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DOE OSTI2026

Classic and Quantum Task-Based Intelligent Runtime for QIRs Running on Multiple QPUs

High-performance computing systems are rapidly evolving into heterogeneous platforms that fuse quantum accelerators with traditional classical processing units (CPUs) and graphical processing units (GPUs). This convergence calls for runtimes capable of managing both classical and quantum workloads in a unified manner. We introduce an intelligent, task-based runtime that marries the Intelligent RuntIme System (IRIS) asynchronous scheduler with a quantum programming stack through the Quantum Intermediate Representation Execution Engine (QIR-EE). Our design allows programs written in the quantum intermediate representation (QIR) to be dispatched concurrently to a variety of back-ends, including multiple quantum simulators and nascent quantum processors, enabling genuine hybrid execution on a single node. To illustrate its practicality, we partition a 4-qubit and 20-qubit circuit into three sub-circuits using quantum circuit cutting via the QCut library. Each sub-circuit is simulated independently by the QIR-EE driver within IRIS, after which a classical post-processing step merges the simulation results to recover the outcome of the original full-circuit computation. This case study demonstrates how finer task granularity can enable the parallel execution and lower the simulation burden per quantum task while preserving overall accuracy, highlighting the feasibility of our hybrid approach.

Miniskar, Narasinga Rao [ORNL] (ORCID:000000018259↗

DOE OSTI2025

Spatiotemporal Automatic Calibration of Infrastructure Lidar, Radar, and Camera with a Global Navigation Satellite System

Robust and accurate perception is important for modern intelligent transportation systems (ITS), which use sensors of various modalities for data fusion to create a digital twin of an intersection. Sensor calibration is an important process that creates a unified coordinate frame for the sensor output data so that it can be used for data fusion. Classical approaches for sensor calibration are time-consuming, require an overlapping field of view for feature matching, and are not feasible for ITS application as they cause disruptions in the flow of traffic. In this paper, we present a spatiotemporal automatic calibration approach to calibrate multiple infrastructure lidar, radar, and cameras installed at a traffic intersection. The approach uses global navigation satellite system (GNSS) positioning information shared by connected vehicles, and when the vehicle is detected by the sensor, we match the sensor detections with the GNSS coordinates. The proposed algorithm is evaluated with a real-world dataset utilizing detections from two radars, cameras, and lidars with a test vehicle instrumented with a post-processing kinematic (PPK)-corrected GNSS driving past the sensors installed at a four-way traffic intersection. The experimental results show that the proposed automatic calibration approach can achieve the transformation with a root mean squared error of less than 0.5 for radar and lidar and less than 2 for camera detections. The ability to rapidly calibrate sensors not only benefits initial installations, but can also be used for system health monitoring, while utilizing available connected vehicle data to test the real-time sensor fidelity and operational status.

ADVANCED PROPULSION SYSTEMS,ENERGY CONSERVATION, C↗

DOE OSTI2024

Spatiotemporal Automatic Calibration of Infrastructure Lidar, Radar, and Camera with a Global Navigation Satellite System: Preprint

ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2026

Microstructure Validation of Graph Theory Model-Derived Cooling Rates in the Wire Arc Additive Manufacturing of ER70S-6 Steel

Wire arc additive manufacturing (WAAM) enables high-rate fabrication of large metallic components, but spatial variations in thermal history can lead to microstructural heterogeneity that requires efficient process models to evaluate. This study evaluates whether cooling rates extracted from a graph theory model (GTM)-based thermal simulation are consistent with the microstructural evolution observed in an ER70S-6 WAAM wall. Thermal histories from the model were analyzed at selected build heights, and cooling rates were extracted from the final thermal excursion through the austenite phase field. Microstructures at corresponding locations were characterized using electron backscatter diffraction (EBSD) to quantify grain size distributions, and pearlite interlamellar spacing was used as an additional indicator of cooling behavior. The modeled cooling rates were highest near the substrate and generally decreased with build height, consistent with the observed reduction in the fine grain fraction and the progressive shift in the grain size distribution as build height increased. Pearlite spacing trends also supported the modeled cooling rate variation. These results indicate that GTM-derived thermal histories can be post-processed into metallurgically meaningful cooling rate estimates for WAAM steel builds and linked to dataset specific empirical grain size distribution relationships for process–thermal history–microstructure assessment.

