CO2-selective membrane reactor process for water-gas-shift reaction with CO2 capture in a coal-based IGCC power plant
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Ovalle-Encinia, Oscar↗
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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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Modern control system applications at Fermilab with Dart and Flutter
The control system at Fermilab is undergoing an unprecedented modernization effort. Hundreds of legacy applications originally developed with technology from the early nineties will be replaced with a suite of modern web applications. The selection of an user interface framework is a key technology decision that will impact on all the applications developed over the next decade. The Controls department at Fermilab has decided to use Google’s Dart language and Flutter framework for future application development. In this paper we will discuss the decision process that selected Dart/Flutter and the development of early general purpose control system applications with the framework.
Diamond, John [Fermilab]↗
Identification Uncertainty in Inverse Material Model Parameter Determination: A Sensitivity‐Based Decision Process for Load Path Selection
This research proposes a sensitivity-based framework for selecting the optimal prescribed loading path for a biaxial cruciform specimen. Optimality here is determined by the direction and magnitude of the prescribed displacement that minimizes the influence of random noise on the material model parameter identification. Using simulated experimental data based on finite element simulation, in this work, we identify the material model parameters of a Ludwik hardening model and plane stress implementation of the Hill-48 yield criterion using finite element model updating (FEMU). Our analysis reveals that the identification (or estimator) uncertainty of model parameters depends on the displacement boundary conditions (i.e., loading sequence) and the ground-truth value of the individual parameters. Optimal experimental design (OED) criteria based on the Fisher information matrix were investigated to mitigate indecision in the choice of optimal load path when the identification uncertainty of different material model parameters optimized at different load paths. The determinant of the Fisher information matrix was chosen here as the more useful metric due to its ability to capture uncertainty of the most influential material model parameters. The proposed framework demonstrates potential for real-time automated load step selection using scalar criteria derived prior to mechanical loading. The framework can be generalized to other geometries, boundary conditions and material models, allowing this procedure to be utilized for different experimental configurations and materials.
Fayad, Samuel S. [University of Illinois at Urbana↗
Selection of a Pair of Experiments to Optimally Reduce Uncertainty in Targeted Nuclear Data
We propose a novel process to select a pair of differential and integral experiments that best reduce uncertainties in targeted 239 Pu nuclear data while compressing the current nuclear data pipeline from 20 to 3 years. 239 Pu nuclear data are poorly understood for neutrons in the intermediate energy range due to sparsity and uncertainty in historical experiments. New experiments targeting this range will enable better understanding of these nuclear data, but choosing the ideal experiments to conduct is challenging. Beginning with a prior distribution represented by samples of nuclear data generated from theory, generalized least squares adjustments are made to incorporate data from historical experiments. To quantify potential uncertainty reduction obtainable from a pair of candidate experiments, we compute the D-optimality criterion of the posterior covariance of intermediate energy range nuclear data compared to the equivalent covariance after additional adjustment to the pair of candidate experiments. Repeating the process for each of many candidate pairs facilitates the final selection. Results support 63 Cu total cross section measurements for differential experiments and alumina and alumina/graphite configurations for integral experiments. This analysis enables choosing differential and integral experiments to be executed concurrently while shortening decision times relative to the current nuclear data pipeline.
73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
Development of Leaching Processes for the Recovery of Rare Earth Elements from Acid Mine Drainage Precipitates
Acid mine drainage (AMD) has been a challenge for mine operators to address and has had significant environmental impacts when there is a failure to address it. Fortunately, there is a regulation in the US that requires the treatment of AMD before water can be discharged into the environment. AMD is generated when sulfide minerals are exposed to water and air from mining. The acidic water then leaches metals from the surrounding rock, creating the potential for environmental contamination of this acidic water containing dissolved metals. AMD can contain high concentrations of metals like iron, aluminum, and manganese and also have been shown to contain trace concentrations of critical minerals, including rare earth elements (REEs). This environmental waste stream is now being researched as a potential source for REEs. There is a patented process for processing and extracting REEs from AMD which produces rare earth oxide preconcentrate (REOP) from AMD treatment precipitates and a final stage mixed rare earth oxide (MREO) product. This body of research examines a selective leaching process for each of these products and determines the activation energy associated with the leaching processes. Acid leaching with HCl was examined to selectively leach REEs from REOP while contaminant metals remained in the solid residue. The maximum leaching recovery of the REEs from the REOP was approximately 90% and an activation energy of 6.4 kJ/mol at pH 3.0. Ammonium chloride leaching was examined to selectively remove contaminant metals from a MREO product. The ammonium chloride process successfully leached major contaminant metals in excess of 85% recovery and had a maximum activation energy of 40.0 kJ/mol.
