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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 1,657 records · Page 92
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

End-to-End Workflow for Machine-Learning-Based Qubit Readout With QICK and hls4ml

In this article, we present an end-to-end workflow for superconducting qubit readout that embeds codesigned neural networks into the quantum instrumentation control kit (QICK). Capitalizing on the custom firmware and software of the QICK platform, which is built on Xilinx radiofrequency system-on-chip field-programmable gate arrays (FPGAs), we aim to leverage machine learning (ML) to address critical challenges in qubit readout accuracy and scalability. The workflow utilizes the hls4ml package and employs quantization-aware training to translate ML models into hardware-efficient FPGA implementations via user-friendly Python application programming interfaces. We experimentally demonstrate the design, optimization, and integration of an ML algorithm for single transmon qubit readout, achieving 96% single-shot fidelity with a latency of 32.25 ns and less than 16% FPGA lookup table resource utilization. Our results offer the community an accessible workflow to advance ML-driven readout and adaptive control in quantum information processing applications.

42 ENGINEERING↗
DOE OSTI2025

The Carbon Budget of Land Conversion: Sugarcane Expansion and Implications for a Sustainable Bioenergy Landscape in Southeastern United States

The expansion of sugarcane onto land currently occupied by improved (IMP) and semi-native (SN) pastures will reshape the U.S. bioenergy landscape. We combined biometric, ground-based and eddy covariance methods to investigate the impact of sugarcane expansion across subtropical Florida on the carbon (C) budget over a 3-year rotation. With 2.3- and 5.1-fold increase in productivity over IMP and SN pastures, sugarcane displayed a C use efficiency (CUE; i.e., fraction of gross C uptake allocated to plant growth) of 0.59, well above that of pastures (0.31–0.23). Sugarcane also had greater C allocation to aboveground productivity and hence, harvestable biomass relative to IMP and SN. Cane heterotrophic respiration over the 3-year rotation (903 ± 335 gC m −2 year −1 ) was 1% and 14% higher than IMP and SN pastures, respectively. These soil C losses responded largely to disturbance over the first year after conversion (1510 ± 227 gC m −2 year −1 ) but declined in subsequent years to an average 599 ± 90 gC m −2 year −1 —well below those of IMP (933 ± 140 gC m −2 year −1 ) and SN (759 ± 114 gC m −2 year −1 ) pastures—despite a significant 40%–61% increase in soil C inputs. Soil C inputs, however, shifted from root-dominated in pastures to litter-dominated in sugarcane, with only 5% C allocation to roots. Reduced decomposition rates in sugarcane were likely driven by changes in the recalcitrance and distribution rather than the size of the newly incorporated soil C pool. As a result, we observed a rapid shift in the net ecosystem C balance (NECB) of sugarcane from a large source immediately following conversion to approaching the net C losses of IMP pastures only 2 years after conversion. The environmental cost of converting pasture to sugarcane underscores the importance of implementing management practices to harness the soil C storage potential of sugarcane in advancing a sustainable bioeconomy in Southeastern United States.

09 BIOMASS FUELS↗
DOE OSTI2025

Life‐cycle greenhouse gas emissions associated with nuclear power generation in the United States

Under the 2022 Inflation Reduction Act, tax credits of up to $3/kgH 2 are available to hydrogen producers if they generate emissions at levels below 0.45 kgCO 2 e/kgH 2 , spurring producers to explore how hydrogen production via electrolysis using electricity generated by nuclear power may qualify for such tax credits. With uranium as a primary fuel for nuclear power plants (NPPs) and no on-site emissions, the upstream emissions associated with nuclear fuel supply chains largely determine the carbon intensity of nuclear energy. Using the GREET (Greenhouse gases, Regulated Emissions, and Energy use in Technologies) model, we evaluated the life-cycle greenhouse gas (GHG) emissions of uranium production and the use of uranium to generate electricity in light water reactor (LWR) NPPs. We evaluated the process chemicals and energy inputs throughout the nuclear fuel supply chain to identify the major contributors to nuclear fuel cycle GHG emissions. Such emissions are estimated at 3.0 gCO 2 e/kWh at NPPs in the United States. The greatest share of nuclear fuel cycle GHG emissions—comprising 53% of total emissions—are associated with electricity consumption throughout the fuel supply chain. We extended the analysis to include an evaluation of the carbon intensity of H 2 production via electrolysis using nuclear power from LWRs. Finally, we examined the impact of future (2035 and 2050) electricity supply chain scenarios on nuclear fuel cycle GHG emissions. Our analysis revealed a decrease of 33% (2035) and 46% (2050) in the carbon intensity of nuclear electricity relative to current nuclear fuel cycle GHG emissions.

