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All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Comparing the cooling effectiveness of operationalisable urban surface combination scenarios for summer heat mitigation

Extreme summer heat in cities exacerbates the vulnerability of urban communities to heatwaves. Vegetative and reflective urban surfaces can help reduce urban heat. This study investigated the impacts of urban trees, green roofs and cool roofs on heat mitigation during average and extreme summer conditions in temperate oceanic Melbourne, Australia. We simulated the city climate using ‘The Air Pollution Model’ (TAPM) at a 1 km spatial resolution over 10 years, which according to our review of the literature, was the most prolonged period for simulation in Melbourne. During a widespread heatwave event, some of the tested scenarios with combined surface parameters could reduce the extreme values of the energy budget components- sensible heat, latent heat, and storage heat fluxes up to seasonal averages compared to the existing situation for Melbourne (control). The scenario with the highest (reasonable maximum) ground-level vegetation, green roofs, and cool roofs could reduce air temperatures up to 2.4 °C. The simulations suggest that a combined strategy with vegetative and high-albedo surfaces will deliver higher effectiveness with maximum cooling benefits and cost-effectiveness than individual strategies in cities. These results suggest the importance of collaborative strategic planning of urban surfaces to make cities healthier, sustainable, and liveable.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Autonomous Emergency Landing for Fixed-Wing Aircraft with Energy-Constrained Closed-Loop Prediction

Here this paper presents a new approach for autonomous motion planning for aircraft suffering from a loss-of-thrust emergency. Specifically, we show how modifications to the Closed-Loop Rapidly exploring Random Trees (CL-RRT) framework combined with controlled energy dissipation can enable rapid and effective kinodynamic motion planning. This CL-RRT Glide algorithm uses closed-loop prediction not only for node connections but also to estimate the remaining energy and prune infeasible paths. This greatly speeds up the search process, which is essential for emergency situations. In addition, we improve the ability of the gliding aircraft to reach a goal position and energy state. We do so by creating a Dissipative Total Energy Control Scheme (TECS). Dissipative TECS enables the glider to lose excess altitude in order to reach a desired energy level. Simulation results illustrate how the proposed methods enable faster motion planning. We also integrate the system into a small unmanned aerial vehicle system and experimentally demonstrate autonomous glide planning and execution during a motor-failure event. This type of algorithm can primarily benefit unmanned aircraft but can also serve to assist pilots in stressful emergency situations.

42 ENGINEERING↗
DOE OSTI2021

Acoustic and Genetic Approaches for Informing Population Status and Trends of Migratory Tree Bats

Through the systematic collection of acoustic and genetic data over time, a weight of evidence may be built that will support an improved understanding around population stability for migratory tree-roosting bat species. This information can be leveraged to infer the influence stressors have on population stability, and subsequently determine the level of response necessary to sustain healthy populations. Here, we synthesize the existing information related to bat population status and trends, uncertainty regarding existing estimates, current and novel methods for assessing population-level data, and recommendations for near-term and long-term studies to reduce uncertainty surrounding population data. Although this report focuses on wind energy development and bats, the methods used are broadly applicable and are being implemented by other industries, academia, government agencies, and nongovernmental organizations. Through efforts outside of the wind energy industry, it is likely we will gain a better understanding of bat populations, but the pace of data collection and analysis will be expedited by further participation by the wind energy industry. In return, the wind energy industry will benefit by contributing to these efforts either by 1) discovering that the population status of the species of interest is stable or growing, which may prevent the need for listing one or more species; or 2) recognizing the population status of certain species is in decline and implementing relatively moderate measures to reduce wind turbine-related mortality in the near term to stave off more stringent measures, which may be necessary if action is delayed.

17 WIND ENERGY↗
DOE OSTI2023

Productivity and Cost of Post-Tornado Salvage Logging in Upper Coastal Plain of South Carolina, USA