36 MATERIALS SCIENCE↗

DOE OSTI2026

Capacities of Entanglement Distribution From a Central Source

Distribution of entanglement is an essential task in quantum information processing and the realization of quantum networks. In our work, we theoretically investigate the scenario where a central source prepares an N -partite entangled state and transmits each entangled subsystem to one of N receivers through noisy quantum channels. The receivers are then able to perform local operations assisted by unlimited classical communication to distill target entangled states from the noisy channel output. In this operational context, we define the EPR distribution capacity and the GHZ distribution capacity of a quantum channel as the largest rates at which Einstein-Podolsky-Rosen (EPR) states and Greenberger-Horne-Zeilinger (GHZ) states can be faithfully distributed through the channel, respectively. We establish lower and upper bounds on the EPR distribution capacity by connecting it with the task of assisted entanglement distillation. We also construct an explicit protocol consisting of a combination of a quantum communication code and a classical-post-processing-assisted entanglement generation code, which yields a simple achievable lower bound for generic channels. As applications of these results, we give an exact expression for the EPR distribution capacity over two erasure channels and bounds on the EPR distribution capacity over two generalized amplitude damping channels. We also bound the GHZ distribution capacity, which results in an exact characterization of the GHZ distribution capacity when the most noisy channel is a dephasing channel.

42 ENGINEERING↗

DOE OSTI2024

Accelerating Multivariate Functional Approximation Computation with Domain Decomposition Techniques⋆

Modeling large datasets through Multivariate Functional Approximations (MFA) provide an elegant way to handle many visualization and scientific analysis workflows. The process necessitates scalable data partitioning methods to compute MFA representations efficiently without compromising the accuracy or continuity of the reconstructed solution. We propose a domain -decomposed method for computing the MFA with B -spline bases, which reduces the total work per task and uses a restricted Additive Schwarz (RAS) method to converge the control point data degrees -of -freedom along subdomain boundaries. We provide an in-depth analysis of the parallel approach with domain decomposition solvers, aiming to minimize local subdomain error residuals and recover high -order continuity at subdomain interfaces with appropriate choices of knot overlaps. The communication cost, determined by the overlap regions in the RAS implementation, is optimized to recover the numerical error profile of the single subdomain case. Our proposed method stands in contrast to previous methods, which typically only recover either C 0 or at best C 1 continuity for arbitrary B -spline degree expansions, or those that require post -processing to blend discontinuities in the reconstructed data. We demonstrate the effectiveness of our approach using analytical and real -world datasets in 1D, 2D, and 3D through both strong and weak scaling studies. The performance results indicate that the overall cost of computing the approximation is directly proportional to the underlying nearest -neighbor communication implementation, and is only weakly dependent on the overlap region size that determines the size of the messages. This finding underscores the efficiency and scalability of our proposed method, making it a promising solution for handling large datasets in scientific workflows.

additive Schwarz solvers↗

DOE OSTI2026

Public Reference Data for Megawatt-Scale Hydrogen Electrolysis - NLR Historical Solar PV

