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DOE OSTI2021

Alvin Tollestrup (1924-2020): A Life in Science

Alvin Tollestrup was a physicist and engineer of uncommon insight, intuition, and ingenuity who was revered in life for his essential leadership in the development of superconducting magnets for high-energy accelerators. He exhibited a matchless mastery of what he called freshman physics — though his mastery hardly ended there — and an uncanny gift for joining careful measurements with transparent calculations to reveal the essence of a subject. To watch Alvin at work was to learn how a true scientist formulates and solves a problem. He had other gifts: a nose for significant problems in both science and technology, the ability to draw talented people to an important cause, and a mentor's instinct for spotting gifted junior colleagues and helping them to mature as engineers and scientists. He was also good company! Alvin brought his keen vision about what constitutes important science, along with powerful ambition for the institution and for fundamental science, to considerat ions about Fermilab's future. His questions were uniformly probing, his opinions unfailingly interesting, and his appetite for work consistently inspiring. Among Alvin's many contributions to experimental particle physics — in a career that spanned seven decades — the rich achievements of the Collider Detector at Fermilab Collaboration, of which he was a founder and an enduring leader, stand out. The discovery of the top quark in 1995 earned worldwide acclaim. He reaped individual accolades as well, including election to the National Academy of Sciences. The National Medal of Technology, conferred in 1989, recognized Alvin’s leading role in developing superconducting magnets for Fermilab's Tevatron. Universities Research Association created the Tollestrup Award for Postdoctoral Research, commemorating Alvin's avuncular support for young scientists. This volume celebrates Alvin Tollestrup’s career and the rich, fulfilling life he led beyond the laboratory. Contributions from colleagues and family illuminate many facets of this remarka! ble figure. May old friends savor the memory of the Alvin they knew, while discovering less-familiar parts of his history; and may readers who did not have the opportunity to know Alvin firsthand find inspiration in his exemplary life in science.

Palmer, Mark↗
DOE OSTI2024

HTGR Validation: NEUP Survey and Database - Data Reporting Standard for HTGR Thermal-Fluid Experiments

Since 2009, the U.S. Department of Energy (DOE) Office of Nuclear Energy's Nuclear Energy University Program (NEUP) has been at the forefront of nuclear research, specifically concentrating on advancing high-temperature gas-cooled reactor (HTGR) technologies. By Fiscal Year 2024, NEUP has authorized 36 projects dedicated to HTGR research, each contributing significantly to the enhancement of our understanding of this technology. The outcomes of these diverse projects have been disseminated through final NEUP reports, peer-reviewed journal articles, and presentations at academic conferences, forming a comprehensive tapestry of knowledge. Despite the substantial value of these findings, their dissemination has been fragmented, posing challenges for accessibility to researchers and policymakers and leading to underutilization of DOE investments. Recognizing this critical gap and its potential consequences for the future of nuclear research, the Advanced Reactor Technologies (ART) Gas-Cooled Reactor (GCR) program conducted an extensive survey of completed and ongoing HTGR NEUP projects. This survey enabled the compilation of crucial data, resulting in the development of a specialized public-access database tailored for computational fluid dynamics and system code validation, specifically designed for HTGR applications. However, the data collection process revealed a significant challenge in central data organization due to individual researchers from different institutes employing varying logics and preferences for recording and documenting experimental data. Consequently, an urgent need has been identified to establish a standardized reporting format for HTGR experimental projects. Addressing this issue is essential for enhancing collaboration, maximizing the impact of DOE investments, and ensuring the seamless advancement of HTGR technologies in nuclear research.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2024

