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At least 343 records · Page 19

Radium Dial Workers: Back to the Future

This report reviews the history of the radium dial workers in the United States, summarizes the scientific progress made since the last evaluation in the early 1990s, and discusses current progress in updating the epidemiologic cohort and applying new dosimetric models for radiation risk assessment. The discoveries of radiation and radioactivity led quickly to medical and commercial applications at the turn of the 20th century, including the development of radioluminescent paint, made by combining radium with phosphorescent material and adhesive. Workers involved with the painting of dials and instruments included painters, handlers, ancillary workers, and chemists who fabricated the paint. Dial painters were primarily women and, prior to the mid to late 1920s, would use their lips to give the brush a fine point, resulting in high intakes of radium. The tragic experience of the dial painters had a significant impact on industrial safety standards, including protection measures taken during the Manhattan Project. The dial workers study has formed the basis for radiation protection standards for intakes of radionuclides by workers and the public. The mortality experience of 3,276 radium dial painters and handlers employed between 1913-1949 is being determined through 2019. The last epidemiologic follow-up was 30 years ago when most of these workers were still alive. Nearly 65% were born before 1920, 37.5% were teenagers when first hired, and nearly 50% were hired before 1930 when the habit of placing brushes in mouths essentially stopped. Comprehensive dose reconstruction techniques are being applied to estimate organ doses for each worker related to the intake of 226Ra, 228Ra, and associated photon exposures. Time dependent dose-response analyses will estimate lifetime risks for specific causes of death. The study of radium dial workers is part of the Million Person Study of low-dose health effects that is designed to evaluate radiation risks among healthy American workers and veterans. Despite being one of the most important and influential radiation effects studies ever conducted, shifting programmatic responsibilities and declining funding led to the termination of the radium program of studies in the early 1990s. Renewed interest and opportunity have arisen. With scientific progress made in dosimetric methodology and models, the ability to perform a study over the entire life span, and the potential applicability to other scenarios such as medicine, environmental contamination and space exploration, the radium dial workers have once again come to the forefront.

61 RADIATION PROTECTION AND DOSIMETRY↗

Does mycorrhizal symbiosis determine the climate niche for Populus as a bioenergy feedstock?

This is the final technical and scientific report for DE-SC0016097, in which we investigated the role of symbiotic fungi in determining the climatic niche of trees. While microbes have long been viewed as agents of disease, recent explorations of the microbiome have led biologists to recognize that beneficial microbes play an equally vital role in maintaining the health of plants and animals. Perhaps the most ubiquitous form of beneficial interaction in terrestrial ecosystems occurs between fungi and plant roots. These fungus-root, or “mycorrhizal”, symbioses involve the reciprocal exchange of plant sugars for soil nutrients obtained by the fungus, such as nitrogen and phosphorous, which are critical for plant growth. It is known that mycorrhizal associations are widespread - occurring in approximately 90% of plant species - and diverse, with single plants able to associate simultaneously with over 100 species of fungi. Despite this, the ecological factors that control large-scale distribution and abundance of mycorrhizal symbioses are still poorly known, making it challenging to predict how mycorrhizal symbioses may change in future climate conditions or how these fungal communities might be manipulated to improve regional agriculture or forestry projects. In this research project we focused on a native North American tree with strong biofuel potential – Populus – to answer fundamental questions about the role of climate, soil environment, and mycorrhizal interactions in determining growth and competition in plant communities. Populus provides a unique opportunity for ecologically relevant experiments because of its widespread distribution across North America and its natural variability in mycorrhizal association types.

59 BASIC BIOLOGICAL SCIENCES↗

Simulating Continuum-based Redshift Measurement in the Roman’s High Latitude Spectroscopic Survey

We investigate the capability of the Nancy Grace Roman Space Telescope’s (Roman) Wide-Field Instrument G150 slitless grism to detect red, quiescent galaxies based on the current reference survey. We simulate dispersed images for Roman reference High-Latitude Spectroscopic Survey (HLSS) and analyze two-dimensional spectroscopic data using the grism Redshift and Line Analysis (Grizli) software. This study focus on assessing Roman grism’s capability for continuum-level redshift measurement for a redshift range of 0.5 ≤ z ≤ 2.5. The redshift recovery is assessed by setting three requirements of: σ z = $\frac{|z–z_{true}|}{1+z}$ ≤ 0.01, signal-to-noise ratio≥ 5 and the presence of a single dominant peak in redshift likelihood function. We find that, for quiescent galaxies, the reference HLSS can reach a redshift recovery completeness of ≥50% for F158 magnitude brighter than 20.2 mag. We also explore how different survey parameters, such as exposure time and the number of exposures, influence the accuracy and completeness of redshift recovery, providing insights that could optimize future survey strategies and enhance the scientific yield of the Roman in cosmological research.