The U.S. Department of Energy and National Laboratory of the Rockies (NLR) demonstrate hydrogen electrolysis from variable sources, hydrogen compression and storage, and hydrogen fuel cell power production using megawatt-scale equipment at NLR’s Flatirons Campus as part of the Advanced Research on Integrated Energy Systems (ARIES) research platform. This dataset represents part of that effort and is intended for academic, national laboratory, industrial, and other stakeholders to plan, design, and validate models of megawatt-scale hydrogen technologies and diverse energy infrastructure nationwide. These data provide a baseline for how existing hydrogen electrolysis technologies perform when coupled with various energy technologies. Future datasets will demonstrate how existing hydrogen fuel cell technologies can provide controllable, dispatchable, and variable power output for artificial intelligence data centers and other variable loads. This dataset entry describes the behavior of a 1.25-MW proton exchange membrane MC250 electrolyzer system, manufactured by Nel Hydrogen , [1] when fed historical data generated by the 430-kW, fixed-axis solar photovoltaic (PV) array located at NLR’s Flatirons Campus. (While the electrolyzer balance of plant supports up to 2.5 MW of electrolysis, NLR only has a single 1.25-MW electrolysis stack.) Solar PV power output data for the 2020 calendar year were categorized on a daily basis by total energy generation and standard deviation. Each day was then ranked by these metrics, and the 25th, 50th, and 100th percentiles were selected. The 75th percentile day did not exhibit sufficient variability to make for a valuable experiment. A similar process was used for the related historical wind dataset . [2] The historical days in 2020 that represented these percentiles are Dec. 19, March 29, and May 4, respectively. The entire solar day’s power profile was then fed through the MC250 electrolyzer. Due to its length, the 100th percentile day experiment was split into two parts, and the final 3 hours of the solar day were not captured. These final 3 hours contained no spikes or dips of interest and simply represented a slow decay of input solar power. Also, a single timestamp (13:13:47 on Jan. 14, 2026) was lost in the hydrogen system supervisory control and data acquisition. Finally, during the 25th percentile experiment (solar day Dec. 19, 2020) data recording was lost from 11:00:13 to 11:14:45. The roughly 15 minutes of the solar profile were rerun at the end of the experiment and spliced into this time slot during post-processing. The electrolysis system controls hydrogen production by varying direct current applied to the stack, from a maximum of 3,000 A to a minimum safe operation of 300 A, or 10%. Because the current–voltage characteristic changes as the stack ages and efficiency degrades, the actual minimum safe operating power changes over time. The historical solar profiles were translated from power (kilowatts) to current (amperes) using a curve fit with calibration data and sent to the electrolyzer power supply at 1-Hz frequency. For more details on the statistical analysis process, see the slide deck “Public Reference Data for Megawatt-Scale Hydrogen Electrolysis: NLR Historical Solar PV Analysis and Profile Generation” accessible with this data entry. These datasets report relevant hydrogen balance-of-plant and system data, all captured at 1 Hz, including hydrogen mass production measured with an Emerson Coriolis flow meter. Each .zip file represents a single solar PV electrolysis experiment and is formatted as: {technology}_{percentile}_{scaling factor} For instance, “solarPV-430kW_25_2x.zip” reports the experiment using the 25th percentile solar data from the historical 2020 solar PV dataset, scaled to 200%. Scaling factors were applied to the generated solar PV power output files to more closely match the 1.25-MW capacity of the electrolyzer. Each .zip folder contains the following files: A .csv file containing raw data. An .xlsx file explaining all the fields in the raw data. A .png plot showing the time series of hydrogen production, electrolysis power consumption, and solar power input. A PDF file detailing the historical solar data statistical analysis used to generate the solar profile. An experiment labeled “characterization_200.zip” demonstrates the MC250 electrolyzer steady-state response with 30-minute load steps for a total duration of 5 hours. Finally, a .csv file is provided with all experiments combined into one dataset labeled "combined_solarPV_experiments.csv". [1] nelhydrogen.com/product/mc-series-electrolyser . [2] data.nlr.gov/submissions/316 .

08 HYDROGEN↗

DOE OSTI2025

Over three decades, and counting, of near-surface turbulent flux measurements from the Atmospheric Radiation Measurement (ARM) user facility

