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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 955 records · Page 53
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

OpenGraphGym: A Parallel Reinforcement Learning Framework for Graph Optimization Problems

This paper presents an open-source, parallel AI environment (named OpenGraphGym) to facilitate the application of reinforcement learning (RL) algorithms to address combinatorial graph optimization problems. This environment incorporates a basic deep reinforcement learning method, and several graph embeddings to capture graph features, it also allows users to rapidly plug in and test new RL algorithms and graph embeddings for graph optimization problems. This new open-source RL framework is targeted at achieving both high performance and high quality of the computed graph solutions. This RL framework forms the foundation of several ongoing research directions, including 1) benchmark works on different RL algorithms and embedding methods for classic graph problems; 2) advanced parallel strategies for extreme-scale graph computations, as well as 3) performance evaluation on real-world graph solutions.

Zheng, Weijian↗
DOE OSTI2024

Manufacturing ODS Steels from GARS Powders by Friction Consolidation and Extrusion

Previous research suggested that friction-based processing is a promising method for fabricating oxide dispersion-strengthened (ODS) steel. In this study, we combined friction consolidation and friction extrusion to successfully manufacture ODS steel rods using precursor powder made with gas atomization reaction synthesis. We examined the microstructure evolution from the initial powder to the final extruded rod, which revealed the dispersion process of Y. Additionally, by comparing the microstructures of three rods extruded at different temperatures, we showed that low-temperature friction extrusion effectively enhanced microstructure uniformity and prevented grain coarsening, leading to improved mechanical properties. Furthermore, our findings provide practical guidelines for adjusting processing parameters in the production of ODS steel using friction-based processing.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Unexpected Changes of Aerosol Burdens With Decreased Convection in the Context of Scale-Aware Convection Schemes

Atmospheric aerosols in global climate models (GCMs) can be removed by both large-scale and convective precipitation. As the horizontal resolution of GCMs increases, it is expected that large-scale precipitation increases and convective precipitation decreases if scale-aware convection schemes are well developed. Here, to explore its impact on aerosol burdens, this study develops a novel method and applies it to the National Center for Atmospheric Research Community Atmosphere Model version 5.3 to mimic the behavior of scale-aware convective schemes. Surprisingly, it is found that aerosol burden increases globally as convective precipitation decreases and large-scale precipitation increases, which can be attributed to the reduced light rain. In the light-rain intensity category, the decreased frequency of convective precipitation is not offset by the increased frequency of large-scale precipitation. Therefore, for aerosol simulations in high-resolution GCMs using scale-aware convection schemes, subgrid convection and large-scale condensation should be coordinated to assure light rain stays the same.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

DIPS-Plus: The enhanced database of interacting protein structures for interface prediction

Abstract In this work, we expand on a dataset recently introduced for protein interface prediction (PIP), the Database of Interacting Protein Structures (DIPS), to present DIPS-Plus, an enhanced, feature-rich dataset of 42,112 complexes for machine learning of protein interfaces. While the original DIPS dataset contains only the Cartesian coordinates for atoms contained in the protein complex along with their types, DIPS-Plus contains multiple residue-level features including surface proximities, half-sphere amino acid compositions, and new profile hidden Markov model (HMM)-based sequence features for each amino acid, providing researchers a curated feature bank for training protein interface prediction methods. We demonstrate through rigorous benchmarks that training an existing state-of-the-art (SOTA) model for PIP on DIPS-Plus yields new SOTA results, surpassing the performance of some of the latest models trained on residue-level and atom-level encodings of protein complexes to date.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Engage the Public in Science and Embrace Future Change with Human-Centric Stories, Art, and Imaginings

