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DOE OSTI2025

Orchestrating Spontaneous Emission With Metasurfaces: Recent Advances in Engineering Thermal, Luminescent, and Quantum Emissions

Metasurfaces have emerged as powerful tools for controlling spontaneous emission, offering unprecedented control over light-matter interactions at sub-wavelength scales. While metasurfaces are traditionally utilized for shaping coherent electromagnetic waves, they have recently extended their capabilities to control incoherent or spontaneous emission. This examines review how metasurfaces can enhance and precisely control properties of thermal, luminescent, and quantum emission. In thermal emission, metasurfaces enable control over spatial, temporal, and spin coherence, offering new possibilities for applications such as energy harvesting, radiative cooling and heat assisted ranging and detection. For luminescent emission, metasurfaces significantly improve emission rates, quantum efficiency, and directionality, driving innovations in lighting and display technologies. For controlling quantized spontaneous emission, metasurfaces are instrumental in enhancing single-photon sources and enabling novel functionalities in quantum states through photon-pair generation, which is vital for quantum communication, meteorology, and computing. Here, despite these advancements several challenges to increase the operational bandwidths, accelerate and develop simulation strategies, and fabrication complexities persist. Emerging trends are also discussed, such as dynamic metasurfaces and their integration with nanophotonic platforms, which could further expand the capabilities of light-emitting metasurfaces.

Luminescence↗

DOE OSTI2026

NLR CSP Optical Facilities: Illuminating the Path Forward Through Innovation and Impact: Agreement 38490

This initiative is a multi-faceted project at the National Laboratory of the Rockies (NLR) aimed at strengthening its Concentrating Solar Power (CSP) Optical Facilities to advance the development of low-cost, high-performance materials for solar and other applications. The project's strategy is built on three pillars: strategic stakeholder engagement, diligent facility maintenance and utilization, and the development of new research capabilities. The overarching goal is to ensure the facilities remain state-of-the-art resources for industry and academia, thereby accelerating the conversion of concentrated sunlight into energy. A key driver of the project is an international Advisory Board, which provides critical guidance on research priorities and industry needs, leading to new collaborations and secured funding. This external engagement, combined with proactive outreach to industry partners, ensures the lab's work remains aligned with real-world challenges, including materials durability and performance certification. Significant efforts in facility maintenance have addressed challenges with aging infrastructure. Notable achievements include the complete refurbishment of the hail-damaged Ultra-Accelerated Weathering System (UAWS) and the successful replacement of a failing 15-year-old Lambda 1050 spectrophotometer with a new-generation model, substantially upgrading material characterization capabilities. These maintenance activities were complemented by achieving a prestigious ISO 9001:2015 certification for the Advanced Optical Materials Labs, formally recognizing the quality and reliability of NLR's measurement capabilities. Despite these successes, challenges remain, including high demand for the High Flux Solar Furnace (HFSF) and intermittent failures of other key instruments. The project has delivered major advancements in research techniques and capabilities. At the Flatirons campus, a new indoor laboratory, was established to house advanced deflectometry and photogrammetry systems for heliostat characterization. For on-sun testing, a novel, actively cooled turning mirror was developed for the HFSF, enabling more realistic testing of particle receivers and components. A collaboration with Virginia Tech successfully demonstrated the high-temperature durability of a new solar absorber coating through extensive cyclic testing. Concurrently, new modeling took place to better predict material degradation on rough, fractal surfaces. In summary, this project has systematically enhanced NLR's CSP Optical Facilities through strategic upgrades, rigorous maintenance, and stakeholder-guided research. By overcoming equipment failures, budgetary constraints, and logistical hurdles, the project has reinforced NLR's role as a central hub for CSP innovation and materials testing. Future work will focus on securing diverse funding, expanding collaborations, and continuing to provide the critical infrastructure needed to accelerate the development and deployment of next-generation technologies.

