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DOE OSTI2024

Distinguishing the XUV-induced Coulomb explosion dynamics of iodobenzene using covariance analysis

The primary and secondary fragmentation dynamics of iodobenzene following its ionization at 120 eV were determined using three-dimensional velocity map imaging and covariance analysis. Site-selective iodine 4d ionization was used to populate a range of excited polycationic parent states, which primarily broke apart at the carbon-iodine bond to produce I+ with phenyl or phenyl-like cations (C n H x + or C n H, with n = 1 – 6 and x = 1 – 5). The molecular products were produced with varying degrees of internal excitation and dehydrogenation, leading to stable and unstable outcomes. This further allowed the secondary dynamics of C 6 H x 2+ intermediates to be distinguished using native-frame covariance analysis, which isolated these processes in their own centre-of-mass reference frames. Furthermore, the mass resolution of the imaging mass spectrometer used for these measurements enabled the primary and secondary reaction channels to be specified at the level of individual hydrogen atoms, demonstrating the ability of covariance analysis to comprehensively measure the competing fragmentation channels of aryl cations, including those involving intermediate steps.

74 ATOMIC AND MOLECULAR PHYSICS↗

DOE OSTI2022

Resonant Ultrasonic Spectroscopy: A Modal-Analysis Approach for Additive Manufacturing

Resonant ultrasonic spectroscopy is a methodology capable of measuring a change in modal resonant frequencies of material structures. It encompasses an inversion technique based on the eigen-frequencies of a simple regular sample geometry to estimate the elastic tensor of a solid. Obtaining an accurate and complete set of resonant frequencies is a critical first step in the RUS process. In this paper a variety of techniques to extract and validate material resonances from complicated RUS measurement spectra are developed. Various processing methodologies employing modal analysis techniques are applied to estimate the underlying resonances that lead to the extraction of the elastic coefficients characterizing the specimen under test. A case study of a simple isotropic material (304 SS) is investigated to analyze the algorithms and evaluate their performance

Coal, lignite, and peat↗

DOE OSTI2025

Pulse-by-pulse transient thermal deformation in crystal optics under high-repetition-rate FEL

Time-domain modeling of the thermal deformation of crystal optics can help define acceptable operational ranges across the pulse-energy repetition-rate phase space. In this paper, we have studied the transient thermal deformation of a water-cooled diamond crystal for a cavity-based X-ray free-electron laser (CBXFEL), either an X-ray free-electron laser oscillator (XFELO) or a regenerative amplifier X-ray free-electron laser (RAFEL), by numerical simulations including finite-element analysis and advanced data processing. Pulse-by-pulse transient thermal deformation of a 50 µm-thick diamond crystal has been performed with X-ray pulse repetition rates between 50 kHz and 1 MHz. Results for temperature and thermal deformation have been compared with the results of transient analysis using a continuous wave (CW) power loading. Temperature and thermal deformation results from pulse-by-pulse transient analysis vary with time about the results for the CW case for the same average power. The variation amplitude increases with pulse energy and decreases with repetition rate. When the repetition rate increases to infinity, both temperature and thermal deformation converge to the results for the CW case. Two critical time scales for the operation of crystal optics in a CBXFEL are (1) first-turn time, i.e. the time for the XFEL pulse to complete the first turn around the cavity so that the crystal sees the recirculated XFEL pulse, and (2) period-end time, i.e. the time that the next electron bunch arrives for the amplification, so that the crystal outcouples the amplified FEL power. For the same average power, simulation results show that the crystal thermal deformation seen by the XFEL beam decreases with repetition rate at the first-turn time of a 300 m-long cavity and increases with repetition rate at the period-end time. For the wavefront preservation requirement of the crystal optics, a pulse-energy versus repetition-rate phase space has been established. The upper bounds of the pulse energy at both first-turn and period-end times decreases with repetition rate, especially at the period-end time. The upper bound of the thermal deformation of the crystal at the period-end time for any repetition frequency can be estimated from the CW case. For a water-cooled diamond crystal of dimension 5 mm × 5 mm × 0.05 mm, the time to reach a quasi steady-state is about 50 ms for temperature and 50 µs for thermal deformation.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2021

Impact Fracture and Fragmentation of Glass via the 3D Combined Finite-Discrete Element Method

