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DOE OSTI2024

A comparative study on deep learning models for condition monitoring of advanced reactor piping systems

Advanced nuclear reactors offer innovative applications due to their portability, reliability, resiliency, and high capacity factors. To operate them on a wider scale, reducing maintenance life-cycle costs while ensuring their integrity is essential. Autonomous operations in advanced nuclear reactors using augmented Digital Twin (DT) technology can serve as a cost-effective solution by increasing awareness about the system’s health. A key component of nuclear DT frameworks is the condition monitoring of safety systems, such as piping-equipment systems, which involves acquiring and monitoring the plant’s sensor data. Here, this research proposes a condition monitoring methodology utilizing deep learning algorithms, such as multilayer perceptions (MLP) and convolutional neural networks (CNNs), to detect degradation and its severity in nuclear piping-equipment systems. Sensor signals are processed to obtain the power spectral density and the Short-Time Fourier transform, and feature extraction methodologies are proposed to develop degradation-sensitive data repositories. The performance of MLP, one-dimensional (1D) CNN, and 2D CNN within the proposed condition monitoring framework is compared using a finite element model of a 3D piping system subjected to seismic loads as the application case study. Various approaches, such as dropout, k-Fold validation, regularization, and early stopping of training the network, are investigated to avoid overfitting the models to the input sensor data. The predictive capability and computational capacity of the deep learning algorithms are also compared to detect degradation in the Z-pipe system of the Experimental Breeder Reactor II (EBRII). The Z-pipe system is subjected to harmonic excitations that represent normal operating loads, such as pump-induced vibrations. The findings of the study indicate that the proposed artificial intelligence (AI)-driven condition monitoring framework demonstrates superior prediction accuracies with a 2D CNN, whereas the MLP exhibits higher computational efficiency.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2018

Elemental Composition of Analogs to Mars Return Samples Studied with X-Ray Fluorescence Imaging at NSLS-II

In 2020, NASA will launch out into the first step of what may be an ambitious, multi-mission campaign with the goal to bring back for analysis in laboratories here to Earth a collection of rock, regolith, and atmosphere samples from Mars. It is the main objective of this endeavor to determine whether or not Mars was ever host to ancient microbial life. Finding evidence for extinct (or extant) life on Mars has the potential to fundamentally alter our view of the origin and evolution of life. It would provide proof for Earth being just one amongst others regarding the question of biological activity in the Solar System. Furthermore, returned Martian samples would provide a rich repository of information on the history of planetary accretion and differentiation, geochronology, atmospheric evolution, and the paleo-environmental history of Mars, adding tremendous additional value to such a mission beyond the issue of extraterrestrial life. Here the potential knowledge to be obtained by such a collection of samples will equal or exceed that of the Apollo lunar sample archive, which even today yields new insights on the history of the Solar System, over 40 years after they were returned to Earth.

36 MATERIALS SCIENCE↗

DOE OSTI2017

OpenMSI Arrayed Analysis Toolkit: Analyzing Spatially Defined Samples Using Mass Spectrometry Imaging

Mass spectrometry imaging (MSI) has primarily been applied in localizing biomolecules within biological matrices. Although well-suited, the application of MSI for comparing thousands of spatially defined spotted samples has been limited. One reason for this is a lack of suitable and accessible data processing tools for the analysis of large arrayed MSI sample sets. In this paper, the OpenMSI Arrayed Analysis Toolkit (OMAAT) is a software package that addresses the challenges of analyzing spatially defined samples in MSI data sets. OMAAT is written in Python and is integrated with OpenMSI (http://openmsi.nersc.gov), a platform for storing, sharing, and analyzing MSI data. By using a web-based python notebook (Jupyter), OMAAT is accessible to anyone without programming experience yet allows experienced users to leverage all features. OMAAT was evaluated by analyzing an MSI data set of a high-throughput glycoside hydrolase activity screen comprising 384 samples arrayed onto a NIMS surface at a 450 μm spacing, decreasing analysis time >100-fold while maintaining robust spot-finding. The utility of OMAAT was demonstrated for screening metabolic activities of different sized soil particles, including hydrolysis of sugars, revealing a pattern of size dependent activities. Finally, these results introduce OMAAT as an effective toolkit for analyzing spatially defined samples in MSI. OMAAT runs on all major operating systems, and the source code can be obtained from the following GitHub repository: https://github.com/biorack/omaat.

