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All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Cybersecurity Center for Secure Evolvable Energy Delivery Systems (SEEDS)

The SEEDS Center has successfully completed its mission to research and develop a plethora of technologies during its six-year timeframe. The Center institutions of the University of Arkansas, the University of Arkansas at Little Rock, Carnegie-Mellon University, Florida International University, Lehigh University, and MIT all worked together with industry partners to define relevant energy sector cybersecurity issues, create projects to address those issues, and execute those projects in roughly two and three-year increments. The short project descriptions below indicate some really keystone areas of research. The teams generally met all of their objectives with only a few exceptions, which is tremendous in an R&D center. In fact, the success of one project led to the creation of a startup company, Bastazo, Inc. that is commercializing the SPARTAN project. In addition to creating new technologies, the Center helped to educate a desperately needed workforce. Lastly, a big success is that the UA seriously followed the mandate of the original program manager to try to become self-sustaining. This effort has resulted in a combined NSF center with the CREDC Center at the University of Illinois, Urbana-Champaign. To summarize the SEEDS effort, great research was funded, students were educated and put into the workforce, technology is being commercialized and offered to the electric sector, and the research efforts are being sustained through additional funding. The effort was an unqualified success.

03 NATURAL GAS↗
DOE OSTI2020

Distribution Development for Residual Inventory at the New York West Valley Site - 20400

The New York State Energy Research and Development Authority (NYSERDA) is the owner of the Western New York Nuclear Service Center (WNYNSC), a 1,351 ha site located approximately 48 km south of Buffalo, New York. In 1962, Nuclear Fuel Services, Inc. (NFS) entered into Agreements with the Atomic Energy Commission and New York State to construct the first commercial reprocessing plant of nuclear fuel in the United States. NFS, a private company, built and operated the spent fuel reprocessing plant and waste disposal facilities, processing 640 Mg of spent nuclear fuel from 1966 to 1972 under an Atomic Energy Commission license. Nuclear fuel reprocessing operations ended in 1972 and never reopened, leaving behind radioactive and chemical wastes. Operations led to contamination in a number of facilities and locations. Some of that contamination has migrated from waste disposal zones to other layers, formations, and features on and off the WNYNSC. Phase I decommissioning activities are ongoing and involve the removal of a number of areas and structures that have been associated with contamination. The purpose of this work is to outline the approach for characterizing contamination not associated with disposed wastes, contaminated structures, or specific releases. In this work, the term, residual radiological activity, is used to describe environmental contamination that exists subsequent to the completion of Phase I decommissioning activities, that is not associated with disposed wastes, contaminated structures, or specific releases. Contamination from the Site was quantified relative to data that characterize the concentrations of radionuclides that exist in background. Background concentrations are those present in the area but having no influence from Site related activities. The existence of residual radiological activity that is elevated relative to background has the potential to contribute to future risks to human health and the environment. As a consequence, the residual inventory information is used to inform the West Valley Probabilistic Performance Assessment (PPA) model to characterize potential future risks to human health and the environment. The centralized West Valley Data Management System (DMS) was the source of information for the data assembled in this analysis. The DMS is a fairly large compilation consisting of thousands of records from investigation studies, with sample dates ranging from 1990 to present. Samples from monitoring wells, boreholes, geoprobe studies, surface water, surface soils, storm water outfalls, ventilation stack filters, plant and animal tissues, and more are included in the DMS. Results are typically reported in units of activity per unit volume. For the purpose of the analyses presented here, all results were converted into consistent units of pCi per unit volume. Since 1990, data have been collected from various locations across the WNYNSC at different times with varying frequency over the course of several decades. As a consequence, a number of potential issues can arise with respect to the assembly of a dataset that is deemed adequate for the characterization of residual radiological activity. These issues were assessed and resolved to the extent possible through careful consideration of the properties of the distributions. The intent was to use data which characterize the current state of the Site. Radionuclides can be designated to one of several groups depending on their origin. In this work the groups considered were 1) Naturally Occurring Radioactive Material (NORM), 2) fallout, and 3) Other (including power plant, medical research, etc). This grouping is a useful construct with respect to the interpretation of fixed laboratory results. For example, NORM radionuclides that exist within a decay chain should have approximately equivalent distributions of concentrations if they are representative of background conditions. Insights such as these can be used as a check to identify sample results that need to be further investigated or omitted due to issues associated with reported results from fixed laboratory analyses. This type of analysis provided a foundation for the assessment of the adequacy of sample results for use in subsequent components of an assessment. The general process for the assessment of residual radiological contamination at the Site consists of a sequence of several steps. First, for each analyte, several statistical tests were performed to assess the weight of evidence against the null hypothesis that the mean of the distribution of concentrations was equal to zero. If the mean of the distribution of concentrations for a given radionuclide was not found to be greater than zero, then it was removed from consideration as a component of the residual radiological contamination. If there was significant evidence to reject the hypothesis of the mean being equal to zero, the second step was to compare the distribution of the data from the Site to that of the corresponding background. A suite of tests was used to compare the distributions of the site and background data. The results of these tests were collectively used to determine if site data are elevated relative to background. The third step was to develop distributions using a Bayesian framework to characterize the distribution of mean of the increment present above background for each of the radionuclides. The Bayesian model implemented allowed for the comparison of site-specific records to background concentrations to better approximate contamination attributed to the Site. A final screening step was employed for radionuclides that exceed background. This screening step compared 95% upper confidence limits (UCLs) from the increment distribution developed in the previous step to the risk screening levels. This approach yields a list of analytes that were determined to be elevated relative to background.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2026

