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DOE OSTI2022

Analysis on the Preliminary Gap Conductance Test with Single Primary Heat Extraction and Removal Emulator

Microreactor is a type of nuclear reactor that is intended to produce thermal energy from the micro and integrated design. Among various microreactor roadmaps, microreactor with heat pipes is one of the promising designs that under active development. At Idaho National Laboratory (INL) and supported by the Department of Energy (DOE) Microreactor Program (MRP), a Single Primary Heat Extraction and Removal Emulator (SPHERE) was developed. The main purpose of SPHRE is to understand thermal performance of heat pipe in the conjunction of heat source without using nuclear material. The heat pipe used in the SPHERE has sodium as a working fluid, with Argon gas in the vapor region, thus this heat pipe is variable conductance heat pipe (VCHP). The volume of non-condensable gas changes with power and elapsed time, so the area covered by non-condensable gas changes. This gives an effect that passively controls the cooling capability of condenser section. This preliminary experiment was intended to perform a gap conductance experiment that reveals heat transfer characteristics of gas at the gap around the heat pipe. However, there are not many experimental data that deals with heat transfer of the gas at the gap around heat pipe. In this research, the preliminary experimental result regarding to the heat pipe performance with different composition of gas at the gap, level of power is presented. The experimental data will be used for the validation study of SOCKEYE as a future plan.

42 ENGINEERING↗

DOE OSTI2021

Machine Learning Predicts the Timing and Shear Stress Evolution of Lab Earthquakes Using Active Seismic Monitoring of Fault Zone Processes

Abstract Machine learning (ML) techniques have become increasingly important in seismology and earthquake science. Lab‐based studies have used acoustic emission data to predict time‐to‐failure and stress state, and in a few cases, the same approach has been used for field data. However, the underlying physical mechanisms that allow lab earthquake prediction and seismic forecasting remain poorly resolved. Here, we address this knowledge gap by coupling active‐source seismic data, which probe asperity‐scale processes, with ML methods. We show that elastic waves passing through the lab fault zone contain information that can predict the full spectrum of labquakes from slow slip instabilities to highly aperiodic events. The ML methods utilize systematic changes in P‐wave amplitude and velocity to accurately predict the timing and shear stress during labquakes. The ML predictions improve in accuracy closer to fault failure, demonstrating that the predictive power of the ultrasonic signals improves as the fault approaches failure. Our results demonstrate that the relationship between the ultrasonic parameters and fault slip rate, and in turn, the systematically evolving real area of contact and asperity stiffness allow the gradient boosting algorithm to “learn” about the state of the fault and its proximity to failure. Broadly, our results demonstrate the utility of physics‐informed ML in forecasting the imminence of fault slip at the laboratory scale, which may have important implications for earthquake mechanics in nature.

58 GEOSCIENCES↗

DOE OSTI2023

Emission factors of industrial boilers burning biomass-derived fuels

Boilers are combustion devices that provide process heat and are integral to many industrial facilities. Historically, outside of the pulp and paper industry, most boilers burned fossil fuels, although interest in decarbonization has been leading to an increased use of renewable fuels in boilers. These boilers, including those in the biorefineries, are often large sources of air pollutant emissions, and the characterization of these emissions is critical to obtaining air permits and ensuring protection of the surrounding air quality. Several industrial boilers and new biorefineries allow utilization of biomass-derived fuels (e.g. wastewater sludge, lignin, etc.) produced during their operation as a fuel for the boiler to meet process energy needs. However, there is limited empirical data on emission factors for the burning of unconventional fuels, such as solid-gas mixtures containing biomass residues. To fill this gap, we carry out a comprehensive data survey, collecting information on emission factors for boilers burning either a single or a mixture of solid and gaseous biomass-derived fuels. We review multiple hard-to-obtain and unconventional data sources, such as air permit applications, stack test data, and industry-sponsored data collection efforts, to compile emission factors for biomass-derived fuels. We then compare this data with wood residue emission factors from the U.S. Environmental Protection Agency’s AP-42 emission factor database. Our results indicate that the emission factors for boilers burning unconventional fuels vary widely depending on the fuel composition, boiler type, and fuel characteristics. Overall, we find that median emission factors of selected biomass-derived fuels are typically lower than those of wood residue boilers in AP-42. The information collected herein could be useful to permitting agencies and industries utilizing boilers who may want to reduce the carbon impact of their facilities by combusting biomass-derived wastes for process energy needs, for more accurate emission estimation for permitting.

