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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 307 records · Page 17
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Green solvent-processable poly(3-hexylthiophene) derivative maintains high hole mobility in diodes

The development of environmentally sustainable fabrication methods for organic solar cells (OSCs) is critical to enable their large-scale adoption. Conventional solution-processed OSCs often rely on halogenated, toxic solvents that pose health and environmental risks, limiting their scalability. This is because π-conjugated polymers tend to have a low solubility in non-halogenated solvents. A common strategy to enhance solubility in alternative solvents is through the incorporation of polar solubilizing groups, often on every repeat unit of at least one monomer. However, too many polar side chains tend to disrupt favorable morphology and decrease charge transport properties. In this study, we demonstrate that a minimal degree of side chain functionalization can improve green solvent processability while preserving electrical performance. We tested this hypothesis using our previously reported random copolymer derivative of poly(3-hexylthiophene) (P3HT) where ∼10 mol% of the side chains are 6-pentanoatehexyl side chains. We find that this low-level functionalization significantly broadens the solvents that can be used to process P3HT to less toxic solvents such as o-xylene and anisole. Furthermore, the copolymer processed from greener solvents showed high hole mobilities (∼10⁻ 3 cm 2 /Vs by the Space-Charge Limiting Current method in diodes), comparable to P3HT films cast from toxic chloroform. These findings suggest that minimal side-chain modification is a viable strategy for expanding green solvent compatibility while preserving electrical performance, paving the way toward more sustainable organic semiconductor designs.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Slug Test Characterization Results for the Upper Three Runs Aquifer at the Chemicals, Metals, and Pesticides Pits Operable Unit

Slug tests were performed on 21 wells screened within the Upper Three Runs aquifer (UTRA) at the Chemical, Metals, and Pesticide (CMP) Pits Operable Unit (OU) to determine unit-specific hydraulic properties. The UTRA is the water table aquifer at CMP Pits and is comprised of three discrete zones that are separated by two semi-continuous confining zones. These zones include the transmissive zone (TZ, unconfined), the middle aquifer zone (MAZ, semi-confined), and the lower aquifer zone (LAZ, semi-confined). The LAZ, the most substantial portion of the UTRA in the area, extends vertically to the green clay confining zone (GCCZ). A subset of wells from each zone was selected for slug testing. The objective of the project was to determine hydraulic properties of the three zones of the UTRA for use in future updates to the CMP pits groundwater flow and contaminant transport modeling.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Fast-rate joining of thermoplastic composites using integrated additive manufacturing and compression molding process

The integration of additive manufacturing (AM) technology with compression molding (CM), has emerged as a high-performance composite manufacturing technology in recent years. In the AM-CM process, quickly deposited AM preform on a mold undergoes a rapid compaction cycle to fabricate structurally robust composite parts due to highly controlled fiber alignment (from AM) and reduced porosity (from CM). Currently, AMCM-based part size is limited by the size of the mold volume, thus posing a challenge to manufacture scalable parts. Here, this work focuses on joining techniques developed to enable fast rate joining of short fiber-reinforced thermoplastic composite parts using the AM-CM process. Acrylonitrile butadiene styrene resin reinforced with 20 wt% short carbon fiber composites was printed onto a flat mold and pressed under a hydraulic press. Fabricated panels were joined by the (a) mechanical impression at the joining interface and (b) over-molded continuous carbon fiber (CCF) sheet. Tensile tests were performed to characterize the joining strength of both mechanical impression-based joints and CCF over molded joints. Among the mechanical impression-based joints, a U-shape channel allowed the fibers to flow between two joint parts, and 280 % increased mechanical properties were observed. The continuous carbon fiber-based over molded joint CCF showed 350 % increase in tensile strength compared to the butt joints.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Distributed Magnetic Field and Temperature Monitoring for Superconducting Radio Frequency Cavities

