Results for “Pioneer” — Page 17

DOE OSTI2025

Correlating Nb-SRF Surface Processing with Evolution of Surface Electronic States

The few nanometers of the surface exposed to RF field plays a major role in defining the RF performance of superconducting cavities. Over the past two decades, several pioneering surface treatment and processing methods have emerged, enabling remarkable improvements in cavity performance by simultaneously achieving high Q with increasing Eacc. These processing methods include: thermal treatment under ultra-high vacuum (UHV) conditions across lo¬¬¬¬¬¬¬w-, mid-, and high-temperature ranges and high temperature treatments under controlled N2 atmosphere. These processes also produce distinct surface oxide configurations with different valence states, thicknesses, and uniformity, as well as different oxygen concentration profiles in bulk Nb. In this work, we are trying to understand how do surface-processing methods and the resulting oxide/oxygen profiles affect the electronic structure of surface and the mechanism of superconductivity? With the help of Fermilab’s in-house X-ray photoemission facility and, in collaboration with the synchrotron-based angle-resolved photoemission (ARPES) facility at Argonne National Laboratory, we are investigating how the valence band structure and density of states (DoS) near the Fermi level modify with different surface treatments. Our observations show that different surface-processing methods lead to distinct evolutions of the valence-band states near the Fermi level during the superconducting transition. This behavior suggests variations in Nb-O orbital hybridizations and points towards the possibility of different underlying mechanisms of superconductivity governed by the surface chemistry and oxide configuration. We also correlate these distinct superconducting mechanisms with RF cavity performance, specifically focusing on measured surface resistance, the nature of the Q-slope, and quench fields observed in SRF measurements. These results will enable us to identify the potential limiting factors and relevant controllable parameters that can be further optimized to improve the performance of SRF cavities.

Tripathi, Malvika [Fermilab]↗

DOE OSTI2025

Hybrid learning techniques for scientific data reduction with performance guarantees

The research initiatives supported by the U.S. Department of Energy (DOE) Grant DE-SC0022265 are fundamentally aimed at pioneering advanced machine learning (ML) techniques for scientific data compression within high-performance computing (HPC) environments. This comprehensive body of work addresses the critical challenge posed by the exponential growth of data generated by scientific simulations in domains such as fusion energy, climate modeling, and computational fluid dynamics (CFD). A core objective is to develop compression algorithms that achieve substantial data reduction—often by orders of magnitude—while rigorously ensuring the fidelity of both the primary data (PD) and scientifically crucial derived quantities of interest (QoI). The methodologies deployed under this grant integrate sophisticated deep learning architectures, prominently featuring autoencoders, advanced generative models like conditional diffusion, and hybrid learning techniques. Key innovations include the development of Guaranteed Autoencoders (GAE) and the Guaranteed Conditional Diffusion with Tensor Correction (GCDTC) framework, which provide explicit, instance-level error bounds on reconstructed data. Furthermore, specialized strategies such as nonlinear constraint satisfaction are employed to preserve the integrity of QoI, a vital requirement for the trustworthiness of downstream scientific analyses. This research also focuses on the design and implementation of scalable, GPU-accelerated software pipelines that seamlessly integrate into existing HPC workflows, ensuring both computational efficiency and practical applicability. The CAESAR framework, for example, unifies foundation and generative models to create an adaptive and efficient compression solution for spatio-temporal scientific data. Collectively, these efforts represent a significant advancement in mitigating the scientific data deluge, enabling more effective data management, accelerated scientific discovery, and optimized utilization of HPC resources.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2025

Final report- UFL - RAPIDS2: A SciDAC Institute for Computer Science, Data, and Artificial Intelligence

