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DOE OSTI2024

On the approximations underpinning fast nuclear cloud models

Fast nuclear cloud models have been used for many decades to predict cloud rise and growth following a nuclear detonation for both emergency response and debris collections mission planning. We present derivations and discussions of the approximations made by such fast nuclear cloud models in common use. In particular, the models we discuss all fundamentally rely on an empirical entrainment parameter to inexpensively model the cloud rise and growth of a buoyant bubble due to entrainment of an ambient air mass on its surface. We show the particular circumstances under which these models are equivalent as well as apply a virtual mass correction throughout them. Aside from some minor differences in representation and the use of the Boussinesq approximation, these models are all very similar and necessitate the specification of an entrainment parameter to accurately determine the cloud rise.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗

DOE OSTI2023

Beyond Conventional Sensing: Hybrid Plasmonic Metasurfaces and Bound States in the Continuum

Fano resonances result from the strong coupling and interference between a broad background state and a narrow, almost discrete state, leading to the emergence of asymmetric scattering spectral profiles. Under certain conditions, Fano resonances can experience a collapse of their width due to the destructive interference of strongly coupled modes, resulting in the formation of bound states in the continuum (BIC). In such cases, the modes are simultaneously localized in the nanostructure and coexist with radiating waves, leading to an increase in the quality factor, which is virtually unlimited. In this work, we report on the design of a layered hybrid plasmonic-dielectric metasurface that facilitates strong mode coupling and the formation of BIC, resulting in resonances with a high quality factor. We demonstrate the possibility of controlling Fano resonances and tuning Rabi splitting using the nanoantenna dimensions. We also experimentally demonstrate the generalized Kerker effect in a binary arrangement of silicon nanodisks, which allows for the tuning of the collective modes and creates new photonic functionalities and improved sensing capabilities. Our findings have promising implications for developing plasmonic sensors that leverage strong light-matter interactions in hybrid metasurfaces.

36 MATERIALS SCIENCE↗

DOE OSTI2026

Utilization of Unmanned Aircraft Systems for Environmental Purposes at the Savannah River Site – 26578

Born in the 1950s, the Savannah River Plant was constructed as a plutonium and tritium production plant. As the Cold War era came and went, a change of direction was signaled when the name of the facility changed from Savannah River Plant to Savannah River Site (1989) – the main mission at Savannah River shifted from “production” to “cleanup.” The Department of Energy is responsible for managing the 310 square miles of land that is the Savannah River Site and the cleanup/remediation activities that occur. The Savannah River National Laboratory utilizes unmanned aircraft systems to assist with some of those environmental monitoring and remediation activities. One function of unmanned aircraft systems at the Savannah River National Laboratory is conducting aerial photography and videography. Various infrastructure evaluations have been performed with unmanned aircraft – facility rooftop inspections, water tower lock verifications, earthen dam surveys, etc. An unmanned aircraft system has been used for progress footage of remediation projects – Dunbarton Bay remediation, Saltstone Disposal Unit construction, etc. Thermal cameras on an unmanned aircraft system are used to help identify cracks/leaks in structures from vantage points not readily accessible by personnel. Virtual tours of different waste units are conducted with unmanned aircraft systems for Site groups and federal/state regulators to save costs and travel time. Additionally, advanced remote sensing technologies are used on unmanned aircraft systems at the Savannah River Site. Light Detection and Ranging uses laser pulses to measure distances to the Earth's surface or other targets to create highly detailed topographic maps that accurately portray terrain (e.g., elevation changes, slopes, and contours). Data collection with a Light Detection and Ranging unit mounted on an unmanned aircraft system is quick and efficient – large-area surveys are conducted in shorter time frames. Because Light Detection and Ranging can penetrate through foliage and vegetation to ground level, it is being used in conjunction with a watershed model to study the General Separations Area of the Site to determine stormwater flow volume and direction, monitor stream stability, and forecast river flow behavior. A hyperspectral camera captures a wide spectrum of electromagnetic wavelengths across numerous narrow bands, beyond what the human eye can see. It provides detailed spectral information about the objects or surfaces in an image, which can be used to identify and analyze materials based on their spectral signatures. A hyperspectral camera mounted on an unmanned aircraft system has been used at the Savannah River Site for non-destructive evaluation of concrete and concrete structures. Unmanned aircraft systems at the Savannah River Site are also used for the application of herbicide on identified rooftops. P-Reactor and R-Reactor are entombed nuclear reactors at the Savannah River Site. Unwanted vegetation is often present on these rooftops – over time roots can penetrate seams, cracks, and joints of the general roof and concrete caps, leading to water ingress and deterioration of the structural components. For time and cost efficiencies as well as in the interest of personnel safety, an unmanned aircraft is used to dispense herbicide on rooftop areas where vegetation is noticed to help alleviate the issues/hazards.

