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At least 253 records · Page 14

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DOE OSTI2025

Assessment of the grindability of robocast silicon carbide

The demand for high-performance lightweight optics has driven interest in silicon carbide (SiC) due to its exceptional thermal stability, hardness, and strength-to-weight ratio. This study investigates the potential of robocasting, an additive manufacturing process, as a viable method for producing lightweighted SiC components for optical applications. Four samples with varied starting powder phases (α and β) and sintering conditions were fabricated and evaluated. Post-sintering surface and form were assessed using coherence scanning interferometry (CSI) and coordinate measuring machine (CMM) techniques. A three-stage grinding process was applied to each sample, with surface roughness assessed at each stage. Results demonstrate that samples with predominantly α-phase SiC and smaller particle sizes achieved superior surface finish, particularly sample D2-α-2135 °C, which displayed the lowest post-grinding Sq value of 0.178 µm. The analysis also indicated no significant print-through effect from the lightweighting structure, or print artifacts, at this stage of grinding. However, β-phase samples showed poorer grindability, increased surface roughness, and pitting. These findings suggest that phase composition and particle size are critical for achieving the desired surface quality in robocast SiC optics. Future work will incorporate additional samples and finer grinding wheels to refine surface quality further, supporting the development of SiC for high-precision optical applications.

Barrett, Taylor [ORNL]↗

DOE OSTI2024

3D printed carbon fiber reinforced carbon as an energy efficient alternative to graphite for EFAS tooling

As Electric Field Assisted Sintering (EFAS) gains more industrial acceptance and use, it becomes more important to develop more efficient means to implement this technology. To this aim, 3D printed continuous carbon fiber reinforced carbon (CCC) was manufactured and fabricated into tooling for EFAS systems as an alternative to traditional graphite tooling. The impact of fiber orientation on the thermal and electrical properties of the CCC was characterized. Sample material was sintered in Tokai G535 graphite tooling, under common processing conditions and compared with CCC tooling. There was nearly 50 % energy savings compared to graphite while maintaining equivalent sample density and microstructure plus keeping ram temperatures 39 % cooler. This is due to spatial control of generated heat and thermal diffusivity within the molds, by means of fiber orientation anisotropy. Finite element modeling of the tooling design supported the experimental results as well as displays the effect of optimization of this 3D printed CCC material.

36 MATERIALS SCIENCE↗

DOE OSTI2024

Results of Long-Term Analyses of the Salt (Macro) Batch 11 Tank 21H Qualification Sample

Savannah River National Laboratory (SRNL) analyzed samples from Tank 21H in support of qualification of Salt Waste Processing Facility (SWPF) Salt Batch 11. This document reports the long-term results of the analyses of the sample of Tank 21H. Analysis of this sample indicates that the material does not display any unusual characteristics or observations, such as visually apparent solids. However, the sample was dark colored. This report satisfies Deliverable 3 of the Technical Task Request (TTR).

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2024

Results of Long-Term Analyses of the Salt (Macro) Batch 11 Tank 21H Qualification Sample

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2024

Results of Long-Term Analyses of the Salt (Macro) Batch 13 Tank 42H Qualification Sample

Savannah River National Laboratory (SRNL) analyzed samples from Tank 42H in support of qualification of Salt Waste Processing Facility (SWPF) Salt Batch 13. This document reports the long-term results of the analyses of the sample of Tank 42H. Analysis of this sample indicates that the material does not display any unusual characteristics or observations, such as visually apparent solids. This report satisfies Deliverable 3 of the Technical Task Request (TTR).

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2025

Results of Long-Term Analyses of the Salt (Macro) Batch 14 Tank 21H Qualification Sample

Savannah River National Laboratory (SRNL) analyzed samples from Tank 21H in support of qualification of Salt Waste Processing Facility (SWPF) Salt Batch 14. This document reports the long-term results of the analyses of the sample of Tank 21H. Analysis of this sample indicates that the material does not display any unusual characteristics or observations, such as visually apparent solids. This report satisfies Deliverable 3 of the Technical Task Request (TTR).

