A machine learning algorithm is integrated with the CJP to perform real‐time defect detection and autonomous correction, enhancing the yield of printed films with high electrical conductivity from 44% to 94%. Concurrent printing and sintering eliminate the need for post‐printing processing, reducing the overall manufacturing time by multiple folds depending on product size. CJP enables direct printing of functional devices on a variety of temperature‐sensitive materials including biological materials. Direct printing of hydration sensors on living plant leaves is demonstrated for long‐duration monitoring of hydration level in the plant. The versatile CJP method opens tremendous opportunities to harmoniously integrate abiotic and biotic materials for emerging applications in wearable/implantable devices and biohybrid systems.

Du, Yipu [Department of Aerospace and Mechanical E↗

DOE OSTI2025

Investigating the mechanism of copper–carbon interactions in ultraconductor materials via in situ thermal X-ray and Raman spectroscopy

Improving energy transfer efficiency is critical to advancing technologies for a more sustainable future. Nanoscale materials, specifically metal–carbon composites such as ultraconductors, have shown promise in this field due to their enhanced electrical and thermal conductivities. However, the origin of the enhancement has yet to be determined. Prior research has primarily explored these materials at room temperature in an attempt to explain this phenomenon, but these materials have not yet been examined under enhanced thermal conditions. This study probes ultraconductor materials during the heating process to uncover the origins of their enhanced thermal conductivity. Understanding the mechanism underpinning the enhanced properties of the material could lead to increased property enhancement and therefore improved performance in energy transfer technologies. In this work we employ in situ thermal X-ray absorption near edge spectroscopy (XANES) and Raman spectroscopy to characterize copper-based covetic materials, revealing how thermal conditions influence the bonding environment and interaction between the copper and infused carbon. Our findings suggest that heating the materials does not result in the formation of chemical bonds between the carbon and copper framework of the material but rather points to a primarily physical interaction within the sample. Furthermore, we hypothesize possible mechanisms underlying the nature of the physical interaction leading to enhanced properties. These insights contribute to a deeper understanding of the material's behavior under relevant thermal conditions and highlight its potential for integration into next-generation energy systems.

36 MATERIALS SCIENCE↗

DOE OSTI2024

Preliminary Results on Process Modeling Tools for Determining Variability in Additively Manufactured Stainless Steel 316 Parts

The Advanced Materials and Manufacturing Technologies program aims to accelerate the development, qualification, demonstration, and deployment of advanced materials and manufacturing technologies to enable reliable and economical nuclear energy. However, the distinct characteristics of additive manufacturing (AM) materials, stemming from their unique processing history, microstructure, and properties, pose significant challenges for the qualification and certification of nuclear components. These challenges primarily arise from component-scale variations in microstructure and properties influenced by local process conditions and geometry, which affect thermal history, melt pool dynamics, and microstructure evolution. Computational modeling tools can play a crucial role in predicting and controlling this variability. This report presents preliminary results on process modeling tools designed to predict microstructure variability in additively manufactured stainless steel 316 parts. It details the software packages and physical modeling approaches employed to simulate an AM component within an automated process modeling workflow. Initial results are demonstrated through comparisons between predicted microstructures and experimental measurements across various representative processing conditions. The report concludes by discussing the challenges inherent in process modeling of AM components and outlines a plan for future development needs.

36 MATERIALS SCIENCE↗

DOE OSTI2026

NLR CSP Optical Facilities: Illuminating the Path Forward Through Innovation and Impact: Agreement 38490

This initiative is a multi-faceted project at the National Laboratory of the Rockies (NLR) aimed at strengthening its Concentrating Solar Power (CSP) Optical Facilities to advance the development of low-cost, high-performance materials for solar and other applications. The project's strategy is built on three pillars: strategic stakeholder engagement, diligent facility maintenance and utilization, and the development of new research capabilities. The overarching goal is to ensure the facilities remain state-of-the-art resources for industry and academia, thereby accelerating the conversion of concentrated sunlight into energy. A key driver of the project is an international Advisory Board, which provides critical guidance on research priorities and industry needs, leading to new collaborations and secured funding. This external engagement, combined with proactive outreach to industry partners, ensures the lab's work remains aligned with real-world challenges, including materials durability and performance certification. Significant efforts in facility maintenance have addressed challenges with aging infrastructure. Notable achievements include the complete refurbishment of the hail-damaged Ultra-Accelerated Weathering System (UAWS) and the successful replacement of a failing 15-year-old Lambda 1050 spectrophotometer with a new-generation model, substantially upgrading material characterization capabilities. These maintenance activities were complemented by achieving a prestigious ISO 9001:2015 certification for the Advanced Optical Materials Labs, formally recognizing the quality and reliability of NLR's measurement capabilities. Despite these successes, challenges remain, including high demand for the High Flux Solar Furnace (HFSF) and intermittent failures of other key instruments. The project has delivered major advancements in research techniques and capabilities. At the Flatirons campus, a new indoor laboratory, was established to house advanced deflectometry and photogrammetry systems for heliostat characterization. For on-sun testing, a novel, actively cooled turning mirror was developed for the HFSF, enabling more realistic testing of particle receivers and components. A collaboration with Virginia Tech successfully demonstrated the high-temperature durability of a new solar absorber coating through extensive cyclic testing. Concurrently, new modeling took place to better predict material degradation on rough, fractal surfaces. In summary, this project has systematically enhanced NLR's CSP Optical Facilities through strategic upgrades, rigorous maintenance, and stakeholder-guided research. By overcoming equipment failures, budgetary constraints, and logistical hurdles, the project has reinforced NLR's role as a central hub for CSP innovation and materials testing. Future work will focus on securing diverse funding, expanding collaborations, and continuing to provide the critical infrastructure needed to accelerate the development and deployment of next-generation technologies.

