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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 613 records · Page 34
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Data for Rewiring Yeast Metabolism for Producing 2,3-Butanediol and Two Downstream Applications: Techno-Economic Analysis and Life Cycle Assessment of Methyl Ethyl Ketone (MEK) and Agricultural Biostimulant Production

Rising concerns for sustainability and global climate change have driven the development of sustainable production pathways for biofuels and chemicals from lignocellulosic biomass via integrated biological and chemical processes. We constructed an engineered Saccharomyces cerevisiae capable of producing 2,3-butanediol (2,3-BDO) from glucose without accumulating ethanol and glycerol, which hinder downstream processing of 2,3-BDO, through extensive metabolic reprogramming. Specifically, we introduced heterologous 2,3-BDO biosynthetic enzymes and deleted the major isozymes of ethanol and glycerol biosynthetic enzymes. In addition, we introduced an NAD+ regenerating Pyruvate-Malate (PM) cycle and enhanced the NAD+ regenerating capability of the PM cycle to resolve the redox imbalance from the deletion of ethanol and glycerol production pathways. The resulting engineered yeast produced 109.9 g/L of 2,3-BDO with a productivity of 1.0 g/L/h and a yield of 0.36 g/g glucose in a fed-batch fermentation. We also conducted techno-economic analysis (TEA) and life cycle assessment (LCA) of the production of methyl ethyl ketone (MEK) through catalytic dehydration of 2,3-BDO. A TEA based on the experimental results indicated that the minimum product selling price (MPSP) was estimated to be $1.90/kg. Regarding cradle-to-grave LCA, 100-year global warming potential (GWP100) and fossil energy consumption (FEC) were found to be 0.37 kg CO2 eq/kg and 3.1 MJ/kg, respectively. These results demonstrated the feasibility of cost-competitive and sustainable bio-based MEK production via yeast fermentation. In addition, we explored the possibility of using the fermentation broth containing 2,3-BDO as a biostimulant inducing drought tolerance in plants. As a result, the yeast 2,3-BDO fermentation broth can induce drought tolerance in Arabidopsis thaliana without a complicated purification process.

Economics↗
DOE OSTI2021

Predicting images for the dynamics of stellar clusters ( π-DOC ): a deep learning framework to predict mass, distance, and age of globular clusters

ABSTRACT Dynamical mass estimates of simple systems such as globular clusters (GCs) still suffer from up to a factor of 2 uncertainty. This is primarily due to the oversimplifications of standard dynamical models that often neglect the effects of the long-term evolution of GCs. Here, we introduce a new approach to measure the dynamical properties of GCs, based on the combination of a deep-learning framework and the large amount of data from direct N-body simulations. Our algorithm, π-DOC (Predicting Images for the Dynamics Of stellar Clusters) is composed of two convolutional networks, trained to learn the non-trivial transformation between an observed GC luminosity map and its associated mass distribution, age, and distance. The training set is made of V-band luminosity and mass maps constructed as mock observations from N-body simulations. The tests on π-DOC demonstrate that we can predict the mass distribution with a mean error per pixel of 27 per cent, and the age and distance with an accuracy of 1.5 Gyr and 6 kpc, respectively. In turn, we recover the shape of the mass-to-light profile and its global value with a mean error of 12 per cent, which implies that we efficiently trace mass segregation. A preliminary comparison with observations indicates that our algorithm is able to predict the dynamical properties of GCs within the limits of the training set. These encouraging results demonstrate that our deep-learning framework and its forward modelling approach can offer a rapid and adaptable tool competitive with standard dynamical models.