Processes mediating the coupling of terrestrial, aquatic, biospheric, and atmospheric systems influence weather, climate, and ecosystem dynamics via transfer of energy, momentum, water, and carbon (or other species). These exchange processes are quantified by measurements of near-surface turbulent fluxes. Understanding processes at these interfaces provides insight toward understanding and predicting current and future states within the Earth system. The Atmospheric Radiation Measurement (ARM) user facility has been conducting measurements of near-surface turbulent fluxes since the early 1990s at long-term fixed locations and shorter-term mobile deployments across the Earth. ARM has utilized two established methods for conducting these measurements: energy balance Bowen ratio (EBBR) and eddy covariance (EC). Primary measurements from the former include sensible and latent heat flux, while the latter also measures fluxes of momentum and carbon (primarily carbon dioxide, with methane fluxes measured at two locations to date). The EBBR systems have been deployed at 22 locations, and, to date, the EC systems have been deployed at over 50 sites, with plans for additional novel site locations in the future. Herein, the history, evolution, and key aspects of these instrument systems are documented, along with information on data quality assurance and post-processing, as well as best use practices. Additionally, three data validation experiments were recently conducted, and their key findings are summarized. Finally, ancillary datasets acquired by ARM, which can contextualize and aid interpretation of the near-surface turbulent flux measurements, are discussed. The datasets described herein include the eddy correlation flux measurement system: 30ECOR (https://doi.org/10.5439/1879993, Sullivan et al., 1997), 30QCECOR (https://doi.org/10.5439/1097546, Gaustad, 2003), ECORSF (https://doi.org/10.5439/1494128, Sullivan et al., 2019a), and associated AmeriFlux and Methane Value-Added Product, AMCMETHANE (https://doi.org/10.5439/1508268, Billesbach, 2011); the energy balance Bowen ratio system: 30EBBR (https://doi.org/10.5439/1023895, Sullivan et al., 1993) and 30BAEBBR (https://doi.org/10.5439/1027268, Gaustad and Xie, 1993); and the carbon dioxide flux measurement system: CO2FLX (https://doi.org/10.5439/1287574, https://doi.org/10.5439/1287575, https://doi.org/10.5439/1287576, Koontz et al., 2015a, b, c; https://doi.org/10.5439/1989774, https://doi.org/10.5439/1989776, https://doi.org/10.5439/1992202, Biraud and Chan, 2002a, b, c). These data can be found by searching the above data stream names at https://adc.arm.gov/discovery/#/results/ (last access: 8 September 2025).

Sullivan, Ryan C. [Argonne National Laboratory (AN↗

DOE OSTI2024

Effect of Processing Parameters on Recrystallization During Hot Isostatic Pressing of Stellite-6 Fabricated Using Laser Powder Bed Fusion Technique

Crack-free Stellite-6 alloy was fabricated using the laser powder bed fusion technique equipped with a heating module as the first attempt. Single tracks were printed with a build plate heated to 400 °C to identify the processing window. Based on the melt pool dimensions, two combinations (sample A: 300 W/750 mm/s and sample B: 275 W/1000 mm/s) were identified to print the cubes. The as-printed microstructure comprised FCC-Co dendrites with M7C3 in the interdendritic region. W-rich M6C particles were found in the overlapping regions between the melt pools, matching the Scheil simulations. However, gas pores were observed due to the higher nitrogen and oxygen content of the feedstock requiring hot isostatic pressing (HIP) at 1250 °C and 150 MPa for 2 h. Sample A was partially recrystallized with slightly coarsened M7C3, while sample B underwent complete recrystallization followed by grain growth along with higher coarsening of the M7C3 after HIP. The varying recrystallization behavior can be attributed to the difference in residual stresses and grain aspect ratio in the as-built condition dictated by laser power and scanning speed. The microhardness after HIP was slightly higher than its wrought counterpart, indicating no severe impact of post-processing on the properties of Stellite-6 alloy.

Sridar, Soumya (ORCID:0000000306775408)↗

DOE OSTI2026

Data, model inputs, and analysis scripts associated with a manuscript on stream intermittency controls across spatial scales in Pacific Northwest watersheds

