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All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Uncertainty Quantification in GADRAS Inverse Modeling

The Gamma Detector Response and Analysis Software (GADRAS) package includes an inverse modeling tool that is helpful in identifying characteristics of unknown radioactive materials. Traditionally, uncertainties in this analysis were derived solely from measurement data quality and the fit of synthetic spectra. This paper aims to rigorously quantify additional sources of uncertainty, focusing on uncertainties arising from measurements being analyzed, Detector Response Function (DRF) characterization, and DRF extrapolation. Applying these findings to the BeRPBall benchmark data set, we demonstrated the impact of these uncertainties on plutonium and polyethylene estimates. The results underscore the importance of incorporating diverse uncertainty sources to enhance the accuracy and reliability of GADRAS’s inverse modeling capabilities.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2025

TropiRoot 1.0: Database of tropical root characteristics across environments

Tropical ecosystems contain the world's largest biodiversity of vascular plants. Yet, our understanding of tropical functional diversity and its contribution to global diversity patterns is constrained by data availability. This discrepancy underscores an urgent need to bridge data gaps by incorporating comprehensive tropical root data into global datasets. Here, we provide a database of tropical root characteristics. This new database, TropiRoot 1.0, will be instrumental in evaluating an array of hypotheses pertaining to root functional ecology and plant biogeography, both within the tropics and relative to other global biomes. The data compilation was conducted by the TropiRoot Initiative, in partnership with the Fine-Root Ecology Database (FRED) and the Global Root Trait (GRooT) database, Colorado State University (CSU) and the Smithsonian Tropical Research Institute (STRI). Literature search and data extraction were conducted between 2020 and 2024. Literature was identified using Web of Science, Scopus, and complemented using the expert knowledge of members of TropiRoot. To provide broad environmental and geographical distributions, literature searches included root characteristics (traits) across global change drivers, natural gradients, and from different continents. We adopted FRED standardized data columns and streamlined the format to enhance accessibility for data extraction across various user groups. This optimized framework resulted in a smaller, yet comprehensive datasheet. To make the database compatible with other global root trait initiatives, column identification was standardized following the codes provided by FRED. These efforts culminated in data extracted from 104 new sources, resulting in more than 8000 rows of data (either species or community data). Most of the data in TropiRoot 1.0 include root characteristics such as root biomass, morphology, root dynamics, mass fraction, architecture, anatomy, physiology, and root chemistry. This initiative represents a 30% increase in the currently available data for tropical roots in FRED. TropiRoot 1.0 contains root characteristics from 25 different countries, where seven are located in Asia, six in South America, five in Central America and the Caribbean, four in Africa, two in North America, and 1 in Oceania. Due to the volume of data, when ancillary data were available, including soil data, these data were either extracted and included in the database or its availability was recorded in an additional column. Multiple contributors checked the entries for outliers during the collation process to ensure data quality. For text-based observations, we examined all cells to ensure that their content relates to their specific categories. For numerical observations, we ordered each numerical value from least to greatest and plotted the values, checking apparent outliers against the data in their respective sources and correcting or removing incorrect or impossible values. Some data (soil and aboveground) have different columns for the same variable presented in different units, including originally published units, but root characteristics data had units converted to match those reported in FRED. By filling a gap from global databases, TropiRoot 1.0 expands our knowledge of otherwise so far underrepresented regions and our ability to assess global trends. This advancement can be used to improve tropical forest representation in vegetation models. The data are freely available and should be cited when used.

