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All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Oppenheimer Study Center Solar Analysis

The thermal solar array on the roof of the Oppenheimer Study Center (03-0207) has been defunct and aging progressively worse for years. This study set out to investigate the current state of the abandoned solar array, its various connected systems, and the viability of replacing the array with modern PV panels. This will help offset the energy usage of the building and bring the building in line with upcoming LANL goals of electrification and net-zero emissions (Exec. Order 14057, Sec. 201-5). This study is intended to be a high level, over-the-shoulder, analysis of impact and feasibility, and shall not be considered as an approved design. Further detailed design and analysis will be necessary beyond the concept feasibility phase of this project.

14 SOLAR ENERGY↗
DOE OSTI2022

Decarbonizing the Building Sector: A Human-Centered Study Focused on Small/Light Commercial Building Energy Equity

Decarbonization of the building sector is no small feat; buildings account for 40% of primary energy consumption, and fossil-fuel combustion in buildings leads to roughly 30% of total greenhouse gas emissions. Energy efficiency, electrification and smart technologies are fundamental strategies to reduce consumption and shift away from fossil-fuel use in buildings. This energy transition carries significant societal risks unless the shift is carried out with equity and justice as a top priority. Low-income, vulnerable and communities of color have higher energy burdens compared to affluent populations. Furthermore, systemic racism and historic exclusionary policies have resulted in increased risks (environmental, climatic, economic, and social) to low-income and communities of color, and underserved communities often do not have financial resources for, or access to, advanced building technologies. The U.S. Department of Energy is funding research to characterize and develop solutions to the challenges of equity and justice that complicate the ability of communities to contribute to goals for decarbonization. Our project has a specific focus on small commercial buildings and the businesses that occupy them. Significantly less is known about the burdens and risks these businesses experience or the challenges they face in pursuing decarbonization, or how those are affected by income and race, in comparison to research on energy equity and justice for diverse households. The project team includes the Pacific Northwest National Laboratory, Arizona State University and Clark Atlanta University. Researchers are conducting semi-structured interviews with small business owners in underserved communities in Phoenix and Atlanta, followed by a survey distributed to the larger community to learn more about the equity and justice issues that communities with different racial, economic, and cultural backgrounds face. Results will help inform an actionable and replicable framework for engaging small commercial building owners/operators to catalyze the reduction of energy burdens and increase equity.

Antonopoulos, Chrissi A.↗
DOE OSTI2022

The safety of pranlukast and montelukast during the first trimester of pregnancy: A prospective, two‐centered cohort study in Japan

Abstract For leukotriene receptor antagonists (LTRAs), especially pranlukast, safety data during pregnancy is limited. Therefore, we conducted a prospective, two‐centered cohort study using data from teratogen information services in Japan to clarify the effects of LTRA exposure during pregnancy on maternal and fetal outcomes. Pregnant women who being counseled on drug use during pregnancy at two facilities were enrolled. The primary outcome of this study was major congenital anomalies. The incidence of major congenital anomalies in women exposed to montelukast or pranlukast during the first trimester of pregnancy was compared with that of controls. Logistic regression analysis was performed to analyze the effects of maternal LTRA use during the first trimester of pregnancy on major congenital anomalies. The outcomes of 231 pregnant women exposed to LTRAs (montelukast n = 122; pranlukast n = 106; both n = 3) and 212 live births were compared with those of controls. The rate of major congenital anomalies in the LTRA group was 1.9%. Multivariable logistic regression analysis revealed that LTRA exposure was not a risk factor for major congenital anomalies (adjusted odds ratio, 0.78; 95% confidence interval, 0.23–2.05; p = 0.653). In addition, no significant difference was detected in stillbirth, spontaneous abortion, preterm birth, and low birth weight between the two groups. The present study revealed that montelukast and pranlukast were not associated with the risk of major congenital anomalies. Our findings suggest that LTRAs could be safely employed for asthma therapy during pregnancy.