NOTE: The manuscript associated with this data package is currently in review. The data may be revised based on reviewer feedback. Upon manuscript acceptance, this data package will be updated with the final dataset and additional metadata. This data package is associated with the manuscript "Hydroclimatic Memory and Watershed Template Shape Stream Intermittency: Multi-scale Attribution Using Process-based Simulation and Explainable ML" by Niroula et al. (2026), submitted to Water Resources Research (WRR). The study investigates the dominant controls on stream intermittency across local, reach, and watershed scales using a coupled process-based simulation and explainable machine-learning framework. Long-term daily simulations from the Advanced Terrestrial Simulator (ATS) were used to generate wetness states and ponded-depth responses over river-corridor cells. These ATS outputs were then aggregated across scales and used to train XGBoost (eXtreme Gradient Boosting) models. SHAP (SHapley Additive exPlanations) was applied to quantify the relative importance of hydroclimatic forcings, watershed template attributes, and antecedent-memory effects in shaping intermittency behavior. The analysis is carried out for three contrasting Pacific Northwest watersheds: Oak Creek (OCW), American River Watershed (ARW), and H.J. Andrews (HJA). Across these testbeds, the package contains ATS-ready watershed inputs, ATS run configuration and selected output files, model-evaluation data products, intermittency-analysis datasets, machine-learning target-feature tables, SHAP outputs, and notebooks used to organize, analyze, and visualize results. At a high level, the package documents a workflow in which ATS provides the physically based simulation backbone and explainable machine learning is used as a post-processing attribution tool. The contents are intended to support interpretation of the manuscript figures and results, provide context for how intermittency metrics were generated at multiple scales, and preserve the key artifacts needed to understand and reuse the analysis workflow. The package contains a high-level directory summary file (`summary.txt`) and four main content folders (1) `evaluation_plots` contains evaluation figures and supporting evaluation datasets; (2) `intermittency_plots` contains intermittency-focused analysis notebook and prepared datasets; (3) `ml-training-and-shap_values_plots` contains ML training inputs, SHAP outputs, and figure-generation notebooks; and (4) `watershed_mesh_and_ats_input` contains ATS model setup materials, forcing inputs, geometry, and selected run files. More specifically, the `evaluation_plots` folder contains the notebook used for ATS evaluation plotting and site-specific evaluation datasets. These include evapotranspiration and water-balance products for three watersheds, as well as an Oak Creek field-measurement discharge file. The `intermittency_plots` folder contains the notebook used for intermittency analysis and the prepared datasets used to analyze intermittent and non-intermittent wetness behavior across the study watersheds. The `ml-training-and-shap_values_plots` folder contains notebooks and outputs for the machine-learning and explainability workflow. This includes the main XGBoost and SHAP notebook(s), a beeswarm plotting notebook, target-feature tables for machine-learning training, SHAP summary tables, and per-sample SHAP value archives. The `watershed_mesh_and_ats_input` folder contains ATS-related watershed inputs and supporting materials. This includes mesh and shape products, ATS-readable LAI and meteorological forcing inputs, selected ATS spinup and transient-run files, and a watershed workflow example notebook. Subdirectories are organized by watershed where applicable.All files are .cpg (codepage files), .csv (comma-separated values), .dbf (database files), .exo (Exodus mesh format), .h5 (HDF5 format), .ipynb (Jupyter notebooks), .pkl (Python pickle), .prj (projection files), .sh (shell scripts), .shp (shapefile geometry), .shx (shapefile index), .txt (text files), or .xml (markup data).

Advanced Terrestrial Simulator↗

DOE OSTI2026

Solubilization of PET in binary mixtures of HFIP and DCM

The dissolution of polyethylene terephthalate (PET) is a critical step for a solvent-based process, yet it typically requires highly corrosive or toxic solvents. Here, we investigate the solubilization and conformational behavior of PET in binary mixtures of hexafluoro-2-propanol (HFIP) and dichloromethane (DCM) as a strategy to reduce HFIP usage while maintaining effective dissolution. Small-angle neutron scattering (SANS) measurements reveal that PET remains molecularly dissolved in HFIP/DCM mixtures up to 50 vol% DCM. Analysis of PET chain conformations shows a transition from Gaussian behavior at low HFIP fractions to more swollen chains at intermediate compositions, accompanied by a counter-intuitive minimum in the radius of gyration at 50% HFIP. Complementary SANS measurements of the binary solvents demonstrate that compositional heterogeneity is maximized at this same solvent composition, suggesting a direct coupling between solvent microstructure and polymer dimensions. Molecular dynamics simulations corroborate the experimental findings, revealing solvent domain formation, preferential solvation of PET by HFIP, and a “caging” effect arising from solvent heterogeneity that leads to polymer coil compaction. Together, these results provide molecular-level insight into polymer behavior in mixed solvent systems and establish HFIP/DCM mixtures as a promising, more sustainable solvent platform for the PET post-process.

