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At least 883 records · Page 49

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DOE OSTI2026

Adaptive optical correction for in vivo two-photon fluorescence microscopy with neural fields

Adaptive optics restore ideal imaging performance in complex samples by measuring and correcting optical aberrations but often require custom-built microscopes with carefully aligned wavefront sensing/shaping devices and can be susceptible to sample motion. Here we describe NeAT, a computational framework using neural fields for adaptive optics two-photon fluorescence microscopy. NeAT estimates wavefront aberration and recovers sample structure from a 3D image stack without requiring external datasets for training. Incorporating motion correction in learning and correcting conjugation errors commonly found in commercial microscopes, NeAT is designed for deployment in biological laboratories for in vivo imaging. We validate NeAT’s performance using a custom-built microscope with a wavefront sensor under varying signal-to-noise ratios, aberration and motion conditions. With a commercial microscope, we demonstrate real-time aberration correction for in vivo morphological and functional imaging in the living mouse brain, with NeAT improving the signal and accuracy of glutamate and calcium imaging of synapses and neurons.

Kang, Iksung↗

DOE OSTI2024

Underwater unexploded ordnance discrimination based on intrinsic target polarizabilities – A case study

Seabed unexploded ordnance that resulted partly from the high failure rate among munitions from more than 80 years ago and from decades of military training and testing of weapons systems poses an increasing concern all around the world. Although existing magnetic systems can detect clusters of debris, they are not able to tell whether a munition is still intact requiring special removal (e.g. in situ detonation) or is harmless scrap metal. The marine environment poses unique challenges, and transferring knowledge and approaches from land to a marine environment has not been easy and straightforward. On land, the background soil conductivity is much lower than the conductivity of the unexploded ordnance and the electromagnetic response of a target is essentially the same as that in free space. For those frequencies required for target characterization in the marine environment, the seawater response must be accounted for and removed from the measurements. The system developed for this study uses fields from three orthogonal transmitters to illuminate the target and four three-component receivers to measure the signal arranged in a configuration that inherently cancels the system's response due to the enclosing seawater, the sea–bottom interface and the air–sea interface for shallow deployments. The system was tested as a cued system on land and underwater in San Francisco Bay – it was mounted on a simple platform on top of a support structure that extended 1 m below and allowed the diver to place metal objects to a specific location even in low-visibility conditions. The measurements were stable and repeatable. Furthermore, target responses estimated from marine measurements matched those from land acquisition, confirming that the seawater and air–sea interface responses were removed successfully. Thirty-six channels of normalized induction responses were used for the classification, which was done by estimating the target principal dipole polarizabilities. Our results demonstrated that the system can resolve the intrinsic polarizabilities of the target, with clear distinctions between those of symmetric intact unexploded ordnance and irregular scrap metal. The prototype system was able to classify an object based on its size, shape and metal content and correctly estimate its location and orientation.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗

DOE OSTI2025

Quantum light: creation, integration, and applications

In today’s rapidly evolving quantum landscape, the generation and manipulation of quantum light not only represent fundamental challenges but also herald unprecedented opportunities in communication, computing, sensing, and imaging. This special issue brings together a collection of contributions that span the entire journey, from the creation of quantum light using novel materials and emitters to its seamless integration into photonic architectures and eventual deployment in advanced quantum applications. This special issue, “Quantum Light: Creation, Integration, and Applications,” features a collection of three review articles, five perspectives, and 23 original research papers, highlighting both the timeliness of the topic and the remarkable breadth and richness of ongoing advancements in the field.

77 NANOSCIENCE AND NANOTECHNOLOGY↗

DOE OSTI2022

Simulations for HEP with SQMS Quantum Hardware (Snowmass2021 - Letter of Interest)

The Superconducting Quantum Materials and Systems (SQMS) Center, a Fermilab lead NQI center, will develop and deploy a beyond state of the art quantum computers. These systems will exhibit long coherent times, and a high degree of connectivity. We are interested in exploring the utility of these devices to address computational challenges in HEP, such as the simulation of strongly coupled quantum field theories. A progression of toy models and simulations can begin to chart a course towards simulating QCD-like systems.

