Results for “important processes” — Page 51

DOE OSTI2022

Elucidating the Initial Steps in α-Uranium Hydriding Using First-Principles Calculations

Hydrogen embrittlement of uranium, which arises due to the formation of a structurally weak pyrophoric hydride, poses a major safety risk in material applications. Previous experiments have shown that hydriding begins on the top or near the surface (i.e., subsurface) of α-uranium. However, the fundamental molecular-level mechanism of this process remains unknown. Here, in this work, starting from pristine α-U bulk and surfaces, we present a systematic investigation of possible mechanisms for the formation of metal hydride. Specifically, we address this problem by examining the individual steps of hydrogen embrittlement, including surface adsorption, subsurface absorption, and the interlayer diffusion of atomic hydrogen. Furthermore, by examining these processes across different facets, we highlight the importance of both (1) hydrogen monolayer coverage and (2) applied tensile strain on hydriding kinetics. Taken together, by studying previously overlooked phenomena, this study provides foundational insights into the initial steps of this overall complex process. We anticipate that this work will guide near-term future development of multiscale kinetic models for uranium hydriding and subsequently identify potential strategies to mitigate this undesired process.

36 MATERIALS SCIENCE↗

DOE OSTI2020

Automated detector simulation and reconstruction parametrization using machine learning

Rapidly applying the effects of detector response to physics objects (e.g. electrons, muons, showers of particles) is essential in high energy physics. Presently available tools for the transformation from truth-level physics objects to reconstructed detector-level physics objects involve manually defining resolution functions. These resolution functions are typically derived in bins of variables that are correlated with the resolution (e.g. pseudorapidity and transverse momentum). This process is time consuming, requires manual updates when detector conditions change, and can miss important correlations. Machine learning offers a way to automate the process of building these truth-to-reconstructed object transformations and can capture complex correlation for any given set of input variables. Such machine learning algorithms, with sufficient optimization, could have a wide range of applications: improving phenomenological studies by using a better detector representation, allowing for more efficient production of Geant4 simulation by only simulating events within an interesting part of phase space, and studies on future experimental sensitivity to new physics.

47 OTHER INSTRUMENTATION↗

DOE OSTI2024

Fitness factors impacting survival of a subsurface bacterium in contaminated groundwater

Many factors contribute to the ability of a microbial species to persist when encountering complexly contaminated environments, including time of exposure, the nature and concentration of contaminants, availability of nutritional resources, and possession of a combination of appropriate molecular mechanisms needed for survival. Herein we sought to identify genes that are most important for survival of Gram-negative Enterobacteriaceae in contaminated groundwater environments containing high concentrations of nitrate and metals using the metal-tolerant Oak Ridge Reservation isolate, Pantoea sp. MT58 (MT58). Survival fitness experiments in which a randomly barcoded transposon insertion (RB-TnSeq) library of MT58 was exposed directly to contaminated Oak Ridge Reservation groundwater samples from across a nitrate and mixed metal contamination plume were used to identify genes important for survival with increasing exposure times and concentrations of contaminants, and availability of a carbon source. Genes involved in controlling and using carbon, encoding transcriptional regulators, and related to Gram-negative outer membrane processes were among those found to be important for survival in contaminated Oak Ridge Reservation groundwater. A comparative genomics analysis of 75 Pantoea genus strains allowed us to further separate the survival determinants into core and non-core genes in the Pantoea pangenome, revealing insights into the survival of subsurface microorganisms during contaminant plume intrusion.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2023

Modeling, analysis, and optimization of complex nuclear processes and facilities via computational methods: The HALEU process case study

