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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 OSTI2025

Transfer of EP and Doping Technology for PIP-II HB650 Cavities from Fermilab to Industry

Fermilab has optimized the surface processing conditions for PIP-II high beta 650 MHz cavities. This encompasses conditions for bulk electropolishing, heat treatment, nitrogen doping, post-doping final electropolishing, and post-processing surface rinsing. The technology has been effectively transitioned to industry. This paper highlights the efforts made to fine-tune the process and to smoothly share them with the partner labs and an associated vendor.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

Transfer of EP and Doping Technology for PIP-II HB650 Cavities from Fermilab to Industry

Fermilab has optimized the surface processing conditions for PIP-II high beta 650 MHz cavities. This encompasses conditions for bulk electropolishing, heat treatment, nitrogen doping, post-doping final electropolishing, and post-processing surface rinsing. The technology has been effectively transitioned to industry. This paper highlights the efforts made to fine-tune the process and to smoothly share them with the partner labs and an associated vendor.

Chouhan, V.↗
DOE OSTI2024

SQMS science advances impact on Rigetti commercial processors

The collaboration between the Superconducting Quantum Materials and Systems Center (SQMS) and Rigetti Computing produced several advancements in our understanding of the role of materials characteristics in quantum processor performance. This partnership leverages SQMS's extensive characterization infrastructure and cutting-edge research in materials, and Rigetti's expertise in quantum hardware and robust nanofabrication to improve precision and performance of Rigetti's test QPUs. Qubit frequency is determined in large part by the properties of Josephson junctions (JJs) made of amorphous oxide tunnel barriers; the Alternating-Bias Assisted Annealing (ABAA) process allows us to tune JJs to their desired frequency [1]. Work by SQMS researchers in characterizing high-precision JJs post-processed (using ABAA) have yielded crucial information on the nature of the structure and chemical bonding uniformity of the ABAA processed amorphous oxides. Performance has also been improved through a comprehensive series of experiments that tested encapsulation and surface treatment. Encapsulation of the niobium metal layer with tantalum resulted in an T1 improvement of 80%, experimentally confirming the role of Nb surface losses in qubit performance [2]. Pre-treatment of the underlying silicon surface prior to JJ fabrication by replacing a buffered oxide etch (BOE) with hydrofluoric acid (HF) followed by aqueous ammonium fluoride (NH4F) has shown a statistically significant improvement of T1 by 22%, and reduction in the number of strongly-coupled TLS [3]. These examples, as well as many other published and ongoing investigations, demonstrate the mutual benefits that come from Rigetti's involvement in the SQMS collaboration. [1] - Pappas, D.P., et al. (2024). https://doi.org/10.1038/s43246-024-00596-z [2] - Bal, M., et al. (2024). https://doi.org/10.1038/s41534-024-00840-x [3] Kopas, C. J. et al. Preprint at https://doi.org/10.48550/arXiv.2408.02863 (2024).

Lachman, Ella↗
DOE OSTI2025

Real-time process monitoring and automated control for direct ink write 3D printing of frontally polymerizing thermosets

Additive manufacturing (AM) enables the fabrication of complex geometries, yet its application to thermosets remains limited by post-processing requirements. Frontal ring-opening metathesis polymerization (FROMP) offers a promising alternative, enabling energy-efficient, in situ curing of freestanding thermoset structures. This study presents a real-time process monitoring and automated control system for direct ink writing (DIW) of FROMP thermosets. By integrating thermochromic leuco dyes and computer vision, we enable real-time polymerization front tracking, allowing autonomous printing parameter adjustments for consistent geometries across resin formulations. The system’s accuracy was validated against manual tracking, demonstrating precise front velocity detection. Its adaptability was confirmed by printing freestanding mechanical springs with different resins, achieving consistent geometries and mechanical properties despite front velocity variations. These findings highlight the potential of automated DIW control for scalable, repeatable, and material-agnostic 3D printing of thermosets.