47 OTHER INSTRUMENTATION↗

DOE OSTI2017

Formularity: Software for Automated Formula Assignment of Natural and Other Organic Matter from Ultrahigh-Resolution Mass Spectra

Ultrahigh-resolution mass spectrometry, such as Fourier transform ion-cyclotron resonance mass spectrometry (FT-ICR MS), can resolve thousands of molecular ions in complex organic matrices. A Compound Identification Algorithm (CIA) was previously developed for automated elemental formula assignment for natural organic matter (NOM). In this work we describe a user friendly interface for CIA, titled Formularity, which includes an additional functionality to perform search of formulas based on an Isotopic Pattern Algorithm (IPA). While CIA assigns elemental formulas for compounds containing C, H, O, N, S, and P, IPA is capable of assigning formulas for compounds containing other elements. We used halogenated organic compounds (HOC), a chemical class that is ubiquitous in nature as well as anthropogenic systems, as an example to demonstrate the capability of Formularity with IPA. A HOC standard mix was used to evaluate the identification confidence of IPA. The HOC spike in NOM and tap water were used to assess HOC identification in natural and anthropogenic matrices. Strategies for reconciliation of CIA and IPA assignments are discussed. Software and sample databases with documentation are freely available from the PNNL OMICS software repository https://omics.pnl.gov/software/formularity.

data analysis↗

DOE OSTI2026

Challenges and Vision for Standardization of Biopolymer Data Sets for Machine Learning

Machine learning (ML) is transforming materials research, yet potential for biopolymer discovery remains constrained by fragmented data and nonstandardized reporting. Biopolymers differ significantly from synthetic polymers, requiring specialized approaches to represent their biosynthetic origins, hierarchical structures, and application-specific metrics. In this Perspective, we identify three core challenges limiting biopolymer representation: information encoding, data quality, and data sharing. We describe the most pressing issues and propose commensurate approaches to address each key challenge. Recommendations include the design and adoption of biopolymer-specific fingerprinting and representation frameworks, development of hybrid human-large language model (LLM) data extraction strategies, and expanding Findable, Accessible, Interoperable, Reusable (FAIR)-compliant repositories. We propose a robust foundation to define interoperable, high-quality data sets that capture the full context of biopolymer materials. Standardized metadata, shared ontologies, and community-driven infrastructure would enable scalable, reproducible workflows and accelerate the ML-driven development of biopolymers.

36 MATERIALS SCIENCE↗

DOE OSTI2017

Out-of-Oblivion Cage Molecules and Their Porous Crystalline Phases

An automated molecular porosity detection approach was developed and applied to PubChem, a repository of ca. 94 million molecules, to discover intrinsically porous cage molecules, which, although previously considered by chemists, have remained in oblivion to the porous solids community as neither their crystal structures nor solid-state porosity have been previously reported. The effort led to identification of six such cage molecules reported over the span of the last two decades. The following crystal structure prediction effort suggested that these molecules can form stable low-energy porous crystalline phases. One of the identified lowest energy phases exhibits zeolite-range porosity with pore diameters of ca. 8 Å and internal surface area of 1070 m 2 /g.

36 MATERIALS SCIENCE↗

DOE OSTI2020

Machine Learning for Materials Scientists: An Introductory Guide toward Best Practices

This Methods/Protocols article is intended for materials scientists interested in performing machine learning-centered research. Herein, we cover broad guidelines and best practices regarding the obtaining and treatment of data, feature engineering, model training, validation, evaluation and comparison, popular repositories for materials data and benchmarking data sets, model and architecture sharing, and finally publication. In addition, we include interactive Jupyter notebooks with example Python code to demonstrate some of the concepts, workflows, and best practices discussed. Overall, the data-driven methods and machine learning workflows and considerations are presented in a simple way, allowing interested readers to more intelligently guide their machine learning research using the suggested references, best practices, and their own materials domain expertise.

