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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 613 records · Page 34
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Dynamic fracture of glass fiber-reinforced ductile polymer matrix composites and loading rate effect

Here, the dynamic fracture of S-2 glass fiber-reinforced polymer matrix composites (FRPMCs) was investigated in this study. The matrix ductility was improved by a recently developed network topology modification technique via mixing partially reacted substructures (mPRS). The composite material was manufactured and characterized by micro-CT scanning and scanning electron microscopy (SEM). Dynamic single-edge notched bending (d-SENB) experiments were performed on the composites by using a modified split-Hopkinson pressure bar. Each specimen’s fracture process was visualized by ultrafast X-ray imaging. Such in-situ radiography enabled identifying the damage initiation below 50-micron scale and inspecting its propagation through the internal structures of opaque composites, thereby accurately quantifying the composites’ mechanical properties. Furthermore, the identical d-SENB experiments were designed and the digital image correlation (DIC) was employed to monitor the stress wave propagation on the composite specimens. The force and deflection measurements were modified and correlated to the physical damage processes. Besides, quasi-static SENB experiments were conducted to identify the loading rate effects on the composites’ fracture behaviors. The force and deflection history, bending stiffness, energy dissipation, and fracture toughness at different loading rates were quantified and compared. Finally, post-fracture analysis by micro-CT scanning and SEM provided physical observations on the variation of the fracture morphology by different loading rates.

42 ENGINEERING↗
DOE OSTI2026

Performance assessment of 3D printed multi-material energy absorber for automotive bumper: pedestrian lower extremity protection

Designing an energy absorber for automotive bumpers involves balancing low-speed and high-speed impacts to ensure safety, reduce repair costs, and meet regulatory standards. Here, this study explores a novel design using multi-material 3D printing and structural optimization to fabricate a lightweight and cost-efficient energy absorber. The design effectively dissipates energy in low-speed collisions and minimizes force transmission in high-speed pedestrain impacts, helping to meet both safety and performance requirements. The energy absorber design combines 20% carbon fiber-reinforced acrylonitrile butadiene styrene (CF-ABS) and thermoplastic polyurethane (TPU) for optimal stiffness and flexibility. It uses 3D-printed lattice structures optimized through finite element simulations to help meet both low-speed and high-speed impact requirements. Full-scale energy absorbers were 3D-printed using optimized CF-ABS/TPU blends and tested under high-speed impact using the Flexible Pedestrian Legform Impactor (Flex-PLI). For fair comparison, a baseline bumper with a traditional triangular lattice structure, also 3D-printed from the same CF-ABS/TPU materials, was similarly tested. Interestingly, both the optimized and baseline 3D-printed energy absorbers showed nearly identical performance, successfully meeting injury limits. Their performances were also benchmarked against an injection-molded energy absorber. While both 3D-printed and injection-molded designs met injury limits, the 3D-printed absorber exhibited a higher tibia bending moment, indicating an opportunity for further optimization. A Techno-Economic Analysis compared the costs of producing energy absorbers using traditional manufacturing and 3D printing. The analysis highlighted that 3D printing offers cost benefits for low to medium production volumes, with the total cost per energy absorber at ∼ $\$$74, compared to traditional methods that become economical beyond 2000 units.

Additive manufacturing↗
DOE OSTI2021

Electrochemical stability, physical, and electronic properties of thermally pre-formed oxide compared to artificially sputtered oxide on Fe thin films in aqueous chloride

Here the electrochemical stability and corrosion behavior of the thermally pre-oxidized and sputter deposited variant of an oxide single-crystalline Fe thin film were compared. Thermal oxides formed Fe 2 O 3 over an inner layer of Fe 3 O 4 while the sputtered oxides were found to have grown Fe 3 O 4 instead of the target stoichiometry of Fe 2 O 3 . Oxide films of both types were stable in pH=9.3 borate buffer solution, however, were altered in pH=9.3, 0.1 M NaCl solution in the case of thermally pre-oxidized Fe thin film. The stability of the oxide differed in pH=4, 0.1 M NaCl where sputtered Fe films were more stable against acidic chemical dissolution than the thermally formed Fe oxide film. These differences were traced by AC/DC electrochemical analysis including electronic defect densities, and molecular identity characterized by ex-situ X-ray photoelectron spectroscopy.

