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DOE OSTI2026

Web-based Preprocessing and Visualization of 3D FIB Tomography Data for Nuclear Fuel Characterization

Three-dimensional (3D) focused ion beam (FIB) tomography enables reconstruction of internal nuclear fuel features that can't be fully evaluated through surface imaging alone. This capability supports characterization of fuel constituents and defects under thermal and irradiation conditions relevant to microreactor development. However, large tomography datasets can create data-handling, loading, and visualization challenges, especially when image-stack preparation and file conversion must be completed with separate tools. The Computational Ultraspatial Tomography Toolkit for High-Resolution Object Analysis Tools (CUTTRHOAT) is an open-source web application being developed to display FIB tomography datasets available through the Nuclear Research Data System (NRDS). The current alpha version requires prepared HDF5 datasets and has limited integrated data-preparation capabilities. This project improves CUTTHROAT by adding dataset-folder selection, automatic input detection, dataset scanning, missing-slice identification, blank-slice insertion, and image-stack-to-HDF5 conversion. Two applications will be compared: the baseline CUTTHROAT alpha workflow and the updated application containing the integrated data-handling and preprocessing functions. Evaluation will consider dataset detection accuracy, conversion success, loading time, rendering responsiveness, application stability, and user interaction. Preliminary results demonstrate successful loading of existing HDF5 files and converted image stacks, while testing also identified performance reductions caused by excessive blank-slice generation. The updated workflow reduces reliance on external preparation tools and supports more direct movement from image stacks to color-code 3D visualization. Future work includes refining missing-slice handling, integrating additional preprocessing functions, like a denoising feature, parsing TIFF metadata for automatic voxel scaling, and adding manual X, Y, and Z voxel-spacing inputs for PNG and JPEG.

36 - MATERIALS SCIENCE↗

DOE OSTI2024

Analysis of the Surface Morphology and Chemical Composition of Zr-Nb3Sn Alloys with Different Zr Concentrations

In the field of superconducting science, Nb has been the material of choice for Radio Frequency (RF) Cavities for years. However, in recent years Nb3Sn has shown to be a promising alternative to Nb. Its combination of a significantly high superconducting transition temperature and a much higher value of upper critical magnetic field (Hc2), has been the reason the alloy has been so widely studied. This project aims to develop materials to further improve the high magnetic field performance of Nb3Sn by improving its upper critical magnetic field and critical current density (Jc). One way to improve the Jc and the Hc2 is to create more grain boundaries in the Nb3Sn alloy allowing them to trap more magnetic flux lines in the materials. To do this the grains must be made smaller which will naturally cause the formation of more grain boundaries. A common way to create smaller grains is by doping an impurity in the alloy. In this study, Nb3Sn was doped with Zr to form Zr-Nb3Sn alloys. This project investigates the surface properties and chemical composition of Zr-Nb3Sn alloys with varying zirconium concentrations (~0.5% and ~24% Zr). Using Scanning Electron Microscopy (SEM), Energy Dispersive Spectroscopy (EDS), and X-ray Photoemission Spectroscopy (XPS), allowed for the characterization of the differences in surface morphology and elemental composition between samples. SEM images were quantified using ImageJ giving insight into grain distribution. The results indicate that increased Zr concentration creates smaller grains in the Zr-Nb3Sn alloy. Furthermore, the ~ 24% Zr grew nanoscale particles on the surface that are possible oxides. XPS data revealed that as Zr concentration increased, the total Nb oxides (NbOx) thickness decreased. Conversely, as Zr concentration increased, the surface Sn oxides (SnOx) also increased slightly. Quantification via ImageJ of SEM data showed that the ~ 0.5% Zr sample had a grain size of ~ 5300 nm2. These results show that Zr inclusion leads to smaller grain formations, which is promising in the world of superconducting RF cavities for high magnetic field applications.