Chardin, Jonathan↗
DOE OSTI2022

Signatures of superconducting triplet pairing in Ni–Ga-bilayer junctions

Ni–Ga bilayers are a versatile platform for exploring the competition between strongly antagonistic ferromagnetic and superconducting phases. We characterize the impact of this competition on the transport properties of highly-ballistic Al/Al 2 O 3 (/EuS)/Ni–Ga tunnel junctions from both experimental and theoretical points of view. While the conductance spectra of junctions comprising Ni (3 nm)–Ga (60 nm) bilayers can be well understood within the framework of earlier results, which associate the emerging main conductance maxima with the junction films’ superconducting gaps, thinner Ni (1.6 nm)–Ga (30 nm) bilayers entail completely different physics, and give rise to novel large-bias (when compared to the superconducting gap of the thin Al film as a reference) conductance-peak subseries that we term conductance shoulders. These conductance shoulders might attract considerable attention also in similar magnetic superconducting bilayer junctions, as we predict them to offer an experimentally well-accessible transport signature of superconducting triplet pairings that are induced around the interface of the Ni–Ga bilayer. We further substantiate this claim performing complementary polarized neutron reflectometry measurements on the bilayers, from which we deduce (1) a nonuniform magnetization structure in Ga in a several nanometer-thick area around the Ni–Ga boundary and can simultaneously (2) satisfactorily fit the obtained data only considering the paramagnetic Meissner response scenario. While the latter provides independent experimental evidence of induced triplet superconductivity inside the Ni–Ga bilayer, the former might serve as the first experimental hint of its potential microscopic physical origin. Finally, we introduce a simple phenomenological toy model to confirm also from the theoretical standpoint that superconducting triplet pairings around the Ni–Ga interface can indeed lead to the experimentally observed conductance shoulders, which convinces that our claims are robust and physically justified. Arranging our work in a broader context, we expect that Ni–Ga-bilayer junctions could have a strong potential for future superconducting-spintronics applications whenever an efficient engineering of triplet-pairing superconductivity is required.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2021

Synthesis and Electrical Properties of a New Compound (BiSe) 0.97 (Bi 2 Se 3 ) 1.26 (BiSe) 0.97 (MoSe 2 ) Containing Metallic 1T-MoSe 2

The synthesis and electrical properties of a new misfit compound containing BiSe, Bi 2 Se 3 , and MoSe 2 constituent layers are reported. The reaction pathway involves competition between the formation of (BiSe) 1+x (Bi 2 Se 3 ) 1+y (BiSe) 1+x (MoSe 2 ) and [(Bi 2 Se 3 ) 1+y ] 2 (MoSe 2 ). Excess Bi and Se are required in the precursor to synthesize (BiSe) 1+x (Bi 2 Se 3 ) 1+y (BiSe) 1+x (MoSe 2 ). High-angle annular dark field-scanning transmission electron microscopy (HAADF-STEM) confirm the stacking sequence of the heterostructure. Small grains of both 2H- and 1T-MoSe 2 are observed in the MoSe 2 layers. X-ray photoelectron spectroscopy (XPS) indicates that there is a significantly higher percentage of 1T-MoSe 2 in (BiSe) 1+x (Bi 2 Se 3 ) 1+y (BiSe) 1+x (MoSe 2 ) than in (BiSe) 0.97 (MoSe 2 ), suggesting that more charge transfer to MoSe 2 occurs due to the additional BiSe layer. The additional charge transfer results in (BiSe) 1+x (Bi 2 Se 3 ) 1+y (BiSe) 1+x (MoSe 2 ) having a low resistivity (14–19 μΩ m) with metallic temperature dependence. Furthermore, the heterogeneous mix of MoSe 2 polytypes observed in the XPS complicates the interpretation of the Hall data as two bands contribute to the electrical continuity.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Modern Approaches to Exact Diagonalization and Selected Configuration Interaction with the Adaptive Sampling CI Method

Recent advances in selected configuration interaction methods have made them competitive with the most accurate techniques available and, hence, creating an increasingly powerful tool for solving quantum Hamiltonians. In this work, we build on recent advances from the adaptive sampling configuration interaction (ASCI) algorithm. We show that a useful paradigm for generating efficient selected CI/exact diagonalization algorithms is driven by fast sorting algorithms, much in the same way iterative diagonalization is based on the paradigm of matrix vector multiplication. We present several new algorithms for all parts of performing a selected CI, which includes new ASCI search, dynamic bit masking, fast orbital rotations, fast diagonal matrix elements, and residue arrays. The ASCI search algorithm can be used in several different modes, which includes an integral driven search and a coefficient driven search. The algorithms presented here are fast and scalable, and we find that because they are built on fast sorting algorithms they are more efficient than all other approaches we considered. After introducing these techniques, we present ASCI results applied to a large range of systems and basis sets to demonstrate the types of simulations that can be practically treated at the full-CI level with modern methods and hardware, presenting double- and triple-ζ benchmark data for the G1 data set. The largest of these calculations is Si$_2$H$_6$ which is a simulation of 34 electrons in 152 orbitals. We also present some preliminary results for fast deterministic perturbation theory simulations that use hash functions to maintain high efficiency for treating large basis sets.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Plant-Nitrifier Interactions in Topsoil and Subsoil