FRED↗
DOE OSTI2025

Data from TropiRoot 1.0 database: tropical root characteristics across environments

TropiRoot 1.0 is a new tropical root database with root characteristics across environment gradients. It has data extracted from 104 new sources, resulting in more than 8000 rows of data (either species or community data). Most of the data in TropiRoot 1.0 includes root characteristics such as root biomass, morphology, root dynamics, mass fraction, architecture, anatomy, physiology and root chemistry. This initiative represents an approximately 30% increase in the currently available data for tropical roots in the Fine Root Ecology Database (FRED). TropiRoot 1.0, contains root characteristics from 25 different countries where seven are located in Asia, six in South America, five in Central America and the Caribbean, four in Africa, two in North America, and 1 in Oceania. Due to the volume of data, when ancillary data was available, including soil data, these data was either extracted and included in the database or their availability was recorded in an additional column. Multiple contributors checked the entries for outliers during the collation process to ensure data quality. For text-based observations, we examined all cells to ensure that their content relates to their specific categories. For numerical observations, we ordered each numerical value from least to greatest and plotted the values, checking apparent outliers against the data in their respective sources and correcting or removing incorrect or impossible values. Some data (soil and aboveground) have different columns for the same variable presented in different units, including originally published units, but root characteristics data had units converted to match the ones reported in FRED. By filling a gap from global databases, TropiRoot 1.0 expands our knowledge of otherwise so far underrepresented regions, and our ability to assess global trends. This advancement can be used to improve tropical forest representation in vegetation models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Deep-learning methods for contrast enhancement and artifact reduction in cryo-electron tomography: a systematic analysis of the state of the art and proposed improvements

Cryo-electron tomography (cryo-ET) has emerged as the preferred technique for visualizing the organization of macromolecular complexes in situ and resolving their structures at subnanometre resolution [Tegunov et al. (2021)View full citation, Nat. Methods, 18, 186–193]. Despite improvements in data quality as a result of advances in detector technology, microscope stability and stage precision, the analysis and interpretation of tomograms remains challenging due to a low signal-to-noise ratio and reconstruction artifacts stemming from experimental constraints in specimen tilt during data collection resulting in a missing wedge in the Fourier space. Recently, self-supervised deep-learning methods have been proposed for contrast enhancement and reduction of resolution anisotropy in reconstructed tomograms. Here, we evaluate several state-of-the-art deep-learning methods which aim to improve the interpretability of cryo-ET reconstructions, with a focus on their performance on downstream tasks of template matching, sub­tomogram averaging and segmentation. We propose new training architectures and a loss function based on Fourier shell correlation that show improved performance over the standard U-Net with L1/L2 losses. We demonstrate our analysis on four diverse experimental datasets: purified 80S ribosomes, in situ Chlamydomonas reinhardtii, immature HIV-1 virus-like particles and INS-1E cells.

contrast enhancement↗
DOE OSTI2025

Post-composing ontology terms for efficient phenotyping in plant breeding

Abstract Ontologies are widely used in databases to standardize data, improving data quality, integration, and ease of comparison. Within ontologies tailored to diverse use cases, post-composing user-defined terms reconciles the demands for standardization on the one hand and flexibility on the other. In many instances of Breedbase, a digital ecosystem for plant breeding designed for genomic selection, the goal is to capture phenotypic data using highly curated and rigorous crop ontologies, while adapting to the specific requirements of plant breeders to record data quickly and efficiently. For example, post-composing enables users to tailor ontology terms to suit specific and granular use cases such as repeated measurements on different plant parts and special sample preparation techniques. To achieve this, we have implemented a post-composing tool based on orthogonal ontologies providing users with the ability to introduce additional levels of phenotyping granularity tailored to unique experimental designs. Post-composed terms are designed to be reused by all breeding programs within a Breedbase instance but are not exported to the crop reference ontologies. Breedbase users can post-compose terms across various categories, such as plant anatomy, treatments, temporal events, and breeding cycles, and, as a result, generate highly specific terms for more accurate phenotyping.

Mathematical & Computational Biology↗
DOE OSTI2026

United States Nuclear Power Reactor Used Nuclear Fuel Database and Applications

The Unified Database (UDB) within STANDARDS serves as the foundational data infrastructure for managing the United States' spent nuclear fuel inventory of 315,111 discharged assemblies totaling 91,036 metric tons of heavy metal. The database organizes this complex inventory through over 200 interconnected tables structured into eight primary attribute categories, supporting integrated analyses across storage, transportation, and disposal domains. Data enters the UDB through the GC-859 Nuclear Fuel Data Survey, which transitioned to web-based collection in 2023, improving data quality through real-time validation. The UDB enables automated generation of input files for nuclear safety analyses, reducing preparation time from weeks to hours while maintaining traceability. Applications include national inventory reporting, Certificate of Compliance assessments, and facility optimization. The three-tier distribution model balances accessibility with security requirements for federal agencies, national laboratories, and research organizations. The UDB provides essential data infrastructure as spent fuel management transitions from site-specific to integrated national campaigns.

