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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 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Circularity in polymers: addressing performance and sustainability challenges using dynamic covalent chemistries

The circularity of current and future polymeric materials is a major focus of fundamental and applied research, as undesirable end-of-life outcomes and waste accumulation are global problems that impact our society. The recycling or repurposing of thermoplastics and thermosets is an attractive solution to these issues, yet both options are encumbered by poor property retention upon reuse, along with heterogeneities in common waste streams that limit property optimization. Dynamic covalent chemistry, when applied to polymeric materials, enables the targeted design of reversible bonds that can be tailored to specific reprocessing conditions to help address conventional recycling challenges. In this review, we highlight the key features of several dynamic covalent chemistries that can promote closed-loop recyclability and we discuss recent synthetic progress towards incorporating these chemistries into new polymers and existing commodity plastics. Next, we outline how dynamic covalent bonds and polymer network structure influence thermomechanical properties related to application and recyclability, with a focus on predictive physical models that describe network rearrangement. Finally, we examine the potential economic and environmental impacts of dynamic covalent polymeric materials in closed-loop processing using elements derived from techno-economic analysis and life-cycle assessment, including minimum selling prices and greenhouse gas emissions. Throughout each section, we discuss interdisciplinary obstacles that hinder the widespread adoption of dynamic polymers and present opportunities and new directions toward the realization of circularity in polymeric materials.

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

Effect of network connectivity on behavior of synthetic Broborg hillfort glasses

There is wide industrial interest in developing robust models of long-term (>100 years) glass durability. Archeological glass analogs, glasses of similar composition, and alteration conditions to those being tested for durability can be used to evaluate and inform such models. Two such analog glasses from a 1500-year-old vitrified hillfort near Uppsala, Sweden have previously been identified as potential analogs for low concentration Fe-bearing aluminosilicate nuclear waste glasses. However, open questions remain regarding the melting environment from which these historic glasses were formed and the effect of these conditions on their chemical durability. A key factor to answering the previous melting and durability questions is the redox state of Fe in the starting and final materials. Past work has shown that the melting conditions of a glass-forming melt may influence the redox ratio value (Fe +3 /ΣFe), a measure of a glass's redox state, and both melting conditions and the redox ratio may influence the glass alteration behavior. Synthetic analogs of the hillfort glasses have been produced using either fully oxidized or reduced Fe precursors to address this question. In this study, the melting behavior, glass transition temperature, oxidation state, network structure, and chemical durability of these synthesized glass analogs is presented. Resulting data suggests that the degree of network connectivity as impacted by the oxidation state of iron impacted the behavior of the glass-forming melt but in this case does not affect the chemical durability of the final glass. Glasses with a lower degree of melt connectivity were found to have a lower viscosity, resulting in a lower glass transition temperature and softening temperature, as well as in a lower temperature of foam onset and temperature of foam maximum. This lower degree of network connectivity most likely played a more significant role in accelerating the conversion of batch chemicals into glass than the presence of water vapor in the furnace's atmosphere. Future work will focus on using the results from this work with outcomes from other aspects of this project to evaluate long-term glass alteration models.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Unveiling mesoscopic structures in distorted lamellar phases through deep learning-based small angle neutron scattering analysis

Hypothesis: The formation of distorted lamellar phases, distinguished by their arrangement of crumpled, stacked layers, is frequently accompanied by the disruption of long-range order, leading to the formation of interconnected network structures commonly observed in the sponge phase. Nevertheless, traditional scattering functions grounded in deterministic modeling fall short of fully representing these intricate structural characteristics. Our hypothesis posits that a deep learning method, in conjunction with the generalized leveled wave approach used for describing structural features of distorted lamellar phases, can quantitatively unveil the inherent spatial correlations within these phases. Experiments and Simulations: This report outlines a novel strategy that integrates convolutional neural networks and variational autoencoders, supported by stochastically generated density fluctuations, into a regression analysis framework for extracting structural features of distorted lamellar phases from small angle neutron scattering data. To evaluate the efficacy of our proposed approach, we conducted computational accuracy assessments and applied it to the analysis of experimentally measured small angle neutron scattering spectra of AOT surfactant solutions, a frequently studied lamellar system. Findings: The findings unambiguously demonstrate that deep learning provides a dependable and quantitative approach for investigating the morphology of wide variations of distorted lamellar phases. It is adaptable for deciphering structures from the lamellar to sponge phase including intermediate structures exhibiting fused topological features. In conclusion, this research highlights the effectiveness of deep learning methods in tackling complex issues in the field of soft matter structural analysis and beyond.

