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DOE OSTI2021

Stratigraphic Identification with Airborne Electromagnetic Methods at the Hanford Site, Washington

Stratigraphic units can influence the fate and transport of subsurface contaminants within groundwater. Units having coarse-grained sediments act as preferential flow pathways, and therefore can accelerate the transport of contaminants to reach human and ecological receptors. At legacy waste sites, detailed knowledge of subsurface stratigraphy can be used for effective monitoring and remediation planning to help minimize risk to human health and the environment. Airborne electromagnetic (AEM) methods can non-invasively provide information on kilometer-scale or larger subsurface stratigraphic features and fill informational gaps in directly sampled data from sparsely located boreholes. In this paper, we present inversion results of a 412 line-km frequency-domain AEM survey to delineate subsurface stratigraphic features at the Hanford Site, located in southeastern Washington State. The inversion was performed using a massively parallel 3D electromagnetic modeling and inversion code, where the modeling is based on solving frequency-domain Maxwell’s equations using an unstructured-mesh finite-element method and the inversion employs a Gauss-Newton optimization scheme. The results are compared to an underlying geologic framework model (GFM), built by interpolating contact depths of stratigraphic units interpreted from site borehole datasets. In areas with good borehole coverage, the inversion results show a good match with the GFM to a depth of about 60 m. Outside of these areas, the inversion results exhibit inconsistencies from the assumptions made to create the GFM, demonstrating that the AEM survey results can be used to improve the understanding of the geological conceptual model.

47 OTHER INSTRUMENTATION↗

DOE OSTI2025

Automated Construction of Artificial Lattice Structures with Designer Electronic States

Manipulating matter with a scanning tunneling microscope (STM) enables the creation of atomically defined artificial structures that host designer quantum states. However, the time-consuming nature of the manipulation process, coupled with the sensitivity of the STM tip, constrains the exploration of diverse configurations and limits the size of the designed features. In this study, we present a reinforcement learning (RL)-based framework for creating artificial structures by spatially manipulating carbon monoxide (CO) molecules on a copper substrate by using the STM tip. The automated workflow combines molecule detection and manipulation, employing deep-learning-based object detection to locate CO molecules and linear assignment algorithms to allocate these molecules to designated target sites. We initially perform molecule maneuvering based on randomized parameter sampling for sample bias, tunneling current set point, and manipulation speed. This data set is then structured into an action trajectory used to train an RL agent. The model is subsequently deployed on the STM for real-time fine-tuning of the manipulation parameters during structure construction. Our approach incorporates path-planning protocols coupled with active drift compensation to enable atomically precise fabrication of structures with significantly reduced human input while realizing larger-scale artificial lattices with the desired electronic properties. Furthermore, using our approach, we demonstrate the automated construction of an extended artificial graphene lattice and confirm the existence of a characteristic Dirac point in its electronic structure. Further challenges regarding the RL-based structural assembly scalability are discussed.

Algorithms↗

DOE OSTI2016

An ensemble model of QSAR tools for regulatory risk assessment

Quantitative structure activity relationships (QSARs) are theoretical models that relate a quantitative measure of chemical structure to a physical property or a biological effect. QSAR predictions can be used for chemical risk assessment for protection of human and environmental health, which makes them interesting to regulators, especially in the absence of experimental data. For compatibility with regulatory use, QSAR models should be transparent, reproducible and optimized to minimize the number of false negatives. In silico QSAR tools are gaining wide acceptance as a faster alternative to otherwise time-consuming clinical and animal testing methods. However, different QSAR tools often make conflicting predictions for a given chemical and may also vary in their predictive performance across different chemical datasets. In a regulatory context, conflicting predictions raise interpretation, validation and adequacy concerns. To address these concerns, ensemble learning techniques in the machine learning paradigm can be used to integrate predictions from multiple tools. By leveraging various underlying QSAR algorithms and training datasets, the resulting consensus prediction should yield better overall predictive ability. We present a novel ensemble QSAR model using Bayesian classification. The model allows for varying a cut-off parameter that allows for a selection in the desirable trade-off between model sensitivity and specificity. The predictive performance of the ensemble model is compared with four in silico tools (Toxtree, Lazar, OECD Toolbox, and Danish QSAR) to predict carcinogenicity for a dataset of air toxins (332 chemicals) and a subset of the gold carcinogenic potency database (480 chemicals). Leave-one-out cross validation results show that the ensemble model achieves the best trade-off between sensitivity and specificity (accuracy: 83.8 % and 80.4 %, and balanced accuracy: 80.6 % and 80.8 %) and highest inter-rater agreement [kappa (κ): 0.63 and 0.62] for both the datasets. The ROC curves demonstrate the utility of the cut-off feature in the predictive ability of the ensemble model. In conclusion, this feature provides an additional control to the regulators in grading a chemical based on the severity of the toxic endpoint under study.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2024