Insights into the mechanisms of NH3 inhibition on Cu-CHA SCR catalysts

This work elucidates the atomic-scale mechanism behind ammonia (NH3) inhibition during the selective catalytic reduction (SCR) of NO? on Cu-CHA catalysts, a key issue limiting low-temperature emission control. Using SCR kinetic analysis, operando electron paramagnetic resonance (EPR) spectroscopy, and density functional theory (DFT), we demonstrate that NH3 inhibition primarily slows the oxidation half-cycle (OHC), while the reduction half-cycle (RHC) remains unaffected. DFT simulations reveal that excess NH3 substantially increases the diffusion barrier for CuI ions, hindering formation of essential CuII-oxo dimer intermediates and thus suppressing OHC kinetics. Operando EPR studies confirm that this inhibition strongly depends on operating temperature and catalyst Cu loading. Our findings highlight strategies to counteract NH3 inhibition, including optimizing Cu loading, precisely managing NH3:NO feed ratios, and enhancing CuI ion mobility through tailored catalyst design and operational adjustments, thereby advancing the efficiency of emission control technologies in automotive applications.

Deka, Dhruba Jyoti↗
DOE OSTI2020

Avatar Tools

Supervised machine learning is the process of using past experience to predict the future. "Ensembles" are a machine-learning meta-method that can be applied to most machine learning algorithms. Ensembles generally greatly improve accuracy, reduce or remove most of the design issues presented by machine learning, and are admirably suited to parallel and distributed computation. The Avatar Tools codes are an implementation of ensembles specifically for decision trees. Some features that distinguish Avatar Tools from other "ensembles for decision trees" codes are: (1) Does the bookkeeping necessary for out of bag (OOB) validation. (2) Can use OOB validation to automatically determine optimal ensemble size. (3) Provides an MPI-based parallel implementation, for distributed operation. (4) Provides convenient tools for cross-validation, to assess the accuracy provided by a training set. SAND2020-3858 M Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

Siefert, Christopher↗
DOE OSTI2026

Estimating Fine-Resolution Shortwave Broadband Albedo of Croplands from Harmonized Landsat and Sentinel-2 Data