The overall objective of the proposed Phase I program was to design, construct and demonstrate a fiber optic sensing system capable of providing temperature and magnetic field measurements with an enhanced spatial resolution that can be implemented over a large surface area (cryomodules) to survey superconducting radio frequency cavities and magnets. A magnetic field sensor capable of detecting fluxes on the order of 1 μT is required to detect the distribution of trapped flux on the cavity surface. A unique distributed magnetic field sensor was successfully designed and constructed to demonstrate the detection of magnetic fluxes less than 500 nT. The sensor leveraged the ultra-high sensitivity of Sentek’s picoDAS to measure the magnetostriction induced vibrations in a commercially available Metglas 2605 SC ribbon that was in physical contact with sensing fiber. Static magnetic fields were detected by applying an alternating current a copper wire proximate to the Metglas 2605SC ribbon to create an AC bias magnetic field. In an alternative approach, an AC bias magnetic field was applied to a special magnetic field sensing fiber with Metglas 2605SC cladding successfully detect a magnetic field flux of a 3 μT. Exhaustive testing was performed to characterize the dependency of sensor response on the direction of the applied magnetic field. Although the special sensing fiber based magnetic field sensor did not exhibit an observable dependence on the direction of the magnetic field, the Metglas 2605SC ribbon sensor exhibited a clear directional dependence. A wide variety of polymer materials were evaluated to enhance the temperature response of an FBG based sensor at cryogenic temperatures. The processing and performance challenges provided the motivation to develop a new simple cryogenic temperature sensor that uses a commercially available fiber optic splice protector. The EVA hot melt tube that becomes adhered to the optical fiber and the polyolefin outer tube that shrinks upon heating in the fusion splicer heater provide the high thermal expansion coefficient necessary to impart a significant strain on the FBG when exposed to cryogenic temperatures. The temperature sensitivity (Δ𝜆𝐵𝑟𝑎𝑔𝑔~ 62 𝑝𝑝𝑚/℃) of the FBG-based sensor was on par with the best reported to date. The simple design, use of readily available cost-effective materials, and well-established processing techniques lends this approach to the creation of hundreds to thousands of temperature sensors on one single optical fiber length. The inherently small form factor also allows for co-location with the distributed magnetic field sensor. In preparation for field testing of the prototype sensing system at the Jefferson Labs in potential Phase II program, several different cable designs were evaluated to package the sensors. The preliminary successful demonstration of fully functional sensing cables provides the foundation for subsequent development efforts to advance the Technology Readiness Level of the technology. The technical feasibility of the proposed approach was successfully demonstrated in this Phase I effort.

43 PARTICLE ACCELERATORS↗
DOE OSTI2026

Dielectric Spectroscopy Cable NDE for Unequal Aging Over Different Lengths

This Pacific Northwest National Laboratory milestone report assesses the effect of unequal aging over different lengths of cable on dielectric spectroscopy (DS) bulk impedance measurements from the cable end. DS measurements can indicate the remaining useful life of cables; however, most benchmark tests are based on accelerated aging of the entire cable. If only a portion of the cable is exposed to a harsh environment (as is frequently the case), the DS measurement will indicate a significantly less-aged cable than if the entire cable were exposed to a uniform environmental stress. The DS measurement is primarily related to the intrinsic insulation material relative permittivity—typically between 1.5 and 3.0. Insulation aging increases permittivity and, correspondingly, the cable’s overall capacitance measured from the cable end. To judge the health of the overall cable system, operators are primarily interested in the condition of the most severely aged section since that is where a failure is most likely to occur. This can be expressed as the dimensionless relative permittivity of the severely aged section divided by the permittivity of a pristine cable. This relative permittivity is an intrinsic material parameter independent of cable length and can serve as a quantitative indication of the insulation condition. If the relative percentages of cable exposed to a harsh and benign environment are known, the DS measurement of the entire cable length, including both pristine and aged sections, can be compensated to predict the relative permittivity (relative to pristine permittivity) of the most severly aged segment. A test was performed on three 50 ft (16 m) cables with 10%, 50%, and 90% of the cable inside a thermal aging oven. Predictably, the aging effect on the DS response was greatest in the 90% sample, followed by the 50% sample, and then the 10% sample. The more interesting result is the ratio of the aged insulation permittivity to the pristine insulation permittivity. Based on prior work, an end-of-life cable has a value of approximately 1.35. If the relative percentages of pristine and severely aged cable lengths are known, and the total cable-length capacitance of the initial pristine cable and the combined pristine plus aged cable are known, the relative aged permittivity (ratio of aged section permittivity / pristine section permittivity) can be estimated. The relative cable lengths exposed to a harsh environment may be known or estimated based on plant layout drawings or cable reflectometry tests that can locate the oven entry and exit points. The compensation was verified using a lumped-parameter model to predict the aged-segment permittivity e2/e1, where e2 is the aged-segment permittivity and e1 is the pristine-segment permittivity. For all three percentages of aged cable lengths, the ratio was quite similar, as would be expected since all cable segments were exposed to the same aging environment. This ratio of aged permittivity to pristine permittivity can be compared to other damage indicating tests, including elongation at break, to assess cable condition.