Arifuzzaman, Arif [ORNL]↗

DOE OSTI2026

Development of large-scale, 3D Printed high temperature ceramic material

Through this collaborative effort, a new 3D printing platform for ceramics called laser-induced slip casting (LIS) was explored and developed for improving the processing and manufacture of silicon carbide (SiC), a high temperature ceramic material. This method prints layers of ceramic slips/slurries with subsequent selective-laser heating to dry each layer to build a 3D structure. The completed and dried part is then sintered. This manufacturing process is inherently lower cost than alternative ceramic printing methods such as binder jet technology or stereolithography when it comes to the feedstock material but is more expensive than robocasting or direct ink writing. However, it has the potential to make more controlled parts with less defects compared to robocasting. The largest cost is the heating source for the printer. With this technique, there is potential to make large ceramic parts in a near-net shape. Further, there is no known commercial manufacturing of 3D printed ceramics that creates a large range of different high-density ceramics at large scale. The goals and outcomes for the development of the new printing technology were: 1) assessing the technology with alumina by characterizing coupons, 2) printing and sintering of silicon carbide (SiC), and 3) characterization and properties testing of materials printed and a scale-up of SiC part(s). Through this collaborative project, it was sought to provide the best solution to achieving highly dense and near-net shaped ceramic parts at large scale and reduced cost. The goal was to produce high density sintered materials for applications in defense, energy generating systems, armor, and wear parts using 3D printing methods. Variables including powder particle sizes, dispersant molecular weights (MWs), binders, printing parameters, and post processing were explored as well as the characterization of the physical properties (add specifics here).

36 MATERIALS SCIENCE↗

DOE OSTI2026

Topsoil bulk geochemical compositions - An updated harmonized global dataset

Mineral weathering is a key biogeochemical process because of the capacity of minerals to stabilize organic matter. However, predicting soil weathering status across large spatial areas still isn’t possible due to a lack of global data and theoretical frameworks. To address this knowledge gap, multiple global datasets of bulk topsoil geochemical compositions have been harmonized using R. These datasets document topsoil bulk geochemical compositions across five continents (n = ~16,000 observations). Source data for these observations include the EuroGEOSurveys Geochemical Baseline Database (FOREGS), the US Geological Survey National Geochemical Database (NASGLP), the Geochemical Atlas of Australia (GAA), the US Geological Survey Alaska Geochemical Database (AGD84), the National Cooperative Soil Survey (NCSS), the European Geochemical Mapping of Agricultural Soil (GEMAS), Ecorespira-Amazon (ERA), the New Zealand Geochemical Baseline Survey (NZ_GBS), and the African Soil Information Service (AFSIS). Major elements observed include Aluminum (Al), Calcium (Ca), Iron (Fe), Potassium (K), Magnesium (Mg), Sodium (Na), Titanium (Ti), Manganese (Mn), Phosphorus (P), Carbon (C), and Sulfur (S). This data package includes the harmonized dataset itself, and the R scripts necessary to harmonize these datasets, in addition to metadata that describes all columns, files, and databases used in this project. Methods & Sampling Step 1 – Databases of geochemical data identified This study aimed to leverage existing measurements of topsoil geochemical data. Databases were first identified and deemed appropriate for inclusion if they were measuring soils and performed these measurements on the <2mm soil fraction. Databases such as NCSS and AGD84 needed more post processing to include in the database and this was done using the NCSS_datamerge_031626 R file and Alaska_USGSmerge_031626 R file, respectively. Step 2 – Database harmonization Once appropriate databases were identified, they were harmonized for ease of analysis using the R script Database_Harmonization_031826. This included removing columns from original datasets that would not be used in analysis (removed columns are noted in the code). Then, data cleaning procedures specific to each dataset were undertaken. This includes standardizing columns to include units and adding metadata columns regarding procedures for analyzing specific elements. Functions for standardizing measurements and units are outline in R files: calculate element_mg_kg_031626, calculate_oxide_wt_perc_031626, change_oxide_caps_031626, and conv_2_numeric_031626. This also included adding a unique identifier for each sample to identify it with its respective database (see CD_ID in data dictionary). Geographic information: Data reflect a compilation of datasets collected globally. Geographic areas covered by each of the datasets include: - EuroGEOSurveys Geochemical Baseline Database (FOREGS) - European continent - North American Soil Geochemical Landscapes (NASGLP) - continental United States and limited parts of Canada (see database key for more details) - National Geochemical Survey of Australia (GAA) - Australia - Alaska geochemical database (AGDB4) - Alaska - National Cooperative Soil Survey (NCSS) - Global measurements, but concentrated in the continental United States - Geochemical data for arable land and land under permanent grass cover in continental Europe (GEMAS) - continental Europe - Ecorespira-Amazon (ERA) - Geochemical data from the Amazon basin - Geochemical baseline data for New Zealand (NZGBS) - New Zealand - Geochemical data collected across continental Africa (AfSIS) - Measurements across Africa

