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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 559 records · Page 31
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Solid-State Transformation of Uranyl Peroxide Materials through High-Level Irradiation

The solid-state transformation of sodium uranyl triperoxide (Na 4 (UO 2 )(O 2 ) 3 ·9H 2 O, NaUT) to sodium uranyl tricarbonate (Na 4 (UO 2 )(CO 3 ) 3 ) by radiolysis was observed for the first time. The exposure of NaUT to 3 MGy gamma irradiation resulted in partial breakdown of the peroxides forming a mixed peroxide and carbonate species. The effects of He-ion irradiation on NaUT were also investigated up to 225 MGy using both hydrated argon and dry argon. The complete conversion to the uranyl tricarbonate phase by 56 MGy was done using hydrated argon, while dry argon did not fully convert showing the importance of water in the system. He-ion irradiated NaUT samples all convert to the tricarbonate phase with time in air post radiation exposure. This transition was monitored via Raman spectroscopy, infrared spectroscopy (IR), and powder X-ray diffraction (PXRD) to further confirm the identity of the final product as the sodium uranyl tricarbonate, čejkaite. Furthermore, this transformation outlines a mechanism for the mobility of uranyl in natural environments and in the Hanford tanks.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗
DOE OSTI2025

Scalable Implementation of Mean-Field and Correlation Methods Based on Lie-Algebraic Similarity Transformation of Spin Hamiltonians in the Jordan–Wigner Representation

Recent work has highlighted that the strong correlation inherent in spin Hamiltonians can be effectively reduced by mapping spins to Fermions via the Jordan−Wigner transformation (JW). The Hartree−Fock method is straightforward in the Fermionic domain and may provide a reasonable approximation to the ground state. Correlation with respect to the Fermionic mean field can be recovered based on Lie-algebraic similarity transformation (LAST) with two-body correlators. Specifically, a unitary LAST variant eliminates the dependence on site ordering, while a nonunitary LAST yields size-extensive correlation energies. Whereas the first recent demonstration of such methods was restricted to small spin systems, we present efficient implementations using analytical gradients for the optimization with respect to the mean-field reference and the LAST parameters, thereby enabling the treatment of larger clusters, including systems with local spins s > $\frac{1}{2}$.

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

SCF Framework, HF Stability, and RPA Correlation for Jordan–Wigner-Transformed Spin Hamiltonians on Arbitrary Coupling Topologies

Mapping spins to fermions via the Jordan–Wigner (JW) transformation can render mean-field (Hartree–Fock, HF) descriptions effective for strongly correlated spin systems. As established in recent work, the application of such approaches is not limited by the nonlocal structure of JW strings or by site ordering because string operators can be absorbed into Thouless rotations of a Slater determinant, and the variational optimization of a unitary Lie-algebraic similarity transformation removes any ordering dependence. Leveraging these ideas, we develop a self-consistent field (SCF) scheme that expresses the mean-field energy as a functional of the single-particle density matrix, providing an alternative to gradient-based optimization of Thouless parameters. We derive the analytical orbital Hessian to diagnose HF stability and compute the ground-state correlation energy through the random-phase approximation (RPA). Benchmark results for the XXZ and J 1 –J 2 model on one- and two-dimensional lattices demonstrate that RPA significantly improves mean-field accuracy.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Efficient 3D Molecular Design with an E(3) Invariant Transformer VAE

This work introduces a three-dimensional (3D) invariant graph-to-string transformer variational autoencoders (VAE) (Vagrant) for generating molecules with accurate density functional theory (DFT)-level properties. Vagrant learns to model the joint probability distribution of a 3D molecular structure and its properties by encoding molecular structures into a 3D-aware latent space. Directed navigation through this latent space implicitly optimizes the 3D structure of a molecule, and the latent embedding can be used to condition a generative transformer to predict the candidate structure as a one-dimensional (1D) sequence. Additionally, we introduce two novel sampling methods that exploit the latent characteristics of a VAE to improve performance. Here, we show that our method outperforms comparable 3D autoregressive and diffusion methods for predicting quantum chemical property values of novel molecules in terms of both sample quality and computational efficiency.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Extreme Metastability of Diamond and its Transformation to the BC8 Post-Diamond Phase of Carbon