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2025

Physically constrained 3D diffusion for inverse design of fiber-reinforced polymer composite materials

Designing fiber-reinforced polymer composites (FRPCs) with a tailored nonlinear stress-strain response is crucial for applications such as energy absorption in crash structures, flexible robotics, and impact-resistant protective gear. However, the inherent complexities of composite materials and the multitude of parameters involved, render traditional design and optimization methods inadequate for achieving effective inverse design of composites. In this paper, we present an AI-based inverse design framework that effectively and efficiently generates FRPCs with targeted nonlinear stress-strain responses. We introduce a physically constrained diffusion model (PC3D_Diffusion) capable of managing the complexities of composite materials and producing detailed, high-quality designs. We propose a loss-guided, learning-free approach to generate physically feasible microstructure designs by explicitly enforcing physical constraints during the generation process. For training purposes, 1.35 million FRPC samples were created, and their corresponding stress-strain curves were computed using established physics-based computational models. The results show that PC3D_Diffusion consistently generates high-quality designs with tailored mechanical behaviors, while guaranteeing compliance with the physical constraints. PC3D_Diffusion advances FRPC inverse design and may facilitate the inverse design of other 3D materials, offering potential applications in industries reliant on materials with custom mechanical properties.

Xu, Pei [Clemson Univ., SC (United States)]↗

DOE OSTI2025

Benchmarking Concentration and Extraction Methods for Wastewater-Based Surveillance of Eight Human Respiratory Viruses: Implications for Rapid Application to Novel Pathogens

To provide early warning and support a rapid response to a novel virus through wastewater surveillance, it would be ideal to understand in advance which concentration and extraction methods are likely to be effective for dPCR-based methods, depending on virus characteristics. In this study, we spiked raw wastewater samples with eight human respiratory viruses and processed them with four methods that concentrate and/or extract nucleic acids from both liquid and solid fractions (Promega, Nanotrap, and InnovaPrep) or only the solid fraction of wastewater (Solids). Our findings provide encouraging evidence that all four methods combined with dPCR could detect an emerging virus in wastewater, although they differed in sensitivity. The pattern of recovery efficiency for adenoviruses, coronaviruses, and influenza A viruses was consistent across methods, with Promega producing higher median recovery efficiencies, while distinct patterns were observed for coxsackieviruses. We also normalized the concentration data with two endogenous fecal indicators, PMMoV and Carjivirus (formerly crAssphage). We found that normalization could reduce method-associated differences if the indicator exhibited a recovery pattern similar to that of the target virus. These findings can guide the selection of concentration and extraction methods for wastewater monitoring based on the properties of target viruses, thus enhancing pandemic preparedness.

Biological and medical sciences↗

DOE OSTI2024

Phase retrieval for refraction-enhanced x-ray radiography using a deep neural network

X-ray refraction-enhanced radiography (RER) or phase contrast imaging is widely used to study internal discontinuities within materials. The resulting radiograph captures both the decrease in intensity caused by material absorption along the x-ray path, as well as the phase shift, which is highly sensitive to gradients in density. A significant challenge lies in effectively analyzing the radiographs to decouple the intensity and phase information and accurately ascertain the density profile. Conventional algorithms often yield ambiguous and unrealistic results due to difficulties in including physical constraints and other relevant information. We have developed an algorithm that uses a deep neural network to address these issues and applied it to extract the detailed density profile from an experimental RER. To generalize the applicability of our algorithm, we have developed a technique that quantitatively evaluates the complexity of the phase retrieval process based on the characteristics of the sample and the configuration of the experiment. Accordingly, this evaluation aids in the selection of the neural network architecture for each specific case. Beyond RER, the model has potential applications for other diagnostics where phase retrieval analysis is required.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2025

Electrical conductivity of multilayered Cu/Nb composites fabricated by accumulative roll bonding

The electrical conductivity of nanolayered copper/niobium composites fabricated using accumulative roll bonding was investigated as a function of layer thickness. Cu/Nb was used as a model system to evaluate the processing-structure–property relationship stemming from the accumulative roll bonding process. The physical properties were compared against samples of individual average layer heights ranging from 193 to 25 nm. The electrical resistivity was measured over a temperature range of ~3–300 K. Analysis on the role of interfaces on temperature dependence is conducted including the residual resistivity ratio and temperature coefficient of resistivity. It was found that electrical resistivity increases with decreasing layer height.