Active Learning for Rapid Targeted Synthesis of Compositionally Complex Alloys

The next generation of advanced materials is tending toward increasingly complex compositions. Synthesizing precise composition is time-consuming and becomes exponentially demanding with increasing compositional complexity. An experienced human operator does significantly better than a novice but still struggles to consistently achieve precision when synthesis parameters are coupled. The time to optimize synthesis becomes a barrier to exploring scientifically and technologically exciting compositionally complex materials. This investigation demonstrates an active learning (AL) approach for optimizing physical vapor deposition synthesis of thin-film alloys with up to five principal elements. We compared AL-based on Gaussian process (GP) and random forest (RF) models. The best performing models were able to discover synthesis parameters for a target quinary alloy in 14 iterations. We also demonstrate the capability of these models to be used in transfer learning tasks. RF and GP models trained on lower dimensional systems (i.e., ternary, quarternary) show an immediate improvement in prediction accuracy compared to models trained only on quinary samples. Furthermore, samples that only share a few elements in common with the target composition can be used for model pre-training. We believe that such AL approaches can be widely adapted to significantly accelerate the exploration of compositionally complex materials.

Chemistry↗

DOE OSTI2018

Analyzing urban ecosystem variation in the City of Dongguan: A stepwise cluster modeling approach

Highlights: • A stepwise cluster modeling approach is proposed for urban ecosystem simulation. • Nonlinear relationship is established among NDVI and multiple drivers. • Vegetation conditions get better gradually from the north to the south of Dongguan. • Ecosystem in the east of Dongguan is sensitive to growth of economy and population. In this study, a stepwise cluster modeling approach (SCMA) is developed for analyzing urban ecosystem variation via Normalized Difference Vegetation Index (NDVI). NDVI is an indicator of vegetation growth and coverage and useful in reflecting urban ecosystem. SCMA is established on a cluster tree that can characterize the complex relationship between independent and dependent variables. SCMA is applied to the City of Dongguan for simulating the urban NDVI and identifying associated drivers of human activity, topography and meteorology without specific functions. Results show that SCMA performances better than conventional statistical methods, illustrating the ability of SCMA in capturing the complex and nonlinear features of urban ecosystem. Results disclose that human activities play negative effects on NDVI due to the destruction of green space for pursuing more space for buildings. NDVI reduces gradually from the south part to the north part of Dongguan due to increased gross domestic product and population density, indicating that the ecosystem in Dongguan is better in the south part. NDVI in the northeast part (dominated by agriculture) is sensitive to the growth of economy and population. More attention should be paid to this part for sustainable development, such as increasing afforestation, planting grass and constructing parks. Precipitation has a positive effect on NDVI due to the promotion of soil moisture that is beneficial to plants’ growth. Awareness of these complexities is helpful for sustainable development of urban ecosystem.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2017

Kip, Version 1.0

This high-performance ray tracing library provides very fast rendering, compact code, type flexibility via C++ 'generic programming' techniques, and ease of use through an application programming interface (API) that functions independently of any GUI, on-screen display, or other enclosing application. Kip supports constructive solid geometry models based on any of several built-in shapes and operators and allows for new, user-defined shapes and operators. Additional features include basic texturing, input/output of models using a simple human-readable file format with full error checking and detailed diagnostics, and support for shared data parallelism. Kip is written in pure, ANSI Standard C++, is entirely platform independent, and is very easy to use, as it requires no build system, configuration or installation scripts, wizards, special preprocessing, makefiles, or external libraries.

