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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 181 records · Page 10
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

GPU-friendly surface model for Monte-Carlo detector simulations

The demands for Monte-Carlo simulation are drastically increasing with the Large Hadron Collider’s high-luminosity upgrade, and are expected to exceed the currently available compute resources. At the same time, modern high-performance computing has adopted powerful hardware accelerators, particularly GPUs. The AdePT and Celeritas projects aim to address the demanding computational needs by leveraging these heterogeneous computing architectures. While both have successfully ported realistic detector simulations to GPUs using the VecGeom library, the complexity of geometry modeling emerged as a bottleneck. Thread divergence and high register usage were degrading the GPU performance. Therefore, a new, GPU-friendly surface-based model has been introduced in the VecGeom library that decomposes the divergent code of the 3D primitive solids into simpler and more balanced surface algorithms. In this work, we present the latest developments, focusing on the additions required to efficiently model complex setups like the CMS Phase-2 geometry. This includes memory reduction techniques, and adding accelerating structures for faster traversal.

Diederichs, Severin [CERN]↗
DOE OSTI2022

Role of Sr doping and external strain on relieving bottleneck of oxygen diffusion in La 2- x Sr x CuO 4-δ

In many complex oxides, the oxygen vacancy formation is a promising route to modify the material properties such as a superconductivity and an oxygen diffusivity. Cation substitutions and external strain have been utilized to control the concentration and diffusion of oxygen vacancies, but the mechanisms behind the controls are not fully understood. Using first-principles calculations, we find how Sr doping and external strain greatly enhances the diffusivity of oxygen vacancies in La 2-x Sr x CuO 4-δ (LSCO) in the atomic level. In hole-doped case (2x > δ), the formation energy of an apical vacancy in the LaO layer is larger than its equatorial counterpart by 0.2 eV that the bottleneck of diffusion process is for oxygen vacancies to escape equatorial sites. Such an energy difference can be reduced and even reversed by either small strain (< 1.5%) or short-range attraction between Sr and oxygen vacancy, and in turn, the oxygen diffusivity is greatly enhanced. For fully compensated hole case (2x ≦ δ), the formation energy of an apical vacancy becomes too high that most oxygen vacancies cannot move but would be trapped at equatorial sites. From our electronic structure analysis, we found that the contrasting change in the formation energy by Sr doping and external strain is originated from the different localization natures of electron carrier from both types of oxygen vacancies.

25 ENERGY STORAGE↗
DOE OSTI2022

Towards a Stronger Foundation: Digitizing Commercial Buildings with Brick to Enable Portable Advanced Applications

Most large commercial buildings have digital controls for their heating, ventilation, and air-conditioning (HVAC) and lighting systems with the potential to implement advanced control strategies and data analytics. However, advanced control strategies and data analytics are rarely deployed at scale due to non-standard naming conventions and heterogenous building configurations. Semantic metadata standards, like Brick, show promise to proliferate these applications across many buildings, but they have not been widely adopted by industry due to barriers such as perceived risk and unfamiliarity with the technology. This paper describes the workflow we established and evaluated while using it to develop over ten Brick models of existing buildings. Through this process, we observed that digitizing existing commercial buildings is a cost and labor-intensive effort in which understanding the buildings’ data streams is the major bottleneck. Yet, we conclude this investment is worthwhile since various use case applications such as fault detection and diagnostics, thermal comfort analysis, and HVAC control optimization can utilize the same Brick model. The paper also explores the challenges and lessons learned we encountered while creating these data models, such as: 1) difficulties in finding metadata descriptions and relationships for existing buildings; 2) handling missing concepts in the schema needed to model a building; 3) lack of guidance on how to structure the data model or how much detail to include; 4) unfamiliarity with technologies, which makes the learning curve steep for applications developers. Finally, we also describe future directions for semantic metadata research and development to make such transformative technologies more accessible to practitioners.

