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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 811 records · Page 45

Low Temperature Tantalum Electrodeposition: Enabling Next Generation Coatings

This project aimed to identify the ligand and solvation conditions necessary for the electrodeposition of refractory metals from low-temperature organic electrolytes. Tantalum (Ta) was chosen as a specific exemplar for programmatic purposes. It was hoped that a deeper understanding of these relationships could facilitate the innovative application of refractory metal coatings on thermally sensitive and geometrically intricate components used in extreme environments related to energy generation and storage, nuclear deterrence, aerospace, defense, and beyond.

36 MATERIALS SCIENCE↗

Investigating the Performance of SF6 Replacement Gases to Enable the Next Generation of Pulsed Power

High voltage switches are essential components in pulsed power systems, where consistent and reliable performance is crucial—particularly as the field explores alternatives to SF 6 as an insulating gas. This project examines the self-break voltage distributions of various gases, with a focus on the low-voltage discharges observed in the lower tail of these distributions. Experimental results revealed that the specific housing design influenced the self-break behavior. Among the tested gases, air demonstrated a more favorable overall distribution compared to SF 6 , albeit requiring higher operating pressures. However, air also exhibited a greater likelihood of extremely low-voltage dropouts, raising concerns about its suitability as a direct replacement for SF 6 . Notably, all gases tested showed a higher-than-expected probability of low-voltage events when considering the tail of the distribution rather than the bulk behavior.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Next-Generation Energy Technologies for Connected and Automated On-Road Vehicles (NEXTCAR) - Predictive Data-Driven Vehicle Dynamics and Powertrain Control: from ECU to the Cloud (Final Scientific/Technical Report)

This project developed and demonstrated a predictive, data-driven vehicle control system designed to improve energy efficiency and driving performance. The team created intelligent self-driving car technology that optimizes fuel and electricity use by proactively planning vehicle actions. By combining Level 4 autonomous driving capabilities with vehicle-to-everything (V2X) connectivity, the system enables vehicles to adjust speed and change lanes in response to traffic signals, surrounding vehicles, and road conditions, reducing unnecessary stops and delays. In testing, the system improved vehicle fuel economy by more than 30% and reduced travel time by approximately 10%, compared to a conventional adaptive cruise control baseline. These results demonstrate the technical effectiveness of using predictive, V2X-enabled strategies, such as traffic light timing and surrounding traffic awareness, to inform real-time vehicle powertrain control and driving behavior. Additionally, a supporting cloud platform was developed to provide dispatch and route recommendations as well as to log vehicle data, demonstrating the economic feasibility of this approach at the fleet level. By optimizing dispatching and routing operations, this technology enables electric fleet operators to use their vehicles more efficiently and reduce reliance on diesel backups, lowering both operating costs and energy consumption. Overall, this project’s technology advances the future of clean, energy-efficient transportation, enabling vehicles and fleets to reduce energy waste, cut costs, and lower emissions through intelligent automation and connectivity.

33 ADVANCED PROPULSION SYSTEMS↗

NEXT Generation Energy Technologies for Connected and Automated On-Road Vehicles (NEXTCAR Phase I & II)

The Ohio State University’s ARPA-E NEXTCAR project was a multi-phase, multi-year research, development, and demonstration program focused on improving the energy efficiency of connected and automated vehicles (CAVs). The team developed and validated advanced vehicle motion and powertrain control algorithms that coordinate propulsion and automation systems to optimize energy use. Key technologies included Dynamic Skip Fire engine control, predictive eco-driving functions such as Eco-Approach and Departure (Eco-AND) and Eco-Adaptive Cruise Control (Eco-ACC), and powertrain-agnostic optimization frameworks for hybrid, plug-in hybrid, and battery electric vehicles. The project successfully demonstrated up to 30% energy-efficiency improvement during real-world testing at the Transportation Research Center and the American Center for Mobility. The outcomes provide a foundation for scalable, cost-effective deployment of energy-optimized CAV technologies across the automotive industry.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Investigation of new superconducting materials for the next generation high-performance RF superconducting cavities for particle accelerators

In this DOE-funded project DE-SC0010081-020 Old Dominion University (ODU) in collaboration with University of Wisconsin (UW) and Jefferson Laboratory have investigated both experimentally and theoretically electromagnetic response and losses in multilayered superconducting structures made of new SRF materials which can push the field and Q performance limits of accelerating cavities.