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

Aggregate attack surface management for network discovery of operational technology

Interconnectivity has become a substratum of technology as the benefits of data-driven functionality are being realized in nearly all industries. Increased connectivity of Operational Technology (OT) exacerbates cyber risks because Industrial Control Systems (ICS) are becoming exposed to the Internet. These exposures are often done inadvertently through misconfigurations as additional network devices come online. Attack surface management (ASM) platforms can be used to identify vulnerabilities by performing external network discovery over the Internet using web spiders. These web spiders enable big data analytics of Internet of Things (IoT) devices as identifiable information of Internet-exposed equipment are archived in searchable databases that are made publicly available. There are a multitude of ASM service providers on the market. Here, this study was conducted to evaluate several commonly known tools to determine the aggregate attack surface of control systems. Queries were crafted by targeting commonly known manufacturers and communication protocols found in OT networks. Identified devices were that categorized based on technology types. Each query was replicated between several tools to target identical ICS equipment. Findings in this paper suggested a significant variance in the exposures discovered by each tool, but unique contributions were identified for each tool when a merged attack surface was derived. Therefore, all tools should be used in aggregate.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Calculating the divided differences of the exponential function by addition and removal of inputs

We introduce a method for calculating the divided differences of the exponential function by means of addition and removal of items from the input list to the function. Our technique exploits a new identity related to divided differences recently derived by F. Zivcovich. We show that upon adding an item to or removing an item from the input list of an already evaluated exponential, the re-evaluation of the divided differences can be done with only O(sn) floating point operations and O(sn) bytes of memory, where [z 0 ,...,z n ] are the inputs and s ∝ max i,j |z i – z j |. We demonstrate our algorithm’s ability to deal with input lists that are orders-of-magnitude longer than the maximal capacities of the current state-of-the-art. Here, we discuss in detail one practical application of our method: the efficient calculation of weights in the off-diagonal series expansion quantum Monte Carlo algorithm.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Materials Fingerprinting Classification

Significant progress in many classes of materials could be made with the availability of experimentally-derived large datasets composed of atomic identities and three-dimensional coordinates. Methods for visualizing the local atomic structure, such as atom probe tomography (APT), which routinely generate datasets comprised of millions of atoms, are an important step in realizing this goal. However, state-of-the-art APT instruments generate noisy and sparse datasets that provide information about elemental type, but obscure atomic structures, thus limiting their subsequent value for materials discovery. The application of a materials fingerprinting process, a machine learning algorithm coupled with topological data analysis, provides an avenue by which here-to-fore unprecedented structural information can be extracted from an APT dataset. As a proof of concept, the material fingerprint is applied to high-entropy alloy APT datasets containing body-centered cubic (BCC) and face-centered cubic (FCC) crystal structures. A local atomic configuration centered on an arbitrary atom is assigned a topological descriptor, with which it can be characterized as a BCC or FCC lattice with near perfect accuracy, despite the inherent noise in the dataset. Finally, this successful identification of a fingerprint is a crucial first step in the development of algorithms which can extract more nuanced information, such as chemical ordering, from existing datasets of complex materials.

36 MATERIALS SCIENCE↗
DOE OSTI2021

EnZymClass: Substrate specificity prediction tool of plant acyl-ACP thioesterases based on ensemble learning

Characterizing the functional properties of plant acyl-ACP thioesterases (TEs), a key enzyme class used in the production of renewable oleochemicals in microbial hosts, experimentally, can be an expensive and time consuming process since it requires manual screening of thousands of candidates in a database. Using amino acid sequence to computationally predict an enzyme’s function might accelerate this process; however obtaining the necessary amount of information on previously characterized enzymes and their respective sequences required by standard Machine Learning (ML) based approaches to accurately infer sequence-function relationships can be prohibitive, especially with a low-throughput testing cycle. Experimental noise, unbalanced dataset where high sequence similarity does not always imply identical functional properties will further prevent robust prediction performance. Herein we present a ML method, Ensemble method for enZyme Classification (EnZymClass), that is specifically designed to address these issues. We used EnZymClass to classify TEs into short, long and mixed free fatty acid substrate specificity categories. While general guidelines for inferring substrate specificity have been proposed before, prediction of chain-length preference from primary sequence has remained elusive for plant acyl-ACP TEs. By applying EnZymClass to a subset of TEs in the ThYme database, we identified two medium chain TEs, ClFatB3 and CwFatB2, with previously uncharacterized activity in E. coli fatty acid production hosts.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Methane liquefaction with an active magnetic regenerative refrigerator