01 COAL, LIGNITE, AND PEAT↗
Probing the Effects of Dimethyl Aluminum Isopropoxide Surface Reaction Byproducts on Atomic Layer Deposition Nucleation
Atomic layer deposition (ALD) processes that leverage a myriad of metal–organic and complementary reactant combinations have been identified to realize precise and conformal thin film growth. However, the effects of the ALD reaction byproducts on nucleation and growth mechanisms are rarely considered. Site-selective ALD processes provide an opportunity for the detailed investigation of uniform surface sites with atomistic accuracy. Intentional pretreatment with a known ALD reaction byproduct – isopropanol – enables a significant improvement in the nucleation rate reproducibility of dimethylaluminum isopropoxide and water ALD on rutile TiO 2 (110). In situ spectroscopic ellipsometry reveals a partially reversible byproduct binding that is site-selective for TiO 2 (110) surface oxygen vacancies. First-principles calculations reveal surface site-specific thermodynamics for adsorption of isopropanol and water that may influence ALD nucleation. In conclusion, the sensitivity of site-selective ALD motivates consideration of secondary surface reactions when designing precision deposition processes, including area- or site-selective ALD reactions.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Improved manufacturability of vacuum thermoforming molds by segmentation
Thin polymer shell components used in consumer goods, the automotive industry, and marine applications are often produced by thermoforming. Depending on the quantity of parts, molding processes can increase throughput significantly when compared to processes such as milling or additive manufacturing. One disadvantage to mold-based manufacturing methods, however, is that molds must be produced to form the required geometry. Mold production is often complex and requires a large capital investment. For large-scale molding processes, mold production is sometimes complicated by the requirement for large working volume machine tools which are capable of machining the cavities to the appropriate tolerances for the selected molding process. Here, this paper focuses on a segmented design for a large draw ratio (>3:1 molded surface area to sheet surface area) thermoforming mold and a cost-benefit analysis of segmentation versus monolithic design. Discussion of the thermoforming tests are included. The primary goals were to improve manufacturability and reduce the overall cost of the mold.
Manufacturability↗
Machine Learning-Driven Optimization of Building Enclosures for Moisture Durability and Thermal Performance
The design of moisture-durable building enclosures with low embodied carbon often involves an iterative process of selecting the materials for the specific exposure conditions to meet the performance requirements. While hygrothermal simulations are commonly used to evaluate moisture durability, they often require advanced expertise for proper implementation. Machine learning (ML) provides a promising alternative by streamlining the design process and minimizing the reliance on complex simulations. This study presents a machine learning-based approach for predicting moisture durability in residential wall assemblies. The ML model was trained to estimate the mold index and maximum moisture content of various layers under typical exposure conditions. The model achieved a high predictive accuracy, with a coefficient of determination (R²) exceeding 0.90 when compared to traditional hygrothermal simulations on materials that were not part of training the ML model. Building on these results, the ML model was developed into a practical tool for optimizing wall assembly designs. This tool allows users to automatically optimize material selections based on energy, moisture, and carbon performance criteria. By incorporating multi-objective optimization, the tool identifies configurations that minimize embodied carbon while maintaining moisture safety and code-compliant thermal performance. Additionally, it provides insights into how material choices influence assembly durability, energy efficiency, and carbon reduction. The tool will be implemented in the Building Science Advisor (BSA) to enhance its performance and provide more granularity on the results. This research highlights the potential for ML-driven tools to simplify the design of high-performance building enclosures, offering architects and engineers a faster, more efficient way to balance critical performance factors.
Salonvaara, Mikael [ORNL] (ORCID:0000000318991554)↗
Understanding Process–Structure Relationships during Lamination of Halide Perovskite Interfaces
Fabrication of halide perovskite (HP) solar cells typically involves the sequential deposition of multiple layers to create a device stack, which is limited by the thermal and chemical incompatibility of top contact layers with the underlying HP semiconductor. One emerging strategy to overcome these restrictions on material selection and processing conditions is lamination, where two half-stacks are independently processed and then diffusion bonded to complete the device. Lamination reduces the processing constraints on the top side of the solar cell to allow new device designs, expanded use of deposition methods, and self-encapsulation of devices. While laminated perovskite solar cells with high efficiencies and novel interlayer combinations have been demonstrated, there is a limited understanding of how the lamination process parameters affect the diffusion-bond quality and material properties of the resulting HP layer. In this study, we systematically vary temperature, pressure, and time during lamination and quantify the resulting impacts on bonded area, grain domain size, and photoluminescence. A design of experiments is performed, and statistical analysis of the experimental results is used to quantitatively evaluate the resulting process–structure–property relationships. The lamination temperature is found to be the key parameter controlling these properties. Furthermore, a temperature of 150 °C enables successful bonding over 95% of the substrate area and also results in increases in apparent grain domain size and photoluminescence intensity. Based on these insights, the lamination temperature of functional perovskite solar cell devices is varied, demonstrating the importance of the resulting bond quality on device performance metrics.