greenhouse gas emissions↗
DOE OSTI2026

Simplex‐based model for nanoparticle grain identification in four‐dimensional scanning transmission electron microscopy data

Grain identification in polycrystalline nanoparticles, for example, determining which crystal phases are present at each spatial location, is fundamental to materials characterisation. This is particularly challenging when grains overlap extensively, as commonly occurs in four-dimensional scanning transmission electron microscopy (4D-STEM) datasets. We propose a simplex-based model (SBM) in which each simplex vertex represents the diffraction pattern (DP) of a pure grain, and the simplex edges and interior represent overlapping grains. Our SBM grain identification algorithm operates on the Bragg disk (BD) data matrix distilled from the 4D-STEM data to identify the grain membership at each scan position, together with a BD feature matrix whose columns represent the DPs for each constituent grain, which is important for identifying the crystal structure of each grain. We solve the model using a two-stage algorithm. In Stage 1, we adapt a linear mixing algorithm to estimate an initial BD feature matrix whose columns represent DPs of potentially overlapping grains. Our Stage 2 algorithm incorporates sparsity considerations to transform the initial BD feature matrix so that its columns represent DPs of pure grains. Using simulated datasets with various grain configurations, we demonstrate that SBM recovers both the BD feature matrix and membership maps more accurately than existing methods, even when a grain lacks any pure region and completely overlaps with other grains.

4D-STEM segmentation↗
DOE OSTI2026

Elongation Factor Tu Acts as a Chaperone to Activate an Antibacterial RNase Toxin

Many Gram-negative bacterial species use contact-dependent growth inhibition (CDI) systems to deliver toxic proteins into neighboring competitors. CDI + strains deploy CdiA effector proteins, which translocate their C-terminal toxin (CT) domains into target bacteria through a receptor-mediated delivery pathway. To protect against auto-intoxication, CDI + bacteria also produce CdiI immunity proteins that neutralize CT toxin activity. Here, we present the crystal structure of the CT·CdiI O32:H37 complex from Escherichia coli O32:H37. CT O32:H37 adopts the same fold as the tRNase domain of colicin D, and the nucleases share similar catalytic centers. However, unlike colicin D, which cleaves the anticodon loops of tRNA Arg isoacceptors, CT O32:H37 exhibits nonspecific RNase activity. Notably, we find that endogenous elongation factor Tu (EF-Tu) co-purifies with the over-produced CT·CdiI O32:H37 complex. Although EF-Tu does not bind stably to CT O32:H37 in the absence of CdiI O32:H37 , the translation factor is required for toxic RNase activity in vitro. AlphaFold 3 modeling and site-directed mutagenesis indicate that CT O32:H37 interacts with the N-terminal GTPase domain of EF-Tu. EF-Tu appears to stabilize residue Trp52 within the hydrophobic core of the toxin, which in turn supports the RNase active site through an unusual hydrogen-bonding interaction with the catalytic His67 residue. Furthermore, EF-Tu is hijacked as an essential co-factor to organize the toxin's catalytic center.

Nhan, Dinh Quan [University of California, Santa B↗
DOE OSTI2025

Differential absorption of circularly polarized light by a centrosymmetric crystal

Crystalline solids are governed by universal structure-property relationships derived from their crystal symmetry, leading to paradigmatic rules on what properties they can and cannot exhibit. A long-held structure-property relationship is that centrosymmetric crystals cannot differentially absorb circularly polarized light. In this study, we demonstrate the design, synthesis, and characterization of the centrosymmetric material Li 2 Co 3 (SeO 3 ) 4 , which violates this relationship not by defying symmetry-imposed selection rules but by invoking a photophysical process not previously characterized for crystalline solids. This process originates from an interference between linear dichroism and linear birefringence, referred to as LD-LB, and involves strong chiroptical signals that invert upon sample flipping. Here, in addition to enabling a chiroptical response under centrosymmetry, this process opens up photonic engineering opportunities based on crystalline solids.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Draft assemblies for three geographically distinct isolates of the human-infecting microsporidium Encephalitozoon intestinalis

Encephalitozoon intestinalis is an obligate intracellular parasite that causes enteritis, bronchitis, conjunctivitis, and/or encephalitis in immunosuppressed and immunocompetent individuals. To better assess its genetic diversity, here we report near-chromosome-level draft genome assemblies for three geographically distinct isolates, quadrupling the number of genome assemblies available for the enigmatic fungal pathogen.

