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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Power and Limitations of Linear Programming Decoder for Quantum LDPC Codes

Decoding quantum error-correcting codes is a key challenge in enabling fault-tolerant quantum computation. In the classical setting, linear programming (LP) decoders offer provable performance guarantees and can leverage fast practical optimization algorithms. Although LP decoders have been proposed for quantum codes, their performance and limitations remain relatively underexplored. In this work, we uncover a key limitation of LP decoding for quantum low-density parity-check (LDPC) codes: certain constant-weight error patterns lead to ambiguous fractional solutions that cannot be resolved through independent rounding. To address this issue, we incorporate a post-processing technique known as ordered statistics decoding (OSD), which significantly enhances LP decoding performance in practice. Our results show that LP decoding, when augmented with OSD, can outperform belief propagation with the same post-processing for intermediate code sizes of up to hundreds of qubits. These findings suggest that LP-based decoders, equipped with effective post-processing, offer a promising approach for decoding near-term quantum LDPC codes.

Gu, Shouzhen [Yale U.]↗
DOE OSTI2025

Power and Limitations of Linear Programming Decoder for Quantum LDPC Codes

Decoding quantum error-correcting codes is a key challenge in enabling fault-tolerant quantum computation. In the classical setting, linear programming (LP) decoders offer provable performance guarantees and can leverage fast practical optimization algorithms. Although LP decoders have been proposed for quantum codes, their performance and limitations remain relatively underexplored. In this work, we uncover a key limitation of LP decoding for quantum low-density parity-check (LDPC) codes: certain constant-weight error patterns lead to ambiguous fractional solutions that cannot be resolved through independent rounding. To address this issue, we incorporate a post-processing technique known as ordered statistics decoding (OSD), which significantly enhances LP decoding performance in practice. Our results show that LP decoding, when augmented with OSD, can outperform belief propagation with the same post-processing for intermediate code sizes of up to hundreds of qubits. These findings suggest that LP-based decoders, equipped with effective post-processing, offer a promising approach for decoding near-term quantum LDPC codes.

Gu, Shouzhen [Yale U.]↗
DOE OSTI2025

Stabilization of [(N 5 ) 2 BX] 2– and [(N 5 ) 2 B 2 X 2 ] 2– (X = H, F, Cl, Br) by Conjugation and Hyperconjugation Effects

The isolation of nucleophilic boron bases has led to a paradigm shift in boron chemistry. Previous studies of the bis(carbene) borylene complexes revealed that these compounds possess strong donor abilities, and their reaction inertness is due to the large steric hindrance between boron reagents and reactant. In the present study, we have theoretically studied the [(N 5 ) 2 BX] 2– and [(N 5 ) 2 B 2 X 2 ] 2– compounds (X = H, F, Cl, Br). Their electronic structures and properties are discussed by using the NBO, LOL, and ELF methods. We found that both π-conjugation and hyperconjugation effects can effectively stabilize the substituted nucleophilic anionic boron compounds [(N 5 ) 2 BX] 2– and [(N 5 ) 2 B 2 X 2 ] 2– . Substituents, especially X = H, stabilize the boron center through highly delocalized π-bonding, involving the formally “empty” in-plane p orbitals of the boron atom. While the halogen substituents have high electron withdrawal ability, leading to systems being less stable, we suggest the borinium anions [(N 5 ) 2 BH] 2– and [(N 5 ) 2 B 2 H 2 ] 2– as possible synthetic targets of novel environmentally friendly catalysts.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

A chemical bonding based descriptor for predicting the role of anharmonicity induced by quantum nuclear effects in hydride superconductors

Quantum nuclear effects (QNEs) can significantly alter a material’s crystal structure and phonon spectra, impacting properties such as thermal conductivity and superconductivity. However, predicting a priori whether these effects will enhance or suppress superconductivity, or destabilize a structure, remains a grand challenge. Herein, we address this unresolved problem by introducing two possible descriptors, based upon the integrated crystal orbital bonding index (iCOBI) or the bond valence function, to predict the influence of QNEs on a crystal lattice’s dynamic stability, phonon spectra and superconducting properties. We find that structures with atoms in symmetric chemical bonding environments exhibit greater resilience to structural perturbations induced by QNEs, while those with atoms in asymmetric bonding environments are more susceptible to structural alterations, resulting in enhanced superconducting critical temperatures.