EARTH SCIENCE > LAND SURFACE > SOILS↗

DOE OSTI2025

SiC Receiver/Reactor by Additive Manufacturing for Concentrated Solar Thermocatalysis with Thermal Energy Storage (Final Technical Report - Public)

The direct use of solar thermal energy provides opportunities for low-cost heating sources for a variety of applications. Ultra-high temperatures around 1000°C are high value and highly useful for energy-demanding industries. Many materials cannot withstand these conditions. In the area of Sustainable Chemicals, further limitations on material stability exist. Combining state-of-the-art materials with new designs provides a promising pathway for harvesting solar thermal energy and performing high temperature chemical processes. However, conventional manufacturing limits the potential for design flexibility. In this project, Additive Manufacturing was combined with advanced materials and new chemical reactor designs. In addition, 24/7 energy is necessary for chemical processing, and designs for ultra-high temperature thermal storage were devised. Specifically, preliminary design of a novel solar thermal receiver was developed in this project and designed to work with thermocatalytic reactors for producing sustainable chemicals and fuels. An ultra-high temperature particle storage system and heat exchangers were proposed to transport ultra-hot air as thermal fluid for the system. On a broader scale, this system could be used to tap solar thermal energy for a centralized facility with capability of transferring that heat to various segments at a full range of temperatures to 1000°C. The project pushed the temperature boundaries past those in current use, and Additive Manufacturing was envisaged for fabricating the receiver to meet requirements of extreme environments. An extensive analysis of silicon carbide additive manufacturing was performed to compare the thermal and mechanical properties of complex geometries compared to conventional material and those manufactured via other methods. The Additive Manufacturing via Binder-Jet printing was optimized and characterized to provide high quality and reproducible components capable of withstanding the proposed extreme environments. The designs for the concentrating solar thermal cavity with ultra-hot air thermal fluid showed high performance in simulations, attributable to the complex optimized geometries of the 3D printed systems. The bright future of Additive Manufacturing with advanced materials developments should provide more options and even higher quality as the technology further develops. Current costs for Additive Manufacturing of advanced ceramics is relatively low, however post-processing of the materials for extreme environments is currently high. There is little industrial-scale infrastructure for these, but it is growing as niche applications become more mainstream. The results of the project can be translated into similar extreme environments for concentrating solar thermal energy as well as its integration with ultra-hot air thermal fluids. A number of industries that require ultra-high temperatures need to electrify or otherwise decarbonize for climate goals, and this project showed that theoretically there is a pathway to do so with direct concentrated solar thermal power.

10 SYNTHETIC FUELS↗

DOE OSTI2025

Tomographic Volumetric Additive Manufacturing of Silica Glass with In Situ Error Calculation and Projection Tiling

The optical, thermal, and chemical properties of glass make it an ideal material for use in a wide range of applications, from optics and chemistry reactionware to consumer products. Tomographic volumetric additive manufacturing (tVAM) creates 3D objects by selectively gelling material within a rotating vial of photo-sensitive material by exposure to a series of tomographically-determined images. Here, in this study, tVAM is investigated for fabrication of complex, centimeter-scale, 3D silica glass parts. By optimizing the post-processing procedure, fully-dense glass parts with up to 2 cm 2 cross sectional area are achieved. The changes in optical properties during printing are leveraged for in situ metrology during printing, and the non-Newtonian nature of the glass-filled photoresin system to facilitate larger fabrication volumes via tiled projections. Printed structures are finally demonstrated for applications as MEMS components, optics, fluidics, and consumer products.