Stefanovic, Peter↗
DOE OSTI2024

Informing forest carbon inventories under the Paris Agreement using ground-based forest monitoring data

Human interactions with forests have shaped Earth's climate for millennia and will continue to do so as we target net-zero emission goals. Accurately characterizing these climate impacts requires making reliable forest carbon data available for forest monitoring and planning. Here, we develop a semi-automated process for submitting forest carbon measurements from the largest relevant scientific database to the International Panel on Climate Change's Emission Factor Database, which currently has sparse forest carbon data. Building this bridge from scientific research to international policy is an important step towards managing forests in a net-zero motivated future. Humans have been influencing Earth's climate via transformative impacts on forests for millennia, and forests are now recognized as critical to climate change mitigation under the Paris Agreement. The efficacy of climate change mitigation planning and reporting depends on quality data on forest carbon (C) stocks and changes. The Emission Factor Database (EFDB) of the International Panel on Climate Change (IPCC) is intended to be a definitive source for such data, but needs comprehensive and well-documented data to be so. To facilitate submission of forest C estimates from scientific studies to EFDB, we develop and document a process for semi-automated data submission from the Global Forest C database (ForC v4.0), which is the largest compilation of ground-based forest C estimates. We then assess the data currently available through ForC and provide recommendations for improving forest data collection, analysis, and reporting. As of September 2024, ForC contained ~19,286 records potentially relevant to EFDB, 1068 of which had been submitted and posted to EFDB. These represented 19% of the total EFDB records for forest land. Records were unevenly distributed across variables and geographic regions. ForC records (37%) reviewed could not be submitted because the original publication lacked required information. In the future, ground-based forest C estimates should target gaps in the record, and studies should ensure that they report all information necessary for inclusion in EFDB. Given that climate change is rapidly impacting the world's forests, timely reporting of recent estimates will be critical to accurate forest C inventories.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

AI Applications to Physics Experiments at Jefferson Lab

We survey how AI/ML is being deployed across Jefferson Lab's experimental and accelerator programs. In EPSCI, Hydra applies computer vision to automate real-time data-quality monitoring across all four experimental halls, replacing manual inspection of hundreds to thousands of histograms per shift. AIEC (AI Experiment Controls) uses ML to stabilize drift chamber gains and is now part of standard CEBAF production running, while AI Optimized Polarization (AIOP) targets autonomous control of polarized targets and photon beam angular alignment. In CASA, cavity fault classification models identify faulted cavities and trip types from waveform data with ~85% and ~78% agreement to labeled data, respectively, and are deployed in production; a separate effort applies LLMs and hybrid search to make the CEBAF operations logbook AI-ready. QCD-focused work includes transformer- and GAN-based generative models for particle-level event simulation, with distributed GAN training scaling studies on Polaris. Additional efforts span ML-on-FPGA for the EIC and a new Data Science Department coordinating anomaly detection, uncertainty quantification, and HPC-scalable ML lab-wide. Collectively, these projects illustrate AI's growing role in improving efficiency across JLab's nuclear physics mission.