Mejia, Edgar Brian [Sandia National Laboratories (↗
DOE OSTI2025

Laser Ablation for Low-Cost Multijunction III-V Solar Cell Mesa Isolations

Eliminating photolithography from solar cell processing is a significant opportunity for cost reduction for III-V solar cells. In this work, we test femtosecond laser ablation and scribing as an alternative to contact photolithography and wet chemical etching for mesa isolation, when processing multijunction cells. We demonstrate that upright multijunction solar cells isolated by using the laser as a scribe to cleave through the substrate had virtually no performance loss when compared to a baseline cell processed with photolithography. By contrast, cells isolated by laser ablating through the active layers have performance losses that cannot be fully eliminated with post-processing etches. This demonstration of photolithography-free mesa isolation with no performance losses is promising for less expensive III-V manufacturing.

14 SOLAR ENERGY↗
DOE OSTI2025

Design, fabrication, simulation, and testing of additively manufactured lattice-based copper heat sinks

Here, this study investigates the potential advantages of lattice structure-based bound metal material extrusion (MEX) 3D printing for fabricating high-performance copper heat sinks. Copper powder-filled polymer filaments, with a copper content of > 90 wt.%, were developed specifically for the bound metal MEX 3D printing process. Three types of structures—planar, strut, and surface lattices—were 3D printed to facilitate efficient heat transfer pathways within the heat sinks. Subsequent post-processing steps, including polymer removal and sintering, were performed to achieve dense copper parts. Hot isostatic pressing was further employed to enhance the sintered density from 93 to 98%. Finite element analysis (FEA) simulations were conducted to assess the heat transfer efficiency of the designs, and heat transfer experiments were performed using a custom setup to validate the simulation results. Additionally, this research explores the use of extended hold times during pre-sintering and a reduced atmosphere to enhance the %IACS values (electrical conductivity) and thermal performance of the bound metal MEX 3D printed copper heat sinks. The investigation combines experimental analysis, including simulations and heat transfer experiments, to gain insights into the structure-material property relationships and optimize the thermal performance of the printed heat sinks.

Ajjarapu, Kameswara Pavan Kumar [Oak Ridge Nationa↗
DOE OSTI2026

Advancing Industry 4.0: Multimodal Sensor Fusion for AI-Based Fault Detection in 3D Printing

Additive manufacturing, particularly fused deposition modeling, is transforming modern production by enabling rapid prototyping and complex part fabrication. However, its layer-by-layer process remains vulnerable to faults such as nozzle clogging, filament runout, and layer misalignment, which compromise print quality and reliability. Traditional inspection methods are costly, time-intensive, and often limited to post-process analysis, making them unsuitable for real-time intervention. In this current study, the authors developed a novel, low-cost, and portable faultdetection system that leverages multimodal sensor fusion and artificial intelligence for real-time monitoring in FDM-based 3D printing. The system integrates acoustic, vibration, and thermal sensing into a non-intrusive architecture, capturing complementary data streams that reflect both mechanical and process-related anomalies. Acoustic and thermal sensors operate in a fully contactless manner, while the vibration sensor requires minimal attachment such that it will not interfere with printer hardware, thereby preserving portability and ease of deployment. The multimodal signals are processed into spectrograms and time-frequency features, which are classified using convolutional neural networks for intelligent fault detection. The proposed system advances Industry 4.0 objectives by offering an affordable, scalable, and practical monitoring solution that improves faultdetection accuracy, reduces waste, and supports sustainable, adaptive manufacturing.