36 MATERIALS SCIENCE↗
DOE OSTI2024

The Interplay of Binary and Quantitative Structure on the Stability of Mutualistic Networks

Synopsis Understanding how the structure of biological systems impacts their resilience (broadly defined) is a recurring question across multiple levels of biological organization. In ecology, considerable effort has been devoted to understanding how the structure of interactions between species in ecological networks is linked to different broad resilience outcomes, especially local stability. Still, nearly all of that work has focused on interaction structure in presence-absence terms and has not investigated quantitative structure, i.e., the arrangement of interaction strengths in ecological networks. We investigated how the interplay between binary and quantitative structure impacts stability in mutualistic interaction networks (those in which species interactions are mutually beneficial), using community matrix approaches. We additionally examined the effects of network complexity and within-guild competition for context. In terms of structure, we focused on understanding the stability impacts of nestedness, a structure in which more-specialized species interact with smaller subsets of the same species that more-generalized species interact with. Most mutualistic networks in nature display binary nestedness, which is puzzling because both binary and quantitative nestedness are known to be destabilizing on their own. We found that quantitative network structure has important consequences for local stability. In more-complex networks, binary-nested structures were the most stable configurations, depending on the quantitative structures, but which quantitative structure was stabilizing depended on network complexity and competitive context. As complexity increases and in the absence of within-guild competition, the most stable configurations have a nested binary structure with a complementary (i.e., anti-nested) quantitative structure. In the presence of within-guild competition, however, the most stable networks are those with a nested binary structure and a nested quantitative structure. In other words, the impact of interaction overlap on community persistence is dependent on the competitive context. These results help to explain the prevalence of binary-nested structures in nature and underscore the need for future empirical work on quantitative structure.

Zoology↗
DOE OSTI2026

Measuring the Energy Consumption and Efficiency of Deep Neural Networks: An Empirical Analysis and Design Recommendations

Addressing the "Red-AI" trend of rising energy consumption by large-scale neural networks, this study investigates the measured energy consumption of training various fully connected neural network architectures. We introduce the BUTTER-E dataset, an augmentation to the BUTTER Empirical Deep Learning dataset, containing energy consumption and performance data from 41,129 individual experimental runs spanning 30,582 distinct configurations: 13 datasets, 20 sizes (trainable parameters), 8 "shapes", and 14 depths on both CPUs and GPUs using node-level watt-meters. This dataset reveals the complex relationship between dataset size, network structure, and energy use. Our analysis uncovers a surprising, hardware-mediated non-linear relationship between energy efficiency and network design, challenging the assumption that reducing the number of parameters or FLOPs is the best way to achieve greater energy efficiency. We propose a straightforward and effective energy model that accounts for network size, computing, and memory hierarchy. Highlighting the need for cache-considerate algorithm development, we suggest a codesign approach to energy efficient network, algorithm, and hardware design. This work contributes to the fields of sustainable computing and Green AI, offering practical guidance for creating more energy-efficient neural networks and promoting sustainable AI.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Effect of Networking Density on the Patterning Performance of Molecular Layer Deposited Alucone Electron Beam/EUV Resists

One of the main barriers to continued device scaling in the era of extreme ultraviolet (EUV) lithography is the need for improved photoresist chemistries to address challenges such as poor EUV sensitivity, inadequate etch resistance, and pattern collapse. Metal-organic photoresists are a promising class of materials that can address many of these challenges, and among them, resists deposited via hybrid molecular layer deposition (MLD) have attracted interest for their unique advantages in thickness control, chemical homogeneity, and compatibility with vacuum processing. However, despite many successful demonstrations of patterning, little is known about how the molecular design of hybrid MLD resists affects their lithographic performance. In this work, we study the effect of the network structure, a common feature among all hybrid MLD resists, via a series of aluminum alkoxide ("alucone") negative tone resists with varying networking density. Their patterning mechanism is investigated via electron beam lithography (EBL)─a common proxy for EUV─and compared to their EUV-induced reactions studied via flood exposure and in situ characterization. In conclusion, we show that the resist with the least networking density demonstrates the best sensitivity and resolution, with the ability to resolve dense line/space gratings as small as 14 nm half pitch via EBL.

electron beam lithography↗
DOE OSTI2020

High-density, fail-in-place switches for computer and data networks

A structure for a network switch. The network switch may include a plurality of spine chips arranged on a plurality of spine cards, where one or more spine chips are located on each spine card; and a plurality of leaf chips arranged on a plurality of leaf cards, wherein one or more leaf chips are located on each leaf card, where each spine card is connected to every leaf chip and the plurality of spine chips are surrounded on at least two sides by leaf cards.