The research initiatives supported by the U.S. Department of Energy (DOE) Grant DE-SC0022265 are fundamentally aimed at pioneering advanced machine learning (ML) techniques for scientific data compression within high-performance computing (HPC) environments. This comprehensive body of work addresses the critical challenge posed by the exponential growth of data generated by scientific simulations in domains such as fusion energy, climate modeling, and computational fluid dynamics (CFD). A core objective is to develop compression algorithms that achieve substantial data reduction—often by orders of magnitude—while rigorously ensuring the fidelity of both the primary data (PD) and scientifically crucial derived quantities of interest (QoI). The methodologies deployed under this grant integrate sophisticated deep learning architectures, prominently featuring autoencoders, advanced generative models like conditional diffusion, and hybrid learning techniques. Key innovations include the development of Guaranteed Autoencoders (GAE) and the Guaranteed Conditional Diffusion with Tensor Correction (GCDTC) framework, which provide explicit, instance-level error bounds on reconstructed data. Furthermore, specialized strategies such as nonlinear constraint satisfaction are employed to preserve the integrity of QoI, a vital requirement for the trustworthiness of downstream scientific analyses. This research also focuses on the design and implementation of scalable, GPU-accelerated software pipelines that seamlessly integrate into existing HPC workflows, ensuring both computational efficiency and practical applicability. The CAESAR framework, for example, unifies foundation and generative models to create an adaptive and efficient compression solution for spatio-temporal scientific data. Collectively, these efforts represent a significant advancement in mitigating the scientific data deluge, enabling more effective data management, accelerated scientific discovery, and optimized utilization of HPC resources.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2026

Laboratory Investigations of Aerosol-Cloud Interactions in an Entraining, Turbulent Environment (Final Technical Report)

The effects that aerosol particles have on clouds in Earth’s atmosphere and the role that clouds play in determining the fate of aerosol particles play a crucial role in determining the human experience on this planet. These interwoven interactions can determine whether it rains or snows, is comfortable, hot or cold, or whether the cloud you gaze at in the sky looks like a tiger or a lump of porridge. How clouds mix with the air surrounding them, a process known as entrainment, further affects these processes. We conducted laboratory experiments of aerosol-cloud interactions in a setting in which we can measure the effects of entrainment – the first laboratory experiments since the pioneering work of Latham and Reed in 1977. We measured the effects of entrainment on aerosol-cloud interactions in the Pi Chamber and also used a state-of-the-art large eddy simulation (LES) to investigate processes in fine detail.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

Mathematical methods for optimal polynomial recovery of high-dimensional systems from noisy data

The goal of our Early Career Research Project (ECRP) is to establish a modern mathematical foundation that will enable next-generation computational methods for polynomial approximation of high-dimensional systems, having a certain set of constraints, from a limited amount of noisy data. Such a foundation is critical to realizing the future potential of the DOE user facilities, and will ultimately empower scientists to address a fundamental question, namely, “how many realizations of a nonlinear manifold are required to recover the entire high-dimensional solution map, with optimal approximation guarantees and minimal computational cost?” The central theme of this effort aims to conquer this challenge by pioneering the development of extraordinarily innovative theoretical analysis and transformational non-intrusive computational methodologies. Such approaches will enable the reconstruction of the entire high-dimensional solution map, with accuracy comparable to the best approximation, while utilizing an optimal number of samples. During this reporting period we have made significant progress on four thrusts.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2025

Seeing is Believing: Autonomous Microscopy and the Data Revolution in Materials Science

This presentation explores the transformative potential of autonomous electron microscopy and artificial intelligence (AI) in accelerating materials science discovery, particularly for energy applications and materials operating in extreme environments. We discuss pioneering self-driving laboratories at NREL designed to intelligently probe material synthesis and degradation across multiple scales, aiming to rapidly bridge the gap between atomic-level understanding and the development of high-performance, reliable materials. Utilizing advanced machine learning techniques, such as few-shot learning and multimodal analysis integrating imaging and spectroscopy, we demonstrate methods to extract actionable descriptors for material behavior, quantify complex microstructural evolution, and statistically link synthesis parameters to defect populations. This AI-driven approach promises to accelerate the creation of predictive materials tailored for specific missions, enabling faster development cycles and enhanced material assurance.

