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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 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Carbohydrate-aromatic interface and molecular architecture of lignocellulose

Abstract Plant cell walls constitute the majority of lignocellulosic biomass and serve as a renewable resource of biomaterials and biofuel. Extensive interactions between polysaccharides and the aromatic polymer lignin make lignocellulose recalcitrant to enzymatic hydrolysis, but this polymer network remains poorly understood. Here we interrogate the nanoscale assembly of lignocellulosic components in plant stems using solid-state nuclear magnetic resonance and dynamic nuclear polarization approaches. We show that the extent of glycan-aromatic association increases sequentially across grasses, hardwoods, and softwoods. Lignin principally packs with the xylan in a non-flat conformation via non-covalent interactions and partially binds the junction of flat-ribbon xylan and cellulose surface as a secondary site. All molecules are homogeneously mixed in softwoods; this unique feature enables water retention even around the hydrophobic aromatics. These findings unveil the principles of polymer interactions underlying the heterogeneous architecture of lignocellulose, which may guide the rational design of more digestible plants and more efficient biomass-conversion pathways.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Prediction of DIII-D Pedestal Structure From Externally Controllable Parameters

The sharp increase of pressure at the edge of a high confinement mode (H-mode) plasma, the pedestal, strongly impacts overall plasma performance. Predicting the pedestal is a necessity to control and optimize tokamak operations. Here, an experimental data-driven machine learning (ML) approach is presented that predicts the pedestal heights and widths of electron density (n e ) and electron temperature (T e ) profiles as well as the separatrix ne from externally controllable parameters such as the plasma shape, heating method and power, and gas puff rate and integrated gas puff. The OMFIT framework was used with DIII-D data to efficiently, robustly, and automatically build a database of pedestal parameters to train machine learning models. Database creation was enabled by the search engine tool for DIII-D data, TokSearch, which parallelizes data fetching, enabling fast searches through basic signals of thousands of DIII-D shots and selection of relevant time intervals. Principal Component Analysis (PCA) separated the database into three clusters that represent classes of plasma shapes that are regularly used in DIII-D. The most important parameters for setting the pedestal structure were plasma current (I p ), toroidal magnetic field (B Φ ), neutral beam heating power (P NBI ) and shaping quantities. The Deep Jointly Informed Neural Networks (DJINN) algorithm was applied to identify suitable neural network (NN) architectures that appropriately capture the features of the pedestal database. Separate NNs were implemented for each pedestal parameter, and ensembling methods were used to improve the prediction accuracy and allowed estimation of the prediction uncertainty. The pedestal predictions of the test dataset lie within the measurement uncertainties of the pedestal parameters. The NN outperformed simple Linear Regression (LR) analysis, indicating non-linear dependencies in the pedestal structure. The presented achievements illustrate a promising path for future research, using feature extraction to infer experimental trends and thereby improve pedestal models as well as deploying NN for a fast pedestal prediction in DIII-D scenario development.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2026

Demonstration of reconstruction-free static magnetic control of DIII-D plasma with deep reinforcement learning

This paper presents the development and experimental validation of a reinforcement learning (RL)-based magnetic controller on the DIII-D tokamak. The controller directly maps raw magnetic diagnostic signals to actuator commands, replacing the traditional isoflux control algorithm based on equilibrium reconstruction. Four RL controllers are trained using the Soft Actor–Critic algorithm with an asymmetric Actor–Critic architecture in the NSFsim simulator. All controllers are deployed in the DIII-D Plasma Control System and operated with a 4 kHz feedback loop. Two randomization strategies are evaluated during training: evolving kinetic profiles and fixed kinetic profiles within each episode. The latter approach is found to better capture experimental deviations in the current density profile and to provide overall improved control performance. Robust operation is demonstrated across heating power scans in both L- and H-mode plasmas, as well as during transient events such as L–H transitions and pellet injections. Control errors in plasma shape and radial position remained within 1.5–2.0 cm and 1 cm, respectively. A notable discrepancy was observed in the vertical X-point position, with errors of up to approximately 4 cm, attributed to the current density distribution mismatches between simulations and experiments.

