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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 217 records · Page 12

Stable Simulation of the Community Atmosphere Model Using Machine‐Learning Physical Parameterization Trained With Experience Replay

In recent years, machine learning (ML) models have been used to improve physical parameterizations of general circulation models (GCMs). A significant challenge of integrating ML models into GCMs is the online instability when they are coupled for long‐term simulation. We present a new strategy that demonstrates robust online stability when the physical parameterization package of an atmospheric GCM is replaced by a deep ML model. The method uses experience replay with a multistep training scheme of the ML model in which the model's own output at the previous time step is used in the training. Predicted physics tendencies in the replay buffer with the most recent errors in the training iterations are reused, making the ML model learn from its own errors. The training method reduces the gap between the offline and online environments of the ML model. The method is used to train the ML model as the physical parameterization of the Community Atmosphere Model (CAM5) with training data from the Multi‐scale Modeling Framework high resolution simulations. Three 6‐year online simulations of the CAM5 are carried out by using the ML physics package. The simulated spatial distributions of precipitation, surface temperature and zonally averaged atmospheric fields demonstrate overall better accuracy than that of the standard CAM5 and benchmark model even without the use of additional physical constraints or tuning. This work is the first to demonstrate a solution to address the online instability problem in climate modeling with ML physics by using experience replay.

54 ENVIRONMENTAL SCIENCES↗

Bringing Alaska's Carbon Ore, Rare Earth, and Critical Minerals (CORE-CM) into Perspective

The final report outlines the outcomes of the Alaska CORE-CM Program, funded by the U.S. Department of Energy under award DE-FE0032050. Led by the University of Alaska Fairbanks and the Alaska Division of Geological and Geophysical Surveys, with assistance from other organizations, the project assessed Alaska's potential for Carbon Ore, Rare Earth Elements, and Critical Minerals (CORE-CM). Leveraging advanced analytical techniques, the project identified high-potential resource basins, evaluated geochemical and satellite data, and conducted targeted field investigations. Findings revealed promising concentrations of critical minerals in legacy samples and newly collected materials. The project also investigated innovative extraction technologies, including BioExtraction and use of supercritical CO2, which show significant promise for sustainable resource recovery. Additionally, the study explored the reuse of waste streams from active mining operations and coal byproducts such as using alkali-activated coal ash to manufacture concrete. Infrastructure and logistical challenges in Alaska’s remote regions are discussed, alongside strategies to establish a Technology Innovation Center aimed at advancing CORE-CM development in Alaska. The report includes actionable insights to support Alaska’s critical role in securing domestic supplies of essential minerals while addressing economic, environmental, and technological challenges.

01 COAL, LIGNITE, AND PEAT↗

Survey of Available Information on U.S. Manufacturing Wastewater and Energy Requirements for Reuse

This report seeks to inform an improved understanding of the energy tradeoff associated with manufacturing water reuse in the United States, in part by developing an analytical framework for understanding when this tradeoff for reuse is beneficial. In order to apply this approach, existing literature and publicly available information on typical contaminants in industrial effluent and available treatment technologies are reviewed and summarized. An existing case study is then discussed in light of how the analytical framework could be applied if sufficient data were available. We conclude that the shortcomings of the available data severely restrict our ability to apply this framework. These limitations are highlighted with the intent of providing the broader research community information on where data gaps need to be filled.

54 ENVIRONMENTAL SCIENCES↗

Use of Historical EM Practices and Policies for Management of Cleanup Program and Address GAO Concerns - 20222