Physical chemistry↗
DOE OSTI2021

Deep graph representations embed network information for robust disease marker identification

We report that the accurate disease diagnosis and prognosis based on omics data rely on the effective identification of robust prognostic and diagnostic markers that reflect the states of the biological processes underlying the disease pathogenesis and progression. In this article, we present GCNCC, a Graph Convolutional Network-based approach for Clustering and Classification, that can identify highly effective and robust network-based disease markers. Based on a geometric deep learning framework, GCNCC learns deep network representations by integrating gene expression data with protein interaction data to identify highly reproducible markers with consistently accurate prediction performance across independent datasets possibly from different platforms. GCNCC identifies these markers by clustering the nodes in the protein interaction network based on latent similarity measures learned by the deep architecture of a graph convolutional network, followed by a supervised feature selection procedure that extracts clusters that are highly predictive of the disease state. By benchmarking GCNCC based on independent datasets from different diseases (psychiatric disorder and cancer) and different platforms (microarray and RNA-seq), we show that GCNCC outperforms other state-of-the-art methods in terms of accuracy and reproducibility.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Transformative Technology for FLASH Radiation Therapy

The general concept of radiation therapy used in conventional cancer treatment is to increase the therapeutic index by creating a physical dose differential between tumors and normal tissues through precision dose targeting, image guidance, and radiation beams that deliver a radiation dose with high conformality, e.g., protons and ions. However, the treatment and cure are still limited by normal tissue radiation toxicity, with the corresponding side effects. A fundamentally different paradigm for increasing the therapeutic index of radiation therapy has emerged recently, supported by preclinical research, and based on the FLASH radiation effect. FLASH radiation therapy (FLASH-RT) is an ultra-high-dose-rate delivery of a therapeutic radiation dose within a fraction of a second. Experimental studies have shown that normal tissues seem to be universally spared at these high dose rates, whereas tumors are not. While dose delivery conditions to achieve a FLASH effect are not yet fully characterized, it is currently estimated that doses delivered in less than 200 ms produce normal-tissue-sparing effects, yet effectively kill tumor cells. Despite a great opportunity, there are many technical challenges for the accelerator community to create the required dose rates with novel compact accelerators to ensure the safe delivery of FLASH radiation beams.

43 PARTICLE ACCELERATORS↗
DOE OSTI2023

Reliability modeling in a predictive maintenance context: A margin-based approach

Current system reliability methods (typically based on fault trees or reliability block diagrams) can effectively propagate reliability data from the asset to the system level in order to identify system critical points. However, employed asset reliability data are an approximated integral representation of the past industrywide operational experience, and they neglect the present asset health status (available, for example, from online monitoring data and diagnostic assessments) and forecasted health projection (when available from prognostic models). Asset health should be informed solely by that specific asset’s current and historical performance data and should not be an approximated integral representation of the past industrywide operational experience (as currently performed by system reliability models through Bayesian updating processes). Sensor data, diagnostic assessments, and prognostic assessments are in fact not considered in plant reliability models used to inform system engineers on the most critical assets. In addition, the propagation of quantitative health data from the asset to the system level is a challenge given the diverse nature and structure of health data elements (e.g., vibration spectra, temperature readings, expected failure time). Ideally, in a predictive maintenance context, system reliability models should support decision making by propagating available health information from the asset to the system level in order to provide a quantitative snapshot of system health and identify the most critical assets. Here, this paper is directly addressing these two goals by proposing a different approach for reliability modeling that relies on asset diagnostic and prognostic assessments, along with monitoring data to measure asset health. The propagation of health data from the asset to the system level is performed through fault tree models not in probability terms, but in terms of margin where margin is the “distance” between the present status and an undesired event (e.g., failure or unacceptable performance). Through a cause-effect lens, while classical reliability models target the effect associated with asset performance, a margin-based approach focuses on the cause of an undesired asset performance (i.e., its health). Hence, thinking of reliability in terms of margins implies decision-making based on causal reasoning. We will show how fault tree models can be solved using a margin language and how this process can effectively assist system engineers to identify the most critical assets.