Lorier, Troy [Savannah River National Laboratory (↗

DOE OSTI2026

Search for Higgsinos in final states with low-momentum lepton-track pairs at 13 TeV

We present a search for the pair production of Higgsinos in final states with large missing transverse momentum and either two reconstructed muons or a reconstructed lepton (muon or electron) and an isolated track. The analyzed data correspond to proton-proton collisions with an integrated luminosity of 137 fb −1 , collected by the CMS experiment at $\sqrt{𝑠}$ =13 TeV in 2016, 2017, and 2018. The signal scenario assumes four nearly mass degenerate Higgsino mass eigenstates: two neutralino states $\tilde{𝜒}^0_2$ and $\tilde{𝜒}^0_1$ with a small mass difference in the range 1–10 GeV and two chargino states $\tilde{𝜒}^±_1$ with an intermediate mass. The analysis focuses on the decay of the heavier neutralino into the lighter one and a virtual 𝑍 boson, which decays into two same-flavor leptons. The leptons have small transverse momentum and/or a small opening angle between the identified muons. An isolated track is used to recover events in which only one of the two leptons is identified. Multivariate discriminants are used to enhance the sensitivity by efficiently rejecting backgrounds from SM processes or misreconstructed tracks and/or leptons. The search explores a unique phase space and probes a previously unexplored region of the signal model parameter space. Mass differences between the two neutralinos are probed down to 1.5 GeV, assuming a Higgsino mass of 100 GeV. The maximum excluded Higgsino mass is 115 GeV.

Hayrapetyan, A. [Yerevan Physics Institute]↗

DOE OSTI2023

Medical Physics Leadership Academy Journal Club (Leadership Club) program: Two-year review in building a community of leaders

The American Association of Physicists in Medicine began the Medical Physics Leadership Academy Journal Club in the fall of 2020. The initiative was launched to provide a forum for medical physicists to learn about leadership topics using published material, discuss and reflect on the material, and consider incorporating the discussed skills into their professional practice. This report presents the framework for the MPLA Journal Club program, describes the lessons learned over the last 2 years, summarizes the data collected from attendees, and highlights the roadmap for the program moving forward.

99 GENERAL AND MISCELLANEOUS↗

DOE OSTI2022

Controlled Environment Agriculture - Farming and Food Access for Healthy Diets

Fruits and vegetables are critical for a healthy diet and USDA guidelines recommend increasing fruit and vegetable consumption for almost all Americans. However, most food system lifecycle assessments do not capture the importance of these food groups in the food system. Moreover, long supply chains and limited access to fresh produce are particularly prevalent in disadvantaged neighborhoods. Many of these communities are food deserts or food swamps characterized by a lack of full-scale groceries, and a prevalence of convenience stores and fast-food restaurants. Stocking of fresh produce is difficult and often unprofitable for small stores. Convenience stores are usually space-limited, unable to achieve economies-of-scale in purchasing and have difficulty managing the short shelf-life of fresh produce. Controlled-Environment (CE) farming, including greenhouses and fully indoor vertical farms, have the potential to address persistent food access issues for disadvantaged communities. CE farming offers several potential advantages for food desert and food swamp neighborhoods, including shorter or non-existent transport distances and fewer middlemen, which could reduce costs. CE farming can be integrated into existing buildings at virtually any scale, providing local jobs and revitalization of community hubs such as shopping malls, or larger-scale farms located in close-in warehouse or industrial areas. Rainwater harvesting and recovery of nutrients from wastewater could further improve food system resilience and circularity. Geospatial modeling of CE farming in case study cities of various sizes and densities will help to quantify the benefits of CE farming. Geospatial modeling can help to answer questions such as whether sufficient collection area is available for rainwater harvesting for a CE farm, and what size of farm would be required to serve the community living within walking distance of the farm. This paper reports on progress in geospatial modeling of CE farming integration into several case study cities.