Altered surface albedo due to land-cover conversions and management is a significant driver of global climate change. Albedo can be directly measured at ground stations, and remote sensing data can be used to scale-up albedo values to regional and global levels. Some previous studies have retrieved fine-resolution (10–30 m) instantaneous albedo and coarse-resolution (500–1000 m) daily mean albedo from remote sensing data, but they all required the input of Moderate Resolution Imaging Spectroradiometer (MODIS) albedo information at 500-m resolution, and none have assembled both instantaneous and daily albedo based exclusively on fine-resolution satellite data. Here, to address this issue, we compiled 387 instantaneous and 346 daily albedo records using field net radiometer measurements from the bioenergy croplands at the W. K. Kellogg Biological Station in southwest Michigan. We then connected these albedo records with a suite of variables derived from harmonized Landsat and Sentinel-2 data through two machine learning algorithms (random forest regression and extreme gradient boosting) to retrieve clear-sky instantaneous and daily shortwave broadband albedo. The performance statistics indicate reasonable accuracy of model results [root-mean-square error (RMSE)] around or below 0.03 except for snow-covered surfaces), suggesting that the retrieval of both instantaneous and daily albedo based exclusively on fine-resolution satellite data is promising. To facilitate the use of fine-resolution albedo products at the global level, future efforts need to include more albedo records of diverse surface cover types, as well as to accurately model daily albedo for cloudy days to address the “clear-sky bias.”

Harmonized Landsat and Sentinel-2↗
DOE OSTI2022

Amine Oxidation Catalyzed by NO 2

Amine oxidation is a major risk in the deployment of amine scrubbing for CO2 capture from post-combustion flue gas. Oxidation probably occurs by dissolved oxygen at elevated temperature, Fe(III)/Fe(II) shuttling between the absorber and the stripper, and by NO2 in the absorber. Amine selection, dissolved oxygen removal, and solvent reclaiming mitigate oxidation due to dissolved oxygen and iron shuttle mechanism, but not by NO2. This work examines piperazine (PZ) oxidation by NO2 in a bench-scale high gas flow reactor (HGF). PZ is an effective second-generation solvent with high CO2 capacity, fast capture rate, good thermal stability, and good resistance to oxidation. Flue gas typically contains 0.5–5 ppm NO2 and 10–100 ppm NO. NO2 at 1 ppm in the flue gas appears to cause significant amine oxidation in pilot plant testing. PZ solution can easily absorb NO2, and it is hypothesized that NO2 can catalytically oxidize PZ through free-radical propagation. The issue may not be solved by a NO2 pre-scrubber since the coexistence of NO and O2 in the flue gas will keep producing non-negligible amounts of NO2 in the ductwork and the absorber after the NO2 incoming with the flue gas has been scrubbed. In addition, NO2 may not only oxidize PZ but also the intermediate degradation products, and it is unknown if the existence of dissolved iron influences the oxidation by NO2. Previous workers have also shown that NO2 is the stoichiometric source of nitrosamine. A comprehensive analysis of NO2 oxidation is needed to address solvent management and its health/environmental impact. The new high gas flow (HGF) reactor focuses only on the absorber environment and is simpler to interpret because it does not include oxidation at high temperatures and by dissolved metal catalysts from corrosion. This paper presents the results of HGF experiments and shows evidence to support a hypothesis of amine oxidation catalyzed by NO2, presents the synergic effect of NO2 and iron on amine oxidation, points out the different effects of NO2 in clean and degraded solvents, and demonstrates that NO2 not only can oxidize amines but also their degradation products.