A driving technical concern for the automobile industry is their assurance that developed windshield products meet Federal safety standards. Besides conducting innumerable glass breakage experiments, product developers also have the option of utilizing numerical approaches that can provide further insight into glass impact breakage, fracture, and fragmentation. The combined finite-discrete element method (FDEM) is one such tool and was used in this study to investigate 3D impact glass fracture processes. To enable this analysis, a generalized traction-separation model, which defines the constitutive relationship between the traction and separation in FDEM cohesive zone models, was introduced. The mechanical responses of a laminated glass and a glass plate under impact were then analyzed. For laminated glass, an impact fracture process was investigated and results were compared against corresponding experiments. Correspondingly, two glass plate impact fracture patterns, i.e., concentric fractures and radial fractures, were simulated. The results show that for both cases, FDEM simulated fracture processes and fracture patterns are in good agreement with the experimental observations. The work demonstrates that FDEM is an effective tool for modeling of fracture and fragmentation in glass.

42 ENGINEERING↗

DOE OSTI2020

Perimatrix of middle ear cholesteatoma: A granulation tissue with a specific transcriptomic signature

Objectives/Hypothesis: To establish comprehensive transcriptomic profiles of cholesteatoma perimatrix tissue and granulation tissue from chronic otitis media (COM) that did not develop cholesteatoma, which can indicate molecular pathways involved in the cholesteatoma perimatrix pathology and invasiveness. Study Design: Retrospective Case Series. Methods: Transcriptome data were obtained from cholesteatoma perimatrix tissue and COM granulation tissue by an Illumina iScan microarray. Differentially expressed genes (DEGs) were subsequently analyzed using both bioinformatical functional annotation and network analysis. Expression of candidate genes (MMP9 and LCN2) was validated by quantitative reverse transcription-polymerase chain reaction (qRT-PCR) on a larger group of samples. Results: Analysis of the transcriptome led to the identification of 169 differentially expressed genes between investigated tissues. Bioinformatic analysis suggested that most significant biological processes involving DEGs were previously described in cholesteatoma pathology. Network analysis identified ERBB2, TFAP2A, and TP63 as major hubs of the DEGs molecular network. Furthermore, it was observed that the cellular component most significantly enriched in DEGs was extracellular space containing 47 DEGs. Using qRT-PCR, it was confirmed that mRNA levels of the major extracellular hub (MMP9) are increased, whereas its interacting molecule (LCN2) mRNA levels were decreased in cholesteatoma perimatrix tissue compared to COM granulation tissue. Conclusions: The current study approach offers an overall look at molecular mechanisms that describe the cholesteatoma entity by focusing exclusively on the perimatrix processes in comparison to COM granulation tissue. The observed differences in gene expression between cholesteatoma perimatrix and COM granulation tissue could suggest novel markers potentially influenced by the perimatrix–matrix molecular interplay, which is not present in COM without cholesteatoma. Level of Evidence: NA. Laryngoscope, 130:E220–E227, 2020. © 2019 The American Laryngological, Rhinological and Otological Society, Inc.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2023

A Deep Learning Pipeline for Optimizing Large-scale Phase Field Simulations

Phase field (PF) simulations are computationally expensive but remain a key analysis tool to understand the complex mechanisms of additive manufacturing (AM) processes. Each PF simulation-aided analysis requires thousands of node hours on leadership-class supercomputers. One of the main goals of these analyses is the study of microstructure evolution during the build process which begins with the onset of nucleation. Nucleation occurs under certain thermomechanical conditions which are not known a priori and many PF simulations are required to identify ranges of input thermo-mechanical parameters that can result in the onset of nucleation. Since many of the simulations do not result in nucleation, an analysis campaign often ends up wasting tremendous amounts of precious computing resources executing nucleation-absent simulations. The goal of this work is to design and train deep learning models to inform a PF simulation about the likelihood of the occurrence of nucleation in a future simulation time-step based on the state summary over a finite number of past time-steps of a running simulation. If the prediction determines that the running simulation is unlikely to reach nucleation in the allotted time, then its execution is stopped immediately ultimately resulting in vast reduction in wasted computations when accrued over all the PF simulations typically performed in a single or multiple analysis campaign(s). The paper presents the performance of a machine learning pipeline that uses a convolutional neural network (CNN) model to learn an embedding which is then used with a self-attention network to build a multi-task deep learning model to predict the likelihood of nucleation. The model also predicts the input parameters used in a simulation. Performance is compared with a baseline pipeline that uses an off-the-shelf LeNet-5 model to learn the initial embedding. Despite their smaller size, performance results indicate significant improvement in accuracy of the proposed models compared to the larger baseline models.