Hatakeyama, Shiro↗
DOE OSTI2025

Craig Energy Center Feasibility Study

Tri-State Generation and Transmission Association Inc. (“Tri-State”) provides wholesale electric power to its member cooperatives and public power districts that collectively deliver electricity to more than a million consumers in Colorado, Nebraska, New Mexico and Wyoming. The Craig Power Station has been an integral part of Tri-State’s dispatchable generation fleet that ensures an affordable, reliable and resilient energy supply for its members. The ownership of the Craig Station Unit 1&2 is a collaborative between Tri-State, Salt River Project Agricultural Improvement and Power District, Platte River Power Authority, PacifiCorp, and Public Service Company of Colorado and is known as the Yampa Project. Tri-State owns all of Craig Unit 3 and is the operator of all three units. All three of the Craig units have planned retirement dates that will not extend beyond 2028 and Tri-State Generation wanted to evaluate carbon-neutral emitting technologies that could provide approximately 300 megawatts (“MWs) of capacity and energy that will be lost after Unit 3 ceases operation, as well as provide a similar level of economic benefits to the Craig community. This report is to serve as a technology feasibility study constrained to the current land area available at the Craig site and was conducted based on the limitations of the region.

25 ENERGY STORAGE↗
DOE OSTI2024

The center for nonlinear studies: A personal history

The Center for Nonlinear Studies (CNLS) was an integral part of my scientific career starting as a Postdoctoral Fellow in 1983 up to my tenure as CNLS Director from 2004 to 2015. As such, I experienced a number of scientific phases of CNLS through almost four decades of foundation, evolution, and transition. Throughout this entire interval, the inspiration and influence of David Campbell guided my way. A proper history of CNLS encompassing all of the many contributors to the CNLS story is beyond my means or purpose here. Instead, I present the history as I experienced it. I emphasize the main scientific accomplishments achieved at CNLS over more than 40 years, but I will also attempt to describe and quantify the attributes that made and continue to make the Center for Nonlinear Studies a special institution of remarkable impact and longevity. Throughout its existence, CNLS owes much to the enduring legacy of David Campbell who laid down the foundations and operating principles that have made it so successful.

Ecke, Robert E. (ORCID:0000000177725876)↗
DOE OSTI2022

Effect of localization on photoluminescence and zero-field splitting of silicon color centers

The study of defect centers in silicon has been recently reinvigorated by their potential applications in optical quantum information processing. A number of silicon defect centers emit single photons in the telecommunication O-band, making them promising building blocks for quantum networks between computing nodes. The two-carbon G-center, self-interstitial W-center, and spin-1/2 T-center are the most intensively studied silicon defect centers, yet despite this, there is no consensus on the precise configurations of defect atoms in these centers, and their electronic structures remain ambiguous. Here, in this work, we employ ab initio density functional theory to characterize these defect centers, providing insight into the relaxed structures, band structures, and photoluminescence spectra, which are compared to experimental results. Motivation is provided for how these properties are intimately related to the localization of electronic states in the defect centers. In particular, we present the calculation of the zero-field splitting for the excited triplet state of the G-center defect as the structure is linearly interpolated from the A-configuration to the B-configuration, showing a sudden increase in the magnitude of the D zz component of the zero-field-splitting tensor. By performing projections onto the local orbital states of the defect, we analyze this transition in terms of the symmetry and bonding character of the G-center defect, which sheds light on its potential application as a spin-photon interface.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2022

Center for Nonlinear Studies (CNLS) Leader Position [Slides]

The Center Director provides scientific leadership and line management of the CNLS while fostering collaborations with scientists throughout the Laboratory. The CNLS Center Director is expected to develop and lead a program to target and create cooperative long-term research programs consistent with the Laboratory’s strategic research objectives, to develop a strong working relationship with the CNLS External Advisory Committee, and to maintain effective working relationships throughout all levels of the Laboratory, government entities, academia and industry. The successful candidate will be expected to maintain an active research program while providing technical vision to nurture and support existing programs of others at the Center.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2023

Natural Language Processing for Text Based Event Extraction: Identifying Events of Interest Related to Worldwide State-Sponsored Civil Nuclear Power