Plants can influence soil microbes through resource acquisition and interference competition, with consequences for ecosystem function such as nitrification. However, how plants alter soil conditions to influence nitrifiers and nitrification rates remains poorly understood, especially in the subsoil. Here, coupling the 15N isotopic pool dilution technique, high throughput sequencing and in situ soil O2 monitoring, we investigated how a deep-rooted perennial grass, miscanthus, versus an adjacent shallow-rooted turfgrass reference shapes nitrifier assembly and function along 1 m soil profiles. In topsoil, the suppression of ammonia (NH3) oxidizing archaea (AOA) and gross nitrification rates in miscanthus relative to the reference likely resulted from nitrifiers being outcompeted by plant roots and heterotrophic bacteria for ammonium (NH4+). The stronger tripartite competition under miscanthus may have been caused in part by the lower soil organic matter (SOM) content, which supported lower gross nitrogen (N) mineralization, the major soil process that produces NH4+. In contrast, below 10 cm soil depth, significantly greater gross nitrification rates were observed in miscanthus compared to the reference. This was likely driven by the significantly lower oxygen (O2) in miscanthus than reference subsoil, which selected against aerobic heterotrophic bacteria but in favor of AOA. Overall, we found that plants can regulate AOA community structure and function through different mechanisms in topsoil and subsoil, with suppression of nitrification in topsoil and enhancement of nitrification in subsoil.

Field Data↗
DOE OSTI2026

Thriving in the Carbon-Aware Market: How to Account for Emissions in the Era of Carbon-Centered Trade Policies

Emerging global policies, such as the European Union's enacted Carbon Border Adjustment Mechanism and similar policies under development in Canada, Australia and the United Kingdom, will place a premium on goods traded into their territories with higher embedded emissions than those produced domestically. U.S. manufacturers could stand to benefit from such policies; given the investments U.S. industry has made to reduce the energy and emission intensities of its operations. For example, the overall GHG intensity of U.S. steel production in 2019 was ~0.96 t CO2/t steel, less than half that of China (~1.97 t CO2/t steel), and bested only by Italy. To realize these benefits, transparent, accurate, interoperable and accepted embedded emissions accounting and calculation methods are required. Achieving this requires overcoming challenges related to data availability, boundary definitions, and product definitions among others, both at individual facilities as well as through value chains. We will present technical findings on methodology considerations and data-availability constraints for determining the emissions of traded goods, using steel as a pilot and leveraging publicly available data. Issues such as determining the appropriate scope for emissions accounting, implications of the specificity of product chosen, emissions allocation in multi-product facilities, and enumeration of emissions for products manufactured across multiple facilities will be discussed. By sharing the results of our efforts, we aim to inform the development and execution of embedded emissions accounting methods from a technical perspective such that U.S. manufacturers can thrive in emerging global markets.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2026

Spatiotemporal and Statistical Mapping of Transition Metal Equilibria in Alkaline Media

Transition metal dissolution and redeposition (D/R) kinetics in alkaline media play a critical role in various chemical and electrochemical processes. Competitive reaction kinetics between different transition metals can modulate individual metal behavior in these processes. To date, these phenomena have remained largely unmeasured, and even when captured, they are difficult to statistically characterize due to their dynamic nature, simultaneous occurrence, and spatially heterogeneous nature. Here, in this study, we develop a statistical analysis framework based on in situ and operando X-ray fluorescence microscopy (XFM) to investigate the relative D/R kinetics of multiple transition metals in alkaline media. By employing statistical analysis, we quantify the spatial distribution of D/R species and assess the rate at which the system reaches equilibrium under varying reaction conditions. We show that pH does not simply change the rate of dissolution and redeposition, but reorganizes the cross-element kinetic correlations among Ni, Fe, and Mn and accelerates the spatial equilibration of D/R events, as quantified through correlation analysis, reaction-rate estimation, probability function distributions, and texture-based monitoring statistics. Additionally, we demonstrate how modifying the solvent environment can influence D/R kinetics, providing a pathway for tuning materials synthesis and process optimization. Our study offers valuable insights into the complex interplay between different transition metals and provides a reliable statistical framework for spatial analysis of diverse imaging data sets, enabling deeper extraction of latent information across multiple modalities.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Visualizing Temporal Topic Embeddings with a Compass

—Dynamic topic modeling is useful at discovering the development and change in latent topics over time. However, present methodology relies on algorithms that separate document and word representations. This prevents the creation of a meaningful embedding space where changes in word usage and documents can be directly analyzed in a temporal context. This paper proposes an expansion of the compass-aligned temporal Word2Vec methodology into dynamic topic modeling. Such a method allows for the direct comparison of word and document embeddings across time in dynamic topics. This enables the creation of visualizations that incorporate temporal word embeddings within the context of documents into topic visualizations. In experiments against the current state-of-the-art, our proposed method demonstrates overall competitive performance in topic relevancy and diversity across temporal datasets of varying size. Simultaneously, it provides insightful visualizations focused on temporal word embeddings while maintaining the insights provided by global topic evolution, advancing our understanding of how topics evolve over time.