42 ENGINEERING↗
DOE OSTI2025

3D Printing of Highly Porous Polypropylene Separators for Lithium‐Ion Batteries Using Fused Deposition Modeling and Thermally Induced Phase Separation

Appearing as one of the key-components of lithium-ion batteries (LIBs), this work specifically focuses on the additive manufacturing (AM) of custom-shape separators, facilitated by the filament material extrusion process, also called fused deposition modeling (FDM). The development and optimization of composite thermoplastic filament feedstocks combining polypropylene and paraffin wax, followed by the 3D printing of the separator membranes is shown. A post-processing step, based on thermal induced phase separation (TIPS), is introduced to promote porosity formation through removal of the paraffin wax sacrificial phase within the 3D printed items. Separators with different polypropylene/paraffin wax ratios are developed and the impact on printability, mechanical strength, porosity, and electrochemical performances, is thoroughly discussed. X-ray micro-computed tomography is employed to assess the geometric fidelity and to detect printing defects in a complex 3D lattice structure. The performance of the 3D printed porous separators is also compared to a commercial separator. This pioneering research establishes a foundation for the creation of porous separators that can adapt to and conform into 3D printed battery architectures with novel form factors, and also creates opportunities for the use of FDM and TIPS for a wide range of applications that employ porous structures beyond the energy storage field.

3D printing↗
DOE OSTI2025

Evaluating the factors influencing accuracy, interpretability, and reproducibility in the use of machine learning classifiers in biology to enable standardization

The complexity and variability of biological data has promoted the increased use of machine learning methods to understand processes and predict outcomes. These same features complicate reliable, reproducible, interpretable, and responsible use of such methods, resulting in questionable relevance of the derived. outcomes. Here we systematically explore challenges associated with applying machine learning to predict and understand biological processes using a well- characterized in vitro experimental system. We evaluated factors that vary while applying machine learning classifers: (1) type of biochemical signature (transcripts vs. proteins), (2) data curation methods (pre- and post-processing), and (3) choice of machine learning classifier. Using accuracy, generalizability, interpretability, and reproducibility as metrics, we found that the above factors significantly mod- ulate outcomes even within a simple model system. Our results caution against the unregulated use of machine learning methods in the biological sciences, and strongly advocate the need for data standards and validation tool-kits for such studies.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Impact of T - and ρ -dependent decay rates and new (n, γ ) cross-sections on the s process in low-mass asymptotic giant branch stars

Aims. We study the impact of nuclear input related to weak-decay rates and neutron-capture reactions on predictions for the slow neutron-capture process (s process) in asymptotic giant branch (AGB) stars. We provide the first database of surface abundances and stellar yields of the isotopes heavier than iron from the Monash models. Methods. We ran nucleosynthesis calculations with the Monash post-processing code for seven stellar structure evolution models of low-mass AGB stars with three different sets of nuclear inputs. The reference set has constant decay rates and represents the set used in the previous Monash publications. The second set contains the temperature and density dependence of β decays and electron captures based on the default rates of nuclear NETwork GENerator (NETGEN). In the third set, we further update 92 neutron-capture rates based on re-evaluated experimental cross sections from the ASTrophysical Rate and rAw data Library. We compare and discuss the predictions of the sets relative to each other in terms of isotopic surface abundances and total stellar yields. We also compare the results to isotopic ratios measured in presolar stardust silicon carbide (SiC) grains from AGB stars. Results. The new sets of models result in a ∼66% solar s-process contribution to the p-nucleus 152 Gd, confirming that this isotope is predominantly made by the s process. The nuclear input updates result in predictions for the 80 Kr/ 82 Kr ratio in the He intershell and surface 64 Ni/ 58 Ni, 94 Mo/ 96 Mo, and 137 Ba/ 136 Ba ratios that are more consistent with the corresponding ratios measured in stardust; however, the new predicted 138 Ba/ 136 Ba ratios are higher than the typical values of the SiC grains. The W isotopic anomalies are in agreement with data from the analyses of other meteoritic inclusions. We confirm that the production of 176 Lu and 205 Pb is affected by too large uncertainties in their decay rates from NETGEN.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