Beginning in FY20, SRNL was funded by the National Nuclear Security Administration’s Office of Defense Nuclear Non-Proliferation Research and Development to develop a prototype natural language processing/natural language understating machine learning-based modeling and analysis pipeline to extract and forecast events of interest from massive open data sources. The working hypothesis within the approach is that contextual shifts in key words and phrases act as indicators of events of interest over time. Therefore, by identifying points in time where contextual shifts occur, events of interest can be extracted along with explicit and implicit connections of entities and activities. The development of the preliminary prototype pipeline proved successful, meriting further testing of the pipeline on more broad topical domains and in a worldwide data environment. Therefore, SRNL, in collaboration with the Sanghani Center for Artificial Intelligence and Data Analytics at Virginia Tech, have continued development with a test case of identifying events of interest related to worldwide state-sponsored civil nuclear power in open data sources. In the first year of this follow-on effort, the team has curated domain-specific data corpuses using an automated scheme and applied the modeling and analysis pipeline. This robust, focused, and efficient approach consists of an ensemble of analyses applied to time dependent word embedding models that are trained on the data corpuses. In this report, the team has demonstrated the capability of the existing pipeline (as development has continued in parallel) by exploring several specific case-studies centered around Rosatom’s international activities regarding the planning, construction, operation, and/or shutdown of nuclear reactors. A basic timeline events has been generated by manually cataloging known “milestone” events that have occurred at reactors in Turkey, Finland, Hungary, and Egypt and compared with the output of the modeling pipeline. In this approach, the team has characterized the lead time using the prototype pipeline, as well as the ability to capture relevant information, which proved 100% successful. A deep dive example of the Akkuyu reactor (Turkey) is presented that shows the breadth of information that can be captured using the approach. In this case study, events were extracted pertaining to the planning/construction of Akkuyu including protests from the population, information campaigns in response to the protests, forged regulatory documents and lawsuits, budgetary/shareholder information, geopolitical tensions, and the various construction milestones. This has demonstrated the pipeline’s utility as a research aid or real-time event extraction tool, where summary-level information and detailed text extractions from millions of articles or Tweets across long time periods can be generated with significantly less effort than current techniques.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2023

Opportunities for wave energy in bulk power system operations

Wave energy resources have high, yet largely untapped potential as candidate generation technology. In this paper, we perform a data-driven analysis to characterize the impact of wave energy integration on bulk-scale power systems and market operations. Through data-driven sensitivity studies centered on an optimization-based production cost modeling formulation, our work characterizes the inflection point beyond which wave integration starts impacting power system operations, considering present day transmission infrastructure. Furthermore, our analysis also considers the joint effects of wave energy integration and system-wide transmission expansion. Finally, potential resilience scenarios such as wildfire-driven transmission contingencies and heat wave events are investigated, whereby the contributions of grid-integrated wave energy in alleviating the effects of the resilience events are analyzed. As our demonstration test bed, we consider a reduced-order network topology for the U.S. Western Interconnection with wave energy generation integrated at carefully selected sites across the coastal areas of Washington, Oregon, and northern California. Our results indicate that over a representative year of operations, wave energy integration systematically reduces locational marginal prices (LMPs) of energy and price volatility, especially during periods of high wave resource availability (winter months for the U.S. west coast). Average, maximum, and minimum of hourly LMPs over a typical year of operation was reduced by 2.95, 51.28, and 1.13 $\$$/MWh respectively (over a baseline scenario with no wave energy integration), when the selected network model had a total of 5000 MW wave power installed capacity during the representative year of study. The effects of wave energy integration can remain localized with existing transmission infrastructure (identified to be most pronounced in the Pacific Northwest region in the example we studied). However, with concurrent transmission expansion, the impacts of wave energy integration are likely to have a higher geographical spread. Our results also indicate that wave energy may be able to assist power system operations during resilience events such as major transmission contingencies and heat wave events, although such benefits might be dependent on factors such as proximity of affected area to wave resources, availability of adequate resource potential and adequate transmission capacity.

16 TIDAL AND WAVE POWER↗
DOE OSTI2021

Molecular Engineering of 2D Nanomaterial Field-Effect Transistor Sensors: Fundamentals and Translation across Innovation Spectrum

We report over the last decade, 2D layered nanomaterials have attracted significant attention across the scientific community due to their rich and exotic properties. Various nanoelectronic devices based on these 2D nanomaterials have been explored and demonstrated, including those for environmental applications. Here, the fundamental attributes of 2D layered nanomaterials for field-effect transistor (FET) sensors and tunneling FET (TFET) sensors, which provide versatile detection of water contaminants such as heavy-metal ions, bacteria, nutrients, and organic pollutants, are discussed. The major challenges and opportunities are also outlined for designing and fabricating 2D nanomaterial FET/TFET sensors with superior performance. Translation of these FET/TFET sensors from fundamental research to applied technology is illustrated through a case study on graphene-based real-time FET water sensors. A second case study centers on large-scale sensor networks for water-quality monitoring to enable intelligent drinking water and river-water systems. Overall, 2D nanomaterial FET sensors have significant potential for enabling a human-centered intelligent water system that can likely be applied to other precarious water supplies around the globe.