As Department of Energy (DOE) Environmental Management (EM) program policies and management practices are revised over time, it might be useful to periodically reflect on the DOE EM program's past to identify practices that have worked and those that might be appropriate for possible reuse in the future. An added impetus to examine alternative approaches is the recent Government Accountability Office (GAO) report entitled 'Nuclear Waste Cleanup - DOE Could Improve Program and Project Management by Better Classifying Work and Following Leading Practices.' The February 2019 GAO report states that EM categorizes most of its work in a way that does not adequately involve independent experts and DOE senior leadership. It also noted that EM has not followed leading practices for program and project management that could help keep the cleanup efforts on schedule and control costs. The GAO report recommended that EM, working with DOE Office of Project Management (PM): - Establish requirements and then assess EM's ongoing operations activities to determine if some activities should be reclassified as capital asset projects based on these newly established requirements. - Review and revise EM's 2017 cleanup policy to include program management leading practices related to scope, cost, schedule performance, and independent reviews. - Update the cleanup policy to require that earned value management (EVM) systems be maintained and used in a way that follows EVM best practices. - Develop a policy to ensure that work is categorized as level of effort (LOE) only in appropriate, specified circumstances, such as when work is not measurable or when measurement is impractical. - Integrate EVM data into EM's performance metrics for operations activities. To assist with improving program performance and implementing audit recommendations, this paper reviewed past successful cleanups such as Weldon Springs, Rocky Flats, Fernald, Ashtabula, Battelle Columbus (abs, and other historical resources and people with experience during the early years of EM program. As such, there are several past practices which may well be suited for reuse today. Some of these practices include: - In 1990's, as the DOE EM cleanup program, known as Environmental Restoration came into effect, the known cleanup scope at each geographic location or site was organized into four levels ranging from overall site-wide cleanup effort to the individual 'release sites', providing high granularity to the program. - The cleanup program was organized by geography (Northwest, Southwest, Eastern Area Programs, etc.). These 'sub-program' managers provided a degree of Headquarters oversight and management allowing upper management (EM-1, 2, and 3) to focus on major decisions and issues. - Budget levels at selected cleanup sites were funded above 'minimum-safety' levels to accelerate actual cleanup work. - Past practice utilized EVM techniques and uniform EM oversight over all discrete project-like activities, providing for a coordinated cleanup effort. - EM managed LOE activities using benchmarking and best in class management practice techniques to help manage costs. - EM managed and reviewed entire site cleanup baselines, not only individual projects, focusing on a successful overall outcome. - Sites had a 'Road map' or 'Management Action Plan' that provided the overall strategy or plan for that site's cleanup - this helped with understanding and coordination of efforts. - EM had a robust lessons-learned program and database to capture and transmit worthy ideas. - EM had well-structured cleanup baseline change control policies to control cost and schedule changes, at the contractor and higher Headquarters levels. Finally, some of the GAO observations may be valid, however it is not clear as to their extent. For example, for some operation types of work, the use of the Level of Effort (LOE) earned value method to track progress may be appropriate. Also, to ensure independence of thought, and to obtain useful insight on past practices, it may be suitable to have former EM employees to assist with understanding past practices or to verify that activities have been properly identified. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

MARSAME Radiological Release Report for Metal Items from Technical Area 53, Set 26

Environmental Protection and Compliance, Environmental Stewardship Group (EPC-ES) has evaluated the survey results for metal items from the Los Alamos Neutron Science Center (LANSCE) at Technical Area 53 (TA-53) and found that the metal items described in Table 1 of this report (identified by Radiation Protection [RP] Tracking Numbers) meet the criteria for unrestricted release under Department of Energy (DOE) Order 458.1 Chg 4, Radiation Protection of the Public and the Environment (DOE 2020) and can be recycled. This conclusion is based on the known history of the metal items and radiation survey data. None of the items in this report are located within radiological areas. Therefore, the items are considered unencumbered and are not subject to the moratorium suspension on metal recycling from DOE facilities. Additionally, Los Alamos National Laboratory (LANL) has determined that there is no practical opportunity for internal DOE reuse of this metal. Process knowledge indicates that these metal items were unlikely to ever be in direct contact with the beam and thus are unlikely to have become activated. Surface contamination measurements (both total and removable) showed either no detectable radioactivity or activity levels within the range of background. All measurements for volumetric contamination were indistinguishable from background based on calculated decision limits. Additionally, all gamma isotopic surveys conducted for defense-in depth showed no identifiable gamma radiation from beam activation.