36 MATERIALS SCIENCE↗

DOE OSTI2025

Seeing is Believing: Autonomous Microscopy and the Data Revolution in Materials Science

This presentation explores the transformative potential of autonomous electron microscopy and artificial intelligence (AI) in accelerating materials science discovery, particularly for energy applications and materials operating in extreme environments. We discuss pioneering self-driving laboratories at NREL designed to intelligently probe material synthesis and degradation across multiple scales, aiming to rapidly bridge the gap between atomic-level understanding and the development of high-performance, reliable materials. Utilizing advanced machine learning techniques, such as few-shot learning and multimodal analysis integrating imaging and spectroscopy, we demonstrate methods to extract actionable descriptors for material behavior, quantify complex microstructural evolution, and statistically link synthesis parameters to defect populations. This AI-driven approach promises to accelerate the creation of predictive materials tailored for specific missions, enabling faster development cycles and enhanced material assurance.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2026

Toward a microscopic picture of hadronization and multi-parton processes

This project advanced the understanding of how quarks and gluons produced in high-energy collisions transform into the hadrons observed in particle detectors, a fundamental process known as quantum chromodynamics (QCD) hadronization. By combining theoretical calculations, quantum simulation methods, and modern AI techniques, the research developed new tools to study multi-parton dynamics and nonperturbative effects that are essential for interpreting data from current and future nuclear physics experiments. Key outcomes include new theoretical frameworks for jet and hadron measurements, pioneering quantum simulation algorithms for real-time dynamics in field theories, and the development of advanced machine-learning models, such as diffusion models and explainable classifiers, to simulate and analyze collider events. These results are directly relevant to experiments at Jefferson Lab, Brookhaven National Laboratory, and the future Electron-Ion Collider, and they also have a broader impact in areas such as quantum information science and data-driven modeling of complex systems. The project supported the training of graduate students and postdoctoral fellows and contributed to the broader scientific community through publications, workshops, and collaborative activities. Overall, this work provides new insights into the microscopic mechanisms of hadron formation and establishes a foundation for future studies at the intersection of nuclear physics, artificial intelligence, and quantum computing.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2025

Neutrino-Argon Cross-Section Measurements from the MicroBooNE Experiment

MicroBooNE is a liquid argon time projection chamber (LArTPC) neutrino detector located along the Fermilab Booster Neutrino Beam and 8 degrees off-axis to the Neutrinos at the Main Injector beam. MicroBooNE collected data from both beams accumulating a large neutrino-argon scattering dataset with a mean neutrino energy of approximately 0.8 GeV. Understanding neutrino-argon interactions is crucial for the next generation of neutrino oscillation experiments including DUNE. MicroBooNE has developed pioneering methodologies and novel reconstruction tools in order to benchmark models at very high sensitivity across the interaction phase space, including for ultra-rare channels. This talk will give an overview of the most recent MicroBooNE neutrino interaction results. These measurements provide invaluable datasets for constraining backgrounds and improving the modelling of neutrino scattering critical for the broader LArTPC neutrino physics program.

Liu, Liang [Fermilab]↗

DOE OSTI2026

"Lignin First" Catalytic Biomass Fractionation: Cooperative Research and Development (Final Report)

In this work, a continuous zwitterionic chromatography using simulated moving beds will be conducted to separate Li and Mg from brines. The existing program that ExxonMobil is carrying out with Clariant and Genomatica is consistent with all pioneer cellulosic fuels plants inasmuch as biomass enters the facility and is processed, in its entirety, through at least the hydrolysis step of biochemical conversion. This approach creates significant operating challenges as biomass can vary widely in composition and mechanical properties across and within a given biomass type. The proposed program involves a first step fractionation using solvolysis and reductive catalysis to generate a lignin oil containing well-defined monomers, dimers, and oligomers and a delignified cellulose/ hemicellulose pulp. The amount of lignin oil vs pulp and the type of monomers produced are expected to vary with biomass type due to differences in lignin composition and chemistry.