42 ENGINEERING↗

DOE OSTI2024

Safety in Artificial Intelligence: Challenges and Opportunities for the U.S. National Labs and Beyond

This report discusses the importance of the critical and underexplored topic of artificial intelligence (AI) safety, as highlighted during the “Strategy Alignment on AI Safety” workshop convened at Lawrence Livermore National Laboratory (LLNL) in April 2024. Through a summary of keynote talks, panel discussions, and breakout sessions, world-leading AI safety experts from academic, industry, national labs, and government agencies clearly agree on the need for and importance of large-scale investments for research and capabilities in AI safety. With the field innovating at unprecedented rates, there is increasing urgency to develop novel evaluation methodologies that allow full considerations of risks/threats of AI technologies in different domains. Quantitative metrics and effective methodologies that can evaluate and audit the “safeness” of how a given AI technology is trained, deployed, or regulated are, at best, nascent for certain scenarios or, more commonly, nonexistent. This maturation gap presents the possibility of serious threats to national security, and further inaction may have serious consequences. Additionally, the gap between the public’s and research community’s perceptions of AI risks/rewards is significant. While numerous voices from the AI community have expressed concern that the risks could be so high that future AI systems could inflict extinction-level damage to humanity if deployed incorrectly, the public largely is aware only of risk in low-impact scenarios. This discrepancy highlights the crucial need for researchers to articulate to governmental bodies what, why, and when various AI risks matter as part of motivating funding requests. Thus, the call to action for this community is to pursue AI safety as a “Big Science” project on a scale comparable to the Manhattan Project. High risks and high payoffs are on the table, but safe AI is a fast-moving target, and large-scale investments are needed to guide development of this technology in a responsible way. We highlight the need for a multilayered solution combining the development of new methods and algorithmic approaches to mitigate threats with an active participation of the government(s) in setting high industry standards and regulations based on state-of-the-art technology. The U.S. Department of Energy (DOE) national laboratories have served as leading institutions for scientific innovation in the U.S. for more than 70 years. Drawing on their expertise in the AI community and their history of safeguarding critical and sensitive information, and as we look to the future, national labs are the best choice for evaluating and safeguarding AI technologies.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2025

Implementation of a turbine farm model into the Energy Exascale Earth System Model for investigation and quantification of global climate impacts

Although there has been widespread deployment of wind farms in the United States, and plans to continue deployment into the future, the complete effects of wind farms on Earth systems are not well understood. The work performed here has incorporated wind farm models into the Energy Exascale Earth System Model (E3SM) capable of simulating the effects of extracting momentum from the atmospheric flow field using power generating wind farms. This new capability will allow scientists to quantify the impacts of wind farm induced changes on Earth systems by exploiting E3SM’s ability to couple atmospheric, oceanic, and biogeochemical (BGC) models on a global scale and monitor precipitation levels, extreme weather events, soil moisture content and jet stream location over decades-long time periods. This tool will be used to inform decision making on wind farm citing and will contribute to the Lab’s ability to assess energy technology impacts on the environment and evaluate the trade-offs between energy infrastructure investments and their impacts on natural systems.

17 WIND ENERGY↗

DOE OSTI2020

Broadband Communications Using High Voltage Transmission Lines - A Review

The possibilities afforded by using an existing transmission line infrastructure for the transport of broadband communications are many ranging from (conventional) A-to-B point-to-point links, to integration of Wi-Fi access points mounted at defined network (power and communications) locations, to operation as a fault-tolerant, rollover backup communications fabric (in conjunction with other transport media, such as optical fiber or terrestrial microwave). As stated by RCR Wireless reporter Jeff Kagan – regarding an ongoing in-field demonstration - “This is so much more cost effective for the company and so much easier for them to deliver an ultra-fast, gigabit speed, wireless internet connection anywhere there are power lines.” The same situation arises for a utility looking to augment or simply deploy a communications fabric in their region via treating the transmission line infrastructure as a communication fabric thereby reducing the cost and complexity of deployment. A circa 2020 review of broadband over powerlines (BPL) is presented here.