36 MATERIALS SCIENCE↗

DOE OSTI2023

Oxide– and Silicate–Water Interfaces and Their Roles in Technology and the Environment

Interfacial reactions drive all elemental cycling on Earth and play pivotal roles in human activities such as agriculture, water purification, energy production and storage, environmental contaminant remediation, and nuclear waste repository management. The onset of the 21st century marked the beginning of a more detailed understanding of mineral aqueous interfaces enabled by advances in techniques that use tunable high-flux focused ultrafast laser and X-ray sources to provide near-atomic measurement resolution, as well as by nanofabrication approaches that enable transmission electron microscopy in a liquid cell. This leap into atomic- and nanometer-scale measurements has uncovered scale-dependent phenomena whose reaction thermodynamics, kinetics, and pathways deviate from previous observations made on larger systems. A second key advance is new experimental evidence for what scientists hypothesized but could not test previously, namely, interfacial chemical reactions are frequently driven by “anomalies” or “non-idealities” such as defects, nanoconfinement, and other nontypical chemical structures. Third, progress in computational chemistry has yielded new insights that allow a move beyond simple schematics, leading to a molecular model of these complex interfaces. In combination with surface-sensitive measurements, we have gained knowledge of the interfacial structure and dynamics, including the underlying solid surface and the immediately adjacent water and aqueous ions, enabling a better definition of what constitutes the oxide– and silicate–water interfaces. This critical review discusses how science progresses from understanding ideal solid–water interfaces to more realistic systems, focusing on accomplishments in the last 20 years and identifying challenges and future opportunities for the community to address. Further, we anticipate that the next 20 years will focus on understanding and predicting dynamic transient and reactive structures over greater spatial and temporal ranges as well as systems of greater structural and chemical complexity. Closer collaborations of theoretical and experimental experts across disciplines will continue to be critical to achieving this great aspiration.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2020

Mobility of Radionuclides in Fractured Carbonate Rocks: Lessons from a Field-Scale Transport Experiment

Current research on radionuclide disposal is mostly conducted in granite, clay, saltstone, or volcanic tuff formations. These rock types are not always available to host a geological repository in every nuclear waste-generating country, but carbonate rocks may serve as a potential alternative. To assess their feasibility, a forced gradient cross-borehole tracer experiment was conducted in a saturated fractured chalk formation. The mobility of stable Sr and Cs (as analogs for their radioactive counterparts), Ce (an actinide analog), Re (a Tc analog), bentonite particles, and fluorescent dye tracers through the flow path was analyzed. The migration of each of these radionuclide analogs (RAs) was shown to be dependent upon their chemical speciation in solution, their interactions with bentonite, and their sorption potential to the chalk rock matrix. The brackish groundwater resulted in flocculation and immobilization of most particulate RAs. Nevertheless, the high permeability of the fracture system allowed for fast overall transport times of all aqueous RAs investigated. This study suggests that the geochemical properties of carbonate rocks may provide suitable conditions for certain types of radionuclide storage (in particular, brackish, high-porosity, and low-permeability chalks). Nevertheless, careful consideration should be given to high-permeability fracture networks that may result in high radionuclide mobility.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗

DOE OSTI2023

Impact of a Biological Chelator, Lanmodulin, on Minor Actinide Aqueous Speciation and Transport in the Environment

Minor actinides are major contributors to the long-term radiotoxicity of nuclear fuels and other radioactive wastes. Here in this context, understanding their interactions with natural chelators and minerals is key to evaluating their transport behavior in the environment. The lanmodulin family of metalloproteins is produced by ubiquitous bacteria and Methylorubrum extorquens lanmodulin (LanM) was recently identified as one of nature’s most selective chelators for trivalent f-elements. Herein, we investigated the behavior of neptunium, americium, and curium in the presence of LanM, carbonate ions, and common minerals (calcite, montmorillonite, quartz, and kaolinite). We show that LanM’s aqueous complexes with Am(III) and Cm(III) remain stable in carbonate-bicarbonate solutions. Furthermore, the sorption of Am(III) to these minerals is strongly impacted by LanM, while Np(V) sorption is not. With calcite, even a submicromolar concentration of LanM leads to a significant reduction in the Am(III) distribution coefficient (K d , from >10 4 to ~10 2 mL/g at pH 8.5), rendering it even more mobile than Np(V). Thus, LanM-type chelators can potentially increase the mobility of trivalent actinides and lanthanide fission products under environmentally relevant conditions. Monitoring biological chelators, including metalloproteins, and their biogenerators should therefore be considered during the evaluation of radioactive waste repository sites and the risk assessment of contaminated sites.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗

DOE OSTI2024

Plutonium Speciation and Oxidation State Distributions in the Presence of Citrate