Salvage harvesting is common in the US South following natural disasters such as tornadoes and hurricanes; nevertheless, few studies have evaluated the productivity and costs of these harvests because of their geographic dispersion and the short interval between natural disasters and salvage harvesting. An Enhanced Fujita Scale 3 (EF3) tornado with winds in excess of 250 km per hour struck Aiken County, South Carolina in April of 2020, uprooting trees and severing other stems above breast height. The goal of this study was to estimate the productivity and cost of salvage harvesting in loblolly pine (Pinus taeda L.) stands following severe tornado damage. Salvage harvests were conducted with a rubber-tired drive-to-tree feller-buncher, grapple skidder, tracked loader, and chipper. All stems were chipped and used to produce energy; no roundwood was produced from the harvests. Elemental time-and-motion studies were conducted in three pulpwood-sized stands (<30 cm large-end diameter) and three sawtimber-sized stands (≥30 cm large-end diameter). Hourly harvesting costs were estimated using the machine rate method and per-ton costs were estimated using a modified version of the Auburn Harvesting Analyzer. Skidding productivity was low in each harvest unit, but especially so in the three pulpwood-sized stands because of stem breakage and low weight per stem. Harvesting costs averaged $\$$29.78 and $\$$19.97 (USD) per tonne (onboard truck) in the pulpwood- and sawtimber-sized stands, respectively. High salvage harvesting costs mean that landowners can expect significantly reduced stumpage prices from these harvests; nonetheless, landowners do benefit from reduced reforestation costs. Harvesting promptly after a tornado can reduce harvesting costs and increase timber value recovery.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Contrasting Responses of Soil Inorganic Carbon to Afforestation in Acidic Versus Alkaline Soils

Afforestation is recommended as an effective approach for carbon sequestration and environmental benefits. However, it remains less clear, and sometimes controversial, regarding how afforestation may impact soil inorganic carbon (SIC), a crucial component of the ecosystem carbon pool. Here, using field data from 619 afforested plots and 163 control plots across northern China, we investigated the relative and absolute differences in SIC between afforested and corresponding control plots. Our results suggested that afforestation increased SIC in acidic soils, while decreased SIC in alkaline soils. Fitting a linear mixed model and further a structure equation model, we found that afforestation-induced soil pH change was the most significant factor regulating SIC responses. In particular, SIC was more sensitive to pH change in more arid areas, where both soil pH and SIC stocks were high. Other factors could indirectly affect SIC responses to afforestation through modulating soil pH and soil organic carbon (SOC) dynamics. Moreover, afforestation-induced SIC changes also varied considerably among different species of tree plantations and across different soil depths. Importantly, in plantations of Pinus sylvestris var. mongholica, Pinus tabuliformis, and Populus spp., changes in SIC caused by afforestation were even comparable to that in SOC. Overall, our findings provide a data-based understanding on the comprehensive soil carbon dynamics following afforestation and its underlying mechanisms. With the increased use of afforestation and reforestation as nature-based solutions to climate change, their associated impacts on SIC need to be taken into account, especially in SIC-rich areas.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Learning curves for drug response prediction in cancer cell lines

Motivated by the size and availability of cell line drug sensitivity data, researchers have been developing machine learning (ML) models for predicting drug response to advance cancer treatment. As drug sensitivity studies continue generating drug response data, a common question is whether the generalization performance of existing prediction models can be further improved with more training data. We utilize empirical learning curves for evaluating and comparing the data scaling properties of two neural networks (NNs) and two gradient boosting decision tree (GBDT) models trained on four cell line drug screening datasets. The learning curves are accurately fitted to a power law model, providing a framework for assessing the data scaling behavior of these models. The curves demonstrate that no single model dominates in terms of prediction performance across all datasets and training sizes, thus suggesting that the actual shape of these curves depends on the unique pair of an ML model and a dataset. The multi-input NN (mNN), in which gene expressions of cancer cells and molecular drug descriptors are input into separate subnetworks, outperforms a single-input NN (sNN), where the cell and drug features are concatenated for the input layer. In contrast, a GBDT with hyperparameter tuning exhibits superior performance as compared with both NNs at the lower range of training set sizes for two of the tested datasets, whereas the mNN consistently performs better at the higher range of training sizes. Moreover, the trajectory of the curves suggests that increasing the sample size is expected to further improve prediction scores of both NNs. These observations demonstrate the benefit of using learning curves to evaluate prediction models, providing a broader perspective on the overall data scaling characteristics. A fitted power law learning curve provides a forward-looking metric for analyzing prediction performance and can serve as a co-design tool to guide experimental biologists and computational scientists in the design of future experiments in prospective research studies.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

Impact of Tree Age and Anatomical Fraction on Forest Residue Pyrolysis and Hydrotreating

