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DOE OSTI2023

Quantifying interfacial energetics of 2D semiconductor electrodes using in situ spectroelectrochemistry and many-body theory

Hot carrier extraction occurs in 2D semiconductor photoelectrochemical cells. Boosting the energy efficiency of hot carrier-based photoelectrochemical cells requires maximizing the hot carrier extraction rate relative to the cooling rate. One could expect to tune the hot carrier extraction rate constant (k ET ) via a Marcus–Gerischer relationship, where k ET depends exponentially on ΔG°' (the standard driving force for interfacial electron transfer). ΔG°' is defined as the energy level difference between a semiconductor's conduction/valence band (CB/VB) minima/maxima and the redox potential of reactant molecules in solution. A major challenge in the electrochemistry community is that conventional approaches to quantify ΔG°' for bulk semiconductors (e.g., Mott–Schottky measurements) cannot be directly applied to ultrathin 2D electrodes. The specific problem is that enormous electronic bandgap changes (>0.5 eV) and CB/VB edge movement take place upon illuminating or applying a potential to a 2D semiconductor electrode. Here, we develop an in situ absorbance spectroscopy approach to quantify interfacial energetics of 2D semiconductor/electrolyte interfaces using a minimal many-body model. Our results show that band edge movement in monolayer MoS 2 is significant (0.2–0.5 eV) over a narrow range of applied potentials (0.2–0.3 V). Such large band edge shifts could change k ET by a factor of 10–100, which has important consequences for practical solar energy conversion applications. We discuss the current experimental and theoretical knowledge gaps that must be addressed to minimize the error in the proposed optical approach.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2024

Frost Tables, NGEE Areas B, C and D, Utqiagvik (Barrow), Alaska, 2012-2014

This dataset represents spatially intensive thaw depth surveys with individual point measurements spaced ~0.5 m apart. The three ~10x10m grids cover an ice wedge and a portion of its two neighboring polygons. The data contain thaw depth, frost table elevation, ground surface elevation, active layer depth and surface water inundation across three seasons (2012, 2013 and 2014) at Utqiagvik (Barrow) NGEE Areas B, C and D. There are a total of 69 extracted files separated by month, year, and area. The data were originally provided in excel workbooks, these files have been preserved within the zip "Original_Files." The files were transformed for archiving with images pulled out into JPEGs, data preserved as CSV files and some methodological information preserved as TXT files. Some functions, features, and formatting were lost during the transformation. Missing data were defined with blank cells. Additional documentation can be found in files labelled with "information.txt" file endings.The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research.The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska.Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).[2024-07-15] Renamed BEO_Thaw_Depth_Frost_Table_Area_*_*_*.csv, BEO_Thaw_Depth_Frost_Table_Area_*_*_*_Plot_1.JPEG, and BEO_Thaw_Depth_Frost_Table_Area_*_*_*_Plot_2.JPEG names that had E or L to be Early or Late. Renamed BEO_Thaw_Depth_Frost_Table_Area_*_*_*_Plot_1.JPEG, BEO_Thaw_Depth_Frost_Table_Area_*_*_*_Plot_2.JPEG, and BEO_Thaw_Depth_Frost_Table_Area_*_information_Plot_1.JPEG to BEO_Thaw_Depth_Frost_Table_Area_*_*_*_Gridpoints.JPEG, BEO_Thaw_Depth_Frost_Table_Area_*_*_*_3Dgraphic.JPEG, and BEO_Thaw_Depth_Frost_Table_Area_*_information_LiDAR_DEM.JPEG, respectively. Updated BEO_Thaw_Depth_Frost_Table_flmd.csv file to reflect above updates.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