97 - MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Understanding effects of chemical complexity on helium bubble formation in Ni-based concentrated solid solution alloys based on elemental segregation measurements

Here, helium bubble formation and swelling were systematically studied in Ni-based concentrated solid solution alloys containing different numbers and types of elements. Our microscopy analysis showed that although increasing the alloy chemical complexity helps suppress bubble formation in general, there is no monotonic relationship between the bubble growth rate and the number of alloying elements. Certain elements (e.g., Fe and Pd) are more effective in suppressing bubble growth than others (e.g., Cr and Mn). Atom probe tomography was applied to accurately measure elemental segregation around bubbles, revealing unique effects of certain alloying elements on vacancy migration towards bubbles. More specifically, the high vacancy mobility via Cr sites leads to a large vacancy flux and an increased bubble size, while the high degree of atomic size mismatch introduced by Pd helps deflect vacancy flow away from bubbles and decrease the amount of swelling. The effects identified in this study provide new strategies to design concentrated solid solutions with superior resistance to swelling.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Effective climate sensitivity distributions from a 1D model of global ocean and land temperature trends, 1970–2021

Abstract Current theoretically based Earth system models (ESMs) produce Effective Climate Sensitivities (EffCS) that range over a factor of three, with 80% of those models producing stronger global warming trends for 1970–2021 than do observations. To make a more observationally based estimate of EffCS, a 1D time-dependent forcing-feedback model of temperature departures from energy equilibrium is used to match measured ranges of global-average surface and sub-surface land and ocean temperature trends during 1970–2021. In response to two different radiative forcing scenarios, a full range of three model free parameters are evaluated to produce fits to a range of observed surface temperature trends (± 2σ) from four different land datasets and three ocean datasets, as well as deep-ocean temperature trends and borehole-based trend retrievals over land. Land-derived EffCS are larger than over the ocean, and EffCS is lower using the newer Shared Socioeconomic Pathways (SSP245, 1.86 °C global EffCS, ± 34% range 1.48–2.15 °C) than the older Representative Concentration Pathway forcing (RCP6, 2.49 °C global average EffCS, ± 34% range 2.04–2.87 °C). The strongest dependence of the EffCS results is on the assumed radiative forcing dataset, underscoring the role of radiative forcing uncertainty in determining the sensitivity of the climate system to increasing greenhouse gas concentrations from observations alone. The results are consistent with previous observation-based studies that concluded EffCS during the observational period is on the low end of the range produced by current ESMs.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Virtual Design and Analysis of the Balancing Element of an External Gear Machine Considering Cavitation and Mixed Lubrication Effects

This report presents a simulation based approach for sizing the axial balance elements of a pressure compensated external gear machine. The reference hydraulic unit must be able to guarantee operation in two quadrants, as either a pump or a motor. It also needs to operate in a wide operating range of speed and pressure, as it is to be used as primary hydraulic unit for an electro-hydraulic actuator (EHA). The design procedure builds upon past work at the author’s center, but it extends it to the case of multiple quadrant units. Also, a unique method for modeling of both mixed lubrication and cavitation that might occur in the lubricating film is used. After describing the design procedure, the paper discusses the features of the balancing of a reference unit, along with the effects of both cavitation and mixed lubrication.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Transformative Technology for FLASH Radiation Therapy: A Snowmass 2021 White Paper

Conventional cancer therapies include surgery, radiation therapy, chemotherapy, and, more recently, immunotherapy. These modalities are often combined to improve the therapeutic index. The general concept of radiation therapy is to increase the therapeutic index by creating a physical dose differential between tumors and normal tissues through precision dose targeting, image guidance, and high radiation beams that deliver radiation dose with high conformality, e.g., protons and ions. However, treatment and cure are still limited by normal tissue radiation toxicity, with many patients experiencing acute and long-term side effects. Recently, however, a fundamentally different paradigm for increasing the therapeutic index of radiation therapy has emerged, supported by preclinical research, and based on the FLASH radiation effect. FLASH radiation therapy (FLASH-RT) is an ultra-high dose-rate delivery of a therapeutic radiation dose within a fraction of a second. Experimental studies have shown that normal tissues seem to be universally spared at these high dose rates, whereas tumors are not. The dose delivery conditions are not yet fully characterized. Still, it is currently estimated that large doses of 10 Gy or more delivered in 200 ms or less produce normal tissue sparing effects yet effectively kill tumor cells. There is a great opportunity, but also many technical challenges, for the accelerator community to create the required dose rates with novel and compact accelerators to ensure the safe delivery of FLASH radiation beams.