Roa, Carlos Duarte↗
DOE OSTI2021

Novel synthesis approach for “stubborn” metals and metal oxides

Significance Atomically precise complex oxides containing “stubborn” elements, such as ruthenium, iridium, and platinum, hold tremendous promise as designer quantum materials for exploring novel electronic, magnetic, superconducting, and topological phases owing to their strong spin–orbit interaction. This study shows a method to synthesize such materials by eliminating the major synthesis bottleneck of low vapor pressure and difficulty in oxidation. This study serves as a “proof-of-concept” allowing us to 1) grow Pt, RuO 2 , and SrRuO 3 thin films by supplying Pt and Ru precursors at 65 to 100 °C in a low-temperature effusion cell, as opposed to the several thousand degrees Celsius needed using electron beam evaporators; 2) reveal bulk-like room-temperature resistivity; and 3) ultimately provide pathways to creating atomically precise quantum structures.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Expanding the substrate scope of pyrrolysyl-transfer RNA synthetase enzymes to include non-$α$-amino acids in vitro and in vivo

The absence of orthogonal aminoacyl-transfer RNA (tRNA) synthetases that accept non-L-α-amino acids is a primary bottleneck hindering the in vivo translation of sequence-defined hetero-oligomers and biomaterials. Here we report that pyrrolysyl-tRNA synthetase (PylRS) and certain PylRS variants accept α-hydroxy, α-thio and N-formyl-L-α-amino acids, as well as α-carboxy acid monomers that are precursors to polyketide natural products. These monomers are accommodated and accepted by the translation apparatus in vitro; those with reactive nucleophiles are incorporated into proteins in vivo. High-resolution structural analysis of the complex formed between one PylRS enzyme and a m-substituted 2-benzylmalonic acid derivative revealed an active site that discriminates prochiral carboxylates and accommodates the large size and distinct electrostatics of an α-carboxy substituent. This work emphasizes the potential of PylRS-derived enzymes for acylating tRNA with monomers whose α-substituent diverges substantially from the α-amine of proteinogenic amino acids. These enzymes or derivatives thereof could synergize with natural or evolved ribosomes and/or translation factors to generate diverse sequence-defined non-protein heteropolymers.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Evaluation of Electron Tomography Capabilities for Shale Imaging

Despite the advantageous resolution of electron tomography (ET), reconstruction of three-dimensional (3D) images from multiple two-dimensional (2D) projections presents several challenges, including small signal-to-noise ratios, and a limited projection range. This study evaluates the capabilities of ET for thin sections of shale, a complex nanoporous medium. A numerical phantom with 1.24 nm pixel size is constructed based on the tomographic reconstruction of a Barnett shale. A dataset of 2D projection images is numerically generated from the 3D phantom and studied over a range of conditions. First, common reconstruction techniques are used to reconstruct the shale structure. The reconstruction uncertainty is quantified by comparing overall values of storage and transport metrics, as well as the misclassification of pore voxels compared to the phantom. We then select the most robust reconstruction technique and we vary the acquisition conditions to quantify the effect of artifacts. We find a strong agreement for large pores over the different acquisition workflows, while a wider variability exists for nanometer-scale features. Furthermore, the limited projection range and reconstruction are identified as the main experimental bottlenecks, thereby suggesting that sample thinning, advanced holders, and advanced reconstruction algorithms offer opportunities for improvement.

3D scanning transmission electron microscopy↗
DOE OSTI2021

A universal method to fabricating porous carbon for Li-O 2 battery

The main bottleneck in the application of biomass-based carbon material in energy field is the development of an economical, nanoscale controllable and universal fabrication method. Herein, we demonstrate that a biomass-derived carbon consisting of loose carbon nanosphere clusters could serve as an ideal cathode material for high performance Li-O 2 batteries. The carbon nanomaterial can be prepared from both biomass derivatives and crude biomass by a low cost, facile and nanoscale controllable method. The open slit-shaped hierarchical pore structure endows the electrode sufficient active surface sites and mass transport channels that are not easily asphyxiated by the discharge products. The concentration of oxygenic groups and structure defects on the surface of obtained carbon materials is very low, which minimizes the side reactions associated with carbon decomposition. Finally, the unique pore structure and surface properties of the carbon electrode give rise to a superhigh specific capacity of 20,300 mA h g -1 and an extremely long cycle life which a cyclic capacity retention of 100% is maintained for 543 cycles with current density of 0.2 mA cm -2 at controlled discharge-charge depths of 500 mA h g -1 .