Utilization of cost-advantaged biomass and waste resources to produce clean, domestic biofuels will be a key factor in decarbonizing transportation in the United States. This study investigates how the distribution of anatomical fractions (stem wood, bark, needles, branches) from pine forest residues of different ages impacts pyrolysis and hydrotreating processes, including product composition, carbon efficiency, economics, and overall sustainability. Commercially sourced 13- and 23-year-old loblolly pine residues were chosen to represent available resources; whole tree thinnings, and tops and branches from merchantable timber, respectively. Detailed characterization of the residue samples, product distributions, bio-oil, char, and gas characterization data are presented for 13 experiments, including the individual fractions, blends, and air-classified samples to reduce ash content. Pyrolysis mass balances averaged 97 +/- 3%. As expected, bio-oil yields were lower for the bark, needles, and blends with high proportions of these (53-56% dry basis), and highest for the debarked stem wood (67-71% dry basis). Lower yields were attributed to the high ash content of the needles, but also to the high lignin content in the bark (>50%), which is hypothesized to have led to increased char formation during pyrolysis. Higher-oil yield was generally correlated with total volatiles, hydrogen, oxygen, glucan, and mannan; somewhat correlated with xylan and acetyl content; and negatively correlated with total carbon, fixed carbon, nitrogen, sulfur, and extractives. Air classification of the residues was effective in reducing ash content (1.5% to 0.64%), extractives (~7% to 2%), and sulfur (0.04% to 0.02%), resulting in a small increase in yield, a significant increase in the GC-detectable proportion of oil (27% to 34%) and a net economic benefit. These experimental results were used to conduct technoeconomic and life cycle analyses, which are also summarized here.