09 BIOMASS FUELS↗

DOE OSTI2024

A global database of dissolved organic matter (DOM) concentration measurements in coastal waters (CoastDOM v1)

Abstract. Measurements of dissolved organic carbon (DOC), nitrogen (DON), and phosphorus (DOP) concentrations are used to characterize the dissolved organic matter (DOM) pool and are important components of biogeochemical cycling in the coastal ocean. Here, we present the first edition of a global database (CoastDOM v1; available at https://doi.org/10.1594/PANGAEA.964012, Lønborg et al., 2023) compiling previously published and unpublished measurements of DOC, DON, and DOP in coastal waters. These data are complemented by hydrographic data such as temperature and salinity and, to the extent possible, other biogeochemical variables (e.g. chlorophyll a, inorganic nutrients) and the inorganic carbon system (e.g. dissolved inorganic carbon and total alkalinity). Overall, CoastDOM v1 includes observations of concentrations from all continents. However, most data were collected in the Northern Hemisphere, with a clear gap in DOM measurements from the Southern Hemisphere. The data included were collected from 1978 to 2022 and consist of 62 338 data points for DOC, 20 356 for DON, and 13 533 for DOP. The number of measurements decreases progressively in the sequence DOC > DON > DOP, reflecting both differences in the maturity of the analytical methods and the greater focus on carbon cycling by the aquatic science community. The global database shows that the average DOC concentration in coastal waters (average ± standard deviation (SD): 182±314 µmol C L−1; median: 103 µmol C L−1) is 13-fold higher than the average coastal DON concentration (13.6±30.4 µmol N L−1; median: 8.0 µmol N L−1), which is itself 39-fold higher than the average coastal DOP concentration (0.34±1.11 µmol P L−1; median: 0.18 µmol P L−1). This dataset will be useful for identifying global spatial and temporal patterns in DOM and will help facilitate the reuse of DOC, DON, and DOP data in studies aimed at better characterizing local biogeochemical processes; closing nutrient budgets; estimating carbon, nitrogen, and phosphorous pools; and establishing a baseline for modelling future changes in coastal waters.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2026

Microscopic Scattering Approach to In-Gap States

We develop a microscopic scattering formalism to describe Yu-Shiba-Rusinov (YSR) states due to a single Cr adatom on the Bi-terminated surface of beta Bi2Pd, by combining ab initio Wannier functions with a real-space Green's function approach in the Bogoliubov-de Gennes formalism[1]. Our framework reproduces key scanning tunneling spectroscopy features, including a single particle-hole asymmetric YSR peak and isotropic dIdV maps around the impurity. Decomposing the YSR states reveals contributions from four nearly degenerate C4v representations, with energy broadening masking their individual signatures. Spin-orbit coupling induces partial spin polarization, while the spatial asymmetry between particle and hole components arises from Cr d-Bi p hybridization. These results highlight the importance of realistic band structures and microscopic modeling for interpreting STM data for magnetic in-gap states on superconductors. Further advances examining layered 2D material surfaces, such as NbSe2, will be described[2]. For this system the superconducting properties are obtained from a full anisotropic Eliashberg calculation of the superconducting order parameter along with the charge density wave gap. Additional features associated with proposals to measure the dynamics of these individual YSR states will be presented. [1] arXiv:2507.08740 [2] arXiv:2507.11856

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2022

Effect of Coaxial Electrode Geometry on the Electric Field Enhancement Factor for a High Voltage Vacuum Gap