Human-centric stories that weave in real scientific data may be able to engage the public in environmental issues that do not yet directly affect them. Science fiction and artistically rendered futuristic scenarios can unleash the imagination and act as a lens to envision technological, social, and cultural aspects of transitioning to clean energy. Cultures with strong oral traditions use stories to record history, develop a shared identity, pass on environmental lessons, and prepare for future change. Through the lenses of our non-physical science disciplines (cultural anthropology and environmental sociology), we discuss challenges to communicating science, the use of narratives to illustrate ecosystem processes, and we report on a collaborative, interdisciplinary workshop and book project that is creating narratives of hope and visions for the future through inspiring art, short stories, and essays. We explore the surprising potential of 'cli-fi,' humor, games, and the research behind emotion and imagination-driven engagement. We describe methods that help people visualize their future well-being and we explore opportunities to spread those methods.

clean energy↗
DOE OSTI2022

The binding of atomic hydrogen on graphene from density functional theory and diffusion Monte Carlo calculations

In this research, density functional theory (DFT) and diffusion Monte Carlo (DMC) methods are used to calculate the binding energy of a H atom chemisorbed on the graphene surface. The DMC value of the binding energy is about 16% smaller in magnitude than the Perdew–Burke–Ernzerhof (PBE) result. The inclusion of exact exchange through the use of the Heyd–Scuseria–Ernzerhof functional brings the DFT value of the binding energy closer in line with the DMC result. It is also found that there are significant differences in the charge distributions determined using PBE and DMC approaches.

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

Optical signatures of multifold fermions in the chiral topological semimetal CoSi

Significance We present a comprehensive combined experimental and theoretical study of optical conductivity in the chiral topological semimetal CoSi based on the development of high-quality crystals. We reveal the presence and the energy range of various exotic multifold quasiparticles in the optical responses and provide experimental evidence for the realization of fourfold spin-3/2 fermions, which were not directly observed previously. Our work is critical to interpreting future optical and transport responses of multifold fermion materials. We believe the methods used in this work will not only stimulate future research in this class of materials but will also provide a strategy for addressing optical signatures of chiral topological fermions in solids.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Structure-Spin-Transport Anomaly in Quasi-One-Dimensional Ba9Fe3Te15 under High Pressure

Recently, a series of novel compounds Ba3MX5 (M= Fe, Ti, V; X=Se, Te) with hexagonal crystal structures composed of quasi-1-dimensional (1D) magnetic chains has been synthesized by our research team using high-pressure and high-temperature methods. The initial hexagonal phases persist to the maximum achievable pressure, while spin configurations and magnetic interactions may change dramatically as a result of considerable reductions in interchain separations upon pressurization. These compounds therefore offer unique possibilities for studying the evolution of intrinsic electronic structures in quasi-1D magnetic systems. Here we present a systematic investigation of Ba9Fe3Te15, in which the interchain separations between trimerized 1D chains (~10.2 Å) can be effectively modulated by external high pressure. The crystal structure especially along the 1D chains exhibits an abnormal expansion at ~5GPa, which accompanies trimerization entangled anomalous mixed-high-low spin transition. An insulator-metal transition has been observed under high pressure as a result of charge-transfer gap closing. Pressure-induced superconductivity emerges at 26GPa, where the charge-transfer gap fully closes, 3D electronic configuration forms and local spin fully collapses.

Zhang, Jun↗
DOE OSTI2024

Review of nonflow estimation methods and uncertainties in relativistic heavy-ion collisions

Abstract Collective anisotropic flow, where particles are correlated over the entire event, is a prominent phenomenon in relativistic heavy-ion collisions and is sensitive to the properties of the matter created in those collisions. It is often measured by two- and multi-particle correlations and is therefore contaminated by nonflow, those genuine few-body correlations unrelated to the global event-wise correlations. Many methods have been devised to estimate nonflow contamination with various degrees of successes and difficulties. Here, we review those methods pedagogically, discussing the pros and cons of each method, and give examples of ballpark estimate of nonflow contamination and associated uncertainties in relativistic heavy-ion collisions. We hope such a review of the various nonflow estimation methods in a single place would prove helpful to future researches.