biofuels↗
DOE OSTI2024

HarDWR - Harmonized Water Rights Records

A dataset within the Harmonized Database of Western U.S. Water Rights (HarDWR). For a detailed description of the database, please see the meta-record v2.0. Changelog v2.0 - Recalculated based on data sourced from WestDAAT - Changed using a Site ID column to identify unique records to using aa combination of Site ID and Allocation ID - Removed the Water Management Area (WMA) column from the harmonized records. The replacement is a separate file which stores the relationship between allocations and WMAs. This allows for allocations to contribute to water right amounts to multiple WMAs during the subsequent cumulative process. - Added a column describing a water rights legal status - Added "Unspecified" was a water source category - Added an acre-foot (AF) column - Added a column for the classification of the right's owner v1.02 - Added a .RData file to the dataset as a convenience for anyone exploring our code. This is an internal file, and the one referenced in analysis scripts as the data objects are already in R data objects. v1.01 - Updated the names of each file with an ID number less than 3 digits to include leading 0s v1.0 - Initial public release Description Here we present an updated database of Western U.S. water right records. This database provides consistent unique identifiers for each water right record, and a consistent categorization scheme that puts each water right record into one of seven broad use categories. These data were instrumental in conducting a study of the multi-sector dynamics of inter-sectoral water allocation changes though water markets (Grogan et al., *in review*). Specifically, the data were formatted for use as input to a process-based hydrologic model, Water Balance Model (WBM), with a water rights module (Grogan et al., *in review*). While this specific study motivated the development of the database presented here, water management in the U.S. West is a rich area of study (e.g., Anderson and Woosly, 2005; Tidwell, 2014; Null and Prudencio, 2016; Carney et al., 2021) so releasing this database publicly with documentation and usage notes will enable other researchers to do further work on water management in the U.S. West. We produced the water rights database presented here in four main steps: (1) data collection, (2) data quality control, (3) data harmonization, and (4) generation of cumulative water rights curves. Each of steps (1)-(3) had to be completed in order to produce (4), the final product that was used in the modeling exercise in Grogan et al. (*in review*). All data in each step is associated with a spatial unit called a Water Management Area (WMA), which is the unit of water right administration utilized by the state in which the right came from. Steps (2) and (3) required use to make assumptions and interpretation, and to remove records from the raw data collection. We describe each of these assumptions and interpretations below so that other researchers can choose to implement alternative assumptions an interpretation as fits their research aims. Motivation for Changing Data Sources The most significant change has been a switch from collecting the raw water rights directly from each state to using the water rights records presented in WestDAAT, a product of the Water Data Exchange (WaDE) Program under the Western States Water Council (WSWC). One of the main reasons for this is that each state of interest is a member of the WSWC, meaning that WaDE is partially funded by these states, as well as many universities. As WestDAAT is also a database with consistent categorization, it has allowed us to spend less time on data collection and quality control and more time on answering research questions. This has included records from water right sources we had previously not known about when creating v1.0 of this database. The only major downside to utilizing the WestDAAT records as our raw data is that further updates are tied to when WestDAAT is updated, as some states update their public water right records daily. However, as our focus is on cumulative water amounts at the regional scale, it is unlikely most records updates would have a significant effect on our results. The structure of WestDAAT led to several important changes to how HarWR is formatted. The most significant change is that WaDE has calculated a field known as `SiteUUID`, which is a unique identifier for the Point of Diversion (POD), or where the water is drawn from. This separate from `AllocationNativeID`, which is the identifier for the allocation of water, or the amount of water associated with the water right. It should be noted that it is possible for a single site to have multiple allocations associated with it and for an allocation to be able to be extracted from multiple sites. The site-allocation structure has allowed us to adapt a more consistent, and hopefully more realistic, approach in organizing the water right records than we had with HarDWR v1.0. This was incredibly helpful as the raw data from many states had multiple water uses within a single field within a single row of their raw data, and it was not always clear if the first water use was the most important, or simply first alphabetically. WestDAAT has already addressed this data quality issue. Furthermore, with v1.0, when there were multiple records with the same water right ID, we selected the largest volume or flow amount and disregarded the rest. As WestDAAT was already a common structure for disparate data formats, we were better able to identify sites with multiple allocations and, perhaps more importantly, allocations with multiple sites. This is particularly helpful when an allocation has sites which cross WMA boundaries, instead of just assigning the full water amount to a single WMA we are now able to divide the amount of water between the number of relevant WMAs. As it is now possible to identify allocations with water used in multiple WMAs, it is no longer practical to store this information within a single column. Instead the stAllocationToWMATab.csv file was created, which is an allocation by WMA matrix containing the percent Place of Use area overlap with each WMA. We then use this percentage to divide the allocation's flow amount between the given WMAs during the cumulation process to hopefully provide more realistic totals of water use in each area. However, not every state provides areas of water use, so like HarDWR v1.0, a hierarchical decision tree was used to assign each allocation to a WMA. First, if a WMA could be identified based on the allocation ID, then that WMA was used; typically, when available, this applied to the entire state and no further steps were needed. Second was the spatial analysis of Place of Use to WMAs. Third was a spatial analysis of the POD locations to WMAs, with the assumption that allocation's POD is within the WMA it should belong to; if an allocation still had multiple WMAs based on its POD locations, then the allocation's flow amount would be divided equally between all WMAs. The fourth, and final, process was to include water allocations which spatially fell outside of the state WMA boundaries. This could be due to several reasons, such as coordinate errors / imprecision in the POD location, imprecision in the WMA boundaries, or rights attached with features, such as a reservoir, which crosses state boundaries. To include these records, we decided for any POD which was within one kilometer of the state's edge would be assigned to the nearest WMA. Other Changes WestDAAT has Allowed In addition to a more nuanced and consistent method of assigning water right's data to WMAs, there are other benefits gained from using the WestDAAT dataset. Among those is a consistent categorization of a water right's legal status. In HarDWR v1.0, legal status was effectively ignored, which led to many valid concerns about the quality of the database related to the amounts of water the rights allowed to be claimed. The main issue was that rights with legal status' such as "application withdrawn", "non-active", or "cancelled" were included within HarDWR v1.0. These, and other water rights status' which were deemed to not be in use have been removed from this version of the database. Another major change has been the addition of the "unspecified water source category. This is water that can come from either surface water or groundwater, or the source of which is unknown. The addition of this source category brings the total number of categories to three. Due to reviewer feedback, we decided to add the acre-foot (AF) column so that the data may be more applicable to a wider audience. We added the ownerClassification column so that the data may be more applicable to a wider audience. File Descriptions The dataset is a series of various files organized by state sub-directories. In addition, each file begins with the state's name, in case the file is separate from its sub-directory for some reason. After the state name is the text which describes the contents of the file. Here is each file described in detail. Note that st is a placeholder for the state's name. stFullRecords_HarmonizedRights.csv: A file of the complete water records for each state. The column headers for each of this type of file are: state - The name of the state to which the allocations belong to. FIPS - The two digit numeric state ID code. siteID - The site location ID for POD locations. A site may have multiple allocations, which are the actual amount of water which can be drawn. In a simplified hypothetical, a farm stead may have an allocation for "irrigation" and an allocation for "domestic" water use, but the water is drawn from the same pumping equipment. It should be noted that many of the site ID appear to have been added by WaDE, and therefore may not be recognized by a given state's water rights database. allocationID - The allocation ID for the water right. For most states this is the water right ID, and what is recommended to use should a right be looked up on a given state's water rights database. The water amounts associated with these IDs tend to be finer scaled than those associated with siteID. It should be noted that some allocations may be extracted from multiple sites, particularly for larger Places of Use. ownerClassification - A classification of the types of owners for water rights. The most common is `Private` which incorporates a wide range of entities. Several classifications would be grouped into a government category, most of which are for the U.S. Federal Government. These allocations could be listed as "Federal", "United States of America", or as the names of any number of federal agencies. The last major grouping of entities is for "Native American"s. priorityDate - The date we use as the water right priority date for our modeling analysis. This is the legal priority date when it is available. However, for some rights, specifically from California and New Mexico, we used a pseudo priority date (e.g. well completion date or start of well drilling date) when a legal priority date was not available. The most questionable dates come from New Mexico, where the only date associated with certain water right records was the date the allocation was recorded in the database. As the allocation record creation tended to be within a few months of the filing of the application of the water right, from manually double checking the water rights, and our analysis focuses on aggregating water rights on the timescale of years, we determined it was acceptable to use such dates to include as many records as possible. primaryBeneficialUse - From the numerous state water use categories, WaDE categorized them into 21 categories WestDAAT. This column is the original WaDE category for the primary water use at the PoD site. allocationBeneficialUse - From the numerous state water use categories, WaDE categorized them into 21 categories for WestDAAT. This column is the original WaDE category