We present an experimental analysis of the change in the electric field enhancement factor with varying gap size and penetration depth (P.D) of cathode into anode for a coaxial vacuum gap, diagnosed using Fowler–Nordheim analysis and optical imaging via scanning electron microscope (SEM) and time integrated Digital single lens reflex camera (DSLR). Data were collected on the Coaxial Gap Breakdown Machine (240 A, 25 kV, 150 ns, 0.1 Hz). Experiments using five different gap sizes at nine different P.Ds are compared over runs comprising 50 shots for each case. The results show a strong link between enhancement factor and gap size, with P.D and surface topology. For large gap sizes, 150, 330, and 700 μm, the average enhancement factor value increases with increasing P.D. For smaller gap sizes, 50 and 100 μm, the average enhancement factor decreases with P.D. SEM imaging before and after plasma formation for each gap size allows for quantifying surface finish, microprotrusion growth, average blast diameter, and an estimation of the surface area breakdowns occupy. Time integrated DSLR imaging analysis of the gap at each shot allows for a determination of the distribution of breakdowns about the circumference of the gap for each case tested. Here, the Fowler–Nordheim analysis allows for a quantitative analysis of the surface roughness of all gap sizes tested. Results show that for large gap sizes, the gap geometry and increasing area of breakdown is the main cause for increasing average enhancement factor. For small gap sizes, the dominant driving factor for small average enhancement factors—that subsequently decrease with P.D—is significant changes in surface topology due to an increased number of breakdowns.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2020

Energy Storage in Long-Term System Models: A Review of Considerations, Best Practices, and Research Needs

Technological change and policy support have heightened expectations for the role of energy storage in power systems, creating a need to enhance representations of energy storage in long-term models to inform decision-making. Energy storage technologies have complex and diverse cost, value, and performance characteristics that make them challenging to model, but there is limited guidance about best practices and research gaps for energy storage analysis. This paper reviews the literature and draws upon our collective experience to provide recommendations to analysts on approaches for representing energy storage in long-term electric sector models, navigating tradeoffs in model development, and identifying research gaps for existing tools and data. The review focuses on national-scale models with technological, temporal, and regional detail given their prevalence in planning and policy, though many insights are transferable to other modeling contexts. It also offers guidance to consumers of model outputs on proper use and interpretation based on model strengths and limitations. In particular, this review demonstrates the importance of capturing how the values of energy storage and other resources change as the system composition changes (e.g. with different levels of storage, renewables deployment, and emissions outcomes). These considerations require model detail like high spatiotemporal resolutions and endogenous investments that global integrated assessment models and price-taker frameworks do not typically resolve. Research gaps include linking tools of different resolutions, developing reduced-form representations of value streams, incorporating hybrid energy storage and renewable systems, and representing longer-duration energy storage technologies.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗

DOE OSTI2022

Multiscale spatial analysis of fracture arrangement and pattern reconstruction using Ripley's K-function

This work presents novel multiscale spatial data analytics using Ripley's K-function, as a measure of spatial interaction, to study one-dimensional arrangement of fractures. Fracture spatial arrangements are classified into clustered, anticlustered, or indistinguishable from random by testing statistical significance of the calculated Ripley's K-function. Characterizations of fracture arrangements are performed as a function of length scale and position. Analysis of the K-function along the study interval identifies where fracture clustering and anticlustering occur. Here, a simulation technique is also introduced here to statistically reconstruct spatial arrangements and to generate fracture realizations that are spatially similar to the fractures observed in the field. With this simulation technique, one can also fill spatial gaps in fracture measurements where data are absent, unreliable, or unused. Synthetic as well as field-measured 1D fracture datasets are used for testing and demonstration. Methods introduced in this work can be readily applied to fracture datasets observed in outcrops, borehole image logs, and cores.

58 GEOSCIENCES↗

DOE OSTI2025

Expediting field-effect transistor chemical sensor design with neuromorphic spiking graph neural networks