Astronomical simulations↗

Data Release 1 of the Dark Energy Spectroscopic Instrument

In 2021 May the Dark Energy Spectroscopic Instrument (DESI) collaboration began a 5 yr spectroscopic redshift survey to produce a detailed map of the evolving three-dimensional structure of the Universe between z = 0 and z ≈ 4. DESI’s principal scientific objectives are to place precise constraints on the equation of state of dark energy, the gravitationally driven growth of large-scale structure, and the sum of the neutrino masses, and to explore the observational signatures of primordial inflation. We present DESI DR1, which consists of all data acquired during the first 13 months of the DESI main survey, as well as a uniform reprocessing of the DESI Survey Validation data, which were previously made public in the DESI Early Data Release. The DR1 main survey includes high-confidence redshifts for 18.7M objects, of which 13.1M are spectroscopically classified as galaxies, 1.6M as quasars, and 4M as stars, making DR1 the largest sample of extragalactic redshifts ever assembled. We summarize the DR1 observations, the spectroscopic data-reduction pipeline and data products, large-scale structure catalogs, value-added catalogs, and describe how to access and interact with the data. In addition to fulfilling its core cosmological objectives with unprecedented precision, we expect DR1 to enable a wide range of transformational astrophysical studies and discoveries.

79 ASTRONOMY AND ASTROPHYSICS↗

Grimsel Test Site - A Successful International Underground Research Laboratory for Many Decades - 20429

For more than 35 years, Nagra and its partners from around the world have been conducting underground research projects at the Grimsel Test Site (GTS, www.grimsel.com) to contribute to the development and confirmation of safe geological disposal concepts and for the characterization of suitable host rock formations. Over the years, the results of this internationally recognized research program have been, and continue to be, incorporated directly into exploration programs, modelling, safety, and engineering feasibility studies on options for deep geological repositories. Each project of the GTS program involves field-testing, laboratory studies, design and modelling tasks, and integrates all scientific and technical aspects. Each project phase is planned with a duration of three to five years to facilitate practical and administrative aspects and allow flexibility for updating the overall project plans with the latest findings. Scientific and engineering interaction among the different projects is ensured via an international steering committee meeting. Hosting an IAEA level C radiation- controlled zone, which allows use of radionuclides, including actinides such as thorium, uranium, neptunium, plutonium and americium, in in-situ experiments is one of the reasons why GTS also developed as a center of excellence for work with radioactive tracers under realistic in-situ boundary conditions. Last year, a new five-year program (2019 to 2023) started which includes projects with a planning horizon of decades. The new five-year program includes a new phase of in-situ experiments using radionuclides such as migration experiments in the Colloid Formation and Migration project (CFM), the Long-Term Diffusion experiment (LTD) and the newly established C-14 and I-129 Migration in cement project (CIM). The 'High Temperature effects on Bentonite' (HotBENT) project is starting in the current phase and is studying the effects of elevated temperatures (>175 deg. C) on bentonite materials. As a generic underground research laboratory (URL) it is expected that the GTS will provide in the coming years a platform for international collaboration, knowledge development and knowledge transfer for the next generation of scientists and engineers in the area of radioactive waste disposal and geosciences. A key role regarding knowledge transfer and training is provided by the well-established Grimsel Training Center (GTC), which (beside many URL related issues) also covers many general aspects of radioactive waste management. In this paper we provide an overview of the current program at the GTS, focusing on the experiments that study the migration of radionuclides through engineered barrier materials and the geosphere. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

Review on Perovskite Solar Cells: From Single‐Junction Devices to Tandem Deployment in Space

Perovskite solar cells (PSCs) have emerged as a transformative photovoltaic technology, offering high power conversion efficiency (PCE) and the potential for cost-effective manufacturing. However, stability and large-scale manufacturing remain critical challenges that must be addressed for widespread adoption. This review provides a roadmap from single-junction perovskite solar cells to tandem deployment in space. First, material-level innovations are discussed, including mixed-cation and low-dimensional perovskites, transport materials, and additives that improve thermal and structural stability while enhancing efficiency. Then, we examine both established industrial standards and emerging scientific protocols aimed at stabilizing PSCs under operational conditions, including tandem cell integration strategies and encapsulation techniques to mitigate performance degradation. Manufacturing scalability is a focal point, where deposition methods and green solvents are explored to improve large-area film uniformity and reduce environmental impact. Additionally, the increasing viability of PSCs in extraterrestrial environments is assessed, with emphasis on their performance in space applications, radiation resistance, and flexible lamination methods for deployment in extreme conditions. Progress across materials innovation, device architectures, stability testing protocols, and both terrestrial and extraterrestrial applications collectively drives perovskite photovoltaics toward higher efficiency, stability, and cost-effectiveness.