Mei, Xinxin [Thomas Jefferson National Accelerator↗
DOE OSTI2024

Graph-learning approach to combine multiresolution seismic velocity models

SUMMARY The resolution of velocity models obtained by tomography varies due to multiple factors and variables, such as the inversion approach, ray coverage, data quality, etc. Combining velocity models with different resolutions can enable more accurate ground motion simulations. Toward this goal, we present a novel methodology to fuse multiresolution seismic velocity maps with probabilistic graphical models (PGMs). The PGMs provide segmentation results, corresponding to various velocity intervals, in seismic velocity models with different resolutions. Further, by considering physical information (such as ray path density), we introduce physics-informed probabilistic graphical models (PIPGMs). These models provide data-driven relations between subdomains with low (LR) and high (HR) resolutions. Transferring (segmented) distribution information from the HR regions enhances the details in the LR regions by solving a maximum likelihood problem with prior knowledge from HR models. When updating areas bordering HR and LR regions, a patch-scanning policy is adopted to consider local patterns and avoid sharp boundaries. To evaluate the efficacy of the proposed PGM fusion method, we tested the fusion approach on both a synthetic checkerboard model and a fault zone structure imaged from the 2019 Ridgecrest, CA, earthquake sequence. The Ridgecrest fault zone image consists of a shallow (top 1 km) high-resolution shear-wave velocity model obtained from ambient noise tomography, which is embedded into the coarser Statewide California Earthquake Center Community Velocity Model version S4.26-M01. The model efficacy is underscored by the deviation between observed and calculated traveltimes along the boundaries between HR and LR regions, 38 per cent less than obtained by conventional Gaussian interpolation. The proposed PGM fusion method can merge any gridded multiresolution velocity model, a valuable tool for computational seismology and ground motion estimation.

Geochemistry & Geophysics↗
DOE OSTI2024

Report on Initial Sodium Testing on the Thermal Hydraulic Experimental Test Article (THETA) (Fiscal Year 2024 Final Report)

The Thermal Hydraulic Experimental Test Article (THETA) is a facility that is used to develop sodium components and instrumentation as well as to acquire experimental data for validation of reactor thermal hydraulic and safety analysis codes. The facility simulates nominal thermal hydraulic conditions as well as protected/unprotected loss of flow accidents in a sodium-cooled fast reactor (SFR). High fidelity distributed temperature profiles of the developed flow field may be acquired with Rayleigh backscatter based optical fiber temperature sensors. The facility was designed in partnership with systems code experts to tailor the experiment to ensure the most relevant and highest quality data for code validation. THETA is comprised of a traditional primary coolant and secondary coolant system. The primary system is submerged in the pool of sodium and consists of a pump, electrically heated core, intermediate heat exchanger, and connected piping and thermal barriers (redan). The secondary system, located outside of the sodium pool, consists of a pump, sodium to air heat exchanger, and connected piping and valves. In fiscal year 2023, thermal stratification tests were completed with the primary system online, while the secondary system was being constructed [1]. These tests had shown that the core barrel and intermediate heat exchanger (IHX) outlet required increased thermal insulation. The THETA primary system was removed from METL, cleaned, thermal insulators installed, and then inserted into METL Test Vessel 4. At the time of this writing the THETA primary and secondary system are operational. During this fiscal year 100+ hours of testing was completed to characterize thermal hydraulic phenomena associated with steady state and transient conditions in a pool type liquid metal cooled reactor. A majority of the testing campaign was completed to satisfy the experimental data acquisition requirements for the GAIN Voucher with Oklo, CRADA 2021-21121. THETA is still operational at the time of this publication and future testing is planned for fiscal year 2025. Work is underway to publish existing and future data to an online database to facilitate collaboration with SFR engineers looking to validate their systems code or computational fluid dynamics models.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2026

Inference of the linear matter power spectrum at z = 0 using DESI DR1 Full-Shape data

