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DOE OSTI2025

FY24 LDRD Annual Report PDF

The Laboratory Directed Research and Development (LDRD) Program at Lawrence Livermore National Laboratory (LLNL) is the Lab's most significant resource for supporting internally directed research and development. It provides investments in cutting-edge science, technology, and engineering. This program expands the frontiers of knowledge, creates capabilities required by our evolving mission needs, and attracts and retains the world's most talented scientists and engineers. In this annual report, we describe the LDRD investment portfolio, provide information to demonstrate the program's value and impact to LLNL's science, technology and engineering capabilities, and showcase LDRD accomplishments across the Lab's mission space.

42 ENGINEERING↗

DOE OSTI2023

Impact Report: Quantum Systems Accelerator

The Quantum Systems Accelerator (QSA) is a U.S. National Quantum Information Science Research Center established in August 2020 and funded by the Department of Energy (DOE) Office of Science. QSA is composed of 15 partner institutions— universities and national laboratories—bringing together pioneers of many of today’s unique quantum information science (QIS) and engineering capabilities. Led by Lawrence Berkeley National Laboratory (Berkeley Lab), with Sandia National Laboratories (Sandia Labs) as the lead partner, 250+ QSA researchers are catalyzing U.S. leadership in a fast-growing field that seeks solutions to the Nation’s and the world’s most pressing problems by harnessing the laws of quantum mechanics.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2020

The GLUEX DIRC program

The GLUEX experiment is located in experimental Hall D at Jefferson Lab (JLab) and provides a unique capability to search for hybrid mesons in high-energy photoproduction, utilizing a ~9 GeV linearly polarized photon beam. The initial, low-intensity phase of GLUEX was recently completed and a high-intensity phase has begun in 2020 which includes an upgraded kaon identification system, known as the DIRC (Detection of Internally Reflected Cherenkov light), utilizing components from the decommissioned BaBar DIRC. The identification of kaon final states will significantly enhance the GLUEX physics program, to aid in inferring the quark flavor content of conventional (and potentially hybrid) mesons. Here, we describe the installation of the GLUEX DIRC and the analysis of initial commissioning data.

47 OTHER INSTRUMENTATION↗

DOE OSTI2025

Status and Performance of the GlueX DIRC

The GlueX experiment is located in experimental Hall D at Jefferson Lab (JLab) and provides a unique capability to search for hybrid mesons in high-energy photoproduction, utilizing a $\sim$9 GeV linearly polarized photon beam. Since 2020 a Detector of Internally Reflected Cherenkov light (DIRC) has been in use at GlueX, utilizing components from the decommissioned BaBar DIRC. In this contribution we will discuss the status and performance of the GlueX DIRC with the data collected to date, including recent developments in the use of machine learning for particle identification.

Stevens, Justin [College of William and Mary, Will↗

DOE OSTI2025

A Grassroots Network and Community Roadmap for Interconnected Autonomous Science Laboratories for Accelerated Discovery

Scientific discovery is being revolutionized by AI and autonomous systems, yet current autonomous laboratories remain isolated islands unable to collaborate across institutions. We present the Autonomous Interconnected Science Lab Ecosystem (AISLE), a grassroots network transforming fragmented capabilities into a unified system that shorten the path from ideation to innovation to impact and accelerates discovery from decades to months. AISLE addresses five critical dimensions: (1) cross-institutional equipment orchestration, (2) intelligent data management with FAIR compliance, (3) AI-agent driven orchestration grounded in scientific principles, (4) interoperable agent communication interfaces, and (5) AI/ML-integrated scientific education. By connecting autonomous agents across institutional boundaries, autonomous science can unlock research spaces inaccessible to traditional approaches while democratizing cutting-edge technologies. This paradigm shift toward collaborative autonomous science promises breakthroughs in sustainable energy, materials development, and public health.