This manuscript reports liquefaction of methane using an active magnetic regenerative refrigerator (AMRR). The AMRR used to cool from 285 K to 135 K has two identical regenerators fabricated with adjacent layers of four ferromagnetic refrigerants, each with sequentially lower Curie temperatures and lesser masses from hot to cold temperatures. The dual multilayer regenerators were assembled in opposition with a single cold region between them. For this set of experiments equal mass of helium gas was pumped through all layers of the regenerators during the two flow steps of the four-step AMR cycle. After successfully reaching 135 K, a compact condensing coil-fin tube heat exchanger (HEX) with a small storage vessel was mounted in the cold region of the dual regenerator assembly to be cooled during the hot-to-cold helium flow from alternatively demagnetized regenerators. The cold HEX was used to cool and liquefy a process stream of methane gas supplied at 295 K and different pressures in different runs. Further, by measuring time to liquefy a known volume of methane at three different pressures, cooling powers of the AMRR as a function of temperature were determined. The resultant data were compared to those predicted by the AMRR performance model. Conclusions from these experiments and suggestions for future work are presented.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

Enabling end-to-end secure federated learning in biomedical research on heterogeneous computing environments with APPFLx

Facilitating large-scale, cross-institutional collaboration in biomedical machine learning (ML) projects requires a trustworthy and resilient federated learning (FL) environment to ensure that sensitive information such as protected health information is kept confidential. Specifically designed for this purpose, this work introduces APPFLx - a low-code, easy-to-use FL framework that enables easy setup, configuration, and running of FL experiments. APPFLx removes administrative boundaries of research organizations and healthcare systems while providing secure end-to-end communication, privacy-preserving functionality, and identity management. Furthermore, it is completely agnostic to the underlying computational infrastructure of participating clients, allowing an instantaneous deployment of this framework into existing computing infrastructures. Experimentally, the utility of APPFLx is demonstrated in two case studies: (1) predicting participant age from electrocardiogram (ECG) waveforms, and (2) detecting COVID-19 disease from chest radiographs. Here, ML models were securely trained across heterogeneous computing resources, including a combination of on-premise high-performance computing and cloud computing facilities. By securely unlocking data from multiple sources for training without directly sharing it, these FL models enhance generalizability and performance compared to centralized training models while ensuring data remains protected. In conclusion, APPFLx demonstrated itself as an easy-to-use framework for accelerating biomedical studies across organizations and healthcare systems on large datasets while maintaining the protection of private medical data.

Biomedical Research↗
DOE OSTI2021

A census of the lung: CellCards from LungMAP

The human lung plays vital roles in respiration, host defense, and basic physiology. Recent technological advancements such as single-cell RNA sequencing and genetic lineage tracing have revealed novel cell types and enriched functional properties of existing cell types in lung. The time has come to take a new census. Initiated by members of the NHLBI-funded LungMAP Consortium and aided by experts in the lung biology community, we synthesized current data into a comprehensive and practical cellular census of the lung. Identities of cell types in the normal lung are captured in individual cell cards with delineation of function, markers, developmental lineages, heterogeneity, regenerative potential, disease links, and key experimental tools. This publication will serve as the starting point of a live, up-to-date guide for lung research at https://www.lungmap.net/cell-cards/. We hope that Lung CellCards will promote the community-wide effort to establish, maintain, and restore respiratory health.