14 SOLAR ENERGY↗

DOE OSTI2024

Innovative Joining Method for Hybrid Composites with Tailored Performance

In various industries, such as renewables and aerospace, the demand for carbon fiber is rapidly growing to meet the requirements of high-performance applications with optimized designs. Traditionally, these optimized designs have been limited to single-material composites due to process constraints. However, this study aims to explore the feasibility of transitioning between two different fiber types for pultrusion and filament winding processes, thereby enhancing design flexibility, and optimizing performance. This research introduces a novel method of hybrid reinforcement through splicing techniques. By employing a splicing method, fibers are merged prior to infusion, resulting in a transition of higher strength compared to traditional joints. Moreover, this technique enables the creation of composites with variable compositions, allowing for the selective placement of properties in a process previously characterized by static properties. The objective of this paper is to evaluate the mechanical properties of dry fiber splicing and its impact on the resulting composite material. Through comprehensive analysis, we aim to provide insights into the feasibility and advantages of employing this innovative splicing technique in composite manufacturing processes.

Guzorek, Steven↗

DOE OSTI2025

Multiscale Characterization of Additive Manufacturing Components with Computed Tomography, 3D X-ray Microscopy, and Deep Learning

Additive manufacturing (AM) facilitates the creation of complex-geometry parts, driving advancements in lightweight aerospace components, high-efficiency engine cooling channels, and customized medical implants. However, ensuring the quality and reliability of AM parts remains challenging due to internal defects, surface irregularities, porosity, and residual trapped powder, which are often inaccessible to traditional inspection methods. Recent developments in X-ray computed tomography (XCT) and 3D X-ray microscopy (XRM), particularly systems equipped with resolution-at-a-distance (RaaD™) capabilities, enable high-resolution, non-destructive evaluation of AM components across multiple scales, from sub-micrometer to macroscopic levels. This paper explores modern XCT and XRM techniques for multiscale characterization of AM parts, focusing on their ability to detect and analyze defects such as porosity, cracks, inclusions, and surface roughness, while offering insights into defect formation mechanisms, material properties, and process-induced variations. The integration of deep learning (DL) frameworks, including Simurgh, DeepRecon, and DeepScout, enhances XCT/XRM workflows by reducing scan times, improving resolution recovery, and enabling accurate defect detection even with limited projection data. These DL-based methods overcome limitations of traditional reconstruction techniques, enabling faster, more reliable characterization of dense materials like Inconel 718 and novel alloys such as AlCe. Applications include process parameter optimization, high-throughput quality control, and multistage AM process evaluation, with DL-enhanced workflows accelerating analysis times from weeks to days. Correlative imaging approaches further validate XCT and XRM data against scanning electron microscopy (SEM) images of physically sectioned samples, confirming the accuracy of DL-based reconstructions and enabling comprehensive defect analysis. While challenges remain in generalizing DL models to diverse materials and imaging conditions, improvements in resolution, noise reduction, and defect detection highlight the transformative potential of these methods. This multiscale and correlative approach enables precise identification and correlation of microstructural features with the overall performance of AM components. By integrating advanced XCT, XRM, and DL techniques, this paper demonstrates a significant leap forward in AM characterization, offering valuable insights into the relationships between processing parameters, microstructure, and part performance, and driving innovations that enhance the quality and reliability of AM products for demanding industrial applications.