25 ENERGY STORAGE↗
DOE OSTI2023

Atomic-scale origin of the low grain-boundary resistance in perovskite solid electrolyte Li 0.375 Sr 0.4375 Ta 0.75 Zr 0.25 O 3

Oxide solid electrolytes (OSEs) have the potential to achieve improved safety and energy density for lithium-ion batteries, but their high grain-boundary (GB) resistance generally is a bottleneck. In the well-studied perovskite oxide solid electrolyte, Li 3x La 2/3-x TiO 3 (LLTO), the ionic conductivity of grain boundaries is about three orders of magnitude lower than that of the bulk. In contrast, the related Li 0.375 Sr 0.4375 Ta 0.75 Zr 0.25 O 3 (LSTZ0.75) perovskite exhibits low grain boundary resistance for reasons yet unknown. Here, we use aberration-corrected scanning transmission electron microscopy and spectroscopy, along with an active learning moment tensor potential, to reveal the atomic scale structure and composition of LSTZ0.75 grain boundaries. Vibrational electron energy loss spectroscopy is applied for the first time to reveal atomically resolved vibrations at grain boundaries of LSTZ0.75 and to characterize the otherwise unmeasurable Li distribution therein. We find that Li depletion, which is a major reason for the low grain boundary ionic conductivity of LLTO, is absent for the grain boundaries of LSTZ0.75. Instead, the low grain boundary resistivity of LSTZ0.75 is attributed to the formation of a nanoscale defective cubic perovskite interfacial structure that contained abundant vacancies. Our study provides new insights into the atomic scale mechanisms of low grain boundary resistivity.

25 ENERGY STORAGE↗
DOE OSTI2023

Floating zone crystal growth, structure, and properties of a cubic Li 5.5 La 3 Nb 1.5 Zr 0.5 O 12 garnet-type lithium-ion conductor

As a promising candidate for solid-state electrolytes in Li-ion batteries, the garnet-type Li-ion conductor series Li 5+x La 3 Nb 2-x Zr x O 12 (LLNZO) (0 ≤ x ≤ 2) exhibits high ionic conductivity at room temperature. However, no previous single-crystal growth or characterization has been reported for LLNZO compositions 0 ≤ x ≤ 1. To obtain a complete understanding of the trend in the structure–property relationship in this class of materials, we used the floating zone (FZ) method to grow a single crystal of Li 5.5 La 3 Nb 1.5 Zr 0.5 O 12 that was 4 mm in diameter and 10 mm in length. Using Laue neutron single-crystal diffraction, two distinct Li sites were observed: tetrahedral 24d and octahedral 96h sites. The maximum entropy method (MEM) based on neutron single-crystal diffraction data was used to map Li nuclear density and estimate that the bottleneck of Li transport exists between neighboring tetrahedral and octahedral sites, and that Li is delocalized between split octahedral sites. Room-temperature Li-ion conductivity in Li 5.5 La 3 Nb 1.5 Zr 0.5 O 12 measured with electrochemical impedance spectroscopy (EIS) was 1.37 × 10 -4 S cm -1 . The Li migration activation energy was estimated to be 0.50 eV from EIS and 0.47 eV from dielectric relaxation measurements. The Li-ion jump attempt rate was estimated to be 1.47 × 10 12 Hz while the time scale of successful migration is 10 -7 to 10 -6 s.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

A Platform Technology for High-throughput Atomically Precise Manufacturing: Mechatronics at the Atomic Scale