Improving and adapting industrial systems to timely meet changing programmatic and market demands is an important goal to achieve, including when operating and maintaining complex nuclear processes and facilities. However, changes to these complex systems are costly, particularly when they are already in place and bounded to stringent requirements and constraints such as when handling radioactive material and contaminated equipment. These conditions often exist when treating spent nuclear fuel remotely within shielded nuclear radiation chambers, commonly referred as hot cells, to condition nuclear material and/or fabricate products for utilization in other nuclear enterprises such as in the manufacture of advanced nuclear fuel. The illustrative case considered here is the production of high assay low enriched uranium (HALEU) products supporting the deployment of advanced nuclear reactors. For the HALEU program, resources invested were and are being systematically analyzed so that these investments are maximized in a facility that is nearly 60 years old. A methodology that has effectively enabled optimized and improvements in the Spent Fuel Treatment (SFT) program, and consequently the HALEU program, involves discrete event simulation as addressed in this article. Here, the quantification of multiple productivity metrics, including material processing rates, cycle times, bottlenecks, number of material transfers as well as equipment, workstation, and material handling utilization, has resulted in a myriad of diverse discoveries and data-informed decisions regarding process layout and constituent, labor levels and schedules, selection of new process units, storage needs, and other critical process configurations. This article describes such a computational capability being applied for decision-making, illustrates its application to an actual process and program, provides illustrative results, and argues how computational methods for the modeling, analysis, and optimization of complex processes and facilities does lead to informed decisions derived from data and not only from intuition.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2024

Site Integration and Regulatory Considerations for a Nuclear Power Plant Colocated with Industrial Facilities: Colocation Studies for a Petroleum Refinery, Methanol Plant, and Wood Pulp Plant

This research explores the colocation of nuclear power plants (NPPs) with industrial applications. Three existing industrial sites were considered to demonstrate the siting process and illuminate technological gaps for future work. The three applications demonstrated for colocation here are a petroleum refinery, a methanol production plant, and a pulp and paper plant. This study uses a modified version of the EPRI siting criteria to explore the geological and demographic characteristics of the location of the current industrial site, as well as exploring external hazards from the industrial plant and its surrounding land use. Data was collected from public databases to estimate site characteristics. We then discuss how the site characteristics may impact the ability to colocate an NPP with an industrial application. The application site and 5 additional sites were explored for each application to give a general indication of the siting implications for an NPP in each area. The hazards for each industrial application was also explored to determine how colocation may impact reactor safety. The following gaps have been identified and should be explored in future research on colocation of NPPs with petroleum refineries, methanol plants, and pulp and paper plants: - There is a variety of industrial use, hazards, and pipelines in the surrounding area. A more thorough review of these hazards should be considered for colocation. - In general, the whole region around some applications seems to have softer soil, with implications for large site preparation costs. Further site investigations should prioritize looking into the geotechnical conditions. - Applications along coastlines are susceptible to flooding and hurricanes. The benefits of colocation should be weighed against the potential design implications. - The benefits of natural gas pipeline infrastructure in place should be explored further. If heat supply from the NPP is not required or not feasible due to the distance between the NPP and the application, there may be an opportunity to supply hydrogen to the plant through an existing pipeline. - Because there are several collocated industrial plants in the regions for the refinery and methanol plant, the benefits of sharing resources from the NPP should be explored further. This may open up additional sites for colocation. The following knowledge gaps were identified for the colocation of NPPs with these three industries, and industrial applications in general. These gaps are: - While the STAND tool contains many important characteristics for the reactor siting process, it is not calibrated for the colocation of NPPs with industrial facilities. - There are aspects of both the NPP and industrial application that need to be quantified for a siting analysis. Particularly, we need to understand the water intake requirements for NPPs and each application. - Further work may focus on adapting the STAND site comparison methodology to comparison of sites for co-location. This will involve using the data documented in this report as a starting point and performing a comprehensive and quantitative comparison. - Without spending significant resources, it would be impossible to gather data for each site to evaluate all aspects of siting. One approach to finding data and understanding its implications to siting is looking at FSARs for existing plants. For example, most sites considered in this study have small Vs30 values, indicating soft soil. However, there are NPPs located in the vicinity of most of the sites (e.g., Waterford Steam Electric Station near New Orleans) and reviewing available site characteristics and geotechnical data for these NPPs, might provide further information for siting. - The siting analysis in this study indicates that colocation of the NPP with the industrial site could be difficult based on external hazards, cooling requirements, weather, or population. We need to determine the impact of distance between the two facilities on cost and quality of energy transport. - This study did not touch on socioeconomic impacts for NPP colocation with industrial facilities. The input-output analysis methodology could be applied to the communities referenced in this study to determine the socioeconomic impact of these projects. - Similarly, the impacts of colocation on emergency planning was not explored in this study. The impacts on emergency planning infrastructure are somewhat related to the socioeconomic impacts, and could be explored using a similar methodology. - This study also did not address physical and cybersecurity, which will be important aspects of co-location [ref] . Cybersecurity will be important, regardless of the distance, but physical security will be important if the facilities are located very closely. Physical security might also be important for the steam lines between the plants, unless they are determined to be non-safety significant. - In many site l