14 SOLAR ENERGY↗
Treatment of antibiotic-manufacturing wastewater enriches for Aeromonas veronii, a zoonotic antibiotic-resistant emerging pathogen
Antibiotic-manufacturing wastewater treatment plants primarily target chemical pollutants, but their processes may select for antibiotic-resistant pathogens and antibiotic resistance genes. Leveraging the combined strengths of deep metagenomic sequencing, 16S rRNA gene sequencing, quantitative polymerase chain reaction, and bacterial culturing, we investigated bacterial communities and antibiotic resistomes across eleven treatment units in a full-scale antibiotic-manufacturing wastewater treatment plant processing wastewater from a β-lactam manufacturing facility. Both bacterial communities and antibiotic resistance gene compositions varied across the treatment units, but were associated. Certain antibiotic resistance gene persisted through treatment, either carried by identical bacterial species, or linked to mobile genetic elements in different species. Despite the satisfactory performance in chemical removal, this plant continuously enriched zoonotic antibiotic-resistant Aeromonas veronii (an emerging pathogen responsible for substantial economic losses in aquaculture and human health) from influent to effluent, probably due to prolonged β-lactam selection pressure and aquatic nature of A. veronii. This enrichment resulted in a significantly higher abundance of A. veronii than other aquatic samples worldwide. Furthermore, the closest evolutionary relative to the retrieved A. veronii was an isolate obtained from the stool of a local diarrhea patient. These findings highlighted a substantial public health risk posed by antibiotic-manufacturing wastewater treatment, underlining its potential role in enriching and disseminating zoonotic antibiotic-resistant pathogens. Beyond chemical monitoring, enhanced surveillance of antibiotic-resistant pathogens and antibiotic resistance genes is needed in effluent discharge standard for antibiotic-manufacturing wastewater treatment plants.
Wang, Xingshuo↗
High-Temperature Fe-Based Fischer–Tropsch Synthesis: Experimentally Validated Kinetic Models Implemented at Pellet and Reactor Scales
Pellet- and reactor-scale models for Fischer–Tropsch synthesis (FTS) with a Fe–K/silica catalyst were developed to investigate the sensitivity of the hydrocarbon products and carbon dioxide selectivity to process conditions and feed composition at high temperature (350–400 °C), moderate pressure (1–10 bar), and a range of H 2 /CO ratios (3–1). The major objective of this paper is to develop, validate, and evaluate a high-temperature FTS model that is then used to assess the feasibility of process integration with syngas production. Since there is limited kinetic data available, in literature at these conditions, bench-scale reactor tests were conducted to obtain operational data for parameter fitting of kinetic expressions used in the model. This resulting kinetic model demonstrated agreement with the experimental data with an R 2 of 0.97 to the testing data set and, thus, was feasible to apply at pellet and reactor scales. Here, multiple pellet sizes were modeled to detail the role of transport limitations as the sphere’s diameter approached and exceeded 1 mm. Application of the reactor model indicated that hydrocarbon selectivity depended strongly on temperature, whereas the ratio of olefin to paraffin products decreased with increasing temperature, pressure, and H 2 /CO ratio. Product selectivity was not sensitive to the conversion of carbon monoxide. Furthermore, the roles of the pressure and H 2 /CO ratio were closely coupled. At a H 2 /CO ratio of 3, only slight variations in selectivity occurred over a pressure range of 1–20 bar, whereas at a ratio of 1, selectivity could vary by as much as 30% over the same pressure range. At pressures below 5 bar and temperatures above 350 °C, minimal selectivity to heavy hydrocarbons (C 12+ ) is obtained, and selectivity to midrange products (C 5–11 ) rapidly declined as pressure dropped below 5 bar, which indicated that an operational pressure of at least 5 bar is needed to achieve reasonable yields in this temperature range. These results, while tentative, provide guidelines for further experimentation and evaluation of integrated FTS processes.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Wasserstein normalized autoencoder for anomaly detection
A novel anomaly detection algorithm is presented. The Wasserstein normalized autoencoder (WNAE) is a normalized probabilistic model that minimizes the Wasserstein distance between the learned probability distribution—a Boltzmann distribution where the energy is the reconstruction error of the autoencoder (AE)—and the distribution of the training data. This algorithm has been developed and applied to the identification of semivisible jets—conical sprays of visible standard model (SM) particles and invisible dark matter states—with the CMS experiment at the CERN LHC. Trained on jets of particles from simulated SM processes, the WNAE is shown to learn the probability distribution of the input data in a fully unsupervised fashion, such that it effectively identifies new physics jets as anomalies. The model exhibits stable, convergent training and recovers strong classification performance for a wide range of signals against the selected background process, for which a standard AE fails because of outlier reconstruction. In addition, the model improves upon standard normalized autoencoders while remaining fully agnostic to the signal. The WNAE directly tackles the problem of outlier reconstruction, a common failure mode of autoencoders in anomaly detection tasks.