Sue, Micah↗

DOE OSTI2020

Shifting agricultural practices to produce sustainable, low carbon intensity feedstocks for biofuel production

The carbon intensity (CI) of biofuel's well-to-pump life cycle is calculated by life cycle analysis (LCA) to account for the energy/material inputs of the feedstock production and fuel conversion stages and the associated greenhouse gas (GHG) emissions during these stages. The LCA is used by the California Air Resources Board's Low Carbon Fuel Standard (LCFS) program to calculate CI and monetary credits are issued based on the difference between a given fuel's CI and a reference fuel's CI. Through the Tier 2 certification program under which individual fuel production facilities can submit their own CIs with their facility input data, the LCFS has driven innovative technologies to biofuel conversion facilities, resulting in substantial reductions in GHG emissions as compared to the baseline gasoline or diesel. A similar approach can be taken to allow feedstock petition in the LCFS so that lower-CI feedstock can be rewarded. Here we examined the potential for various agronomic practices to improve the GHG profiles of corn ethanol by performing feedstock-level CI analysis for the Midwestern United States. Our system boundary covers GHG emissions from the cradle-to-farm-gate activities (i.e. farm input manufacturing and feedstock production), along with the potential impacts of soil organic carbon change during feedstock production. We conducted scenario-based CI analysis of ethanol, coupled with regionalized inventory data, for various farming practices to manage corn fields, and identified key parameters affecting cradle-to-farm-gate GHG emissions. The results demonstrate large spatial variations in CI of ethanol due to farm input use and land management practices. In particular, adopting conservation tillage, reducing nitrogen fertilizer use, and implementing cover crops has the potential to reduce GHG emissions per unit corn produced when compared to a baseline scenario of corn–soybean rotation. This work shows a large potential emission offset opportunity by allowing feedstock producers a path to Tier 2 petitions that reward low-CI feedstocks and further reduce biofuels' CI. The prevalence of significant acreage that has not been optimized for CI suggests that policy changes that incentivize optimization of this parameter could provide significant additionality over current trends in farm efficiency and adoption of conservation practice.

09 BIOMASS FUELS↗

DOE OSTI2022

Correlated color temperature is not a suitable proxy for the biological potency of light

Using a simulation based on a real, five-channel tunable LED lighting system, we show that Correlated Color Temperature (CCT) is not a reasonable predictor of the biological potency of light, whether characterized with CIE melanopic Equivalent Daylight Illuminance (mel-EDI), Equivalent Melanopic Lux (EML) (a scalar multiple of mel-EDI), or Circadian Stimulus (CS). At a photopic corneal illuminance of 300 lx and Rf ≥ 70, spectra can vary in CS from 17 to 41% across CCTs from 2500 to 6000 K, and up to 23% at a single CCT, due to the choice of spectrum alone. The CS range is largest, and notably discontinuous, at a CCT of 3500 K, the location of the inflection point of the CS model. At a photopic corneal illuminance of 300 lx and Rf ≥ 70, mel-EDI can vary from 123 to 354 lx across CCTs from 2500 to 6000 K and can vary by up to 123 lx at a fixed CCT (e.g., 196 to 319 lx at 5000 K). The range of achievable mel-EDI increases as CCT increases and, on average, decreases as color fidelity, characterized with IES TM-30 Rf, increases. These data demonstrate that there is no easy mathematical conversion between CS and mel-EDI when a spectrally diverse spectra set of spectral power distributions is considered.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2023

Mitigation of ventilation air methane (VAM) using novel methanotrophic coating materials: a technical analysis