High‐Concentration Antibody Formulation via Solvent‐Based Dehydration

Abstract Although subcutaneous (SC) delivery is the preferred administration route for immunotherapies and other biologics for improved patient compliance and lower healthcare costs, it necessitates high‐concentration antibody formulations. However, high‐concentration antibody solutions face significant instabilities and prohibitively high viscosities. Other approaches for high‐concentration formulations have been developed, including non‐aqueous solutions, which can be irritating or painful, and antibody‐laden hydrogel microparticles, which require centrifugation and are limited to concentrations <300 mg mL −1 . This work presents a new formulation process wherein the antibody is concentrated and encapsulated into hydrogel microparticles via solvent‐based dehydration. The final dosage form is an aqueous particle suspension with a formulation concentration of 360 mg mL −1 . In this process, microparticles are synthesized continuously, and antibody precipitation is realized simultaneously to dehydration, which allows for higher antibody concentrations. Antibody phase behavior and precipitation–dehydration kinetics are analyzed. The antibody is structurally and functionally stable in the microparticle post‐processing and after 4 months. Injectability of the suspension meets clinical standards with glide force <20 N. For the first time, an aqueous antibody formulation at high concentrations comparable to non‐aqueous formulations is presented, ideal for subcutaneous administration. The process is envisioned to be generalizable as a platform for SC delivery in multiple clinical applications.

Zheng, Talia [Department of Chemical Engineering M↗
DOE OSTI2025

SetBERT: the deep learning platform for contextualized embeddings and explainable predictions from high-throughput sequencing

MOTIVATION: High-throughput sequencing (HTS) is a modern sequencing technology used to profile microbiomes by sequencing thousands of short genomic fragments from the microorganisms within a given sample. This technology presents a unique opportunity for artificial intelligence to comprehend the underlying functional relationships of microbial communities. However, due to the unstructured nature of HTS data, nearly all computational models are limited to processing DNA sequences individually. This limitation causes them to miss out on key interactions between microorganisms, significantly hindering our understanding of how these interactions influence the microbial communities as a whole. Furthermore, most computational methods rely on post-processing of samples which could inadvertently introduce unintentional protocol-specific bias. RESULTS: Addressing these concerns, we present SetBERT, a robust pre-training methodology for creating generalized deep learning models for processing HTS data to produce contextualized embeddings and be fine-tuned for downstream tasks with explainable predictions. By leveraging sequence interactions, we show that SetBERT significantly outperforms other models in taxonomic classification with genus-level classification accuracy of 95%. Furthermore, we demonstrate that SetBERT is able to accurately explain its predictions autonomously by confirming the biological-relevance of taxa identified by the model. AVAILABILITY AND IMPLEMENTATION: All source code is available at https://github.com/DLii-Research/setbert. SetBERT may be used through the q2-deepdna QIIME 2 plugin whose source code is available at https://github.com/DLii-Research/q2-deepdna.

Ludwig, David W↗
DOE OSTI2025

Slicing Solutions for Wire Arc Additive Manufacturing

Both commercial and research applications of wire arc additive manufacturing (WAAM) have seen considerable growth in the additive manufacturing of metallic components. However, there remains a clear lack of a unified paradigm for toolpath generation when slicing parts for WAAM deposition. Existing toolpath generation options typically lack the appropriate features to account for all complexities of the WAAM process. This manuscript explores the key slicing challenges specific to toolpaths for WAAM geometry and pairs each consideration with multiple solutions to mitigate most negative effects on completed components. These challenges must be addressed to minimize voids, prevent bead collapse, and ensure deposited components accurately approximate the desired geometry. Slicing considerations are grouped into four general categories: geometric, process, thermal, and productivity. Geometric considerations are addressed with overhang compensation, corner-sharpening, and toolpath-smoothing features. Process considerations are addressed with start point configuration and controls for the bead lengths and end points. Thermal and productivity considerations are addressed with island optimization, multi-material printing, and connected insets. Finally, tools for the post-processing of generated G-code are explored. Overall, these solutions represent a critical set of slicing features used to improve generated toolpaths and the quality of the components deposited with those toolpaths.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Microstructural evaluation of the creep behavior in L-PBF Ni-based superalloys