Kannan, Ramakrishnan {ramki}↗

DOE OSTI2025

Ghidra Galore

SAND2025-00232O Ghidra Galore is a software tool that provides a test harness for running an analysis script in multiple versions of Ghidra, an open-source binary disassembler, using Docker. It allows users to understand how different versions of Ghidra evolve over time by analyzing a corpus of input files. The software includes a Python script that wraps the Docker functionality, simplifying the process of running the analysis script across different Ghidra versions. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

Crussell, Jonathan [Sandia National Lab. (SNL-CA),↗

DOE OSTI2024

Disruptive Technology for Carbon Negative Commodity Chemicals

This project was designed to develop an economically attractive process for the carbon negative production of the commodity biochemical succinic acid. To compete against petroleum derived biochemicals, the process must deliver high raw material conversion efficiency and high volumetric productivity to reduce both recurring and capital-related costs, respectively. The proposed process introduces additional electrons sourced from hydrogen into the biosynthetic pathway so that the carbon from two CO2 molecules can be combined with each glucose to significantly increase succinic acid yields. The process also uses ultrafiltration to continuously remove the product from the bioreactor to avoid product inhibition and extend the productive life of the cell extract. Genetic deletions and specific enzyme removal during extract preparation more efficiently direct both atomic and electronic resources toward product formation. A preliminary technoeconomic analysis indicates that these process innovations made possible by cell-free production will enable large scale production with attractive ROI and profitability. This two-year project provided some 29 distinct insights, analytical advances, and process improvements that resulted in a demonstration of process feasibility. However, an estimated two to three years of additional development will be required to achieve convincing pilot scale demonstrations that will motivate large scale investments.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2024

A Practical Solver for Scalar Data Topological Simplification

This paper presents a practical approach for the optimization of topological simplification, a central pre-processing step for the analysis and visualization of scalar data. Given an input scalar field f and a set of “signal” persistence pairs to maintain, our approaches produces an output field g that is close to f and which optimizes (i) the cancellation of “non-signal” pairs, while (ii) preserving the “signal” pairs. In contrast to pre-existing simplification algorithms, our approach is not restricted to persistence pairs involving extrema and can thus address a larger class of topological features, in particular saddle pairs in three-dimensional scalar data. Our approach leverages recent generic persistence optimization frameworks and extends them with tailored accelerations specific to the problem of topological simplification. Extensive experiments report substantial accelerations over these frameworks, thereby making topological simplification optimization practical for real-life datasets. Our approach enables a direct visualization and analysis of the topologically simplified data, e.g., via isosurfaces of simplified topology (fewer components and handles). We apply our approach to the extraction of prominent filament structures in three-dimensional data. Specifically, we show that our pre-simplification of the data leads to practical improvements over standard topological techniques for removing filament loops. Here, we also show how our approach can be used to repair genus defects in surface processing. Finally, we provide a C++ implementation for reproducibility purposes.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2026

Haze processing of atmospheric particles during wintertime in the Indo-Gangetic Plains

The impacts of haze on visibility, air quality, and climate are not well quantified due to a lack of understanding of the evolution of the mixing state and phase state of atmospheric particles during haze processing. The variability of the mixing state of atmospheric particles contributes significantly to uncertainties associated with the estimated aerosol radiative forcing. We collected particle samples in January 2018 from the highly polluted Indo-Gangetic plain during hazy conditions to study haze-processed particles. Single particle analysis using multi-modal micro-spectroscopy techniques revealed an abundance (40–70% by number) of potassium-rich sulfate particles from biomass-burning influenced smoke. Tilted view imaging showed that most of the organic particles that had inorganic potassium and sulfate inclusions were liquid-like while those without inclusions were more semi-solid. High-resolution mass spectrometry analysis revealed a significant presence of organosulfates and nitroxy-organosulfates in the morning samples (24%) compared to the afternoon samples (9%), despite higher relative humidity in the afternoon. Overall, our results highlight the significant contribution of both organic and inorganic sulfate to the total particulate sulfur budget during haze processing in winter, when anthropogenic emissions such as household burning, agricultural burning, and vehicular emissions are major contributors to particle mass.