NO2, iron, piperazine (PZ), solvent oxidation, sol↗
DOE OSTI2024

Precision Agriculture using Networks of Degradable Analytical Sensors (PANDAS) (Final Technical Report)

Precision agriculture, where sensing of soil, environment and crop conditions are used to precisely synchronize inputs (such as water and fertilizer) to crop needs enhances input use efficiency. This can improve yields and farm profitability while mitigating environmental losses, improving soil carbon content and substantially decreasing energy use for food, feed and fuel crops. Unfortunately, farmers are not yet able to harness the full potential of these management technologies as there is a lack of available management information, and there is therefore a need for sensors that are able to economically measure spatio-temporal variability in soil and crop properties of extremely heterogeneous farm fields precisely at high resolution and at low cost. Real-time, in-situ monitoring of agricultural soil conditions is today carried out using devices that limit the total number of nodes that can be used economically to typically one per acre or less. Higher spatio-temporal resolution sensing would enable more precise agricultural input optimization, with significant benefits to the farmer and the environment. In order to address this issue, this project focused on developing additively manufactured, biodegradable, soil sensors with predicted costs of < $\$$1 per unit to monitor crop inputs (such as water and fertilizer) that predictably, harmlessly degrade away into the soil when no longer needed. These sensor nodes should be easy to place, accurately and continuously monitor soil and crop conditions for an entire season, be read remotely using existing farm equipment, require no ongoing maintenance, not impede farm operations and produce no persistent waste. This approach could enable a >100× increase in information density over current solutions for precision farming of row and other crops, and lead to significant reductions in input energy use and provide increased yield for biofuel crops. Over the course of this project the team at the University of Colorado Boulder, University of California Berkeley, and Colorado State University/Kansas State University investigated a wide range of printable biodegradable electronic materials and sensor designs for determining soil moisture and soil nitrate concentration. These efforts expanded the available materials set for printed soil degradable electronic materials, particularly for conductors, enabling high conductivity and stability. Printed soil moisture and nitrate sensors with suitable sensitivity and selectivity were developed and characterized. Low power and passive wireless electronic systems were integrated with the soil sensors, and testing was carried out with completed sensors to understand their functionality under agricultural conditions. Additionally, other sensor types enabled by the biodegradable materials set created during this project, such as soil microbial activity sensors, were also developed and demonstrated. Project outputs include 10 peer reviewed publications, 4 patent applications, 21 technical presentations, 3 PhD thesis, 10 media reports, 8 additional grants worth over $\$$6M, and the formation of 3 start-up companies.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

Assessing the Effectiveness of Large-Scale Environmental Restoration: Challenges and Opportunities

A recent National Academies consensus report addresses monitoring and assessment of cumulative effects of large-scale and multiple restoration projects within the context of long-term environmental change. Fines and penalties from the Deepwater Horizon oil spill in the Gulf of Mexico (GoM) have supported hundreds of restoration projects at spatial scales not often possible in the past. Here, in this paper, the report committee members and staff provide personal reflections from our time working on the study. We found that gaps in data collection, issues with data accessibility, and a lack of synthesis and analysis are hindering the ability to answer a basic question: What are the impacts of these many restoration efforts on improving ecosystem health and productivity in the GoM at the regional and Gulf wide scale? Restoration efforts are occurring in environments where many trends are changing and exhibiting higher variability than in the past, suggesting that previously successful restoration practices may no longer be adequate to compensate for the effects of environmental changes and variability. Our proposed approach to these challenges includes employing emerging monitoring technologies; using conceptual models; devising an adaptive management framework; rethinking restoration outcome goals; assessing cumulative effects; and undertaking rigorous synthesis and analysis of existing information on long-term environmental trends and restoration efforts. Restoration scientists and practitioners working in the GoM have an unprecedented opportunity to demonstrate large-scale environmental recovery if advances in monitoring, synthesis, assessment, and action are taken quickly. We are cautiously optimistic that, with mid-course adjustments, continued progress toward large-scale environmental recovery is possible.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Multi-Agent Hierarchical Deep Reinforcement Learning for HVAC Control With Flexible DERs