Hayrapetyan, Aram [Yerevan Phys. Inst.]↗
Decision-making based on Markov decision process in integrated artificial reasoning framework—Part I: Theory
This paper presents a decision-making framework based on an integrated artificial reasoning framework and Markov decision process (MDP). The integrated artificial reasoning framework provides a physics-based approach that converts system information into state transition models, and the analysis result will be represented by the transition probabilities that can be used with an MDP to find a traceable and explainable optimal pathway. A dynamic Bayesian network (DBN) is well suited for representing the structure of an MDP. The causality information among process variables (or among subsystems) is mathematically represented in a DBN by the conditional probabilities of the node’s states provided different probabilities of the parent node’s states. To define node states in a physically understandable manner, we used multilevel flow modeling (MFM). An MFM follows the fundamental energy and mass conservation laws and supports the selection of process variables that represent the system of interest so that causal relations among process variables are properly captured. An MFM-based DBN supports developing state transition models in an MDP to capture the effect of process variables of system having physical relations. The operators of the target system can capture stochastic system dynamics as multiple subsystem state transitions based on their physical relations and uncertainties coming from component degradation or random failures. We analyzed a simplified exemplary system to illustrate an optimal operational policy using the suggested approach.
Markov decision process↗
Non-Equilibrium Actinide Radiation Chemistry and the Nuclear Fuel Cycle
Actinides are inherently unstable elements that frequently coexist with other radioisotopes, generating intense ionizing radiation fields that drive the formation of non equilibrium oxidation states. These transient species exert a profound mechanistic influence on the radiation response of actinide containing systems due to their unique redox chemistry. Despite their importance, they remain poorly understood, yet such insight is essential for advancing actinide science and accurately predicting radiation driven behavior. Actinide separations—critical for nuclear energy technologies, strategic deterrence, space exploration, and nuclear medicine—depend on precise control of actinide oxidation states to recover targeted elements from complex matrices such as used nuclear fuel. However, during these processes, actinides, their coordination complexes, and the separation media are all exposed to intense, multicomponent (alpha, beta, gamma, etc.) radiation fields that can alter process efficiency, selectivity, and chemical stability. Understanding, controlling, and mitigating radiation induced reactions is therefore key to innovating and optimizing next generation separation technologies. This seminar will provide an overview of the nuclear fuel cycle and non equilibrium actinide radiation chemistry in the context of recovering actinides from used nuclear fuel, with a particular emphasis on direct dissolution–based reprocessing strategies. We will explore time resolved electron pulse radiolysis and alpha and gamma dose accumulation studies, integrated with multiscale computational modeling, to elucidate the molecular level roles of radiation driven, non equilibrium actinide species in process performance and in the radiolytic stability of organic ligands used for actinide recovery. These insights offer new pathways for designing advanced separation methods and next generation solvent systems, with broad implications for the future of the nuclear fuel cycle.