08 HYDROGEN↗

DOE OSTI2025

Site Integration and Regulatory Considerations for an NPP Colocated with a Petroleum Refinery, Methanol Plant, and Wood Pulp Plant

This research explores the colocation of nuclear power plants (NPPs) with industrial applications. Three existing industrial sites were considered to demonstrate the siting process and illuminate technological gaps for future work. The three applications demonstrated for colocation here are a petroleum refinery, a methanol production plant, and a pulp and paper plant. This study uses a modified version of the EPRI siting criteria to explore the geological and demographic characteristics of the location of the current industrial site, as well as exploring external hazards from the industrial plant and its surrounding land use. Data was collected from public databases to estimate site characteristics. We then discuss how the site characteristics may impact the ability to colocate an NPP with an industrial application. The application site and 5 additional sites were explored for each application to give a general indication of the siting implications for an NPP in each area. The hazards for each industrial application was also explored to determine how colocation may impact reactor safety. The following gaps have been identified and should be explored in future research on colocation of NPPs with petroleum refineries, methanol plants, and pulp and paper plants: - There is a variety of industrial use, hazards, and pipelines in the surrounding area. A more thorough review of these hazards should be considered for colocation. - In general, the whole region around some applications seems to have softer soil, with implications for large site preparation costs. Further site investigations should prioritize looking into the geotechnical conditions. - Applications along coastlines are susceptible to flooding and hurricanes. The benefits of colocation should be weighed against the potential design implications. - The benefits of natural gas pipeline infrastructure in place should be explored further. If heat supply from the NPP is not required or not feasible due to the distance between the NPP and the application, there may be an opportunity to supply hydrogen to the plant through an existing pipeline. - Because there are several collocated industrial plants in the regions for the refinery and methanol plant, the benefits of sharing resources from the NPP should be explored further. This may open up additional sites for colocation. The following knowledge gaps were identified for the colocation of NPPs with these three industries, and industrial applications in general. These gaps are: - While the STAND tool contains many important characteristics for the reactor siting process, it is not calibrated for the colocation of NPPs with industrial facilities. - There are aspects of both the NPP and industrial application that need to be quantified for a siting analysis. Particularly, we need to understand the water intake requirements for NPPs and each application. - Further work may focus on adapting the STAND site comparison methodology to comparison of sites for co-location. This will involve using the data documented in this report as a starting point and performing a comprehensive and quantitative comparison. - Without spending significant resources, it would be impossible to gather data for each site to evaluate all aspects of siting. One approach to finding data and understanding its implications to siting is looking at FSARs for existing plants. For example, most sites considered in this study have small Vs30 values, indicating soft soil. However, there are NPPs located in the vicinity of most of the sites (e.g., Waterford Steam Electric Station near New Orleans) and reviewing available site characteristics and geotechnical data for these NPPs, might provide further information for siting. - The siting analysis in this study indicates that colocation of the NPP with the industrial site could be difficult based on external hazards, cooling requirements, weather, or population. We need to determine the impact of distance between the two facilities on cost and quality of energy transport. - This study did not touch on socioeconomic impacts for NPP colocation with industrial facilities. The input-output analysis methodology could be applied to the communities referenced in this study to determine the socioeconomic impact of these projects. - Similarly, the impacts of colocation on emergency planning was not explored in this study. The impacts on emergency planning infrastructure are somewhat related to the socioeconomic impacts, and could be explored using a similar methodology. - This study also did not address physical and cybersecurity, which will be important aspects of co-location [ref] . Cybersecurity will be important, regardless of the distance, but physical security will be important if the facilities are located very closely. Physical security might also be important for the steam lines between the plants, unless they are determined to be non-safety significant. - In many site l

08 - HYDROGEN↗

DOE OSTI2021

Microfluidic Liquid-Liquid Extraction Chip with Integrated Raman Sensors (Phase I and Phase II Final Report)