Staley, Martin↗

DOE OSTI2023

An extended focused assessment with sonography in trauma ultrasound tissue-mimicking phantom for developing automated diagnostic technologies

Medical imaging-based triage is critical for ensuring medical treatment is timely and prioritized. However, without proper image collection and interpretation, triage decisions can be hard to make. While automation approaches can enhance these triage applications, tissue phantoms must be developed to train and mature these novel technologies. Here, we have developed a tissue phantom modeling the ultrasound views imaged during the enhanced focused assessment with sonography in trauma exam (eFAST). The tissue phantom utilized synthetic clear ballistic gel with carveouts in the abdomen and rib cage corresponding to the various eFAST scan points. Various approaches were taken to simulate proper physiology without injuries present or to mimic pneumothorax, hemothorax, or abdominal hemorrhage at multiple locations in the torso. Multiple ultrasound imaging systems were used to acquire ultrasound scans with or without injury present and were used to train deep learning image classification predictive models. Performance of the artificial intelligent (AI) models trained in this study achieved over 97% accuracy for each eFAST scan site. We used a previously trained AI model for pneumothorax which achieved 74% accuracy in blind predictions for images collected with the novel eFAST tissue phantom. Grad-CAM heat map overlays for the predictions identified that the AI models were tracking the area of interest for each scan point in the tissue phantom. Overall, the eFAST tissue phantom ultrasound scans resembled human images and were successful in training AI models. Tissue phantoms are critical first steps in troubleshooting and developing medical imaging automation technologies for this application that can accelerate the widespread use of ultrasound imaging for emergency triage.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2023

Smart Data Mapping for Connecting Power System Model and Geospatial Data

Knowing the geospatial locations of power system model elements is the foundation for analyzing system vulnerability to natural hazards and connecting loads with end users and their communities. However, power system models and geospatial data for power grid assets may have been developed asynchronously without close coordination. Creating a direct mapping between the two may be a challenging task, considering heterogeneous data structures, target uses, historical legacies, and human errors. This work aims to build an automatic data mapping workflow to connect power system model elements and geospatial data for transmission network, and to support energy grid resilience studies for Puerto Rico. The primary steps in this workflow include constructing graphs using geospatial data, and aligning them to the transmission networks defined in the power system data. The results have been evaluated against existing manual mapping practices for part of the Puerto Rico Power Grid model to illustrate the performance of such auto-mapping solutions.

Resilience, geospatial data, grid transmission net↗

DOE OSTI2025

Report of the 2025 Workshop on Next-Generation Ecosystems for Scientific Computing: Harnessing Community, Software, and AI for Cross-Disciplinary Team Science

This report summarizes insights from the 2025 Workshop on Next-Generation Ecosystems for Scientific Computing: Harnessing Community, Software, and AI for Cross-Disciplinary Team Science, which convened more than 40 experts from national laboratories, academia, industry, and community organizations to chart a path toward more powerful, sustainable, and collaborative scientific software ecosystems. To address urgent challenges at the intersection of high-performance computing (HPC), AI, and scientific software, participants envisioned agile, robust ecosystems built through socio-technical co-design—the intentional integration of social and technical components as interdependent parts of a unified strategy. This approach combines advances in AI, HPC, and software with new models for cross-disciplinary collaboration, training, and workforce development. Key recommendations include building modular, trustworthy AI-enabled scientific software systems; enabling scientific teams to integrate AI systems into their workflows while preserving human creativity, trust, and scientific rigor; and creating innovative training pipelines that keep pace with rapid technological change. Pilot projects were identified as near-term catalysts, with initial priorities focused on hybrid AI/HPC infrastructure, cross-disciplinary collaboration and pedagogy, responsible AI guidelines, and prototyping of public-private partnerships. This report presents a vision of next-generation ecosystems for scientific computing where AI, software, hardware, and human expertise are interwoven to drive discovery, expand access, strengthen the workforce, and accelerate scientific progress.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2022

Need for advanced research reactors for the next-generation reactor physics, analysis tools, and technology