42 ENGINEERING↗
DOE OSTI2023

Deconvoluting Structures of Component Plant Biopolymers Using Deuterium Labeled Brassica oleracea Stems

Several Brassica species are cultivated globally for the production of seed oils as food, lubricants, and increasingly biofuels. The stem and leaf residues of these herbaceous dicotyledonous crops constitute another feedstock for biofuels and other products. Plant cell walls are complex, multipolymeric structures that consist primarily of polysaccharides and lignin. Cellulose chains coalesce to form crystalline microfibrils while the amorphous biopolymers, hemicellulose, and lignin form a network structure and fill the interstitial space. Neutron scattering has been used for the structural study of the assembly and deconstruction of the plant cell walls. However, similar neutron sensitivity of the different amorphous biopolymers has made structural association to individual component biopolymers nontrivial and ambiguous. To improve the association of structural features to specific biopolymer components, partial deuteration of the plant cell wall can be employed to increase the difference in the neutron scattering length density between amorphous carbohydrate and lignin plant polymers. Vegetative stems from partially deuterated Brassica oleracea acephala (kale) plants were obtained commercially, and the plant cell wall structures were studied by contrast-variation small-angle neutron scattering (CV-SANS) and Fourier-transform infrared spectroscopy (FTIR). FTIR results indicated that deuterium substitution for hydrogen in the carbohydrates was higher than that in lignin. By combining CV-SANS and FTIR results, the neutron scattering length density (nSLD) of the polysaccharides and lignin was determined to match nSLD of 65%:35% and 48%:52% D 2 O/H 2 O solvent mixtures, respectively. These nSLD values were higher than the nSLD values of polysaccharides and lignin for H 2 O grown biopolymers. The nSLD increase correlates to replacing about 42.5% of hydrogens present as both C–H and O–H groups in cellulose with deuterium atoms, while only 21% for lignin. In conclusion, this study lays the foundation to use partially deuterated plants to deconvolute structural features of the different component biopolymers, especially cellulose, hemicellulose, and lignin, of the plant cell wall without introducing unintended structural modifications due to the pretreatment extraction processes.

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

Inelastic relaxation processes in amorphous sodium silicates

During fracture amorphous oxides exhibit irreversible processes, including inelastic and nonrecoverable relaxation effects in the process zone surrounding the crack tip. Here, classical molecular dynamics simulations were used with a reactive forcefield to evaluate inelastic relaxation processes in five amorphous sodium silicate compositions. Overall, the 20% Na 2 O-SiO 2 (NS20) composition exhibited the most inelastic relaxation, followed by the 15% Na 2 O-SiO 2 (NS15) composition, the 25% Na 2 O-SiO 2 (NS25) composition, and finally the 10% (NS10) and 30% (NS30) Na 2 O-SiO 2 compositions. Coordination analysis of the Na + ions identified that during inelastic relaxation the Na+ ions were increasingly coordinated by nonbridging oxygens (NBOs) for the NS10 and NS15 compositions, which was supported by radial analysis of the O-Na-O bond angles surrounding the crack tip. Across the sodium silicate compositional range, two different inelastic relaxation mechanism were identified based on the amount of bridging oxygens (BOs) and NBOs in the Na+ ion coordination shell. At lower (NS10) and higher (NS30) sodium compositions, the entire structured relaxed toward the crack tip. In contrast at intermediate sodium concentrations (NS20) the Na + ion migrates toward the crack tip separately from the network structure. By developing a fundamental understanding of how modified silica systems respond to static stress fields, we will be able to predict how varying amorphous silicate systems exhibit slow crack growth.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Anomalously strong viscosity behavior in mixed oxy-sulfide Na 4 P 2 S 7-x O x invert glasses

In this paper, the first observations have been made of anomalously strong viscosity behavior and correspondingly strong bulk glass formation and resistance to crystallization in a melt quenched (MQ) prepared series of invert, modifier content greater than the glass former content, mixed oxy-sulfide Na 4 P 2 S 7-x O x , 0 ≤ x ≤ 5, (NaPSO) glasses. We find that these glasses even with 67 mole % modifier, Na 2 S, and only 33 mole% glass former P 2 S 5-x O x , exhibit a calorimetric fragility index, m cal , as small as 29 compared to typical values of ~ 60 to 80 for alkali silicate glasses with > 50 mole% SiO 2 and the theoretical minimum m cal of ~ 15. The m cal values were converted to kinetic viscosity fragility, m vis , values and used with the measured glass transition temperatures, T g s, to produce viscosity curves for the glasses using the Mauro-Yue-Ellison-Gupta-Allan (MYEGA) model. The viscosity of the glass with a minimum m cal value of x = 2.5 was then shown to exhibit a viscosity that was as strong as, and for some compositions stronger than, the viscosity of alkali aluminosilicate glasses which have three dimensional network structures compared to the one dimensional chain structures observed in these MOS invert glasses. Consistent with these small values of fragility indices and therefore predicted higher viscosity above the glass transition temperature, T g , in the supercooled liquid regime, these glasses are also particularly resistant to crystallization. For example, the x = 2.5 glass composition exhibits a crystallization temperature, T C , more than 150K above its T g . We find that the calorimetric fragility of these glasses varies systematically with the extent of network forming bridging oxygens (BOs) in the glasses. The structural change caused by incrementally replacing sulfur with oxygen has been found to have consistent, large effects on the thermal properties.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Hemp fiber reinforced dual dynamic network vitrimer biocomposites with direct incorporation of amino silane