As demand for electricity continues to increase worldwide, the world’s nuclear power generating capacity will continue to grow. Nuclear power is the most environmentally benign way of producing electricity on a large scale. The long-term successful use of nuclear power, however, is critically dependent upon adequate and safe processing and disposal of spent nuclear fuels. A very important feature of nuclear energy is that spent fuels can be reprocessed to recover fissile and fertile materials that can then be used as fresh fuel for nuclear power plants. The DOE-NE Fuel Cycle Research and Development (FCR&D) is currently developing nuclear material reprocessing technologies. In a typical nuclear fuel reprocessing system, centrifugal contactors are used as liquid-liquid extraction devices where two immiscible liquids are mixed at high speeds using a rotor, which creates a fine dispersion of droplets of organic phase in an aqueous phase that contains the analyte. Understanding the extraction efficiency at these contactors through modeling and simulation is important in the development of reprocessing technologies and the optimization of current technologies such as the PUREX process. The outcome of this program will be a liquid-liquid microfluidic flow cell chip with embedded spectroscopic sensors for the analysis of extracted analytes. This device will be useful in the optimization of new fuel reprocessing schemes as well as existing reprocessing processes by providing a microfluidic modeling platform to optimize extraction parameters. The Phase I and Phase II work developed a microfluidic chip design that allows integration of Raman and absorption fiber optic probes and successfully demonstrated the feasibility of using Raman and absorption probes to detect key analytes that partition into the aqueous and organic phases.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2025

Regulatory Sandboxes and Other Processes to Expedite Utility Adoption of Advanced Grid Technologies

Advanced grid technologies are increasingly important to enable electric transmission and distribution systems to meet growing demands. However, traditional regulatory processes typically lag technological advancements. Regulatory sandboxes, which provide a structured environment for testing new technologies and business approaches under modified rules to increase the speed of adoption, aim to bridge the gap between grid needs and opportunities to deliver solutions at scale. This report examines the role of regulatory sandboxes in promoting utility innovation, highlights examples of successful sandbox mechanisms, and provides emerging best practices for designing and implementing regulatory sandboxes.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2022

Salt-Assisted 2H-to-1T' Phase Transformation of Transition Metal Dichalcogenides [plus supplemental information]

Phase engineering of nanomaterials (PEN) has demonstrated great potential in the fields of catalysis, electronics, energy storage and conversion, and condensed matter physics. Recently, transition metal dichalcogenides (TMDs) with unconventional metastable phases (e.g., 1T and 1T') have attracted increasing research interest due to their unique and appealing physicochemical properties. However, there is still a lack of a simple, universal, and controlled method for the preparation of large-scale and high-purity unconventional-phase TMD crystals, restricting their further fundamental study and practical applications. Here, a facile, one-step salt-assisted general strategy is reported for the controlled phase transformation of commercially available TMDs with conventional 2H phase, yielding a large amount of metastable 1T'-phase TMDs, including WS 2 , WSe 2 , MoS 2 , and MoSe 2 . It is found that the easily accessible metal salts, such as K 2 C 2 O 4 ·H 2 O, K 2 CO 3 , Na 2 CO 3 , Rb 2 CO 3 , Cs 2 CO 3 , KHCO 3 , NaHCO 3 , and NaC 2 O 4 , can be used to assist the 2H-to-1T' phase transformation, greatly simplifying the synthetic process for producing metastable 1T'-TMDs. Importantly, this method can also be used to prepare 1T'-TMD alloys, such as 1T'-WS 2x Se 2(1–x) . We report this newly developed strategy is robust and highly effective, which can also be used for the phase engineering of other materials with various polymorphs.

1T′ phase↗

DOE OSTI2025

Manipulating the Assembly and Architecture of Fibrillar Silk

Silk is a unique and exceptionally strong biological material. However, no synthetic method has yet come close to replicating the properties of natural silk. This shortfall is attributed to an insufficient understanding of both silk nanofibril structure and the mechanism of formation. Here in situ atomic force microscopy (AFM) and photo-induced force microscopy (PiFM) is utilized to investigate the formation process and define the basic structural paradigm of individual silk nanofibrils. By visualizing the multistage process of silk nanofibril formation, the importance of conformational transformations along the assembly pathway is revealed. Unfolded silk structures initially accumulate into amorphous clusters, which then evolve into crystal nuclei via conformational transformation into β-crystallites. Nanofibril elongation then occurs through the attachment of silk molecules at a single end of the nanofibril tip; this is facilitated through the formation of a new amorphous cluster that then repeats the aforementioned conformational transformation. However, enzymatic digestion of the amorphous regions leads to direct, rapid elongation of β-crystalline fibers. These findings imply that the energy landscape is characterized by shallow minima associated with intermediate states, which can be eliminated by introducing β-crystallites, and motivate research into the directed modification of the silk assembly pathway to select for features beneficial to specific applications.