Mathai, Susan [Pacific Northwest National Laborato↗

DOE OSTI2021

Processes contributing to cloud dissipation and formation events on the North Slope of Alaska

Abstract. Clear-sky periods across the high latitudes have profound impacts on the surface energy budget and lower atmospheric stratification; however an understanding of the atmospheric processes leading to low-level cloud dissipation and formation events is limited. A method to identify clear periods at Utqiaġvik (formerly Barrow), Alaska, during a 5-year period (2014–2018) is developed. A suite of remote sensing and in situ measurements from the high-latitude observatory are analyzed; we focus on comparing and contrasting atmospheric properties during low-level (below 2 km) cloud dissipation and formation events to understand the processes controlling clear-sky periods. Vertical profiles of lidar backscatter suggest that aerosol presence across the lower atmosphere is relatively invariant during the periods bookending clear conditions, which suggests that a sparsity of aerosol is not frequently a cause for cloud dissipation on the North Slope of Alaska. Further, meteorological analysis indicates two active processes ongoing that appear to support the formation of low clouds after a clear-sky period: namely, horizontal advection, which was dominant in winter and early spring, and quiescent air mass modification, which was dominant in the summer. During summer, the dominant mode of cloud formation is a low cloud or fog layer developing near the surface. This low cloud formation is driven largely by air mass modification under relatively quiescent synoptic conditions. Near-surface aerosol particles concentrations changed by a factor of 2 around summer formation events. Thermodynamic adjustment and increased aerosol presence under quiescent atmospheric conditions are hypothesized as important mechanisms for fog formation.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

Weapon Systems Risk-Assessment Tool Review

To anticipate, and potentially mitigate, future problems in aging weapon systems, four unique riskassessment techniques were analyzed, including root-cause analysis tools, six-sigma problemsolving approaches, and lean six-sigma tools. Identifying the most efficient process, or tool, is crucial for successful application to current and future weapon systems, subsystems, and components. The following processes were reviewed: Fault Tree Analysis, Failure Modes and Effects Analysis, Bow-Tie Analysis, and Hazard and Operability Study. A systematic assessment was performed to determine the most desirable method, and included investigating qualitative versus quantitative characteristics, scope, process durations, advantages, and limitations. Presented results will outline the study findings and further illustrate a capacity to identify future issues and/or concerns, and ultimately, reduce risk.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗

DOE OSTI2023

Use of Longitudinal Serum Analysis and Machine Learning to Develop a Classifier for Cancer Early Detection

Early detection of solid tumors through a simple screening process, such as the proteomic analysis of biofluids, has the potential to significantly alter the management and outcomes of cancers. The application of advanced targeted proteomics measurements and data analysis strategies to uniformly collected serum or plasma samples would enable longitudinal studies of cancer risk, progression, and response to therapy that have the potential to significantly reduce cancer burden in general. In this article, we describe a generalizable workflow combining robust, multiplexed targeted proteomics measurements applied to longitudinal samples from the Department of Defense Serum Repository with a Random Forest machine learning method for developing and initially evaluating the performance of candidate biomarker panels for early detection of cancers. The effectiveness of this approach was demonstrated in a cohort of 175 head and neck squamous cell carcinoma patients. The outlined protocols include methods for sample preparation, instrument analysis, and data analysis and interpretation using this workflow.

Longitudinal analysis, machine learning, cancer, e↗

DOE OSTI2024

Residual Stress Measurements of Cr-coated Cladding

Accident-tolerant fuel concepts for light-water reactor applications have been developed and tested in diverse research programs around the world. Industry teams have developed coated cladding concepts using different coating application methods, such as physical vapor deposition and coldspraying. Recently, variations in thickness and application method were investigated, and their effects on the microstructure and mechanical properties were reported. The results suggest an increase in performance in an unirradiated state, however, it remains unclear what phenomenon drove this performance. One explanation to this performance is associated with the coating process imparting a residual stress. Residual stress analysis was performed on coatings produced with various process parameters to understand how a thin layer of Cr impacts the mechanical properties after coating application and after simulated operating conditions. The gathered information is intended to address safety concerns or support margin identification related to accident-tolerant fuels and to support subsequent modeling and simulation efforts.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2022