The Data Mine model for accessible partnerships in data science

Abstract The Data Mine at Purdue University is a pioneering experiential learning community for undergraduate and graduate students of any background to learn data science. The first data‐intensive experience embedded in a large learning community, The Data Mine had nearly 1300 students in academic year (AY) 2022–2023 and nearly 1700 students for AY 2023–2024. The Data Mine embodies data‐infused education, research, and collaboration. Students learn Python, R, SQL, and shell‐scripting, while working on weekly projects within a high‐performance computing (HPC) cluster. In the Corporate Partners cohort, students work on teams of 5–15 students, led by a paid student team leader. Each cohort follows an Agile approach, working on data‐intensive projects provided by industry partners and mentored by company employees. Students develop professional and data skills throughout the academic year, from August through April. Many students return in subsequent years to the program, increasing their tenure with a Corporate Partner. Student teams are inherently interdisciplinary; students from 133 different majors are involved in the program, ranging from new incoming students through PhD level students. These interdisciplinary teams of students bring new perspectives to challenging problems in which data science is a key part of the solution. The interdisciplinary teams foster an environment of synthesis with ideas and solutions. Students come together with different life experiences, different levels of technical skill, but also varying ways they navigate paths to solutions because of the variety of majors represented, resulting in a more creative and robust solution than a traditional data science program. This article is categorized under: Applications of Computational Statistics > Education in Computational Statistics

Betz, Margaret A.↗
DOE OSTI2021

ECO: the Evidence and Conclusion Ontology, an update for 2022

The Evidence and Conclusion Ontology (ECO) is a community resource that provides an ontology of terms used to capture the type of evidence that supports biomedical annotations and assertions. Consistent capture of evidence information with ECO allows tracking of annotation provenance, establishment of quality control measures, and evidence-based data mining. ECO is in use by dozens of data repositories and resources with both specific and general areas of focus. ECO is continually being expanded and enhanced in response to user requests as well as our aim to adhere to community best-practices for ontology development. The ECO support team engages in multiple collaborations with other ontologies and annotating groups. Here we report on recent updates to the ECO ontology itself as well as associated resources that are available through this project. ECO project products are freely available for download from the project website (https://evidenceontology.org/) and GitHub (https://github.com/evidenceontology/evidenceontology). ECO is released into the public domain under a CC0 1.0 Universal license.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Optimal transport for 𝑒/𝜋 0 particle classification in LArTPC neutrino experiments

The efficient classification of electromagnetic activity from 𝜋 0 and electrons remains an open problem in the reconstruction of neutrino interactions in liquid argon time projection chamber (LArTPC) detectors. We address this problem using the mathematical framework of optimal transport (OT), which has been successfully employed for event classification in other high energy physics contexts and is ideally suited to the high-resolution calorimetry of LArTPCs. Using a publicly available simulated dataset from the MicroBooNE Collaboration, we show that OT methods achieve state-of-the-art reconstruction performance in 𝑒/𝜋 0 classification. The success of this first application indicates the broader promise of OT methods for LArTPC-based neutrino experiments.

Neutrino detection↗
DOE OSTI2025

A Grassroots Network and Community Roadmap for Interconnected Autonomous Science Laboratories for Accelerated Discovery

Scientific discovery is being revolutionized by AI and autonomous systems, yet current autonomous laboratories remain isolated islands unable to collaborate across institutions. We present the Autonomous Interconnected Science Lab Ecosystem (AISLE), a grassroots network transforming fragmented capabilities into a unified system that shorten the path from ideation to innovation to impact and accelerates discovery from decades to months. AISLE addresses five critical dimensions: (1) cross-institutional equipment orchestration, (2) intelligent data management with FAIR compliance, (3) AI-agent driven orchestration grounded in scientific principles, (4) interoperable agent communication interfaces, and (5) AI/ML-integrated scientific education. By connecting autonomous agents across institutional boundaries, autonomous science can unlock research spaces inaccessible to traditional approaches while democratizing cutting-edge technologies. This paradigm shift toward collaborative autonomous science promises breakthroughs in sustainable energy, materials development, and public health.

Ferreira da Silva, Rafael [Oak Ridge National Labo↗
DOE OSTI2020

Joint Appointments: General Principles for Joint Appointments between LANL and University Partners

The Joint Appointment Program provides opportunities for LANL and partner universities to recruit, retain, and recognize premier scientists and engineers by offering them the ability to work in both the LANL and university environments. These interactions enhance the ability of both institutions to achieve their respective research and development missions while providing increased academic strength to educational institutions.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2021