09 BIOMASS FUELS↗

DOE OSTI2025

PSEC5 Physical Verification

Ultra-Fast timing detectors, having a time resolution of 10pSec or less, are becoming increasingly important for many scientific disciplines, including High Energy Physics, Nuclear Physics, Medical Imaging and more. When developing ultra-fast detectors such as MCPs, LAPPDs, LGADs, SNSPDs, it is necessary to have access to a readout system that is low power, can support many channels and is cost effective. The PSEC5 chip was an application specific integrated circuit (ASIC) developed by the pioneering group of Prof. Henry Frisch at University of Chicago and micro electronic engineers at Fermi National Accelerator Laboratory. Three boards were designed, two to implement the readout system for an LAPPD and one to only characterize the PSEC1 chip. The main goal of the project shifted to setting up a testing environment to focus on the characterization of the chip. My student and I tested and characterized this ASIC using the third chip-on-board printed circuit board. Results show the successful fabrication and operation of some of the key building blocks and lead to the suggestions on modification for the second iteration of the chip.

Rico Aniles, H. D. [Unlisted]↗

DOE OSTI2026

Making an s1G Self-Preserving Environmental DNA (eDNA) Filter from Sustainable Materials

Environmental DNA (eDNA) sampling of aquatic environments is a powerful new tool for resource managers to use in detecting the presence of rare and endangered species. The use of eDNA for species detection is quickly becoming an industry-standard methodology in environmental science, and the technology sector is moving quickly to create purpose-built tools for eDNA sampling. Smith-Root, Inc. pioneered the new technology of self-preserving eDNA filters that automatically preserve the DNA captured on filters by desiccating the internal filter membrane. This capability saves the sampling technician significant time in the field and drastically reduces the potential for sample contamination. One main constraint of this new technology, however, is the necessity of single-use plastics for eDNA sampling. Single-use designs are employed because sampling equipment must be devoid of potential contaminating DNA, and the time required to sterilize equipment is often not cost-effective for practitioners. Thus, this technology is creating a significant new source of plastic waste—something that eDNA practitioners, and society, would like to avoid. This project explores the use of alternative bio based polymers and composites as replacements for incumbent industrial standards for eDNA filtration systems, with an emphasis on ease of processing and biodegradable options. 8 candidate materials, 3 for the top filter housing and 5 for the bottom filter housing, were investigated to replace incumbent housing materials. Of the 8 candidates, 2 materials were identified for future investigation which met performance requirements, were bio-based and could potentially offer better disposal options like soil degradation or composting, features sought by the customers of these products. The top housing candidate, material 1, has comparable performance to the incumbent material and equally manufacturable. The bottom housing candidate, material 6, improved desiccation performance by 81% after 1 hour in comparison to the incumbent material, is 100% bio-based, compostable and can be sourced domestically throughout the U.S.A.

36 MATERIALS SCIENCE↗

DOE OSTI2025

Mesoscale Modeling for Restructuring and Fragmentation in High Burnup UO 2

This report summarizes the mesoscale modeling conducted in fiscal year 2025 under the Nuclear Energy Advanced Modeling and Simulation (NEAMS) program, focusing on the microstructural evolution and restructuring in high burnup UO 2 nuclear fuel and its impact on fuel fragmentation. We developed a pioneering phase-field model to simulate restructuring behavior across different regions of high burnup fuel, including the dark zone and rim region. A grand-potential-based phase-field model is employed to concurrently evaluate subgrain formation and the growth of fission gas bubbles within the fuel. An energy-based subgrain formation criterion was introduced to simulate the restructuring process. The effects of temperature and burnup rate were studied to capture how each of these parameters influences the characteristics of the restructured fuel. Subgrain formation was observed to initiate around existing fission gas bubbles and proceed toward triple junctions, grain boundaries, and grain interiors. Under a given subgrain formation rate, the rate of restructuring increases with rising fuel temperature. The restructuring occurs faster with higher burnup rate. A restructuring bias was observed within the microstructure, due to the variation in defect accumulation when comparing different grains. Microstructures corresponding to the dark zone and rim region can be obtained by parameterizing the model with the appropriate defect production rate, as determined based on the burnup rate and temperature. The predicted microstructures are consistent with experimental observations of the restructured regions. Based on the mesoscale simulations, a mechanistic model for restructuring and grain size evolution was implemented in BISON. Thus, this work provides a first-of-its-kind restructuring model for different regions of high-burnup fuel to BISON. It was shown that the model predicts appropriate grain size evolution along fuel radius as those observed in experiments. This model enables BISON to predict the effect of restructuring on the fission gas release. Finally, the phase-field fracture simulation with dark zone specific microstructures were presented to provide the fragmentation criteria for the dark zone. This work introduces a first-of-its-kind restructuring model for high burnup fuel, significantly enhancing BISON's predictive capabilities.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2026