controlled environment agriculture↗

DOE OSTI2026

Throughput Estimation of Data Transport Networks From Digital Twin Measurements

Digital twins of networked infrastructures, known as Virtual Infrastructure Twins (VITs), are increasingly used for software development, pre-deployment testing, and design space exploration. While VITs avoid the costs and potential disruptions associated with experiments on operational networks, their throughput measurements are typically not sufficiently accurate for performance profiling of wide-area networks that they emulate. Here, machine learning (ML) methods are developed to transform these inaccurate VIT network throughput measurements to closely match in peak and overall profile of those from a physical testbed or production network. First, a micro kernel network reflecting a physical network is utilized to collect one-time measurements on a host to support this ML transformation. Then, a generic multi-modal ML method is developed to learn a map that transforms measurements from subsequent VITs on the same host to match past, current and follow-on testbed and cloud networks. ML generalization equations are derived to establish its correctness and probabilistically guarantee its generalization accuracy. Experimental results are presented for a variety of VIT hosts with target testbed and cloud networks; they include a case study of a four-site science ecosystem wherein inaccurate convex VIT measurement profiles are transformed into accurate concave profiles of target networks.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2021

Hydrothermal Liquefaction: Path to Sustainable Aviation Fuel

A variety of technologies are currently under development for producing sustainable aviation fuel (SAF), and one of the most promising technologies is the hydrothermal liquefaction (HTL) of low-cost waste feedstocks. Organizations across the globe are conducting research and development on HTL technology at scales varying from the laboratory to demonstrations focused on various aspects of technology development. The identification of more advanced and sustainable solutions to maximize the fuel yield while optimizing fuel properties and achieving cost parity with conventional fuels is the major focus in developing HTL technology. This workshop on the application of HTL to produce SAF, coordinated by the U.S. Department of Energy Bioenergy Technologies Office, Commercial Aviation Alternative Fuels Initiative, and Pacific Northwest National Laboratory (PNNL), was held virtually on November 17–19, 2020. A broad spectrum of experts from industry, academia, national laboratories, and government from across the globe participated in the workshop, contributing their ideas, insights, and perspectives. A series of keynote presentations, plenary presentations, and breakout sessions provided an interdisciplinary framework for sharing information and building collaboration. This document provides an overview of the content discussed in the presentations as well as the breakout session discussion. Diverse stakeholder perspectives were gathered, and this wealth of information collectively provides an update on the current state of the field and identifies the key research opportunities.

09 BIOMASS FUELS↗

DOE OSTI2021

Science Federation Emulation Testbed: Demonstration of VFSIE Functionalities

The Virtual Federated Science Instrument Environment (VFSIE) is a digital twin of a federated infrastructure of multiple sites with science instruments and computing systems that are geographically dispersed. It emulates the federation using containers and virtual hosts (vhosts) connected over local and wide-area networks for the main purpose of supporting the development and testing of science workflows and software stacks, which will subsequently transition to the physical infrastructure. In this demo, we present VFSIE of a four sites scenario and illustrate the following use cases: (i) execution of federation stack components that expose resources to enable a science user to execute workflows, (ii) execution of remote control commands of beam-line instruments to collect measurements and position the sensor, and (iii) remote execution and access of Jupyter Notebook for tomographic image computations

Al Najjar, Anees↗

DOE OSTI2024

Influences of lidar scanning parameters on wind turbine wake retrievals in complex terrain