ARENA Test Bed↗
DOE OSTI2025

Initial steady-state core simulation capability or thermal and pool-type molten salt reactors, coupling reactor physics, thermal-hydraulics, and evolving chemistry

This report presents the development and validation of an initial steady-state multiphysics capability for molten salt reactors (MSRs) under the Nuclear Energy Advanced Modeling and Simulation (NEAMS) program in Fiscal Year 2025. The framework integrates neutronics, thermal-hydraulics, species transport, and thermochemistry to capture the coupled dynamics of liquid-fueled systems. Implementation and testing were performed on two representative designs: the Molten Salt Reactor Experiment (MSRE), a thermal-spectrum, channeled-flow reactor, and the Lotus Molten Salt Reactor (L-MSR), a fast-spectrum, pool-type reactor. The modeling suite employs Griffin for reactor physics and depletion, Pronghorn and SAM for thermal-hydraulics, Thermochimica for chemistry, and Saline for thermophysical properties, with benchmarking and validation carried out against historical MSRE data, experimental flow-loop measurements, and reference depletion calculations from Monte Carlo codes. The framework demonstrated the ability to reproduce key reactor behaviors including temperature feedback, reactivity losses, delayed neutron precursor transport, xenon poisoning, and redox potential evolution. The results confirm the feasibility and accuracy of the coupled models in predicting steady-state and selected transient MSR behaviors. This latter ones are used in this report as a proxy indicating that the steady-state models from which the transient starts are accurate. For MSRE, validation showed good agreement with pump start-up and natural circulation tests, while for the L-MSR, benchmarking confirmed hydraulic calibration and consistency of neutronics–thermal coupling. The tools also provided new insights into species transport, noble metal deposition, and salt solidification dynamics. On the Xenon transport front, the code is validated against the steady state Xenon poisoining measurement and showed good agreement with the experimental value. Identified areas for future work include advanced void transport modeling, three-dimensional simulations, improved alloy corrosion models, and tighter integration with high-fidelity Monte Carlo codes. These developments provide a foundation for high-fidelity MSR simulations that can support reactor design optimization, safety assessments, and long-term operational strategies.