Feng, Yicheng (ORCID:0000000169010870)↗
DOE OSTI2024

Scalable measurement error mitigation via iterative bayesian unfolding

Measurement errors are a significant obstacle to achieving scalable quantum computation. To counteract systematic readout errors, researchers have developed postprocessing techniques known as measurement error mitigation methods. However, these methods face a tradeoff between scalability and returning nonnegative probabilities. In this paper, we present a solution to overcome this challenge. Our approach focuses on iterative Bayesian unfolding, a standard mitigation technique used in high-energy physics experiments, and implements it in a scalable way. We demonstrate our method on experimental Greenberger-Horne-Zeilinger state preparation on up to 127 qubits and on the Bernstein-Vazirani algorithm on up to 26 qubits. Compared to state-of-the-art methods (such as M3), our implementation guarantees valid probability distributions, returns comparable or better-mitigated results, and does so without a noticeable time and memory overhead. Published by the American Physical Society 2024

Pokharel, Bibek (ORCID:0000000342841353)↗
DOE OSTI2024

Analysis of heat transfer and AuNPs-mediated photo-thermal inactivation of E. coli at varying laser powers using single-phase CFD modeling

In the wake of the COVID-19 pandemics, the demand for innovative and effective methods of bacterial inactivation has become a critical area of research, providing the impetus for this study. The purpose of this research is to analyze the AuNPs-mediated photothermal inactivation of E. coli. Gold nanoparticles irradiated by laser represent a promising technique for combating bacterial infection that combines high-tech and scientific progress. The intermediate aim of the work was to present the calibration of the model with respect to the gold nanorods experiment. The purpose of this work is to study the effect of initial concentration of E. coli bacteria, the design of the chamber and the laser power on heat transfer and inactivation of E. coli bacteria. Using the CFD simulation, the work combines three main concepts. 1. The conversion of laser light to heat has been described by a combination of three distinctive approximations: a- Discrete particle integration to take into account every nanoparticle within the system, b- Rayleigh-Drude approximation to determine the scattering and extinction coefficients and c- Lambert–Beer–Bourger law to describe the decrease in laser intensity across the AuNPs. 2. The contribution of the presence of E. coli bacteria to the thermal and fluid-dynamic fields in the microdevice was modeled by single-phase approach by determining the effective thermophysical properties of the water-bacteria mixture. 3. An approach based on a temperature threshold attained at which bacteria will be inactivated, has been used to predict bacterial response to temperature increases. The comparison of the thermal fields and temporal temperature changes obtained by the CFD simulation with those obtained experimentally confirms the accuracy of the light-heat conversion model derived from the aforementioned approximations. The results show a linear relationship between maximum temperature and variation in laser power over the range studied, which is in line with previous experimental results. It was also found that the temperature inside the microchamber can exceed 55 °C only when a laser power higher than 0.8 W is used, so bacterial inactivation begins. The experimental data allows to determinate the concentration of nanoparticles. This parameter is introduced into the mathematical model obtaining the same number of AuNPs. However, this assumption introduces a certain simplification, as in the mathematical model the distribution of nanoparticles is uniform. This work is directly connected to the use of gold nanoparticles for energy conversion, as well as the field of bacterial inactivation in microfluidic systems such as lab-on-a-chip. Presented mathematical and numerical models can be extended to the entire spectrum of wavelengths with particular use of white light in the inactivation of bacteria. This work represents a significant advancement in the field, as to the best of the authors’ knowledge, it is the first to employ a single-phase computational fluid dynamics (CFD) approach specifically combined with the thermal inactivation of bacteria. Moreover, this research pioneers the use of a numerical simulation to analyze the temperature threshold of photothermal inactivation of E. coli mediated by gold nanorods (AuNRs). The integration of these methodologies offers a new perspective on optimizing bacterial inactivation techniques, making this study a valuable contribution to both computational modeling and biomedical applications.

36 MATERIALS SCIENCE↗
DOE OSTI2025

HALLUFIELD: DETECTING LLM HALLUCINATIONS VIA FIELD-THEORETIC MODELING

A research-focused Python package that implements our hallucination-detection method for large language models(LLMs). The code computes stability signals from LLM predictions across various hyperparameters sweeps and combines free-energy/entropy–style metrics to flag likely hallucinations, with tunable thresholds for batch scoring. The repo includes evaluation scripts, config files, and example notebooks to reproduce benchmark results and ablations; it depends on standard open-source libraries (PyTorch, Hugging Face) and runs on CPU/GPU. The repository examples only use public models/datasets only.