Abstract. Scanning lidars enable the collection of spatially distributed measurements of turbine wakes and the estimation of wake properties such as magnitude, extent, and trajectory. Lidar-based characterizations, however, may be subject to distortions due to the observational system. Distortions can arise from the resolution of the measurement points across the wake, the projection of the winds onto the beam, averaging along the beam probe volume, and intervening evolution of the flow over the scan duration. Using a large-eddy simulation and simulated measurements with a virtual lidar model, we assess how scanning lidar systems may influence the properties of the retrieved wake using a case study from the Perdigão campaign. We consider three lidars performing range-height indicator sweeps in complex terrain, based on the deployments of lidars from the Danish Technical University (DTU) and German Aerospace Center (DLR) at the Perdigão site. The unwaked flow, measured by the DTU lidar, is well-captured by the lidar, even without combining data into a multi-lidar retrieval. The two DLR lidars measure a waked transect from different downwind vantage points. In the region of the wake, the observation system reacts to the smaller spatial and temporal variations of the winds, allowing more significant observation distortions to arise. While the measurements largely capture the wake structure and trajectory over its 4–5 D extent, limited spatial resolution of measurement points and volume averaging lead to a quicker loss of the two lobes in the near wake, smearing of the vertical bounds of the wake (< 30 m), wake center displacements up to 10 m, and dampening of the maximum velocity deficit by up to a third. The virtual lidar tool, coupled with simulations, provides a means for assessing measurement capabilities in advance of measurement campaigns.

17 WIND ENERGY↗

DOE OSTI2026

Untargeted, tandem mass spectrometry (LC/MS-MS) metaproteomes from soil samples in control and warming plots in Blodgett Forest, CA (2014-2021)

The pathways of carbon transport and loss through and from soils—soil organic matter (SOM) depolymerization to dissolved organic carbon and mineralization to carbon dioxide (CO2)—are fundamentally driven by microbial activity, which is strongly regulated by environmental conditions. As part of Lawrence Berkeley National Laboratory (LBNL) Terrestrial Ecosystem Science (TES) Belowground Biogeochemistry Science Focus Area (SFA), we have established a novel whole-soil long-term warming experiment at the University of California (UC) Blodgett Forest Research Station (Sierra Nevada) in 2014, where we study the role of biogeochemical, microbial and geochemical process interactions in SOM decomposition and stabilization. This package contains soil metaproteomics data in the context of site specific metagenomes from soil depth profiles in three paired control and warming plots from a temperate mixed forest in Northern California. Each paired plot had been subjected to experimental warming since June 2014 to simulate a predicted climate change scenario for northern California. These metaproteomes were collected in 2018 after 4.5 years of warming from five depth intervals (0-10 cm, 10-30 cm, 30-45 cm, 45-60 cm, 60-80 cm). For protein identification, the collected spectra were searched following a target-decoy search strategy against a database of metagenome predicted proteins (covering 96 samples from 2014 to 2021) representing the complete sequence diversity at the site. Data was searched with mass spectrometry database search tool (MS-GF+) using Pacific Northwest National Laboratory (PNNL)'s Data Management System (DMS) Processing pipeline. The metagenomes are published as part of another data package. Raw metaproteomic data and the data products from MS-GF+ are deposited in the Mass Spectrometry Interactive Virtual Environment (MassIVE) database under accession no. MSV000097826. Here we present a dataset that includes spectral counts for the detected proteins across samples (EMSL50964_BrodieAllMAGs_Globals_SC.txt), the sequences of the detected proteins, and sample metadata file that contains site information for the soil metaproteome samples.

Belowground Biogeochemistry Science Focus Area↗

DOE OSTI2021

Biological Carbon Sequestration for Climate and Soil Health

The United States recently launched the Net Zero World Initiative on decarbonization. As temperatures and sea levels rise, strong action is needed to mitigate high atmospheric CO 2 levels. In response to this need, a joint workshop between the US Department of Energy’s Oak Ridge National Laboratory (ORNL) and Lawrence Berkeley National Laboratory (LBNL) was convened to assess the challenges and opportunities within biological carbon (C) capture. The virtual workshop on Biological Carbon Sequestration for Climate and Soil Health focused on three aspects of biological C capture research: natural systems, engineered systems, and modeling. For this workshop, the scope of natural systems included unmanaged (e.g., grasslands, wetlands, natural forests) and managed (e.g., agricultural lands, commercial forests, forest biofuels) ecosystems. The engineered systems group covered topics in genetically modified plants and microbes, synthetic microbiomes, and altered plant–microbe interactions for enhancing C capture, storage, and utilization. The modeling group focused on computational tools and data collection to predict and assess the effectiveness of new biocarbon sequestration advances. Participant expertise included plant and microbial systems biology, synthetic biology, soil biogeochemistry, and techno-economic analysis. Together, participants outlined key scientific advances in support of biological C capture and storage. Among the major findings of the meeting were the need to better measure C flux within natural, agricultural, and fabricated ecosystems for experimentation; better understand the role of soil composition, plant roots, and the rhizosphere (i.e., the soil surrounding the plant root) in the underground storage of organic C; and design plant–microbe systems for maximal C storage.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