2D nanomaterials↗
DOE OSTI2022

How a Formate Dehydrogenase Responds to Oxygen: Unexpected O 2 Insensitivity of an Enzyme Harboring Tungstopterin, Selenocysteine, and [4Fe–4S] Clusters

The reversible two-electron interconversion of formate and CO 2 is catalyzed by both nonmetallo- and metallo-formate dehydrogenases (FDHs). The latter group comprises molybdenum- or tungsten-containing enzymes with the metal coordinated by two equivalents of a pyranopterin cofactor, a cysteinyl or selenocysteinyl (Sec) ligand supplied by the polypeptide, and a catalytically essential terminal sulfido ligand. In addition, these biocatalysts incorporate one or more [4Fe–4S] clusters for facilitating long-distance electron transfer. However, an interesting dichotomy arises when attempting to understand how the metallo-FDHs react with O 2 . Whereas existing scholarship portrays these enzymes as being unable to perform in air due to extreme O 2 lability of their metal centers, studies dating as far back as the 1930s emphasize that some of these systems exhibit formate oxidase (FOX) activity, coupling formate oxidation to O 2 reduction. Therefore, to reconcile these conflicting views, we explored context-dependent functional linkages between metallo-FDHs and their cognate electron acceptors within the same organism vis-à-vis catalysis under atmospheric O 2 . Here, we report the discovery and characterization of an O 2 -insensitive FDH2 from the sulfate-reducing bacterium Desulfovibrio vulgaris Hildenborough (DvH) that ligates tungsten, Sec, and four [4Fe–4S] clusters. By advancing a robust expression platform for its recombinant production, we eliminate both the requirement of nitrate or azide during purification and reductive activation with thiols and/or formate prior to catalysis. Because the distinctive spectral signatures of formate-reduced DvH-FDH2 remain invariant under anaerobic and aerobic conditions, we benchmarked the enzyme activity in air, identifying CO 2 as the catalytic product. Full reaction progress curve analysis discloses a high catalytic efficiency when probed with a high-potential artificial electron acceptor. Furthermore, we show that DvH-FDH2 enables near-stoichiometric hydrogen peroxide production without superoxide release to achieve O 2 insensitivity. Notably, simultaneous electron transfer to cytochrome c and O 2 reveals that metal-based electron bifurcation is operational in this system. Taken together, our work proves the co-occurrence of redox bifurcated FDH and FOX activities within a metalloenzyme scaffold. These findings set the stage for uncovering previously unknown O 2 -insensitive flavin-based electron bifurcation mechanisms, as well as for developing authentic formate/air biofuel cells, engineering O 2 -stable FDHs and biohybrid metallocatalysts, and discerning formate bioenergetics of gut microbiota.

13C NMR↗
DOE OSTI2021

Clinical knowledge extraction via sparse embedding regression (KESER) with multi-center large scale electronic health record data

The increasing availability of electronic health record (EHR) systems has created enormous potential for translational research. However, it is difficult to know all the relevant codes related to a phenotype due to the large number of codes available. Traditional data mining approaches often require the use of patient-level data, which hinders the ability to share data across institutions. In this project, we demonstrate that multi-center large-scale code embeddings can be used to efficiently identify relevant features related to a disease of interest. We constructed large-scale code embeddings for a wide range of codified concepts from EHRs from two large medical centers. We developed knowledge extraction via sparse embedding regression (KESER) for feature selection and integrative network analysis. We evaluated the quality of the code embeddings and assessed the performance of KESER in feature selection for eight diseases. Besides, we developed an integrated clinical knowledge map combining embedding data from both institutions. The features selected by KESER were comprehensive compared to lists of codified data generated by domain experts. Features identified via KESER resulted in comparable performance to those built upon features selected manually or with patient-level data. The knowledge map created using an integrative analysis identified disease-disease and disease-drug pairs more accurately compared to those identified using single institution data. Analysis of code embeddings via KESER can effectively reveal clinical knowledge and infer relatedness among codified concepts. KESER bypasses the need for patient-level data in individual analyses providing a significant advance in enabling multi-center studies using EHR data.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2023

Why is the winner the best?