43 PARTICLE ACCELERATORS↗

Advancements in HF-free bipolar pulsed electropolishing for next-generation superconducting cavities

Hydrofluoric acid (HF)-free bipolar pulsed electropolishing (BPEP) offers an environmentally sustainable alternative to conventional Buffered Chemical Polishing (BCP) and Electropolishing (EP) techniques for superconducting radiofrequency (SRF) cavities. Recent studies at Jefferson Lab have demonstrated that a single-cell 1.3 GHz niobium cavity processed using HF-free BPEP achieved an accelerating gradient Eacc of 35 MV/m with a quality factor Q0 of 1E10 at 2 K, following extensive research and optimization. This talk will highlight the challenges encountered in developing this technique, key insights gained from experimental studies, and ongoing efforts to enhance its capabilities. In particular, we will explore its potential for refining Nb3Sn-coated niobium cavities via vapor diffusion techniques and for electroplating Nb3Sn films onto various cavity substrates, contributing to the advancement of high-performance SRF systems.

Tian, Hui [Thomas Jefferson National Accelerator F↗

Recent Collaborations and Innovations to Demonstrate Next-Generation Techniques for Monitoring Subsurface Carbon Storage

World Carbon Capture, Utilization, and Storage (CCUS) Conference, Bergen, Norway, September 1–4, 2025. This talk provides a high-level overview of many novel and sustainable carbon storage-monitoring methods to accelerate the deployment of CCUS technologies at future CCUS sites across the United States. The Energy & Environmental Research Center’s work impacts the general CCUS industry by providing novel low-impact methods for tracking the injected plume’s migration and more autonomous data collection and processing techniques for performing assurance monitoring. Specifically, the results benefit 1) CCUS community members through knowledge sharing of lessons learned; 2) CCUS operators through commercialization of additional methods, including improvements to workflows and simplification of fieldwork; and 3) CCUS project stakeholders through implementation of low-impact and more autonomous monitoring solutions.

02 PETROLEUM↗

The Institute for Nuclear Science to Inspire the next Generation of a Highly Trained workforce (INSIGHT) at FRIB

The proposed INSIGHT Center at FRIB has two objectives: (1) provide a center to support and coordinate a nationwide traineeship effort; and (2) offer traineeships at FRIB by leveraging its scientific opportunities. This will provide an environment to: (i) recruit and retain undergraduate students in (nuclear) physics and sustain and/or increase their interest, confidence, and enthusiasm in this field; (ii) provide participants with a toolset to become effective independent researchers who pursue further research opportunities as undergraduates; and (iii) encourage participants to pursue graduate studies and potential careers in nuclear science, or related STEM fields.

07 ISOTOPE AND RADIATION SOURCES↗

Quantum Computing in Next-Generation Transportation Optimization

We explore how quantum computing (QC) can advance transportation optimization, with a focus on two high-impact areas: traffic signal control and vehicle electrification with grid integration. As transportation systems grow in complexity, classical optimization methods increasingly struggle to deliver scalable and efficient solutions, particularly for real-time, data-rich environments. This work identifies key challenges within these two domains where QC may offer advantages, particularly in handling combinatorial decision spaces and dynamic constraints. We begin by outlining the limitations of classical approaches for traffic signal control optimization and electric vehicle charging coordination, highlighting where computational limitations arise. Previous quantum formulations are presented and new formulations are proposed to demonstrate how emerging quantum algorithms, including quantum annealing and the Quantum Approximation Optimization Algorithm, could be leveraged to reformulate and address these problems. We also evaluate the suitability of current quantum hardware and discuss recent trends that indicate when QC may become a viable tool for transportation applications. While acknowledging the present limitations of QC technologies, this poster emphasizes the importance of preparing quantum-compatible models today. By reviewing and establishing formulations that align with the strengths of quantum algorithms, researchers and practitioners can better position themselves to take advantage of QC advancements as they occur. This work aims to provide a practical, forward-looking perspective on the near-term potential of quantum computing in transportation optimization.