42 ENGINEERING↗

DOE OSTI2025

Assessment and Simulation of Particulate Transport for Delivery of Solid Amendments into the Subsurface: FY25 Status Report

For particulate-based amendments to be viable for field-scale remediation at the Hanford Site (e.g., the 200 DV-1 Operable Unit), amendment particles need to be delivered a reasonable distance away from an injection well to provide cost effective in situ treatment. Field-scale particle transport models can estimate spatial deposition of amendment particles in the subsurface, which is critical for developing an overall remediation strategy. However, field-scale particle simulations are currently limited due to insufficient simulation capability and a lack of experimental data to validate and parameterize particle transport models. During this fiscal year, the following progress has been made toward a field-scale particle transport modeling evaluation: (1) in addition to the two particle transport models implemented last FY, four additional particle transport models have been implemented within PFLOTRAN; (2) a Python-based pre-screening tool was finalized, enabling users to quickly estimate the particle radius of influence (ROI) for any given particle-amendment system; and (3) an initial compatibility assessment was completed using both particle transport simulations deployed through the pre-screening tool, in conjunction with general guidelines to (a) identify the most suitable amendment particle sizes for various Hanford sediments and (b) evaluate amendment-delivery fluid compatibility. The preliminary compatibility assessment revealed that for amendment delivery success to the various Hanford target formations, amendment particle sizes will likely need to be smaller than the amendment sizes tested in the DV-1 treatability study. It is recommended that amendment particles be decreased in size, or alternative smaller size amendments be obtained from the manufacture, prior to any further experimental testing. Also, preliminary testing suggests that xanthan gum may be the most broadly compatible delivery fluid. Planned laboratory experiments will be instrumental in validating and refining the PFLOTRAN particulate transport model formulations, ultimately enabling predictive capabilities to facilitate the design of field-scale amendment delivery systems. This work consists of acquiring new theoretical or experimental knowledge. The information associated with this report should not be used as design input or operating parameters without additional qualification.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2026

Improving High Resolution Offshore Wind Resource Assessments and Forecasts using Observations in the MA/RI Lease Areas

The third Wind Forecast Improvement Project (WFIP3) sought to improve understanding of the physical phenomena in the atmosphere and ocean that dictate the structure and variability of wind and thermodynamic fields within the Marine Atmospheric Boundary Layer (MABL). WFIP3 conducted a comprehensive 18-month observational study over the Northeast U.S. outer continental shelf, a high use coastal zone, using a 3-D multiscale sensor array to highly resolve the temporal, vertical, and horizontal structure of the coupled atmospheric and oceanic boundary layers. Multiple land-based study sites adjacent to the coastal ocean observed surface meteorology and vertical profiles of atmospheric properties via passive infrared and microwave radiometers, active lidars and radars, and radiosondes. At sea, an array of surface flux buoys and two vertical profiling lidar buoys observed both atmospheric and oceanic properties, augmented by land-based oceanographic radar systems and routine ship-based surveys. Intensive observations of the MABL over the ocean was done from an air-sea interaction flux tower and extended deployments of a large barge platform. WFIP3 focused on mesoscale and sub-mesoscale flows -- including sea breezes, low-level jets, low-level clouds, and coastal storms -- and the ability of advanced numerical model parameterizations to represent them within fully coupled oceanic and atmospheric modeling systems and foundational weather forecast models. Numerous critical forecasting phenomena were observed, however the project was terminated prior to the completion of the field observational period and the analysis period.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

Challenges in simulating economic effects of climate change on global agricultural markets