14 SOLAR ENERGY↗

DOE OSTI2025

Fluorescence time profile measurement of LAB based liquid scintillator in response to medium relativistic ion particles

Liquid scintillator is widely used in particle physics experiments due to its high light yield, good timing resolution, scalability and low cost. Certain liquid scintillators exhibit pulse shape discrimination capabilities because of difference in fluorescence timing properties induced by different particles. Its fluoresence timing properties have been measured mostly for radioactive decay sources at MeV energies. Here, we present a novel measurement of fluorescence time properties of Linear Alkyl Benzene (LAB) based liquid scintillator in response to high-energy ions of hydrogen (Z = 1), helium (Z = 2) and krypton at around 200–300 MeV/u for the first time. We compared the results to those from radioactive sources and observed a distinct dE / dX dependence, regardless of the particle type. These findings are essential for physics searches such as the diffuse supernova neutrino background in large liquid scintillator detectors like JUNO, and are also critical towards understanding the underlying scintillation timing mechanism.

Ion identification systems↗

DOE OSTI2025

Overview of the Magnetic Resonance Capabilities at INL

The aim of this presentation is to give a broad overview of the magnetic resonance capabilities at Idaho National Laboratory and how the technique is implemented within the different directorates at the lab. Historically, the high-field NMR instrumentation has been used by our Energy & Environment Science and Technology (EES&T) directorate primarily as a characterization tool for synthetic chemistry work, but we have been actively trying to expand the use of the instrumentation into our Nuclear Science and Technology (NS&T) and National and Homeland Security (NHS) directorates. The expanded scope of work has ranged from solid-state characterization of biomass, analysis of electrolyte materials from lithium ion batteries, quantification of extracted critical materials, and measurement of metal-ligand complexes using ligands proposed for use in the nuclear fuel cycle.

37 - INORGANIC, ORGANIC, PHYSICAL AND ANALYTICAL C↗

DOE OSTI2025

Machine learning and process-based modeling of spatiotemporal changes in active layer thickness across Alaska

Permafrost degradation poses a growing threat to infrastructure stability and ecosystem resilience in the rapidly warming Arctic. We investigated the spatiotemporal dynamics of active layer thickness (ALT) across Alaska by integrating field observations, environmental datasets, a physically based Stefan model, and machine learning (ML) techniques. Using weather projections from the Coupled Model Intercomparison Project Phase 6 under two Shared Socioeconomic Pathways (SSP 2-4.5 and SSP 5-8.5), we assessed ALT sensitivity to projected future weather conditions. The random forest (RF) model outperformed the Stefan approach in predicting ALT on the training dataset (R² = 0.84 vs. 0.53) but demonstrated lower generalizability on the test dataset (R² = 0.24 vs. 0.54). The root mean square error (RMSE) for the RF model for training and testing ranged from 14 to 22 cm, compared to 17 and 18 cm for the Stefan model. Variable importance analysis revealed that mean annual temperature and slope angle were the strongest predictors of ALT, accounting for 19% and 18% of the variance, respectively, followed by sediment transport index (14%) and stream power index (11%). Comparative analysis of baseline ALT predictions showed the Stefan model tended to project a thicker active layer (mean ± SD: 65 ± 16 cm), compared to the RF model (mean ± SD: 59 ± 8.8) cm). Both models indicated a latitudinal gradient in ALT, with shallower depths at higher latitudes. Projected ALT increases by 2100 were estimated at 3.3 ± 2.2 cm under SSP 2-4.5 and 5.9 ± 4.0 cm under SSP 5-8.5 for the ML model, whereas the Stefan model projected substantially larger increases of 13 ± 2.6 cm (SSP 2-4.5) and 28 ± 4.4 cm (SSP5-8.5). Spatial analysis showed the greatest ALT increases in northern Alaska, with relatively smaller changes in southern regions. These findings highlight the complex, multifactorial nature of ALT dynamics and the value of hybrid modeling approaches. As rising temperatures accelerate permafrost thaw, changes in ALT can disrupt ecosystems, damage infrastructures, and enhance the release of stored soil carbon, highlighting the urgent need for improved predictive capabilities to inform adaptation strategies in the Arctic.