Economics↗
DOE OSTI2021

Resilience of terrestrial and aquatic fauna to historical and future wildfire regimes in western North America

Wildfires in many western North American forests are becoming more frequent, larger, and severe, with changed seasonal patterns. In response, coniferous forest ecosystems will transition toward dominance by fire-adapted hardwoods, shrubs, meadows, and grasslands, which may benefit some faunal communities, but not others. We describe factors that limit and promote faunal resilience to shifting wildfire regimes for terrestrial and aquatic ecosystems. We highlight the potential value of interspersed nonforest patches to terrestrial wildlife. Similarly, we review watershed thresholds and factors that control the resilience of aquatic ecosystems to wildfire, mediated by thermal changes and chemical, debris, and sediment loadings. We present a 2-dimensional life history framework to describe temporal and spatial life history traits that species use to resist wildfire effects or to recover after wildfire disturbance at a metapopulation scale. The role of fire refuge is explored for metapopulations of species. In aquatic systems, recovery of assemblages postfire may be faster for smaller fires where unburned tributary basins or instream structures provide refuge from debris and sediment flows. We envision that more-frequent, lower-severity fires will favor opportunistic species and that less-frequent high-severity fires will favor better competitors. Along the spatial dimension, we hypothesize that fire regimes that are predictable and generate burned patches in close proximity to refuge will favor species that move to refuges and later recolonize, whereas fire regimes that tend to generate less-severely burned patches may favor species that shelter in place. Looking beyond the trees to forest fauna, we consider mitigation options to enhance resilience and buy time for species facing a no-analog future.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

The phylogenetic roots of addiction: compulsive drug seeking, natural and drug-sensitive reward, and the acquisition of learned habits

Our rational faculties permit us humans to maximize the utility of our actions. We perform a fundamental type of cost-benefit analysis in which we frame a problem, assign values to the different paths, and then choose from among a set of available options, the course that promises the most favorable outcome. So why then does addiction appear to be so impervious to the associated costs, so unaffected by undesired consequences, and ultimately so resistant to cognitive oversight? The answer may likely be found in the fact that the drivers for compulsive drug seeking and drug taking are located in affective brain circuits, circuits that are structured and patterned by learning with repeated activation. These deep processes exhibit significant resistance to control by our cognitive faculties. The general consensus is that addiction arises from mechanisms that overvalue the magnitude of reward, discount the associated risks, and thereby bias individuals towards compulsive pursuit of addictive drugs. Behavioral disruption and dependence appear to arise at the intersection of a number of connected but separate phenomena: expectations for the occurrence of specific events, behaviors that seek encounters with them, the ability to notice and learn nonrandom, co-occurring conditions, the prediction and valuation of consequences, the forming of enduring memories, and the drivers of focused behavior through compulsion, habits, and acquired routines. It is important to recognize that each one of these individual faculties are present and well developed across the entire phylogenetic tree of bilateral metazoans. The goal of this special volume is dedicated to exploring the degree to which inherent elements can account for addiction and addiction-associated phenomena. In much of the literature on addiction, the underlying processes are often viewed as distinctly mammalian, arising, in part, from the strong cognitive capacities of this taxon. Some phenomena may even be regarded to exist only in primates, or even solely in humans. This supposition arises from the fact that studies are conducted almost exclusively in mammals and primates, while evolutionary antecedents of the behavior are rarely considered. A more comprehensive perspective that examines drug reward and reinforcement in a wider range of organisms demonstrates that many of the component traits are actually well developed across the greater metazoan lineage, and they may well predate the emergence of a mammalian clade by a wide margin. The collection of papers assembled here supports the notion that the capacity to associate cues and quences has not arisen in mammals de novo. Rather, the neural mechanisms for detecting contingencies and for predicting future outcomes are very deeply rooted across broad phylogenetic divisions. Our understanding of an ability to associate paired events has been enriched by work in invertebrate preparations in both classical and operant conditioning scenarios [Cook and Carew, 1986, 1989a, 1989b]. The ability to learn allows us to connect cues and behavioral actions to their associated consequences. Pavlovian conditioning enriches surrounding cues with predictive value. Outcomes with positive valence generate appetitive responses and approach to the associated cues, while those perceived as aversive bring cue avoidance and withdrawal. Humans are not the only life forms capable of such short- and long-term modulations of behavior