Volumetrically Absorbing Thermal Insulator (VATI) for High-temperature Receivers

This project seeks to exploit volumetric absorption of concentrated solar irradiation in a thin, high-temperature, thermally conductive medium. High efficiency thermal conversion is achieved by volumetric absorption of both incoming solar irradiation (short wavelength) and emitted irradiation (long wavelength), demonstrating a volumetrically absorbing thermally insulating (VATI) effect. The thermalized energy is conducted to the back wall, enclosing the working fluid (molten salt or supercritical CO 2 ). Reliance on conductive thermal transport requires a thermally conductive medium, in the absence of which large temperature gradients drive losses due to emission. The project’s outcomes are important to realize a cost-effective approach to reduce optical and thermal losses from CSP receivers at high temperatures (720°C). High-temperature stable and commercially available porous SiC structures (open-cell foams) were explored as a volumetrically absorbing and thermally insulating layer. To the best of our knowledge, the current state-of-the-art for industrially deployed coatings is Pyromark. However, Pyromark suffers degradation at 700+°C and diurnal temperature changes. This necessitates periodic recoating, and the downtime increases the overall levelized cost of energy (LCOE) production. On the other hand, contemporary research activities have generated significant advances in the development of selective emitter coatings, which present challenges with costs, scalability, and stability. The pursued approach alleviates these concerns by developing a receiver that utilizes the inherent structure of high-temperature stable porous materials to enable robust and cost-effective receivers which require no periodic maintenance downtimes. The overall goal of the project is to experimentally demonstrate a figure of merit (FOM) of 0.92 at a temperature of 720°C and solar irradiation of 1000x concentration with porous receivers. The relevant crystallographic (phase) optical and thermal properties of porous SiC were first characterized. Second, the 3-D geometry of the porous SiC was analyzed using micro-X-ray tomography and converted to CAD data using image processing analysis. Utilizing this 3-D geometry and relevant optical/thermal properties, Monte Carlo- Ray Tracing (MCRT) analysis was performed to extract important parameters governing solar-thermal energy conversion such as extinction coefficient (β, 1/m), scattering albedo (ω) and the scattering phase function (Φ). These properties were then integrated into an in-house radiative and conduction transport model to solve for temperature and transport fluxes characterizing the solar-thermal energy conversion. This model was utilized to predict the FOM for various SiC porous geometries and to identify the highest possible FOM. Testing of the optimized porous structures will be accomplished with a custom-built high-accuracy (< ±4%) FOM measurement test-stand and a 1000x solar concentrator. When neglecting convective losses and resistance at the open boundary and the back wall, the optimized SiC foam leads to a FOM of 0.84. This FOM does not surpass the performance of Pyromark 2500. Yet, conversely to Pyromark 2500, SiC is stable at high temperatures and does not degrade over time. Also, it is possible to boost the FOM of SiC by engineering its effective thermal conductivity and its scattering albedo. A FOM of 0.92 is predicted for an optimized foam by accounting for convective losses and resistance at the open boundary and the back wall. This largely exceeds the FOM of Pyromark 2500 (~0.86) predicted by neglecting convective losses and resistance. Therefore, further engineering of the foam could lead to unprecedented FOMs.

14 SOLAR ENERGY↗

DOE OSTI2025

Influence of americium complexation on the radiation-induced chemical reactivity of sulfophenyl bistriazinyl pyridine (SO 3 -Ph-BTP) towards the nitrate radical

Sulfophenyl bistriazinyl pyridine (SO 3 -Ph-BTP) is a hydrophilic organic ligand used to separate actinides and lanthanides. Dose accumulation and time-resolved studies have previously provided insight into the radiolytic stability and degradation pathways of SO 3 -Ph-BTP in reprocessing environments, but no study has yet addressed the impact of minor actinide complexation on the radiation chemistry of this ligand. To begin to fill this knowledge gap, a systematic, time-resolved study exploring the reactivity of the nitrate radical (NO 3 • ) with SO 3 -Ph-BTP in the presence of trivalent americium, Am(III), has been conducted, which demonstrates enhanced reactivity (an order of magnitude faster) upon metal complexation.