Measurements of galaxy distributions at large cosmic distances capture clustering from the past. In this study, we use a cosmological model to translate these observations into the present-day galaxy distribution. Specifically, we reconstruct the 3D linear matter power spectrum at redshift z = 0 using Dark Energy Spectroscopic Instrument (DESI) Year 1 (DR1) galaxy clustering data and Cosmic Microwave Background (CMB) observations, assuming the ΛCDM model, and compare it to the result assuming the w 0 w a CDM model. Building on previous state-of-the-art methods, we apply Effective Field Theory (EFT) modelling of the galaxy power spectrum to account for small-scale effects in the 2-point statistics of galaxy data. Implementation of the EFT approach improves the modelling of the galaxy power spectrum, providing a more robust consistency test of the assumed cosmological model. By casting both CMB and galaxy clustering observations, spanning distinct redshift regimes, into k-space, we can identify discrepancies between the datasets of different redshifts, which would indicate potential inaccuracies in the assumed expansion history. While previous studies have shown consistency with ΛCDM, this work extends the analysis with higher-quality data to further test the expansion histories of both ΛCDM and w 0 w a CDM. Our findings show that both ΛCDM and w 0 w a CDM provide consistent fits to the linear matter power spectrum recovered from DESI DR1 data.

cosmological parameters from LSS↗
DOE OSTI2025

Ocelot: An Interactive, Efficient Distributed Compression-As-a-Service Platform With Optimized Data Compression Techniques

Large volumes of data generated by scientific simulations, genome sequencing, and other applications need to be moved among clusters for data collection/analysis. Data compression techniques have effectively reduced data storage and transfer costs. However, users' requirements on interactively controlling both data quality and compression ratios are non-trivial to fulfill. Here, we propose a novel Compression-as-a-Service (CaaS) platform called Ocelot with four important contributions: (1) It offers real-time visualization, interactive compression, and transfer of scientific datasets. (2) It incorporates new strategies for compressing diverse types of datasets more effectively than traditional methods. (3) It provides an effective method for estimating the compression ratio and execution time of compression tasks. (4) Experiments on multiple real-world datasets on geographically distributed computers show that Ocelot can significantly improve data transfer efficiency with a performance gain of more than 10x in computing clusters with relatively slow networks.

compression as a service (CaaS)↗
DOE OSTI2024

Field Validation of MVA Technology for Offshore CCS: Novel Ultra-High-Resolution 3D Marine Seismic Technology (P-Cable) (Final Report)

The objectives of the proposed study were to deploy and validate a specific monitoring technology, high-resolution 3D marine seismic (HR3D), appropriate for large-demonstration and commercial-scale offshore CCS sites. The project accomplished successful acquisition two HR3D seismic surveys. The first HR3D dataset was over the offshore injection site of the Tomakomai, Japan integrated pilot CCS project, which at the time of survey acquisition was actively injecting CO 2 . The first survey also represented a successful international collaboration between the DOE NETL program and Japan’s national CCS program and was the first successful acquisition and use of HR3D over an active CO 2 injection site (Meckel, Feng et al. 2019). The Tomakomai HR3D survey successfully tested a novel 4-streamer HR3D system array in which, for the first time, no cross-cable (aka “P-Cable”) was utilized and only four GeoEel streamers were used instead of the standard 12-streamer configuration. Consequently, this was not, strictly speaking, a deployment of the “P-Cable” system of (Planke and Berndt 2004) but rather a modified version, thereof, and it is the first known demonstration of the modified system configuration. One very positive outcome from the Japanese collaboration earlier in the project was the ability to learn from the Japanese how they used tail buoys with GPS to determine the position of the seismic source and receivers in time and space. Based on that experience, GCCC designed and built six GPS receivers that could be used to position the streamer receivers and the seismic source via tail buoys. A fundamental advance that was made on the original design, was the ability to directly power the tail buoy GPS units and transfer data through the streamers (i.e., vs. the batteries used at Tomakomai). The bulkiness of the GPS batteries caused drag and episodic surging of the buoys, which affected data quality by lifting up the tail end of the streamers so the receivers were not at the same depth. The units were tested onshore for accuracy and functionality, and the design was subsequently and successfully tested in marine acquisition mode during the SLP survey acquisition. The marine acquisition test and survey satisfied Subtasks 2.2.2, Novel Positioning Technology Selection and Subtask 2.2.3, Novel Positioning Technology Deployment. Results of the novel positioning technology selection (Subtask 2.2.2) were considered successful and will be incorporated in future HR3D seismic acquisition projects to reduce costs, improve deployment safety at sea, and integrate both seismic and data recording via a single data transfer through the streamers to the recording system. The project also established a permitting process through NETL NEPA compliance, which included an Environmental Assessment in a marine setting and is required for conducting these types of surveys using Federal funding. The permitting process charted a “boilerplate,” which can allow future surveys related to other funded projects to move forward more expeditiously. Future improvements that could be considered are more robust seals on the GPS module and stronger materials (especially joints) on tail buoy fabrication. These would increase fixed costs, but would be advisable and probably more economic long-term if multiple HR3D surveys are planned. Project Accomplishments include: • Pre-survey Sensitivity Study • Marine geochemistry methods and data analysis • Successful HR3D seismic dataset acquired @ Tomakomai active CO 2 injection marine site • Developed advanced seismic processing techniques • No NRMS anomalies detected in overburden; Demonstration of containment • Repeatability study • Second survey collected @ San Luis Pass, TX • 4D application using positioning techniques developed in the project for monitoring were successful