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Integrating Carbon Capture, Utilization, & Sequestration into Chemical Pulp Mills

The U.S. pulp and paper industry presents a unique and largely untapped opportunity for large- scale carbon dioxide removal (CDR). Unlike most industrial sectors, pulp mills rely heavily on biomass, meaning that much of their carbon emissions originate from atmospheric CO₂ that was recently captured by plants. If this biogenic CO₂ can be captured and permanently stored, pulp mills can be transformed from carbon emitters into net carbon removal facilities. This project was motivated by that opportunity and aimed to develop and evaluate integrated, low-cost strategies for capturing, utilizing, and sequestering CO₂ within existing chemical pulping operations. The scope of this work focused on four complementary innovations designed to integrate seamlessly into kraft pulp mill infrastructure: (1) in situ CO₂ capture within the recovery cycle, (2) oxy-fuel retrofitting of the rotary lime kiln to produce a high-purity CO₂ stream, (3) ex situ CO₂ capture and mineralization using pulp mill residues (dregs, grits, and lime mud), and (4) beneficial reuse of these residues as mineral carbonate fertilizers. The project combined process modeling, laboratory experimentation, life cycle assessment (LCA), and field trials to evaluate the technical feasibility, economic viability, and environmental impact of these approaches. The results demonstrate that pulp mills can serve as effective platforms for carbon removal when equipped with integrated carbon capture systems. Process modeling showed that combining sodium spiking with oxy-fuel calcination significantly enhances CO₂ capture efficiency while reducing costs by up to 31% compared to conventional configurations. Experimental work further revealed that calcination behavior in high-CO₂ environments differs substantially from traditional systems, leading to the development of a new kinetic model that predicts reaction rates under these conditions. This model provides essential design guidance for next-generation decarbonized lime kilns. In parallel, the project demonstrated that alkaline mineral residues generated during pulping operations can be repurposed as a sustainable alternative to agricultural lime. Across a wide range of soils in the southeastern United States, these materials performed equivalently to commercial lime in adjusting soil pH while offering lower greenhouse gas emissions and reduced cost. Field and greenhouse studies confirmed that crop and tree growth responses were comparable, supporting their viability as a drop-in replacement. This co-product pathway provides a practical utilization strategy that offsets costs and improves overall system economics. A major contribution of this project is the first comprehensive life cycle assessment of carbon removal in pulp and paper systems across multiple system boundaries. Results show that retrofitted mills can achieve carbon removal efficiencies ranging from 12% to 92%, depending on how the system is defined. This finding highlights a critical issue in carbon accounting: reported performance is highly sensitive to methodological choices. By explicitly quantifying these differences, this work provides valuable guidance for policymakers, carbon registries, and project developers working to standardize carbon removal metrics. From a commercialization perspective, the technologies investigated in this project are well- aligned with existing industrial infrastructure, minimizing the need for entirely new facilities. 3 DE-EE0009413 Industry engagement throughout the project—including collaboration with pulp and paper companies, equipment manufacturers, and carbon removal developers—has accelerated the transition from research to deployment. Notably, a commercial developer is actively pursuing carbon capture projects at pulp mills in the southeastern United States and has cited this research as a contributing foundation. The emergence of voluntary carbon markets and long-term offtake agreements further strengthens the business case for implementation. The broader public benefits of this work are significant. By enabling large-scale carbon removal using existing industrial systems, this approach offers a near-term pathway to reduce atmospheric CO₂ concentrations while supporting domestic manufacturing and rural economies. The reuse of industrial residues as fertilizers reduces reliance on mined materials, lowers costs for farmers, and decreases environmental impacts associated with conventional lime production. In addition, the project has supported workforce development by training graduate students and researchers in carbon capture technologies, helping to build capacity in a critical area of national interest. In conclusion, this project demonstrates that integrated carbon capture, utilization, and sequestration in pulp mills is both technically feasible and economically promising. By combining process innovation, experimental validation, and systems-level analysis, the work advances the understanding of how biomass-based industries can contribute to climate mitigation. The findings provide a strong foundation for commercial deployment and offer a scalable solution for transforming a major U.S. industry into a source of durable carbon removal.