38 - RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCL↗

DOE OSTI2023

Coupled Hydromechanical Modeling and Assessment of Induced Seismicity at FWU: Utilizing Time-Lapse VSP and Microseismic Data

The objective of this work is to utilize integrated geomechanics, field vertical seismic profile (VSP) and microseismic data to characterize the complex subsurface stress conditions at the Farnsworth Unit (FWU). The model is based on a five-spot sector model extracted from a primary geomechanical model. The five-spot well injection pattern is characterized by extensive reservoir characterization data, such well logs, extracted cores and borehole geophone data, to facilitate the detailed examination of stress changes and microseismic event occurrences. The study utilizes field vertical seismic volumes acquired from the injection well 13-10A. The seismic volumes successfully provided snapshots of the behavior of the reservoir at distinct times. The use of VSP and microseismic data provided direct and indirect estimates of the dynamic stress changes occurring in the overburden, reservoir and underburden rock formations. In order to illuminate the stress regions and identify rocks that have undergone inelastic failure, microseismic event occurrences were utilized. Microseismic activity has been detected at the FWU; further study of its locations, timing, and magnitude was needed to deduce the nature of the changing stress state. The results of the study revealed that microseismic events were successfully modeled within the Morrow B formation. Moment magnitudes of seismic events were within the same magnitudes for events in the reservoir, suggesting the suitability of the model. The results of the study showed that the computed moment magnitudes for seismic events were insignificant to warrant safety concerns. The study findings showed the usefulness of coupled hydromechanical models in predicting the subsurface stress changes associated with CO 2 injection. The knowledge gained from this study will serve as a guideline for industries planning to undertake underground CO 2 storage, and characterize the subsurface stress changes.

02 PETROLEUM↗

DOE OSTI2024

A Simulated Evaluation of Powder Flowability Through a Partially Obstructed Consumable in Blown Powder Directed Energy Deposition Systems

Abstract In the interest of continued industrialization of metal additive manufacturing in modern production environments, cost is often referenced as a primary deterrent to new adopters. Conventional economic models for additive systems, processes, and supply chains often focus on specific process applications with little generalizability, or they neglect significant costs associated with production such as machine maintenance and consumable part replacement. Compounding the latter issue are substantial knowledge gaps in consumable part wear characterization for additive and other convergent manufacturing systems. In coaxial blown powder directed energy deposition systems, gas atomized metal powder is wasted during material deposition at a rate that is partly dependent on present wear phenomena in a consumable nozzle housed in the cladding head assembly. The price and lead time required to replace the nozzle incentivizes its reuse even when visibly worn. Often this initiates a process quality decline in the form of underbuilt geometry and internal defects due to losses in powder catchment efficiency. While depositing H13 steel using a hybrid manufacturing machine tool equipped with such a deposition system, a unique partial clog with a bridge-like structure formed at the consumable nozzle exit when supporting argon gas flows failed mid-process. To further understand coaxial multi-phase powder flow in the event of support gas failure, a computational fluid dynamics simulation is tailored to relevant process parameters, H13 powder material profile, and machine operator observations collected after the incident. The resulting differences in powder flow compared to control gas flow parameters is presented and discussed. The powder flowability and performance of the clogged nozzle is then assessed by using an optical profilometer to extract the profile of the clog and recreate the clog geometry within the simulation environment. In past work this simulation has been experimentally validated for a 316L steel powder material profile and used specifically for analyzing powder stream geometry and catchment efficiency. After the initial powder flow characterization, the clog is removed, and the nozzle is reprofiled. After removing the obstructing clog, the newly unobstructed nozzle geometry, the original off the shelf nozzle geometry, and additional nozzle profiles exploring different consumable refurbishment strategies are reevaluated in the simulation. Powder catchment efficiency for all variant nozzle geometries and relevant flow variables are compared and discussed, along with potential mitigation strategies for optimizing powder flowability with worn consumables. This work expands on the known morphology of blown powder obstructions and wear defects present in consumable coaxial nozzles while discussing pragmatic simulation driven responses to unanticipated subsystem failure in hybrid manufacturing machining platforms.