54 ENVIRONMENTAL SCIENCES↗

Pseudonymization at Scale: OLCF’s Summit Usage Data Case Study

The analysis of vast amounts of data and the processing of complex computational jobs have traditionally relied upon high performance computing (HPC) systems, which offer reliable and efficient management of large-scale computational and data resources. Understanding these analyses’ needs is paramount for designing solutions that can lead to better science, and similarly, understanding the characteristics of the user behavior on those systems is important for improving user experiences on HPC systems. A common approach to gathering data about user behavior is to extract workload characteristics from system log data available only to system administrators. Recently at Oak Ridge Leadership Computing Facility (OLCF), however, we unveiled user behavior about the Summit supercomputer by collecting data from a user’s point of view with ordinary Unix commands.In this paper, we discuss the process, challenges, and lessons learned while preparing this dataset for publication and submission to an open data challenge. The original dataset contains personal identifiable information (PII) about the users of OLCF which needed be masked prior to publication, and we determined that anonymization, which scrubs PII completely, destroyed too much of the structure of the data to be interesting for the data challenge. We instead chose to pseudonymize the dataset, which reduced the linkability of the dataset to the users’ identities. Pseudonymization is significantly more computationally expensive than anonymization, and the size of our dataset, which is approximately 175 million lines of raw text, necessitated the development of a parallelized workflow that could be reused on different HPC machines. We demonstrate the scaling behavior of the workflow on two leadership class HPC systems at OLCF, and we show that we were able to bring the overall makespan time from an impractical 20+ hours on a single node down to around 2 hours. As a result of this work, we release the entire pseudonymized dataset and make the workflows and source code publicly available.

Maheshwari, Ketan↗

P SI4 1.4: Open-source software for high-throughput quantum chemistry

PSI4 is a free and open-source ab initio electronic structure program providing implementations of Hartree–Fock, density functional theory, many-body perturbation theory, configuration interaction, density cumulant theory, symmetry-adapted perturbation theory, and coupled-cluster theory. Most of the methods are quite efficient, thanks to density fitting and multi-core parallelism. The program is a hybrid of C++ and Python, and calculations may be run with very simple text files or using the Python API, facilitating post-processing and complex workflows; method developers also have access to most of PSI4 's core functionalities via Python. Job specification may be passed using The Molecular Sciences Software Institute (MolSSI) QCSCHEMA data format, facilitating interoperability. We announce a rewrite of our top-level computation driver, and concomitant adoption of the MolSSI QCARCHIVE INFRASTRUCTURE project, makes the latest version of PSI4 well suited to distributed computation of large numbers of independent tasks. The project has fostered the development of independent software components that may be reused in other quantum chemistry programs.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Investigating Benefits and Challenges of Converting Retiring Coal Plants into Nuclear Plants

A coal-to-nuclear (C2N) transition means siting a nuclear reactor at the site of a recently retired coal power plant. Three overarching questions from the C2N transition guide this research: where in the United States are retired coal facilities located and what factors make a site feasible for transition; what factors of technology, cost, and project timeline drive investor economics over such a decision; and how will C2N impact local communities? The study team evaluated the siting characteristics of recently retired plants and those operating coal-fired power plant sites run by a utility or an independent power producer utilizing publicly available data to screen U.S. coal power plant sites to nuclear-feasible locations. After screening all retired coal sites to a set of 157 potential candidates and screening operating sites to a set of 237 candidates, the study team estimates that 80% of retired and operating coal power plant sites that were evaluated have the basic characteristics needed to be considered amenable to host an advanced nuclear reactor. For the recently retired plant sites evaluated, this represents a capacity potential of 64.8 GWe to be backfit at 125 sites. For the operating plant sites evaluated, this represents a capacity potential of 198.5 GWe to be backfit at 190 sites. This report evaluates a case study for the detailed impacts and potential outcomes from a C2N transition. Based on the nuclear technology choices and sizes evaluated to replace a large coal plant of 1,200 MWe generation capacity at the case study site, nuclear overnight costs of capital could decrease by 15% to 35% when compared to a greenfield construction project, through the reuse of infrastructure from the coal facility. Nuclear replacement designs can have a lower capacity size because nuclear power plants run at higher capacity factors than coal power plants. In the case study replacing coal capacity with 924 GWe of nuclear capacity, the study team found regional economic activity could increase by as much as $\$275 million$ and add 650 new, permanent jobs to the region of analysis. The evaluated site choice in the report is hypothetical for analysis purposes only and based on available data and documented assumptions. Consequently, the findings only inform at a general level. A community, investor, or other interested stakeholder can use these results to set up a detailed, in-depth analysis for a specific application of interest, such as evaluating a C2N transition of a specific coal power plant and a specific nuclear technology design. The report was subjected to independent peer reviews by experts in systems engineering and regional economic modeling to evaluate analysis and assumptions.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