Full text of publication follows. There is an urgent need for design and deployment of advanced research and test reactors in support of design, licensing and operation of advanced power reactors and education of next generation nuclear workforce. Existing research reactors mostly were designed and constructed decades ago with the main objectives of training operators, performing reactor physics experiments, and educating nuclear engineers and scientists. There are already gaps and significant concern about future capabilities for the existing research reactor facilities to address modern instrumentation and/or flexible environments for: performing reactor physics studies for advanced designs which have significantly different core materials forms and compositions, reactor shapes and size; validation of advanced high-fidelity software; development of machine learning algorithms for enhancement of human-machine collaboration in support of reactor monitoring, operation and safeguards; and, effective education of the next-generation workforce. The authors will focus on the need for advanced research reactors to improve and validate fast and accurate simulation tools for high-fidelity modeling and analysis of nuclear reactors in support of their design, optimization, licensing, operation, and monitoring. In the past, simulation tools were limited to relatively coarse models using approximate methodologies that benefited from two main factors: i) allowance for large margins and tolerances; ii) ability to construct prototype (e.g., zero power) reactors for adjustment of approximate methodologies. The next generation reactors have to be designed mainly by using novel high-fidelity computational tools that are accurate and fast, and therefore can be used for parametric studies and uncertainty quantification. To sufficiently demonstrate the accuracy of these tools, advanced research reactors are needed. The authors argue the need for new computational paradigms such as the MRT (Multistage, Response- function Transport) methodology which has resulted in the development of the novel high-fidelity RAPID (Real-time Analysis for Particle-transport and In-situ Detection) code system. Such code systems have to be robust in modeling any complex system, and should be fast and accurate, henceforth their uncertainties can be quantified at reasonable costs. Again, advanced research reactors are needed for the validation of the fidelity and accuracy of new computational tools. (authors)

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

DOE OSTI2022

Vertex finding in neutrino-nucleus interaction: a model architecture comparison

We compare different neural network architectures for machine learning algorithms designed to identify the neutrino interaction vertex position in the MINERvA detector. The architectures developed and optimized by hand are compared with the architectures developed in an automated way using the package “Multi-node Evolutionary Neural Networks for Deep Learning” (MENNDL), developed at Oak Ridge National Laboratory. While the domain-expert hand-tuned network was the best performer, the differences were negligible and the auto-generated networks performed as well. There is always a trade-off between human, and computer resources for network optimization and this work suggests that automated optimization, assuming resources are available, provides a compelling way to save significant expert time.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2023

A machine learning approach for identifying variables associated with risk of developing neutralizing antidrug antibodies to factor VIII

A key unmet need in the management of hemophilia A (HA) is the lack of clinically validated markers that are associated with the development of neutralizing antibodies to Factor VIII (FVIII) (commonly referred to as inhibitors). This study aimed to identify relevant biomarkers for FVIII inhibition using Machine Learning (ML) and Explainable AI (XAI) using the My Life Our Future (MLOF) research repository. The dataset includes biologically relevant variables such as age, race, sex, ethnicity, and the variants in the F8 gene. In addition, we previously carried out Human Leukocyte Antigen Class II (HLA-II) typing on samples obtained from the MLOF repository. Using this information, we derived other patient-specific biologically and genetically important variables. These included identifying the number of foreign FVIII derived peptides, based on the alignment of the endogenous FVIII and infused drug sequences, and the foreign-peptide HLA-II molecule binding affinity calculated using NetMHCIIpan. The data were processed and trained with multiple ML classification models to identify the top performing models. The top performing model was then chosen to apply XAI via SHAP, (SHapley Additive exPlanations) to identify the variables critical for the prediction of FVIII inhibitor development in a hemophilia A patient. Using XAI we provide a robust and ranked identification of variables that could be predictive for developing inhibitors to FVIII drugs in hemophilia A patients. These variables could be validated as biomarkers and used in making clinical decisions and during drug development. The top five variables for predicting inhibitor development based on SHAP values are: (i) the baseline activity of the FVIII protein, (ii) mean affinity of all foreign peptides for HLA DRB 3, 4, & 5 alleles, (iii) mean affinity of all foreign peptides for HLA DRB1 alleles), (iv) the minimum affinity among all foreign peptides for HLA DRB1 alleles, and (v) F8 mutation type.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2017

Computed Tomography Radiomics Predicts HPV Status and Local Tumor Control After Definitive Radiochemotherapy in Head and Neck Squamous Cell Carcinoma