DeWitte, Lisa↗

DOE OSTI2022

The association between vaginal microbiota disorders and early missed abortion: A prospective study

Abstract Introduction The objective of this study was to explore the association between disordered vaginal microbiota and missed abortion to ascertain potential causes of missed abortion related to vaginal microbiology. Material and methods This study was a prospective cohort study with a comparison group (reference group). Vaginal secretions from the posterior fornix of women in the early stages of pregnancy were collected in the Xiangya Third Hospital of Central South University in Changsha, Hunan, China, from November 2018 to November 2019. A total of 54 cases of missed abortion (case group) and 50 cases of normal pregnancy requiring induced abortion (reference group) were analyzed. Bacterial DNA was extracted, hybridized with gene‐specific primers, and then detected using a bacterial chip. The composition and relative abundance of vaginal microbiota in the two groups were compared using α‐diversity analysis, β‐diversity analysis, and the linear discriminant analysis effect size method. Results The α‐diversity analysis showed that the Simpson index of the case group was lower than that of the reference group, whereas the Shannon index in the case group was higher. The relative abundance of Firmicutes in the case group (42.52%) was lower than in the reference group (51.03%, p < 0.05), as was the relative abundance of Lactobacillus (case group 16.51%, reference group 23.00%; p < 0.05). Interestingly, levels of Mycoplasma genitalium and Ureaplasma were lower in the case group ( p < 0.05). The relative abundance of Lactobacillus crispatus , Lactobacillus jensenii , and Lactobacillus gasseri was also significantly lower in the case group than in the reference group ( p < 0.05). The pathways enriched in the case group were predominantly related to metabolism, whereas the genetics‐related pathways were predominantly enriched in the reference group. Conclusions Bacteria are more diverse and unevenly distributed in patients with missed abortion. Decreases in the proportion of vaginal Lactobacillus and changes in Lactobacillus species in these patients may increase the chance of genital tract pathogenic bacterial infection. To our knowledge, our study was the first to observe that a decrease of Firmicutes levels in the vaginal microbiota might impair energy metabolism and have an association with missed abortion.

Sun, Dan↗

DOE OSTI2023

Co-located temperature and electrical resistivity measurements for permafrost mapping, Teller 27, Teller 47 and Kougarok 64, Seward Peninsula, Alaska, late summers of 2018, 2019, 2021, and 2022

This data set contains co-located shallow soil (0.8m below ground level) and electrical resistivity at various depth extracted from Electrical Resistivity Tomography (ERT) measurements. Data were acquired at three watersheds on the southern Seward Peninsula during the late summers of 2018, 2019, 2021, and 2022. The three watersheds are located along the Nome-Teller Highway at mile markers 27 and 47, and along Kougarok Road mile marker 64. The co-located temperature and electrical resistivity data were used to (1) map the spatial extend of near surface permafrost, and (2) for supervised classification of permafrost bodies. This dataset contains .csv, .txt, .srv files and flmd reporting format with data dictionary. Find information about the .srv files at "https://e4d-userguide.pnnl.gov/e4d_guide/elec/e4d_e4d-survey.html". The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research. The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska. Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

X-ray absorption spectroscopy of lanmodulin-derived peptides bound to rare earth elements

A sustainable and robust supply chain of rare earth elements (REEs) is necessary to meet our consumer, national security and clean energy goals. However, current intra-REE separation technologies (e.g. solvent extraction) are costly and carry a heavy environmental burden. Therefore, the development of new aqueous based ligands that are selective for individual REEs will be integral in future REE production systems. To develop these ligands, an understanding of how ligand coordination structure relates to selectivity is imperative. We used X-ray absorption spectroscopy (XAS) to observe the local structure around four lanthanide (Ln) ions (La, Ce, Pr and Nd) complexed by water and several relevant chelating ligands [lanmodulin EF-hand 1 peptides (LanM1), ethylenediaminetetraacetic acid (EDTA), aminotris(methylenephosphonic acid) (ATMP) and citric acid]. To collect these liquid-phase XAS spectra, we developed a new flow cell that prevents bubble interference and beam damage to the samples. In the X-ray absorption near-edge structure (XANES), we observed energy shifts in the white line, white line broadening and differences in the white line intensity of different Ln–ligand complexes between ligands. In the extended X-ray absorption fine structure (EXAFS), we distinguished differences in peak intensity and distance between coordinating ligands. Differences in the local coordination structure between Ln–LanM1 peptide complexes were more subtle compared with the other ligands (La–water, La–EDTA, La–ATMP and La–citric acid complexes). Further XANES and EXAFS studies, in combination with modelling and other techniques, could greatly improve our structural knowledge of how these aqueous ligands bind Ln ions and how they can be used to design more selective ligands for more efficient and sustainable REE separations.