Cluster analysis↗
DOE OSTI2022

Dual Targeting Factors Are Required for LXG Toxin Export by the Bacterial Type VIIb Secretion System

Bacterial type VIIb secretion systems (T7SSb) are multisubunit integral membrane protein complexes found in Firmicutes that play a role in both bacterial competition and virulence by secreting toxic effector proteins. The majority of characterized T7SSb effectors adopt a polymorphic domain architecture consisting of a conserved N-terminal Leu-X-Gly (LXG) domain and a variable C-terminal toxin domain. Recent work has started to reveal the diversity of toxic activities exhibited by LXG effectors; however, little is known about how these proteins are recruited to the T7SSb apparatus. In this work, we sought to characterize genes encoding domains of unknown function (DUFs) 3130 and 3958, which frequently cooccur with LXG effector-encoding genes. Using coimmunoprecipitation-mass spectrometry analyses, in vitro copurification experiments, and T7SSb secretion assays, we found that representative members of these protein families form heteromeric complexes with their cognate LXG domain and in doing so, function as targeting factors that promote effector export. Additionally, an X-ray crystal structure of a representative DUF3958 protein, combined with predictive modeling of DUF3130 using AlphaFold2, revealed structural similarity between these protein families and the ubiquitous WXG100 family of T7SS effectors. Interestingly, we identified a conserved FxxxD motif within DUF3130 that is reminiscent of the YxxxD/E “export arm” found in mycobacterial T7SSa substrates and mutation of this motif abrogates LXG effector secretion. Overall, our data experimentally link previously uncharacterized bacterial DUFs to type VIIb secretion and reveal a molecular signature required for LXG effector export.

24 antibacterial toxins↗
DOE OSTI2026

EXERGETIC: De-Risking Next-Generation Resilient Geothermal Hybrids via At-Scale Evaluation Using Virtual Emulation Digital Twin Environment for Efficient Operation

The DOE-GTO-funded project, award number 5.1.2.12, entitled "EXERGETIC - De-risking Next Generation Resilient Geothermal Hybrids via at-Scale Evaluation Using a Virtual Emulation Digital Twin Environment for Efficient Operation," advances the solution to these challenges by developing and validating a geothermal co-emulation environment implemented at the National Laboratory of the Rockies (NLR)'s Advanced Research on Integrated Energy Systems (ARIES) platform. This framework enables the de-risking of next-generation geothermal and geothermal hybrid systems through high-fidelity modeling, real-time digital emulation, advanced control strategies, and techno-economic assessment. The project focused on geothermal hybrid configurations that integrate geothermal power plants with concentrated solar power and underground thermal energy storage, enabling enhanced efficiency, flexibility, and grid support capabilities. The main goal of this project was the development of a geothermal digital co-emulation environment to demonstrate the technical and economic value of geothermal hybrid systems and their contribution to grid stability and flexibility. The EXERGETIC framework combined physics-based models, controls, and real assets at ARIES, including digital real-time simulators (DRTS), a 20-MW-scale controllable grid interface (CGI), and a 2-MW conventional generator. Detailed transient models were developed for the key subsystems of a hybrid geothermal plant, including parabolic trough solar collectors, reservoir thermal energy storage (RTES), and a binary Organic Rankine Cycle (ORC) power plant. The ORC model explicitly captured thermal inertia and off-design operation and integrated control strategies to dynamically respond to electric load profiles. The models were validated against published experimental and numerical studies, demonstrating strong agreement and confirming the accuracy and robustness of the modeling approach. The resulting digital twin represents geothermal-solar-storage systems at multiple scales (1 MW to 100 MW) and enables realistic emulation of grid-connected operation. The control architecture allows the geothermal resource to provide stable baseload generation, while solar and stored thermal energy supply flexible, dispatchable support during periods of high demand or variable grid conditions. A key contribution of the EXERGETIC project is the demonstration that geothermal hybrid systems can be designed to be active grid assets rather than passive baseload generators. Using the ARIES platform, the digital twin was evaluated under multiple grid scenarios, including load following, voltage support at the distribution level, and frequency response at the transmission level. Results show that hybrid geothermal systems can respond effectively to dynamic grid conditions, providing inertia-like behavior, primary frequency support, and voltage regulation through coordinated control. In addition to the performance and grid services capability analysis of geothermal and hybrid geothermal systems, the EXERGETIC project also focused on scalability and techno-economic analysis of geothermal hybrid plants. In particular, for the scalability analysis, machine-learning (ML)-based surrogate models were trained using data generated from the geothermal digital twin under different grid-connected scenarios and plant capacities. These ML models demonstrated strong interpolation and extrapolation capabilities across plant sizes, accurately reproducing both steady-state and transient responses with very low errors. Regarding the techno-economic analysis, plant performance results were integrated with cost models for hybrid geothermal systems, and the levelized cost of electricity (LCOE) was used as the main economic metric to evaluate system performance across a range of system capacities, solar shares, solar multiples, and storage durations. Results indicate that economies of scale significantly reduce geothermal LCOE as plant capacity increases, with large-scale systems (25-100 MW) achieving substantially lower costs than small plants. Hybridization with solar thermal energy and storage further improves economic performance by increasing capacity utilization and enabling flexible dispatch. In addition, thermal storage plays a critical role in reducing LCOE by maximizing geothermal, solar, and stored energy resources. In summary, the results from this project demonstrate that geothermal hybrid systems represent a promising alternative for increasing the energy conversion efficiency of geothermal technologies, contributing to the preservation of geothermal resources, and supporting the transition of geothermal plants from traditional baseload resources into flexible, resilient, and cost-competitive energy conversion technologies.