42 - ENGINEERING↗
DOE OSTI2025

Evaluating the potential of short-term instrument deployment to improve distributed wind resource assessment

Distributed wind projects, which are connected at the distribution level of an electricity system or in off-grid applications to serve specific or local energy needs, often rely solely on wind resource models to establish wind speed and energy generation expectations. Historically, anemometer loan programs have provided an affordable avenue for more accurate onsite wind resource assessment, and the lowering cost of lidar systems has shown similar advantages for more recent assessments. While a full 12 months of onsite wind measurement is the standard for correcting model-based long-term wind speed estimates for utility-scale wind farms, the time and capital investment involved in gathering onsite measurements must be reconciled with the energy needs and funding opportunities that drive expedient deployment of distributed wind projects. Much literature exists to quantify the performance of correcting long-term wind speed estimates with 1 or more years of observational data, but few studies explore the impacts of correcting with months-long observational periods. This study aims to answer the question of how short you can go in terms of the observational time period needed to make impactful improvements to model-based long-term wind speed estimates. Three algorithms, multivariable linear regression, adaptive regression splines, and regression trees, are evaluated for their skill at correcting long-term wind resource estimates from the European Centre for Medium-Range Weather Forecasts Reanalysis version 5 (ERA5) using months-long periods of observational data from 66 locations across the US. On average, correction with even 1 month of observations provides significant improvement over the baseline ERA5 wind speed estimates and produces median bias magnitudes and relative errors within 0.22 m s −1 and 4 percentage points of the median bias magnitudes and relative errors achieved using the standard 12 months of data for correction. However, in cases when the shortest observational periods (1 to 2 months) used for correction are not well correlated with the overlapping ERA5 reference, the resultant long-term wind speed errors are worse than those produced using ERA5 without correction. Summer months, which are characterized by weaker relative wind speeds and standard deviations for most of the evaluation sites, tend to produce the worst results for long-term correction using months-long observations. The three tested algorithms perform similarly for long-term wind speed bias; however, regression trees perform notably worse than multivariable linear regression and adaptive regression splines in terms of correlation when using 6 months or less of observational data for correction. Translating the analysis to wind energy, median relative errors in the capacity factor are on average within 10 % using 1 month of training. If the observation period used for correction is not well correlated with the reference data, however, misrepresentation of the observed capacity factor can be substantial. The risk associated with poor correlation between the observed and reference datasets decreases with increasing training period length. In the worst-correlation scenarios, the median capacity factor relative errors from using 1, 3, and 6 months are within 47 %, 26 %, and 16 %, respectively.

17 WIND ENERGY↗
DOE OSTI2025

The development and application of the stirred‐reactor coupon analysis (SRCA) test method

A new technique, termed the stirred‐reactor coupon analysis (SRCA) method, has been developed to measure the rate of glass dissolution in forward‐rate conditions. Monolithic glass coupons are partially masked with an inert material before placement in a large volume of well‐mixed solution with known chemistry and temperature for a predetermined duration. After the test, the mask is removed, and the difference in step height between the protected area and the exposed corroded portions of the sample coupon is measured to determine the extent of glass dissolution. The step height is converted to a rate measurement using the test duration and glass density. Test parameters such as sample surface preparation and test duration were evaluated to determine their effects on the measured rates. Additionally, results from an interlaboratory study (ILS) consisting of 12 laboratories from 11 different institutions are presented, where each laboratory performed 12 independent tests. When removing experimental outlier data, the 95% reproducibility limits for the SRCA method has no statistical difference with previously published standardized test methods used to determine the forward rate of glass dissolution. Overall, this paper describes steps necessary to perform the test method and provides the statistical calculations to evaluate test accuracy.