EDTA↗

DOE OSTI2020

ESI-FTICR-MS Analysis of Organic Matter during Incubations with Nitrogen Addition, Teller Road Site, Seward Peninsula, Alaska

This dataset contains the results of the molecular characterization of water-extractable organic matter collected during soil microcosm incubations of tundra soil. The microcosms contained soil collected in April 2017 from the NGEE Arctic Teller Road Site at mile marker 27 (TL_MM27) near Intensive Site 9 (“Toeslope”) and Intensive Site 5 (“Plateau”). The incubations were conducted under an atmosphere of N2 at 8°C for 55 days, with or without addition of ammonium chloride (NH4Cl). Soils were extracted with deionized water and extracted using Bond Elut PPL cartridges prior to analysis. Molecular characterization was performed using electrospray ionization Fourier transform ion cyclotron resonance mass spectrometry (ESI- FTICR-MS). Two data files are provided with this dataset. The first is the summary, by soil type and treatment, of the mass spectrometry results for formulas (as a % of the total) containing C, H, and O only, and with N, S, and other elements. These formulas were then grouped into nine biochemical classes of compounds based on O:C and H:C ratios. The second file provides soil sample characteristics and the processed high-resolution mass spectrometry results by soil type and treatment.The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a 10-year research effort (2012-2022) to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy’s Office of Biological and Environmental Research.The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska.Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy’s Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

DRIPS: A framework for dimension reduction and interpolation in parameter space

Reduced-order models are often used to describe the behavior of complex systems, whose simulation with a full model is too expensive, or to extract salient features from the full model’s output. We introduce a new model-reduction framework DRIPS (dimension reduction and interpolation in parameter space) that combines the offline local model reduction with the online parameter interpolation of reduced-order bases (ROBs). The offline step of this framework relies on dynamic mode decomposition (DMD) to build a low-rank linear surrogate model, equipped with a local ROB, for quantities of interest derived from the training data generated by repeatedly solving the (nonlinear) high-fidelity model for multiple parameter points. The online step consists of the construction of a parametric reduced-order model for each target/test point in the parameter space, with the interpolation of ROBs done on a Grassman manifold and the interpolation of reduced-order operators done on a matrix manifold. The DMD component enables DRIPS to model (typically low-dimensional) quantities of interest directly, without having to access the (typically high-dimensional and possibly nonlinear) operators in a high-fidelity model that governs the dynamics of the underlying high-dimensional state variables, as required in projection-based reduced-order modeling. A series of numerical experiments suggests that DRIPS yields a model reduction, which is computationally more efficient than the commonly used projection-based proper orthogonal decomposition; it does so without requiring a prior knowledge of the governing equation for quantities of interest. Furthermore, for the nonlinear systems considered, DRIPS is more accurate than Gaussian-process interpolation (Kriging).

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2022

Robust Event Classification Using Imperfect Real-world PMU Data

Here, this paper studies robust event classification using imperfect real-world phasor measurement unit (PMU) data. By analyzing the real-world PMU data, we find it is challenging to directly use this dataset for event classifiers due to the low data quality observed in PMU measurements and event logs. To address these challenges, we develop a novel machine learning framework for training robust event classifiers, which consists of three main steps: data preprocessing, fine-grained event data extraction, and feature engineering. Specifically, the data preprocessing step addresses the data quality issues of PMU measurements (e.g., bad data and missing data); in the fine-grained event data extraction step, a model-free event detection method is developed to accurately localize the events from the inaccurate event timestamps in the event logs; and the feature engineering step constructs the event features based on the patterns of different event types, in order to improve the performance and the interpretability of the event classifiers. Based on the proposed framework, we develop a workflow for event classification using the real-world PMU data streaming into the system in real time. Using the proposed framework, robust event classifiers can be efficiently trained based on many off-the-shelf lightweight machine learning models. Numerical experiments using the real-world dataset from the Western Interconnection of the U.S power transmission grid show that the event classifiers trained under the proposed framework can achieve high classification accuracy while being robust against low-quality data.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗

DOE OSTI2023

Fission with Exotic Nuclei (Abbreviated Report)

Nuclear fission is a key mechanism involved in the synthesis of heavy elements in the Cosmos and is the primary explanation for the stability of superheavy elements. Nevertheless, our knowledge of fission remains extremely fragmented. Most experiments have been conducted only on a tiny number of stable actinide nuclei and are often incomplete, leading to gaps in our basic understanding of the process. For many radioactive isotopes, basic fission data such as the charge or mass distribution of the fragments is unknown. These gaps cannot always be filled by simulation alone. Common fission models contain too many free parameters and lack predictive power. In contrast, the fundamental theory of fission under development at LLNL is much more predictive, but its current computational cost is too high to be used extensively for data evaluations. A unique window of opportunity to resolve these limitations has recently opened: the U.S. nuclear science community is ramping up major experimental programs at the Facility for Rare Isotope Beams (FRIB, the DOE flagship facility in low-energy nuclear science), and techniques from machine learning have shown great potential to simplify the use of a fundamental, quantum-mechanical theory of fission. This project has two components. On the experimental side, we acquired and deployed at the HIGS facility a new dual Frisch-Grid ionization chamber to measure correlated fragment-mass, kinetic energy, and angular distributions of fission fragments from induced fission. This new device was used to perform measurements of charge, mass and total kinetic energy of fission fragments in the photofission of 238 U and eight gamma-ray beam energies between 6.2 and 13 MeV, which allowed extracting high-precision independent yields for this reaction. The device was also used to perform measurements of the same quantities in the neutron-induced fission of 234 U with monoenergetic beams of energy between 5 and 8 MeV. In parallel, we collaborated with a team at Commissariat à l’énergie atomique et aux énergies alternatives (CEA) to perform a series of measurements of fission yields in inverse kinematics for the two isotopes of 236 U and 240 Pu. The experiment took place at the Grand Accélérateur National d’Ions Lourds in France in June 2023. The deployment of the VAMOS spectrometer with a new array called PISTA allowed determining the excitation energy of the fissioning system within 1 Mega-electronvolts. The second component of the project involved using deep neural networks to build fast and reliable emulators of our current fission models. In an invited paper published in Frontier in Physics, we showed that autoencoders could successfully compress nuclear wavefunctions in nuclear density functional theory. We achieved a dimensionality reduction of the order of two orders of magnitude while keeping the error in the total energy to less than 0.01%. In a second paper submitted to Physical Review Letters in June 2023 with our collaborators at CEA, we showed that variational autoencoders can learn the collective degrees of freedom driving the fission process.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2023

Materials characterization: Can artificial intelligence be used to address reproducibility challenges?

Material characterization techniques are widely used to characterize the physical and chemical properties of materials at the nanoscale and, thus, play central roles in material scientific discoveries. However, the large and complex datasets generated by these techniques often require significant human effort to interpret and extract meaningful physicochemical insights. Artificial intelligence (AI) techniques such as machine learning (ML) have the potential to improve the efficiency and accuracy of surface analysis by automating data analysis and interpretation. In this perspective paper, we review the current role of AI in surface analysis and discuss its future potential to accelerate discoveries in surface science, materials science, and interface science. We highlight several applications where AI has already been used to analyze surface analysis data, including the identification of crystal structures from XRD data, analysis of XPS spectra for surface composition, and the interpretation of TEM and SEM images for particle morphology and size. We also discuss the challenges and opportunities associated with the integration of AI into surface analysis workflows. These include the need for large and diverse datasets for training ML models, the importance of feature selection and representation, and the potential for ML to enable new insights and discoveries by identifying patterns and relationships in complex datasets. Most importantly, AI analyzed data must not just find the best mathematical description of the data, but it must find the most physical and chemically meaningful results. In addition, the need for reproducibility in scientific research has become increasingly important in recent years. The advancement of AI, including both conventional and the increasing popular deep learning, is showing promise in addressing those challenges by enabling the execution and verification of scientific progress. By training models on large experimental datasets and providing automated analysis and data interpretation, AI can help to ensure that scientific results are reproducible and reliable. Although integration of knowledge and AI models must be considered for the transparency and interpretability of models, the incorporation of AI into the data collection and processing workflow will significantly enhance the efficiency and accuracy of various surface analysis techniques and deepen our understanding at an accelerated pace.