Purpose: This study aimed to predict local tumor control (LC) after radiochemotherapy of head and neck squamous cell carcinoma (HNSCC) and human papillomavirus (HPV) status using computed tomography (CT) radiomics. Methods and Materials: HNSCC patients treated with definitive radiochemotherapy were included in the retrospective study approved by the local ethical commission (93 and 56 patients in the training and validation cohorts, respectively). Three hundred seventeen CT radiomic features, including those based on shape, intensity, texture, and wavelet transform, were calculated in the primary tumor region. Cox and logistic regression models were built to predict LC and HPV status, respectively. The best-performing features in the univariable analysis were included in the multivariable analysis after the exclusion of redundant features. The quality of the models was assessed using the concordance index (CI) for modeling of LC and receiver operating characteristics area under the curve (AUC) for HPV status prediction. The radiomics LC model was compared to a model incorporating clinical parameters (tumor stage, volume, and HPV status) and a mixed model. Results: A radiomic signature comprising 3 features was significantly associated with LC (CI{sub training} = 0.75 and CI{sub validation} = 0.78), showing that tumors with a more heterogeneous CT density distribution are at risk for decreased LC. The addition of clinical parameters to the radiomics model slightly improved the model in the training cohort but not in the validation cohort. Another radiomic signature showed good performance in HPV status prediction (AUC{sub training} = 0.85 and AUC{sub validation} = 0.78) and indicated that HPV-positive tumors have a more homogenous CT density distribution. Conclusions: Heterogeneity of HNSCC tumor density, quantified by CT radiomics, is associated with LC after radiochemotherapy and HPV status.

62 RADIOLOGY AND NUCLEAR MEDICINE↗

DOE OSTI2018

Safety Impacts of Automated Vehicles in Mixed Traffic

Previous research have shown that Automated Vehicles (AV) have the potential to improve transportation thanks to the possibility to perform some control functions without driver input (1). A question that remains is what are the safety impacts of the interaction between human-driven vehicles with different levels of automation at a particular intersection?The main objective of this paper is to build the behavioral framework to simulate different levels of automation and to study the safety impact of these levels under different penetration scenarios. Real-life connected vehicle data were incorporated to revise the Wiedemann car-following model and simulate the behavior of human-driven vehicles, level 3 and level 5 AVs.

Ramin, Arvin↗

DOE OSTI2021

Drosophila models to study causative genes for human rare intractable neurological diseases

Highlights: • Drosophila models targeting some ALS-causing genes are described. • Drosophila models targeting some CMT-causing genes are described. • Drosophila models targeting some Sotos syndrome-causing genes are described. • Genes and pathways that commonly interact with ALS- and/or CMT-causing genes are described. Drosophila is emerging as a convenient model for investigating human diseases. Functional homologues of almost 75% of human disease-related genes are found in Drosophila. Amyotrophic lateral sclerosis (ALS) is a severe neurodegenerative disease that causes defects in motoneurons. Charcot-Marie-Tooth disease (CMT) is one of the most commonly found inherited neuropathies affecting both motor and sensory neurons. No effective therapy has been established for either of these diseases. In this review, after overviewing ALS, Drosophila models targeting several ALS-causing genes, including TDP-43, FUS and Ubiquilin2, are described with their genetic interactants. Then, after overviewing CMT, examples of Drosophila models targeting several CMT-causing genes, including mitochondria-related genes and FIG 4, are also described with their genetic interactants. In addition, we introduce Sotos syndrome caused by mutations in the epigenetic regulator gene NSD1. Lastly, several genes and pathways that commonly interact with ALS- and/or CMT-causing genes are described. In the case of ALS and CMT that have many causative genes, it may be not practical to perform gene therapy for each of the many disease-causing genes. The possible uses of the common genes and pathways as novel diagnosis markers and effective therapeutic targets are discussed.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2021

Automatic identification and quantification of dense microcracks in high-performance fiber-reinforced cementitious composites through deep learning-based computer vision

