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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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DOE OSTI2018

Competing Grain Boundary and Interior Deformation Mechanisms with Varying Sizes

In typical coarse-grained alloys, the dominant plastic deformations are dislocation gliding or climbing, and material strengths can be tuned by dislocation interactions with grain boundaries, precipitates, solid solutions, and other defects. With the reduction of grain size, the increase of material strengths follows the classic Hall-Petch relationship up to nano-grained materials. Even at room temperatures, nano-grained materials exhibit strength softening, or called the inverse Hall-Petch effect, as grain boundary processes take over as the dominant deformation mechanisms. On the other hand, at elevated temperatures, grain boundary processes compete with grain interior deformation mechanisms over a wide range of the applied stress and grain sizes. This book chapter reviews and compares the rate equation model and the microstructure-based finite element simulations. The latter explicitly accounts for the grain boundary sliding, grain boundary diffusion and migration, as well as the grain interior dislocation creep. Therefore the explicit finite element method has clear advantages in problems where microstructural heterogeneities play a critical role, such as in the gradient microstructure in shot peening or weldment. Furthermore, combined with the Hall-Petch effect and its breakdown, the above competing processes help construct deformation mechanism maps by extending from the classic Frost-Ashby type to the ones with the dependence of grain size.

Zhang, Wei↗

DOE OSTI2017

Revisiting the conversion reaction voltage and the reversibility of the CuF2 electrode in Li-ion batteries

Deviation between thermodynamic and experimental voltages is one of the key issues in Li-ion conversion-type electrode materials; the factor that affects this phenomenon has not been understood well in spite of its importance. In this work, we combine first principles calculations and electrochemical experiments with characterization tools to probe the conversion reaction voltage of transition metal difluorides MF2 (M = Fe, Ni, and Cu). We find that the conversion reaction voltage is heavily dependent on the size of the metal nanoparticles generated. The surface energy of metal nanoparticles appears to penalize the reaction energy, which results in a lower voltage compared to the thermodynamic voltage of a bulk-phase reaction. Furthermore, we develop a reversible CuF2 electrode coated with NiO. Electron energy loss spectroscopy (EELS) elemental maps demonstrate that the lithiation process mostly occurs in the area of high NiO content. This suggests that NiO can be considered a suitable artificial solid electrolyte interphase that prevents direct contact between Cu nanoparticles and the electrolyte. Thus, it alleviates Cu dissolution into the electrolyte and improves the reversibility of CuF2.

Seo, Joon Kyo↗

DOE OSTI2021

Manifold learning for coarse-graining atomistic simulations: Application to amorphous solids

In this work, we introduce a generalized machine learning framework to probabilistically parameterize upper-scale models in the form of nonlinear PDEs consistent with a continuum theory, based on coarse-grained atomistic simulation data of mechanical deformation and flow processes. The proposed framework utilizes a hypothesized coarse-graining methodology with manifold learning and surrogate-based optimization techniques. Coarse-grained high-dimensional data describing quantities of interest of the multiscale models are projected onto a nonlinear manifold whose geometric and topological structure is exploited for measuring behavioral discrepancies in the form of manifold distances. A surrogate model is constructed using Gaussian process regression to identify a mapping between stochastic parameters and distances. Derivative-free optimization is employed to adaptively identify a unique set of parameters of the upper-scale model capable of rapidly reproducing the system's behavior while maintaining consistency with coarse-grained atomic-level simulations. The proposed method is applied to learn the parameters of the shear transformation zone (STZ) theory of plasticity that describes plastic deformation in amorphous solids as well as coarse-graining parameters needed to translate between atomistic and continuum representations. We show that the methodology is able to successfully link coarse-grained microscale simulations to macroscale observables and achieve a high-level of parity between the models across scales.