36 MATERIALS SCIENCE↗

DOE OSTI2025

Predictive links between microbial communities and biological oxygen utilization in the Arctic Ocean

Microbial metabolism influences rates of net community production (NCP), exerting a direct biological control on marine oxygen and carbon fluxes. In the Arctic, it is increasingly important to understand and quantify this process, as ecological and oceanographic conditions shift due to changing climate. Here, we describe potential ecological links between pelagic microbial diversity and an NCP precursor, biological oxygen utilization, using machine learning and paired observations of community structure and metabolic activity from a seasonally and spatially variable transect of the Arctic Ocean (2019–2020 MOSAiC Expedition). Community structure was determined using 16S (prokaryotic) and 18S (eukaryotic) rRNA gene amplicon sequencing, and metabolic activity was derived from ΔO 2 /Ar. Using self-organizing maps, we identified clear successional patterns in observed microbial community structure that were seasonally driven in the upper ocean and vertically stratified with depth. Metabolic activity was also stratified, with a primarily net heterotrophic water column (median −1.5% biological oxygen saturation), excepting periodic oxygen supersaturation (maximum: 13.6%) within the mixed layer. Using DNA sequences as predictor variables, we then constructed a random forest regression model that reliably reconstructed biological oxygen concentrations (root mean squared error = 4.14 μmol kg −1 ). Top predictors from this model were from heterotrophic (bacteria) or potentially mixotrophic (dinoflagellate) taxa. These analyses highlight biologically driven diagnostic tools that can be used to expand biogeochemical datasets and improve the microbial perspectives and metabolisms represented in ecological models of net productivity and carbon flux in a changing Arctic Ocean.

Chamberlain, Emelia J. [Univ. of San Diego, San Di↗

DOE OSTI2022

Modeling the role of lateral surface flow in low‐relief polygonal tundra

Abstract Ice‐wedge polygon troughs play an important role in controlling the hydrology of low‐relief polygonal tundra regions. Lateral surface flow is confined to troughs only, but it is often neglected in model projections of permafrost thermal hydrology. Recent field and modeling studies have shown that, after rain events, increases in trough water levels are significantly more than the observed precipitation, highlighting the role of lateral surface flow in the polygonal tundra. Therefore, understanding how trough lateral surface flow can influence polygonal tundra thermal hydrology is important, especially under projected changes in temperatures and rainfall in the Arctic regions. Using an integrated cryohydrology model, this study presents plot‐scale end‐of‐century projections of ice‐wedge polygon water budget components and active layer thickness with and without trough lateral surface flow under the Representative Concentration Pathway 8.5 scenario. Trough lateral surface flow is incorporated through a newly developed empirical model, evaluated against field measurements. The numerical scenario that includes trough lateral surface flow simulates discharge (outflow from a polygon) and recharge (rain‐induced inflow to a polygon trough from upslope areas), while the scenario that does not include trough lateral surface flow ignores recharge. The results show considerable reduction (about 100–150%) in evapotranspiration and discharge in rainy years in the scenarios ignoring trough lateral surface flow, but less effect on soil water storage, in comparison with the scenario with trough lateral surface flow. In addition, the results demonstrate long‐term changes (~10–15 cm increase) in active layer thickness when trough lateral surface flow is modeled. This study highlights the importance of including lateral surface flow processes to better understand the long‐term thermal and hydrological changes in low‐relief polygonal tundra regions under a changing climate.