Predictive Assessment of the Chemical Composition of Coal Ash in Reserve at U.S. Disposal Sites

In the United States, more than 2 Gt of coal combustion residuals (i.e., coal ash) are stored in hundreds of disposal units. Recent federal regulations mandate the closure or retrofitting of most coal ash impoundments, presenting significant challenges for waste management. These regulatory pressures also present opportunities to reuse coal ash. However, the quality and quantity of discarded coal ash across the U.S. are not well known, even though this information is crucial for spurring its reuse for conventional and new material applications. This study describes a predictive model for the major element composition of coal ash in reserve at disposal sites of major U.S. coal-fired power plants. This model was constructed from coal purchase records of 705 power stations from 1973 to 2022 and was trained on coal ash composition data, showing that coal ash elemental composition is strongly associated with the source of feedstock coal. The model showed regional shifts in the major element contents of ash produced by power plants in the last 50 years, particularly for calcium and iron (expressed as %CaO and %Fe2O3), as power stations changed their source of coal over this time frame. Our approach enables an estimation of chemical composition for ash stored in waste impoundments at individual power stations. Such information can help to delineate the regional market resource potential of supplementary cements for concrete and other material innovations that would utilize coal ash harvested from disposal sites across the U.S.

01 COAL, LIGNITE, AND PEAT↗

Circularity Futures Workshop Series: Summary Report

The aim of this report is to synthesize key feedback received from the three-part Circularity Futures workshop series held in Spring 2024. The workshop series was conducted by the National Renewable Energy Laboratory (NREL) on behalf of U.S. Department of Energy, Office Energy Efficiency and Renewable Energy (EERE), and was broken into three workshops: Workshop 1 - Circularity Analysis Needs and Priorities; Workshop 2 - Circularity Metrics and Indicators; and Workshop 3 - Circularity Data. Together, the workshops focused on identifying the existing priorities and gaps in the circularity modeling space, understanding different stakeholders' use and interpretation of circularity metrics and indicators, identifying common data gaps and data quality challenges, and assessing the robustness of available solutions. The workshop series brought a diverse group of stakeholders - including representatives from U.S. government offices, national labs, nonprofit organizations, industry, and academia - to collect first-hand feedback on needs, priorities, challenges and opportunities in the circularity modeling and analysis space. The workshop discussions highlighted numerous common needs, priorities and challenges among the interviewed groups. Several topics were frequently discussed, including: 1) Circularity as a pathway for sustainable economic growth: While circularity is generally defined in terms of resource conservation and reducing wasteful disposal of materials, participants agreed that circular strategies should serve broader economic, environmental, and social goals. It is therefore crucial for circularity analysis to look beyond waste reduction and instead evaluate a variety of impact metrics such as cost savings, job creation, air quality, and pollutant emissions. Mutli-criteria decision-making frameworks may be useful for making sense of disparate metrics and evaluating tradeoffs between impact categories.; 2) Economic and social factors are not well understood: Underdevelopment of existing end-of-life (EOL) management infrastructure, inconsistent standardization codes and policy space in reusing recycled content, and suboptimal collection and sorting strategies collectively contribute to uncertainty about the economic potential of circular pathways. The latter observation is consistent among all technologies but more emphasized for renewable energy systems. Social impacts of circularity practices are less understood and less researched than other sustainability aspects.; 3) Inconsistent methods for assessing emerging technologies: LCA and TEA results vary widely depending on the assumptions made with regards to market adoption of new technologies. Emerging technologies suffer limited availability of data needed to conduct a robust circularity analysis. Yet, understanding projected impacts of proposed nascent technology is a key need for different stakeholder groups.; and 4) Lack of temporally and geospatially explicit data: There is a need for open data that represents variations in circularity technologies over time and location. The lack thereof leads to aggregated and potentially misrepresented results in circularity analysis. Sensitivity analyses should be included to verify whether options perceived as more sustainable align with real-world practices.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