36 MATERIALS SCIENCE↗

DOE OSTI2018

Proteomic profile changes associated with diminished expression of T-cell intracellular antigens reveal a hormesis response

T-cell intracellular antigen (TIA) proteins function as regulators of cell homeostasis by controlling global gene expression in response to dynamic regulatory changes and environmental stress. Here, we used two-dimensional differential in-gel electrophoresis (2D-DIGE) and mass spectrometry (MALDI-TOF/TOF) to identify protein changes associated with the down-regulated expression of TIA proteins. We detected 30 differentially expressed proteins (DEPs), 24 of which were identified, and some of these DEPs were validated by western blotting. In silico analysis showed that DEPs were associated with metabolic processes, detoxification and proteostasis. We mapped the DEPs to the available biological pathways and networks, which included the metabolism of small molecules such as sugars, lipids, amino acids, and nucleotides. Our findings support previous studies and suggest that low expression of TIA proteins might act as a potential adaptive switch to link gene expression reprogramming to a proliferative phenotype mediated by a hormesis phenomenon.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2025

GenAI4UQ: A software for forward and inverse uncertainty quantification using conditional generative AI

We introduce GenAI4UQ, a software package for forward and inverse uncertainty quantification in model calibration, parameter estimation, and ensemble forecasting. GenAI4UQ leverages a generative AI-based conditional modeling framework to address limitations of traditional inverse modeling techniques, such as Markov Chain Monte Carlo (MCMC) methods. By replacing computationally intensive iterative processes with a direct, learned mapping, GenAI4UQ enables efficient calibration of input parameters and generation of predictions directly from observations. The software supports rapid ensemble forecasting with robust uncertainty quantification while maintaining computational and storage efficiency. Built-in auto-tuning of hyperparameters simplifies model training, ensuring accessibility for users with varying expertise. Its versatile conditional generative framework is applicable across diverse scientific domains. While GenAI4UQ offers significant advantages in flexibility and efficiency, users should interpret its uncertainty estimates with caution in data-sparse scenarios, as the model may overestimate uncertainty—an effect common to all surrogate-based approaches including MCMC with surrogate models. Despite this, GenAI4UQ transforms inverse modeling by providing a fast, reliable, and user-friendly solution. It empowers researchers and practitioners to quickly estimate parameter distributions and generate model predictions for new observations, facilitating efficient decision-making and advancing the state of uncertainty quantification in computational modeling.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2022

A Robust Neural Network for Extracting Dynamics from Electrostatic Force Microscopy Data

Advances in scanning probe microscopy (SPM) methods such as time-resolved electrostatic force microscopy (trEFM) now permit the mapping of fast local dynamic processes with high resolution in both space and time, but such methods can be time-consuming to analyze and calibrate. Here, we design and train a regression neural network (NN) that accelerates and simplifies the extraction of local dynamics from SPM data directly in a cantilever-independent manner, allowing the network to process data taken with different cantilevers. We validate the NN’s ability to recover local dynamics with a fidelity equal to or surpassing conventional, more time-consuming, calibrations using both simulated and real microscopy data. We apply this method to extract accurate photoinduced carrier dynamics on n = 1 butylammonium lead iodide, a halide perovskite semiconductor film that is of interest for applications in both solar photovoltaics and quantum light sources. Lastly, we use SHapley Additive exPlanations to evaluate the robustness of the trained model, confirm its cantilever-independence, and explore which parts of the trEFM signal are important to the network.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2023

Hierarchical Self-Assembly of Carbon Dots into High-Aspect-Ratio Nanowires

Here, we report a spontaneous and hierarchical self-assembly mechanism of carbon dots prepared from citric acid and urea into nanowire structures with large aspect ratios (>50). Scattering-type scanning near-field optical microscopy (s-SNOM) with broadly tunable mid-IR excitation was used to interrogate details of the self-assembly process by generating nanoscopic chemical maps of local wire morphology and composition. s-SNOM images capture the evolution of wire formation and the complex interplay between different chemical constituents directing assembly over the nano- to microscopic length scales. We propose that residual citrate promotes tautomerization of melamine surface functionalities to produce supramolecular shape synthons comprised of melamine-cyanurate adducts capable of forming long-range and highly directional hydrogen-bonding networks. This intrinsic, heterogeneity-driven self-assembly mechanism reflects synergistic combinations of high chemical specificity and long-range cooperativity that may be harnessed to reproducibly fabricate functional structures on arbitrary surfaces.