Jan, Ahmad↗

DOE OSTI2021

Understanding Electrical Conduction and Nanopore Formation During Controlled Breakdown

Controlled breakdown has recently emerged as a highly appealing technique to fabricate solid-state nanopores for a wide range of biosensing applications. This technique relies on applying an electric field of approximately 0.4–1 V nm –1 across the membrane to induce a current, and eventually, breakdown of the dielectric. Although previous studies have performed controlled breakdown under a range of different conditions, the mechanism of conduction and breakdown has not been fully explored. Here, electrical conduction and nanopore formation in SiN x membranes during controlled breakdown is studied. It is demonstrated that for Si-rich SiN x , oxidation reactions that occur at the membrane-electrolyte interface limit conduction across the dielectric. However, for stoichiometric Si 3 N 4 the effect of oxidation reactions becomes relatively small and conduction is predominately limited by charge transport across the dielectric. Several important implications resulting from understanding this process are provided which will aid in further developing controlled breakdown in the coming years, particularly for extending this technique to integrate nanopores with on-chip nanostructures.

36 MATERIALS SCIENCE↗

DOE OSTI2021

Precipitate Phase Stability and Mechanical Properties of Alloy 263 and Variants in Wrought or Cast Form

Alloy 263 is a candidate Ni-based superalloy for use in advanced ultra-supercritical (AUSC) power plants that are targeted to operate above 700 °C. Exposure times in this type of environment are considerable with target creep properties specified at 100,000 h making phase stability an important parameter in the alloy selection process. In this investigation, the microstructure of alloy 263 and two modified compositions in both cast and cast/wrought forms were investigated after exposure at 800 °C for various times ranging from 1,000 h to 10,000 h. Tension and creep properties were assessed for all alloys in both forms. Modification to the Ti and Al concentrations successfully slowed down the γ' to η transformation while doubling the γ' fraction. Thin elongated η needles or platelets formed in the wrought products while thicker, plate-like, η precipitates were found in the castings. The effect of the various microstructures on the creep properties were determined using isothermally aged specimens aged at 800 °C for 3,000 h and 10,000 h prior to creep screening. Strong precipitation of the η phase was found to considerably decrease creep life and increase minimum creep rate. By modifying the Ti and Al content, the creep life and ductility of the new formulation tested on specimens isothermally aged for 3,000 and 10,000 h were near that of the nominal alloy in its standard aged condition.

Detrois, Martin↗

DOE OSTI2025

Machine Learning in the Context of Laser-Induced Breakdown Spectroscopy

The integration of machine learning (ML) with Laser-Induced Breakdown Spectroscopy (LIBS) has revolutionized the analytical capabilities of LIBS. The combi-nation of both methods enables more accurate and efficient data analysis. While LIBS itself is a powerful technique for elemental analysis, the vast amount of spectral data it generates can be hard to interpret. Machine learning addresses these challenges by leveraging algorithms that can learn from data, identify patterns, and make predictions without explicit programming for the interpretation of each specific task. In LIBS application, ML techniques are used to enhance various analytical processes. For example, ML algorithms can classify materials based on their spectral fingerprints, predict the concentration of elements in a sample, and identify underlying patterns within complex datasets. Here, this application improves the precision of LIBS analyses while significantly reducing the time required for data processing and interpretation. In this chapter, the fundamental concepts of ML will be discussed first. Following this, the process of data splitting and the importance of feature selection will be examined. Several machine learning methods will then be closely examined, exploring how each can benefit LIBS analysis and highlighting their respective advantages and shortcomings. This structured approach will provide a comprehensive understanding of the integration of ML in the context of LIBS analysis.

47 OTHER INSTRUMENTATION↗

DOE OSTI2023

Tutorial: Lessons Learned for Behavior Analysts from Data Scientists

Big data is a computing term used to refer to large and complex data sets, typically consisting of terabytes or more of diverse data that is produced rapidly. The analysis of such complex data sets requires advanced analysis techniques with the capacity to identify patterns and abstract meanings from the vast data. The field of data science combines computer science with mathematics/statistics and leverages artificial intelligence, in particular machine learning, to analyze big data. This field holds great promise for behavior analysis, where both clinical and research studies produce large volumes of diverse data at a rapid pace (i.e., big data). This article presents basic lessons for the behavior analytic researchers and clinicians regarding integration of data science into the field of behavior analysis. We provide guidance on how to collect, protect, and process the data, while highlighting the importance of collaborating with data scientists to select a proper machine learning model that aligns with the project goals and develop models with input from human experts. Here, we hope this serves as a guide to support the behavior analysts interested in the field of data science to advance their practice or research, and helps them avoid some common pitfalls.