flexible PSCs↗

Connecting research and teaching introductory cell and molecular biology using an Arabidopsis mutant screen

A complex research project was translated into a Course-based Undergraduate Research Experience (CURE), which was implemented in sections of an introductory Cell and Molecular Biology laboratory course. The research laboratory generated an engineered plant line producing a growth-inhibiting, lipid-derived plant hormone and mutagenized this line. Students in the CURE cultured the mutagenized plant population and selected and characterized suppressor mutants. They learned to observe phenotypes related to the biosynthesis and perception of the plant hormone and explored the genetic and biochemical basis of these phenotypes. As the students studied the relevant genetic, molecular and biochemical concepts during this CURE, they were able to translate this knowledge into practice and develop scientific arguments. This CURE was a successful collaboration between the teaching lab and the research lab. It benefited both parties as the students had a real-life, deep learning experience in scientific methodology, while the research lab gathered data and materials for further studies

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

High Energy Physics Network Requirements Review (Final Report, July-October 2020)

Throughout 2020, ESnet and the Office of High Energy Physics (HEP) of the DOE SC organized an ESnet requirements review of HEP-supported activities. Preparation for this event included identification of key stakeholders: program and facility management, research groups, technology providers, and a number of external observers. These individuals were asked to prepare formal case study documents about their relationship to the HEP program to build a complete understanding of the current, near-term, and long-term status, expectations, and processes that will support the science going forward. A series of pre-planning meetings better prepared case study authors for this task, along with guidance on how the review would proceed in a virtual fashion.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

Model-Form Epistemic Uncertainty Quantification for Modeling with Differential Equations: Application to Epidemiology

Modeling real-world phenomena to any degree of accuracy is a challenge that the scientific research community has navigated since its foundation. Lack of information and limited computational and observational resources necessitate modeling assumptions which, when invalid, lead to model-form error (MFE). The work reported herein explored a novel method to represent model-form uncertainty (MFU) that combines Bayesian statistics with the emerging field of universal differential equations (UDEs). The fundamental principle behind UDEs is simple: use known equational forms that govern a dynamical system when you have them; then incorporate data-driven approaches – in this case neural networks (NNs) – embedded within the governing equations to learn the interacting terms that were underrepresented. Utilizing epidemiology as our motivating exemplar, this report will highlight the challenges of modeling novel infectious diseases while introducing ways to incorporate NN approximations to MFE. Prior to embarking on a Bayesian calibration, we first explored methods to augment the standard (non-Bayesian) UDE training procedure to account for uncertainty and increase robustness of training. In addition, it is often the case that uncertainty in observations is significant; this may be due to randomness or lack of precision in the measurement process. This uncertainty typically manifests as “noisy” observations which deviate from a true underlying signal. To account for such variability, the NN approximation to MFE is endowed with a probabilistic representation and is updated using available observational data in a Bayesian framework. By representing the MFU explicitly and deploying an embedded, data-driven model, this approach enables an agile, expressive, and interpretable method for representing MFU. In this report we will provide evidence that Bayesian UDEs show promise as a novel framework for any science-based, data-driven MFU representation; while emphasizing that significant advances must be made in the calibration of Bayesian NNs to ensure a robust calibration procedure.

97 MATHEMATICS AND COMPUTING↗

FESAC Decadal Study: Clean, multi-purpose fusion power from small field-reversed configuration (FRC) reactors

This paper urges greatly expanded research into scientific and technical areas of aneutronic field-reversed-configuration devices (FRCs), specifically relatively small FRCs whose fusion power is below 50 MW and above 0.5 MW. These properties greatly increase safety, allow rapid exploration of incremental improvements, enable implementation of improved technologies, ameliorate materials issues, reduce financial risk, ease siting and maintenance requirements, simplify incorporation into the electrical grid, reduce need for long-distance transmission lines, expand the potential markets for these reactors, and provide the US and US industry with leadership positions in clean energy. This route to fusion power, first into niche markets not accessible to the mainline (D-T tokamak) approach to fusion, would demonstrate the reliability and safety of fusion and its competitive advantages against established technologies in the electricity-generation market.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

An Integrated Framework for Risk Assessment of Safety-related Digital Instrumentation and Control Systems in Nuclear Power Plants: Methodology Advancement and Application