77 NANOSCIENCE AND NANOTECHNOLOGY↗

DOE OSTI2022

Electroluminescence from Single-Walled Carbon Nanotubes with Quantum Defects

Individual single-walled carbon nanotubes with covalent sidewall defects have emerged as a class of photon sources whose photoluminescence spectra can be tailored by the carbon nanotube chirality and the attached functional group/molecule. Here we present electroluminescence spectroscopy data from single-tube devices based on (7, 5) carbon nanotubes, functionalized with dichlorobenzene molecules, and wired to graphene electrodes. We observe electrically generated, defect-induced emissions that are controllable by electrostatic gating and strongly red-shifted compared to emissions from pristine nanotubes. The defect-induced emissions are assigned to excitonic and trionic recombination processes by correlating electroluminescence excitation maps with electrical transport and photoluminescence data. At cryogenic conditions, additional gate-dependent emission lines appear, which are assigned to phonon-assisted hot-exciton electroluminescence from quasi-levels. Similar results were obtained with functionalized (6, 5) nanotubes. We also compare functionalized (7, 5) electroluminescence data with photoluminescence of pristine and functionalized (7, 5) nanotubes redox-doped using gold(III) chloride solution. This work shows that electroluminescence excitation is selective toward neutral defect-state configurations with the lowest transition energy, which in combination with gate-control over neutral versus charged defect-state emission leads to high spectral purity.

36 MATERIALS SCIENCE↗

DOE OSTI2025

Overlapping qubits from non-isometric maps and de Sitter tensor networks

The emergence of a local effective theory from a more fundamental theory of quantum gravity with seemingly fewer degrees of freedom is a major puzzle of theoretical physics. A recent approach to this problem is to consider general features of the Hilbert space maps relating these theories. In this work, we construct approximately local observables, or overlapping qubits, from such non-isometric maps. We show that local processes in effective theories can be spoofed with a quantum system with fewer degrees of freedom, with deviations from actual locality identifiable as features of quantum gravity. For a concrete example, we construct two tensor network models of de Sitter space-time, demonstrating how exponential expansion and local physics can be spoofed for a long period before breaking down. Our results highlight the connection between overlapping qubits, Hilbert space dimension verification, degree-of-freedom counting in black holes, holography, and approximate locality in quantum gravity.

Quantum information↗

DOE OSTI2021

Inverse design of two-dimensional materials with invertible neural networks

The ability to readily design novel materials with chosen functional properties on-demand represents a next frontier in materials discovery. However, thoroughly and efficiently sampling the entire design space in a computationally tractable manner remains a highly challenging task. To tackle this problem, we propose an inverse design framework (MatDesINNe) utilizing invertible neural networks which can map both forward and reverse processes between the design space and target property. This approach can be used to generate materials candidates for a designated property, thereby satisfying the highly sought-after goal of inverse design. We then apply this framework to the task of band gap engineering in two-dimensional materials, starting with MoS 2 . Within the design space encompassing six degrees of freedom in applied tensile, compressive and shear strain plus an external electric field, we show the framework can generate novel, high fidelity, and diverse candidates with near-chemical accuracy. We extend this generative capability further to provide insights regarding metal-insulator transition in MoS 2 which are important for memristive neuromorphic applications, among others. This approach is general and can be directly extended to other materials and their corresponding design spaces and target properties.