Improving the sensitive and selective detection of analytes in a variety of applications requires accelerating the rational design of field-effect transistor (FET) chemical sensors. Achieving high-performance detection relies on identifying optimal probe materials that can effectively interact with target analytes, a process traditionally driven by chemical intuition and time-consuming trial-and-error methods. To address the difficulties in probe screening for FET sensor development, this work presents a methodology that combines neuromorphic machine learning (ML) architectures, specifically a hybrid spiking graph neural network (SGNN), with an enriched dataset of physicochemical properties through semi-automated data extraction using large language models. Achieving a classification accuracy of 0.89 in predicting sensor sensitivity categories, the SGNN model outperformed traditional ML techniques by leveraging its ability to capture both global physicochemical properties and sparse topological features through a hybrid modeling framework. Next-generation sensor design was informed by the actionable insights into the connections between material properties and sensing performance offered by the SGNN framework. Through virtual screening for the detection of per- and polyfluoroalkyl substances (PFAS) as a use case, the effectiveness of the SGNN model was further validated. Density functional theory simulations confirmed graphene as a promising active material for PFAS detection as suggested by the SGNN framework. By bridging gaps in predictive modeling and data availability, this integrated approach provides a strong foundation for accelerating advancements in FET sensor design and innovation.

Ferreira, Rodrigo Pires [Univ. of Chicago, IL (Uni↗

DOE OSTI2022

Investigation of Multiple Data Streams for Gearbox Bearing Fault Prediction Through Machine-Learning Models

Operations and maintenance (O&M) cost of wind plant accounts up to 30% of total energy cost, which can be reduced through continuous monitoring and successfully detecting incipient wind turbine failures. To accomplish this, condition monitoring and predictive maintenance systems are being implemented in wind industry to support O&M decision making. A wide range of approaches for condition monitoring and fault prediction have been developed. These approaches generally use historical data of wind turbines collected by Supervisory Control and Data Acquisition (SCADA) system to identify patterns that lead to failure. These SCADA data show the overall condition of a wind turbine and can be leveraged to detect when the turbine's performance is degrading and to identify if a fault is developing. However, it becomes challenging to predict the failure of a specific wind turbine gearbox bearing, because the SCADA data are often not directly linked to the component. To bridge the gap, we have investigated features calculated from SCADA data using physics-based models and the gearbox design over the years. The damaged metric we used in the physics domain is frictional energy. Combining these physics domain variables with SCADA data as inputs to various machine learning models for gearbox bearing fault prediction, we have demonstrated the benefits of leveraging both physics and data domain models. It was an attempt to improve frictional-energy-based damage metric by adding data domain inputs, as we had learned that the frictional-energy-based damage metric alone is not sufficient to single out failed bearings from healthy. As condition monitoring data (either vibration or oil debris data) has become available at more and more wind plants, we would like to evaluate whether by adding the condition monitoring data can help further improve the performance of frictional-energy-based damage metric for gearbox bearing fault prediction. Both cases by modeling through various machine learning algorithms are discussed in this study along with some observations.

fault prediction↗

DOE OSTI2023

Temperature Dependence of Band Gap Renormalization in High-T Sensor Materials via First-Principles and Experimental Corroboration

Understanding the temperature dependence of functional properties of high-T gas sensing materials is vital for their applications in combustion environments. The electron-phonon coupling that derives the electronic structure change with temperatures is a key property of interest as it affects other sensing responses. Herein, we assess the temperature dependence of band gap renormalization in metal oxides and perovskites by employing Allen-Heine-Cardona theory with first-principles simulations and corroborate with experimental observation. The calculated temperature-dependent band gap changes of these materials studied are in good agreement with in-house experimental data, proving that the theory can adequately predict renormalization on the band gap in the system of interest. The predicted and measured band gap variations are characterized using an analytical model, which can provide useful insights on the simulated zero-temperature band gaps. Based on the available data, a set of 53 metal oxides and perovskites were identified as potential high-T gas sensors. A machine learning model has been developed to predict the band-gap change by capturing the overall trend of the empirical parameters with respect to a reduced feature obtained by transforming the set of available physical features.