15 GEOTHERMAL ENERGY↗
DOE OSTI2022

Fundamentals of hydrogen storage in nanoporous materials

Abstract Physisorption of hydrogen in nanoporous materials offers an efficient and competitive alternative for hydrogen storage. At low temperatures (e.g. 77 K) and moderate pressures (below 100 bar) molecular H 2 adsorbs reversibly, with very fast kinetics, at high density on the inner surfaces of materials such as zeolites, activated carbons and metal–organic frameworks (MOFs). This review, by experts of Task 40 ‘Energy Storage and Conversion based on Hydrogen’ of the Hydrogen Technology Collaboration Programme of the International Energy Agency, covers the fundamentals of H 2 adsorption in nanoporous materials and assessment of their storage performance. The discussion includes recent work on H 2 adsorption at both low temperature and high pressure, new findings on the assessment of the hydrogen storage performance of materials, the correlation of volumetric and gravimetric H 2 storage capacities, usable capacity, and optimum operating temperature. The application of neutron scattering as an ideal tool for characterising H 2 adsorption is summarised and state-of-the-art computational methods, such as machine learning, are considered for the discovery of new MOFs for H 2 storage applications, as well as the modelling of flexible porous networks for optimised H 2 delivery. The discussion focuses moreover on additional important issues, such as sustainable materials synthesis and improved reproducibility of experimental H 2 adsorption isotherm data by interlaboratory exercises and reference materials.

08 HYDROGEN↗
DOE OSTI2020

Consequences of product inhibition in the quantification of kinetic parameters

While the potential for product inhibition in catalytic reactions is well known, the impact of neglected inhibition on measured kinetic parameters is often overlooked. The presence of product inhibition, most often caused by the competitive adsorption of products with reactants on catalytic active sites, is difficult to determine a priori for an arbitrary catalytic system. The significance of product inhibition relies on the concentration of the products, their adsorption thermodynamics on catalytically relevant sites, and process parameters such as temperature and pressure. When inhibition is significant, however, apparent activation energies and reaction orders vary from the differential-reactor apparent activation energy by a factor of 1/(1 - δ), where δ is the total inhibition order (e.g., the factor (1 - δ) = 1.6 for a system with product inhibition of -0.6 order). This is illustrated here with the kinetics of NO oxidation over Cu ion clusters (Cu x O y ) in Cu-SSZ-13, for which the product NO 2 inhibits the forward reaction. Furthermore, in the presence of inhibition, when only reactants are fed to a flow reactor or placed in a batch reactor, there is often no practical conversion that is low enough to guarantee differential behavior. Inclusion of products in the feed solves this problem, allowing accurate determination of kinetic parameters such as apparent reaction orders and activation energies. We also demonstrate that evaluation of the necessity of co-feeding products to assure measurement of differential-reactor data in a given catalytic system is straightforward from a plot of the log of the rate (or conversion) versus the log of the space time in a flow reactor or elapsed time in a batch reactor. We encourage inclusion of this test in all kinetic analyses that are reasonably approximated by power law rate expressions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Supply chain optimization of sustainable aviation fuel from carinata in the Southeastern United States