36 MATERIALS SCIENCE↗

DOE OSTI2023

Hemispherical power asymmetry in intensity and polarization for Planck PR4 data

Abstract One of the foundations of the Standard Model of Cosmology is statistical isotropy, which can be tested, among other probes, through the study of the Cosmic Microwave Background (CMB). However, a hemispherical power asymmetry on large scales has been reported for WMAP and Planck data by different works.The statistical significance is above 3σfor temperature, suggesting a directional dependence of the local power spectrum, and thus a feature beyond the ΛCDM model. With the third release of the Planck data (PR3), a new analysis was performed including the E-mode polarization maps, finding an asymmetry at a modest level of significance. In this work, we perform an asymmetry analysis in intensity and polarization maps for the latest Planck processing pipeline (PR4). We obtain similar results to those obtained with PR3, with a slightly lower significance (2.8% for the Sevem method)for the amplitude of the E-mode local variance dipole as well as a significant variability with the considered mask.In addition, a hint of a possible T-E alignment between the asymmetry axes is found at the level of ∼ 5%. For the analysis, we have implemented an alternative inpainting approach in order to get an accurate reconstruction of the E-modes. More sensitive all-sky CMB polarization data, such as those expected from the future LiteBIRD experiment, are needed to reach a more robust conclusion on the possible existence of deviations from statistical isotropy in the form of a hemispherical power asymmetry.

Astronomy & Astrophysics↗

DOE OSTI2016

Accelerated iterative beam angle selection in IMRT

Purpose: Iterative methods for beam angle selection (BAS) for intensity-modulated radiation therapy (IMRT) planning sequentially construct a beneficial ensemble of beam directions. In a naïve implementation, the nth beam is selected by adding beam orientations one-by-one from a discrete set of candidates to an existing ensemble of (n − 1) beams. The best beam orientation is identified in a time consuming process by solving the fluence map optimization (FMO) problem for every candidate beam and selecting the beam that yields the largest improvement to the objective function value. This paper evaluates two alternative methods to accelerate iterative BAS based on surrogates for the FMO objective function value. Methods: We suggest to select candidate beams not based on the FMO objective function value after convergence but (1) based on the objective function value after five FMO iterations of a gradient based algorithm and (2) based on a projected gradient of the FMO problem in the first iteration. The performance of the objective function surrogates is evaluated based on the resulting objective function values and dose statistics in a treatment planning study comprising three intracranial, three pancreas, and three prostate cases. Furthermore, iterative BAS is evaluated for an application in which a small number of noncoplanar beams complement a set of coplanar beam orientations. This scenario is of practical interest as noncoplanar setups may require additional attention of the treatment personnel for every couch rotation. Results: Iterative BAS relying on objective function surrogates yields similar results compared to naïve BAS with regard to the objective function values and dose statistics. At the same time, early stopping of the FMO and using the projected gradient during the first iteration enable reductions in computation time by approximately one to two orders of magnitude. With regard to the clinical delivery of noncoplanar IMRT treatments, we could show that optimized beam ensembles using only a few noncoplanar beam orientations often approach the plan quality of fully noncoplanar ensembles. Conclusions: We conclude that iterative BAS in combination with objective function surrogates can be a viable option to implement automated BAS at clinically acceptable computation times.

61 RADIATION PROTECTION AND DOSIMETRY↗

DOE OSTI2016

WE-FG-202-11: Longitudinal Diffusion MRI for Treatment Assessment of Sarcoma Patients with Pre-Operative Radiation Therapy

Purpose: Diffusion weighted MRI (DWI) is a promising imaging technique for early prediction of tumor response to radiation therapy. A recently proposed longitudinal DWI strategy using a Co-60 MRI guided RT system (MRIgRT) may bring functional MRI guided adaptive radiation therapy closer to clinical utility. We report our preliminary results of using this longitudinal DWI approach performed on the MRIgRT system for predicting the response of sarcoma patient to preop RT. Methods: Three sarcoma patients who underwent fractionated IMRT were recruited in this study. For all three patients DWI images were acquired immediately following his/her treatment. For each imaging session, ten slices were acquired interleaved with the b values covering the gross tumor volume (GTV). The diffusion images were processed to obtain the ADC maps using standard exponential fitting for each voxel. Regions of interest were drawn in the tumor on the diffusion images based on each patient’s clinical GTV contours. Each patient subsequently underwent surgery and the tumor necrosis score was available from standard pathology. The ADC values for each patient were compared to the necrosis scores to assess the predictive value of our longitudinal DWI for tumor response. Results: Each patient underwent 3 to 5 diffusion MRI scans depending on their treatment length. Patient 1 had a relatively unchanged ADC during the course of RT and a necrosis score of 30% at surgery. For patient 2, the mean ADC values decreased from 1.56 × 10-3 to 1.12 × 10-3 mm2/s and the patient’s necrosis score was less than 10%. Patient 3 had a slight increase in the ADC values from 0.59 × 10-3 to 0.71 × 10-3 mm2/s and patient’s necrosis score was 50%. Conclusion: Based on limited data from 3 patients, our longitudinal changes in tumor ADC assessed using the MRIgRT system correlated well with pathology results.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2022