Topology and spectral entanglement in cavity-mediated photon scattering

Here, we develop a microscopic diagrammatic theory for cavity-mediated photon scattering in a topological one-dimensional insulator described by the Su–Schrieffer–Heeger model. Within the velocity-gauge formulation, we derive the photon self-energy and vertex corrections arising from virtual electron–hole excitations coupled to a quantized cavity mode, and we evaluate the resulting polariton dispersion and two-photon correlation spectra. Our analysis shows that vacuum fluctuations of the cavity field induce a momentum-resolved self-energy that mixes conduction and valence bands through virtual photon exchange, producing interband hybridization and avoided crossings in the electronic dispersion. This “cavity dressing” is symmetry-dependent, vanishing at the Brillouin-zone edge where the dipole matrix element is zero, and its strength is controlled by the spatial coherence range ζ ≈ (l c /a) 2 of virtual excitations. We further examine how the cavity modifies nonlinear optical observables, including the Kerr nonlinearity and biphoton spectral entanglement, and identify the regimes where these effects become sensitive to the underlying topological phase. The theoretical framework established here provides a unified description of light–matter coupling in topological and polaritonic systems, bridging solid-state cavity QED with the emerging field of cavity-modified quantum materials. Our results suggest that engineered photonic environments can coherently reshape the electronic landscape of topological insulators, offering new routes to control collective electronic and optical phenomena through vacuum-field fluctuations.

74 ATOMIC AND MOLECULAR PHYSICS↗

DOE OSTI2025

A virtual Frisch-grid geometry-based CZT gamma detector for in-field radioisotope identification

Here, we present a Virtual Frisch-Grid geometry-based CZT gamma detector developed for identifying different radioisotopes over an energy range from a few keV up to 2 MeV, and useful for efficient characterization of CZT crystals. The detector is built with a 3 x 3 matrix of CZT crystals, each measuring approximately 6 mm x 6 mm x 15 mm. The charge generated within the sensor’s active volume is read out via an anode connected directly to the AVG3_Dev integrated circuit. A current signal induced by charge drift is collected on side pads of the crystals, enabling reconstruction of a 3D interaction position. This paper discusses the design, development, and performance of the standalone, mobile detector system, which integrates the AVG3_Dev readout IC developed at Brookhaven National Laboratory, a high-speed FPGA-based with per-channel digital signal processing, and embedded system capabilities. The device is compact, battery-powered, and supports wireless data streaming, making it suitable for field operations for radioisotope identification.

47 OTHER INSTRUMENTATION↗

DOE OSTI2024

An Autonomous Critical Data Extrapolator for the AGN-201m

Nuclear nonproliferation serves as a key goal, being undertaken by the International Atomic Energy Agency (IAEA). To recognize proliferation there are two pathways that states, who intend to use nuclear material for malicious purposes can take, diversion can misuse. Diversion is when fissile nuclear material is declared to the IAEA for non-weapon purposes, but then covertly removed. If the source of nuclear material, that is not declared and not fissionable, is placed inside the reactor core to create fissile material used to create weapons then the state is using the second pathway of proliferation, misuse. With the emerging development in areas of simulation and machine learning the creation of virtual models of reactor systems, digital twins, serve as a potential method to identify proliferation through detecting anomalous behavior in the reactor. A digital twin for a physical nuclear reactor has never been developed, as digital twins serve as an emerging technology. To investigate the process for development and use of a digital twin for a nuclear reactor Idaho State University’s AGN-201m serves as the nuclear reactor used for development of this digital twin. A data acquisition system has been installed to the reactor system allowing for the transfer of collected data from a reactor operation to Idaho National Laboratory’s Deeplynx data warehouse. When utilizing data to train reactor physics and machine learning models, a significant challenge encountered is the initial state of the data. Nuclear proliferation will have the capacity to be detected when the reactor immediately starts up, nor will it occur after the reactor shuts down. Generally, it will be detected when the reactor is operating at some desired power over a sufficient period for that specific reactor design. For the AGN-201m this will be when the reactor is critical (generally 1 mW or above) for a timespan that is within or less than the range of a regular business day. Datasets sent to Deeplynx have had to be manually cut to when the reactor is critical based on plots of power levels. This method is inefficient and laborious, especially when using multiple datasets at once to train a model. To provide a more streamlined approach an automated critical data extrapolator is developed, with capabilities of recognizing when the reactor operation first reaches criticality, and when the reactor undergoes a SCRAM and is shutdown.