The main objective of this project is to invent the necessary enabling technologies for high throughput atomically precise manufacturing (APM). APM is an emerging technology that refers to any manufacturing capability that enables fabrication of atomically precise structures, components, and devices under programmable control. APM will require positional assembly at the atomic and/or molecular scale, as well as at the nano and microscales using hierarchical assembly to create products ranging from nanoscale and quantum devices to macroscale systems and materials. This project builds on the expectation that commercial viability of APM will depend on a high level of parallelism to achieve the required throughput, a capability that currently does not exist. This project is the first attempt to address this key technological bottleneck.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2021

Transfer-Free Synthesis of Atomically Precise Graphene Nanoribbons on Insulating Substrates

The rational bottom-up synthesis of graphene nanoribbons (GNRs) provides atomically precise control of widths and edges that give rise to a wide range of electronic properties promising for electronic devices such as field-effect transistors (FETs). Since the bottom-up synthesis commonly takes place on catalytic metallic surfaces, the integration of GNRs into such devices requires their transfer onto insulating substrates, which remains one of the bottlenecks in the development of GNR-based electronics. Herein, we report on a method for the transfer-free placement of GNRs on insulators. This involves growing GNRs on a gold film deposited onto an insulating layer followed by gentle wet etching of the gold, which leaves the nanoribbons to settle in place on the underlying insulating substrate. Scanning tunneling microscopy and Raman spectroscopy confirm that atomically precise GNRs of high density uniformly grow on the gold films deposited onto SiO 2 /Si substrates and remain structurally intact after the etching process. We have also demonstrated transfer-free fabrication of ultrashort channel GNR FETs using this process. A very important aspect of the present work is that the method can scale up well to 12 in. wafers, which is extremely difficult for previous techniques. Our work here thus represents an important step toward large-scale integration of GNRs into electronic devices.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Scaling pair count to next galaxy surveys

ABSTRACT Counting pairs of galaxies or stars according to their distance is at the core of real-space correlation analyses performed in astrophysics and cosmology. Upcoming galaxy surveys (LSST, Euclid) will measure properties of billions of galaxies challenging our ability to perform such counting in a minute-scale time relevant for the usage of simulations. The problem is only limited by efficient access to the data, hence belongs to the big data category. We use the popular Apache Spark framework to address it and design an efficient high-throughput algorithm to deal with hundreds of millions to billions of input data. To optimize it, we revisit the question of non-hierarchical sphere pixelization based on cube symmetries and develop a new one dubbed the ‘Similar Radius Sphere Pixelization’ (SARSPix) with very close to square pixels. It provides the most adapted indexing over the sphere for all distance-related computations. Using LSST-like fast simulations, we compute autocorrelation functions on tomographic bins containing between a hundred million to one billion data points. In each case, we achieve the construction of a standard pair-distance histogram in about 2 min, using a simple algorithm that is shown to scale, over a moderate number of nodes (16–64). This illustrates the potential of this new techniques in the field of astronomy where data access is becoming the main bottleneck. They can be easily adapted to other use-cases as nearest-neighbours search, catalogue cross-match or cluster finding. The software is publicly available from https://github.com/astrolabsoftware/SparkCorr.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Silicon Nanosheets as Candidates for Silicon-Based Optoelectronics

The growth in computational ability over the past decades has positively impacted global development, the economy, healthcare, and science. As on-chip components are approaching the atomic scale, alternative paradigms are needed to address the thermal and electronic issues that impose bottlenecks for computing. One approach to address this is with optoelectronics. However, silicon—the backbone of microelectronics—is a poor choice due to its indirect bandgap, while existing optoelectronic materials are incompatible with CMOS infrastructure. Monolayer silicon nanosheets (SiNSs) are an intriguing material that exhibit photoluminescence, and are compositionally-compatible with the CMOS process. Here, we synthesize and characterize monolayer SiNSs, and show spectroscopic evidence that they exhibit a quasi-direct bandgap, which is corroborated by DFT calculations. We probe their thermal stability, demonstrating their structure and photoluminescence are stable beyond the required operating temperatures for computing applications. These optoelectronic properties, CMOS-compatibility, and stability make SiNSs a viable candidate for silicon-based photonics.