Strategies for Superconducting Transmon Qubits with Millisecond T1 Relaxation Time

Enhancing coherence in superconducting transmon qubits requires suppressing dielectric loss and quasiparticle-mediated dissipation at metal–substrate, metal–vacuum, and substrate–vacuum interfaces. We recently demonstrated a five-fold enhancement of the energy relaxation time (T₁) by encapsulating Nb thin films with a low-loss passivation layer that inhibits NbOₓ formation, thereby reducing two-level system (TLS) participation at the metal surface. To extend T₁ into the millisecond regime, we are implementing a comprehensive materials- and process-level optimization strategy. This includes engineered substrate surface treatments, evaluation of alternative low-loss superconducting and dielectric material stacks, development of ultra-low-loss capping layers, and redesigns of transmon geometries to suppress electric-field participation ratio in lossy regions. Additionally, we are pioneering novel etching processes to further lower surface participation ratios. We are also exploring novel Josephson-junction materials and device layouts to enhance coherence further. We report T₁ measurements from these efforts, with the leading devices achieving relaxation times>1 ms.

Crisa, Francesco↗

DOE OSTI2026

LDRD FY25 Program Overview

As Lawrence Livermore National Laboratory’s (LLNL’s) Laboratory Directed Research and Development (LDRD) program enters its fifth decade of leading-edge research and development, its impact and importance have never been stronger. The program continues to advance strategic investments in pioneering science, technology, and engineering, ensuring LLNL will be ready to deliver on our mission as it evolves over the coming decades. Investing in LDRD research, and the people who perform this critical work, gives LLNL the ability to sustain our role as a leader in the Department of Energy and National Nuclear Security Administration enterprise. The LDRD program enables high-risk, high-payoff research that anticipates emerging threats and future mission needs. By nurturing the ingenuity of the Lab’s greatest asset, its people, LDRD funding advances not only our research but also grows and nurtures our workforce: engaging future innovators with student mentoring, challenging postdoctoral researchers to apply their skills to support national security, and strengthening the leadership skills of early career staff. This annual report documents how LDRD investments advance LLNL’s science, technology, and engineering across our mission space. To assess LDRD’s impact we track both short and long-term metrics such as peer-reviewed publications, number of students, or professional fellows. In addition to reviewing these metrics, I encourage you to delve deeper into the breadth of science and technology that illustrate the strategic value of this research portfolio. For instance, a recent exploratory research project used advanced manufacturing to construct miniaturized three-dimensional ion traps for a quantum computer with reduced quantum error rates to enable applications that address national security missions and support basic science. Another project has delved into studying detonation by examining deflagration to enhance the safety and security of the nuclear weapons stockpile. LDRD researchers are also deploying AI agents on two of the world’s most powerful supercomputers to automate and accelerate inertial confinement fusion experiments. Other teams are delivering more accurate optical constants to enable improved validation for aluminum to advance atomic and molecular physics models. LDRD-driven discoveries of how metals deform under extreme conditions strengthen our ability to model and design materials for demanding national security environments. National security challenges are increasingly complex and continuously evolving. LDRD focuses our most innovative science and technology on these challenges, ensuring the Laboratory is developing creative, forward-leaning solutions for our nation and the world. The following pages feature highlights of published scientific advances, patents, and honors that stem from LDRD investments. As you read this report, I hope you will understand how these investments position the Laboratory, and our partners, to meet the demands of the decades ahead.