This report documents activities performed by Idaho National Laboratory (INL) during fiscal year (FY) 2024 for the U.S. Department of Energy (DOE) Light Water Reactor Sustainability (LWRS) Program, Risk Informed Systems Analysis (RISA) Pathway, Digital Instrumentation and Control (DI&C) Risk Assessment project. The goal of the RISA Pathway is to optimize safety margins and minimize uncertainties to achieve economic efficiencies while maintaining high levels of safety. This is accomplished by providing scientific basis to better represent safety margins and factors that contribute to cost and safety, and by developing new technologies that reduce operating costs. The research efforts for FY 2024 encompass methodology refinement and exploration. The efforts include: (1) The implementation of a natural language processing tool to expedite key aspects of the reliability analysis methods developed by INL; (2) advances to support intersystem CCF analysis by providing guidance for and identification of coupling mechanisms that may contribute to CCF; (3) the investigation of how generative artificial intelligence tools can aid in hazard analysis and diversity and defense in depth (i.e., D3) assessments; (4) Industry collaboration, allowing the demonstration of and INL's risk assessment tools to support risk assessment of DI&C systems at early and late stages of development; (4) a roadmap for the development of a software for each of INL's risk assessment tools; (5) The development of a theory and methodology manual for a risk quantification methodology; (6) the development of a reliability analysis for machine learning (ML)-integrated control systems.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

Small Penetrator Instrument Concept for the Advancement of Lunar Surface Science

Fundamental scientific objectives concerning the surface and subsurface material and dynamics of the Moon are the drivers for the use and advancement of penetrators, which emplace a suite of scientific instruments by impact into a planetary surface, typically at velocities of dozens to hundreds of meters per second. Small lunar penetrators are poised to become a valuable new tool for lunar science and exploration during the next decade. These low-cost ballistic probes can be deployed in large numbers from orbit, or from descending robotic or crewed vehicles, in order to explore and characterize the diversity of extreme lunar shallow subsurface environments. In this paper, we describe the general overview of penetrator objectives, potential instrumentation, and how these would benefit the advancement of lunar science at various extreme environments.

47 OTHER INSTRUMENTATION↗

Machine-learning-based dynamic-importance sampling for adaptive multiscale simulations

Multiscale simulations are a well-accepted way to bridge the length and time scales required for scientific studies with the solution accuracy achievable through available computational resources. Traditional approaches either solve a coarse model with selective refinement or coerce a detailed model into faster sampling, both of which have limitations. Here, we present a paradigm of adaptive, multiscale simulations that couple different scales using a dynamic-importance sampling approach. Our method uses machine learning to dynamically and exhaustively sample the phase space explored by a macro model using microscale simulations and enables an automatic feedback from the micro to the macro scale, leading to a self-healing multiscale simulation. As a result, our approach delivers macro length and time scales, but with the effective precision of the micro scale. Our approach is arbitrarily scalable as well as transferable to many different types of simulations. Overall, our method made possible a multiscale scientific campaign of unprecedented scale to understand the interactions of RAS proteins with a plasma membrane in the context of cancer research running over several days on Sierra, which is currently the second-most-powerful supercomputer in the world.

59 BASIC BIOLOGICAL SCIENCES↗

Optimizing Aluminum Bonding: Exploring Surface Roughness and Contact Angle Effects through Plasma and Acid Etching Contrasts

The use of adhesives to replace traditional techniques like welding and riveting has been on the rise in various industries as increasingly complex geometries and situations for bonding is required. It’s well known in the scientific community that an adhesive bond is only as efficient as the surface preparation used beforehand. Plasma treatments have been shown to gently clean the surface by only removing the top-most layer from the adherend and “charging” the surface instead of berating it. Whereas chemical treatments “etch” the surface in hopes of increasing surface area by increasing surface roughness. This method can be difficult to control quantitatively and can generate waste leading to serious environmental concerns. This study aims to address the encountered differences between plasma treatments and acid etching on aluminum substrates for adhesive bonding. Treating the surface of adherends plays a crucial role in promoting a good bond between adhesive and adherend. In this study, the effects of plasma and acid treatments are categorized using surface roughness measurements as well as contact angle which have been shown to correlate to mechanical strength. When evaluating different surface treatments, it is notable to mention that not only surface roughness and contact angle play a role in determining which method is most effective but also the consequences of the treatment itself. It was found that although chemical etching was the most effective in increasing roughness and decreasing contact angle, it generates a large amount of waste and is not sustainable compared to plasma work which uses a renewable process. The results from this work provide a deeper understanding of the relationship between surface treatments and surface roughness/contact angle and how this relates to a stronger adhesive bond.