We explored the speciation and kinetics of the Pu(VI)-citrate and Pu(III)-citrate systems (pH m = 2.5–11.0, I = 0.1 M NaCl, T = 23 °C, O 2 (g) < 2 ppm) using ultraviolet–visible–near-infrared (UV–vis–NIR) spectrophotometry, solvent extraction, and PHREEQC modeling. Formation constants were determined for PuO 2 (HcitH)(aq) (log K° 1,1 = 1.09 ± 0.05) and PuO 2 (HcitH)(citH) 3– (log K° 1,2 = -0.20 ± 0.07), and evidence for (PuO 2 ) m (citH –k ) n (OH) x 2m(3+k)n–x was identified under alkaline conditions. Pu(VI) species were found to be less stable in the presence of citrate than in the absence of citrate (t ≤ 168 days); the rate of reduction increased with increasing pH. Further, the direct reduction of Pu(VI) to Pu(IV) was required to fit experimental data in the presence of citrate but did not improve the fit for Pu in the absence of citrate. We also observed increased Pu(III) stability in the presence of citrate (t ≤ 293 days), with higher concentrations of Pu(III) favored at lower pH. Finally, we provide evidence of a radiolysis-driven mechanism for the citrate-mediated reduction of plutonium that involves electron transfer from the oxidative breakdown of citrate. Our work highlights the need to investigate the redox effect of organic ligands on plutonium oxidation states under repository-relevant conditions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2026

Comparability of Liquid Chromatography Tandem Mass Spectrometry Analysis of Dissolved Organic Matter across Laboratories

Non-targeted liquid chromatography tandem highresolution mass spectrometry (LC−MS/MS) is increasingly applied for the structure-resolved chemical analysis of dissolved organic matter (DOM). With new developments in MS instrumentation and analysis software, the approach has gained substantial momentum over the past decade. However, achieving high-quality analytical data that is reproducible and comparable across laboratories can be a bottleneck in non-targeted metabolomics and organic matter chemical analysis, especially for data reuse in repository-scale analyses. Understanding the capabilities as well as challenges of comparing LC−MS/MS data from different laboratories is necessary for inferring global trends from public data sets. To illuminate instrumentation factors that drive differences and variability, we used a standardized data analysis pipeline, including classical (CMN) and featurebased molecular networking (FBMN), to analyze data from a ring trial by 24 laboratories on identical sample sets of algal and DOM extracts that were mixed in predefined concentrations and spiked with standards. Our results showed that data sets from similar mass spectrometer types with unified instrument parameters were qualitatively comparable, resolving the same general trends and shared mass spectral features. Interlaboratory comparability was best for high-intensity features, while low-intensity features showed greater detection variability. Our analysis also highlights challenges when comparing data from instruments with different acquisition rates or operating with less standardized methods. Lastly, we provide recommendations for data integration, public data sharing, standardization, and best practices for standardized LC−MS/MS data acquisition, which will be critical for long-term time series and intercomparability of DOM chemical analyses.

DOM↗

DOE OSTI2020

Structural Features of Zirconium-Based Metal–Organic Frameworks Affecting Radiolytic Stability

Metal–organic frameworks (MOFs) NU-1000 and UiO-66 are herein exposed to two different gamma irradiation doses and dose rates and analyzed to determine the structural features that affect their stability in these environments. MOFs have shown promise for the capture and sensing of off-gases at civilian nuclear energy reprocessing sites, nuclear waste repositories, and nuclear accident locations. However, little is understood about the structural features of MOFs that contribute to their stability levels under the ionizing radiation conditions present at such sites. This study is the first of its kind to explore the structural features of MOFs that contribute to their radiolytic stability. Both NU-1000 and UiO-66 are MOFs that contain Zr metal-centers with the same metal absorption cross section. However, the two MOFs exhibit different linker connectivities, linker aromaticities, node densities, node connectivities, and interligand separations. In this study, NU-1000 and UiO-66 were exposed to high (423.3 Gy/min, 23 min, and 37 s) and low (0.78 Gy/min, 4320 min) dose rates of 60 Co gamma irradiation. NU-1000 displayed insignificant radiation damage under both dose rates due to its high linker connectivity, low node density, and low node connectivity. However, low radiation dose rates caused considerable damage to UiO-66, a framework with lower aromaticity and smaller interligand separation. Finally, results suggest that chronic, low-radiation environments are more detrimental to Zr MOF stability than acute, high-radiation conditions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2019