COVID↗
DOE OSTI2026

Comparison of structurally diverse simulation models for prediction of epidemic outcomes caused by a long-distance dispersed pathogen

Long-distance dispersal (LDD) pathogens pose substantial challenges for epidemic control due to their ability to generate new infection foci at great distances. While various modeling approaches have been developed to understand and manage such outbreaks, little work has compared how models of different structures behave under shared conditions. Here, in this study, we compare four structurally distinct epidemiological models — EPIMUL, GEMF, PoPS, and Warwick — each adapted to simulate the spread of wheat stripe rust (WSR), a wind-dispersed LDD pathogen, under identical epidemiological parameters and dispersal kernel. Using data from a controlled field experiment, we evaluate the ability of each model to replicate disease prevalence under nine intervention scenarios that vary in timing and culling area. While the models differ substantially in design — ranging from spatial grid-based to network-based and raster-based frameworks — the shared dispersal kernel allowed for close alignment in their predictions. All models accurately captured general epidemic trends, particularly the strong effect of early intervention on disease suppression. We qualitatively compared their behavioral responses across scenarios and also evaluated an ensemble prediction by averaging across model outputs. Our findings highlight how integrating shared epidemiological components into distinct modeling frameworks can improve consistency and accuracy, while reinforcing the importance of early culling in managing LDD pathogen outbreaks.

Dispersal kernel↗
DOE OSTI2025

Design, synthesis, evaluation and X-ray structural studies of potent HIV-1 protease inhibitors containing substituted oxaspirocyclic carbamates as the P2 ligands

Here, we report here the design, synthesis and evaluation of a series of HIV-1 protease inhibitors that incorporate substituted oxaspirocyclic carbamate derivatives to serve as the P2 ligands. Various substituted ligand derivatives were synthesized in a racemic manner, using a tandem Prins/pinacol reaction as the key reaction. This reaction sets the relative stereochemistry of the oxaspirocyclic template in a highly diastereoselective manner. Reaction of the resulting ketone with enantiopure (S)-tert-butyl sulfinamide provided a convenient pathway to resolve the oxaspirocyclic ketone derivatives. The absolute stereochemical identity was determined by X-ray crystallography. The structure-activity studies demonstrate the effect of the stereochemistry of the oxaspirocyclic ring systems as well as the substitution effect on the aromatic ring. Several inhibitors exhibited potent HIV-1 protease inhibitory activity. One of these inhibitors displayed subnanomolar HIV-1 protease affinity and also exhibited potent antiviral activity. A high-resolution X-ray crystal structure of this inhibitor-bound HIV-1 protease show that the oxaspirocyclic P2 ligand forms an unconventional C–H⋯O bond with the backbone carboxyl group of Gly48’ and an interesting N–H … π interaction with the aromatic ring in the S2 subsite of HIV-1 protease active site.

Antiviral↗
DOE OSTI2020

Enabling fast charging of lithium-ion batteries through secondary- /dual- pore network: Part I - Analytical diffusion model

Battery performance is strongly correlated with electrode microstructural properties. Enabling fast charging of lithium-ion batteries requires improved through-plane ionic diffusion that can be achieved through, among other strategies, structured electrodes with a secondary- or dual-pore network (SPN). In this work, an analytical model investigates the impact of such an SPN on ionic diffusion with a composite electrode, considering various pore-channel geometries and comparing to standard electrodes with identical gravimetric- and volumetric-specific theoretical capacities. Relevant SPN design parameters and tortuosity coefficients are identified according to three optimization objectives that aim to balance the improved overall through-plane diffusion, thanks to the coarse aligned channels, and degraded in-plane diffusion because of the porous matrix densification required to maintain gravimetric- and volumetric-specific theoretical capacities. The model indicates that a relatively low amount of SPN is required and that electrodes with high through-plane tortuosity and low in-plane tortuosity benefit most from such architecture.

25 ENERGY STORAGE↗
DOE OSTI2022

Electrochemical noise studies on localized corrosion of Ni and Ni-20Cr in molten ZnCl 2

The electrochemical noise (ECN) technique was employed to study corrosion of two model systems, i.e. pure Ni and a binary Ni-20 wt%Cr (Ni20Cr) alloy in molten ZnCl 2 at 623 K. Here we measured ECN transients in current and open-circuit potential from two nominally identical Ni-Ni and Ni20Cr-Ni20Cr electrodes and one galvanic Ni-Ni20Cr electrode pair. The behavior of ECN is quite distinct among the three-electrode systems, and it is correlated with the various microscopic observations of micro- and nano-scale morphological features. Based on the ECN study coupled with the microstructural analysis, the origins of ECN in the molten salt environment as well as the mechanisms of localized corrosion in the three systems are discussed.