Diamond possesses exceptional physical properties due to its remarkably strong carbon–carbon bonding, leading to significant resilience to structural transformations at very high pressures and temperatures. Despite several experimental attempts, synthesis and recovery of the theoretically predicted post-diamond BC8 phase remains elusive. Through quantum-accurate multimillion atom molecular dynamics (MD) simulations, we have uncovered the extreme metastability of diamond at very high pressures, significantly exceeding its range of thermodynamic stability. We predict the post-diamond BC8 phase to be experimentally accessible only within a narrow high pressure–temperature region of the carbon phase diagram. The diamond to BC8 transformation proceeds through premelting followed by BC8 nucleation and growth in the metastable carbon liquid. Here we propose a double-shock compression pathway for BC8 synthesis, which is currently being explored in experiments at the National Ignition Facility.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2020

Mechanistic Insights into Superlattice Transformation at a Single Nanocrystal Level Using Nanobeam Electron Diffraction

Understanding the mechanism and ultimately directing nanocrystal (NC) superlattice assembly and attachment have important implications on future advances in this emerging field. Here, we use 4D-STEM to investigate a monolayer of PbS NCs at various stages of the transformation from a hexatic assembly to a nonconnected square-like superlattice over large fields of view. Maps of nanobeam electron diffraction patterns acquired with an electron microscope pixel array detector (EMPAD) offer unprecedented detail into the 3D crystallographic alignment of the polyhedral NCs. Our analysis reveals that superlattice transformation is dominated by translation of prealigned NCs strongly coupled along the <11n>AL direction and occurs stochastically and gradually throughout single grains. We validate the generality of the proposed mechanism by examining the structure of analogous PbSe NC assemblies using conventional transmission electron microscopy and selected area electron diffraction. Here, the experimental results presented here provide new mechanistic insights into NC self-assembly and oriented attachment.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Low-Cost Transformation of Biomass-Derived Carbon to High-Performing Nano-graphite via Low-Temperature Electrochemical Graphitization

Graphite, an essential component of energy storage devices, is traditionally synthesized via an energy-intensive thermal process (Acheson process) at ~3300 K. However, the battery performance of such graphite is abysmal under fast-charging conditions, which is deemed essential for the propulsion of electric vehicles to the next level. Herein, a low-temperature electrochemical transformation approach has been demonstrated to afford a highly crystalline nano-graphite with the capability of tuning interlayer spacing to enhance the lithium diffusion kinetics in molten salts at 850 °C. The essence of our strategy lies in the effective electrocatalytic transformation of carbon to graphite at a lower temperature that could significantly increase the energy savings, reduce the cost, shorten the synthesis time, and replace the traditional graphite synthesis. The resulting graphite exhibits high purity, crystallinity, a high degree of graphitization, and a nanoflake architecture that all ensure fast lithium diffusion kinetics (~2.0 × 10 –8 cm2 s –1 ) through its nanosheet. Such unique features enable outstanding electrochemical performance (~200 mA h g –1 at 5C for 1000 cycles, 1C = 372 mA g –1 ) as a fast-charging anode for lithium-ion batteries. Overall, this finding paves the way to make high energy-density fast-charging batteries that could boost electromobility.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Molecular Transformation of Dissolved Organic Matter during the Processes of Wildfire, Alum Coagulation, and Disinfection Using ESI(-) and ESI(+) FT-ICR MS

Forest fires alter the quality of terrestrial dissolved organic matter (DOM) flushed into source water, which could be further changed during alum coagulation and serve as potential disinfection byproduct (DBP) precursors in water facilities. To unveil this molecular transformation, we applied 12T Fourier transform ion cyclotron resonance mass spectrometry on a controlled study which used the leachates of black and white ashes (collected immediately after the 2013 Rim Fire, CA) along with unburned white fir (Abies concolor) and ponderosa pine (Pinus ponderosa). Here, results showed that the assigned features in DOM from burned ashes had a smaller molecular weight (MW), contained more unsaturated, aromatic, and oxidized and less fraction of labile compounds, and were enriched with nitrogen, relative to unburned litters. After alum coagulation, we observed the disappeared features with higher MW and S/C and N/C atom ratios than the resistant features. During the following disinfections, the disappeared features serving as the potential DBP precursors had higher MW and unsaturation degree, contained more S- and N-containing functionalities, and were more aromatic, compared to the resistant features. Our study suggested organic compounds with small MW and high aromaticity as the targets for water facilities to improve post-fire drinking water quality.