Tailored Perovskite Waste Forms for Plutonium Trapping

Perovskite ceramics have been extensively studied as host matrixes for radionuclide entrapment for nuclear waste disposal. As an expansion of these investigations, cerium, neodymium, and plutonium were incorporated into a perovskite phase, ACu 3 FeTi 3 O 12 (A = Nd, Ce, Pu), using sol–gel methods under oxidizing and reducing atmospheres. The targeted materials contained varying levels of Ce 3+ and Nd 3+ on the A site, yielding potential compositions of Nd 1–x Ce x Cu 3 FeTi 3 O 12 (x = 0, 0.1, 0.2, 0.3, 0.4, 0.8). However, interrogation of these materials shows that the maximum Ce 3+ loading is achieved near x ≈ 0.2. A single composition with plutonium was targeted, Nd 0.9 Pu 0.1 Cu 3 FeTi 3 O 12 , in order to properly model more realistic loading levels for a repository-destined material. These compounds were characterized using powder X-ray diffraction with Rietveld refinements of the structures and by a variety of spectroscopic techniques. Finally, the data suggest that, in order to achieve Pu 3+ substitution onto the A sites in the Nd 0.9 Pu 0.1 Cu 3 FeTi 3 O 12 , a reducing atmosphere must be employed. Otherwise, the redox activity of plutonium results in substitution onto multiple sites in the material as well as the formation of secondary phases such as TiO 2 .

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗

DOE OSTI2023

NWChem: Recent and Ongoing Developments

In this paper we summarize developments in the NWChem computational chemistry suite since the last major release (NWChem 7.0). Specifically, we focus on functionalities, along with input blocks, that are currently accessible in the current stable release (NWChem 7.2) and master branches, interfaces to quantum computing simulators, interfaces to external libraries, the NWChem GitHub repository, and containerization of NWChem executable images. In conclusion, some of the ongoing developments that will be available in the near future are also discussed.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2023

Machine Learning Full NMR Chemical Shift Tensors of Silicon Oxides with Equivariant Graph Neural Networks

The nuclear magnetic resonance (NMR) chemical shift tensor is a highly sensitive probe of the electronic structure of an atom and furthermore its local structure. Recently, machine learning has been applied to NMR in the prediction of isotropic chemical shifts from a structure. Current machine learning models, however, often ignore the full chemical shift tensor for the easier-to-predict isotropic chemical shift, effectively ignoring a multitude of structural information available in the NMR chemical shift tensor. Here we use an equivariant graph neural network (GNN) to predict full 29 Si chemical shift tensors in silicate materials. The equivariant GNN model predicts full tensors to a mean absolute error of 1.05 ppm and is able to accurately determine the magnitude, anisotropy, and tensor orientation in a diverse set of silicon oxide local structures. When compared with other models, the equivariant GNN model outperforms the state-of-the-art machine learning models by 53%. The equivariant GNN model also outperforms historic analytical models by 57% for isotropic chemical shift and 91% for anisotropy. The software is available as a simple-to-use open-source repository, allowing similar models to be created and trained with ease.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2017

Is the Supporting Information the Venue for Reproducibility and Transparency?

Rudshteyn, Benjamin↗

DOE OSTI2017

Is the Supporting Information the Venue for Reproducibility and Transparency?

Making research data, software, and data processing tools readily available to the public could significantly enhance the impact of scientific publications. Openness and transparency could address reproducibility concerns and accelerate scientific progress.(1, 2) While archives and repositories would be preferable for securing data in the long term, the Supporting Information (SI) already allocates significant space to provide auxiliary files, links, and essential information needed to make scientific findings immediately reproducible as well as data processing protocols and numerical procedures executable. Furthermore, the SI document could ensure that data that might not fit in the tight confines of a journal article lives online even when laboratories move on to other projects, close, and lose track of their data. The change for openness and transparency might be difficult to embrace, depending on the specific nature of the project. Nevertheless, the trend to make raw research data and open-source software packages available to the public has been building steady momentum. Openness in research data and software sharing is already making transformative contributions to the communication of research findings, critical for ensuring reproducibility as well as training of the next generation of scientists.

Rudshteyn, Benjamin↗