Natural fiber composites are inexpensive and renewable alternatives to traditional fiber reinforced polymers (FRPs). However, existing natural fiber composites primarily rely on petrochemical based thermosetting matrices, which are difficult to recycle due to their stable crosslinked network structures. To address this challenge, it is desirable to develop natural fiber composites with inherently recyclable biobased matrices. In this study, we developed a dual dynamic network vitrimer matrix from hempseed oil and limonene derivatives and demonstrated its application in hemp fiber reinforced composites. To improve the weak interface between hemp fiber and the polymer matrix, a common challenge in the realm of natural fiber composites, we directly incorporated amino silane into the vitrimer matrix. The amino silane participated in the polymer network, increasing the crosslink density and toughening the matrix, as evidenced by a significantly improved impact strength from 3.5 kJ/m 2 to 10.3 kJ/m 2 . Moreover, the incorporation of amino silane resulted in a lower water absorption by 11% in 7-day soaking for the composites, demonstrating improved fiber/matrix interfacial interaction. Furthermore, our biobased vitrimer matrix exhibits both imine and hydroxy-ester dynamic bonds, which enable the recycling of the biocomposite through a mild and cost-effective aminolysis process (100°C, 3h, ambient pressure). The decomposed polymer matrix was successfully reused as a polyol for polyurethane adhesives, and the surface morphology of recovered hemp fibers was analyzed and compared with that of the original fibers. Furthermore, these findings will help broaden the use of vitrimers for practical natural fiber composite applications, raise awareness of the problem of recycling natural fiber composite waste, and shed light on the interfacial challenge of natural fiber reinforced vitrimer composites.

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

B‐Staging and Crosslinking of Polycarbosilane at Room Temperature: Cure Mechanism and Properties

The viscosity of polycarbosilane (PCS) polymers is not advantageous for free forming. Various fillers and heat are used to obtain a formable paste. Due to the low yield stress, structures and preforms tend to slump or resin will flow out, especially during the curing of the polymer. B-staging of PCS allows for a more stable structure from room temperature until the full cure of the allyl groups occurs with heat. Additionally, hydrosilation is an effective means of crosslinking at room temperature and controlling viscosity. A network structure was formed in SMP-10 using silane and a vinyl-based crosslinker to bridge each polymer chain. Pt addition catalysts were added to enhance the increase in viscosity to make a lightly crosslinked gel to aid in thickening and forming and to improve the ceramic yield at 1000°C. In conclusion, this enables several options for controlling rheology and improving the properties of preceramic polymers to avoid slumping during curing.

inorganic polymers↗
DOE OSTI2026

Tuning the Mechanical Properties of Crosslinked Copolymers via Sequence and Solvent‐Selective Swelling for Vat Photopolymerization

Block copolymers (BCPs) offer distinct advantages for vat photopolymerization by enabling mechanically programmable network structures through microphase-separated morphologies that can be kinetically trapped during curing, yielding properties unattainable in homogeneous resins. However, the respective roles of repeat-unit sequence and solvent environment, together with their interplay in directing network formation and mechanical performance, remain unclear. Here, we synthesize a series of CO 2 -based polycarbonate copolymers comprising a crosslinkable glassy poly(vinyl cyclohexene carbonate) (PVCHC, A block) and a non-crosslinkable soft poly(propylene carbonate) (PPC, B block). The polymer sequence is systematically varied (ABA, BAB, and statistical), and solvent choice controls block-selective swelling to jointly control gelation behavior, microphase morphology, and mechanical response through changes in the accessibility and local environment of photocrosslinkable vinyl groups during network formation, as revealed by photorheology and small angle x-ray scattering. By tuning polymer sequence and curing solvent, we transform nominally identical formulations from brittle to highly ductile materials, achieving a three-orders-of-magnitude range in toughness (0.003 to 9.1 MJ m −3 ). These results establish clear structure–processing–property relationships and identify polymer sequence and selective solvation as powerful strategies for programming both printability and performance of block copolymer resins for additive manufacturing.