Additive manufacturing↗

DOE OSTI2024

Radiation-induced alteration of apatite on the surface of Mars: first in situ observations with SuperCam Raman onboard Perseverance

Abstract Planetary exploration relies considerably on mineral characterization to advance our understanding of the solar system, the planets and their evolution. Thus, we must understand past and present processes that can alter materials exposed on the surface, affecting space mission data. Here, we analyze the first dataset monitoring the evolution of a known mineral target in situ on the Martian surface, brought there as a SuperCam calibration target onboard the Perseverance rover. We used Raman spectroscopy to monitor the crystalline state of a synthetic apatite sample over the first 950 Martian days (sols) of the Mars2020 mission. We note significant variations in the Raman spectra acquired on this target, specifically a decrease in the relative contribution of the Raman signal to the total signal. These observations are consistent with the results of a UV-irradiation test performed in the laboratory under conditions mimicking ambient Martian conditions. We conclude that the observed evolution reflects an alteration of the material, specifically the creation of electronic defects, due to its exposure to the Martian environment and, in particular, UV irradiation. This ongoing process of alteration of the Martian surface needs to be taken into account for mineralogical space mission data analysis.

Science & Technology - Other Topics↗

DOE OSTI2024

TuFF internal WRAP for Rapid Pipeline Repair (TuFF iWRAP)

The goal of “TuFF internal WRAP for Rapid Pipeline Repair” (TuFF iWRAP) program was to develop a novel material system and placement process to fabricate structural pipe within the existing deteriorated pipelines without disruption of gas delivery. The team (University of Delaware – Center for Composite Materials (UD-CCM) and Plitzie Inc.) addressed this challenge by developing a new material feedstock and pipe in pipe (PIP) repair strategy. This allows the potential for significant cost reduction and has minimum operational impact on gas customers. The new robotic based placement design allows discontinuous placement of pipe sections creating a stand-alone structural liner within the legacy pipeline without the need for pipe shutdown. Here, the material is supplied using a tethered material feeding system and is placed and UV cured with the internal Wound Rapid Automated Placement (iWRAP) system. This provides maximum placement efficiency capable of traversing 90 angle bends in 12-inch pipe and overall design customization to meet pipe repair requirements (e.g., variable wall thickness, bridging gaps, etc.). UV-curable fiber reinforced composite material has been optimized to meet structural performance and placement/cure times. Superior strength and fatigue life has been demonstrated by improving fiber-matrix adhesion using new fiber sizing for UV resins. Rapid cure approaches using new liner and resins have been evaluated with industry. The appropriate design of the section joints has been developed and tested. We estimate coating time to be ~100 hours per mile enabling typical pipe repair within 1 week.

03 NATURAL GAS↗

DOE OSTI2024

Invertible Temper Modeling using Normalizing Flows and the Effects of Structure Preserving Loss

Advanced manufacturing research and development is typically small-scale, owing to costly experiments associated with these novel processes. Deep learning techniques could help accelerate this development cycle but frequently struggle in small-data regimes like the advanced manufacturing space. While prior work has applied deep learning to modeling visually plausible advanced manufacturing microstructures, little work has been done on data-driven modeling of how microstructures are affected by heat treatment, or assessing the degree to which synthetic microstructures are able to support existing workflows. We propose to address this gap by using invertible neural networks (normalizing flows) to model the effects of heat treatment, e.g., tempering. The model is developed using scanning electron microscope imagery from samples produced using shear-assisted processing and extrusion (ShAPE) manufacturing. This approach not only produces visually and topologically plausible samples, but also captures information related to a sample’s material properties or experimental process parameters. We also demonstrate that topological data analysis, used in prior work to characterize microstructures, can also be used to stabilize model training, preserve structure, and improve downstream results. We assess directions for future work and identify our approach as an important step towards end-to-end deep learning system for accelerating advanced manufacturing research and development.

Howland, Sylvia↗

DOE OSTI2024

Stabilizing Nickel‐Rich Cathodes in Aqueous Process through Nanocellulose as Water Barrier

Nickel-rich LiNi 0.8 Co 0.1 Mn 0.1 O 2 (NMC 811) cathode offers high voltage and high specific capacity, making it promising for high energy density batteries. However, large-scale manufacturing of aqueous-processed NMC 811 electrodes remains challenging due to proton exchange causing material decomposition and capacity loss. This work addresses this issue by constructing an in situ nanocellulose protective layer for NMC 811 particles via electrostatic interactions during the slurry preparation. For the first time, the interatomic spacing between inter-chains of nanocellulose is measured through wide-angle X-ray scattering and demonstrate the ability to effectively confine interlayer water using atomistic simulations. Moreover, this nanocellulose coverage simultaneously minimizes Li + surface segregation and mitigates water infiltration. Owing to less material decomposition during the aqueous processing, nanocellulose-protected NMC electrodes exhibit higher initial coulombic efficiency (83% vs 62% at 0.1C) and capacity (133 vs 59 mAh g −1 at 6C) than unprotected electrodes. Additionally, optimized aqueous-processed NMC electrodes offer comparable or even superior electrochemical properties compared to the electrodes fabricated using the conventional toxic organic solvent, N-methyl-2-pyrrolidone. Consequently, the developed approach enables affordable, sustainable aqueous processing for Nickel-rich NMC 811 cathodes with excellent electrochemical performances.

25 ENERGY STORAGE↗