Biological and medical sciences↗
DOE OSTI2025

Hybrid PDES Simulation of HPC Networks Using Zombie Packets

Although high-fidelity network simulations have proven to be reliable and cost-effective tools to peer into architectural questions for high-performance computing (HPC) networks, they incur a high resource cost. The time spent in simulating a single millisecond of network traffic in the highest detail can take hours, even for static, well-behaved traffic patterns such as uniform random. Surrogate models offer a significant reduction in runtime, yet they cannot serve as complete replacements and should only be used when appropriate. Thus, there is a need for hybrid modeling, where high-fidelity simulation and surrogates run side-by-side. Here, we present a surrogate model for HPC networks in which: packets bypass the network, while the network state is left untouched, i.e., suspended. To bypass the network, we use historical data to estimate the arrival time at which every packet should be scheduled at; to suspend the network, all in-flight packets are scheduled to arrive at their destinations, and are kept in the system to awaken as zombies when switching back to high-fidelity. Speedup for a hybrid model is relative to the proportion of surrogate to high-fidelity. This light-weight surrogate obtained up to 76× speedup. Keeping the zombies in the network showed an increase in the accuracy of the high-fidelity simulation on restart when compared to restarting the network from an empty state.

HPC networks↗
DOE OSTI2026

qSIEVE: Efficient qLDPC Memory via Systolic Movement in Atom Arrays

As quantum machines have scaled up in their number of qubits, significant research has turned towards increasing their fidelity with quantum error correction codes. Although promising results have been shown with the surface code, which only requires near-neighbor connections between qubits, the high qubit overhead of such local codes promises to be problematic. Consequently, recent work has explored non-local quantum LDPC (qLDPC) codes, which have good asymptotic encoding rates. Despite theoretical progress, hardware implementations of these codes have been a longstanding challenge. At the experimental level, demonstrations of movement based communication on atom arrays suggest this is a powerful new primitive to achieve non-local connectivity. Leveraging this, we present a protocol for implementing non-local qLDPC codes in hardware. Our protocol, qSIEVE, is a co-design of such codes with movement in atom arrays. qSIEVE defines a restricted family of qLDPC codes that can be implemented efficiently with systolic movement. We then quantify the utility of qSIEVE in the context of a complete fault tolerant architecture. We compare the cost of implementing benchmark programs in a standard, surface code only architecture and a mixed architecture where data is stored in qLDPC memory with qSIEVE and loaded to surface codes for computation.

Quantum error correction↗
DOE OSTI2025

Modeling the Nucleation and Growth of Lead Sulfate Particles on Lead Electrodes

Lead-acid batteries (LABs) play a pivotal role in the energy storage sector with applications spanning from starting-lighting-ignition batteries to grid energy storage. Passivation of lead negative electrodes by PbSO 4 particles is a fundamental mechanism limiting the performance of LAB. In this regard, an electrochemical model is developed that simulates the nucleation and growth (N&G) dynamics of PbSO 4 particles on a flat lead electrode, responsible for its passivation. The model considers the electrochemical reactions between lead electrode and sulfuric acid, N&G of PbSO 4 particles, passivation of the lead surface, and the ternary transport of PbSO 4 (aq), bisulfate, and protons in H 2 SO 4 electrolyte. The model is validated with a dataset of cyclic voltammetry (CV) responses collected at several scan rates and H 2 SO 4 concentrations. The model shows remarkable qualitative and quantitative agreement with the experimental data including CV peak features, discharge capacity, and particle size. The model was employed to explore key N&G quantities, such as supersaturation, nucleation rate, particle count, growth rate, particle size, and surface coverage, and to examine how their interactions influence electrode utilization. Parametric studies were also conducted to evaluate how scan rates and acid concentrations influence the previously mentioned N&G quantities and, subsequently, the utilization of the electrode.

25 ENERGY STORAGE↗
DOE OSTI2025

Choline Chloride-Based Water-in-Salt Electrolyte for Efficient Iron Electrodeposition

Electrochemical production of iron is a promising low-cost and modular approach to replace the traditional blast furnace. Aqueous electrolytes for iron electrolysis are advantageous as they can be operated at near-ambient temperatures, but they suffer from inefficiencies due to the parasitic hydrogen evolution reaction. In this work, we identify a new water-in-salt electrolyte (WiSE) based on choline chloride (ChCl) for high coulombic efficiency (>85%) iron deposition. Electrochemical analysis of the partial current densities of iron plating and hydrogen co-evolution revealed that, at optimal WiSE compositions, water reduction is kinetically suppressed resulting in an increase in the Fe plating efficiency. Decreased coordination of water and increased coordination of choline’s alcohol group with the Fe 2+ ion were observed through 1 H NMR providing evidence that water reduction is kinetically suppressed in WiSE. Additionally, Raman spectroscopy revealed that complexation effects (with Cl – ) reduce Fe 2+ diffusion coefficients and corresponding limiting currents as the ChCl concentration is increased. This results in an optimal WiSE composition (4 M ChCl + 1 M FeCl 2 ) that provides kinetic suppression of HER but also low transport resistance to Fe plating yielding 85% coulombic efficiency at high current densities.

