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DOE OSTI2026

Effect of 316 stainless steel powder processing conditions on microstructure

One parameter that has both economic and performance effects in the manufacturing of steel parts is the powder feedstock used. 316 L steel powder can be produced through water atomization or gas atomization, where both the powder size and the atomization process determine the cost of powder feedstock. Gas atomized powder is spherical and has a lower oxygen content than water atomized powder, making it the preferred choice for performance, but comes at a higher financial cost. Here, in this study, various 316 L powder batches are characterized to understand physical properties and microstructural variations. Density and flowability were determined using gas pycnometry and Hall flow tests. Powder morphology and porosity were qualitatively analyzed via optical microscopy and scanning electron microscopy, Finally, transmission electron microscopy was used to determine changes in phases present. Spherical gas atomized powder of 30–100 μm contained Mn$-$Si rich oxides both on the surface and within the matrix. Meanwhile, irregular water atomized powder 30 μm in diameter contained silicon oxides without manganese. All powder particles contained a nickel rich cellular structure which was shown to support the formation of additional phases. While literature has observed general microstructural features across 316 L powder particles, transmission electron microscopy (TEM) in this study has identified variations in precipitation and cell structures. Information regarding morphology, flowability, density, and phase distributions can be applied to understand variations in part consolidation, which will in turn show which powder aspects are significant for production.

Chemical segregation↗

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 OSTI2026

Lignin Extraction and Condensation as a Function of Temperature, Residence Time, and Solvent System in Flow-through Reactors

Solvolytic extraction of lignin from biomass is a critical step in lignin-first biorefining, including the reductive catalytic fractionation (RCF) process. Key to optimal RCF processing is the ability to rapidly extract lignin from biomass at high delignification extents and transfer the lignin molecules to a catalyst surface in a time frame that minimizes lignin condensation reactions. Here, we use a flow-through reactor to study the effects of temperature (175–250 °C), residence time (9 to 36 min), and solvent composition (methanol and methanol–water) on lignin extraction and condensation. We evaluated three metrics at each condition: total delignification, delignification rate, and extent of condensation, the latter measured by a decrease in monomer yield for batch hydrogenolysis reactions of solvolysis liquor compared to batch RCF reactions. We observe that delignification is predominantly determined by temperature, while residence time dictates the lignin condensation extent. Moreover, the extent of both extraction and condensation increased in the methanol–water solvent system compared to that in the methanol system. Lignin extracted in methanol is stable up to 18-min residence times at or below 225 °C, while a majority of the lignin extracted in methanol–water is condensed with a 9-min residence time at 200 °C. These results can inform reactor designs and solvent selection for lignin-first biorefining processes that aim to physically separate the biomass and catalyst.

biorefining↗

DOE OSTI2025

Lignin Extraction and Condensation as a Function of Temperature, Residence Time, and Solvent System in Flow-through Reactors

Solvolytic extraction of lignin from biomass is a critical step in lignin-first biorefining, including the reductive catalytic fractionation (RCF) process. Key to optimal RCF processing is the ability to rapidly extract lignin from biomass at high delignification extents and transfer the lignin molecules to a catalyst surface in a time frame that minimizes lignin condensation reactions. Here, we use a flow-through reactor to study the effects of temperature (175-250 °C), residence time (9 to 36 min), and solvent composition (methanol and methanol-water) on lignin extraction and condensation. We evaluated three metrics at each condition: total delignification, delignification rate, and extent of condensation, the latter measured by a decrease in monomer yield for batch hydrogenolysis reactions of solvolysis liquor compared to batch RCF reactions. We observe that delignification is predominantly determined by temperature, while residence time dictates the lignin condensation extent. Moreover, the extent of both extraction and condensation increased in the methanol-water solvent system compared to that in the methanol system. Lignin extracted in methanol is stable up to 18-min residence times at or below 225 °C, while a majority of the lignin extracted in methanol-water is condensed with a 9-min residence time at 200 °C. These results can inform reactor designs and solvent selection for lignin-first biorefining processes that aim to physically separate the biomass and catalyst.