09 BIOMASS FUELS↗
DOE OSTI2022

Understanding Fission Gas Bubble Distribution and Zirconium Redistribution in Neutron-irradiated U-Zr Metallic Fuel Using Machine Learning

U-10wt.% Zr (U-10Zr) based metallic fuel is the leading candidate for next-generation sodium cooled fast reactor in United States. Currently, Idaho National Laboratory (INL) has been the leading national laboratory for research, development, and demonstration (RD&D) on metallic fuel. Advanced post-irradiation characterization will help to understand fuel microstructure and property change during irradiation, benefiting fuel qualification for commercial application. Characterization capabilities ranging from sub-nanometer to micrometer, such as scanning electron microscopy (SEM), focused ion beam (FIB) sampling, transmission electron microscopy (TEM) characterization, and local thermal conductivity microscopy (TCM), have been utilized recently on irradiated U-10Zr fuel samples to gain a better understanding of nuclear fuel microstructure and property evolution inside a reactor. The FIB/SEM coupled with energy dispersive X-ray spectroscopy (EDS) can capture the essential information to achieve better understanding of fuel behaviors. Inside a nuclear reactor, the phase and microstructure of U-10Zr is constantly changing under neutron bombardment. For example, the gaseous fission product atoms have a limited solubility inside fuel matrix and tend to precipitate out in bubble form, which not only contribute to fuel thermal conductivity degradation but also provide a shortcut for movement of fission products, i.e. lanthanides. The resultant deposition of lanthanides at the cladding inner surface will potentially trigger a chemical reaction/interaction between nuclear fuel and cladding at reactor operational conditions, threatening fuel integrity and safety. FIB/SEM coupled with EDS can provide the fission bubble information as well as probe into phase separation or Zr redistribution, which is fundamental to predict the fuel performance. With high velocity image data generating method, such as FIB/SEM, an automatic way to extract the microstructural information quantitively can better serve the needs from post irradiation characterization. A trained machine learning model, named Decision Tree, is employed to generate a bubble classifier and to categorize bubbles into three categories: isolated bubble, connected without lanthanides, and connected with lanthanides bubbles[3]. This work presents a showcase of this approach on six regions of a fuel cross-section along the radial temperature gradient. We obtained distributions of bubble categories and porosity rates along the six regions. Moreover, a secondary phase U-Zr2 was determined and found on regions 5 and 6. The secondary phase fraction was increasing from 15.61% in region 5 to 34.79% in region 6 based on this approach . This quantitative data offers insights into the lanthanide migration and potentially thermal conductivity degradation. This information from machine learning will be fed into fuel design code for better prediction of fuel performance.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2021