NucMesh: nuclear reactor geometry creation and mesh generation module in NEMoSys

NucMesh is a parameterized geometry and mesh generator for nuclear reactors developed within the Nuclear Energy Modeling System NEMoSys at Illinois Rocstar. NEMoSys is a platform developed for mesh generation, adaptive refinement, and solution verification. NucMesh is implemented to be generalized and extensible with a robust computer aided design engine and multiple mesh generation algorithms for unstructured triangular, quad-dominant, and structured quadrilateral meshing. In this paper, we present the geometric and meshing features of NucMesh. Geometrically objects are constructed bottom-to-top and overlaps are addressed automatically. A sophisticated object tracking algorithm prevents data from being lost for segmented objects. We discuss the primitive objects of circle and polygons that constitute the module and show how they are used with example inputs. Arrays of primitives and arrays of arrays are utilized to build large assemblies of objects. The concept of saved objects is discussed to demonstrate how repetitive objects can be reused easily and augmented in place. Three dimensional meshes can be obtained through mesh extrusion where all materials and side sets are extended to three dimensions. We show that side sets can be defined nearly anywhere within the geometry and can then be applied to the mesh. Finally, example reactor meshes are demonstrated for the Idaho National Laboratory Advanced Test Reactor and Los Alamos National Lab Empire reactor, both of which use control drums that NucMesh handles easily. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

Offshore Advanced Infrastructure Integrity Model (AIIM) Dashboard

The Advanced Infrastructure Integrity Model (AIIM) is a multivariate, multi-machine learning modeling technology applied to evaluate the integrity of offshore energy infrastructure (e.g., pipelines, platforms) in the U.S Gulf Region. Offshore energy infrastructure plays an essential role in ensuring access to safe and secure energy for the United States. According to the U.S. Energy Information Administration (EIA), production in the U.S. Gulf Region accounts for 15% of total crude and 5% of total natural gas from the United States. Many of these structures have been operating for close to or past their design life, while others have the chance of attrition before return on investment. To better understand the potential for reuse or life extension opportunities, an assessment of the infrastructure integrity is critical to inform safe decision making. Assessing structural integrity, AIIM provides key insights that inform infrastructure use and reuse, as well as hazard prevention planning, in support of stakeholders including researchers and industry.

Advanced Infrastructure Integrity Model↗

Multichannel Analysis of Surface Waves Accelerated (MASWAccelerated): Software for efficient surface wave inversion using MPI and GPUs

Multichannel Analysis of Surface Waves (MASW) is a technique frequently used in geotechnical engineering and engineering geophysics to infer 1D layered models of seismic shear wave velocities in the top tens to hundreds of meters of the subsurface. We aim to accelerate MASW calculations by capitalizing on modern computer hardware available in the workstations of most engineers: multiple cores and graphics processing units (GPUs). We propose new parallel and GPU accelerated algorithms for computing 1D MASW inversion, and provide software implementations in C using Message Passing Interface (MPI) and CUDA. These algorithms take advantage of sparsity that arises in the problem, and the work balance between processes considers typical data trends. We compare our methods to an existing open source Matlab MASW tool. Our serial C implementation achieves a 2x speedup over the Matlab software, and we continue to see improvements by parallelizing the problem with MPI. Here we see nearly perfect strong and weak scaling for uniform data, and improve strong scaling for realistic data by repartitioning the problem to process mapping. By utilizing GPUs available on most modern workstations, we observe an additional 1.3x speedup over the serial C implementation on the first use of the method. We typically repeatedly evaluate theoretical dispersion curves as part of an optimization procedure, and on the GPU the kernel can be cached for faster reuse on later runs. We observe a 3.2x speedup on the cached GPU runs compared to the serial C runs. This work is the first open-source parallel or GPU-accelerated software tool for MASW imaging, and should enable geotechnical engineers to fully utilize all computer hardware at their disposal.