3D seismic GPS positioning↗
DOE OSTI2025

Vision Foundation Models in Remote Sensing: A survey

Artificial intelligence (AI) technologies have profoundly transformed the field of remote sensing (RS), revolutionizing data collection, processing, and analysis. Traditionally reliant on manual interpretation and task-specific models, RS research has been significantly enhanced by the advent of foundation models (FMs)—large-scale pretrained AI models capable of performing a wide array of tasks with unprecedented accuracy and efficiency. This article provides a comprehensive survey of FMs in the RS domain. We categorize these models based on their architectures, pretraining datasets, and methodologies. Through detailed performance comparisons, we highlight emerging trends and the significant advancements achieved by those FMs. Additionally, we discuss technical challenges, practical implications, and future research directions, addressing the need for high-quality data, computational resources, and improved model generalization. Our research also finds that pretraining methods, particularly self-supervised learning (SSL) techniques like contrastive learning (CL) and masked autoencoders (MAEs), remarkably enhance the performance and robustness of FMs. This survey aims to serve as a resource for researchers and practitioners by providing a panorama of advances and promising pathways for the continued development and application of FMs in RS.

data models↗
DOE OSTI2025

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↗
DOE OSTI2026

Connecting ambient toxicity testing with community-level responses of benthic macroinvertebrates in an impacted stream in East Tennessee, USA

Single-species laboratory toxicity tests are a standard tool for evaluating potential impairment of freshwater systems; however, it remains uncertain how well they reflect community-level impacts in natural environments. This study presents a multi-decadal dataset (2005-2025) pairing ambient toxicity testing with macroinvertebrate surveys along Bear Creek on the Oak Ridge Reservation (Tennessee, USA) downstream of an industrial complex to assess the ability of laboratory tests using stream water to track community-level effects. Biannual three-brood Ceriodaphnia dubia tests from 2005 to 2025 often showed reduced reproduction at select sites. Integrating water quality data showed strong positive correlations between sublethal toxicity and specific conductance. Macroinvertebrate diversity metrics, family-level occurrence, and densities were also associated with conductance and contemporaneous C. dubia responses. Laboratory-measured sublethal toxicity was a stronger indicator of macroinvertebrate change than conductance alone, although responses varied among sites and seasons. At the site with the highest diversity, densities and richness of Ephemeroptera, Plecoptera, Trichoptera (EPT) and non-EPT taxa were significantly related to C. dubia reproduction, with greater toxicity corresponding to lower diversity. At the family level, some pollution-tolerant taxa were more prevalent and at higher densities during periods of sublethal toxicity, while some sensitive families were absent or reduced. These patterns may reflect site-specific mixtures of acute and chronic stressors, with laboratory toxicity tests more effectively capturing short-term impacts. Overall, these multi-decadal observations suggest that laboratory toxicity tests can help track water-quality changes linked to shifts in aquatic community diversity, despite variable responses reflecting the complexity of dynamic stressors in this impacted freshwater system.