As electricity consumption in commercial and residential buildings continues to rise, reducing energy costs presents an increasing challenge. Heating, ventilating, and air-conditioning (HVAC) systems, which typically account for 40%-50% of a building's energy use, are prime targets for energy savings. Intelligent control of HVAC temperature through the exploitation of HVAC load flexibility brings significant potential to reduce energy consumption and electricity expenses. The nonlinear models of HVAC systems challenge traditional control methods, while the uncertainty introduced by HVAC load flexibility complicates distributed energy resource (DER) management using conventional optimal dispatch techniques. In response to these challenges, we propose a hierarchical multi-agent deep reinforcement learning (DRL) approach. The lower-level agents focus on balancing comfort and energy conservation, while the upper-level DRL agents optimize the use of DERs to reduce peak demand based on the control outcomes of the HVAC by the lower-level agents. Here, in the upper-level agents, we incorporate a multi-agent structure based on ensemble learning, which acts based on historical and current data without relying on precise load forecasting to address the delayed rewarding issue in DRL. This allows for the effective reduction of energy costs. The proposed method is tested using a real-world microgrid comprising 413 buildings in Southern California, and the results demonstrate that our approach can significantly reduce overall electricity bills while ensuring the comfort of consumers and residents.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

Development of Efficient Process for Manufacturing of Thermoplastic Composites with Tailored Properties (CRADA 511)

This is a collaborative effort between Battelle Memorial Institute as manager and operator of Pacific Northwest National Laboratory (PNNL) and ESI North America Inc. (“ESI” or “Participant”) to apply computation and data analytics to the challenge of light weighting with a focus on the battery enclosures of electric vehicles (EVs). EVs use heavy batteries to increase range and power. A complex-shaped battery enclosure is required to meet a host of challenging performance requirements. The ability to virtually develop composite parts such as battery enclosure with tailored properties to meet required performance will be highly valuable to the automotive industry. However, efficient simulation of composite-manufacturing processes remains a challenging issue since simulation involves multiscale models in space and time, highly non-linear and anisotropic behavior, strongly coupled multi-physics, and complex geometries. This work will advance the state of the art by reducing the computational burden of composite optimization by using simulation data from a limited number of configurations off-line and then developing a reduced order model (ROM) using data analytics and machine learning (ML). Develop a data driven approach to link features of the material and manufacturing processes to the mechanical properties of thermoplastic composite parts.

42 ENGINEERING↗
DOE OSTI2025

Collaborative: in situ visual analytics technologies for extreme scale combustion simulations

This project aims to drastically enhance the usability of in situ analysis and visualization for extreme-scale scientific simulations. Current exascale computing capabilities promise to offer greater predictive ability of simulations and to further push the frontiers of science and technology. However, to validate the simulation output at extreme scale, examine the modeled phenomena, and discover previously unknowns from the output data, the output must be reduced or transformed in situ as it is being generated during the simulation such that the amount of data to examine and store is kept to a minimum. Such in situ approaches allow us to process and analyze the data and any embedded geometry to an extent that would be prohibitively expensive, if not impossible, to perform as a post hoc task. While in situ processing has been demonstrated to be a feasible and promising approach, its full potential has not yet been leveraged. In this project, we have developed comprehensive enhancements to in situ technology based on probability distributions in data. Our research focuses on jointly developing new ways of interacting with massive statistical samples while creatively utilizing new state-of-the-art computational resources to push the boundaries of in situ exploration. Moreover, we have developed new time-dependent techniques to enable previously unattainable capabilities in areas such as intelligent simulation steering and precise feature identification. We have experimentally studied our design and implementation at NERSC and OLCF, and are able to leverage existing in situ infrastructures whenever possible. While the exemplar in this project is combustion, many other fields for which turbulent transport is important, e.g., fusion, climate, astrophysics among others, encounter similar issues as simulations scale up to the exascale. This project shows its potential to generate high impact on DOE missions since the resulting technology promises to improve scientists’ ability to rapidly and correctly interpret and tune extreme-scale simulations, leading to new scientific understanding and advancements.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Materials for Energy Management across the Electromagnetic Spectrum (MEMES) (LDRD Report)