09 BIOMASS FUELS↗

DOE OSTI2025

Round Robin Measurements of Molten Salt Properties for LiF-NaF-KF (FLiNaK) and NaCl-KCl Mixtures

The development, operation, and regulation of nuclear reactors that utilize molten salts as fuel or as heat transfer media require knowledge of the thermal properties of the salt systems and quantification of the corresponding uncertainties. Knowledge of molten salt properties is also necessary for applications in material synthesis, processing, separations, solar thermal power generation, and energy storage. A round robin was conducted with national laboratory and university participants from twenty-one laboratories in five countries to compare property measurements, to better understand uncertainties, and to identify possible best practices. Two salt mixtures, each from a common batch, were distributed to participants for evaluation: equimolar NaCl-KCl and 45.0LiF-13.7NaF-41.3KF mol % (FLiNaK). Measurements were performed to determine the major constituent composition, oxygen content, density, thermal expansivity, melting point, and thermal conductivity. Error analysis was performed on each measurement for uncertainty quantification for each type of property that was explored. Finally, the resulting discussion of the methodologies used in this work is meant to lay the groundwork for the development of standard methods and reference materials for future high-temperature property measurements on halide melts.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2026

Evaluation of Pu Solubility in Glass for Sludge Batch 11

Stainless-steel clad Pu from Japan’s Fast Critical Assembly (FCA) reactor is currently being dispositioned at the Savannah River Site. The electrolytic dissolver, operated by Savannah River Nuclear Solutions in H-Canyon, is being utilized to dissolve the material. The resulting solutions are transferred to the Concentration, Storage, and Transfer Facilities, operated by Savannah River Mission Completion (SRMC), for subsequent vitrification at the Defense Waste Processing Facility (DWPF). In support of the FCA mission startup, a preliminary evaluation was conducted by the Savannah River National Laboratory to assess the impact of the FCA discards on the liquid waste system.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2026

Bio-based oxalic acid production in Issatchenkia orientalis enables sustainable rare earth recovery

The growing demand for rare earth elements (REEs) in clean energy and high-tech industries underscores the need for sustainable recovery methods and a reliable supply of processing chemicals. Here, we establish a microbial platform using the acid-tolerant yeast Issatchenkia orientalis SD108 to produce bio-oxalic acid for REE recovery. By introducing an oxaloacetate cleavage pathway and applying metabolic engineering, the engineered strain produces 39.53 g·L -1 oxalic acid at pH 4.0 in fed-batch fermentation. The crude fermentation broth, used without purification, efficiently precipitates over 99% neodymium (Nd), 99% dysprosium (Dy), and 98% lanthanum (La) from individual REE chloride solutions. Recovery from a low-grade ore leachate achieves over 99% total recovery. X-ray diffraction (XRD) and Fourier transform infrared spectroscopy (FTIR) confirm that REE oxalates precipitated with bio-oxalic acid closely resemble those obtained using commercial oxalic acid. Techno-economic analysis (TEA) and life cycle assessment (LCA) further demonstrate that bio-oxalic acid can be produced at a competitive price of $1.79·kg -1 while reducing carbon intensity (CI) by 112% to 63.5% with and without electricity displacement, respectively, relative to the fossil-based benchmark. These results highlight bio-oxalic acid as a green, economically viable alternative to synthetic oxalate for sustainable REE recovery.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2025

Design and full core fuel performance assessment of high burnup cores for 4-loop PWRs