36 MATERIALS SCIENCE↗

DOE OSTI2026

pop-cosmos : redshifts and physical properties of KiDS-1000 galaxies

ABSTRACT Principled Bayesian inference of galaxy properties has not previously been performed for wide-area weak-lensing surveys with millions of sources. We address this gap by applying the pop-cosmos generative model to perform spectral energy distribution (SED) fitting for 4 million KiDS (Kilo-Degree Survey)-1000 galaxies. Calibrated on deep COSMOS2020 photometric data, pop-cosmos specifies a physically motivated prior over the galaxy population up to $z \simeq 6$ in stellar population synthesis (SPS) parameter space. Using the Speculator SPS emulator with GPU (graphics processing unit)-accelerated Markov Chain Monte Carlo sampling, we perform full posterior inference at 8.2 GPU seconds per galaxy, obtaining joint constraints on galaxy redshifts and physical properties. We validate photometric redshifts against $\sim \!185\,\!000$ KiDS galaxies cross-matched to Dark Energy Spectroscopic Instrument Data Release 1 spectroscopic samples, achieving low bias ($2\times 10^{-3}$), scatter ($\sigma _{\mathrm{MAD}}=0.03$), and outlier fraction (3.2 per cent) for the Bright Galaxy Survey, with comparable performance (bias $3\times 10^{-2}$, $\sigma _{\mathrm{MAD}}=0.05$, 1.0 per cent outliers) for luminous red galaxies (LRGs). Within the LRG sample, we identify massive, dusty, star-forming contaminants at $z \simeq 0.4$ satisfying standard colour selections for quenched populations. We infer trends in stellar mass, star formation, metallicity, and dust across five tomographic redshift bins consistent with established scaling relations. Using specific star formation rate constraints, we identify $\sim$7 per cent of KiDS-1000 galaxies as quenched, versus 37 per cent implied by conservative colour cuts. This enables the construction of weak-lensing samples defined by physical properties while mitigating intrinsic alignment systematics and preserving statistical power. Our analysis validates pop-cosmos out of sample, establishing it as a scalable approach for galaxy evolution and cosmological analyses with photometric surveys.

Halder, Anik [Institute of Astronomy and Kavli Ins↗

DOE OSTI2024

Search for the baryon number and lepton number violating decays τ − → Λ π − and τ − → Λ ¯ π − at Belle II

We present a search for the baryon number B and lepton number L violating decays τ − → Λ π − and τ − → Λ ¯ π − produced from the e + e − → τ + τ − process, using a 364 fb − 1 data sample collected by the Belle II experiment at the SuperKEKB collider. No evidence of signal is found in either decay mode, which have | Δ ( B − L ) | equal to 2 and 0, respectively. Upper limits at 90% credibility level on the branching fractions of τ − → Λ π − and τ − → Λ ¯ π − are determined to be 4.7 × 10 − 8 and 4.3 × 10 − 8 , respectively. Published by the American Physical Society 2024

Adachi, I. (ORCID:0000000322870173)↗

DOE OSTI2025

Measurement of 𝑑 2⁢ 𝜎/𝑑⁢|$\vec{q}$|⁢𝑑⁢𝐸 avail in charged current 𝜈 𝜇 -nucleus interactions at ⟨𝐸 𝜈 ⟩=1.86 GeV using the NOvA Near Detector

Double- and single-differential cross sections for inclusive charged-current 𝜈 𝜇 -nucleus scattering are reported for the kinematic domain 0 to 2 GeV/𝑐 in three-momentum transfer and 0 to 2 GeV in available energy, at a mean 𝜈 𝜇 energy of 1.86 GeV. The measurements are based on an estimated 995,760 𝜈 𝜇 charged-current (CC) interactions in the scintillator medium of the NOvA Near Detector. The subdomain populated by 2-particle-2-hole (2p2h) reactions is identified by the cross section excess relative to predictions for 𝜈 𝜇 -nucleus scattering that are constrained by a data control sample. Models for 2-particle-2-hole processes are rated by 𝜒 2 comparisons of the predicted-versus-measured 𝜈 𝜇 CC inclusive cross section over the full phase space and in the restricted subdomain. Shortfalls are observed in neutrino generator predictions obtained using the theory-based València and SuSAv2 2p2h models.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2024

Isotropic negative thermal expansion in ZrW 2 O 8 and HfW 2 O 8 from 1100 to 1275°C

Abstract ZrW 2 O 8 (ZrO 2 •2WO 3 ) and HfW 2 O 8 (HfO 2 •2WO 3 ) have been the focus of thermal expansion studies due to their isotropic negative thermal expansion (NTE) measured previously at temperatures below 775°C. This work presents measurements of these materials at their thermodynamically stable temperature ranges of 1105 and 1257°C for ZrW 2 O 8 and 1105–1276°C for HfW 2 O 8 , where they were characterized with in situ, powder X‐ray diffraction. The linear coefficients of thermal expansion were measured to be −5.52 × 10 −6 and −4.87 × 10 −6 °C −1 for ZrW 2 O 8 and HfW 2 O 8 , respectively. The mechanism leading to this NTE is discussed. Powder samples were synthesized by a solution‐based process called the organic–inorganic steric entrapment method. In situ characterization in air was carried out at the National Synchrotron Light Source II using a hexapole lamp, optical furnace and the Advanced Photon Source using a quadrupole lamp, optical furnace to achieve elevated temperatures.