99 GENERAL AND MISCELLANEOUS↗

DOE OSTI2022

Performance Study of Virtual Frisch Grid CdZnTeSe Detectors

Nuclear detectors for x-ray and gamma-ray spectroscopy and imaging are a vital tool in many homeland security, medical imaging, astrophysics and other applications. Most of these applications require room-temperature operation due to the operational constraints imposed by a cryogenic cooling system. CdZnTe (CZT) has been the main material with the desired detection properties, and CZT crystals have been used commercially for three decades. However, CdZnTe still suffers from long-standing issues of high densities of performance-limiting intrinsic defects such as Te inclusions and networks of dislocation walls (sub-grain boundaries). A recently invented new quaternary material CdZnTeSe showed excellent material properties for radiation detection. The material was found to be free from dislocation networks, possess reduced Te inclusions, and have better compositional homogeneity. Virtual Frisch grid detectors were fabricated from crystals taken from a CdZnTeSe ingot that was grown by the traveling heater method. The detectors were fabricated from an as-grown ingot, bypassing the post-growth annealing process commonly practiced for industrial-grade CZT. The performances of the detectors were studied with different Frisch grid lengths using an amplifier shaping time ranging from 1–6 µs. The detectors showed high-quality spectroscopic performance with an as-measured energy resolution of ~1.1% at 662 keV for an optimum Frisch grid length of 3 mm. The charge collection was observed to enhance for longer Frisch grids.

47 OTHER INSTRUMENTATION↗

DOE OSTI2026

State of the art, gaps, and prospects in fusion materials theory and modelling

Advancing the theory and simulation of materials for fusion applications remains a key component of global roadmaps aimed at delivering much-needed fusion power. Especially as the drive for commercial application increases, prototypes must be designed against radiation damage before the relevant experimental data can be collected and cost reductions that are possible by testing materials in silico become even more important. Here, we summarise the state of the art as it emerged during the 7 th Fusion Materials Theory & Modelling Workshop that took place in 2024, with the aim to highlight present gaps and future directions for the fusion materials modelling community. Of particular interest were the effects of transmutations, chemical complexity with the development of novel alloys and interatomic potentials, advancements in modelling high-dose microstructures, comparison with experimental data and multiscale models for structural assessment relying on high-performance computing and virtual reality.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2023

DeepONet-grid-UQ: A trustworthy deep operator framework for predicting the power grid’s post-fault trajectories

This paper proposes a novel data-driven method for the reliable prediction of the power grid’s post-fault trajectories, i.e., the power grid’s dynamic response after a disturbance or fault. Here, the proposed method is based on the recently proposed concept of Deep Operator Networks (DeepONets). Unlike traditional neural networks that learn to approximate functions, DeepONets are designed to approximate nonlinear operators, i.e., mappings between infinite-dimensional spaces. Under this operator framework, we design a novel and efficient DeepONet that (i) takes as inputs the trajectories collected before and during the fault and (ii) outputs the predicted post-fault trajectories. In addition, we endow our method with the much-needed ability to balance efficiency with reliable/trustworthy predictions via uncertainty quantification. To this end, we propose and compare two novel methods that enable quantifying the predictive uncertainty. First, we propose a Bayesian DeepONet (B-DeepONet) that uses stochastic gradient Hamiltonian Monte-Carlo to sample from the posterior distribution of the DeepONet trainable parameters. Then, we design a Probabilistic DeepONet (Prob-DeepONet) that uses a probabilistic training strategy to enable quantifying uncertainty at virtually no extra computational cost. Finally, we validate the proposed methods’ predictive power and uncertainty quantification capability using the New York-New England power grid model.

24 POWER TRANSMISSION AND DISTRIBUTION↗