Increasing the fuel discharge burnup of current light water reactors (LWRs) promises reductions in fuel cycle and/or operations costs. By assuming a constant core power density, the economic gain is enabled by better fuel utilization and/or an increased capacity factor. In this effort to investigate greater than 62 MWd/kgU maximum rod average burnup for 110+ kW/l core power density, two core designs have been developed for a standard 17x17, 193 fuel assemblies pressurized water reactor (PWR). The levelized unit cost methodology is employed to evaluate fuel cycle, operation and maintenance, and capital cost impacts and to examine the economic viability of both core design pathways. Core design and optimization are performed using the commercial STUDSVIK code package. Fuel performance analysis is realized in full core configuration via auditing FRAPCON4.1, FAST1.2, and the high-fidelity code BISON. To provide a realistic assessment, the core design process takes into consideration best practices in current PWR core design. It features acceptable performance in terms of various core design constraints on maximum allowable peaking and boron concentration. Gadolinia (Gd2O3) is used as a burnable poison with a maximum of 9 wt% concentration while feeding 89 or 77 fuel assemblies in a 3-batch refueling scheme. Full core fuel performance simulation, which allows for characterization of relevant fuel temperatures, plenum pressures, stresses, and strains, is performed with respect to two bounding burnup levels. Such performance is potentially licensable for the 18-month high burnup core (<68 MWd/kgU peak pin), while it is more challenging for the 24-month high burnup core design pathway (<75 MWd/kgU peak pin). Maximum rod plenum pressure is identified as the most limiting fuel performance parameter. Here, while the scope of the present study focuses on the steady-state plus overpower conditions, the acceptability of the new discharge burnup has to be further assessed by considering uncertainties and impacts under accident scenarios in the future.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2025

Online learning of quadratic manifolds from streaming data for nonlinear dimensionality reduction and nonlinear model reduction

Here, this work introduces an online greedy method for constructing quadratic manifolds from streaming data, designed to enable in situ analysis of numerical simulation data on the Petabyte scale. Unlike traditional batch methods, which require all data to be available upfront and take multiple passes over the data, the proposed online greedy method incrementally updates quadratic manifolds in one pass as data points are received, eliminating the need for expensive disk input/output operations as well as storing and loading data points once they have been processed. A range of numerical examples demonstrate that the online greedy method learns accurate quadratic manifold embeddings while being capable of processing data that far exceed common disk input/output capabilities and volumes as well as main-memory sizes.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2025

Direct Feed High-Level Waste APPS Model Glass Testing (DFHLW APPS) Matrix, Phase 2

This report summarizes the data collected during the batching and melting of a second matrix of Direct Feed High-Level Waste (DFHLW) glasses generated using the preliminary enhanced waste glass models (EWG2.5) and the Britton and Anderson (2024) preliminary DFHLW feed vector. The purpose of these glasses is two-fold: 1. Validate EWG2.5 glass calculations being used in the Aspen Process Performance Simulation (APPS) model. 2. Evaluate and ultimately improve the glass property models and formulation methods used for design of DFHLW glasses as part of an iterative process of data collection and model refinement. Some of the 16 APPS2 glasses tested did not satisfy all target property constraints due to the limited data on DFHLW glass supporting the EWG2.5 models. • One glass, APPS2-10, formed nepheline on canister centerline cooling (CCC) heat-treatment and failed the product consistency test (PCT) response limits. This glass also had high B and Cr release rates for the toxicity characteristic leaching procedure (TCLP). All other glasses were found to satisfy the PCT and TCLP constraints for both quenched and CCC samples. • One glass, APPS2-08, had higher than acceptable viscosity due to magnetite crystallization. • One glass, APPS2-09, formed greater than 2 vol% crystals at 950 °C. As the glass design criterion was that the temperature at 2 vol% crystal (T 2% ) be less than 950 °C, only one glass failed the criteria. However, this criterion is being reevaluated. Four additional glasses formed crystal fractions between 1 and 2 vol% at 950 °C (APPS2-03, -08, -12, and -14). • Four glasses – APPS2-01, -02, -04, and -16 – failed the Monofrax K-3 refractory neck corrosion (k neck ) design limit of 0.04 in. at 1208 °C for 6 d. This is another criterion being reevaluated. Four additional glasses (APPS2-05, -06, -11, and -13) exhibited 0.025 = k neck = 0.04 in. • All 16 glasses passed the sulfur solubility and TCLP constraints. The measured property values were compared to predicted values using EWG2.5 and a selection of other existing models. A few models (e.g., electrical conductivity, TCLP) were found to be adequate for designing DFHLW glasses in the near future, while others require refits or offsets. It is recommended that new property models be developed for EWG3.0, as a large amount of DFHLW glass property data (> 14 × existing data) is expected to be collected in the compositional spaces where no data was previously available. To enable near-term calculations and formulations for designing DFHLW glasses and processing rate estimations, a formulation algorithm with minor modifications will be developed, EWG2.6.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2024