37 - INORGANIC, ORGANIC, PHYSICAL AND ANALYTICAL C↗
Non-Equilibrium Actinide Radiation Chemistry and the Nuclear Fuel Cycle
Invited John and Naomi Fackler Lectureship in Chemistry and English seminar at Valparaiso University, IN, USA. Actinides are inherently unstable elements that frequently coexist with other radioisotopes, generating intense ionizing radiation fields that drive the formation of non-equilibrium oxidation states. These transient species exert a profound mechanistic influence on the radiation response of actinide-containing systems due to their unique redox chemistry. Despite their importance, they remain poorly understood, yet such insight is essential for advancing actinide science and accurately predicting radiation-driven behavior. Actinide separations—critical for nuclear energy technologies, strategic deterrence, space exploration, and nuclear medicine—depend on precise control of actinide oxidation states to recover targeted elements from complex matrices such as used nuclear fuel. However, during these processes, actinides, their coordination complexes, and the separation media are all exposed to intense, multicomponent (alpha, beta, gamma, etc.) radiation fields that can alter process efficiency, selectivity, and chemical stability. Understanding, controlling, and mitigating radiation-induced reactions is therefore key to innovating and optimizing next-generation separation technologies. This seminar will provide an overview of the nuclear fuel cycle and non-equilibrium actinide radiation chemistry in the context of recovering actinides from used nuclear fuel, with a particular emphasis on direct-dissolution–based reprocessing strategies. We will explore time-resolved electron pulse radiolysis and gamma dose accumulation studies to elucidate the molecular-level roles of radiation-driven, non-equilibrium actinide species in process performance and in the radiolytic stability of organic ligands used for actinide recovery. These insights offer new pathways for designing advanced separation methods and next-generation solvent systems, with broad implications for the future of the nuclear fuel cycle.
37 - INORGANIC, ORGANIC, PHYSICAL AND ANALYTICAL C↗
Advancing Ethanol-to-Jet cost Effectiveness via direct conversion to n -Butene-Rich olefins and Co-Product Valorization
Ethanol is a promising feedstock for sustainable aviation fuel production; however, conventional routes face significant energy and cost challenges, particularly due to the ethanol dehydration step to ethylene. Here, this study leverages breakthrough experimental data to perform comprehensive techno-economic and life-cycle assessments of an innovative ethanol-to-jet process. The process employs a single-step catalytic conversion, enabled by multifunctional Cu-ZrO 2 /SBA-16 catalyst, to directly upgrade ethanol into a mixed olefin stream rich in n-butene. The single-step conversion eliminates the costly ethanol dehydration step in the conventional process. High selectivity toward n-butene offers key advantages: it simplifies downstream oligomerization into jet-range hydrocarbons and enables the co-production of renewable n-butene alongside sustainable aviation fuel. The analysis estimates a minimum fuel selling price as low as $\$$2.50 per gallon, whether using corn ethanol or cellulosic ethanol from corn stover. Life cycle CO 2 equivalent emissions are projected to be as low as 10.6 g CO 2 eq/MJ sustainable aviation fuel, representing over 70% reduction compared to conventional petroleum-based jet fuel. This one-step ethanol upgrading approach not only facilitates SAF and n-butene co-production but also provides operational flexibility. The ability to tailor product outputs allows the ethanol-to-jet process to adapt to varying feedstocks, incentive programs, and market dynamics, ultimately enhancing the economic viability of sustainable aviation fuel production.
Xu, Yiling [Pacific Northwest National Laboratory ↗
Selection Algorithm for electron neutrino charged current interactions in SBND
The Short Baseline Neutrino (SBN) program at Fermilab is a joint proposal by three experimental collaborations primarily for the investigation of the cause behind the low-energy electron-like event excess observed by the MiniBoone experiment. This dissertation focuses on the near detector of the project, named the Short Baseline Near Detector (SBND), a Liquid Argon Time Projection Chamber apparatus which will conduct searches for sterile neutrinos in the mass range of 1 ${eV}^2/{c}^4$, as well as provide cross-section measurements for neutrino interactions in argon and perform other beyond the standard model studies.\\ \indent As is the case for all detectors in the program, the SBND will use the Booster Neutrino Beam as its source, which will provide it with both muon and electron neutrinos. Given that the ability to discern between the neutrino flavors will be crucial to the fulfillment of the detector's physics goals, the objective of this work is to provide the collaboration with a tool capable of doing so. As such, we here present the development process for an inclusive selection algorithm for the identification of electron neutrino charged current (CC) events regardless of their interaction channel. This is done through a combination of traditional techniques, such as the implementation of cuts on the reconstructed interaction properties, with the use of the Convolutional Visual Network, a machine learning algorithm capable of classifying particle interactions through the analysis of the topology of their final states. With this approach, we have developed a selection process that is capable of identifying $\nu_e$ CC interactions across a wide range of topologies with 34.4\% efficiency, as well as a purity of 91.2\%, making it especially promising for use in cross section studies.
Freire, Hector Moya [ABC Federal U.] (ORCID:000900↗