Revisiting trends in the exchange current for hydrogen evolution

Nørskov and collaborators proposed a simple kinetic model to explain the volcano relation for the hydrogen evolution reaction on transition metal surfaces such that j 0 = k 0 f(ΔG H ) where j 0 is the exchange current density, f(ΔG H ) is a function of the hydrogen adsorption free energy ΔG H as computed from density functional theory, and k 0 is a universal rate constant. Herein, focusing on the hydrogen evolution reaction in acidic medium, we revisit the original experimental data and find that the fidelity of this kinetic model can be significantly improved by invoking metal-dependence on k 0 such that the logarithm of k 0 linearly depends on the absolute value of ΔG H . Here, we further confirm this relationship using additional experimental data points obtained from a critical review of the available literature. Our analyses show that the new model decreases the discrepancy between calculated and experimental exchange current density values by up to four orders of magnitude. Furthermore, we show the model can be further improved using machine learning and statistical inference methods that integrate additional material properties.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

PPPL Laboratory Directed Research and Development (Project Final Reports, FY2018 - FY2020)

The U.S. Department of Energy’s (DOE) Princeton Plasma Physics Laboratory (PPPL) is a collaborative national center for fusion energy science, basic sciences, and advanced technology. The Laboratory has three major missions: (1) to develop the scientific knowledge and advanced engineering to enable fusion to power the U.S. and the world; (2) to advance the science of nanoscale fabrication for technologies of tomorrow; and (3) to further the development of the scientific understanding of the plasma universe from laboratory to astrophysical scales. PPPL’s Laboratory Directed Research and Development (LDRD) program supports and encourages creativity and innovation and contributes to its long-term viability. New scientific and technical research areas emerge and are nurtured through the program. Furthermore, new capabilities are developed to enable the Laboratory to meet its and DOE’s missions. The program is used to systematically diversify the Laboratory’s programs and mission. In the last few years, the program has started projects in nanomaterial synthesis, microelectronics, advanced x-ray spectroscopy, high-energy-density physics, superconducting magnet technology, machine learning and artificial intelligence, 3D magnetic fields to optimize fusion plasmas, integration of permanent magnets with simple high-field magnets to reduce the cost of producing complex 3D magnetic fields, advanced computational methods for predictive understanding and control of fusion plasma, development of quantum computing algorithms for plasma physics, liquid metal plasma-facing components for fusion reactors, virtual engineering, and plasma-based space propulsion. The program is also the vehicle to recruit and train talented scientists and engineers with the new skills needed to perform the Laboratory’s mission. Many of the new hires through the program go on to become world-class scientists and engineers in their fields. This report provides descriptions and accomplishments of those LDRD projects that were completed during fiscal years 2018 through 2020.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Satellite-Based Solar Forecasting Including Maintenance of Computer and Storage Equipment at CSU/CIRA (CRADA Final Report)

The Cooperative Institute for Research in the Atmosphere (CIRA) at Colorado State University (CSU) proposes to continue its research partnership with the NREL in Golden, CO. The partnership resonates with the goals outlined in a Memorandum of Understanding between the Department of Energy and the Department of Commerce (with the National Oceanic and Atmospheric Administration (NOAA)) signed in 2011. This CRADA will sustain the research partnership and provide an avenue and resources for ongoing collaborations, including the maintenance of NREL-furnished computer and storage equipment at CSU/CIRA. There is no formal exchange of funds in this CRADA; support above and beyond the in-kind items outlined in this document will come on a best-effort basis and/or in association with formal sponsored-research enabled collaborations. The research conducted under this CRADA continues to work toward enabling and improving short-term solar energy forecasting as developed under previous CIRA/CSU and NREL collaborations, with specific focus on point-based (e.g., solar farm) forecasting on the 1-3 hour timeframe. Under the best-effort actions during the CRADA period, technical maintenance of CIRA's existing satellite-based solar forecasting algorithm ("CIRACast"; Miller et al., 2017) was continued to maintain operability with evolving datasets. These efforts benefit the public by maintaining national capability for point solar forecasting using geostationary observation platforms, and laying the groundwork for future technical improvements as opportunities arise.