Electrification of the United States has been a major driver of economic growth, powering industrialization, modern manufacturing, and the digital economy. Today, further economic and environmental benefits can be realized by improving the energy efficiency of the technologies we have come to rely on. This project focused on enhancing the energy efficiency of two cornerstone technologies of modern society: electronic displays and building climatization. While these two technologies operate in different parts of the electromagnetic spectrum (the visible and infrared, respectively), they share a common potential technological solution: electrically-driven displays that change reflectivity. In this work, we developed the first multipixel multicolor reflective display that achieves multi-colorization with fast-changing structural color. In the course of this demonstration, we quantified the structural changes that occur across different time and length scales and resolved device sealing issues, enabling operation of these devices over months (as opposed to days previously). We furthermore developed two reflectivity changing devices in the infrared and demonstrated how these technologies can be used to reduce climatization demands for both smart window and smart wall technologies. Implementing such a device in a scaled down mock room resulted in 10 degree Celsius change.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

Supply Chain Management in Cyber Grid Guard Framework

Grid modernization has impeded innovative power grid applications and energy resources that are increasingly distributed. Blockchain/distributed ledger technology (DLT) has the potential to enhance the resilience of the electric infrastructure, particularly in a decentralized and distributed environment. The benefits of blockchain are to ensure asset information and lifecycle events are secure and traceable and identify potential malicious modification of data. Oak Ridge National Laboratory (ORNL) has developed a framework, Cyber Grid Guard (CGG), incorporating blockchain. The system implements a low-energy, fast, and robust enhancement to system trustworthiness within and across electric grid systems, including substations, control centers, and metering infrastructures. Currently, one of the major concerns is supply chain attacks. There have been several recent attacks that have significantly impacted critical infrastructures and organizations around the world. This document focuses on how CGG can be used to address the supply chain issue.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

A successful start for mLife

mLife made its debut with the publication of the inaugurating issue on March 30, 2022. We are pleased to have published 42 high-quality articles, including reviews, research articles, and application notes, which cover microbial genetics, biochemistry and physiology, ecology and evolution, pathogenic and public health microbiology, and so forth. Nine of the articles concern technical innovations and applications in areas including genome editing, single-cell analysis, and database. To pay tribute to the great microbiologist Carl Woese on the 10th anniversary of his death, the Journal published a special section containing three thought-provoking articles in the last issue of the year. mLife and its collaborating publisher Wiley have made great efforts to increase the Journal's national and continental representation. As a result, the Journal received submissions from over 20 countries in five continents last year. The authors of the published articles are from China, United States, Great Britain, France, Canada, Ger many, Japan, Switzerland, and Austria.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

ASDFL: An adaptive super‐pixel discriminative feature‐selective learning for vehicle matching

Abstract There are a large number of cameras in modern transportation system that capture numerous vehicle images continuously. Therefore, automatic analysis of these vehicle images is helpful for traffic flow management, criminal investigations and vehicle inspections. Vehicle matching, which aims to determine whether two input images depict an identical vehicle, is one of the core tasks in vehicle analysis. Recent relevant studies have focused on local feature extraction instead of global extraction, since local details can provide crucial cues to distinguish between cars. However, these methods do not select local features; that is, they do not assign weights to local features. Therefore, in this research, we systematically study the vehicle matching task, and present a novel annotation‐free local‐based deep learning method called Adaptive super‐pixel discriminative feature‐selective learning (ASDFL) to address this issue. In ASDFL, vehicle images are segmented into clusters of super‐pixels of similar size by considering the location and colour similarities of pixels without using any component‐level annotation. These super‐pixels are deemed to be the virtual components of vehicles. Moreover, a convolutional neural network is used to extract the deep features of these virtual components. Thereafter, an instance‐specific mask generation module driven by the extracted global features is enhanced to produce a mask to select the most distinctive virtual components of each vehicle image pair in the feature space. Finally, the vehicle matching task is accomplished by classifying the selected virtual component features of each imaged vehicle pair. Extensive experiments on two popular vehicle identification benchmarks demonstrate that our method is 1.57% and 0.8% more accurate than the previous baselines in a vehicle matching task on the VeRi and VehicleID datasets, respectively, which demonstrates the effectiveness of our method.