This presentation at ICAM 2024 Conference focuses on the commonalities and differences in the creep rupture behavior and creep mechanisms for three distinct classes of laser powder-bed fusion (L-PBF) Ni-based superalloys (γ’-precipitate strengthened Haynes® 282®, γ’/γ”/δ-precipitate strengthened Alloy 718, and solid-solution strengthened Alloy 625) as compared to conventionally processed counterparts. A comparison of Larson-Miller parameter plots establishes that these alloys perform statistically within the bounds established for the wrought product, despite having dissimilar microstructural features and other artefacts associated with PBF-LB manufacturing and post-processing heat treatment. To understand the failure and the impact of composition, minor phases, and deformation defects on creep behavior, the fractography has been performed and microstructures have been evaluated in detail with SEM-EDS, EBSD, and HAADF-STEM. The underlying diffusional and dislocation creep mechanisms associated with this microstructural evaluation is discussed. This work is supported by NETL-FWP-1022408 Advanced Turbines.

Sudbrack, Chantal↗
DOE OSTI2025

Multi-physics Topology OPtimization and Additive Manufacturing for High-temperature Heat Exchangers

This research significantly advances the understanding of high-temperature heat exchanger design through an integrated approach that combines topology optimization (TO), triply periodic minimal surface (TPMS) structures, additive manufacturing (AM) and thermohydraulic testing. Each of these components contributes uniquely to a unified, high-performance design, fabrication and testing workflow. Topology optimization serves as the foundation of the design methodology by providing a systematic way to determine the most effective material layout for separating hot and cold fluids while maximizing thermal performance. The researchers introduced a novel three-material optimization framework using two density fields to represent hot fluid, cold fluid, and solid domains. This approach enables automated discovery of optimal shapes and flow paths that cannot be intuitively designed, especially under constraints imposed by manufacturing technologies. Furthermore, constraints such as minimal wall thickness and overhang angles were embedded into the optimization process, ensuring that resulting designs are not only thermally efficient but also manufacturable using modern additive techniques. In parallel, the study delves into the use of Gyroid-based TPMS geometries for constructing the core of the heat exchanger. TPMS structures are known for their high surface area, excellent fluid mixing capabilities, and minimal pressure drop characteristics. The researchers applied a data-driven modeling framework using Heteroscedastic Sparse Gaussian Process Regression (HSGPR) combined with genetic algorithms. This allowed for the rapid evaluation and optimization of key geometric parameters such as frequency, iso-value, and phase shift. The result was a set of Gyroid structures tailored for high heat transfer and low flow resistance, demonstrating clear improvements over conventional straight-channel designs. After the designing process, additive manufacturing played a critical role by turning these highly complex, optimized geometries into physical components. Utilizing Laser Powder Bed Fusion (LPBF) with Haynes 282, the study demonstrated the feasibility of fabricating these heat exchangers at high precision. Post-processing methods, including dilation-erosion operations, were applied to ensure local features adhered to self-supporting constraints. The fabricated structures were then subjected to thermohydraulic testing under conditions representative of supercritical CO 2 Brayton cycles, validating the predicted performance and confirming the viability of the full design-to-fabrication pipeline. Finally, thermohydraulic testing across the above studies served as a crucial experimental validation of advanced heat exchanger. Under consistent high-temperature and high-pressure conditions using supercritical CO 2 , the testing demonstrated that both TO and Gyroid-based TPMS designs significantly outperformed conventional straight-channel HXs. The TO design achieved a 115% increase in UA and NTU and a 27.6% boost in gravimetric power density, while the data-driven optimized Gyroid design delivered a 166% increase in UA and NTU and improved effectiveness from 68.7% to 86.1%. These results validate the simulation models, confirm the manufacturability of complex geometries under AM constraints, and provide key insights into design-performance trade-offs, thereby advancing the development of high-efficiency, compact heat exchangers for extreme environments.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Influence of the as-built microstructure on the recrystallization of an additively manufactured Inconel939 Ni-based superalloy