chemical durability↗
DOE OSTI2025

Hydraulic Conductivity Measurements, Utqiagvik (Barrow), Alaska, 2014

Six individual ice cores were collected from the Barrow Environmental Observatory in Barrow, Alaska, in May of 2013 as part of the Next Generation Ecosystem Experiment (NGEE). Each core was drilled at a different location to varying depths. After drilling, the cores were stored in coolers packed with dry ice and flown to Lawrence Berkeley National Laboratory (LBNL) in Berkeley, CA. 3-dimensional images of the cores were constructed using medical X-ray computed tomography (CT) scanner at 120kV. Hydraulic conductivity samples were extracted from these cores at LBNL Richmond Field Station in Richmond, CA, in February 2014 by cutting 5 to 8 inch segments using a chop saw. Samples were packed individually and stored at -20C freezing temperatures to minimize any changes in structure or loss of ice content prior to analysis. Hydraulic conductivity was determined through falling head tests using a permeameter [ELE International, Model #: K-770B] (Appendix A). Samples were placed in a latex membrane via a membrane stretcher while frozen. Use of a membrane stretcher made the membranes easier to secure and minimized contact with the sample. A clear polycarbonate sleeve, fabricated with a stainless steel ring at the bottom to keep the sleeve from floating, was placed around the sample inside the permeameter to minimize deformation during analysis. The permeameter was filled with water and 1.0 PSI of air was applied for confining pressure during sample defrost. Outflow valves were left open to allow for incremental thawing and samples were left to thaw for approximately 12 hours. After approximately 12 hours of thaw, initial falling head tests were performed. When the flow was significantly too fast or too slow, the analysis was stopped and the burette size was adjusted accordingly (i.e. a larger diameter burette was used for flows that were faster than desired or a smaller diameter burette was used for flows that were slower than desired). Two to four measurements were collected on each sample and collection stopped when the applied head load exceeded 25% change from the original load. Analyses were performed between 2 to 3 times for each sample. The final hydraulic conductivity calculations were computed using methodology of Das et al., 1985.The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a 15-year research effort (2012-2027) to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research. The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska. Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

NuQKD: A Modular Quantum Key Distribution Simulation Framework for Engineering Applications

Abstract An experimental Quantum Key Distribution (QKD) implementation requires advanced costly hardware, unavailable in most research environments, making protocol testing and performance evaluation complicated. This has been a major motivation for the development of QKD simulation frameworks, to allow researchers to obtain insight before proceeding into practical implementations. Several simulators have been introduced over the recent years. However, only four are publicly available, only one of which models equipment imperfections. Currently, no open‐source simulator includes all following capabilities: channel attenuation modelling, equipment imperfections and effects on key rates, estimation of elapsed time during classical and quantum‐ channel processes, use of truly random binary sequences for qubits and measurement bases, shared ‐bit fraction customization. In this paper, we present NuQKD, an open‐ source modular, intuitive simulator, featuring all the above capabilities. NuQKD establishes communication between two computer terminals, accepts custom user inputs (iterations, raw key size, attacker interception rate etc.) and evaluates the sifted key, Quantum Bit Error Rate (QBER), elapsed communication time, and more. NuQKD capabilities include optical fiber and free ‐space simulation, modeling of equipment/channel imperfections, bitstrings from True Random Number Generators, decoy‐state protocol support, and automated evaluation of performance metrics. We expect NuQKD to enable convenient and accurate representation of actual experimental conditions.

Gkouliaras, Konstantinos↗
DOE OSTI2024

Material property characterization of 3D printed polypropylene wood plastic composites

Abstract Wood flour (WF) at 10 wt.% and 20 wt.% loadings was used as a reinforcing filler to enhance the applicability of polypropylene (PP) for 3D printing. After performing printability tests of PP wood plastic composites (WPCs), the mechanical properties of both injection‐molded and 3D‐printed PP WPC specimens were explored. Test specimens were prepared from 3D printed hexagons for analyzing the mechanical properties. Adding WF to neat PP increased the storage modulus and the glass transition temperature while decreasing the degree of crystallinity and the coefficient of thermal expansion, while enhancing the printability of neat PP. The tensile strength, tensile modulus of elasticity, flexural strength, and flexural modulus of elasticity of injection‐molded neat PP improved by up to 21%, 59%, 30%, and 56%, respectively, with 20 wt.% WF. However, the impact strength of injection‐molded neat PP decreased by 85%, with 20 wt.% WF. After 3D printing, the tensile strength and tensile modulus of elasticity of printed neat PP increased by up to 84% and 60%, respectively, with 20 wt.% WF. The flexural strength and flexural modulus of elasticity of neat printed PP remained unchanged compared to those of PP filled with 20 wt.% WF, while the impact strength decreased by 87%. Highlights WF was used as a reinforcement in polypropylene designed for 3D printing A pilot‐scale, pellet‐fed 3D printer was used to print hollow hexagons, and then test specimens were fabricated from these hexagons for mechanical properties The tensile and flexural properties of 3D printed neat polypropylene improved, while the impact strength decreased after adding 20 wt.% WF to the neat PP.