Perovskite grain wrapping by converting interfaces and grain boundaries into robust and water-insoluble low-dimensional perovskites

Stabilizing perovskite solar cells requires consideration of all defective sites in the devices. Substantial efforts have been devoted to interfaces, while stabilization of grain boundaries received less attention. Here, we report on a molecule tributyl(methyl)phosphonium iodide (TPI), which can convert perovskite into a wide bandgap one-dimensional (1D) perovskite that is mechanically robust and water insoluble. Mixing TPI with perovskite precursor results in a wrapping of perovskite grains with both grain surfaces and grain boundaries converted into several nanometer-thick 1D perovskites during the grain formation process as observed by direct mapping. The grain wrapping passivates the grain boundaries, enhances their resistance to moisture, and reduces the iodine released during light soaking. The perovskite films with wrapped grains are more stable under heat and light. The best device with wrapped grains maintained 92.2% of its highest efficiency after light soaking under 1-sun illumination for 1900 hours at 55°C open-circuit condition.

14 SOLAR ENERGY↗

DOE OSTI2023

QASMTrans

A transpiler that incorporates several key features. It includes a mapping and routing method, a process for decomposing gates into basis gates compatible with IBM machines, and a remapping method designed to enhance simulation performance. Compared to the state-of-art transpiler qiskit, we have dramatically speed up in various benchmarks.

Hua,, Fei↗

DOE OSTI2017

Partial differential equations preconditioner resilient to soft and hard faults

We present a domain-decomposition-based preconditioner for the solution of partial differential equations (PDEs) that is resilient to both soft and hard faults. The algorithm reformulates the PDE as a sampling problem, followed by a solution update through data manipulation that is resilient to both soft and hard faults. This reformulation allows us to recast the problem as a set of independent tasks, and exploit data locality to reduce global communication. We discuss two different parallel implementations: (a) a single program multiple data (SPMD) version based on a one-to-one mapping between subdomain and MPI processes responsible for both state and computation; and (b) an asynchronous server–client implementation where all state information is held by the servers and clients are designed solely as computational units. We present a scalability comparison of both implementations under nominal conditions, showing efficiency within ~80% for up to 12,000 cores. We present a resilience analysis under different fault scenarios based on the server–client implementation. This framework provides resiliency to hard faults such that if a client crashes, it stops asking for work, and the servers simply distribute the work among all of the other clients alive. Erroneous subdomain solves (e.g. due to soft faults) appear as corrupted data, which is either rejected if that causes a task to fail, or is seamlessly filtered out during the regression stage through a suitable noise model. Three different types of faults are modeled: hard faults modeling nodes (or clients) crashing; soft faults occurring during the communication of the tasks between server and clients; and soft faults occurring during task execution. We demonstrate the resiliency of the approach for a 2D elliptic PDE, and explore the effect of the faults at various failure rates.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2020

Utah FORGE InSAR Data from 2020

Interferometric Synthetic Aperture Radar data from the TerraSAR-X and the TanDEM-X satellite missions operated by the German Space Agency (DLR). Interferometric pairs (interferograms) were created using generic mapping tool GMT-SAR processing software (see link in Resources). Data from January through December 2020.

15 GEOTHERMAL ENERGY↗

DOE OSTI2021

Utah FORGE InSAR Data from 2021

15 GEOTHERMAL ENERGY↗