Park, Jongwoo↗

DOE OSTI2024

Verification, Validation, and Calibration Through a Causal Lens

While typical validation and verification approaches focus on identifying the associations between data elements using statistical and machine learning methods, the novel methods in this paper focus instead on identifying causal relationships between data elements. Statistical and machine-learning-based approaches are strictly data-driven, meaning that they provide quantitative comparison measures between data sets without explicitly considering the hypotheses behind them. This can lead to the erroneous conclusion that, if two data sets are close enough, the models that generated them are similar. In addition, when experimental and simulated data differ to an extent that fails to meet the acceptance criteria, calibration techniques are used to tweak simulation model parameters to reduce the gap between the two types of data. This produces the false expectation that a simulation model will match reality. The methods presented in this paper move away from these strictly data-driven methods for validation and calibration toward more robust, model-driven methods based on causal inference. Causal inference aims to identify the possible mechanisms that might have generated data. Thus, this analysis targets the prediction of the effects when one (or more) of the identified mechanisms are altered. There are many approaches to identify, quantify, and illustrate causal relationships. For the scope of this paper, directed graphs are employed as causal models. If the directed graph lacks cycles, it is known as a directed acyclic graph. A node in such a graph represents an observed data element while a directed edge connecting two nodes represents a causal relationship between two variables. The developed causal methods are designed to extract causal models from simulation models and experimental data. Causal models capture the causal relationships between data elements (e.g., simulated and experimental data). In this context, validation and verification are performed by comparing causal models. The proposed approach does not only inform system analysts on how a simulation model matches real-world data, but also identifies elements of the simulation model that should be revised when discrepancies between simulation and experimental data are observed. Through these causal methods, analysts can identify the portion of the model equation(s) that are behind an edge connecting two variables. Hence, once the structural differences between causal models have been determined, model calibration can occur by changing only those model parameters that impact the identified causal relationships.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2024

VALIDATION, VERIFICATION, AND CALIBRATION THROUGH A CAUSAL LENS

This paper presents an alternative method based on causal inference to perform validation, verification, and calibration of simulation models. While classical validation and verification approaches focus on the identification of the associations between data elements using statistical and machine learning methods, the novel methods in this paper focus instead on the identification of causal relationships between data elements. Statistical and machine learning-based approaches are strictly data-driven, meaning that they provide quantitative comparison measures between datasets without explicitly considering the hypotheses behind them. This can lead to the erroneous conclusion that, if two data sets are close enough, then the models that generated them are similar. In addition, when experimental and simulated data differ to an extent that fails to meet the acceptance criteria, calibration techniques are used to tweak simulation model parameters to reduce the gap between the two types of data. This produces the false expectation that a simulation model will match reality. The methods presented in this paper move away from these strictly data-driven methods for validation and calibration toward more robust, model-driven methods based on causal inference. Causal inference aims to identify the possible mechanisms that might have generated data. Thus, this analysis targets the prediction of the effects when one (or more) of the identified mechanisms are altered. There are many approaches to identify, quantify and illustrate causal relationships. For the scope of this paper, directed graphs are employed as causal models. If the directed graph lacks cycles it is known as a directed acyclic graph (DAG). A node in such a graph represents an observed data element while a directed edge connecting two nodes represents a causal relationship between two variables. The developed causal methods are designed to extract causal models from simulation models and from experimental data. Causal models capture the causal relationships between data elements (e.g., simulated and experimental data). In this context, validation and verification are performed by comparing causal models. The proposed approach does not only inform system analysts on how a simulation model matches real-world data, but also identifies elements of the simulation model that should be revised when discrepancies between simulation and experimental data are observed. Through these causal methods, analysts have a means to identify the portion of the model equation(s) that are behind an edge connecting two variables. Hence, once the structural differences between causal models have been determined, model calibration can occur by changing only those model parameters that impact the identified causal relationships.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2020

Modeling the pseudogap metallic state in cuprates: Quantum disordered pair density wave

We present a way to quantum-disorder a pair density wave and propose it to be a candidate of the effective low-energy description of the pseudogap metal which may reveal itself in a sufficiently high magnetic field that suppresses the d-wave pairing. The ground state we construct is a small-pocket Fermi liquid with a bosonic Mott insulator in the density-wave-enlarged unit cell. At low energy, the charge density is mainly carried by charge 2e bosons, which develop a small insulating gap. As an intermediate step, we discuss the quantum disordering of a fully gapped superconductor and its excitation spectrum. A simplified 1D model, which we solve numerically, is used to illustrate the introduced concepts. Here, we discuss a number of experimental consequences. The interplay between the electron and the small-gap boson results in a step-function background in the electron spectral function which may be consistent with existing angle-resolved photoemission spectroscopy data. Optical excitation across the boson gap can explain the onset and the magnitude of the mid infrared absorption reported long ago.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2025