14 SOLAR ENERGY↗
DOE OSTI2022

SEIN: Breaking Barriers Resilient Energy System Analysis [Slides]

The Solar Energy Innovation Network (SEIN) is a collaborative research program that supports multi-stakeholder teams in researching and sharing solutions to real-world challenges associated with solar energy adoption. The Breaking Barriers project was selected to participate in the Solar Energy Innovation Network, Round 2, and was led by Groundswell, a D.C.-based clean energy project developer. The Breaking Barriers team included Partnership for Southern Equity, Atlanta University Center campus facility managers and professors, the City of Atlanta's Neighborhood Planning Unit T, and Georgia Power Company. The project aimed to design and construct innovative urban energy resiliency hubs integrating microgrid technology, solar generation, and energy storage in Atlanta colleges and communities. The hubs will help these historically Black colleges and universities (HBCUs) and the energy-burdened broader community in West Atlanta be more resilient, in addition to informing new course curricula at Atlanta University Center campuses. With many possible options for the system's battery size, the Breaking Barriers team needed insight into the relationships between BESS size, economic performance, and resilience at Spelman College's Manley Center. These insights are crucial for entering procurement negotiations with project developers, establishing resilience capabilities that the HBCU campuses can plan around, and guiding the team's fundraising targets. This analysis includes estimates of PV and battery performance, costs, savings, and resilience for multiple battery sizes. In order to provide power during a grid outage, the resilient energy system also needs to be connected to the Manley Center in a safe and island-able manner (electrically isolated from the grid). Analysis of potential electrical configurations and estimated setup costs is key to successfully entering a required interconnection agreement with Georgia Power, as well as informing requests for engineering firms to construct the system. This analysis includes conceptual options and rough order of magnitude cost estimates for electrically interconnecting the resilient energy system to the Manley Center and the grid. The Breaking Barriers team used this analysis to select preferred system characteristics and design for the resilient energy system.

14 SOLAR ENERGY↗
DOE OSTI2023

Algal Biomass Production via Open Pond Algae Farm Cultivation: 2022 State of Technology and Future Research

The annual State of Technology (SOT) assessment is an essential activity for platform research conducted under the Bioenergy Technologies Office (BETO). It allows for the impact of research progress (both directly achieved in-house at the National Renewable Energy Laboratory [NREL] and furnished by partner organizations) to be quantified in terms of economic improvements in the overall biofuel production process for a particular biomass processing pathway, whether based on terrestrial or algal biomass feedstocks. As such, initial benchmarks can be established for currently demonstrated performance, and progress can be tracked toward out-year goals to ultimately demonstrate economically viable biofuel technologies. NREL's algae SOT benchmarking efforts focus both on front-end algal biomass production and separately on back-end conversion to fuels through NREL's "combined algae processing" (CAP) pathway. The production model is based on outdoor long-term cultivation data, enabled by comprehensive algal biomass production trials conducted under the Development of Integrated Screening, Cultivar Optimization, and Verification Research (DISCOVR) consortium efforts, driven by data furnished by Arizona State University (ASU) at the Arizona Center for Algae Technology and Innovation (AzCATI) testbed site. The CAP model is based on experimental efforts conducted primarily under NREL research and development projects. This report focuses on front-end algal biomass production, documenting the pertinent algal biomass cultivation parameters that were input to the NREL open pond algae farm model. Through partnerships under DISCOVR, collaborators at ASU furnished details on cultivation performance metrics including biomass productivity and harvest densities for recent growth trials done at the AzCATI site. The resulting biomass productivity was calculated at 18.5 g/m2/day (ash-free dry weight [AFDW], annual average) for seasonal cultivation of Picochlorum celeri, Tetraselmis striata LANL1001, and Monoraphidium minutum 26B-AM biomass strains at the ASU site. Picochlorum celeri achieved the best productivity from May to September, with Monoraphidium minutum 26B-AM being used in October, November, March, and April, and Tetraselmis striata employed during winter months (December through February). Beyond the standard SOT models, in Appendix C of this report we also present an industry case study evaluating several scenarios reflective of outdoor cultivation data furnished by an industry collaborator. This case study provides a supplementary datapoint on work being performed elsewhere achieving comparable cultivation productivity with more favorable compositional quality, producing biomass enriched in lipids as may be more optimal for conversion upgrading to fuels and products.