The mechanical properties of Kel-F 800 (FK-800) as a function of crystallinity

Kel-F 800 is a copolymer of chlorotrifluoroethylene PTFE (75 wt. %) and vinylidene fluoride PVDF (25 wt. %). It has previously been used as a PBX binder for insensitive explosives such as PBX 9502 and LX-17. 3M started production of Kel-F 800 in 1957 and small-scale batches continued to be made until 2002 when production ceased due to environmental concerns regarding one of the emulsifiers used during production. Around 2000 the Kel-F 800 name was changed to FK-800 to avoid trademark concerns because rights to produce another polymer with a similar tradename (Kel-F 81) had been sold to another manufacturer. The Kel designation came from the original manufacturer of PCTFE (Kel-F 81), the Kellog company. To avoid confusion this document will only refer to Kel-F 800. In 2006, production of small-scale batches of Kel-F 800 was started again by 3M in response to customer enquiries. This new material, the first blended batch is referred to as LOT 1, was produced with a different emulsifier than used previously. Because Kel-F 800 is made in a small batch reactor, considerable variation in crystallinity can be expected from lot to lot and year to year. In many ways, this is not significant since the material is dissolved in a solvent (often MEK, ethylmethyl ketone or ethyl acetate) for PBX production purposes. This destroys the as received crystallinity and the resulting crystallinity in the processed material is a function of polymer molecular weight and thermal history. Producing large billets of Kel-F 800 from solvent extraction is not practical and so a compression molding technique has been used above the melting temperature. This method also removes residual crystallinity from the supplied granules. The molecular weight of a polymer can be estimated by several techniques, the most common being gel permittivity chromatography (GPC), size exclusion chromatography (SEC) and shear rheometry measurements of polymer/solvent solutions. Changes in molecular weight will affect the crystallization rate and the maximum crystallinity reached for a specific thermal history. Both references agree that the new LOT 1 material molecular weight falls within the deviation found from averaging previous historical lots of Kel-F 800.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2025

NLR HPC Eagle Jobs Data and Additional Energy Metrics

Overview: Anonymized job-level records from the Eagle high-performance computing (HPC) system at the National Laboratory of the Rockies (NLR). Each record represents a Slurm batch job with scheduling metadata, resource requests, resource utilization, CPU/GPU energy consumption, and efficiency metrics. Sensitive fields (user, account, job name) are replaced with cryptographic hashes. System & Timeframe: Eagle was a 2,000-node, 8-petaflop system operated at NLR from 2019–2024. Data covers the full operational lifetime of the system. Slurm data was processed nightly; timestamps are in Mountain Time. Funding provided by the U.S. Department of Energy, EERE. Files: esif.hpc.eagle.job-anon.zip — Core anonymized job records (Hive-partitioned Parquet) esif.hpc.eagle.job-anon-energy-metrics.zip — Same records with additional iLO and Ganglia energy metrics datacard.md — Full dataset documentation ~13.8 million rows, 62 variables. Readable with PyArrow, pandas, DuckDB, Apache Spark, or any Parquet-compatible tool. Data Collection: Jobs collected via sacct through a pipeline: Eagle Jobs API → Redpanda → StreamSets → HPCMON API → PostgreSQL. Node-level power from iLO (HP Integrated Lights-Out); GPU power from Ganglia monitoring, joined to jobs via node lists and time ranges. Preprocessing: Anonymization of name, user, and account fields via cryptographic hashing Derived columns: queue_wait, cpu_eff, max_mem_eff Simplified job state mapping (e.g., "CANCELLED BY 12345" → "CANCELLED") QoS accounting rules (buy-in, standby, or Slurm QoS value) CPU energy estimated from TDP (200W, Intel Xeon Gold 6154, 18 cores) Timezone-aware columns (_tz) sourced from LEX accounting database to correctly handle DST transitions Key Variables: Scheduling: job_id, partition, state_simple, submit_time_tz, start_time_tz, end_time_tz, queue_waitResources: nodes_req/used, processors_req/used, memory_req, wallclock_req/used, gpus_requested Efficiency: cpu_eff, max_mem_eff Energy: cpu_energy_tdp_estimated_max/used_watt_hours, node_energy_total_watt_hours (iLO), gpu0/1_energy_total_watt_hours (Ganglia) Partitions: bigmem, bigmem-8600, bigscratch, csc, dav, ddn, debug, gpu, haswell, long, mono, short, standard Job States: CANCELLED, COMPLETED, FAILED, NODE_FAIL, OUT_OF_MEMORY, PENDING, RUNNING, TIMEOUT QoS Levels: Unknown, normal, buy-in, debug, penalty, high, standby Important Notes: Non-_tz timestamp columns may be off by one hour across DST boundaries; use _tz columns for time difference calculations Energy fields are null for jobs without monitoring coverage Job step records and raw Slurm JSONB fields are excluded from this extract Do not attempt to re-identify individuals from hashed fields