DIII-D↗
DOE OSTI2024

Magnetic resonance insights into the heterogeneous, fractal-like kinetics of chemically recyclable polymers

Moving toward a circular plastics economy is a vital aspect of global resource management. Chemical recycling of plastics ensures that high-value monomers can be recovered from depolymerized plastic waste, thus enabling circular manufacturing. However, to increase chemical recycling throughput in materials recovery facilities, the present understanding of polymer transport, diffusion, swelling, and heterogeneous deconstruction kinetics must be systematized to allow industrial-scale process design, spanning molecular to macroscopic regimes. To develop a framework for designing depolymerization processes, we examined acidolysis of circular polydiketoenamine elastomers. We used magnetic resonance to monitor spatially resolved observables in situ and then evaluated these data with a fractal method that treats nonlinear depolymerization kinetics. This approach delineated the roles played by network architecture and reaction medium on depolymerization outcomes, yielding parameters that facilitate comparisons between bulk processes. These streamlined methods to investigate polymer hydrolysis kinetics portend a general strategy for implementing chemical recycling on an industrial scale.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Linear thermomagnetic energy harvester for low-grade thermal energy harvesting

Low-grade thermal energy, either from waste heat or from natural resources, constitutes an enormous energy reserve that remains to be fully harvested. Harvesting low-grade heat is challenging because of the low Carnot efficiency. Among various thermal energy harvesting mechanisms available for capturing low-grade heat (temperature less than 100 degrees C), the thermomagnetic effect has been found to be quite promising. In this study, we demonstrate a scalable thermomagnetic energy harvester architecture that exhibits 140% higher power density compared to the previously published spring-mass designs. The alternating force required to oscillate the thermomagnetic mass is generated through the interaction between two magnetic forces in opposite directions. We employed numerical modeling to illustrate the behavior of a thermomagnetic device under different operating conditions and to obtain the optimal hot-side and cold-side temperatures for continuous mode operations. A miniaturized thermomagnetic harvester was fabricated and experiments were conducted to systematically evaluate the performance. The prototype was found to exhibit an oscillation frequency of 0.33 Hz, a work output of 0.6 J/kg/cycle, and a power density of 0.2 W/kg of gadolinium under the temperature difference of 60 K.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

Tailoring Spin-Wave Channels in a Reconfigurable Artificial Spin Ice

Artificial spin ices are ensembles of geometrically arranged interacting nanomagnets that have shown promising potential for the realization of reconfigurable magnonic crystals. Such systems allow for the manipulation of spin waves on the nanoscale and their potential use as information carriers. However, there are presently two general obstacles to the realization of artificial spin-ice-based magnonic crystals: the magnetic state of artificial spin ices is difficult to reconfigure and the magnetostatic interactions between the nanoislands are often weak, preventing mode coupling. We demonstrate, using micromagnetic modeling, that coupling a reconfigurable artificial spin-ice geometry made of weakly interacting nanomagnets to a soft magnetic underlayer creates a complex system exhibiting dynamically coupled modes. These give rise to spin-wave channels in the underlayer at well-defined frequencies, based on the artificial spin-ice magnetic state, which can be reconfigured. Finally, these findings open the door to the realization of reconfigurable magnonic crystals with potential applications for data transport and processing in magnonic-based logic architectures.

25 ENERGY STORAGE↗
DOE OSTI2024

Celeritas: Accelerating Geant4 with GPUs

Celeritas [1] is a new Monte Carlo (MC) detector simulation code designed for computationally intensive applications (specifically, High Lumi- nosity Large Hadron Collider (HL-LHC) simulation) on high-performance heterogeneous architectures. In the past two years Celeritas has advanced from prototyping a GPU-based single physics model in infinite medium to implementing a full set of electromagnetic (EM) physics processes in complex geometries. The current release of Celeritas, version 0.3, has incorporated full device-based navigation, an event loop in the presence of magnetic fields, and detector hit scoring. New functionality incorporates a scheduler to offload electromagnetic physics to the GPU within a Geant4-driven simulation, enabling integration of Celeritas into high energy physics (HEP) experimental frameworks such as CMSSW. On the Summit supercomputer, Celeritas performs EM physics between 6 and 32 faster using the machine’s Nvidia GPUs compared to using only CPUs. When running a multithreaded Geant4 ATLAS test beam application with full hadronic physics, using Celeritas to accelerate the EM physics results in an overall simulation speedup of 1.8–2.3× on GPU and 1.2× on CPU.