Hwang, Sungjun↗
DOE OSTI2024

Additive manufacturing of sandwich panels with continuous fiber reinforced high modulus composite facings

Abstract An improved approach consisting of a combination of fiber placement and fused filament fabrication is introduced for the additive manufacture (AM) of structural grade sandwich beams. Here, sandwich beams are additively manufactured using in‐situ deposition and consolidation of continuous fiber unidirectional facings made from a commingled yarn system of e‐glass fiber (~50% vol.) and amorphous PET, and a hexagonal honeycomb core structure made from PETG. Both facings and the sandwich core are manufactured on a single machine, in one sequence (skin‐core‐skin), employing the benefit of matrix compatibility to create autohesion at the interfaces. Flexural and transverse shear rigidity are determined experimentally and compared with analytical predictions and show correlation to within 3%. Flexural strength and core shear strength are also measured. Post‐mortem examinations show that core fracture and core facing debond were the dominant failure mode in flexure. Single cantilever beam tests were performed to evaluate core facing debond toughness. Subsequently, surface preheat using infrared heaters was utilized to increase autohesion between core and facing. The results show debond toughness was increased 4 times using infrared heating. This research effort presents a manufacturing approach that has the potential for the AM of stiff, well bonded, structural grade sandwich beams, in an integrated sequence, employing in‐situ consolidation to the facings, without the need for the use of intermediate adhesives for skin‐to‐core bonding. Highlights An improved additive manufacturing technique for making sandwich panels is developed. Sandwich panel facings have fiber volume fractions of approximately 50%. Surface preheat improves core‐to‐facing debond toughness by a factor of 4. Top and bottom facings are consolidated during manufacture leading to better properties. Experimental results are compared to analytical predictions and show good correlation.

17 WIND ENERGY↗
DOE OSTI2025

Robust Spectral Anomaly Detection in EELS Spectral Images via 3D Convolutional Variational Autoencoders

Abstract A 3D Convolutional Variational Autoencoder (3D‐CVAE) is introduced for automated anomaly detection in electron energy‐loss spectroscopy spectrum imaging (EELS‐SI) data. This approach leverages the full 3D structure of EELS‐SI data to detect subtle spectral anomalies while preserving both spatial and spectral correlations across the datacube. By employing cross‐entropy loss and training on bulk spectra, the model learns to reconstruct bulk features characteristic of the defect‐free material. In exploring methods for anomaly detection, both the 3D‐CVAE approach and principal component analysis (PCA) are evaluated, testing their performance using FeL‐edge ΔEpeak shifts designed to simulate material defects. These results show that 3D‐CVAE achieves superior anomaly detection and maintains consistent performance across various shift magnitudes. The method demonstrates clear bimodal separation between bulk and anomalous spectra, enabling reliable classification. Further analysis verifies that lower‐dimensional representations are robust to anomalies in the data. While performance advantages over PCA diminish with decreasing anomaly concentration, our method maintains high reconstruction quality even in challenging, noise‐dominated spectral regions. This approach provides a robust framework for unsupervised automated detection of spectral anomalies in EELS‐SI data, particularly valuable for analyzing complex material systems.