Materials Science↗

DOE OSTI2024

Final Technical Report - Rapid Surface Microanalysis using a Low Temperature Plasma

This project focused on improving our current understanding and scientific knowledge in the area of plasma-surface interactions and plasma assisted material synthesis related to advanced microelectronics and nanotechnology. Current challenges include: controlling the interaction of Low Temperature Plasma (LTP) with a single layer of atoms to manufacture integrated circuits, continued miniaturization of integrated circuits, LTP processing of material surfaces and thin films to enable industrial scale fabrication of advanced microelectronics, synthesis of new materials, nanomaterials, nanotubes, and complex materials, Technology developed in this subtopic is of value to either (i) enable scans of surfaces (~1 sq. cm area) using various microscopies (electron, optical, other) at high resolution (micron or sub-micron resolution) rapidly (hours or days rather than years to complete a high-resolution scan of such a large surface area), or (ii) enable scans of surfaces (~1 sq. cm area) using various microscopies (electron, optical, other) at relatively low resolution rapidly, then apply algorithms to select spots for micron-scale imaging. Sputtering occurs when particles of a solid material are ejected from its surface by energetic particles from a plasma. While the degradation of the solid material and the subsequent deposition of the ejected material onto vulnerable surfaces are the usual subjects of sputtering studies, plasma science has yet to be combined with sputtering to create new diagnostics devices and systems. Small changes in the design of the plasma discharge device make it possible to create broad plasma beams for rapid scanning or small plasma beams to obtain the distribution of ejected elements with micron resolution. In the high-resolution use, the ion flux is extracted from the gas-discharge plasma and focused by a spherical emission surface to micron sizes onto the target specimen, providing very local sputtering and local elemental analysis. We call this “self-focusing”. The radiation from the excited and ionized sputtered atoms is recorded by a spectrometer through a window and fiberglass cable and analyzed with standard software packages used for optical glow discharge spectroscopy. Computer simulations of beam formation were used to verify and optimize the designs to be tested. A prototype was designed, constructed, and used to start experiments of beam formation.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2022

2021 NIRT Mini Drill (After Action Report)

During the summer and fall of 2021, several functional area drills were held that focused on exercising Consequence Management’s (CM) ability to extract and use data from RadResponder for the purpose of answering intermediate-phase questions presented as technical inject requests for information (RFI) in Sandia National Laboratories (SNL) Consequence Management Operational System (COSMOS) software. The scenario chosen was that of Northern Lights 2016 (NL16) which was a large-scale nuclear power plant (NPP) release exercise in the state of Minnesota. The NL16 data was extracted from the Radiological Assessment and Monitoring System (RAMS) event where it was created and was reformatted for implanting to a new RadResponder event. Next, the beta-version of a laboratory sample data simulator was used to generate more sample data that was injected to the event. Five “mini-drills” were devised with each prompt defined by a data-based need. For each drill, a team of assessment and NARAC scientists worked the problem using the drill prompt and the available data in RadResponder. The teams held a kickoff meeting, had several days to work the problem, and then reported their results as well as observations in a hotwash. Several areas for improvement in both the software and process were identified during the course of these drills. This report will document the process of addressing each RFI and the discovered gaps in both software capability and methodology so that they can be considered for future development and investment by the CM and NIRT programs.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗

DOE OSTI2020

Primary $\gamma$-ray intensities and $\gamma$-strength functions from discrete two-step $\gamma$-ray cascades in radiative proton-capture experiments