We report carinata is a purpose-grown oilseed feedstock for renewable fuels, including sustainable aviation fuel (SAF) that can replace conventional aviation fuel (CAF). Given carinata is a new crop in the Southeastern United States, it is crucial to analyze its sustainability from a supply chain perspective. This study developed a mixed-integer linear programming (MILP) model and simulated it for 20 years, starting from a farm (county-level data) and ending at the airport. About 2.06 million ha in Alabama, Florida, and Georgia combined were found suitable for carinata production. Given the three-year rotation period, about 0.69 million hectares can be cultivated annually, approximately 65% of which was in Georgia. About 2.4% of the combined SAF annual demand of four major airports (about 210 thousand t) in the study area is satisfied at that level of carinata cultivation. However, all available SAF was supplied to the Atlanta airport as this decision minimizes the supply chain cost. A total of 1343 storage units, one oil extraction mill, and one biorefinery were needed to meet the overall demand. We found that SW Georgia is the top supplier of carinata seeds. The unit cost of production and carbon intensity were estimated to be $\$$0.89 L -1 (or $\$$26.79 GJ -1 ) and 0.91 kg CO 2 e L -1 (or 27.28 kg CO 2 e GJ -1 ), respectively. This carbon intensity of carinata-based SAF was 67.8% lower than that of CAF. With variations included in SAF demand, yield, and soil carbon sequestration, carbon savings remained between 66.5% and 67.8%. Given the GHG advantage of SAF over CAF, there is justification for subsidies required to make SAF competitive.

10 SYNTHETIC FUELS↗
DOE OSTI2022

Agroecosystem model simulations reveal spatial variability in relative productivity in biomass sorghum and maize in Iowa, USA

Biomass sorghum (Sorghum bicolor L. Moench) is an annual C 4 grass that has emerged as a candidate bioenergy crop but has not been widely grown in the United States. Corn (Zea mays L.), another annual C 4 grass, has been produced on a large scale in the United States for biofuels. Iowa leads the nation in both corn and ethanol production. The high productivity of corn in Iowa creates the research question: could biomass sorghum be as or more productive than corn in the state in terms of bioenergy? Efforts to use crop models to fill the gaps left by field experimentation on biomass sorghum have also been limited thus far. To address our research question, we collected biophysical data on biomass sorghum grown in Iowa for two growing seasons, used them to develop a biomass sorghum module in an agroecosystem model (Agro-IBIS), and predict the potential performance of biomass sorghum across Iowa compared to maize. Despite dry conditions in 2019 and 2020, average biomass sorghum yields were 17.20 Mg ha –1 . By comparison, average corn aboveground biomass was higher in 2020 (22.02 Mg ha –1 ). Soil cores indicated average belowground biomass of 1.46 Mg ha –1 , with roots concentrated near the surface (73% of biomass above 50 cm in a 1 m core). When biomass sorghum model parameters were calibrated with measured values, model output was in close agreement with measured biomass (slope = 0.932, R 2 = 0.91) and evapotranspiration (slope = 0.757, R 2 = 0.64). Subsequent regional simulations revealed a notable latitudinal gradient in biomass sorghum yield, with a strong linear relationship between yield and seasonal growing degree-days (R 2 = 0.89). When these yields were compared to simulated corn aboveground biomass yields, only 3.4% of the state had biomass sorghum yields that were significantly higher than corn. This number was reduced to 0.3% when comparing the crops in terms of energy ethanol yield. Thus, we conclude biomass sorghum must be improved to be competitive with corn as an annual biofuel crop in Iowa. This study provides a baseline against which to compare advancements in sorghum breeding for biomass and stress tolerance in Iowa.

Agro-IBIS↗
DOE OSTI2021

DETECTING FIRE WITH MACHINE LEARNING-ENABLED VISUAL MONITORING FOR NUCLEAR POWER PLANT ENVIRONMENTS

Nuclear power plants are experiencing significant cost challenges to remain competitive with other energy-generation utilities. Unlike other industries, the cost of operation and maintenance activities is mostly attributed to workforce costs. To mitigate this, nuclear power plant stakeholders are increasingly interested in the development and deployment of machine learning methods to potentially automate or augment manually intensive tasks to reduce costs, especially for monitoring activities. One monitoring function that is visually demanding and that can occur frequently to meet the requirements of a fire protection program is visually monitoring an area for fire occurrence. Currently, fire watch activities consist of a worker physically stationed at a given location with the sole responsibility of observing a given area to ensure a fire is detected and mitigated promptly. This effort focused on the development and evaluation of a suitable deep convolutional neural network to classify individual video frames at a sub-second frequency for the occurrence of “fire” and “no fire” in varying industrial environments similar to nuclear power plants. It is believed that a trained neural network model could be integrated with existing facility video surveillance camera feeds to generate alerts when fire inferences occur in individual frames captured at sub-second temporal resolutions. Extensive effort was dedicated to identifying and curating suitable imagery training data representing varying environments and scene settings with and without flame features to maximize generalization in nuclear power plant environments. The data collection effort resulted in the aggregation of a large, labeled image library exceeding 12,000 images to support model training for diverse industrial environments. A deep neural network model incorporating parallel multi-scale capabilities was developed and trained to support accurate image-based detection of flame incidents of varying sizes and spectral feature properties within heterogeneous scenes. Analysis results show that the trained model can achieve high inference accuracy despite heterogeneous scene environments and components. Testing accuracy exceeded 95.0 percent with very low false positive and false negative inferences.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2025