Bhattarai, Manish [Los Alamos National Labs]↗
DOE OSTI2021

Purification of native histidine-rich protein 2 (nHRP2) from Plasmodium falciparum culture supernatant, infected RBCs, and parasite lysate

Background: Despite the widespread use of histidine-rich protein 2 (HRP2)-based rapid diagnostic tests (RDTs), purified native HRP2 antigen is not standardly used in research applications or assessment of RDTs used in the field. Methods: This report describes the purification of native HRP2 (nHRP2) from the HB3 Plasmodium falciparum culture strain. As this culture strain lacks pfhrp3 from its genome, it is an excellent source of HRP2 protein only and does not produce the closely-related HRP3. The nHRP2 protein was isolated from culture supernatant, infected red blood cells (iRBCs), and whole parasite lysate using nickel-metal chelate chromatography. Biochemical characterization of nHRP2 from HB3 culture was conducted by SDS-PAGE and western blotting, and nHRP2 was assayed by RDT, ELISA, and bead-based immunoassay. Results: Purified nHRP2 was identified by SDS-PAGE and western blot as a –60 kDa protein that bound anti-HRP-2 monoclonal antibodies. Mouse anti-HRP2 monoclonal antibody was found to produce high optical density readings between dilutions of 1:100 and 1:3,200 by ELISA with assay signal observed up to a 1:200,000 dilution. nHRP2 yield from HB3 culture by bead-based immunoassay revealed that both culture supernatant and iRBC lysate were practical sources of large quantities of this antigen, producing a total yield of 292.4 µg of nHRP2 from two pooled culture preparations. Assessment of nHRP2 recognition by RDTs revealed that Carestart Pf HRP2 and HRP2/pLDH RDTs detected purified nHRP2 when applied at concentrations between 20.6 and 2060 ng/mL, performing within a log-fold dilution of commercially-available recombinant HRP2. The band intensity observed for the nHRP2 dilutions was equivalent to that observed for P. falciparum culture strain dilutions of 3D7 and US06 F Nigeria XII between 12.5 and 1000 parasites/µL. Conclusions: Purified nHRP2 could be a valuable reagent for laboratory applications as well as assessment of new and existing RDTs prior to their use in clinical settings. These results establish that it is possible to extract microgram quantities of the native HRP2 antigen from HB3 culture and that this purified protein is well recognized by existing monoclonal antibody lines and RDTs.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

An initial investigation of accuracy required for the identification of small molecules in complex samples using quantum chemical calculated NMR chemical shifts

The majority of primary and secondary metabolites in nature have yet to be identified, representing a major challenge for metabolomics studies that currently require reference libraries from analyses of authentic compounds. Using currently available analytical methods, complete chemical characterization of metabolomes is infeasible for both technical and economic reasons. For example, unambiguous identification of metabolites is limited by the availability of authentic chemical standards, which, for the majority of molecules, do not exist. Computationally predicted or calculated data are a viable solution to expand the currently limited metabolite reference libraries, if such methods are shown to be sufficiently accurate. For example, determining nuclear magnetic resonance (NMR) spectroscopy spectra in silico has shown promise in the identification and delineation of metabolite structures. Many researchers have been taking advantage of density functional theory (DFT), a computationally inexpensive yet reputable method for the prediction of carbon and proton NMR spectra of metabolites. However, such methods are expected to have some error in predicted 13 >C and 1 H NMR spectra with respect to experimentally measured values. This leads us to the question–what accuracy is required in predicted 13 C and 1 H NMR chemical shifts for confident metabolite identification? Using the set of 11,716 small molecules found in the Human Metabolome Database (HMDB), we simulated both experimental and theoretical NMR chemical shift databases. We investigated the level of accuracy required for identification of metabolites in simulated pure and impure samples by matching predicted chemical shifts to experimental data. We found 90% or more of molecules in simulated pure samples can be successfully identified when errors of 1 H and 13 C chemical shifts in water are below 0.6 and 7.1 ppm, respectively, and below 0.5 and 4.6 ppm in chloroform solvation, respectively. In simulated complex mixtures, as the complexity of the mixture increased, greater accuracy of the calculated chemical shifts was required, as expected. However, if the number of molecules in the mixture is known, e.g., when NMR is combined with MS and sample complexity is low, the likelihood of confident molecular identification increased by 90%.