This study investigates the influence of the as-built microstructure on the recrystallization (RX) behavior and mechanical properties of the Ni-based superalloy Inconel 939 produced by laser powder bed fusion (PBF-LB/M). Two distinct as-built microstructures were obtained by varying the hatch distance (h d ): a columnar, strongly textured condition (h d =50, termed h d 50) and an equiaxed, weakly textured condition (h d =70, termed h d 70)). Both were subjected to nine solution treatments combining three temperatures (1100, 1150, and 1200 °C) and three holding times (1, 4, and 8 h). Comprehensive microstructural characterization was conducted to assess grain morphology, texture, grain boundary character, dislocation density, and precipitate distribution. Recrystallization was found to be significantly slower than in cast counterparts, requiring higher temperatures and longer times for completion. The initial microstructure plays a decisive role: full RX was achieved only in hd70 specimens after treatment at 1200 °C for 8 h, whereas hd50 samples exhibited delayed and incomplete RX under identical conditions. This behavior is attributed to the finer grain size and higher fraction of high-angle grain boundaries in hd70, which promote recrystallization. Mechanical testing revealed that hd70 samples subjected to a 1200 °C/8 h treatment followed by standard double ageing show higher yield and tensile strengths across the investigated temperature range than both printed and cast Inconel939 processed under conventional conditions, albeit with slightly reduced ductility. The enhanced mechanical performance is attributed to the larger grain size, which limits grain boundary sliding. These results demonstrate the critical importance of controlling the as-built microstructure and tailoring post-processing strategies to optimize high-temperature performance of PBF-LB/M Inconel939.

Inconel939↗
DOE OSTI2024

TEAMER: Crossflow Turbine Fairing Geometry Optimization - Report and CFD Modeling Files

The dataset includes computational fluid dynamics (CFD) models and simulation files for crossflow turbines as well as a detailed project report. The report documents the project undertaken by the Ocean Renewable Power Company (ORPC) to design and optimize a modular fairing for the Modular RivGen Marine Hydrokinetic (MHK) turbine, which enhances the efficient deployment and operation of turbine arrays. The project focused on optimizing the hydrodynamic performance of the fairing using CFD, with an emphasis on two key geometric parameters: the fairing's cross-sectional shape and the spacing between the rotor and the fairing. The analysis aimed to maximize net power output while also assessing discretized loading to evaluate ultimate and fatigue loads on the turbine components. The numerical modeling was conducted using both the commercial CFD software Star-CCM+ and the open-source code openFOAM, with the latter utilizing the actuator line library, turbinesFOAM. This dual-code approach was intended to increase confidence in the results and demonstrate the viability of using open-source tools for high-fidelity marine energy modeling. This dataset includes all necessary files for actuator line simulations in openFOAM, as well as 2D blade-resolved CFD results, along with Python and Java scripts for setting up and post-processing simulations.

16 TIDAL AND WAVE POWER↗
DOE OSTI2025

A Performance Model of In-Situ Techniques

The computational capacity of High-Performance Computing (HPC) systems increases continuously with the rapid development of central processing units (CPUs) and graphic processing units (GPUs), while the in-/output (IO) subsystem develops relatively slowly and storage capacity is also limited. Data-intensive applications, which are designed to leverage the high computational capacity of HPC resources, typically generate a considerable amount of data for post-processing visualizations and data analytics. The limited IO speed and storage space could lead to constraints in the actual performance of these applications and, therefore, scientific discovery. In-situ techniques, where data is visualized/analysed while still in memory rather than through disk, can contribute to alleviating these problems as they can reduce or even fully avoid data writing/reading through the IO subsystem to/from storage. However, the overall efficiency of insitu techniques crucially depends on the characteristics of both the in-situ tasks and the applications, and the resource distribution among them. Therefore, choosing the right in-situ approach (synchronous, asynchronous, or hybrid) and resource allocation is essential to minimize overhead and maximize the benefits of concurrent execution. In this paper, we present a performance model of in-situ techniques to find the most beneficial in-situ approach and the preferred resource configuration. We verify the high accuracy of our approach with over 6800 measurements and provide use cases with different applications.

Ju, Yi [Max Planck Computing and Data Facility, Ga↗