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2026

Plutonium Retention by Crystalline Silicotitanate under Hyperalkaline Conditions Relevant to Tank-Side Cesium-Removal at the Hanford Site

Crystalline silicotitanate (CST) is used in Hanford’s Tank-Side Cesium-Removal (TSCR) process to selectively remove Cs-137 from highly caustic, nitrate-rich tank supernatants. Recent testing with actual waste samples suggests that CST can also retain measurable plutonium (Pu), which could affect radiological classification and disposal pathways for spent CST. To quantify this behavior, Pu partitioning to CST was studied under Hanford-relevant conditions using batch-contact experiments in a representative simulant (2 M NaNO3, 0.7 M NaOH). Isotherm data were measured and distribution ratios calculated, with Cs+ uptake used as benchmark. Under low-carbonate conditions, Pu was retained strongly by CST in systems initially contacted with either PuO2 nanoparticles (Pu(IV)) or aqueous Pu(VI), with distribution ratios of ~2,200–3,700 mL/g, generally exceeding those for Cs+ (~400–1,000 mL/g). Increasing carbonate concentration strongly reduced PuO2 nanoparticle retention; at [Na2CO3] = 1 M, distribution ratios decreased by up to one order of magnitude to roughly 100–300 mL/g. Electron microscopy suggests that Pu retention involves a combination of mechanisms such as PuO2 NP aggregation induced by CST leachate components, and association with CST bead surfaces.

Neumann, J.↗

DOE OSTI2025

MOOSE ProbML: Parallelizable Probabilistic Machine Learning and Uncertainty Quantification Capabilities

The Multiphysics Object Oriented Simulation Environment (MOOSE) is a widely used open- source finite element software for performing multiphysics multiscale simulations in a massively parallel fashion. Recently, the computational team at Idaho National Laboratory (INL) has implemented Probabilistic Machine Learning (ProbML) capabilities in MOOSE—in a parallelized fashion—and enable active learning with large-scale computational models for tasks such as surrogate model development, scale bridging, forward/inverse uncertainty quantification (UQ), Bayesian optimization, etc. This presentation summarizes these developments in MOOSE along with demonstrations on several real applications relevant to nuclear energy. At the fundamental level, samplers like Monte Carlo/Latin Hypercube, variance reduction, parallelized Markov Chain Monte Carlo (MCMC) support uncertainty propagation in both forward and inverse settings. These samplers can be integrated with the Gaussian processes (GP) suite in MOOSE, which offer several variants like scalar GPs, multi-output GPs, and deep GPs, to enable active learning. These GPs can be tuned using gradient-based optimization methods like Adam and its variants or gradient-free methods like the elliptical slice sampler (a variant of MCMC adept under Gaussian settings) for more complex covariance kernels or likelihoods whose gradient computations can be cumbersome. A variety of batch acquisition functions permit parallelized evaluation of the computational model and support different learning objectives with high efficiency like Bayesian inference, global surrogate development, optimization, etc. Furthermore, libtorch integration supports training, evaluation, and re-training of neural networks and other complex machine learning models in active learning settings. The impacts of these developments are shown on several real applications: (1) nuclear fuel inverse UQ and model inadequacy assessment using the Kennedy O’Hagan framework; (2) uncertainty aware surrogate modeling for additive manufacturing to predict field quantities; (3) nuclear reactor rare events analysis; and (4) complex fluid flow prediction using a global surrogate with quantified prediction uncertainty. Finally, the outlook of MOOSE ProbML is discussed for both outer-loop and inner-loop computations in the broad view to accelerate fuels and materials qualification, address gaps in knowledge and data, and assess new reactor/fuel systems.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2025