Materials Science↗

DOE OSTI2024

NuMI/LBNF Horn and Stripline Welding

Focusing horns for secondary particles are critical components for creating a stable beam of neutrinos. These components need to survive in a harsh environment and withstand high stresses. Extending the lifetime of the horns is critical as spare fabrication takes approximately two years and has many subcomponents with strict quality control. Two key aspects of the fabrication process include the inner conductor CNC TIG welding and the friction stir welding of the stripline pieces. The process for welding requires steps such as sample welding, x-ray imaging, and tensile pull tests. Having a perfect weld retains as much of the original strength of the base metal and reduces the risk of failure. As FNAL ramps up in power to 2.4MW, the lifetime of the horn and stripline will more heavily rely on continuing to have high quality welding procedures and thorough quality assurance.

Orea, Adrian↗

DOE OSTI2025

Probing Condensed-Phase Structure and Dynamics in Hierarchical Zeolites and Nanosheets for Catalytic Upgradation of Biomass (Final Report)

Understanding complex reaction pathways in systems governed by multi-scale collective interactions across time and length scales remains a central scientific challenge. This project was guided by the hypothesis that the interplay among oligomers, solvents, and active sites can be tuned by a suitable choice of solvation environment and pore architecture in solid-acid catalysts to direct chemical transformations relevant to biomass conversion. Zeolites and zeolite nanosheets were used as model platforms, allowing for the interaction of macromolecules with the surface of the zeolite nanosheets and with smaller pores that host catalytically active sites. To investigate these coupled phenomena, we employ a multi-scale computational framework that integrates molecular-level descriptions with advanced sampling approaches to capture key physical and chemical interactions. Our work through this project improved fundamental understanding of how reactants and solid-acid catalysts interact in solvent-rich environments, thereby enabling the rational design of catalytic systems that upgrade biomass with enhanced selectivity and energy efficiency. In addition, the project developed advanced sampling methodologies critical for disentangling complex, reactive processes in multi-component catalytic environments.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2025

Open Specy 1.0: Automated (Hyper)spectroscopy for Microplastics

Microplastic spectral analysis is one of the most time-consuming processes in studying microplastic pollution, often requiring days per sample. Researchers are transitioning to automated batch and hyperspectral image analysis techniques to enhance efficiency. Open Specy, initially aimed at manual single-spectrum analysis, has now integrated automated methods. This updated version, Open Specy 1.0, introduces several new features, including two algorithms for automated processing (smoothing and particle compression), an extensive library containing over 40,000 open-source Raman and FTIR spectra, and two machine learning classifiers (logistic regression and k medoids) developed from this library. Furthermore, it includes a revamped user interface, an R package, and a benchmark data set for testing future advancements in automated techniques. Researchers evaluated various configurations for hyperspectral smoothing, particle identification, compression, and splitting, to achieve combined recovery rates between 50 and 150% particle counts, identities, and sizes with a coefficient of variation (CV) of less than 40% (the accredited standard). Mean absorbance times the standard deviation provided a consistent particle identification. Hyperspectral smoothing led to a 96% combined recovery rate and reduced variability (CV = 38%) compared to the 86% recovery (CV = 83%) of nonsmoothed controls. Additionally, compressing spectra for particles was significantly faster (>3x) and showed similar accuracy but with reduced variability than processing each pixel individually. Key challenges persist in automating spectral analysis, particularly in refining particle splitting algorithms, and improving identification routines to minimize false positives and negatives. In conclusion, new methods in sample preparation for better stabilization and dispersion of particles could overcome some of these issues.

13 HYDRO ENERGY↗

DOE OSTI2025

Conditional Pseudo-Reversible Normalizing Flow for Surrogate Modeling in Quantifying Uncertainty Propagation

We introduce a conditional pseudo-reversible normalizing flow (PR-NF) that directly learns conditional probability distributions from noisy physical models to efficiently quantify both forward and inverse uncertainty propagation. Traditional surrogate modeling approaches approximate only the deterministic component of physical models, requiring separate noise characterization and computationally expensive sampling methods for inverse problems. Here, in this work, we develop the conditional PR-NF model to directly learn and efficiently generate samples from the conditional probability density functions (PDFs). The training process utilizes dataset consisting of input-output pairs without requiring prior knowledge about the noise and the function. Once trained, our model efficiently generates samples from conditional PDFs for any input within the training domain. Moreover, the pseudo-reversibility feature allows for the use of fully connected neural network architectures, which simplifies the implementation and enables theoretical analysis. We provide a rigorous convergence analysis of the conditional PR-NF model, showing its ability to converge to the target conditional PDF using the Kullback−Leibler divergence. To demonstrate the effectiveness of our method, we apply it to several benchmark tests and a real-world geologic carbon storage problem.

97 MATHEMATICS AND COMPUTING↗