Johnson, Seth R.↗
DOE OSTI2024

Effects of Rolling Reduction on Critical Current Density and Microstructure of Bi-2212 Wires

Bi-2212 Rutherford cables have been fabricated into flat racetrack coils and canted-cosine-theta dipole magnets. The performance gap between the magnets made with Rutherford cables and the “short-sample-limit” is about 30%. To better understand the influence of Rutherford cable processing on the strand performance, we studied three Bi-2212 wires with filament architectures of 37 × 18 and 55 × 18 and diameters of 0.8 and 1.0 mm. To simulate the deformation caused by cabling process, the three wires were rolled with thickness reductions ranging from 10% to 30%. The aspect ratios of rolled strands are between 1.29 and 2.05. The low aspect-ratio wire is also an interesting form for fabricating solenoid coils with higher packing density. The round and rolled strands were heat-treated under 50 bar and with maximum heat treatment temperatures of 885.5 °C and 890.5 °C. The rolling deformation reduced filament size uniformity, resulting in filament merging in fully heat-treated wires. It was found that rolling reduction reduced wire critical current density (JE) by 16 to 18%, but the JE decrease saturated at 15 to 20% of the thickness reduction. It is believed that the reduced JE results from the filament merging caused by rolling and non-uniform shrinking during overpressure heat treatment.

Bi-2212 wire↗
DOE OSTI2026

Terahertz‐Nanoscale Visualization of the Microscopic Spin‐Charge Architecture of Colossal Magnetoresistive Switching

Resolving sub-10 nm spin switching and the associated terahertz (THz) electrodynamics during the colossal magnetoresistance (CMR) transition is a definitive frontier in reaching the fundamental spatial, temporal, and energy-dissipation limits of spin-electronics. Yet, simultaneous control of high magnetic field, cryogenic environment, and nanometer resolution has remained an elusive benchmark for THz nanoscopy, leaving the local THz dynamics of these transitions largely unexplored. Here, we overcome these limitations by utilizing a custom-built cryogenic magneto-THz scattering-type scanning near-field optical microscopy (cm-THz-sSNOM) to resolve the near-field THz spectroscopic evolution of the magnetic field-driven CMR transition in a manganite single crystal. Our measurements provide a nanoscale visualization of the THz conductivity, capturing the moment that magnetic-field-induced spin switching triggers the transition from an antiferromagnetic insulator to a ferromagnetic metal. An ellipsoidal near-field model reveals a multi-scale transition initiated by 1–2 nm isolated spin-flip sites at low magnetic fields, which coalesce into ∼15 nm conducting regions as the threshold field is approached. These results provide an nano-THz view of CMR switching, establishing an analysis framework for mapping spin–charge–lattice–orbit–coupled dynamics at spatial scales that transcend the nominal sSNOM resolution.

Haeuser, Samuel [Iowa State University, Ames, IA (↗
DOE OSTI2024

Molecular-level architecture of Chlamydomonas reinhardtii’s glycoprotein-rich cell wall

Abstract Microalgae are a renewable and promising biomass for large-scale biofuel, food and nutrient production. However, their efficient exploitation depends on our knowledge of the cell wall composition and organization as it can limit access to high-value molecules. Here we provide an atomic-level model of the non-crystalline and water-insoluble glycoprotein-rich cell wall of Chlamydomonas reinhardtii . Using in situ solid-state and sensitivity-enhanced nuclear magnetic resonance, we reveal unprecedented details on the protein and carbohydrate composition and their nanoscale heterogeneity, as well as the presence of spatially segregated protein- and glycan-rich regions with different dynamics and hydration levels. We show that mannose-rich lower-molecular-weight proteins likely contribute to the cell wall cohesion by binding to high-molecular weight protein components, and that water provides plasticity to the cell-wall architecture. The structural insight exemplifies strategies used by nature to form cell walls devoid of cellulose or other glycan polymers.

09 BIOMASS FUELS↗
DOE OSTI2020

Materials loss measurements using superconducting microwave resonators

The performance of superconducting circuits for quantum computing is limited by materials losses. In particular, coherence times are typically bounded by two-level system (TLS) losses at single photon powers and millikelvin temperatures. The identification of low loss fabrication techniques, materials, and thin film dielectrics is critical to achieving scalable architectures for superconducting quantum computing. Superconducting microwave resonators provide a convenient qubit proxy for assessing performance and studying TLS loss and other mechanisms relevant to superconducting circuits such as non-equilibrium quasiparticles and magnetic flux vortices. In this review article, we provide an overview of considerations for designing accurate resonator experiments to characterize loss, including applicable types of losses, cryogenic setup, device design, and methods for extracting material and interface losses, summarizing techniques that have been evolving for over two decades. Results from measurements of a wide variety of materials and processes are also summarized. Finally, we present recommendations for the reporting of loss data from superconducting microwave resonators to facilitate materials comparisons across the field.