Abstract Many approaches have been used to investigate climate change impacts on agriculture. However, several caveats remain in this field: (i) analyses focus only on a few major crops, (ii) large differences in yield impacts are observed between projections from site-based crops models and Global Gridded Crop Models (GGCMs), (iii) climate change impacts on livestock are rarely quantified, and (iv) several causal relations among biophysical, environmental, and socioeconomic aspects are usually not taken into account. We investigate how assumptions about these four aspects affect agricultural markets, food supply, consumer well-being, and land use at global level by deploying a large-scale socioeconomic model of the global economy with detailed representation of the agricultural sector. We find global welfare impacts several times larger when climate impacts all crops and all livestock compared to a scenario with impacts limited to major crops. At the regional level, food budget can decrease by 10 to 25% in developing countries, challenging food security. The role of land area expansion as a major source of adaptation is highlighted. Climate impacts on crop yields from site-based process crop models generate more challenging socioeconomic outcomes than those from GGCMs. We conclude that the agricultural research community should expand efforts to estimate climate impacts on many more crops and livestock. Also, careful comparison of the GGCMs and traditional site-based process crop models is needed to understand their major implications for agricultural and food markets.

Gurgel, Angelo C. (ORCID:0000000183319508)↗

DOE OSTI2025

Applications of explainable artificial intelligence in renewable energy research

Researchers in renewable energy are applying deep learning (DL) to a variety of problems from diverse renewable energy domains, such as biofuels, wind, solar, power systems, buildings, vehicles, and transportation systems. Improvements in accuracy may be demonstrated using DL in laboratory settings. However, the lack of interpretability of DL models poses a practical limitation to their utility in advancing scientific knowledge and in the deployment of DL models in safety-critical energy systems. In this article, we discuss explainable artificial intelligence (XAI) as one pathway toward more interpretable DL models. We explore a brief timeline of U.S. national laboratory interest in XAI, an overview and taxonomy of methods in the field of XAI, and a selection of applications across renewable energy research domains. We conclude by highlighting pivotal areas where XAI can accelerate innovation in artificial intelligence for renewable energy research and other essential future directions.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2025

From tides to seasons: How cyclic tidal drivers and plant physiology interact to affect carbon cycling at the terrestrial-estuarine boundary (Final technical report)

Coastal ecosystems are among the most biologically and biogeochemically active and diverse systems on Earth. Because they act as important linkages between terrestrial ecosystems and the open ocean, their incorporation in Earth system models (ESMs) is critical to predict coastal and global responses to environmental changes. However, they vary greatly in the magnitude of tides and the volume and timing of freshwater input from land, making it challenging to model the major biogeochemical reactions that control productivity and greenhouse gas emissions across coastal terrestrial aquatic interfaces (TAIs). Our overall objective was to improve mechanistic process understanding and modeling of tidal wetland hydro-biogeochemistry in coastal TAIs. We established a new flux tower site (Ameriflux US-PLo) in the oligohaline part of the Parker River to continuously monitor ecosystem-scale carbon fluxes under temporally varying salinity conditions. The site is co-located with long-term monitoring plots of the Plum Island Ecosystems LTER project. We installed wells and redox sensors in the marsh interior and creek bank, established biomass monitoring plots and deployed novel optode sensors in both locations. We used this data to parameterize plant-mediated transport in PFLOTRAN and tested the impact of soil heterogeneity on porewater constituents and gas fluxes. We collected observations of root oxygen release with a novel planar optode system in the field. Flux data collected during the measurement period encompasses a large variation in salinity ranging from drought to record precipitation years. We developed a method to extract functional relationships from the flux data using artificial neural networks, identifying salinity thresholds for CH 4 fluxes. Finally, we are using the coupled ELM-PFLOTRAN model to test the impact of antecedent hydrological conditions on the salinity-CH 4 flux relationship. This grant contributed to the professional development of one postdoc, three research assistants and one graduate student. The sensor data has been shared with external collaborators.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2026