Materials science↗

DOE OSTI2025

Direct‐Write Printed Contacts to Layered and 2D Materials

Advancements in fabrication methods have shaped new computing device technologies. Among these methods, depositing electrical contacts to the channel material is fundamental to device characterization. Novel layered and 2D materials are promising for next-generation computing electronic channel materials. Direct-write printing of conductive inks is introduced as a surprisingly effective, significantly faster, and cleaner method to contact different classes of layered materials, including graphene (semi-metal), MoS 2 (semiconductor), Bi-2212 (superconductor), and Fe 5 GeTe 2 (metallic ferromagnet). Based on the electrical response, the quality of the printed contacts is comparable to what is achievable with resist-based lithography techniques. These devices are tested by sweeping gate voltage, temperature, and magnetic field to show that the materials remain pristine post-processing. This work demonstrates that direct-write printing is an agile method for prototyping and characterizing the electrical properties of novel layered materials.

2D materials↗

DOE OSTI2024

Assessment of frequency and amplitude dependence on the cyclic degradation of polyurethane foams

Many energy absorption applications utilize flexible polymeric foams for their viscoelastic properties. It is desired that the material will perform consistently across repeated compression cycles. This study examines the effect of fatigue at low strain rates on the viscoelasticity of open-cell polyurethane foam. Six polyurethanes of the same base composition with two porosities (70% and 80%) and three chemical indexes (79i, 100i, and 121i) are tested. Large deformation cyclic compression of the foams is conducted on a universal testing system (UTS). These data are then post-processed leveraging dynamic mechanical analysis Fourier transform rheology to characterize changes in the viscoelasticity of the materials over fatigue cycles. Results show that foams can increase or decrease in stiffness up to 10% over 10 4 cycles. Specifically, higher chemical index, higher excitation frequency, and larger excitation amplitude correlate with a more pronounced decrease in stiffness. Damping can also change by 15% and correlates with chemical index and excitation frequency. Consequently, the findings suggest that internal foam structure and bulk material properties as well as applied loading parameters affect the viscoelastic fatigue response of flexible polymeric foams.

36 MATERIALS SCIENCE↗

DOE OSTI2025

Spatial mapping of dissolved methane using an in situ sensor in Puget Sound

Release of methane, as gas bubbles or in the dissolved phase, from the seafloor has been observed in coastal waters (< 200 m) and deep ocean basins (> 1000 m). Methane dissolution within the water column affects the geochemistry of the surrounding water, leading to localized oxygen loss and potential escape to the atmosphere, particularly from shallower sites. Traditional methods for detecting and quantifying dissolved methane rely on collecting discrete water samples for ship- or land-based ex situ analysis and post processing. Here, we report on the use of a reduced response time, in situ methane sensor, the Sensor for Aqueous Gases in the Environment (SAGE), for detecting and quantifying dissolved methane concentrations in a wide range of seafloor environments. During a Fall 2022 research cruise on the R/V Thomas G. Thompson in Puget Sound, SAGE was integrated onto a towed conductivity/temperature/depth rosette and deep-sea camera system with live-stream 1 Hz telemetry and used to spatially map the concentration of methane approximately 1 m above the seafloor. The site had been previously identified as an active methane plume field characterized by gas bubbles, fluid venting, and a faulted seabed. The widespread background dissolved concentration of methane measured by SAGE was 83 nM, and a range of 78–670 nM was observed throughout the survey. The results highlight the capacity of SAGE to map the spatial and temporal variability of dissolved methane concentrations in situ and to identify and localize sites of variable methane emissions from the seafloor.

Padilla, Alexandra M. [Woods Hole Oceanographic In↗