Climate sciences↗

DOE OSTI2025

Hydra: An AI-Based Framework for Interpretable and Portable Data Quality Monitoring

Hydra is an advanced framework designed for training and managing AI models for near real time data quality monitoring at Jefferson Lab. Deployed in all four experimental halls, Hydra has analyzed over 2 million images and has extended its capabilities to offline monitoring and validation. Hydra utilizes computer vision to continually analyze sets of images of monitoring plots generated 24/7 during experiments. Generally, these sets of images are produced at a rate and quantity that is exceedingly difficult for shift crews to effectively monitor. Significant effort has been devoted to enhancing Hydra’s user interface, to ensure that it provides clear, actionable insights for shift workers and other users. Gradient Weighted Class Activation Maps (GradCAM) provide added interpretability, allowing users to visualize important regions of the image for classification. Hydra has been containerized to enable the creation of portable demos and seamless integration with container-based technologies such as Kubernetes and Docker. With the user interface enhancements and containerization, Hydra can be rapidly deployed for new use cases and experiments. This talk will describe the Hydra framework, its user interface and experience, and the challenges inherent in its design and deployment.

Britton, Thomas [Thomas Jefferson National Acceler↗

DOE OSTI2024

ISO_Fortran_binding_m v0.1.0

The Fortran programming language standard defines a broad feature set supporting the interoperability of Fortran programs with program written according to the C programming language standard. Among Fortran's C-interoperability features is a a C header file "ISO_Fortran_binding.h" This header file defines the interface to various C data structures and functions that C programs may use to access Fortran data entities. The ISO_Fortran_binding_m software defines a native Fortran module that presents an interface to these same data structures and functions. ISO_Fortran_bind_m thus enables Fortran programs to access and manipulate Fortran entities in ways that precisely mirror what C programs can do using ISO_Fortran_binding.h. ISO_Fortran_binding_m facilitates writing portable standard-conforming Fortran programs that emulate non-interoperable features, e.g., dynamic polymorphism, in a standard-conforming interoperable way similar but broader than what is demonstrated in Berkeley Lab's Caffeine software [1]. ISO_Fortran_binding_m also enables a Fortran programmer to extend Fortran's capabilities to emulate certain C functionality such as memory address arithmetic or computing C's "sizeof" function. [1] https://github.com/BerkeleyLab/caffeine/blob/213e3df1c319f0663306354414f352acda42a24f/src/caffeine/collective_subroutines/co_reduce_s.f90#L88 [2] https://github.com/BerkeleyLab/ISO_Fortran_binding_m/blob/0c585362bb4f2c72cf9049c800a7115b529ec533/src/iso_fortran_binding_m.F90#L196

Rouson, Damian↗

DOE OSTI2024

AiiDA-INQ plugin

The AiiDA-INQ plugin will allow the INQ code developed at the lab to utilize the AiiDA workflow manager which enables high-throughput workflows. This includes provenance tracking, restart capabilities, job calculators, and other capabilities

Keilbart, NathanD↗

DOE OSTI2025

Development of Thin Gap GEM-µRWELL Hybrid Detectors at Jefferson Lab

Over the past few decades, Micro Pattern Gaseous Detector (MPGD) technologies have been increasingly adopted as tracking detector options in High Energy and Nuclear Physics experiments thanks to their good spatial resolution, high-rate capability, stability and more importantly their ability for large area coverage at a relatively low cost compared to the alternative. The thin gap GEM-µRWELL hybrid detector is the latest addition to the MPGD family, that was introduced to vastly improve the spatial resolution capability of gaseous trackers when deployed in the barrel region to cover large angular acceptance of the central tracker in a collider experiment. In this talk, I will re-introduce the concept and motivation for the development of thin gap GEM-µRWELL hybrid technology with an emphasis on the initial studies that establish the proof-of-concept of the technology. I will then discuss the more recent results from latest beam test campaign at Jefferson Lab in May 2025 to study detector efficiency performance with various gas mixtures. I will also briefly present the ongoing activities to develop large area thin gap GEM-µRWELL tracking detectors for the ePIC experiment of the future Electron Ion Collider as well as the exploration of the technology to provide large area tracking options to the muon system of experiments at a future Higgs Factory Collider such as the FCC-ee for example. Finally, I will conclude with some perspectives on new ideas under exploration to develop the next generation of thin gap MPGD technologies with enhanced timing and spatial resolution capabilities