36 MATERIALS SCIENCE↗

DOE OSTI2026

Strategies for Superconducting Transmon Qubits with Millisecond T1 Relaxation Time

Enhancing coherence in superconducting transmon qubits requires suppressing dielectric loss and quasiparticle-mediated dissipation at metal–substrate, metal–vacuum, and substrate–vacuum interfaces. We recently demonstrated a five-fold enhancement of the energy relaxation time (T₁) by encapsulating Nb thin films with a low-loss passivation layer that inhibits NbOₓ formation, thereby reducing two-level system (TLS) participation at the metal surface. To extend T₁ into the millisecond regime, we are implementing a comprehensive materials- and process-level optimization strategy. This includes engineered substrate surface treatments, evaluation of alternative low-loss superconducting and dielectric material stacks, development of ultra-low-loss capping layers, and redesigns of transmon geometries to suppress electric-field participation ratio in lossy regions. Additionally, we are pioneering novel etching processes to further lower surface participation ratios. We are also exploring novel Josephson-junction materials and device layouts to enhance coherence further. We report T₁ measurements from these efforts, with the leading devices achieving relaxation times>1 ms.

Crisa, Francesco↗

DOE OSTI2026

Green Methanol via an Integrated Direct Air Capture, CO 2 Electrolyzer, and Hydrogenation Reactor

This project pioneered a groundbreaking reactor design to produce green methanol by harnessing the electrochemical CO 2 reduction reaction (eCO 2 RR), a cornerstone of power-to-fuels technology. The effort integrated three innovative technologies to achieve carbon-neutral methanol production at a target cost of under $\$$800/ton: 1. Direct Air Capture (DAC): Using a cutting-edge sorbent material developed at Holocene, scalable models were developed to integrate captured atmospheric CO₂ into the reactor system. 2. Intermediate-Temperature CO 2 Electrolyzer: Developed by the University of Tennessee (UTK), this electrolyzer utilizes a cost-effective, proton-conducting solid acid electrolyte (CsH 2 PO 4 , CDP) and a mixed-metal oxide cathode. It achieves high faradaic efficiencies (>98%) by effectively suppressing hydrogen evolution at high current densities, converting CO 2 to CO with remarkable selectivity. 3. Catalysis and Reactor Engineering: Oak Ridge National Laboratory (ORNL) contributed world-class expertise in heterogeneous catalysis and reactor design. Their advanced ASPEN modeling drove systems integration and supported techno-economic and life cycle analyses. This effort was further bolstered by partnerships with industry leaders Air Company and Plug Power, who provided critical guidance on scaling, systems engineering, and the integration of water electrolyzers into large-scale operations. During Phase 1, the team focused on modeling and validating a lab-scale reactor demonstrating the feasibility of the integrated approach. Key accomplishments include a 52% increase in current density at 0.8 V while maintaining >98% CO faradaic efficiency, successful 10× scale-up of the electrolyzer with performance within 5% of coin-cell results, best-in-class durability (168-hour test at 0.6 V with 0.14 mA/cm 2 -h degradation), validated TEA confirming the $\$$800/ton methanol target, and completed preliminary LCA showing potential for net-negative GHG emissions under renewable energy scenarios.

10 SYNTHETIC FUELS↗

DOE OSTI2020

DOE Early Career: Elucidating Chirality-induced Magnetism and Magnetoelectric Functionalities in Layered Chiral Hybrid Metal Halide Perovskites

This final report summarizes the research conducted under the DOE Early Career Award DE SC0020992, which aimed to elucidate the fundamental origins of chirality-induced spin selectivity and magnetoelectric functionalities in 2D chiral hybrid metal halide systems. By integrating spin-orbitronics with magneto-optics, this project successfully pioneered new paradigms for controlling spin and orbital degrees of freedom via chirality in solution-processed hybrid organic-inorganic semiconductors.

36 MATERIALS SCIENCE↗