Ferreira da Silva, Rafael [Oak Ridge National Labo↗

DOE OSTI2020

Integral LOCA fragmentation test on high-burnup fuel

Increasing the fuel burnup limit in light water reactors is sought to enhance fuel cycle economics and requires establishing a technical basis. Experimental observations of severe fuel fragmentation under loss-of-coolant-accident conditions at Halden and Studsvik had raised the need for additional considerations during the development of this technical basis. These test data suggested that the burnup threshold for high burnup fuel fragmentation may be influenced by pre-transient power. Additional loss-of-coolant-accident test data is therefore valuable to complement these tests and enhance the current state of understating. Oak Ridge National Lab has developed the Severe Accident Test Station capable of examining the oxidation kinetics and accident response of irradiated fuel and cladding materials for design basis accident and beyond design basis accident scenarios. Severe Accident Test Station provides various temperature profiles, steam, and the thermal shock conditions necessary for integral loss of coolant accident testing, defueled oxidation quench testing, and high-temperature beyond design basis accident testing. Severe Accident Test Station has been successfully installed and demonstrated in the Irradiated Fuels Examination Laboratory at Oak Ridge National Lab. Furthermore, descriptions of the in-cell re-fabrication capabilities and assembly of the loss-of-coolant-accident test train are provided. Installation of the Severe Accident Test Station system and in-cell re-fabrication restores United States capability to examine postulated and extended loss-of-coolant-accident conditions on spent fuel and cladding and provides a platform for evaluating advanced fuel and accident-tolerant fuel cladding concepts. Lastly, three in-cell integral loss-of-coolant-accident test were performed in the Severe Accident Test Station and compared to the Nuclear Regulatory Commission sponsored loss-of-coolant-accident test as well as the original loss-of-coolant-accident test performed at Argonne National Lab. Finally, the results of these tests as well as all publicly available integral loss-of-coolant-accident test were used to validate a threshold for high burnup fuel fragmentation.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2023

Polarized electron injector for positron production at CEBAF

As a part of the effort to expand the capabilities of CE-BAF 12 GeV (Continuous Electron Beam Accelerator Facility) at Jefferson Lab, the addition of a polarized positron source is considered. This capability would provide acceleration of high duty-cycle polarized posi-trons, with spin >60% polarization, through the same main CEBAF accelerator machine with appropriate mag-net field reversals and linac phasing to the four CEBAF experimental halls. To produce this positron beam, a high average current (3-10 mA) highly polarized electron beam with energy of 100 ? 150 MeV is required at the positron source target. The focus of this paper is the de-sign of that polarized electron beam injector. We will describe the production and delivery of a >3 mA highly polarized electron beam. We will discuss different aspects of the design, the photocathode gun, beam dynamics simulation results, spin manipulation, bunching and accelerating process and final electron beam parameters.

Kazimi, R.↗

DOE OSTI2023

Characterizing PuO 2 Powder at Oak Ridge National Laboratory for the Multi-Lab Plutonium Process Signatures Campaign FY 2022

This report highlights the development of advanced analytical capabilities at Oak Ridge National Laboratory to characterize PuO 2 powder to support the Multi-Lab Plutonium Process Signatures Campaign in FY 2022. Two samples from a batch of 76 statistical precipitation runs were packaged and shipped from Pacific Northwest National Laboratory to Oak Ridge National Laboratory at the beginning of FY 2022. Techniques were used to characterize the sample, including powder X-ray diffraction, Raman spectroscopy, and diffuse reflectance spectroscopy. A brief description of each technique, the results and discussion, and future work are described in this report. Opportunities to expand current capabilities and include additional analytical capabilities for the next campaign will also be discussed.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2023

Infrastructure Perception and Control Laboratory [ITS Research Lab]

U.S. Department of Energy national laboratories host world-class advanced computing and modeling capabilities. The National Renewable Energy Labowratory (NREL) inaugurated the Infrastructure Perception and Control Laboratory (IPC Lab) to advance mobility reach into real-world applications.

99 GENERAL AND MISCELLANEOUS↗

DOE OSTI2020

Sub-Ångström-resolution MicroED Using a Direct Detection Camera

The recent development of microcrystal electron diffraction (MicroED) in electron cryo-microscopy (cryo-EM) has addressed many of the challenges of X-ray crystallography [1], filling a long-standing need in organic chemistry for rapid and accurate high-resolution structural characterization [2]. Like X-ray crystallography, this is also a diffraction technique requiring crystalline specimens, but because high energy electrons have a more favorable ration of elastic to inelastic scattering [3], MicroED can acquire data using crystals that are orders of magnitude smaller than those needed for X-ray crystallography—even as small as 50 nm in length [4]. Additionally, the instrumentation required for MicroED is readily available, as many chemistry and biology labs already have access to cryo-capable transmission electron microscopes (TEM).

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2024

Model-predictive optimal control of ferrofluidic microrobots in three-dimensional space