09 BIOMASS FUELS↗
DOE OSTI2024

Algal Biomass Production via Open Pond Algae Farm Cultivation: 2023 State of Technology and Future Research

The annual State of Technology (SOT) assessment is an essential activity for platform research conducted under the Bioenergy Technologies Office (BETO). It allows for the impact of research progress (both directly achieved in-house at the National Renewable Energy Laboratory [NREL] and furnished by partner organizations) to be quantified in terms of economic improvements in the overall biofuel production process for a particular biomass processing pathway, whether based on terrestrial or algal biomass feedstocks. As such, initial benchmarks can be established for currently demonstrated performance, and progress can be tracked toward out-year goals to ultimately demonstrate economically viable biofuel technologies. NREL's algae SOT benchmarking efforts historically focused both on front-end algal biomass production and separately on back-end conversion to fuels through NREL's "combined algae processing" (CAP) pathway. The production model is based on outdoor long-term cultivation data, enabled by comprehensive algal biomass production trials conducted under the Development of Integrated Screening, Cultivar Optimization, and Verification Research (DISCOVR) consortium efforts, driven by data furnished by Arizona State University (ASU) at the Arizona Center for Algae Technology and Innovation (AzCATI) testbed site. The CAP model is based on experimental efforts conducted primarily under NREL research and development projects. This report focuses on front-end algal biomass production, documenting the pertinent algal biomass cultivation parameters that were input to the NREL open pond algae farm model. Through partnerships under DISCOVR, collaborators at ASU furnished details on cultivation performance metrics including biomass productivity and harvest densities for recent growth trials done at the AzCATI site. The resulting biomass productivity was calculated at 16.7 g/m 2 /day (ash-free dry weight [AFDW], annual average) for seasonal cultivation of Picochlorum celeri TG2 and Monoraphidium minutum 26B-AM biomass strains at the ASU site. Picochlorum celeri achieved the best productivity from April to September, with Monoraphidium minutum 26B-AM being used between October and March. Tetraselmis striata LANL1001, usually part of the strain rotation in previous cultivation SOTs, was supplanted by Monoraphidium minutum 26B-AM in this year's outdoor cultivation trials. Finally, building from an industry case study presented in the 2022 SOT report, in the Appendix of this report we provide an update on further improved data furnished by an industry collaborator and resultant impacts on economics reflecting several seasonal scenarios. This case study provides a supplementary datapoint on work being performed elsewhere with a more dedicated focus on improved compositional quality, producing biomass enriched in lipids as may be more optimal for conversion upgrading to fuels and products.

09 BIOMASS FUELS↗
DOE OSTI2026

Computational Modeling of Atmospheric Processes at Texas Southern University

Texas Southern University (TSU) is strengthening its research program in atmospheric chemistry and physics with a climate science emphasis by leveraging partnerships with the U.S. Department of Energy’s Atmospheric Radiation Measurement (ARM) Facility, Brookhaven National Laboratory (BNL), and the Tracking Aerosol Convection Interactions ExpeRiment (TRACER). This RDPP-supported program focuses on secondary organic aerosols (SOAs) and reactive atmospheric species that influence cloud formation, precipitation processes, and radiative forcing. SOAs play a critical role in cloud microphysics and Earth’s energy balance, yet the chemical and physical mechanisms governing SOA–cloud interactions remain a significant source of uncertainty in predictive climate models. Through computational modeling, observational data analysis, and national laboratory collaboration, this program develops a skilled cohort of students trained in atmospheric science, environmental data analysis, and climate-relevant modeling. These research experiences build technical competencies that are transferable to careers in government laboratories, academia, and industry. By engaging students from historically underrepresented communities in high-impact climate research, TSU expands participation in the atmospheric sciences workforce while contributing meaningful scientific insights to DOE-supported ARM research activities. This partnership strengthens national capacity in climate science and supports the development of the next generation of atmospheric researchers.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

On the implications of positive W mass shift

We investigate the phenomenological implications of the recent W mass measurement by the CDF collaboration, which exhibits tension with the standard model (SM) electroweak fit. Performing the fit to the electroweak observables within the SM effective field theory, we find that the new physics that contributes either to the determination of the electroweak vacuum expectation value, or to the oblique parameters, can improve the agreement with data. The best description is obtained from a fit where flavor universality is not required in the new physics operators, with 2 to 3 σ indications for several nonzero Wilson coefficients. We point out that top partners with order TeV masses could lead to the observed shift in the W mass.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2025