Qin, Rong↗
DOE OSTI2025

Annual Status Report (FY 2024): Performance Assessment for the Integrated Disposal Facility

The purpose of this Annual Summary Report (ASR) for Fiscal Year (FY) 2024 is to evaluate the continued adequacy of the Integrated Disposal Facility (IDF) Performance Assessment (PA) and Disposal Authorization Statement (DAS). This report consolidates relevant monitoring data, modeling analyses, and regulatory reviews to demonstrate a reasonable expectation that the PA objectives and performance measures will be met, as required under DOE O 435.1. The ASR follows the guidance in DOE-STD-5002-2017, which provides a framework for maintaining the validity of the DAS through periodic assessment of facility performance and compliance with waste disposal requirements. The IDF is a near-surface disposal facility designed to receive and permanently dispose of low-level waste (LLW) and mixed low-level waste (MLLW) generated from Hanford Site operations. The facility consists of two double-lined disposal cells equipped with leak detection and leachates recovery systems to ensure environmental protection. Waste planned for disposal includes vitrified low-activity waste (LAW) and solid secondary waste (SSW) from the Hanford Waste Treatment and Immobilization Plant (WTP). At the end of FY 2024, the IDF had not yet received any waste, as it remains in a pre-operational state. Disposal activities will begin with the hot commissioning of the WTP LAW Vitrification Facility using the Direct-Feed Low-Activity Waste (DFLAW) approach in Calendar Year (CY) 2025. This ASR justifies the continued adequacy of the PA and DAS by reviewing key documents and data sources. these sources are listed in Table A-2 in Appendix A.4): The Operating Disposal Authorization Statement (ODAS) for the IDF (DOE-EM, 2021) remains in effect, with no outstanding conditions or key issues affecting its implementation. Based on the comprehensive review of PA analyses, monitoring data, and regulatory compliance activities, this ASR concludes that the IDF remains in compliance with DOE O 435.1, and there is reasonable assurance that the PA performance objectives will be met once disposal operations commence in CY 2025.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2022

Roadmap to Iodine and Mercury Abatement Materials Selection in Nuclear Waste Processing Off-Gas Streams

This work provides a guide for candidate mercury (Hg) and iodine-129 (129I) abatement material identification, screening, evaluation, technical gap identification, and bench-scale testing prioritization while also conceptualizing a materials deployment roadmap for implementation of new materials in the Hanford Waste Treatment and Immobilization Plant (WTP) Low-Activity Waste (LAW) Facility at Hanford in Richland, Washington, and elsewhere in the U.S. Department of Energy (DOE) complex for similar applications. The study was prompted by the need to replace the Kombisorb BAT-37 due to uncertainties in Hg and 129I capture performance and future availability for use in the WTP LAW Facility secondary off-gas system, specifically the Carbon Adsorber units. However, replacement of this material could also mitigate two other issues with Kombisorb BAT-37 (and its successor BAT II 37): (1) fire safety risk due to exothermic heat generated by adsorption reactions between the carbon material and Hg and (2) the risk of low retention of 129I in glass and subsequent downstream impacts on secondary liquid waste treatment at the Effluent Treatment Facility.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2025

Biologically Influenced Transport of the +3 Actinide Oxidation State from the WIPP Environment, Using Neodymium as an Analog

This report provides an update to a previously issued report on the bioassociation of the +3 actinide analog, neodymium, with Waste Isolation Pilot Plant (WIPP) microbial isolates (LCO ACP-32, Biologically Influenced Transport of the +3 Actinide Oxidation State from the WIPP Environment, Using Neodymium as an Analog ). Results contribute to updates for the biocolloid parameters for the 2026 Compliance Recertification Application (CRA-2026).

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