43 PARTICLE ACCELERATORS↗
DOE OSTI2025

Comparison of Sequence Component-Based Fault Detection and Relay Coordination Algorithms in Inverter-Based Networks

Protection of inverter-based microgrids using sequence component-based relaying schemes is a promising solution. These methods offer several advantages, including lower computational requirements, compatibility with commercial relay systems, and cost-effectiveness compared to communication-based approaches. This article investigate the performance of various sequence component based schemes with the objective of identifying the algorithms that provide the best fault detection and relay coordination, solely relying on local voltages and current at relay terminals. Positive, negative and zero sequence impedance, admittance and power detection algorithms were tested on modified IEEE 13 bus test network for various shunt faults (LG, LL, LLG, LLL). Hardware-in-the-loop validation was achieved using the Typhoon real-time simulator, interfacing with a SEL 751 relay. This research demonstrates that while several algorithms are capable of detecting faults with sufficient accuracy, only a few are effective in achieving proper coordination. Validation results indicate that the negative sequence power approach provides the best performance in both fault detection and coordination.

Patel, Deepika [ORNL] (ORCID:0000000341099994)↗
DOE OSTI2026

Multiscale modeling of packed-bed microwave reactors and estimation of intrinsic materials' permittivity

Modeling of packed-bed microwave reactors relies on an accurate representation of particle size, shape, and distribution within the bed, as well as the particles' dielectric properties. The measured permittivity of microwave susceptors (powders or structured materials) depends on the geometric features of the particles and the porosity of the bed, as well as the specific form factor of a structured material. These are effective properties and cannot be used to analyze other reactor configurations unless the geometric effects are removed. Therefore, we introduce a methodology for extracting the intrinsic particle permittivity from experimentally measured effective permittivity by combining cavity-based measurements with multiscale simulations and machine learning. Further, we develop the first multiscale model of packed-bed microwave reactors that incorporate particle effects (geometric features, random packing, and particle contact). This approach bridges macroscopic observables with mesoscopic physics, enabling analysis of local hotspots, arcing, and contact effects that control reactor performance. Using polymer-based spherical activated carbon (PBSAC) and silicon carbide (SiC) as examples, we demonstrate that the inferred particle permittivity is consistent with independent experimental heating profiles we collect from microwave reactors without adjustable parameters. Finally, this methodology establishes a foundation for predictive, multiscale design of microwave packed-bed reactors that explicitly accounts for particle-scale effects, enabling the estimation of intrinsic permittivity for the first time.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Effects of substitutional Ga on the phonons of δ -phase Pu based on density functional theory calculations

The delta phase of Pu is stabilized by Ga doping, but the mechanism of this stabilization remains an open question. Density functional theory calculations focused on how Ga doping affects the phonons sheds some light on the phonons' contribution to the stabilization. Here, the calculated phonon modes of Ga-doped delta phase Pu fall into two distinct types: localized, high frequency Ga-dominated phonon modes, and Pu-dominated modes at lower frequencies. Increasing the Ga concentration has an effect on the Pu-dominated phonon modes opposite to that of compression: higher-frequency modes soften, and lower-frequency modes stiffen. The latter provides an indication that the stabilization mechanism is not due to a thermodynamic contribution from the phonons. Furthermore, the stiffened phonon modes include candidate modes that describe possible pathways into low-temperature phases, suggesting that doping with Ga could impede such pathways.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2020

Oxidation kinetic modelling of Fe-based oxygen carriers for chemical looping applications: Impact of the topochemical effect