Ferrofluid microrobots have emerged as promising tools for minimally invasive medical procedures. Their unique properties to navigate complex fluids and reach otherwise inaccessible regions of the human body have enabled new applications in targeted drug delivery, tissue engineering, and diagnostics. Here, this paper proposes a model-predictive controller for the external magnetic manipulation of ferrofluid microrobots in three dimensions (3D). The internal optimization routine of the controller determines appropriate changes in the applied electromagnetic field to minimize the deviation between the actual and desired trajectories of the microrobot. A linear system governing locomotion is derived and used as the equality constraints of the optimization problems associated with the feedback index. In addition to ferrofluid droplets, the controller presented in this work may be applied to other magnetically-pulled microrobots. Several experiments are performed to validate the controller and showcase its ability to adapt to changes in system parameters such as the desired tracking trajectory and the size, orientation, deformation, and velocity of the microrobot. The accuracy of the controller is analyzed for each experiment, and the average error is found to be within 0.25 mm for small velocities. An additional experiment is performed to demonstrate significant improvement over a PID controller that is optimally tuned using Bayesian optimization. The results presented in this paper suggest that the proposed control algorithm could enable new microrobotic capabilities in minimally invasive medical procedures, lab-on-a-chip applications, and microfluidics.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2021

Design of a 10 MeV beamline at the Upgraded Injector Test Facility for e-beam irradiation

Electron beam irradiation near 10 MeV is suitable for wastewater treatment. The Upgraded Injector Test Facility (UITF) at Jefferson Lab is a CW superconducting linear accelerator capable of providing an electron beam of energy up to 10 MeV and up to 100 µA current. This contribution presents the beam transport simulations for a beamline to be used for the irradiation of wastewater samples at the UITF. The simulations were done using the code General Particle Tracer with the goal of obtaining an 8 MeV electron beam of radius (3-σ) of ~2.4 cm. The achieved energy spread is ~74.5 keV. The space charge effects were investigated when the bunch charge is varied to be up to 1000 times and the results showed that they do not affect the beam quality significantly.

Li, X.↗

DOE OSTI2020

LANSCE 21st Century Deterrence

The LANSCE accelerator is an important tool in the assessment and certification of the nation’s nuclear stockpile. LANSCE provides critical materials and nuclear data needed to develop a predictive capability for the performance of nuclear weapons. As the stockpile ages, materials are replaced, and manufacturing processes change, it is critical that we understand how these changes impact device performance. Dynamic proton radiography and neutron diffraction are ideally suited to contribute to this need. These capabilities are essential to enabling the NNSA labs to become more responsive and agile in advancing the deterrent of the future.

43 PARTICLE ACCELERATORS↗

DOE OSTI2021

Automated Transit Fleet Electrification Planning in Response to Route Dynamics, Vehicle Utilization, and Local Climate

Transit electrification is difficult, requiring the consideration of many factors including route dynamics, vehicle utilization, and local ambient temperatures. Incumbent planning tools typically rely upon static vehicle efficiency numbers which do not consider local factors specific to each fleet. This paper summarizes a collaboration between NREL and Microgrid Labs, which produced an automated sizing tool capable of producing vehicle battery and motor sizing for drop-in electrification. Furthermore, the workflow also exports the electric-vehicle supply equipment needed to support fleet electrification. The paper summarizes the model theory and includes a sample pilot study performed in support of the Via Mobility transit fleet.

33 ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2021

Applying Anodic Stripping Voltammetry to Complex Wastewater Streams for Rapid Metal Detection (Final Report)

The overall objective of this project is the development of a lab-on-a-chip (LOC) electrochemical sensor capable of accurately measuring heavy metal (lead (Pb), cadmium (Cd), and arsenic (As)) concentrations in complex aqueous streams such as wastewater. The specific goals include: i) identify how heavy metal speciate and transform during the different stages of the wastewater treatment train, and explore pre-treatment methods that will de-complex heavy metals, making them available to ASV, ii) develop appropriate electrodes for ASV using organic/inorganic nanomaterials, and iii) integrate pre-treatment steps and specialized electrodes into an LOC sensor device capable of autonomously determining heavy metal concentrations in complex wastewater streams.

99 GENERAL AND MISCELLANEOUS↗

DOE OSTI2021

Design of a 10 MeV beamline at the Upgraded Injector Test Facility for e-beam irradiation

Li, X.↗

DOE OSTI2023

2024 Capability Review Update [Slides]

LANL appears competitive with peer NNSA labs, below most DOE Science labs, but trending down - why? Publication rates or counts are an indicator of productivity. Field-weighted citation impact (FWCI) is an indicator of impact.

99 GENERAL AND MISCELLANEOUS↗

DOE OSTI2021

November Mega-AI Newsletter

Mega AI, an internal investment at Pacific Northwest National Laboratory, aims to develop next-generation artificial intelligence (AI) capabilities unique to the Department of Energy national lab complex to address research gaps in large-scale multimodal representation learning, multitask inferences, and the need for increased generalizability, rapid adaptivity, and usability of AI technologies. In this newsletter, we highlight recent developments in the research community on next-gen AI technologies focusing on massive-scale model development, deployment and evaluation, data and code availability, model interactions, and new features and capabilities that are relevant to Mega AI’s goals and science and security applications.

Volkova, Svitlana↗