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

Structural Transformation and Degradation of Cu Oxide Nanocatalysts during Electrochemical CO 2 Reduction

The electrochemical CO 2 reduction reaction (CO 2 RR) holds enormous potential as a carbon-neutral route to the sustainable production of fuels and platform chemicals. The durability for long-term operation is currently inadequate for commercialization, however, and the underlying deactivation process remains elusive. A fundamental understanding of the degradation mechanism of electrocatalysts, which can dictate the overall device performance, is needed. In this work, we report the structural dynamics and degradation pathway of Cu oxide nanoparticles (CuO x NPs) during the CO 2 RR by using in situ small-angle X-ray scattering (SAXS) and X-ray absorption spectroscopy (XAS). The in situ SAXS reveals a reduction in the size of NPs when subjected to a potential at which no reaction products are detected. At potentials where the CO 2 RR starts to occur, CuO x NPs are agglomerated through a particle migration and coalescence process in the early stage of the reaction, followed by Ostwald ripening (OR) as the dominant degradation mechanism for the remainder of the reaction. As the applied potential becomes more negative, the OR process becomes more dominant, and for the most negative applied potential, OR dominates for the entire reaction time. The morphological changes are linked to a gradual decrease in the formation rate for multicarbon products (C 2 H 4 and ethanol). Other reaction parameters, including reaction intermediates and local high pH, induce changes in the agglomeration process and final morphology of the CuO x NPs electrode, supported by post-mortem ex situ microscopic analysis. The in situ XAS analysis suggests that the CuO x NPs reduced into the metallic state before the structural transformation was observed. The introduction of high surface area carbon supports with ionomer coating mitigates the degree of structural transformation and detachment of the CuO x NPs electrode. These findings show the dynamic nature of Cu nanocatalysts during the CO 2 RR and can serve as a rational guideline toward a stable catalyst system under electrochemical conditions.

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

Deep Learning for Subsurface Flow: A Comparative Study of U‐Net, Fourier Neural Operators, and Transformers in Underground Hydrogen Storage

Subsurface flow research is essential for the sustainable management of natural resources and the environment. Deep learning (DL) has significantly advanced this field by developing efficient and accurate surrogate models to replace computationally expensive physics‐based simulations. These surrogate models are commonly used to predict the spatiotemporal evolution of state variables, such as gas saturation and reservoir pressure, in heterogeneous geological formations. Despite the various DL models applied to this task, there is a lack of studies systematically comparing their performance. This absence of comparative analysis leads to somewhat arbitrary DL model selection in subsurface flow research, resulting in suboptimal performance and potentially inaccurate predictions. To bridge this gap, we conduct a systematic comparison study of three popular DL architectures—U‐Net, Fourier Neural Operators (FNO), and Segmentation Transformer (SETR)—in surrogate modeling of underground hydrogen storage (UHS). We focus on UHS due to its promise of enhancing clean energy resilience and its cyclic operational conditions that represent common scenarios in various subsurface applications. We evaluate the models based on accuracy, training cost, and inference speed. The comparison shows that U‐Net achieves the highest accuracy, followed by SETR and FNO. Despite its lower accuracy, FNO has the highest inference speed. SETR offers competitive accuracy with the least training memory usage, demonstrating the potential of transformers in learning subsurface flow. Our results provide guidance for selecting DL models for surrogate modeling in a wide range of subsurface flow problems.

42 ENGINEERING↗
DOE OSTI2021

CRL4A DTL degrades DNA-PKcs to modulate NHEJ repair and induce genomic instability and subsequent malignant transformation