Abstract Ventilation air methane (VAM) is a potent greenhouse gas source originating from geological wells, current and extinct mineshafts and other terrestrial conduits venting methane to the atmosphere, contributing to global methane emissions and disproportionate warming potential. Herein, we introduce the concept of the methanotrophic material as an engineering solution. Such materials should be capable of converting methane at ambient temperatures and pressures to a binder product, capturing and permanently sequestering the methane while simultaneously restricting its further emission. While such materials are currently under research development, this goal is supported and facilities by the mathematical framework, introduced and used herein, to evaluate the ability to convert methane, using currently published activity data. We include a case study of the conversion of a characteristic stream of VAM (0.6% methane in air, 1.7 × 10 8 l hr −1 equivalent to 100 000 standard cubic feet per minute). We show that when appropriately designed, such systems require a surface coverage of less than 1000 m of mine tunnel length (equivalent to 20 000 m 2 areal coverage) in order to reduce the methane emission from this stream by over 99%. Finally, we highlight formaldehyde as a reactive intermediate of methane oxidation which may itself be incorporated into these coating materials. As a component of binders and polymers already used ubiquitously in commercial products, this intermediate ultimately allows these systems to sequester the carbon from methane in a stable and solid form. The results presented here are easily extended to the treatment of other methane streams—either more concentrated or dilute—and the results herein will guide the design and development of a new class of carbon-negative materials.

Environmental Sciences & Ecology↗

DOE OSTI2022

Prototype Integrated Hydrogen Fuel Cell Powered Data Center (Cooperative Research and Development Final Report)

Data centers are the backbone of the modern economy-from the server rooms that power small- to medium-sized organizations, to the enterprise-class data centers that support American corporations, and the server farms that run cloud computing services. Rather than a traditional grid-centric data center approach, work under the CRADA will be directed towards implementing a proof-of-concept "hydrogen-based" data center utilizing hydrogen fuel cells, electrolyzers, and solar photovoltaics as key building blocks. This integrated proof-of-concept builds on preliminary work done by HPE and its partners Mercedes-Benz Research & Development North America, Inc. (MBRDNA) and Power Innovations, to dramatically simplify the electrical infrastructure in the data center, leverage the latest automotive-scale hydrogen fuel cell technologies, and tightly couple renewably generated DC power directly to the Information Technology (IT). While some limited aspects of this concept have been demonstrated previously the entire integrated proof of concept proposed here has not yet been realized.

08 HYDROGEN↗

DOE OSTI2022

Wet Waste Hydrothermal Liquefaction and Biocrude Upgrading to Hydrocarbon Fuels: 2022 State of Technology

Data from Pacific Northwest National Laboratory’s (PNNL) conversion hydrothermal liquefaction (HTL) program for wet waste was used to update the pathway techno-economic analysis (TEA) for the fiscal year 2022 State of Technology (2022 SOT). Figure S.1 shows the modeled minimum fuel selling price (MFSP) for the 2022 SOT, along with the previous years’ SOTs (Snowden-Swan et al. 2020, 2021, 2022). These costs are for a HTL plant scale of 110 dry ton/day sludge feed and a larger centralized upgrading plant scale of 38 million gallons/year biocrude feed, commensurate with the design case. All costs were updated to 2020 dollars. Corresponding cost breakdowns and technical parameters for each case are given in Appendix B. In previous years’ analyses options with and without ammonia (NH 3 ) stripping treatment of the HTL aqueous phase recycle stream were included in the analysis to account for cases with direct recycle of untreated HTL aqueous phase back to the wastewater treatment plant. In the FY21 SOT assessment however, system boundaries for the analysis were adjusted to reflect separate ownership/operatorship for the HTL plant and with that, nutrient surcharge fees associated with disposal of the aqueous phase wastewater to a municipal sewer system were incorporated to provide an improved accounting of true disposal costs for a standalone plant. With these changes, there is no significant cost difference between the case including ammonia removal and the case excluding ammonia removal and therefore the “no NH3 removal” options will not be included in the 2022 and future SOTs.

09 BIOMASS FUELS↗

DOE OSTI2022

CA Special Studies: Updates to the Groundwater Pathway Radiological Dose

This environmental calculation file (ECF) summarizes the groundwater pathway exposure assumptions and methodologies used to revise radionuclide specific unit dose factors (UDFs) published in CP-64491, Hanford Site Composite Analysis Data Package – Exposure Scenarios and Radionuclide Specific Dose Conversion Factors. Specifically, this ECF describes the updated exposure-related parameters used to calculate radionuclide specific UDFs based on recommendations from the Low-Level Waste Disposal Facility Federal Review Group (LFRG) and updates to Adult External DCFs included in EPA 402-R-93-081, External Exposure to Radionuclides in Air, Water, and Soil hereinafter called Federal Guidance Report [FGR]).