36 MATERIALS SCIENCE↗

Optimizing Aluminum Bonding: Exploring Surface Roughness and Contact Angle Effects through Plasma and Acid Etching Contrasts.

The use of adhesives to replace traditional techniques like welding and riveting has been on the rise in various industries as increasingly complex geometries and situations for bonding is required. It’s well known in the scientific community that an adhesive bond is only as efficient as the surface preparation used beforehand. Plasma treatments have been shown to gently clean the surface by only removing the top-most layer from the adherend and “charging” the surface instead of berating it. Whereas chemical treatments “etch” the surface in hopes of increasing surface area by increasing surface roughness. This method can be difficult to control quantitatively and can generate waste leading to serious environmental concerns. This study aims to address the encountered differences between plasma treatments and acid etching on aluminum substrates for adhesive bonding. Treating the surface of adherends plays a crucial role in promoting a good bond between adhesive and adherend. In this study, the effects of plasma and acid treatments are categorized using surface roughness measurements as well as contact angle which have been shown to correlate to mechanical strength. When evaluating different surface treatments, it is notable to mention that not only surface roughness and contact angle play a role in determining which method is most effective but also the consequences of the treatment itself. It was found that although chemical etching was the most effective in increasing roughness and decreasing contact angle, it generates a large amount of waste and is not sustainable compared to plasma work which uses a renewable process. The results from this work provide a deeper understanding of the relationship between surface treatments and surface roughness/contact angle and how this relates to a stronger adhesive bond

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Reactivity in cement pastes bearing fine fraction concrete and glass from construction and demolition waste: Microstructural analysis of viability

Converting construction and demolition waste (CDW) into secondary raw materials is one of the priorities of environmental policy and circular economy strategy. This study analysed the variation in eco-efficient paste reactivity with OPC replacement ratio (5% to 10%) and hydration time (2 d, 28 d and 90 d). Three types of CDW were explored: two (calcareous and siliceous) consisting of fine (4 AH{sub 13} and C{sub 4}AcH{sub 11}, ettringite and portlandite, were the same as observed in OPC hydration. These findings attest to the scientific viability recycling such CDWs as mineral additions in eco-cement manufacture and consequently eliminating the need for stockpiling these materials at recycling/storage plants.

36 MATERIALS SCIENCE↗

Towards Exascale Astrophysics of Mergers and Supernovae (TEAMS)

The TEAMS project brought together cutting-edge simulations, theoretical insights, and collaborative efforts to deepen our understanding of some of the universe’s most extreme phenomena—supernovae, neutron star mergers, and the powerful signals they emit. Using one of the largest suites of 3D supernova simulations ever conducted, researchers uncovered new insights into how massive stars explode, how those explosions vary by stellar mass, and what conditions lead to the birth of neutron stars or black holes. They also studied the radiation and gravitational wave signals emitted during these events, revealing how future observations can be used to uncover what happens deep inside collapsing stars. The team developed improved tools for modeling how light and neutrinos behave in such explosive environments, enabling more accurate predictions of what astronomers might observe. Work also explored how the chemical composition and geometry of kilonovae—the visible explosions that follow neutron star mergers—influence their signals and can reveal the origins of heavy elements like gold. These efforts not only advanced scientific knowledge, but also trained a new generation of researchers at the intersection of astrophysics, computational science, and nuclear theory.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

Probabilistic Neural Computing with Stochastic Devices

Abstract The brain has effectively proven a powerful inspiration for the development of computing architectures in which processing is tightly integrated with memory, communication is event‐driven, and analog computation can be performed at scale. These neuromorphic systems increasingly show an ability to improve the efficiency and speed of scientific computing and artificial intelligence applications. Herein, it is proposed that the brain's ubiquitous stochasticity represents an additional source of inspiration for expanding the reach of neuromorphic computing to probabilistic applications. To date, many efforts exploring probabilistic computing have focused primarily on one scale of the microelectronics stack, such as implementing probabilistic algorithms on deterministic hardware or developing probabilistic devices and circuits with the expectation that they will be leveraged by eventual probabilistic architectures. A co‐design vision is described by which large numbers of devices, such as magnetic tunnel junctions and tunnel diodes, can be operated in a stochastic regime and incorporated into a scalable neuromorphic architecture that can impact a number of probabilistic computing applications, such as Monte Carlo simulations and Bayesian neural networks. Finally, a framework is presented to categorize increasingly advanced hardware‐based probabilistic computing technologies.

Misra, Shashank↗