Chemistry↗
DOE OSTI2025

The three-point form factor of Tr ϕ 3 to six loops

We study the three-point form factor of the length-three half-BPS operator (Tr ϕ 3 ) in planar $\mathcal{N}$ = 4 Super-Yang-Mills theory, using analyticity and integrability methods. We find that the functions describing the form factor in perturbation theory live in the same restrictive space of multiple polylogarithms as the one describing the form factor of the stress-tensor operator (Tr ϕ 2 ). Furthermore, we find that the leading-order data in the collinear limit provided by the form factor operator product expansion (FFOPE) is enough to fix the form factor uniquely, at least through six loops. We perform various tests of our results using the subleading FFOPE corrections. We also analyze the form factor in the Regge limit where two Mandelstam invariants are large; we obtain a compact representation for the form factor in this limit which is valid to all orders in the coupling.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

ESM data downscaling: a comparison of super-resolution deep learning models

Abstract Climate projections at fine spatial resolutions are required to conduct accurate risk assessment for critical infrastructure and design adaptation planning. Generating these projections using advanced Earth system models (ESM) requires significant computational resources. To address this issue, various statistical downscaling techniques have been introduced to generate fine-resolution data from coarse-resolution simulations. In this study, we evaluate and compare five deep learning-based downscaling techniques, namely, super-resolution convolutional neural networks, fast super-resolution convolutional neural network ESM, efficient sub-pixel convolutional neural network, enhanced deep residual network (EDRN), and super-resolution generative adversarial network (SRGAN). These techniques are applied to a dataset generated by the Energy Exascale Earth System Model (E3SM), focusing on key surface variables such as surface temperature, shortwave heat flux, and longwave heat flux. Models are trained and validated using paired fine-resolution (0.25 $$^{\circ }$$ ∘ ) and coarse-resolution (1 $$^{\circ }$$ ∘ ) monthly data obtained from a 9-year simulation. Next, blind testing is performed using monthly data obtained from two different years outside of the training and validation set. To evaluate the efficiency of each technique, different statistical metrics are used, including mean squared error (MSE), peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), and learned perceptual image patch similarity (LPIPS). The results show that EDRN outperforms other algorithms in terms of PSNR, SSIM, and MSE, but struggles to capture fine-scale features in the data. In contrast, SRGAN, a generative model that uses perceptual loss, excels in capturing fine details at boundaries and internal structures, resulting in lower LPIPS than other methods.

Pawar, Nikhil M. (ORCID:0000000211613289)↗
DOE OSTI2025

Effect of particle size and moisture on flow performance of loblolly pine anatomical fractions: Experimental findings and model predictions

The rising energy demand has highlighted biomass as a promising next-generation energy source. However, commercializing biomass-derived energy faces challenges, particularly in handling biomass feedstock. Factors like particle size, shape, moisture content, and surface roughness significantly impact biomass flowability. This study addresses a crucial knowledge gap by examining the effects of particle size and moisture content on the flow behavior and shear properties of different anatomical fractions of loblolly pine (Pinus taeda). The bulk shear behavior was examined using a Schulze ring shear tester, while flow performance was tested through gravity-driven flow experiments in a variable wedge-shape hopper. Results were incorporated into empirical and machine learning-based flow prediction models to evaluate their accuracy and limitations. The study found that samples with higher moisture content show higher unconfined yield strength. The critical arching distance increased with particle size, e.g., from approximately 13 and 33 mm for 2- and 6-mm whole chips, respectively at a 32-degree inclination angle. Conversely, the flow rate decreased for a given hopper opening as particle size increased. For instance, at a 60-mm hopper opening and a 32-degree inclination angle, the mass flow rates for 2- and 6-mm whole chips were 7.83 and 6.42 tonne/h, respectively. The empirical model consistently overpredicted the mass flow rate for all anatomical fractions, while the machine learning model more accurately predicted the central tendency of flow rate but was insensitive to varying tissue proportions. These novel findings provide comprehensive characterization of anatomical fractions, reveal significant combined effects of particle size and moisture content on biomass flow behavior, and demonstrate a better predictive accuracy of a machine learning model, all of which are useful for optimizing material handling strategies and biomass utilization technologies in the industry.