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

What's in a Name? Developing a Standardized Taxonomy for HVAC System Faults

Faults occurring in heating, ventilation and air-conditioning (HVAC) systems have significantly negative impacts on building energy consumption, occupant comfort, and indoor air quality. In the past thirty years, extensive research has been conducted on fault detection and diagnostics (FDD) methods, and there are now dozens of commercially available FDD software tools. Growing adoption of FDD tools has the potential to generate a massive and useful data set on fault characteristics. However, the lack of a unifying taxonomy is a significant barrier to efficient analysis and evaluation of FDD outputs. Therefore, there is a strong need to develop a robust taxonomy which can better represent and interpret FDD output data. This paper documents the development of a unifying taxonomy for HVAC system faults in commercial buildings, with initial focus on air handling units, variable air volume terminal units, and roof top unit systems. The developed fault taxonomy employs both a physical hierarchy of HVAC equipment and a cause-effect relationship model as tools to better understand and support root cause analysis for HVAC faults. A variable air volume terminal unit is used as an example to demonstrate the application of the developed fault taxonomy. The taxonomy has short-term application in a major U.S. study on fault prevalence, and promises longer term benefits to FDD software developers and building operators by creating a foundation for improved approaches to identifying and resolving HVAC faults.

Chen, Yimin↗
DOE OSTI2022

Deep learning-based spatio-temporal estimate of greenhouse gas emissions using satellite data

Accurate estimation of greenhouse gases (GHGs) emissions is very important for developing mitigation strategies to climate change by controlling and reducing GHG emissions. This project aims to develop multiple deep learning approaches to estimate anthropogenic greenhouse gas emissions using multiple types of satellite data. NO2 concentration is chosen as an example of GHGs to evaluate the proposed approach. Two sentinel satellites (sentinel-2 and sentinel-5P) provide multiscale observations of GHGs from 10-60m resolution (sentinel-2) to ~kilometer scale resolution (sentinel-5P). Among multiple deep learning (DL) architectures evaluated, two best DL models demonstrate that key features of spatio-temporal satellite data and additional information (e.g., observation times and/or coordinates of ground stations) can be extracted using convolutional neural networks and feed forward neural networks, respectively. In particular, irregular time series data from different NO 2 observation stations limit the flexibility of long short-term memory architecture, requiring zero-padding to fill in missing data. However, deep neural operator (DNO) architecture can stack time-series data as input, providing the flexibility of input structure without zero-padding. As a result, the DNO outperformed other deep learning architectures to account for time-varying features. Overall, temporal patterns with smooth seasonal variations were predicted very well, while frequent fluctuation patterns were not predicted well. In addition, uncertainty quantification using conformal inference method is performed to account for prediction ranges. Overall, this research will lead to a new groundwork for estimating greenhouse gas concentrations using multiple satellite data to enhance our capability of tracking the cause of climate change and developing mitigation strategies.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Electronic Structure, Bonding and Reactivity in f-Element Chemistry (Final Technical Report)

The proposed research aims to further develop and apply advanced inorganic spectroscopic methods to f-element systems, including well-defined compounds, in-situ generated complexes (including potentially complex mixtures) and transient species. Our long term goal is to develop and apply advanced inorganic spectroscopic methods in f-element chemistry in order to advance our understanding of electronic structure, bonding and reactivity in f-element systems up to the level currently available for d-block systems. A further goal of our program is the training and development of graduate students in the application of advanced inorganic spectroscopic methods for the study of f-element systems, which is critically underdeveloped at US academic institutions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