Ryan, Bradley↗
DOE OSTI2024

Anode Upcycling via Tailored Solvent Treatment

To achieve a truly closed-loop direct recycling process for lithium-ion batteries, all component materials must be recovered. To date, direct recycling method development has primarily focused on the high-value transition-metal cathode materials, while the inherently lower-value graphite has been challenging to recover in a cost-effective manner. However, end-of-life graphite contains a unique engineered value due to the presence of the solid electrolyte interphase (SEI). Growth of the SEI during the cell's active lifetime stabilizes the electronically reactive graphite surface through an irreversible consumption of Li, and thus necessitates both excess lithiation of the cathode and a costly and time-intensive formation procedure during manufacturing. An optimized pre-formed SEI that capitalizes on existing SEI components from end-of-life batteries has the potential to significantly reduce cathode lithiation requirements and eliminate the critical bottleneck of formation cycling during cell remanufacturing. Further, retaining Li at the anode obviates the need for a separate Li leaching and recovery step, improving the overall efficiency of the direct recycling line. In this work, we present a novel approach to "upcycling" spent graphite through use of tailored chemical treatment to remove adverse (i.e., highly resistive and/or poorly passivating) SEI species while retaining beneficially passivating components. We have explored a rational set of solvents spanning a range of polarity, proticity, and molecular size to evaluate structure-property-performance relationships between applied solvent(s), removed and remaining SEI species, and electrochemical response of the resulting graphite product. Further, we have developed and optimized a robust and holistic analysis procedure that couples symmetric-cell electrochemical testing, multi-modal materials characterization, and advanced electrochemical modeling. These analysis results inform a set of correlative metrics for graphite performance relative to both solvent properties and upcycled SEI composition. We demonstrate effective tunability in the residual SEI composition by varying solvent identity and concentration, and report on several promising solvent systems that achieve comparable or performance to pristine graphite.

anode recycling↗
DOE OSTI2026

A Comprehensive Comparative Study of Active Learning Schemes for Nanophotonics Design

We present a benchmarking study of active learning (AL) schemes for designing planar multilayer nanophotonic metamaterials, where the design tasks are formulated as binary optimization problems. Different surrogate models, including factorization machine (FM), Gaussian process regression (GPR), and convolutional neural network (CNN), combined with different optimization methods, including exhaustive enumeration, discrete particle swarm optimization (DPSO), quantum annealing (QA), hybrid QA, and simulated annealing are studied. The benchmark cases investigated range from small problems with short binary lengths (N = 25) to large problems with N up to 100, focusing on the design of two classes of photonic structures, including antireflective coatings for the long-wavelength infrared region and transparent radiative coolers. For small problems, CNN coupled with DPSO in AL achieves the best performance. As N increases, FM with QA outperforms GPR and CNN. For FM-based AL, hybrid QA yields the best optimization results, particularly in high-dimensional cases (N = 100). These results demonstrate that the optimization method can significantly affect in AL performance as N increases, and that QA-based optimization can provide practical routes for mitigating the optimization bottleneck in high-dimensional problems.

Jung, Serang [Kyung Hee University, Korea]↗
DOE OSTI2025

Criegee Intermediates Compete Well with OH as a Cleaning Agent for Atmospheric Amides