33 ADVANCED PROPULSION SYSTEMS↗

Development of Next Generation Hierarchical Hybrid Cu-Si anode Batteries via Direct-Ink Writing Application: End of (6th) Month Report - November 2025

Sustainable renewable energy continues to be in dire need to effectively combat global warming. Emerging technology for electric vehicles/ devices remains in high demand that is not only lower in cost, more efficient, but safer in comparison to commercial materials on the market. Although first-generation lithium-ion batteries have exhibited extensive commercial application, conventional graphite no longer meets this increasing demand as an efficient anode material. Due to the fact that graphite has a subpar theoretical specific capacity (372 mAh g -1 ), thus significant limitations in rate capability (for potential faster charging at higher C-rates currently commercially available.). Alternatively, silicon has gained significant attention as a superior candidate to potentially surpass graphite. Due to silicon’s exceedingly high theoretical capacity (4,200 mAh g -1 ) in comparison to standard graphite, its abundance thus in turn it’s low-cost, in addition to exhibiting a significantly low working potential (< 0.4 V vs Li/Li + ). However, one of the main (and most detrimental) challenges is silicon’s tendency to expand in volume (> 300%) upon discharge as it begins the lithiation process. As a direct result, it causes not only for the particles to both crack and pulverize under mechanical stress as the volume continues to expand and contract during cycling. Upon assembling the cell, it needs to undergo ‘charging’ for initially discharging/ ‘activating’ the cell, otherwise commonly known as the ‘formation’ step. As a result a solid electrolyte interface (SEI) layer begins to form at the anode surface because some of the electrolyte begins to react during the formation process. However, this (SEI) layer is deemed as a ‘protective’ interlayer because in theory it prevents further reaction as the cell continues to cycle. However, due to the volume change it causes significant degradation at the interface. As cracking starts to occur at the anode surface, it results in a ‘new’ altered surface with each cycle that causes further reaction with the electrolyte as a byproduct quickly consuming active Li/ and more electrolyte. Thus, fracturing this ‘protective layer,’ causing significantly higher impedance as a result, and in turn a decline in capacity due to active Li-loss. In addition to the active material exfoliating off from the current collector, further contributing to the steady decline in capacity and overall performance. Current state of the art Si-anode batteries on the market range between a maximum content of 5-10 Si wt%. It has been previously reported Tesla has utilized SiO x -C anodes containing 5 wt% Si within their ‘Model 3/ Model X’ electric vehicles. However, more recent ‘Model 3’ vehicles have started to incorporate 10 Si wt%, in which they were able to increase their energy density upwards by approximately 30%. Recent effort has been focused on continuing to increase the wt% of Si being utilized, eventually to 100 wt% of Si, to maximize the energy density even further.

36 MATERIALS SCIENCE↗

AI-driven neutrino diagnostics and radiation-hard beam instrumentation for next-generation neutrino experiments

The Long Baseline Neutrino Facility (LBNF) at Fermilab will deliver a high-intensity, multi-megawatt neutrino beam to the Deep Underground Neutrino Experiment (DUNE), enabling precision tests of the three-neutrino paradigm, CP violation searches, neutrino mass ordering determination, and supernova neutrino studies. To accelerate DUNE’s physics reach and ensure robust beam operations, we propose an integrated AI-driven framework with real-time diagnostics and radiation-hardened instrumentation. At its core is a Real-Time Beam Integrity Monitor using a physics-informed Digital Twin. By reconstructing pion phase space from muon profiles and exploiting magnetic horn optic linearity, it enables spill-by-spill beam correction and flux stabilization. By using this approach, flux-related systematics could be reduced from 5% to 1%, potentially accelerating the discovery of CP violations by four to six years. Complementing this, a US–Japan R&D effort will deploy a LAPPD-based muon monitor in the NuMI beamline. ToF measurements can be acquired with picosecond precision using this radiation-hard system, enhancing sensitivity to horn chromatic effects. Simulations confirm strong response to these effects. ML models predict beam quality and horn current to sub-percent accuracy from muon data, enhancing anomaly detection and stability. This scalable, AI-enabled strategy improves beam fidelity, reduces systematics, and sets a new standard for high-power accelerator operations.

Ganguly, Sudeshna [Fermilab] (ORCID:00000003163482↗

Development of Next-Generation Additive Chemistry for Direct Air Capture Sorbents (CRADA Final Report)

Introducing antioxidant additives into amine-based DAC sorbents can extend their lifetime. These sorbents are readily prepared by physically mixing additives with amines, a straightforward approach using commercially available materials. Advancing this strategy requires understanding how additives function under varying conditions, especially humidity. This project aims to reveal how humidity and additive chemistry influence oxidative degradation of PEI-based sorbents, levereging LLNL’s expertise in physics-based computational modeling, and Global Thermostat (GT)’s expertise in materials synthesis, characterization, and degradation kinetics testing, to gain fundamental insights into the chemistries and mechanisms of PEI oxidative degradation, and develop design principles that enhance sorbent durability.

36 MATERIALS SCIENCE↗