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2021

Transport Cost for Carbon Removal Projects With Biomass and CO2 Storage

Strategies to remove carbon from the atmosphere are needed to meet global climate goals. Promising strategies include the conversion of waste biomass to hydrogen, methane, liquid fuels, or electricity coupled with CO 2 capture and storage (CCS). A key challenge for these projects is the need to connect geographically dispersed biomass supplies with geologic storage sites by either transporting biomass or CO 2 . We assess the cost of transport for biomass conversion projects with CCS using publicly available cost data for trucking, rail, and CO 2 pipelines in the United States. We find that for large projects (order of 1 Mt/yr CO 2 or greater), CO 2 by pipeline is the lowest cost option. However, for projects that send most of the biomass carbon to storage, such as gasification to hydrogen or electricity production, biomass by rail is a competitive option. For smaller projects and lower fractions of carbon sent to storage, such as for pyrolysis to liquid fuels, CO 2 by rail is the lowest cost option. Assessing three plausible example projects in the United States, we estimate that total transport costs range from $24/t-CO 2 stored for a gasification to hydrogen project traversing 670 km to $\$ 36$/t for a gasification to renewable natural gas project traversing 530 km. In general, if developers have flexibility in choosing transport mode and project type, biomass sources and storage sites can be connected across hundreds of kilometers for transport costs in the range of $20-40/t-CO 2 stored. Truck and rail are often viable modes when pipelines cannot be constructed. Distances of 1,000 km or more can be connected in the same cost range when shared CO 2 pipelines are employed.

09 BIOMASS FUELS↗

DOE OSTI2023

Next Generation Hydrogen Station Analysis

This presentation was part of the U.S. Department of Energy Hydrogen Program's Annual Merit Review and Peer Evaluation Meeting, reporting on project TA042, hydrogen refueling station evaluation.

CDP↗

DOE OSTI2024

1.1 eV GaInAs Cell Development for Dual-Use Solar and 1070 nm Laser Power Converters

Dual-use power converter cells can receive solar and laser power simultaneously to generate current, an application relevant to space and terrestrial industries. This study investigates two concepts of solar cells optimized for dual-use 1070 nm laser and solar power conversion, a single-junction and triple-junction cell. Because it is bandgap-tuned for a 1070 nm laser, the 1.1 eV junction is incorporated into each of the two designs, making its development key to the success of both concepts. Efficiency data for a one junction GaInAs cell demonstrates a laser conversion efficiency of 38% at 1070nm wavelength without an anti-reflection coating. However, the single-junction device requires optimization to reduce short wavelength absorption of the broad solar spectrum. In both devices, the graded buffer layers in the GaInAs cell affects the cell's performance by reducing threading dislocations in the active junction. However, the buffer in the three-junction device also acts as a lateral transport layer and so affects the fill factor depending on its sheet resistance. By varying the buffer thickness, we demonstrate a direct relationship between buffer thickness and sheet resistance reduction, while considering implications to open-circuit voltages. We also performed resistance modeling to determine the optimal grid spacing and thickness of the grid fingers to minimize losses due to sheet resistance and grid shading.

junctions↗

DOE OSTI2024

1.1 eV GaInAs Cell Development for Dual-Use Solar and 1075 nm Laser Power Converters