Systems Analysis of Biomass and Coal Co-firing Power Plants with Deep Carbon Capture Toward Net-zero Emissions

Achieving a net-zero emission economy in the United States requires integrating diverse low-carbon and negative-emission technologies into the existing fossil fuel-dominant power fleet. Potential technologies from the low-carbon portfolio include renewable power, fossil power with carbon capture and storage (CCS), bioenergy with CCS (BECCS), and direct air capture (DAC). Renewable power is a clean energy source but has to pair with costly battery storage to provide dispatchable electricity. Fossil power with CCS offers dispatchable electricity yet still relies on DAC to offset residual emissions, even when deploying deep CCS with more than 90% CO2 capture. Coal-biomass co-firing with CCS, a subset of BECCS, is a reliable energy production technology that can be retrofitted from existing electricity generation units (EGUs). Power plant retrofit maximizes the use of the current U.S. coal power fleet without the need for large-scale deployment of new renewable power, battery storage, or DAC. Retrofitting coal-biomass co-firing with deep CCS in EGUs is a promising option, but not a universal solution. Biomass co-firing at a power plant introduces economic challenges and indirectly poses pressure on land and water resources. Meanwhile, retrofitting deep CCS affects plant efficiency and raises electricity generation costs. Overall, the technical feasibility and economic viability of plant retrofits vary across EGUs, as they are contingent upon the regional availability of biomass, unit-specific characteristics, site-specific fuel supply costs, and adjacent CO2 storage potential. Government incentives like 45Q can improve the retrofit viability, though the impact requires further quantification. A comprehensive analysis at the unit level is essential to address the question regarding the fate of the U.S. coal-fired electricity generation fleet toward the net-zero emission goal. This study conducts a systematic techno-economic-environmental assessment of EGUs to identify the viability of biomass co-firing and deep CCS retrofits in the U.S. coal-fired power fleet. Specifically, it characterizes the techno-economic performance of deep carbon capture, estimates life cycle greenhouse gas (GHG) emissions, and conducts a fleet-level assessment on retrofit viability. The key objectives are (1) to estimate the unit-specific performance and retrofitted cost under various biomass co-firing levels and CO2 capture rates; (2) to determine the possibility of reaching net-zero emission at the fleet level; (3) to quantify the cumulative capacities that are suitable for plant retrofits under current and future biomass supply scenarios; and (4) to improve the understanding of policy impacts on such retrofits to help the power sector’s transition to a net-zero economy. Techno-economic Model of Deep Carbon Capture. This study develops the performance and economic models for Monoethanolamine-based post-combustion CO2 capture at 95–99% capture rates. The process is simulated in Aspen Plus, analyzing the performance of carbon capture technology by varying the plant sizes, solvent lean loading, CO2 concentrations, and flue gas inlet temperature. Based on the key inputs and output parameters of CO2 capture, a reduced-order performance model of deep carbon capture is formulated. In addition, an engineering-economic model integrating the performance metrics is developed to estimate the capital as well as operation and maintenance (O&M) costs. Capital cost estimations follow the framework of the Integrated Environmental Control Model (IECM) and incorporate data regressions from three technical reports by IECM, the National Energy Technology Laboratory (NETL), and the National Renewable Energy Laboratory. The O&M cost estimation utilizes the actual inventory consumption rate and labor requirements. Both performance and cost models are embedded into IECM v13.0-beta, a fossil-fuel power plant modeling tool. Life Cycle Assessment of Power Plants. This study estimates the GHG emissions of power plants through life cycle assessment (LCA). The LCA scope includes fuel supply, combustion-based power generation, and CO2 transport and storage. The fuel-based life cycle module is designed following the framework of the NETL Unit Process Library and CO2U LCA Guidance Toolkit. The module is then incorporated into IECM v13.0-beta. The process-based LCA is applied to estimate the GHG emissions of coal and biomass supply, coal- and coal-biomass co-firing power plant operation, as well as CO2 pipeline transport and geographical sequestration. An uncertainty analysis is conducted to quantify the variability and uncertainty associated with the LCA using the Latin Hypercube Sampling (LHS) method. Fleet-level Assessment. This study evaluates the technical and economic feasibility of selected coal-fired EGUs, examines the role of tax credits in retrofit viability, and assesses the competitiveness of retrofitted units against other low-carbon options. Unit screening identifies EGUs for the study, focusing on new, efficient baseload units with air pollution controls. The power plant databases are then established to organize unit-specific information on performance and operating conditions from the relevant public databases. Biomass for co-firing retrofits is selected based on home and neighboring county availability, ensuring sustained operation with at least a 5% co-firing level. The CO2 storage site is determined by state-level storage potential, with ArcGIS Pro and NETL CO2 Saline Storage Cost Model used to identify the optimal balance between the nearest transport distances and affordable storage costs. The latest IECM v13.0-beta is then employed to configure and evaluate the eligible EGUs with or without the deployment of deep CCS and biomass co-firing. A supply curve is established to illustrate the cumulative installed capacity suitable for retrofits at different cost levels. A sensitivity analysis on tax credits for carbon sequestration is performed. Finally, a unit-level cost comparison is conducted among retrofitted plants, renewable power with battery storage, and abated fossil fuels with DAC. Expected Results. This study evaluates the technical, economic, and environmental metrics of each EGU across an array of CO2 capture rates and biomass co-firing level scenarios. Unit-level comparisons will identify critical factors influencing technical performance. The supply curves with and without tax incentives will provide insights into the impact of tax credits on biomass co-firing and CCS deployment. The cost comparisons with renewables and DAC-retrofit will assess the competitiveness of the retrofitted units. Life cycle emissions from each unit will be assessed to identify the scenarios under which net-zero emissions can be achieved. These analyses are expected to determine the total coal-fired capacity suitable for serving as a low-carbon energy source with or without tax incentives. The study results are novel in identifying optimal unit-specific strategies for producing carbon-neutral power, whether through retrofitting EGUs with deep CCS, biomass co-firing, DAC, or installing renewable power with battery. The findings will provide insight into nationwide efforts to ensure reliable, affordable, and low-carbon electricity. It also will inform investment decisions and policies in the deployment of deep carbon capture and negative emission technologies for a net-zero energy future.