Highlights: • A method is presented to detect, locate, quantify, and visualize dense microcracks in HPFRCC. • Quantification of dense microcracks is realized using deep learning method for the first time. • The presented method uses deep learning models that are trained using a realistic dataset size. • The presented method has a high computation efficiency for identifying and quantifying cracks. • The presented method provides crack width with errors up to 50 μm and a R{sup 2} value of 0.984. High-performance fiber-reinforced cementitious composites (HPFRCCs) feature high mechanical strengths, crack resistance, and durability. Under excessive loading, HPFRCCs demonstrate dense microcracks that are difficult to identify using existing methods. This study presents a computer vision method for identification, quantification, and visualization of microcracks in HPFRCCs based on deep learning. The presented method integrates multiple deep learning models and computer vision techniques in a hierarchical architecture. The crack pattern (e.g., number, width, and spacing of cracks) are automatically determined from pictures without human intervention. This study shows that the presented method achieves an accuracy of 0.992 for crack detection and an accuracy finer than 50 μm (R{sup 2} > 0.984) for quantification of crack width when deep learning models are trained using only 200 pictures of HPFRCCs and 200 pictures of conventional concrete with incorporation of data augmentation. The presented method is expected to be also applicable to other materials featuring complex cracks.

36 MATERIALS SCIENCE↗

DOE OSTI2019

Small Molecule IL-36γ Antagonist as a Novel Therapeutic Approach for Plaque Psoriasis

IL-36 cytokines are pro-inflammatory members of the IL-1 family that are upregulated in inflammatory disorders. Specifically, IL-36γ is highly expressed in active psoriatic lesions and can drive pro-inflammatory processes in 3D human skin equivalents supporting a role for this target in skin inflammation. Small molecule antagonists of interleukins have been historically challenging to generate. Nevertheless, we performed a small molecule high-throughput screen to identify IL-36 antagonists using a novel TR-FRET binding assay. Several compounds, including 2-oxypyrimidine containing structural analogs of the marketed endothelin receptor A antagonist Ambrisentan, were identified as hits from the screen. A-552 was identified as a the most potent antagonist of human IL-36γ, but not the closely related family member IL-36α, was capable of attenuating IL-36γ induced responses in mouse and human disease models. Additionally, x-ray crystallography studies identified key amino acid residues in the binding pocket present in human IL-36γ that are absent in human IL-36α. A-552 represents a first-in-class small molecule antagonist of IL-36 signaling that could be used as a chemical tool to further investigate the role of this pathway in inflammatory skin diseases such as psoriasis.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2020

Foreword Special issue on the Kilopower Project, Kilowatt Reactor Using Stirling TechnologY (KRUSTY) Test

This special issue of Nuclear Technology contains full-length, peer-reviewed papers describing the design work leading up to and the results of the Kilowatt Reactor Using Stirling TechnologY (KRUSTY) test. KRUSTY was the centerpiece of the National Aeronautics and Space Administration (NASA) Kilopower Project to design, build, and test a space nuclear reactor. This test was the first such test since the end of the Space Nuclear Auxiliary Power (SNAP) project at the end of the 1960s. In this issue, the introduction paper presents the goals of the Kilopower Project and the potential missions this reactor concept could serve in NASA. Kilopower was intended to serve both human exploration needs on planetary surfaces as well as science needs for deep-space exploration. The design work for the experiment by Poston and the power conversion development by Gibson present the pre-work required to perform the eventual KRUSTY test. A paper on regulatory analysis follows, to show the path used to gain approval of the proposed experiment. Then, the early zero-power critical experiments are presented by Sanchez and Grove. These experiments were essential data used to enhance model predictions prior to the high-temperature test. Next, Poston presents the three experiments (warm criticals) that increase the temperature in an incremental fashion prior to the final experiment. These experiments were used to achieve final regulatory approval of the final high-temperature experiment. The last paper by Poston presents the results of the steady-state and transient testing of the reactor at full power and at the design temperature. These results show that the reactor design and as-built experiment met all of the requirements that NASA had developed for the system. Any experiment of this magnitude was accomplished only because of the hard work and dedication of a large number of people at multiple institutions, including the NASA Glenn Research Center, NASA Marshall Space Flight Center, Y-12 National Security Site, Los Alamos National Laboratory, and the Nevada National Security Site prime contractor (formerly National Security Technologies and now the Mission Support and Test Services). The project was jointly funded by the Space Technology Mission Directorate at NASA and the Criticality Safety Program at the National Nuclear Security Administration (NNSA). A special thanks to Jerry McKamy and Angela Chambers at the NNSA and Lee Mason and Don Palac at NASA for their support and guidance. For the authors and multitude of staff that performed this work, the passion for space nuclear reactors was the key to making this experiment a reality.

99 GENERAL AND MISCELLANEOUS↗