Genomic instability induced by DNA damage and improper DNA damage repair is one of the main causes of malignant transformation and tumorigenesis. DNA double strand breaks (DSBs) are the most detrimental form of DNA damage, and nonhomologous end-joining (NHEJ) mechanisms play dominant and priority roles in initiating DSB repair. A well-studied oncogene, the ubiquitin ligase Cullin 4A (CUL4A), is reported to be recruited to DSB sites in genomic DNA, but whether it regulates NHEJ mechanisms of DSB repair is unclear. Here, we discovered that the CUL4A-DTL ligase complex targeted the DNA-PKcs protein in the NHEJ repair pathway for nuclear degradation. Overexpression of either CUL4A or DTL reduced NHEJ repair efficiency and subsequently increased the accumulation of DSBs. Moreover, we demonstrated that overexpression of either CUL4A or DTL in normal cells led to genomic instability and malignant proliferation. Consistent with the in vitro findings, in human precancerous lesions, CUL4A expression gradually increased with increasing malignant tendency and was negatively correlated with DNA-PKcs and positively correlated with γ-H2AX expression. Collectively, this study provided strong evidence that the CUL4A-DTL axis increases genomic instability and enhances the subsequent malignant transformation of normal cells by inhibiting NHEJ repair. These results also suggested that CUL4A may be a prognostic marker of precancerous lesions and a potential therapeutic target in cancer.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Calcium promotes persistent soil organic matter by altering microbial transformation of plant litter

Abstract Calcium (Ca) can contribute to soil organic carbon (SOC) persistence by mediating physico-chemical interactions between organic compounds and minerals. Yet, Ca is also crucial for microbial adhesion, potentially affecting colonization of plant and mineral surfaces. The importance of Ca as a mediator of microbe-mineral-organic matter interactions and resulting SOC transformation has been largely overlooked. We incubated 44 Ca labeled soils with 13 C 15 N labeled leaf litter to study how Ca affects microbial transformation of litter and formation of mineral associated organic matter. Here we show that Ca additions promote hyphae-forming bacteria, which often specialize in colonizing surfaces, and increase incorporation of litter into microbial biomass and carbon use efficiency by approximately 45% each. Ca additions reduce cumulative CO 2 production by 4%, while promoting associations between minerals and microbial byproducts of plant litter. These findings expand the role of Ca in SOC persistence from solely a driver of physico-chemical reactions to a mediator of coupled abiotic-biotic cycling of SOC.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

TransPolymer: a Transformer-based language model for polymer property predictions

Accurate and efficient prediction of polymer properties is of great significance in polymer design. Conventionally, expensive and time-consuming experiments or simulations are required to evaluate polymer functions. Recently, Transformer models, equipped with self-attention mechanisms, have exhibited superior performance in natural language processing. However, such methods have not been investigated in polymer sciences. Herein, we report TransPolymer, a Transformer-based language model for polymer property prediction. Our proposed polymer tokenizer with chemical awareness enables learning representations from polymer sequences. Rigorous experiments on ten polymer property prediction benchmarks demonstrate the superior performance of TransPolymer. Moreover, we show that TransPolymer benefits from pretraining on large unlabeled dataset via Masked Language Modeling. Experimental results further manifest the important role of self-attention in modeling polymer sequences. We highlight this model as a promising computational tool for promoting rational polymer design and understanding structure-property relationships from a data science view.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Feature engineering descriptors, transforms, and machine learning for grain boundaries and variable-sized atom clusters

Abstract Obtaining microscopic structure-property relationships for grain boundaries is challenging due to their complex atomic structures. Recent efforts use machine learning to derive these relationships, but the way the atomic grain boundary structure is represented can have a significant impact on the predictions. Key steps for property prediction common to grain boundaries and other variable-sized atom clustered structures include: (1) describing the atomic structure as a feature matrix, (2) transforming the variable-sized feature matrix to a fixed length common to all structures, and (3) applying a machine learning algorithm to predict properties from the transformed matrices. We examine how these steps and different combinations of engineered features impact the accuracy of grain boundary energy predictions using a database of over 7000 grain boundaries. Additionally, we assess how different engineered features support interpretability, offering insights into the physics of the structure-property relationships.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Fourier-based three-dimensional multistage transformer for aberration correction in multicellular specimens

High-resolution tissue imaging is often compromised by sample-induced optical aberrations that degrade resolution and contrast. Although wavefront sensor-based adaptive optics (AO) can measure these aberrations, such hardware solutions are typically complex, expensive to implement and slow when serially mapping spatially varying aberrations across large fields of view. Here we introduce AOViFT (adaptive optical vision Fourier transformer)—a machine learning-based aberration sensing framework built around a three-dimensional multistage vision transformer that operates on Fourier domain embeddings. AOViFT infers aberrations and restores diffraction-limited performance in puncta-labeled specimens with substantially reduced computational cost, training time and memory footprint compared to conventional architectures or real-space networks. We validated AOViFT on live gene-edited zebrafish embryos, demonstrating its ability to correct spatially varying aberrations using either a deformable mirror or postacquisition deconvolution. By eliminating the need for the guide star and wavefront sensing hardware and simplifying the experimental workflow, AOViFT lowers technical barriers for high-resolution volumetric microscopy across diverse biological samples.