58 GEOSCIENCES↗

Results from an Aeromagnetic Survey to Detect Steel-Cased Wells at a Marcellus Shale Well Site in Washington County, Pennsylvania

Pennsylvania has a 150-year history of oil and gas production—the longest of any state—and this enduring activity has resulted in the drilling of more than 300,000 recorded wells. However, unknown wells likely exist because innumerable wells were drilled during Pennsylvania’s intense early oil and gas history when incomplete records were kept of well locations. There is concern that early wells are likely to be ineffectively sealed because there were no laws that required plugging when the wells were abandoned. Today, many undocumented and unplugged wells are thought to be in areas of emerging shale gas and shale oil development where open wellbores can provide a pathway for undesired upward migration of fluids and gas from hydraulically fractured reservoirs. Due to this concern, Pennsylvania regulators have asked operators to locate orphaned and abandoned wells within a 1,000-ft buffer of proposed new wells. The objective of this report is to demonstrate that high-resolution aeromagnetic surveys, historic air photos, and Light Detection and Ranging (LiDAR) imagery can be rapid and effective methods to reconnoiter large, forested areas of moderate terrain for the presence of abandoned wells. These well-finding methods were evaluated at a proposed Marcellus Shale gas drilling site in Washington County, Pennsylvania, where the methods collectively located 18 confirmed wells: 15 wells were identified from aeromagnetic surveys, two wells were identified from inspection of historical air photos, and one well was identified by evaluation of state-wide LiDAR imagery. Only six wells were previously known, and their locations, as recorded in Pennsylvania’s statewide oil and gas wells database (PA/IRIS/WIS), were often too inaccurate for the wells to be found in the dense underbrush. Twelve wells identified in this study were abandoned, unmarked, and undocumented. Aeromagnetic surveys locate wells by detecting the unique magnetic signature of vertical, steel well casing, which is depicted on magnetic maps as a “bull’s eye” type anomaly that is centered directly over the well. However, when wells were drilled and found to be sub-economic, their casing was sometimes pulled and salvaged for reuse. Such wellbores provide no magnetic response and go undetected if all casing was removed. Oftentimes attempts to retrieve well casing were not 100% successful. For example, historical records for one well in the study area indicate that the well was completed in 1902 as a dry hole and that, to the extent possible, the casing was pulled for reuse. However, a section of 10-in. diameter steel casing was not recovered and remains at an unknown depth in the wellbore. This well was easily detected by the aeromagnetic survey although only deep casing remained in the well. To mitigate for the likelihood that wellbores exist where most or all casing has been removed, this study augmented aeromagnetic data with historic air photos and digital terrain models generated from LiDAR datasets—both databases are publicly available at no cost for areas within Pennsylvania. These complementary methods located three wells where the aeromagnetic anomaly, although present, was subtle and overlooked. Together, these methods determined accurate locations for six known wells within the study area and located 12 previously unknown wells. Although it is not certain that these methods successfully located all wells in the study area, the application of these methods does represent a significant improvement over relying on existing databases for well locations. For the Appendix to the report, see: https://www.netl.doe.gov/energy-analysis/details?id=b46c417a-7c9e-4d25-b810-e6248b0217f4</p>