additive manufacturing↗
DOE OSTI2021

A TTL-based Approach for Content Placement in Edge Networks

Edge networks are promising to provide better services to users by provisioning computing and storage resources at the edge of networks. However, due to the uncertainty and diversity of user interests, content popularity, distributed network structure, cache sizes, it is challenging to decide where to place the content, and how long it should be cached. In this paper, we study the utility optimization of content placement at edge networks through timer-based (TTL) policies. We propose provably optimal distributed algorithms that operate at each network cache to maximize the overall network utility. Our TTL-based optimization model provides theoretical answers to how long each content must be cached, and where it should be placed in the edge network. Extensive evaluations show that our algorithm significantly outperforms path replication with conventional caching algorithms over some network topologies.

Panigrahy, Nitish K.↗
DOE OSTI2023

Sub-nanosecond RF synchronization for MIMO software defined radio sensor networks

This disclosure presents distributed and decentralized synchronization for wireless transceivers. The disclosed system, device, and method achieve sub-nanosecond synchronization using low-cost commercial off the shelf software defined radios. By providing a decentralized mechanism that does not rely on a hierarchical master-slave structure, networks constructed as disclosed are robust to sensor drop-out in contested or harsh environments. Such networks may be used to create phased array radars and communication systems without requiring wired connections to distribute a common clock or local oscillator reference.

Moghaddam, Mahta↗
DOE OSTI2020

International network of nuclear structure and decay data evaluators

Compilation, evaluation and dissemination of nuclear data are arduous tasks that rely on contributions from experts in both the basic and applied sciences communities whose efforts are coordinated by the International Atomic Energy Agency (IAEA). The Evaluated Nuclear Structure Data File (ENSDF) includes the most extensive and comprehensive set of nuclear structure and decay data evaluations performed by the international network of Nuclear Structure and Decay Data evaluators (NSDD) under the auspices of the IAEA. In this report we describe the recent NSDD activities supported by the IAEA and provide some future perspectives.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2020

A New Approach to Distributed Hypothesis Testing and Non-Bayesian Learning: Improved Learning Rate and Byzantine-Resilience

Here, we study a setting where a group of agents, each receiving partially informative private signals, seek to collaboratively learn the true underlying state of the world (from a finite set of hypotheses) that generates their joint observation profiles. To solve this problem, we propose a distributed learning rule that differs fundamentally from existing approaches, in that it does not employ any form of “belief-averaging”. Instead, agents update their beliefs based on a min-rule. Under standard assumptions on the observation model and the network structure, we establish that each agent learns the truth asymptotically almost surely. As our main contribution, we prove that with probability 1, each false hypothesis is ruled out by every agent exponentially fast, at a network-independent rate that is strictly larger than existing rates. We then develop a computationally-efficient variant of our learning rule that is provably resilient to agents who do not behave as expected (as represented by a Byzantine adversary model) and deliberately try to spread misinformation.

42 ENGINEERING↗
DOE OSTI2022

Structural heterogeneity in non-crystalline Te x Se1−x thin films

Rapid crystallization behavior of amorphous TexSe1−x thin films limits the use of these alloys as coatings and in optoelectronic devices. Understanding the short- and medium-range ordering of the amorphous structure and the fundamental physics governing the crystallization of the films is crucial. Although the lack of long range crystalline order restricts the characterization of the amorphous films, electron microscopy offers a way to extract information about the nanoscale ordering. In this paper, the local ordering of amorphous TexSe1−x thin films with x=0.22, 0.61, 0.70, 0.90, and 1 grown by thermal evaporation is investigated using radial distribution function (RDF) and fluctuation electron microscopy (FEM) analysis. RDF results show that the nearest-neighbor distances of selenium (Se) and tellurium (Te) in their crystalline structure are preserved, and their bond lengths increase with the addition of Te. Density functional theory (DFT) calculations predict structures with interatomic distances similar to those measured experimentally. Additionally, fluctuations in atomic coordination are analyzed. Medium range order (MRO) analysis obtained from FEM and DFT calculations suggests that there are at least two populations within the chain network structure, which are close to the Se–Se and Te–Te intrachain distances. For the binary alloy with x > 0.61, TexSe1−x, Te–Te like populations increase and Te fragments might form, suggesting that the glass forming ability decreases rapidly.

Sari, Bengisu (ORCID:0000000244217098)↗