Gnanvo, Kondo [Thomas Jefferson National Accelerat↗

DOE OSTI2024

EVs@Scale High-Power Charging (HPC) Pillar Deep-Dive Technical Meeting

EVs@Scale Lab Consortium is addressing challenges, developing solutions, and enabling technologies for transportation electrification ecosystem. The consortium has five research pillars one of which focuses on high power charging (HPC). HPC pillar brings together hardware and software expertise, capabilities, and facilities related to high power EV charging, charge management, and grid integration. Deep-dive technical meetings are organized twice a year and provides an opportunity for more industry engagement and technical feedback for the national labs throughout the project lifecycle. High-Power Charging pillar has two active projects: (i) Next-Gen Profiles (NGP) and (ii) High-Power Electric Vehicle Charging Hub Integration Platform (eCHIP). This presentation summarizes the progress made in both projects during the past six months focusing on specific topics. There will be two deep-dive technical meetings twice a year. Every deep-dive meeting will focus on different aspects of the project progress.

ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2024

Nov. 2024 EVs@Scale High-Power Charging Deep Dive Technical Meeting

ADVANCED PROPULSION SYSTEMS,DIRECT ENERGY CONVERSI↗

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 OSTI2026

Solving challenges in QCD 3D partonic distributions

The superior luminosity and high polarization of CEBAF, combined with the high resolution and excellent particle identification capabilities of its detectors, as well as the ability for multidimensional and multiparticle detection using polarized targets, make Jefferson Lab uniquely positioned to disentangle the genuine intrinsic transverse structure of hadrons encoded in 3D partonic distributions—particularly in the kinematic regime dominated by valence quarks. The inclusion of Jefferson Lab data on semi-inclusive and hard exclusive hadron production has the potential to dramatically advance our understanding of non-perturbative QCD dynamics. Although a wealth of data from various Jefferson Lab experiments is already available, its incorporation into phenomenological studies has been slow, and a significant portion of data from other leptoproduction experiments is still missing from global fits. In this contribution, we discuss the existing challenges and outline a path forward for improving the analysis of low center-of-mass electroproduction experiments in general, and Jefferson Lab data in particular.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2025

Virtual to Physical: Reinforcement Learning to Optimize SNS Particle Accelerator Controls

Complex accelerators must have control systems that can handle dynamic nonlinear environments. This makes traditional control methods unsuitable as they can struggle to adapt to these uncertainties. This provides an ideal environment for reinforcement learning algorithms as they are adaptable and generalizable. We present a reinforcement learning pipeline that can effectively handle the dynamics of a complex accelerator. We test and prove our pipelines capabilities on multiple environments including the Spallation Neutron Source (SNS) and the Beam Test Facility (BTF) at Oakridge National Lab (ORNL). Due to the limited time available to train an online algorithm like reinforcement learning on a real accelerator, we utilize a virtual twin accelerator (VIRAC) developed by ORNL to pretrain the policy and show its ability to converge in the virtual environment. We then test the adaptability of the pretrained RL model by applying it on the real accelerator and comparing the results. Utilizing our Scientific Optimization and Controls Toolkit (SOCT) and open-source standards such as Gymnasium we create and solve for a MEBT orbit correction problem in the SNS and an emittance maximization problem in the BTF. We show how Twin Delayed Deep Deterministic Policy Gradient (TD3) can solve this optimization environment in the virtual accelerator and transfer this policy onto the real accelerator for inference and model retraining. We show how reinforcement learning can be utilized as a control system for complex accelerators and provide a model pipeline for how an implementation performs and can be adapted to new accelerator control problems.

Kasparian, Armen [Thomas Jefferson National Accele↗

DOE OSTI2026

Unraveling the Dynamics of Nucleosome Arrays

The organization of genomic DNA into chromatin is a fundamental determinant of genome stability, regulation, and cellular function. Nucleosomes, the basic repeating units of chromatin, assemble into higher-order structures whose organization and heterogeneity remain difficult to characterize using conventional ensemble-averaged techniques. A key need in the field is the development of experimental approaches capable of directly visualizing nucleosome assemblies and their structural variability at the single-molecule level. This LDRD Lab-Wide project focused on establishing and evaluating atomic force microscopy (AFM)–based approaches for the characterization of nucleosome assemblies. The work emphasized experimental workflows for preparing, imaging, and assessing multi-nucleosome systems, rather than isolated single nucleosomes. Through method development and exploratory measurements, the project demonstrated the feasibility of applying scanning probe microscopy to investigate chromatin-relevant assemblies and provided preliminary insight into the strengths and limitations of this approach for future quantitative studies. Results and lessons learned from this effort were disseminated to the broader scientific community through multiple national conference presentations, helping to position LLNL for continued work in chromatin and genome organization research.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2024