Automatic Calibration and Health Monitoring of Infrastructure Sensors

Smart transportation infrastructure relies on networks of heterogeneous sensors - cameras, radars, and lidars - continuously monitoring traffic conditions. However, executing the initial spatial calibration of multiple sensors and the subsequent health monitoring presents significant operational challenges. Environmental factors, mechanical vibrations, and gradual drift cause spatial misalignment, degrading fusion performance and tracking accuracy. Traditional calibration approaches require manual intervention with specialized targets or survey equipment, resulting in service interruptions and high maintenance costs. This work presents an automated framework for initial calibration and continuous health monitoring without human intervention or service disruption. Our approach addresses two critical problems: (1) detecting when sensors become miscalibrated during operation, and (2) automatically re-establishing spatial alignment using only operational traffic data. The health monitoring component analyzes measurement innovations - differences between sensor observations and predicted object states - to detect systematic biases indicative of calibration drift. By computing bias magnitude, directional consistency, and rejection rates, the system identifies miscalibrations as small as 0.5 meters. Unlike traditional methods requiring known calibration targets, our diagnostic operates continuously on live traffic observations, enabling early detection before fusion quality degrades. The automatic recalibration algorithm leverages overlapping sensor fields-of-view and temporal correlation of vehicle observations. Using graph-based optimization, the system automatically discovers which sensor pairs observe common regions, estimates pairwise spatial transformations using RANSAC-based robust estimation, and jointly optimizes all sensor poses through bundle adjustment. The framework handles practical deployment challenges, including different sensor sampling rates (1-10 Hz), varying installation positions, unknown orientations, and limited overlap regions (>10%). When approximate sensor positions are available from installation surveys (+/-1m accuracy), the algorithm additionally estimates sensor orientations, refining both position and rotation to sub-meter and sub-degree accuracy. We validate the framework on multi-hour traffic datasets from six heterogeneous sensors with sampling rates ranging from 1 Hz to 10 Hz. Results demonstrate successful calibration even with sparse overlap (<20%) and automatic detection of miscalibrations exceeding 0.8 meters. This work enables a "deploy-and-forget" sensor infrastructure that maintains calibration autonomously, reducing maintenance costs while improving tracking accuracy. The techniques generalize beyond transportation to any multi-sensor monitoring application requiring robust spatial alignment, including smart cities, industrial monitoring, and surveillance systems.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2024

Sulfuric Acid New Particle Formation Field Campaign Report

This campaign aimed to measure gaseous sulfuric acid concentrations at the surface level using a newly developed sulfuric acid dimethylamine-reactive condensation particle counter (SAD-RCPC). Sulfuric acid is a key nucleation precursor and is the likely driving compound for new particle formation at the Atmospheric Radiation Measurement (ARM) user facility’s Southern Great Plains (SGP) observatory. The SAD-RCPC will be deployed in the future at ARM SGP (AFC010010: "Vertically Resolved Nucleation Precursors at SGP") scheduled for 2024. The campaign was in conjunction with Eleanor Browne's campaign (AFC010075: Boundary-layer gradients in new particle formation) and took place from 05/7/23 to 5/21/23. The measured sulfuric acid concentrations by the SAD-RCPC will be compared to concentrations measured by Browne's chemical ionization mass spectrometer (CIMS). The SAD-RCPC consisted of a glass flow reactor coupled to a 1-nm diethylene glycol condensation particle counter (DEG CPC). Sampled air was mixed with a high concentration of dimethylamine vapor (~1 ppbv). Sulfuric acid within the sampled gas nucleated with dimethylamine to form detectable 1-nm particles. The measured particle concentration was converted to sulfuric acid concentration using nucleation reaction kinetics. The pulse height distribution of particles detected by the DEG CPC was observed using a multi-channel analyzer. Large pre-existing particles were separated from the freshly formed particles in the SAD-RCPC from the pulse height distribution (PHD). During the short campaign, seven new particle formation events were observed. The SAD-RCPC detected an increase in sulfuric acid concentration, but this also coincided with an increase in pre-existing <3 nm particle concentrations. In addition, Browne's CIMS detect sulfuric acid concentrations in the range of 10 5 -10 6 cm -3 .