Alshaabi, Thayer [Howard Hughes Medical Institute,↗
DOE OSTI2020

RIFTA: A Robust Iterative Fourier Transform-based dwell time Algorithm for ultra-precision ion beam figuring of synchrotron mirrors

With the rapid evolution of synchrotron X-ray sources, the demand for high-precision X-ray mirrors has greatly increased. Single nanometer profile error is required to keep imaging capability at the diffraction limit. Ion Beam Figuring (IBF), as a highly deterministic surfacing technique, has been used for ultra-precision finishing of mirrors. One crucial step that guides the IBF process is dwell time calculation. A valid dwell time solution should be non-negative and duplicate the shape of the desired removal map. Another important aspect is to minimize the total dwell time. In this study, we propose a Robust Iterative Fourier Transform-based dwell time Algorithm (RIFTA) that automatically fulfills these requirements. First, the thresholded inverse filtering in Fourier transform-based deconvolution is stabilized and automated by optimizing the threshold value using the Nelder-Mead simplex algorithm. Second, a novel two-level iterative scheme is proposed to guarantee the minimized total dwell time with its non-negativity at each dwell point. Third, a bicubic resampling is employed to flexibly adapt the calculated dwell time map to any IBF process intervals. The performance of RIFTA is first studied with simulation, followed by a comparison with the other state-of-the-art dwell time algorithms. We then demonstrate with an experiment that, using the dwell time calculated by the RIFTA, the total dwell time is shortened by a factor of two and the RMS in a 5 × 50 mm clear aperture was reduced from 3.4 nm to 1.1 nm after one IBF run, which proves the effectiveness and the efficiency of the proposed algorithm.

42 ENGINEERING↗
DOE OSTI2020

Correlating work hardening with co-activation of stacking fault strengthening and transformation in a high entropy alloy using in-situ neutron diffraction

Transformation induced plasticity (TRIP) leads to enhancements in ductility in low stacking fault energy (SFE) alloys, however to achieve an unconventional increase in strength simultaneously, there must be barriers to dislocation motion. While stacking faults (SFs) contribute to strengthening by impeding dislocation motion, the contribution of SF strengthening to work hardening during deformation is not well understood; as compared to dislocation slip, twinning induced plasticity (TWIP) and TRIP. Thus, we used in-situ neutron diffraction to correlate SF strengthening to work hardening behavior in a low SFE Fe 40 Mn 20 Cr 15 Co 20 Si 5 (at%) high entropy alloy, SFE ~ 6.31 mJ m –2 . Cooperative activation of multiple mechanisms was indicated by increases in SF strengthening and γ-f.c.c. → ε-h.c.p. transformation leading to a simultaneous increase in strength and ductility. The present study demonstrates the application of in-situ, neutron or X-ray, diffraction techniques to correlating SF strengthening to work hardening.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Physics-constrained machine learning for electrodynamics without gauge ambiguity based on Fourier transformed Maxwell’s equations

We utilize a Fourier transformation-based representation of Maxwell’s equations to develop physics-constrained neural networks for electrodynamics without gauge ambiguity, which we label the Fourier–Helmholtz–Maxwell neural operator method. In this approach, both of Gauss’s laws and Faraday’s law are built in as hard constraints, as well as the longitudinal component of Ampère–Maxwell in Fourier space, assuming the continuity equation. An encoder–decoder network acts as a solution operator for the transverse components of the Fourier transformed vector potential, $\hat{A}_⟂(k,t)$, whose two degrees of freedom are used to predict the electromagnetic fields. This method was tested on two electron beam simulations. Among the models investigated, it was found that a U-Net architecture exhibited the best performance as it trained quicker, was more accurate and generalized better than the other architectures examined. We demonstrate that our approach is useful for solving Maxwell’s equations for the electromagnetic fields generated by intense relativistic charged particle beams and that it generalizes well to unseen test data, while being orders of magnitude quicker than conventional simulations. We show that the model can be re-trained to make highly accurate predictions in as few as 20 epochs on a previously unseen data set.

97 MATHEMATICS AND COMPUTING↗