Performance of a coarsely pixelated LAPPD photosensor for the SoLID gas Cherenkov detectors

The SoLID spectrometer's gas Cherenkov counters require photosensors that operate in a high luminosity and high background environment. The reference design features arrays of 9 or 16 tiled multi-anode photomultipliers (MaPMTs), distributed across 32 sectors, to serve the light-gas and heavy-gas Cherenkov counters, respectively. To assess the viability of a pixelated INCOM Large Area Picosecond Photodetector (LAPPD TM ) as an alternative photosensor to replace MaPMT arrays in either detector, we evaluated its performance under realistic SoLID running conditions in Hall C at the Thomas Jefferson National Accelerator Facility (Jefferson Lab). The results of this test confirmed that the coarse-pixelated (2.5 × 2.5 cm 2 pixel size) LAPPD is capable of handling the total projected signal and background rates of the three pillar SoLID experiments. The tested photosensor detected Cherenkov signals with the capability of separating single-electron events from pair production events while rejecting background. Although the design was not aimed at ring-imaging Cherenkov detectors, Cherenkov disk images were captured in two different gas radiators. Through a direct comparison with a GEANT4 simulation, we confirmed the experimental performance of the LAPPD.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2026

SoLID Program at JLab

An overview of the Solenoidal Large Intensity Device (SoLID) and its scientific program will be given in this talk. SoLID is a spectrometer/detector system proposed to exploit the full potential of the Jefferson Lab (JLab) 12 GeV energy upgrade. SoLID will push the limit of luminosity frontier in hadronic physics with its unique capability to handle very high rates with large acceptance under high luminosity (1037-39/cm2/s). A rich and vibrant scientific program has been developed for SoLID, including but not limited to the precision study of the 3d nucleon structure in both momentum space using Semi-Inclusive Deep Inelastic Scattering (SIDIS) and coordinate space using Deep Virtual Exclusive Reactions (DVER), probing physics beyond the Standard Model with Parity Violating Deep Inelastic Scattering (PVDIS), and investigating the gluonic field contribution to the proton structure and proton mass via J/¿ threshold production. The SoLID collaboration has developed a robust, low risk and flexible conceptual design, with a base line design capable of accomplishing its scientific goals and flexibility to adopt the cutting-edge technology. Detector subsystems have been tested with prototypes in realistic high luminosity conditions and are demonstrated to function well under extremely challenging environment to satisfy the requirements of planned experiments.

Chen, Jian-Ping [Thomas Jefferson National Acceler↗

DOE OSTI2026

RDPP: Accelerating Diversity in DOE Climate Science and Resilience Research (Final Report)

The scope of the project was set out to accelerate the inclusion of diversity into the Department of Energy (DOE) Earth and Environmental Systems Sciences Division (EESSD) relevant climate science and resilience research to inclusively advance solutions. The Project Objectives were to usher in equitable use-inspired climate-related research with underrepresented Minorities of which this project helped fund 7 HU graduate students work with the DOE (three of which will graduate in Spring 2025). The two key aims underpinning that core goal were AIM1: developing partnerships (18 organized engaged, see partners list) and AIM2: Developing capabilities (3 visits to DOE facilities, 5 DOE partners visits to HU, increased visiting faculty participation in BNL-DOE lab, secured 5 grants together totaling 1.2 million in funds for HU). The project objectives were highly successful as they were designed to ambitiously pull together DOE lab researchers with the long-standing and successful transdisciplinary climate science research programs of the PI and local DC groups. The major outcomes of this RDPP program will be in the new fundamentally inclusive partnerships with DOE and HU tasked to understand the urban-rural impacts due to climate change in the US, Eastern South Atlantic (ESA) Region, related to energy issues driven by heat stress and the water cycle. Overall, this project contributes to the DOE and science community vision for catalyze connections for project-ready underrepresented minorities (URMs) at a prominent HBCU to DOE projects supported by the Biological and Environmental research (BER) Program; particularly, the Earth and Environmental Systems Sciences Division (EESSD).

54 ENVIRONMENTAL SCIENCES↗