Dual-use power converter cells can receive solar and laser power simultaneously to generate current, an application relevant to space and terrestrial industries. This study investigates two concepts of solar cells optimized for dual- use 1070 nm laser and solar power conversion, a single-junction and triple-junction cell. Because it is bandgap-tuned for a 1070 nm laser, the 1.1 eV junction is incorporated into each of the two designs, making its development key to the success of both concepts. Efficiency data for a one junction GaInAs cell demonstrates a laser conversion efficiency of 38% at 1070nm wavelength without an anti-reflection coating. However, the single-junction device requires optimization to reduce short wavelength absorption of the broad solar spectrum. In both devices, the graded buffer layers in the GaInAs cell affects the cell's performance by reducing threading dislocations in the active junction. However, the buffer in the three-junction device also acts as a lateral transport layer and so affects the fill factor depending on its sheet resistance. By varying the buffer thickness, we demonstrate a direct relationship between buffer thickness and sheet resistance reduction, while considering implications to open-circuit voltages. We also performed resistance modeling to determine the optimal grid spacing and thickness of the grid fingers to minimize losses due to sheet resistance and grid shading.

dual-use↗

DOE OSTI2025

Search for Axions in Magnetic White Dwarf Polarization at Lick and Keck Observatories

We present the most sensitive search to date for light axion-like particles with masses below a micro-eV, using spectropolarimetric data collected from the Lick and Keck Observatories. The conversion of optical photons emitted from the surface of a magnetic white dwarf (MWD) into axions in the strong magnetic field around the star induces a nearly wavelength-independent linear polarization in the observed starlight. We analyze the Stokes parameters $(U, Q, I)$ measured with the Kast spectrograph at the Lick Observatory toward the MWDs SDSS J033320+000720 and ZTF J190132+145807, and with the LRISp-ADC instrument at the Keck Observatory toward ZTF J190132+145807, SDSS J002129+150223, and SDSS J100356+053825 to search for this effect. The data show no evidence of axion-induced linear polarization, and we set world-leading constraints on the axion-photon coupling $|g_{aγγ}| \lesssim 1.7 \times 10^{-12} \,\mathrm{GeV}^{-1}$ at the $95\%$ confidence level for masses $m_a \lesssim 2 \times 10^{-7}\,\mathrm{eV}$.

FOS: Physical sciences↗

DOE OSTI2026

Empirical Comparison of Machine Learning Approaches for Black-Box Modeling of Power Conversion System Dynamics

Inverter-based resources are key components in modern power systems, but accurately modeling their complex behavior can be challenging. Standard, generic converter models often oversimplify inverter dynamics, leading to significant errors in predicting performance. In this work, we compare several data-driven machine learning (ML) approaches for inverter modeling, performing experiments on power conversion systems, systematically varying input conditions, and recording the resulting voltages and currents. The ML models were then trained on this measured data to capture the inverter's dynamic response and to predict the inverter's output current. A performance comparison between the four ML models under study is conducted, laying the foundation for future work on hardware implementation for real-time inference.

30 DIRECT ENERGY CONVERSION↗

DOE OSTI2024

The Mu2e experiment

The Mu2e experiment, currently under construction at Fermilab, will search for neutrinoless mu->e conversion in the field of an aluminum atom. A clear signature of this chargedlepton flavor violating two-body process is given by the monoenergetic conversion electron of 104.97 MeV produced in the final state.An 8 GeV/c pulsed proton beam interacting on a tungsten target will produce the pions decaying in muons; a set of superconducting magnets will drive the negative muon beam to a segmented aluminum target where the stopped muons will eventually convert to electrons; a set of detectors will be used to both identify conversion electrons and reject beam and cosmic backgrounds.The experiment will need 3-5 years of data-taking to achieve a factor of $10^4$ improvement on the current best limit on the conversion rate.After an introduction to the physics of Mu2e, we will report on the status of the different components of the experimental apparatus. The updated estimate of the experiment’s sensitivity and discovery potential will be presented.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2024

Harnessing large language models’ zero-shot and few-shot learning capabilities for regulatory research