Chemical looping is a promising technology for fossil fuel utilization due to its high fuel conversion efficiency with in-situ CO 2 capture capability. Metal oxide are used as oxygen carriers (OCs) and circulate between a fuel reactor and an air reactor to perform reduction and oxidation reactions, respectively. In general, OC exiting the fuel reactor is not reduced fully to its metallic state due to many factors including carbon deposition and OC deactivation. Therefore, the effect of the initial reduction state on the OC oxidation in the air reactor is a significant parameter for consideration in developing the oxidation kinetic model. The objective of this work is to develop a physically significant kinetic model that applies to the oxidation of both initially fully and partially reduced OC with air. For this study, 1.5 mm Fe-based OC particle supported with TiO 2 was used as the model OC particle due to its complex multistep reaction nature. The oxidation kinetics were experimentally investigated in a thermogravimetric analyzer (TGA). Results indicate a significant difference in the oxidation rate profile for the OCs when oxidized from an initially fully reduced compared to an initially partially reduced state. Elemental mapping via energy-dispersive X-ray spectroscopy (EDS) reveals a shrinking-core type topochemical pattern across the OC particle, which was identified to be the cause of the dependency of kinetics on the initial reduction state. A generalized kinetic model was developed based on the observed shrinking-core behavior without presuming any rate-determining steps and experimentally validated over a broad range of temperatures (800–1000 °C) and oxygen concentrations (5, 7, 10, and 15 mol%). Impacts of particle porosity, size, and core-shell structure on the OC oxidation kinetics were analyzed in the developed oxidation kinetic model to suggest methods of improving the oxidation rate of the OC without modifying the chemical composition.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Rheology of lignin and lignin-based solutions, dispersions, gels, polymer blends, and melts

Lignin is an abundant resource that finds application in energy and sustainable materials development. In addition to the utilization of lignin in three-dimensional (3D) printing and hydrogel production, recent studies have reported the use of lignin as a liquid fuel additive. The characterization of the rheological properties of lignin and its derivatives is a dynamic and evolving field, underpinned by advances in experimental, analytical, and modeling techniques. Here, this review provides a comprehensive overview of the recent progress in the study of lignin rheology, highlighting the interplay between structure, modification, and flow behavior in lignin-based solutions, dispersions, gels, polymer blends, and melts. A specific highlight of this review is how lignin concentration affects the rheological properties of lignin-based solutions and dispersions. Furthermore, the effect of lignin type on the properties of 3D-printed lignin-based composites is discussed. For polymer systems, this review discussed lignin-in-polymer solutions separately from lignin-filled polymer systems. Finally, challenges and perspectives on lignin rheology are presented.

Dispersions↗
DOE OSTI2022

Progress and prospects for cuprate high temperature superconductors under pressure

Since the discovery of high temperature superconductivity in La 5–x Ba x Cu 5 O 5(3–y) at an unprecedented T c of 35K over 35 years ago, high pressure experiments have played a critical role in understanding cuprate superconductivity. Soon after its discovery, compression experiments on La 5–x Ba x Cu 5 O 5(3–y) revealed a large dT c /dP, motivating the study of ‘chemical pre-compression’ in the material that led to the 90K Superconductor YBa 2 Cu 3 O 7–y . Cuprate superconductors discovered subsequently exhibited a range of Tc from 30K-140K, including the commonly studied Bi, Tl, and Hg based families. Here, pressure has large effects on superconductivity in these materials, including raising T c in the Hg based cuprates from 140K to 164K at 30 GPa. Reviewing past experiments indicate that pressure dopes holes into the CuO 2 planes common to all cuprates. Further detailed high-pressure studies of these materials should deepen our understanding of cuprate superconductivity and the possibility of reaching still higher T c in cuprates.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2024

Expanding the representation of aerosol, cloud, and precipitation processes with graph network-based simulators

We explored a novel framework for simulating the small-scale processes that drive the evolution of aerosol, cloud, and precipitation particles, which are a critical gap in the predictive understanding of weather and climate. Particle-based methods have emerged as an effective tool for modeling aerosol-cloud-precipitation interactions, but existing particle-based models are computationally too expensive to simulate the large domains relevant for the atmosphere or to represent the full suite of relevant processes. The lack of a comprehensive and efficient reference model is a critical bottleneck in our understanding of cloud and precipitation processes and our ability to parameterize these processes for regional- and global-scale simulations. To address this need, we explored an approach to accelerate and expand particle-based models using a new machine learning approach, graph network-based simulators (GNS). Rather than modeling the evolution of the system by numerically integrating continuity equations, the GNS represents dynamics through learned message passing. Our aim was to develop fast and accurate surrogate models for particle-based simulations. We explored applying GNS to simulate cloud droplet transport, growth, and evaporation under turbulent conditions, but we found the GNS over-smoothed the simulations. We then applied the GNS to simulate aerosol dynamics through gas condensation and found the GNS was able to reproduce the benchmark, physics-based simulation with high accuracy.

54 ENVIRONMENTAL SCIENCES↗