Elucidating the chemical kinetics of stabilized Criegee intermediates (sCIs) in the atmosphere is critically important for climate modeling. Here, we report a class of very rapid bimolecular reactions of two sCIs, namely, CH 2 OO and syn-CH 3 CHOO, with amides. We used electronic structure calculations and kinetics calculations to elucidate a universal mechanism by which the oxygen atom of the carbonyl group in an amide is added to the carbon atom of the COO group in the sCI with simultaneous transfer of the amide hydrogen to the terminal oxygen atom of the carbonyl oxide of the sCI. The barriers for the mechanism are submerged below reactants by ∼9 kcal/mol, which means that the tight transition state is not the rate-determining step, and the rate constants are determined by loose free energy bottlenecks between the reactants and the precursor complexes. We calculate the rate constants due to these loose and barrierless transition states by variable-reaction-coordinate variational transition-state theory. We find that bimolecular reactions of sCIs with amides are very rapid, with rate constants 1 to 5 × 10 –10 cm 3 molecule –1 s –1 , which is a factor of 2 faster than the rate constants of carboxylic acids with sCIs and 10 2 faster than the rate constants of OH reactions with amides. This has the consequence that, under a wide range of conditions, sCIs are the major sink for atmospheric amides, in contrast to the usual assumption that OH radicals are the dominant sinks.

Amides↗
DOE OSTI2025

Thermophilic Chassis-Enabled High-Throughput Selection of a Thermostable Fluorogenic Reporter

Thermostable proteins show increased shelf life and performance at elevated temperatures and under harsh conditions, resulting in lower costs for various industrial and biotechnological applications. However, due to a limited understanding of the relationship between stability and function, protein stabilization remains primarily a trial-and-error approach. Therefore, building a combinatorial library of mutations predicted to improve stability, followed by experimental testing, represents a markedly improved methodology. However, the lack of high-throughput approaches to screen even a moderately sized library presents a major bottleneck in the field. Here, in this study, we use a thermophile, Parageobacillus thermoglucosidasius (Ptherm) to rapidly screen combinatorial libraries consisting of rationally designed thermostabilizing mutations (∼10 3 –10 4 ) of a mesophilic fluorescent reporter, Y-FAST. On a Petri dish, microbial growth at an elevated temperature and exposure to fluorogen yielded several colonies of Ptherm that showed distinct fluorescence at 55 and 68 °C in our two sequentially generated libraries using Rosetta and ProteinMPNN, respectively. The Y-FAST variants isolated from fluorescent colonies were brighter than Y-FAST and showed higher resistance to thermal and chemical denaturation. AlphaFold-predicted structures and MD simulations revealed stability-enhancing salt bridges and hydrogen bond networks in the isolated FAST variants. The moderately thermostable FAST (tsFAST) and hyperstable FAST (hsFAST) were then demonstrated as translation reporters for protein expression and folding at elevated temperatures, such as 55 and 68 °C. Our approach of combinatorial library generation and high-throughput screening in a thermophilic chassis could, in principle, be extended to other proteins fused to these translation reporters. Furthermore, the hsFAST protein is small─half the size of the green fluorescent protein─and does not require oxygen for maturation, making it ideal for engineering extremophilic anaerobes for biosensing and bioconversion.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Machine Learning-enabled Scalable Performance Prediction of Scientific Codes

Hardware architectures become increasingly complex as the compute capabilities grow to exascale. Here, we present the Analytical Memory Model with Pipelines (AMMP) of the Performance Prediction Toolkit (PPT). PPT-AMMP takes high-level source code and hardware architecture parameters as input and predicts runtime of that code on the target hardware platform, which is defined in the input parameters. PPT-AMMP transforms the code to an (architecture-independent) intermediate representation, then (i) analyzes the basic block structure of the code, (ii) processes architecture-independent virtual memory access patterns that it uses to build memory reuse distance distribution models for each basic block, and (iii) runs detailed basic-block level simulations to determine hardware pipeline usage. PPT-AMMP uses machine learning and regression techniques to build the prediction models based on small instances of the input code, then integrates into a higher-order discrete-event simulation model of PPT running on Simian PDES engine. We validate PPT-AMMP on four standard computational physics benchmarks and present a use case of hardware parameter sensitivity analysis to identify bottleneck hardware resources on different code inputs. We further extend PPT-AMMP to predict the performance of a scientific application code, namely, the radiation transport mini-app SNAP. To this end, we analyze multi-variate regression models that accurately predict the reuse profiles and the basic block counts. We validate predicted SNAP runtimes against actual measured times.

97 MATHEMATICS AND COMPUTING↗