Biomass Co-firing↗
DOE OSTI2026

Two-phase flow numerical analysis of electrode geometry for alkaline water electrolyzers

Hydrogen is a promising component of a future energy-secure and efficient economy, but its competitiveness depends on reducing production costs. One strategy is to operate alkaline water electrolyzers at higher current densities to increase output. However, this intensifies performance losses due to gas bubble accumulation, which blocks transport pathways and deactivates electrochemically active surfaces. Enhancing bubble evacuation through electrode design is therefore essential. Previous studies have explored various approaches — such as modifying surface morphology, applying sonication or pressure modulation, and introducing surfactants — but these efforts have addressed a limited range of conditions due to the complexity of two-phase flow and electrode geometries. Experiments have also largely been focused on either cell level improvements, which lack the information necessary to isolate each contributing factor, or on modified geometries that are not relevant to practical cell operation. From a modeling perspective, conventional Eulerian multiphase models do not track the complex gas–liquid interfacial dynamics and often neglect surface tension and contact angle effects, reducing their predictive accuracy. To provide insights on the effects of different electrode geometries on the performance of alklaine water electrolyzers this work employs an immersed boundary volume-of-fluid method to simulate bubble behavior in 3D porous electrodes. Multiple base electrode geometries, typically used in practice, with varying porosity are evaluated under a constant surface gas generation rate. Simulation data is analyzed to quantify electrode gas coverage, bubble size dynamics and other relevant metrics. Results show that porosity strongly influences bubble accumulation on electrode surfaces, with higher porosity reducing gas coverage, and its not strictly dependent on the electrode geometry. However, the electrode’s base geometry significantly affects gas accumulation at the separator gap, independent of porosity. A foam electrode geometry resulted in the lowest gas coverage of all electrodes with a median volumetric gas coverage of 11%, but at the cost of a 70% reduction in active area compared with the largest surface area electrode, while gyroid electrodes showed the best trade-off between gas coverage, particularly at the separator surface, and electrochemically active area. In conclusion, the results highlight the need for holistic electrode design strategies.

Alkaline↗