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

Enhancing the activity of Fe-N-C oxygen reduction reaction electrocatalysts by high-throughput exploration of synthesis parameters

Here, the most active class of platinum group metal-free (PGM-free) oxygen reduction reaction (ORR) electrocatalysts in acidic electrolytes are those synthesized by heat treatment of iron, carbon, nitrogen precursors (Fe-N-C). Due to the large number of possible precursor compounds, a small fraction of the synthesis variable space has been explored. Correlation of synthesis variables with Fe speciation and ORR activity has been limited. In this work, an automation platform and a multi-port ball-milling were utilized to evaluate the effects of synthesis variables, such as identity of iron precursor, iron loading, and carbon and nitrogen sources on the ORR activity of iron -nitrogen-carbon catalysts in acidic electrolyte. The ORR activity is correlated with catalyst Fe speciation determined using Fe K-edge X-ray absorption spectroscopy (XAFS).

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2023

Iron redox behavior and oxygen reduction activity of Fe-N-C electrocatalysts in different electrolytes

The iron redox behavior and oxygen reduction reaction (ORR) activity of Fe-N-C ORR electrocatalysts synthesized by a variety of techniques were investigated as a function of the identity of the electrolyte anion (bisulfate/sulfate or perchlorate) at a constant pH. In situ X-ray absorption spectroscopy data support the assignment of the redox peaks in the voltammograms to the Fe 3+ /Fe 2+ redox couple. It was found that for a given Fe-N-C catalyst, there is a correlation between the Fe redox couple peak potential and the ORR activity in perchloric acid electrolyte, but not in sulfuric acid electrolyte. While a higher Fe redox couple potential (≥ 110 mV higher) was observed in perchloric acid electrolyte, a higher ORR activity was obtained in sulfuric acid electrolyte. Here, the higher ORR activity observed in sulfuric acid than perchloric acid was correlated with the higher peak current and larger faradaic charge for the Fe redox couple. A study of the Fe redox behavior using a cavity microelectrode, eliminating the impact of ionomer, showed that the interaction of H 2 SO 4 with Fe-N-C is stronger than that of HClO 4 and that Fe redox in both electrolytes is a reversible surface electrochemical reaction.

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

Geometrical and chemical effects on the electrochemistry of single-wall carbon nanotube (SWCNT) network electrodes

Single-wall carbon nanotube (SWCNT) network is a promising electrode material for bio detection. Unfortunately, the associations between their physical as well as chemical properties and observed electrochemical performance are not known. This hinders any systematic optimization of the network properties towards specific analytes. Here we present a consistent physicochemical and electrochemical characterization of differently treated SWCNT networks. The results unambiguously show that (i) even if the electrochemical properties of different electrodes are practically identical when assessed by surface insensitive outer sphere redox (OSR) probes their behavior with inner sphere redox (ISR) probes can be drastically different. Further, (ii) the choice of the modification method (structural, chemical, electrochemical) heavily depends on nature of the target analyte, which are typically ISR probes. Although, (iii) chemical changes in the carbon phase appeared to be minor, effects of different treatments on oxidation states of Fe appeared to have a strong effect on the electrochemical performance of the networks in the case of ISR probes.

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

High‑performance cold‑climate heat pump using tandem compressors with and without vapor injection: Laboratory investigation and field demonstration

Cold‑climate air‑source heat pumps must maintain heating capacity and stable operation at low ambient temperatures for residential applications. This study adapts tandem single-speed compressors—proven in commercial systems —for residential CCHPs, integrating VI and validating performance across laboratory and extreme field conditions to achieve cold-climate targets. That is to achieve ≥75% of the rated heating capacity at −25.0 °C relative to 8.3 °C and heating coefficient of performance (COP) greater than 4.0 at 8.3 °C. Laboratory testing confirmed that both configurations met these targets while operating within acceptable discharge‑temperature limits. Relative to the non‑VI configuration, the VI system provided up to 15% higher heating capacity and up to 9% higher heating COP under identical conditions, yielding a 5.6–9.1% increase in heating seasonal performance factor (HSPF). Field tests in Ohio and Alaska, with minimum outdoor temperatures of −25.0 °C and −34.0 °C, respectively, showed stable operation, minimal supplemental heating (<3.2%), low defrost penalties, and consistent output. Tandem‑compressor architectures proves promising for cold-climate residential heating.

Hu, Yifeng [ORNL] (ORCID:0000000242875185)↗