Background: Reaction rates of radiative capture reactions can play a crucial role in the nucleosynthesis of heavy nuclei in explosive stellar environments. These reaction rates depend strongly on $\gamma$-ray decay widths in the reaction products, which are, for nonresonant capture reactions at high excitation energies, derived from the $\gamma$-ray strength function and the nuclear level density. Recently, the ratio method was applied to primary $\gamma$ rays observed from ($\textit{d, p}$) reactions and nuclear resonance fluorescence measurements to extract the dipole strength in atomic nuclei and to test the generalized Brink-Axel hypothesis. Purpose: The purpose of this work is to apply the ratio method to primary $\gamma$-ray intensities of the 63,65 Cu($\textit{p},\gamma$) reactions to extract $\gamma$-ray strength information on the nuclei 64,66 Zn. Here, the impact of spin distribution, total $\gamma$-ray decay widths, level densities, and width fluctuations on the application of the ratio method will be discussed. Additionally, by comparing the relative $\gamma$-ray strength at different excitation energies, conclusions on the validity of the generalized Brink-Axel hypothesis can be made. Method: The radiative proton capture reaction measurements have been performed at the HORUS $\gamma$-ray spectrometer of the University of Cologne at one excitation energy for each reaction. Primary $\gamma$-ray intensities have been determined by normalizing secondary $\gamma$-ray transitions in two-step cascades using their absolute branching ratio. The ratio method was applied to the measured primary $\gamma$-ray intensities as well as to previous measurements by Erlandsson et al. at different excitation energies. Results: The relative strength function curve for 64 Zn from our measurement shows no significant deviation from the previous measurement at a different excitation energy. The same is true for 66 Zn where both measurements were at almost the same excitation energy. Absolute $\gamma$-strength function values have been obtained by normalizing the relative curves to quasiparticle random phase approximation calculations because of the absence of experimental data in the respective energy region. Conclusion: The generalized Brink-Axel hypothesis, i.e., the independence of the strength function on the excitation energy, seems to hold in the studied energy region and nuclei. The method to obtain primary $\gamma$-ray intensities from two-step cascade spectra was shown to be a valuable and sensitive tool although its uncertainties are connected to the knowledge of the low-energy level scheme of the investigated nucleus. The scaling in the ratio method should be taken with care, because the relative strength is not a simple sum of $f_{E1}$ and $f_{M1}$ but a somewhat complex linear combination dependent on the excitation energy of the nucleus.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2021

Particle image velocimetry analysis with simultaneous uncertainty quantification using Bayesian neural networks

Particle image velocimetry (PIV) is an effective tool in experimental fluid mechanics for extracting flow fields from images. Recently, convolutional neural networks (CNNs) have been used to perform PIV analysis with accuracy on par with classical methods. Here we extend the use of CNNs to analyze PIV data while providing simultaneous uncertainty quantification on the inferred flow field. The method we apply in this paper is a Bayesian convolutional neural network (BCNN) which learns distributions of the CNN weights through variational Bayes. In order to demonstrate the utility of BCNNs for the PIV task, we compare the performance of three distinct BCNN models with simple architectures. The first network estimates flow velocity from image interrogation regions only. Our second model learns to infer velocity from both the image interrogation regions and interrogation region cross-correlation maps. Finally, our best performing network infers velocities from interrogation region cross-correlation maps only. We find that BCNNs using interrogation region cross-correlation maps as inputs perform better than those using interrogation windows only as inputs and discuss reasons why this may be the case. Additionally, we test the best performing BCNN on a full synthetic test image pair and a real image pair from the 1st International PIV Challenge. We show that ~98% of true particle displacements from the full synthetic image pair can be captured within the BCNN's 95% confidence intervals, and that the BCNN's performance on the real image pair is quantitatively similar to that of algorithms tested in the 1st International PIV Challenge. Finally, we show that BCNNs can be generalized to be used with multi-pass PIV algorithms with a moderate loss in accuracy, which may be overcome by future work on finetuning and training schemes. So to our knowledge, this is the first use of Bayesian neural networks to perform PIV.

47 OTHER INSTRUMENTATION↗