Sequence length scaling in vision transformers for scientific images on frontier

Vision Transformers (ViTs) are pivotal for foundational models in scientific imagery, including Earth science applications, due to their capability to process large sequence lengths. While transformers for text have inspired scaling sequence lengths in ViTs, adapting these for ViTs introduces unique challenges. We develop distributed sequence parallelism for ViTs, enabling them to handle up to 1M tokens. Our approach, leveraging DeepSpeed-Ulysses and Long-Sequence-Segmentation with model sharding, is the first to apply sequence parallelism in ViT training, achieving a 94% batch scaling efficiency on 2,048 AMD-MI250X GPUs. Evaluating sequence parallelism in ViTs, particularly in models up to 10B parameters, highlighted substantial bottlenecks. We countered these with hybrid sequence, pipeline, and flash attention strategies, to scale beyond single GPU memory limits. Our method significantly enhances climate modeling accuracy by 20% in temperature predictions, marking the first training of a vision transformer model to convergence with a sequence length of 188K tokens, using full self-attention.

Tsaris, Aristeidis (aris) [ORNL] (ORCID:0000000277↗

DOE OSTI2025

A Zero-Emission Process for Direct Reduction of Iron by Hydrogen Plasma in a Rotary Kiln Reactor

This project’s goal was to demonstrate a hydrogen plasma (H-plasma)-rotary kiln process for reducing iron ore to iron as part of the steel manufacturing process. The H-plasma provides a greater thermodynamic driving force for reducing iron ores than thermal processes such as the DRI process, enabling lower reaction temperatures. We estimated that our process technology can reduce energy consumption by 45% compared to the blast furnace process and ~15% compared to the DRI process. Steel manufacturing produces about 1.8 tons of CO2/ton of steel with iron ore reduction accounting for about one-third of the CO2 produced in the overall manufacturing process. We estimated our process has the potential to reduce GHG emissions from ironmaking by 35% with today’s grid and by up to 88% with a future low-carbon grid while being cost competitive with the current blast furnace route. We demonstrated reduction of hematite and magnetite rich materials at temperatures from 600 to 800°C. We achieved 90-95% metallization on 100 gr samples in batch reduction experiments in the H-plasma rotary kiln furnace at 600-650°C. Attempts to perform tests in a continuous operation mode identified problems with the ore feed mechanism. We identified solutions but there was not time nor budget to correct these for this project

36 MATERIALS SCIENCE↗

DOE OSTI2025

A Zero-Emission Process for Direct Reduction of Iron by Hydrogen Plasma in a Rotary Kiln Reactor

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2025

Thermodynamic modeling of countercurrent chemical looping reverse water gas shift process for redox material screening

The reverse water gas shift (RWGS) reaction is a key pathway for CO 2 utilization, particularly within Power-to-X process chains aimed at sustainable fuel and chemical production. Countercurrent chemical looping (CL-RWGS) using non-stoichiometric oxides can overcome equilibrium limitations of conventional RWGS reactors, enabling significantly higher CO 2 conversions. However, modeling the limiting performance of such systems is challenging due to their multiphase nature and coupled spatial and temporal variation in chemical composition. In this work, we present a discretized batch equilibrium model that simulates CL-RWGS reactors as a series of localized equilibrium exchanges between gas and solid elements. The model is numerically stable, computationally efficient, and free of kinetic source terms, making it well-suited for parametric studies and system-level integration. It is validated against established convection–diffusion models and shown to predict reasonable upper bounds on experimental results. Application of the model to a range of oxygen carrier materials identifies cerium–zirconium solid solutions, particularly Ce 0.80 Zr 0.20 O 2 , as a promising class offering superior oxygen storage characteristics compared to state-of-the-art La 0.6 Sr 0.4 FeO 3 . This framework provides a robust platform for materials screening, reactor sizing, and performance optimization in chemical looping systems. The model implementation is available as open-source software to support further research and development.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