04 OIL SHALES AND TAR SANDS↗

Correlation Calculations for the Russian Pu Metal Fast Experiments

Nuclear criticality experiments are often conducted in campaigns with multiple variations. These experiments reuse the same basic components, like the fuel, moderator, or positioning machines. The components have uncertainties in their geometry and composition that propagate to models of the experiments. Shared components create shared uncertainty between the $k_{eff}$ of benchmarks. The shared uncertainty is commonly quantified with a covariance, or correlation coefficient. These covariances can impact criticality safety and nuclear data validation applications. While benchmark evaluations tabulate an experiment’s uncertainty, they often lack a detailed calculation of correlations between experiments. Even some very commonly used benchmarks, like the Russian Pu Metal Fast (PMF) experiments, have missing correlations. This paper presents our approach to calculate the correlations for five of the Russian PMF experiments. The experiments share hemispherical Pu shells that induce a correlation between modeled $k_{eff}$ values. We estimated the correlations with simplified and detailed models of the experiments through linear-perturbation theory. The correlations between the experiments vary significantly between the detailed vs. simplified models. We also investigate how the correlations affect validation metrics of the experiments.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DIVA/DeviceEditor v6.1.2

DIVA is an end-to-end DNA design and construction management platform that streamlines how researchers design, build, and receive sequence-verified DNA constructs. Through a web-based BioCAD interface (DeviceEditor), researchers independently design DNA constructs and submit them to a centralized queue with a single action. Designs progress transparently through standardized states which allow researchers to track status and access finished constructs via a central DNA repository. Submitted designs are reviewed by dedicated staff for feasibility and optimization, reducing costly failures and improving downstream execution. Automated DNA assembly software optimizes construction strategies by reusing existing parts where possible and sourcing synthetic DNA only when needed. Standardized, sequence-agnostic assembly methods enable many independent constructs to be built in parallel using lab automation, dramatically increasing throughput. High-throughput next-generation sequencing is used to verify construct accuracy, with flexible platforms selected based on task requirements. Throughout the process, detailed success and failure data are captured and analyzed, enabling continuous improvement of assembly protocols. Compared to traditional, manual DNA construction workflows, DIVA offers higher scalability, transparency, reproducibility, and data-driven optimization.

Plahar, Hector [Lawrence Berkeley National Laborat↗

Developing a Prototype Methodology to Rank CO2-EOR Wells and Assess Their Reuse Potential for Geologic Carbon Sequestration

This study presents a prototype methodology for evaluating the reuse potential of Class II CO₂-enhanced oil recovery (CO₂-EOR) wells as Class VI wells for geologic carbon sequestration. The approach focuses on assessing wellbore construction materials—casing, cement, tubing, and packers—based on U.S. Environmental Protection Agency (EPA) Class VI well conversion guidelines. Utilizing Python scripts and JSON representations, the methodology automates checks on digitized Texas Railroad Commission (TRRC) data to rank wells based on regulatory and integrity criteria. Key factors include casing integrity, cementing techniques, tubing compatibility, and packer selection. Due to limitations in digitized data, manual verification is required for certain sections. Future enhancements include incorporating non-digitized data via web scraping and machine learning. This research provides a practical framework for well owners and regulators, supporting informed decision-making for sustainable CO₂ storage.

carbon sequestration↗

MARSAME Radiological Release Report for Metal Items from TA 53, Set 20

EPC-ES has evaluated the survey results for metal items from the Los Alamos Neutron Science Center (LANSCE) and found that the metal items described in Table 1 of this report (identified by RP Tracking Numbers) meet the criteria for unrestricted release under DOE Order 458.1 Radiation Protection of the Public and the Environment (DOE 2020) and can be recycled. This conclusion is based on the known history of the metal items and on radiation survey data (see the completed RP-Form-031 LANSCE Metals Clearance Log for each item). None of the items in this report are located within radiological areas. Therefore, the items are considered unencumbered and are not subject to the moratorium suspension on metal recycling from Department of Energy facilities. Additionally, LANL has determined that there is no practical opportunity for internal DOE reuse or recycling of this metal. Process knowledge indicates that these metal items were unlikely to ever be in direct contact with the beam and thus are unlikely to have become activated. Surface contamination measurements (both total and removable) showed either no detectable radioactivity or activity levels within the range of background. All measurements for volumetric contamination were indistinguishable from background based on calculated decision limits. Additionally, all gamma isotopic surveys conducted for defense-in-depth showed no identifiable gamma radiation from beam activation.

54 ENVIRONMENTAL SCIENCES↗