Life cycle assessment of utilizing freshly cut urban wood: A case study

Large amounts of urban wood resources are generated through tree removals in urban areas. Therefore, there is a growing interest to improve the environmental performance of the urban wood supply chain by quantifying the environmental impacts of creating high-value products from urban tree removals to enhance its utilization. By surveying existing urban wood utilization operations for primary data in two major cities: Baltimore, Maryland, and Milwaukee, Wisconsin, a new life cycle inventory (LCI) dataset per m 3 of dry urban hardwood lumber and live edge slabs was created. Incorporating the new LCI data into life cycle assessment method, the total global warming (GW) impact of converting urban trees to kiln-dried lumber was estimated to be 122 kg CO 2 eq and 336 kg CO 2 eq per m 3 of lumber produced for Baltimore and Milwaukee, respectively. In both cases, the total environmental impact of the product was dominated by the conversion of urban trees to kiln dried lumber at the sawmill processing stage. Using scenario analysis for the source of heat generation used in kiln (force) drying wood, substituting wood fuel from coproducts for natural gas in a boiler for Milwaukee resulted in a substantial reduction in fossil-derived GW impact. Urban and traditional hardwood lumber production follow similar GW impact trajectories over their life-cycle stages with the lumber processing stage having the highest environmental impacts for both. Finally, increasing the overall lumber yield and decreasing kiln (force) drying would substantially improve the environmental performance of urban hardwood lumber and help make it comparable to traditional lumber.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

Facilitated transport membranes for H 2 purification from coal-derived syngas: A techno-economic analysis

A single-stage membrane process has been designed for using facilitated transport membranes (FTMs) to decarbonize the coal-derived syngas from an integrated gasification combined cycle (IGCC) power plant. The necessary process model and costing method have also been developed to assess the technical feasibility and process economics. In order to account for the carrier saturation phenomenon associated with FTMs, a homogeneous reactive diffusion model is integrated into the process model. The techno-economic study reveals that the mitigated carrier saturation upon bulk CO 2 removal can lead to appreciable increases in the CO 2 permeance and CO 2 /H 2 selectivity, which can be utilized to achieve 95% CO 2 purity and 95% H 2 recovery with a CO 2 /H 2 selectivity of 50 at the complete carrier saturation. FTMs with different facilitated transport characteristics can also be arranged in a hybrid membrane configuration to render a H 2 recovery of 99% and a cost of electricity of $\$118.5$ /MWh, which is 12.5% lower than that of the benchmark Selexol process.

42 ENGINEERING↗

DOE OSTI2022

Mercury transformation processes in nature: Critical knowledge gaps and perspectives for moving forward

We report the transformation of mercury (Hg) in the environment plays a vital role in the cycling of Hg and its risk to the ecosystem and human health. Of particular importance are Hg oxidation/reduction and methylation/demethylation processes driven or mediated by the dynamics of light, microorganisms, and organic carbon, among others. Advances in understanding those Hg transformation processes determine our capacity of projecting and mitigating Hg risk. Here, we provide a critical analysis of major knowledge gaps in our understanding of Hg transformation in nature, with perspectives on approaches moving forward. Our analysis focuses on Hg transformation processes in the environment, as well as emerging methodology in exploring these processes. Future avenues for improving the understanding of Hg transformation processes to protect ecosystem and human health are also explored.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

BoxScore - A real-time beam-diagnosis program for the CAEN digitizer x730 series

BoxScore is a real-time beam diagnosis and monitoring program for the CAEN x730 series digitizer, developed for the ATLAS in-flight system at Argonne National Laboratory. The CAEN x730 series digitizer, with built-in Digital Pulse Processing for the Pulse-Height-Analysis, digitizes the input signal in real-time and processes it using a trapezoidal filter. BoxScore reads the digitizer’s buffer directly, builds and saves events to local files, plots histograms for particle identification, and outputs the rates of selected isotopes every second. Implementation of BoxScore has shortened the time needed for in-flight beam-tuning and has potential applications for other nuclear physics experiments.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