47 OTHER INSTRUMENTATION↗
DOE OSTI2022

Multiscale petrophysical characterization and flow unit classification of the Minnelusa eolian sandstones

Integration of petrophysical and geological information is critical to simulation of subsurface carbon storage (GCS). In this sense, two depositional facies were identified from the core description and well-log interpretation, namely massive (MS) and cross-bedded (CB) facies groups. Additionally, pore-scale characteristics were studied by a combination of techniques, e.g. Nuclear Magnetic Resonance (NMR) and mercury intrusion capillary pressure (MICP). Scanning electron microscope (SEM) and petrographic analyses show that the pore structure is dominantly controlled by the depositional environment and dolomite cementation. NMR-T 2 distributions of MS and CB facies show triple and quadruple modes, respectively. In addition, MICP of high- and low-permeability MS facies samples, and their CB facies group mixtures were collected. The MS sample pore-throat size distribution is uni-modal, while the triple-modal characteristic of the mixtures indicates heterogeneous pore structures at the sub-core scale for CB facies. The reliably estimates of porosity and permeability for both facies groups via NMR techniques and the MLR (Multiple Linear Regression) approach demonstrate the applicability of these techniques to eolian sandstone. Moreover, irreducible water saturation via the T 2 -cutoff method correlates strongly with T 2LM instead of porosity. Finally, the rock quality index and flow zone indicator were calculated based on Combinable Magnetic Resonance (CMR) log interpretations. This provides direct connection to properties measured in the well. Four flow units were classified for both facies groups. Finally, results show that better reservoir quality with significant heterogeneities is observed in the CB facies. This study highlights the importance integrating multiscale petrophysical properties including facies, pore architecture and diagenesis analysis with core- to log-scale property characterization. The results herein validate our reservoir characterization and flow unit classification in eolian reservoirs.

58 GEOSCIENCES↗
DOE OSTI2025

Solving the Bernstein-Vazirani problem using Majorana-based topological quantum algorithms

Executing quantum algorithms using Majorana zero modes—a major milestone for the field of topological quantum computing—requires a platform that can be scaled to large quantum registers, can be controlled in real time and space, and a braiding protocol that uses the unique properties of these exotic particles. Here, we demonstrate the first successful simulation of a Majorana-based, fault-tolerant quantum algorithm to solve the Bernstein-Vazirani problem in two-dimensional magnet-superconductor hybrid structures from initialization to read-out of the final many-body state. Utilizing the Majorana zero modes’ topological properties, we introduce an optimized braiding protocol for the algorithm and a scalable architecture for its implementation with an arbitrary number of qubits. We visualize the algorithm protocol in real time and space by computing the non-equilibrium density of states, which is proportional to the time-dependent differential conductance, and the non-equilibrium charge density, which assigns a unique signature to each final state of the algorithm.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2020

Skeletides: A Modular, Simplified Physical Model of Protein Secondary Structure

Three-dimensional (3D) models are essential for visualization and conceptual understanding of complex architectures such as protein structure. Although there is a plethora of software platforms that allow digital depictions of protein structure at an atomic level in silico, physical models are needed to convey an intuitive understanding of biomolecular architecture. However, it is a challenge to represent all the relevant features of proteins in a single physical model due to their sheer structural complexity. Here, we describe a modular protein model that focuses only on representation of the secondary structure—the underlying structural skeleton. The simplified model consists of amino acid units, which can be linked together to reproduce the two most fundamental structural features of protein secondary structure: the relative positions of the amino acid alpha carbon atoms, and the intra-main chain hydrogen bonding pattern. We use 3D printing and magnets to create a set of three modular amino acid building blocks, which when linked together into a chain, can faithfully represent alpha helices, beta sheets, and turns. These simple to make models can be used to quickly assemble the alpha carbon trace of an entire protein domain, which conveys a tactile experience of the complexity of protein skeletal architecture. These models highlight the modularity of protein structure: where a single structural unit, the amino acid, can be linked together to form a larger, regular secondary structure. These models also have a propensity to spontaneously organize into alpha helices and beta sheets, as demonstrated by their ability to autonomously assemble when placed in a circulating water tank. These models provide a missing educational tool to expand knowledge of protein structure, foster deeper insight into protein folding, and inspire greater interest in biomacromolecular architecture.