Sinclair, Nicholas Scott [Case Western Reserve Uni↗
DOE OSTI2025

Decoupling Failure Pathways in Li-O 2 Cells Operated Under Lean Electrolyte Conditions

The operation of lithium-oxygen (Li-O 2 ) batteries under lean electrolyte conditions offers higher energy density but leads to rapid degradation and short cycle life. Although cathode passivation and electrolyte decomposition occur in all regimes, we show that under lean electrolyte conditions, failure is primarily driven by progressive electrolyte consumption at the lithium/solid electrolyte interphase (SEI), rather than by irreversible cathode passivation. Poor wetting of the lithium surface results in heterogeneous SEI growth and high local current densities, which accelerate electrolyte loss and cell failure. Strategies aimed at improving interfacial stability, including optimized wetting and SEI forming additives, significantly extend cycle life without compromising energy density. Our results establish anode-electrolyte interactions as the dominant degradation mechanism under lean electrolyte conditions, and emphasize the need to engineer a stable Li/SEI interface for long-lasting Li-O 2 batteries.

Córdoba, Daniel [Argonne National Laboratory (ANL)↗
DOE OSTI2026

Cobalt-Free Lithium and Manganese Rich Cathodes for Electric Vehicle Applications: Influence of Inherent Properties on Temperature-Dependent Performance

Lithium- and manganese-rich (LMR) oxides are attractive options as next-generation, earth-abundant cathodes. Despite inherent properties that result in less-than-ideal performance, advancements in this class of cathodes toward commercialization have been steady. Herein, we report a benchmark study on the influence these inherent properties exert on the discharge rate of Co-free, LMR//graphite (Gr) cells as a function of temperature. Because low-temperature performance is an essential consideration in the design of electric vehicle cells, understanding the limitations of current LMR systems is essential in understanding their current applicability as well as strategies for improvement. This work identifies two major limitations in the rate performance of LMR//Gr cells and suggests several areas of focus where practical strategies may provide improvements.

25 ENERGY STORAGE↗
DOE OSTI2025

SIDDA: SInkhorn Dynamic Domain Adaptation for Image Classification

SInkhorn Dynamic Domain Adaptation (SIDDA) supplements the experiments presented in 2501.14048, SIDDA: SInkhorn Dynamic Domain Adaptation for Image Classification with Equivariant Neural Networks. SIDDA introduces a semi-supervised, automatic domain adaptation method that leverages Sinkhorn divergences to dynamically adjust the regularization in the optimal transport plan and the weighting between classification and domain adaptation loss terms during training.

Pandya, Sneh [Fermi National Accelerator Laborator↗
DOE OSTI2025

Q-OPT:Quantum Optimization Toolkit

SF-2025-003 Quantum computing has the potential to solve classical optimization problems. To bring these algorithms into practical use, a comprehensive, high-performance and user-friendly toolkit is essential. The Q-OPT: Quantum Optimization Toolkit is a collection of software tools designed to support complete end-to-end framework for quantum optimization.

Hovland, Paul [Argonne National Laboratory (ANL), ↗
DOE OSTI2025

pyAXEAP1-GUI package

SF-25-076 pyAXEAP1-GUI package is an X-ray emission spectroscopy (XES) data analysis package in the Python language. It can transfer the collected images from a 2D detector into spectra and display XES and RXES data as 3Dimages, 2D images and 1D profiles.

Berno, Alexander↗
DOE OSTI2026

Adapt: A Weather Radar Data Analysis and Nowcasting Platform for Informed Adaptive Scanning

SF-26-021 Adapt is a data processing platform for real-time data analysis, short term prediction of targets convective cells and tracking for archived data. It provides tools for downloading, processing, segmenting, projecting, analyzing, and visualizing storm cell data from weather radar. The pipeline includes cell detection, motion estimation using optical flow, cell property extraction, and persistence to NetCDF and SQLite/Parquet for guiding adaptive scanning.

Raut, Bhupendra Ashokrao [Argonne National Laborat↗