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

Database-wide hazard modelling of the onset of DIII-D tearing modes with field features

The rate of onset (hazard) of tearing modes is modelled probabilistically using statistical learning algorithms. Axisymmetric energy-density equilibrium fields are taken as raw high-dimensional input features which are reduced with principal component analysis. Signal processing of non-axisymmetric magnetics fluctuation array data provides the target information from which to learn. Model selection, visualization and calibration assessment procedures are detailed. Here, the analysis is deployed at large scale across the DIII-D tokamak database. Standard model selection criteria suggest that the energy-density post-processed feature is a better choice for modelling the onset rate compared to the non-processed equilibrium reconstruction solution. Two example applications of the learned rate function are demonstrated: (i) proximity-to-onset discharge monitoring and (ii) database analysis showing an (expected) observational global trend that the general hazard increases as a plasma performance metric increases. An important connection between the hazard function and its use as a conditional probability generator is reviewed in the Appendix.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2025

A gravity-based mounting approach for large-scale cryogenic calorimeter arrays

Cryogenic calorimeters are among the leading technologies for searching for rare events. The CUPID experiment is exploiting this technology to deploy a tonne-scale detector to search for neutrinoless double-beta decay of 100 Mo. The CUPID collaboration proposed an innovative approach to assembling cryogenic calorimeters in a stacked configuration, held in position solely by gravity. This gravity-based assembly method is unprecedented in the field of cryogenic calorimeters and offers several advantages, including relaxed mechanical tolerances and simplified construction. To assess and optimize its performance, we constructed a medium-scale prototype hosting 28 Li 2 MoO 4 crystals and 30 Ge light detectors, both operated as cryogenic calorimeters at the Laboratori Nazionali del Gran Sasso (Italy). Despite an unexpected excess of noise in the light detectors, the results of this test proved (i) a thermal stability better than ±0.5 mK at 10 mK, (ii) a good energy resolution of Li 2 MoO 4 cryogenic calorimeters, (6.6 ± 2.2) keV FWHM at 2615 keV, and (iii) a Li 2 MoO 4 light yield measured by the closest light detector of 0.36 keV/MeV, sufficient to guarantee the particle identification requested by CUPID.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2024

Exploring Li-Ion Transport Properties of Li 3 TiCl 6 : A Machine Learning Molecular Dynamics Study

We performed large-scale molecular dynamics simulations based on a machine-learning force field (MLFF) to investigate the Li-ion transport mechanism in cation-disordered Li 3 TiCl 6 cathode at six different temperatures, ranging from 25°C to 100°C. In this work, deep neural network method and data generated by ab − initio molecular dynamics (AIMD) simulations were deployed to build a high-fidelity MLFF. Radial distribution functions, Li-ion mean square displacements (MSD), diffusion coefficients, ionic conductivity, activation energy, and crystallographic direction-dependent migration barriers were calculated and compared with corresponding AIMD and experimental data to benchmark the accuracy of the MLFF. From MSD analysis, we captured both the self and distinct parts of Li-ion dynamics. The latter reveals that the Li-ions are involved in anti-correlation motion that was rarely reported for solid-state materials. Similarly, the self and distinct parts of Li-ion dynamics were used to determine Haven’s ratio to describe the Li-ion transport mechanism in Li 3 TiCl 6 . Obtained trajectory from molecular dynamics infers that the Li-ion transportation is mainly through interstitial hopping which was confirmed by intra- and inter-layer Li-ion displacement with respect to simulation time. Ionic conductivity (1.06 mS/cm) and activation energy (0.29eV) calculated by our simulation are highly comparable with that of experimental values. Overall, the combination of machine-learning methods and AIMD simulations explains the intricate electrochemical properties of the Li 3 TiCl 6 cathode with remarkably reduced computational time. Thus, our work strongly suggests that the deep neural network-based MLFF could be a promising method for large-scale complex materials.

Selvaraj, Selva Chandrasekaran (ORCID:000000029023↗

DOE OSTI2022

NucD-23 Nuclear Diagnostics Development: The future of neutron imaging and gamma reaction history

The Neutron Imaging team has deployed novel, active detection systems along two lines of sight at NIF. Studies aimed to characterize NIS sensitivity to implosion shape will be carried out to further extend reconstruction capabilities. Additionally, an improved camera timing system that will reduce uncertainties stemming from bang time jitter will also be fielded. The NIF GRH data recording system is more then 10 years old needs to be updated. Bandwidth and noise are a key limiting factor for improving data quality. New high bandwidth and low noise components will be tested at OMEGA in advance before fielding the components at NIF.

42 ENGINEERING↗