09 - BIOMASS FUELS↗
DOE OSTI2025

SAXS Assistant: Automated SAXS analysis for structural discovery in biologics and polymeric nanoparticles

Small-angle x-ray scattering (SAXS) is a powerful technique for assessing macromolecular structure. High-throughput SAXS is limited by the time-consuming and, at times, subjective nature of SAXS data interpretation. Here, we present SAXS Assistant, a Python-based script that streamlines SAXS data analysis to extract features for machine learning (ML) and key structural parameters, including the Guinier radius of gyration (R g ), pair distance distribution function (PDDF)-derived R g , maximum particle dimension (D max ), and Kratky plots. The script builds upon BioXTAS RAW and validates reliability via Guinier/PDDF R g agreement, an important indicator of well-measured data sets. For assistance in D max estimation, a multilayer perceptron regressor was trained with 1940 data files from the Small Angle Scattering Biological Data Bank. The model achieved a test set performance R 2 = 0.90 and mean absolute error = 11.7 Å. Training exclusively with experimental data translates analyses from researchers, including experts in the field, to the ML model, which helps assess D max estimations from PDDF. Gaussian mixture model clustering was implemented to classify profiles into structural classes based on entries in the Small Angle Scattering Biological Data Bank. Users may therefore assess the similarity between experimental samples and known biomolecular shapes within the mapped repository entries. This probabilistic clustering aids in quantifying information from Kratky and generating shape-descriptive features. SAXS Assistant accelerates SAXS data analysis through enforced quality control, ML-ready outputs, and flags for low-confidence results. In addition to providing the ability to analyze large data sets at high throughput, this tool is versatile and may serve researchers in both biological and synthetic polymer research fields.

36 MATERIALS SCIENCE↗
DOE OSTI2025

LABQ3: Bayesian method for quantification of mineral compositions and nano-scale elemental mapping of 3D synchrotron XCT data

Quantitative analysis of mineral compositions is essential in understanding geochemical, mineralogical and environmental processes. Fine-resolution 3D imaging is widely done using synchrotron X-ray computed tomography (XCT), but existing analyses are limited to visualization and segmentation. This paper presents a new method, Linear Attenuation Bayesian Quantitative 3D-mapper (LABQ3), based on the linearity of X-ray attenuation with respect to elemental concentrations. To address the random variability in attenuation measurements, LABQ3 employs Bayesian decision theory to minimize classification error, using reference attenuation distributions from scans of pure mineral standards. To demonstrate LABQ3 and test its performance, we studied precipitated carbonate samples. XCT scans were done at multiple energies using the transmission X-ray microscope (TXM) at beamline 32-ID-C of the Advanced Photon Source at Argonne National Laboratory. The reconstructed 3D images have a voxel size of 20 nm. Analyses revealed rich nano-scale compositional heterogeneity within individual particles. A mixture of calcium and cadmium produced an overall stoichiometric composition of (Ca 0.78 ,Cd 0.22 )CO 3 , with some voxels containing nearly pure CdCO 3 . The addition of zinc led to an overall stoichiometric composition of 33% Ca, 28% Cd, 39% Zn, with a nearly pure CaCO 3 core and compositional zonation through the rim. These compositional gradients are related to temporal sequences of carbonate mineral formation where Cd precipitated at the beginning in (Ca,Cd)CO 3 , while Cd and Zn precipitated at the end in (Ca, Cd,Zn)CO 3 . Results differ from bulk analyses using Inductively Coupled Plasma-Mass Spectrometry (ICP-MS), showing that LABQ3 provides particle-specific insights. LABQ3 distinguishes itself by quantifying chemical compositions along a continuum, making it different from XCT analyses based on segmentation. LABQ3 allows simultaneous acquisition of morphology and chemical composition in 3D, facilitating the interpretation of chemical gradients of trace elements, quantification of solid solution compositions, inferences about temporal sequences of mineral precipitation, and addressing other concerns about solid-phase chemistry.

58 GEOSCIENCES↗