Big Data For Operation and Maintenance Cost Reduction

The purpose of this research is to develop a first-of-a-kind framework for integrating Big Data capability into the daily activities of our current fleet of nuclear power plants. Big Data is traditionally defined as data sets with high volume, velocity, and heterogeneity, and the existing Big Data analytics capabilities are now widely popular in fields such as finance, weather, e-commerce, healthcare and sports. In the nuclear industry, while the volume and velocity of data may present computational challenges for existing analytics capabilities, data heterogeneity are seen to present the major challenge. This research project mainly focuses on incorporating the wide range of data heterogeneities in nuclear power plants into an integrated Big Data Analytics capability. The primary end-product of this project is a Big Data framework that is capable of dealing with the large volume and heterogeneity of the data found in nuclear power plants to extract timely and valuable information on equipment performance. The framework can generate system insights that are actionable relations between measurable impacts and the corresponding maintenance action plans and enable optimization of plant operation and maintenance based on the extracted information. The developed framework is capable of handling heterogeneous data including both image data and time-series sensor data. Specifically, this developed framework includes the following components. The first component is an overarching maintenance ontology which includes system insights required by maintenance optimization. The maintenance ontology interacts with other components in the developed framework. The second component handles Piping & Instrumentation Diagram (P&ID) data. It can be used to extract system components and their relations automatically from the P&IDs. This extracted information is stored in the first component, i.e., maintenance ontology, and is also used as input to the third component, i.e., a tool for generating the fault tree for the corresponding system. The generated fault tree in turn is stored in the ontology for assessing risk that is used as a criterion in maintenance policy optimization. The fourth component is a tool for inferring the parameters in the Markov degradation model for a nuclear system. It uses basic information from the ontology. The fifth component is a tool for assessing the degradation level using sensor measurement data, for example, pressure, flowrate. This tool can be used for determining corrective maintenance actions. The results obtained from components four and five are returned to the ontology. The sixth component of the framework is a tool for optimizing the maintenance policy for a nuclear system of interest. It takes certain basic information from the ontology, e.g., costs of maintenance actions and system failures, as input, and returns the optimal maintenance policy to the ontology. This tool can be used for determining predictive maintenance actions. A set of experiments have also been conducted to verify the algorithms developed in this project for nuclear system degradation monitoring. The experiments are based on four solenoid valves, similar to the ones used in nuclear power plants. The analyses based on the experimental data using two algorithms, i.e., the Randomized Window Decomposition (RWD) algorithm and the particle filtering algorithm, and the results are introduced in the report. The Big Data framework developed in this project can be used as a support tool in daily activities of plant operation and maintenance and will reduce current costs while maintaining or improving safety levels. Overall, the project will not only benefit existing reactors, however it will open new frontiers to realize the long overdue value of Big Data Analytics in the nuclear sphere.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2026

Ecological and genomic variation in ectomycorrhizal fungal exploration types

Ectomycorrhizal fungi (EMF) produce mycelia with variable extension and complexity, which can be classified according to soil ‘exploration types’ (ETs). ETs have received attention as one of the few mycorrhizal trait frameworks, but without an empirical classification of ET functional diversity and environmental preferences, understanding and interpreting EMF biogeographic patterns has been difficult. We conducted a synthesis combining: comparative EMF genomics to describe functional divergence in decomposition and nutrient cycling genes across ETs; and EMF trait distribution modeling across continental Europe, pairing soil and root EMF surveys to establish biogeographic ET niche profiles. We demonstrate a signature of ETs encoded in EMF genomes, which is independent from phylogeny and linked to biomass production strategies. EMF ET relative abundances were separated by soil, root, and dominant tree leaf type habitats and exhibited unique correlations with forest biotic (e.g. plant productivity and plant pathogen densities) and abiotic (e.g. nitrogen deposition and soil pH) conditions. These findings support a theory that EMF niche partitioning can be partially explained by extraradical mycelial traits, with underlying variation in ET biogeography likely arising from distinct decomposition and nutrient cycling potentials. We also identify important limitations to this trait framework and provide a guided outlook for future research.

biogeography↗
DOE OSTI2020

Automatic Classification of Biological Targets in a Tidal Channel Using a Multibeam Sonar

Multibeam sonars are widely used for environmental monitoring of fauna at marine renewable energy sites. However, they can rapidly accrue vast volumes of data, which poses a challenge for data processing. Here, using data from a deployment in a tidal channel with peak currents of 1–2 m s –1 , we demonstrate the data-reduction benefits of real-time automatic classification of targets detected and tracked in multibeam sonar data. First, we evaluate classification capabilities for three machine learning algorithms: random forests, support vector machines, and k-nearest neighbors. For each algorithm, a hill-climbing search optimizes a set of hand-engineered attributes that describe tracked targets. Here, the random forest algorithm is found to be most effective—in postprocessing, discriminating between biological and nonbiological targets with a recall rate of 0.97 and a precision of 0.60. In addition, 89% of biological targets are correctly classified as either seals, diving birds, fish schools, or small targets. Model dependence on the volume of training data is evaluated. Second, a real-time implementation of the model is shown to distinguish between biological targets and nonbiological targets with nearly the same performance as in postprocessing. From this, we make general recommendations for implementing real-time classification of biological targets in multibeam sonar data and the transferability of trained models.

16 TIDAL AND WAVE POWER↗