Abstract Large language models (LLMs) are sophisticated AI-driven models trained on vast sources of natural language data. They are adept at generating responses that closely mimic human conversational patterns. One of the most notable examples is OpenAI's ChatGPT, which has been extensively used across diverse sectors. Despite their flexibility, a significant challenge arises as most users must transmit their data to the servers of companies operating these models. Utilizing ChatGPT or similar models online may inadvertently expose sensitive information to the risk of data breaches. Therefore, implementing LLMs that are open source and smaller in scale within a secure local network becomes a crucial step for organizations where ensuring data privacy and protection has the highest priority, such as regulatory agencies. As a feasibility evaluation, we implemented a series of open-source LLMs within a regulatory agency’s local network and assessed their performance on specific tasks involving extracting relevant clinical pharmacology information from regulatory drug labels. Our research shows that some models work well in the context of few- or zero-shot learning, achieving performance comparable, or even better than, neural network models that needed thousands of training samples. One of the models was selected to address a real-world issue of finding intrinsic factors that affect drugs' clinical exposure without any training or fine-tuning. In a dataset of over 700 000 sentences, the model showed a 78.5% accuracy rate. Our work pointed to the possibility of implementing open-source LLMs within a secure local network and using these models to perform various natural language processing tasks when large numbers of training examples are unavailable.

Biochemistry & Molecular Biology↗

DOE OSTI2020

Feasibility of Process Intensification of Water-Gas Shift Reaction Using a Microreactor with Integrated Cooling

Hydrogen is an increasingly attractive low-carbon energy carrier for a variety of stationary and mobile applications. The water-gas shift (WGS) reaction is a key processing step used for large-scale hydrogen production via the steam methane reforming process. However, the thermodynamics and kinetics of the reaction are such that standard two-stage adiabatic reactors used in these systems are large, increasing catalyst volume and cost. To intensify the process and realize the economical distributed production of hydrogen, adiabatic WGS microreactors with integrated cooling directly regulate the reaction temperature via integrated coolant channels to promote higher conversion within a smaller reactor volume. This study investigates the conversion efficiency of a single WGS microchannel operating under such cooling conditions. A COMSOL Multiphysics model is developed and validated with isothermal experimental data from the literature. The model is then used to evaluate improvements in conversion efficiency when the reaction is cooled via a specified wall temperature profile. Lastly, the model is modified to include cooling channels with a secondary fluid that can practically achieve a similar conversion profile as the specified wall temperature profile previously applied. Initial results show that reactor conversion can be significantly increased by the inclusion of appropriate cooling and that there is a potential for the recovery of energy from the reaction stream that can be used for other applications within the overall process.

energy recovery↗

DOE OSTI2023

Techno-Economic Case Study: Low-Temperature Conversion Performance Based on Isolated Anatomical Fractions of Corn Stover

This report summarizes analysis conducted to support a case study under the Feedstock-Conversion Interface Consortium (FCIC) focused on techno-economic analysis (TEA) modeling to quantify the yield and cost ramifications for processing isolated anatomical fractions of corn stover through a low-temperature conversion (biochemical pathway) biorefinery. It is hypothesized that different individual anatomical fractions of corn stover vary in composition and recalcitrance, such that processing each fraction on its own e.g. through dedicated campaigns may enable economic benefits relative to processing the whole stover material. To investigate this concept, we worked with FCIC researchers to obtain data on compositional analysis, as well as processing conditions and yields, for conversion of whole corn stover plus three individual anatomical fractions (cobs, husks, and stalks) across key steps of the biorefinery conversion process within FCIC research scope, namely pretreatment and enzymatic hydrolysis. After running the data through the TEA models, fractionated cobs and husks achieved higher fuel yields and lower minimum fuel selling prices than whole corn stover, but stalks fared worse than whole stover on both metrics. The most direct takeaway from this assessment is that a biorefinery would stand to benefit economically from maximizing the use of cob and husk fractions while avoiding or minimizing the use of stalks, though recognizing practical constraints with this approach in maintaining an equivalent processing capacity (fixed at 2,000 dry metric tonne per day in all cases).

09 BIOMASS FUELS↗