LandScan mosaic enables high-resolution gridded population estimates with explicit uncertainty

Gridded population datasets represent high-resolution distributions of human occupancy, enabling informed decision-making across a broad range of fields. These data products are valuable for assessing environmental risk, urban development, disaster preparedness and resource allocation—areas where accurate population estimates directly enhance policy effectiveness and optimize resource distribution. Despite the importance of gridded population datasets, traditional population modeling approaches often overlook inherent uncertainties in the estimation process. This limitation can create a false sense of certainty in population estimates, potentially leading to flawed decisions by those who rely on the data. To address this methodological gap, we introduce a probabilistic machine learning modeling framework, LandScan Mosaic, that explicitly incorporates uncertainty into the population modeling process. Our approach systematically quantifies uncertainty in three key modeling parameters of the LandScan HD gridded population dataset: building use types, floor counts, and occupancy rates. By employing Monte Carlo simulations, we propagate these uncertainties through the modeling process, yielding probability distributions of population counts in place of deterministic point estimates. We demonstrate the practical application of this framework in Iloilo City, Philippines, using structured decision-making techniques and our probabilistic estimates to identify and prioritize areas most affected by projected flooding, supporting targeted interventions that address both economic and social risks. In doing so, we propose a population-specific approach for incorporating confidence into structured decision making processes. Through a comparative analysis with conventional deterministic approaches and point estimate approaches, including LandScan HD and WorldPop, we evaluate how the incorporation of machine learning and uncertainty influences decision rankings. This research advances population distribution modeling by offering a robust, quantitative approach that explicitly accounts for uncertainty in the underlying data, along with guidance for how users can apply uncertainty in their decision-making.

Environmental sciences↗

DOE OSTI2024

Magnon gap tuning in lithium-doped MnTe

Data in this DOI includes: 1) Powder Diffraction at HFIR POWDER (HB-2A) of pure MnTe and 5%-lithium doped MnTe at various temperatures ranging from T = 4 - 360 K for pure MnTe and T = 4 - 290 K for 5% lithium doped MnTe. 2) Inelastic neutron scattering at ARCS of pure MnTe and 5% lithium doped MnTe at T = 10 K measured with incident energy Ei = 30 meV and 150 meV, and corresponding background files (i.e. empty can measurements)

diffraction↗

DOE OSTI2020

Materials Data on AlGaP2 by Materials Project

(AlP)(GaP) is Chalcopyrite-like structured and crystallizes in the tetragonal P-4m2 space group. The structure is three-dimensional. Ga3+ is bonded to four equivalent P3- atoms to form GaP4 tetrahedra that share corners with four equivalent GaP4 tetrahedra and corners with eight equivalent AlP4 tetrahedra. All Ga–P bond lengths are 2.39 Å. Al3+ is bonded to four equivalent P3- atoms to form AlP4 tetrahedra that share corners with four equivalent AlP4 tetrahedra and corners with eight equivalent GaP4 tetrahedra. All Al–P bond lengths are 2.38 Å. P3- is bonded to two equivalent Ga3+ and two equivalent Al3+ atoms to form corner-sharing PAl2Ga2 tetrahedra.

36 MATERIALS SCIENCE↗

DOE OSTI2026

Roadmap for transforming heterogeneous catalysis with artificial intelligence

Artificial intelligence (AI) is poised to transform heterogeneous catalysis, opening avenues for catalytic materials discovery. By uncovering intricate patterns in high-dimensional data, AI has been reshaping our pursuit of sustainable catalytic processes across the energy, environmental and chemical sectors. This promise, however, hinges on overcoming fundamental barriers, including limitations in data availability and quality, challenges in the generalizability and interpretability of data-augmented decisions, and the persistent gap between in silico predictions and experiments. Furthermore, we outline a forward-looking roadmap for deeply integrating AI into heterogeneous catalysis with an AI-ready data ecosystem, multimodal foundation models, and ultimately autonomous laboratories to accelerate the development of next-generation catalytic technologies via AI-empowered human–machine collaboration.

Computational methods↗