Stevenson, Louise [ORNL] (ORCID:0000000349679897)↗
DOE OSTI2024

Initial Testing of an In Situ Load Retention Aging Vessel

A thermal aging vessel instrumented with load cells was fabricated. The primary function of the vessel is to continuously monitor the in situ load retention of up to three compressed polymer coupons undergoing thermally accelerated aging under nitrogen. A secondary function is to enable gas sampling of the vessel headspace during thermal aging. Heating of the vessel is achieved using a custom heater jacket. To improve upon our conventional aging study methods which require periodic interruption of aging to perform load testing in an Instron machine at room temperature, this technology aims to automate/facilitate data acquisition/analysis, improve data quality, and enable uninterrupted compression of the polymer which represents the service condition. As an example case to assess functionality of the in situ vessel, the load retention of a siloxane elastomer material additively manufactured by direct-ink-writing (DIW) was measured at three different isothermal aging temperatures for ~1 month. Initial compression of the coupons while near the aging temperature was achieved by temporarily opening the heated vessel to access the interior chamber and manually tightening four nuts to drive the heated compression plate down onto the heated coupons. Initial testing demonstrated achievement of the primary load retention monitoring function. Unfortunately, the vessel leaked which prevented gas sampling; an active purge was used to maintain a nitrogen atmosphere. Welded or otherwise sealed joints, which could be implemented in a future design, would likely eliminate leak paths. To apply time-temperature superposition (TTS), a technique used to provide long-term prediction of the load retention from short-term isothermal data, the load retention needed to be calculated relative to the load at an estimated “equilibrium” time, after most of the transient viscoelastic physical relaxation occurred. The peak load immediately after compression could not be used as the load retention basis for two reasons: (1) age-related changes must be isolated from non-age-related physical relaxation before applying TTS and (2) the manual mechanism used to compress the specimens at the aging temperature was neither smooth nor repeatable which affected the peak load value. To better understand the effect of the mode of initial compression on the measured load, and possibly better estimate “equilibrium” physical relaxation times, systematic stress relaxation experiments were performed using an Instron machine with a thermal chamber. At a given temperature, the DIW polymer was compressed to a fixed strain in either a stepped or continuous manner at two different rates, then held at that strain for 24 hrs. The results indicated that, at a given temperature, the different stress relaxation curves appeared to converge to the same curve at some “equilibrium” time when the non-age-related physical relaxation was mostly complete. Though this observation suggests that the discontinuous manual compression employed by the vessel is feasible, a compression mechanism that is rapid, smooth, and repeatable would enhance its use.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Precision Neutrino Oscillation Physics with the Daya Bay and DUNE Experiments

The team supported by this grant made significant contributions to the final results of the Daya Bay Reactor Antineutrino Experiment. This experiment utilized eight identically designed antineutrino detectors positioned at varying distances from six 2.9 GW th nuclear reactors to precisely measure the oscillation parameters that govern antineutrino disappearance at short (<2 km) baselines. Our group played a leading role in the calibration and data quality efforts, both of which have been crucial for all final results. Additionally, we co-led the development of an independent measurement of the neutrino mixing angle θ 13 and the atmospheric mass splitting using a sample of antineutrinos identified via neutron capture on hydrogen. Lastly, we laid the groundwork for a search for seasonal modulation in Daya Bay’s measured muon flux using the final dataset, a result expected to be published soon. Simultaneously, our team ramped up its participation in the Deep Underground Neutrino Experiment (DUNE). This experiment will employ a powerful neutrino beam from Fermilab in Illinois directed to the Homestake mine in South Dakota to address some of the most pressing questions in neutrino physics, including the ordering of neutrino masses and whether neutrinos violate the CP symmetry. Our work focused on the development of the pixelated and modularized Liquid Argon Time-Projection Chamber technology that is being prepared for DUNE’s Near Detector. Our group took responsibility for the development, testing, and maintenance of the firmware for the control boards of the detector’s charge readout system and played an active role in analyzing data produced by the very first fully integrated prototypes.

2x2 Demonstrator↗