36 MATERIALS SCIENCE↗
DOE OSTI2024

A galactic approach to neutron scattering science

Neutron scattering science is leading to significant advances in our understanding of materials and will be key to solving many of the challenges that society is facing today. Improvements in scientific instruments are actually making it more difficult to analyze and interpret the results of experiments due to the vast increases in the volume and complexity of data being produced and the associated computational requirements for processing that data. New approaches to enable scientists to leverage computational resources are required, and Oak Ridge National Laboratory (ORNL) has been at the forefront of developing these technologies. We recently completed the design and initial implementation of a neutrons data interpretation platform that allows seamless access to the computational resources provided by ORNL. For the first time, we have demonstrated that this platform can be used for advanced data analysis of correlated quantum materials by utilizing the world's most powerful computer system, Frontier. In particular, we have shown the end-to-end execution of the DCA++ code to determine the dynamic magnetic spin susceptibility χ(q, ω) for a single-band Hubbard model with Coulomb repulsion U/t = 8 in units of the nearest-neighbor hopping amplitude t and an electron density of n = 0.65. The following work describes the architecture, design, and implementation of the platform and how we constructed a correlated quantum materials analysis workflow to demonstrate the viability of this system to produce scientific results.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Revealing structure and shaping priorities in plant and fungal cell wall architecture via solid-state NMR

Plant and fungal cell walls are essential for growth, adaptation, and survival, with their intricate architectures dictating both resistance to stress and susceptibility to antifungal or biomass-degrading strategies. Understanding how these walls are built, remodeled, and function at the molecular level is therefore central to both clinical and biotechnological applications. Solid-state nuclear magnetic resonance (ssNMR) has emerged as a uniquely powerful tool for this purpose, as it reveals the structure, dynamics, and interactions of intact biopolymers without disrupting their native organization. Using this approach, recent studies have shown how structural polymorphism, polymer-polymer interactions, and species-specific remodeling govern mechanical integrity, drug resistance, and stress adaptation. Applications highlighted here include lignin-carbohydrate packing during plant stem maturation, fungal wall reorganization under treatment by wall-targeting antifungals such as echinocandin and nikkomycin, and the functional diversity of glucans, chitins, and mannans. Together, these insights uncover conserved principles of polymer assembly across kingdoms while informing new opportunities for antifungal development and biomass utilization. Ongoing advances in sensitivity and resolution are expected to broaden the reach of ssNMR and further accelerate its role in linking structural heterogeneity to biosynthetic complexity and biological function.

Antifungal↗
DOE OSTI2026

Tokamak Energy’s pre-concept design for a fusion power plant: an overview of ST-E1

Climate change and rapidly rising energy demand, driven in part by artificial intelligence and data-centre growth, create an urgent need for stable, low-carbon, and abundant power. Fusion is a promising long-term solution, yet its commercialisation faces a fundamental paradox in today’s investment environment: pilot plants are essential to de-risk physics, engineering, and operations, but their limited lifetime energy output and high upfront costs make them difficult to finance. This paper presents Tokamak Energy’s response: ST-E1, a pre-concept design for a low-aspect-ratio tokamak power plant engineered specifically to overcome this challenge. ST-E1 is designed from the outset for phased operation—pilot and commercial phases, with an upgrade phase in between—with emphasis on commercial viability, maintainability, nuclear engineering, modularity, and upgradability. A key design principle is the deliberate separation of long-lived assets, such as the magnet cage and vacuum vessel, from replaceable in-vessel systems. This provides an attractive and credible investment approach to generate operational data and de-risk key technologies while preserving most capital-intensive assets for later commercial phases. The architecture supports continuous optimisation toward high net electric power (targeting 800–1000 MW net electric), a normalised capital expenditure of $\$$ 12–14k/kW of net electric power, and high availability (targeting > 80%). A tokamak core with a 5 m major radius, aspect ratio of 2.3, and on-plasma axis toroidal field of 5.25 T was selected to meet these objectives. This paper summarises the ST-E1 design philosophy, principal features, and development methodology. It introduces a Focus Collection of 11 papers detailing the pre-concept design of the entire tokamak and corresponding plant.

ST-E1↗