42 ENGINEERING↗

DOE OSTI2020

Stability of plutonium oxide nanoparticles in the presence of montmorillonite and implications for colloid facilitated transport

The physical and chemical stability of PuO 2 nanoparticles (intrinsic colloids) in groundwater will control their transport and may affect the performance of high-level radioactive waste repositories. In this study, we examined the chemical stability of two types of PuO 2 nanoparticles at both 25 and 80 °C. The “alkaline” PuO 2 nanoparticles were prepared by neutralizing an acidic Pu(IV) solution with dilute NaOH to pH ~9.5. The “acidic” PuO 2 nanoparticles were precipitated from 0.1 M nitric acid by heating a Pu(IV) solution at 60–80 °C for 30 min. The chemical stability of these PuO 2 nanoparticles was tested in the presence of montmorillonite, a common mineral in the environment and potentially relevant backfill material in some nuclear waste repository designs. The “alkaline” PuO 2 nanoparticles were found to be unstable over a timescale of months at both 25 and 80 °C, with elevated temperature enhancing their dissolution rates and sorption to montmorillonite. PuO 2 nanoparticle dissolution rates decreased with increasing Pu concentration, consistent with solution saturation. The “acidic” PuO 2 nanoparticles appeared to remain stable for much longer than the “alkaline” PuO2 nanoparticles. The “alkaline” PuO 2 nanoparticle dissolution rate constants were as high as 10 -11.9±0.4 mol m -2 s -1 and 10 -11.2±0.2 mol m -2 s -1 at 25 and 80 °C, respectively, while the “acidic” PuO 2 nanoparticle dissolution rate at 80 °C was 10 -13.5 mol m -2 s -1 . Based on transmission electron microscopy, the “alkaline” and “acidic” PuO 2 nanoparticles were of similar size (2.5–4.5 and 2–3 nm nanoparticles, respectively). However, the “acidic” PuO 2 nanoparticles formed more ordered nanoparticle aggregates. Our results suggest that the specific conditions experienced during PuO 2 nanoparticle formation could significantly affect the stability of PuO 2 in the presence of competing sorption processes and, in turn, the relative importance of intrinsic versus pseudocolloid transport in the environment.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗

DOE OSTI2025

Optimizing the optimizer for physics-informed neural networks and Kolmogorov-Arnold networks

Physics-Informed Neural Networks (PINNs) have revolutionized the computation of PDE solutions by integrating partial differential equations (PDEs) into the neural network’s training process as soft constraints, becoming an important component of the scientific machine learning (SciML) ecosystem. More recently, physics-informed Kolmogorv-Arnold networks (PIKANs) have also shown to be effective and comparable in accuracy with PINNs. In their current implementation, both PINNs and PIKANs are mainly optimized using first-order methods like Adam, as well as quasi-Newton methods such as BFGS and its low-memory variant, L-BFGS. However, these optimizers often struggle with highly nonlinear and non-convex loss landscapes, leading to challenges such as slow convergence, local minima entrapment, and (non)degenerate saddle points. In this study, we investigate the performance of Self- Scaled BFGS (SSBFGS), Self-Scaled Broyden (SSBroyden) methods and other advanced quasi-Newton schemes, including BFGS and L-BFGS with different line search strategies. These methods dynamically rescale updates based on historical gradient information, thus enhancing training efficiency and accuracy. We systematically compare these optimizers – using both PINNs and PIKANs – on key challenging PDEs, including the Burgers, Allen-Cahn, Kuramoto-Sivashinsky, Ginzburg-Landau, and Stokes equations. Additionally, we evaluate the performance of SSBFGS and SSBroyden for Deep Operator Network (DeepONet) architectures, demonstrating their effectiveness for data-driven operator learning. Our findings provide state-of-the-art results with orders-of-magnitude accuracy improvements without the use of adaptive weights or any other enhancements typically employed in PINNs. More broadly, our work reveal insights into the effectiveness of quasi-Newton optimization strategies in significantly improving the convergence and accurate generalization of PINNs and PIKANs.

97 MATHEMATICS AND COMPUTING↗