International benchmarking competitions have become fundamental for the comparative performance assessment of image analysis methods. However, little attention has been given to investigating what can be learnt from these competitions. Do they really generate scientific progress? What are common and successful participation strategies? What makes a solution superior to a competing method? To address this gap in the literature, we performed a multi- center study with all 80 competitions that were conducted in the scope of IEEE ISBI 2021 and MICCAI 2021. Statistical analyses performed based on comprehensive descriptions of the submitted algorithms linked to their rank as well as the underlying participation strategies revealed common characteristics of winning solutions. These typically include the use of multi-task learning (63%) and/or multi-stage pipelines (61%), and a focus on augmentation (100%), im- age preprocessing (97%), data curation (79%), and post- processing (66%). The “typical” lead of a winning team is a computer scientist with a doctoral degree, five years of experience in biomedical image analysis, and four years of experience in deep learning. Two core general development strategies stood out for highly-ranked teams: the reflection of the metrics in the method design and the focus on analyzing and handling failure cases. According to the organizers, 43% of the winning algorithms exceeded the state of the art but only 11% completely solved the respective domain problem. The insights of our study could help researchers (1) improve algorithm development strategies when approaching new problems, and (2) focus on open research questions revealed by this work.

Eisenmann, Matthias↗
DOE OSTI2022

NSAID use and clinical outcomes in COVID-19 patients: a 38-center retrospective cohort study

Abstract Background Non-steroidal anti-inflammatory drugs (NSAIDs) are commonly used to reduce pain, fever, and inflammation but have been associated with complications in community-acquired pneumonia. Observations shortly after the start of the COVID-19 pandemic in 2020 suggested that ibuprofen was associated with an increased risk of adverse events in COVID-19 patients, but subsequent observational studies failed to demonstrate increased risk and in one case showed reduced risk associated with NSAID use. Methods A 38-center retrospective cohort study was performed that leveraged the harmonized, high-granularity electronic health record data of the National COVID Cohort Collaborative. A propensity-matched cohort of 19,746 COVID-19 inpatients was constructed by matching cases (treated with NSAIDs at the time of admission) and 19,746 controls (not treated) from 857,061 patients with COVID-19 available for analysis. The primary outcome of interest was COVID-19 severity in hospitalized patients, which was classified as: moderate, severe, or mortality/hospice. Secondary outcomes were acute kidney injury (AKI), extracorporeal membrane oxygenation (ECMO), invasive ventilation, and all-cause mortality at any time following COVID-19 diagnosis. Results Logistic regression showed that NSAID use was not associated with increased COVID-19 severity (OR: 0.57 95% CI: 0.53–0.61). Analysis of secondary outcomes using logistic regression showed that NSAID use was not associated with increased risk of all-cause mortality (OR 0.51 95% CI: 0.47–0.56), invasive ventilation (OR: 0.59 95% CI: 0.55–0.64), AKI (OR: 0.67 95% CI: 0.63–0.72), or ECMO (OR: 0.51 95% CI: 0.36–0.7). In contrast, the odds ratios indicate reduced risk of these outcomes, but our quantitative bias analysis showed E-values of between 1.9 and 3.3 for these associations, indicating that comparatively weak or moderate confounder associations could explain away the observed associations. Conclusions Study interpretation is limited by the observational design. Recording of NSAID use may have been incomplete. Our study demonstrates that NSAID use is not associated with increased COVID-19 severity, all-cause mortality, invasive ventilation, AKI, or ECMO in COVID-19 inpatients. A conservative interpretation in light of the quantitative bias analysis is that there is no evidence that NSAID use is associated with risk of increased severity or the other measured outcomes. Our results confirm and extend analogous findings in previous observational studies using a large cohort of patients drawn from 38 centers in a nationally representative multicenter database.

60 APPLIED LIFE SCIENCES↗