Clarifying Terminology in Microbial Ecology: A Call for Precision in Scientific Communication

The rapid evolution of microbiology as a field of research has led to the introduction of new terminology and the adaptation of existing terms. However, inconsistencies in the use of these terms, including variations across different scientific disciplines, can lead to confusion and miscommunication within the scientific community. This article discusses the importance of precise terminology in microbiome research, highlighting examples where terms have been misused or redefined without clear justification. We also present a list of frequently used terms in microbial ecology along with their specific definitions. We argue that the misuse of terminology can hinder scientific progress by creating ambiguity and misunderstanding. To address this, we propose a set of guidelines for the consistent use of key terms and provide clear definitions for some of the most commonly misused or newly introduced terms in the field. The definitions provided herein will also function as a guide for young researchers new to the field of microbial ecology. Accurate and consistent use of terminology is crucial for effective communication and collaboration in microbiology research. By adhering to standardised definitions, researchers can ensure that their work is clearly communicated and contributes meaningfully to the progress of science.

definitions↗
DOE OSTI2020

ASCR@40: Four Decades of Department of Energy Leadership in Advanced Scientific Computing Research

Throughout its long history, the Office of Advanced Scientific Computing Research (ASCR) has built the critical technologies to ensure U.S. leadership in energy science and national security. It has made its parent agency, the Department of Energy (DOE) and its Office of Science, the world’s recognized leader in computational science. ASCR’s stated mission is “to discover, develop, and deploy computational and networking capabilities to analyze, model, simulate, and predict complex phenomena important to the DOE.” To accomplish this goal, ASCR oversees a large complex of computing and networking facilities and is responsible for procuring, deploying and operating high-performance computing (HPC), networking and storage resources; conducting basic research in mathematics and computer science; developing and sustaining a large body of software; and collaborating with other Office of Science programs, academia and industry. ASCR’s computational science leadership has a long history, predating even DOE’s inception. Applied mathematics and advanced computing were both elements of the Manhattan Project’s Theoretical Division. In the 1950s, DOE’s predecessor, the Atomic Energy Commission, created a mathematics program to develop and apply digital computing by supporting researchers at universities and AEC laboratories. Several organizational and name changes later, this program would grow and become ASCR.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Development of Data Reporting Standards for High-Temperature Gas-cooled Reactor (HTGR) Nuclear Energy University Program (NEUP) Thermal-Fluid Experiments

Since 2009, the U.S. Department of Energy (DOE) Office of Nuclear Energy's Nuclear Energy University Program (NEUP) has been at the forefront of nuclear research, specifically concentrating on advancing high-temperature gas-cooled reactor (HTGR) technologies. By Fiscal Year 2023, NEUP has authorized 35 projects dedicated to HTGR research, each contributing significantly to the enhancement of our understanding of this technology. The outcomes of these diverse projects have been disseminated through final NEUP reports, peer-reviewed journal articles, and presentations at academic conferences, forming a comprehensive tapestry of knowledge. Despite the substantial value of these findings, their dissemination has been fragmented, posing challenges for accessibility to researchers and policymakers and leading to underutilization of DOE investments. Recognizing this critical gap and its potential consequences for the future of nuclear research, the Advanced Reactor Technologies (ART) Gas-Cooled Reactor (GCR) program conducted an extensive survey of completed and ongoing HTGR NEUP projects. This survey enabled the compilation of crucial data, resulting in the development of a specialized public-access database tailored for computational fluid dynamics and system code validation, specifically designed for HTGR applications. However, the data collection process revealed a significant challenge in central data organization due to individual researchers from different institutes employing varying logics and preferences for recording and documenting experimental data. Consequently, an urgent need has been identified to establish a standardized reporting format for HTGR experimental projects. Addressing this issue is essential for enhancing collaboration, maximizing the impact of DOE investments, and ensuring the seamless advancement of HTGR technologies in nuclear research.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