Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “difference scaling”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 685 records · Page 38
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Multiscale Structural Characterization of a Smectic Liquid Crystalline Elastomer upon Mechanical Deformation Using Neutron Scattering

Liquid crystalline elastomers (LCEs) exhibit unique shape memory behavior due to the combination of liquid crystalline orientation and rubber elasticity. Multiscale structural characterization of these materials upon deformation is crucial to understanding their structure–property relationships. In this work, the structure evolution of an epoxy-based, main-chain LCE with deuterated flexible spacers upon uniaxial mechanical deformation is investigated at different length scales. Wide-angle and small-angle X-ray scattering (WAXS/SAXS) reveal the presence of smectic polymorphism and rotations of the smectic domains upon mechanical stretching, which is also confirmed by small-angle neutron scattering (SANS). Importantly, the selective deuteration enables an improved neutron scattering contrast between the smectic and amorphous domains. SANS patterns of the deformed, deuterated LCE exhibit strong scattering streaks that are not observed in SAXS or SANS of hydrogenated LCE, indicating the presence of highly aligned amorphous domains. The macroscopic orientation also results in the formation of structures in the micrometer scale revealed by the ultrasmall-angle neutron scattering (USANS) experiment.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Structure of Diisobutylene Maleic Acid Copolymer (DIBMA) and Its Lipid Particle as a “Stealth” Membrane-Mimetic for Membrane Protein Research

The study of membrane proteins remains challenging, especially in a native membrane environment. Recently, major progress has been made using maleic acid copolymers, such as styrene maleic acid, to purify membrane proteins and study them directly with native lipids associated with the membrane. Additional maleic acid copolymers, such as diisobutylene maleic acid (DIBMA) membrane-mimetic systems, are being developed and found to have improved spectroscopic properties and pH stability. We studied DIBMA and its lipid particles in solution to better understand its assembly, without and with the lipids, to provide an insight regarding how to use it in solution for better membrane extraction. Using small-angle neutron and X-ray scattering (SANS/SAXS), we show that DIBMA organizes into structures of different size scales at various concentrations and ionic strengths. The polymer performed reasonably well under most solvent conditions except in very low concentrations and high-salt conditions that could result in limited interaction with lipids. To explore DIBMA lipid particles as a suitable membrane-mimetic system for neutron scattering studies of membrane proteins, we measured and determined the contrast-matching point of DIBMA to be ~12% (v/v) D 2 O — similar to that of most protiated lipid molecules but distinct from that of regular protiated proteins — providing a natural contrast for separating their neutron scattering signals. Using SANS contrast variation, we demonstrated that the scattering from the whole lipid particle can be annihilated. Further, we determined that a well-defined lipid nanodisc structure with DIBMA was contrast-matched. These results demonstrate that the DIBMA lipid particle is an outstanding “stealth” membrane-mimetic for membrane proteins. The results provide a structural framework for understanding the organization and assembly process of the polymer itself and the lipid molecules. Such an understanding is imperative for structural techniques such as cryo-electron microscopy, nuclear magnetic resonance, small-angle scattering, and other biophysical techniques.

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

Laser-Induced Trapping of Metastable Amorphous AlO x /C (2.5 < x ≤ 3.5) Nanocomposites: Implications for Use as Solid Phase Gas Generators

The synthesis of kinetically "frozen" metastable nanostructures remains elusive. This limitation has severely restricted the current paradigms in materials discovery. We overcame this challenge by phase-stabilizing unusually hyperoxidized and metastable amorphous aluminum oxide (a-AlO x ; 2.5 < x ≤ 3.5) nanostructures. Specifically, we employed laser ablation synthesis in solution (LASiS) as a one-pot nonequilibrium technique to achieve this pivotal advancement. Structural and compositional characterizations of the as-synthesized material reveal highly disordered a-AlO x nanoparticles that are remarkably stabilized by interfacial monolayers of ordered carbon atoms. Both structure and chemical composition were confirmed with disparate characterization methods at different length scales. The nanoparticles of sizes less than10 nm were stable even at elevated temperatures. Only at temperatures higher than 750 °C do the a-AlO x structures undergo a solid-solid phase transition that culminates in the formation of stable α-Al 2 O 3 while releasing excess trapped gases. In conclusion, such disruptive materials can find applications in emerging technologies seeking metastable phase-change materials for solid phase gas generators, batteries, neuromorphic computing, and energetic additives.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

High-Resolution Imaging of Unstained Polymer Materials

Electron microscopy has played an important role in polymer characterization. Traditionally, electron diffraction is used to study crystalline polymers while transmission electron microscopy is used to study microphase separation in stained block copolymers and other multiphase systems. We describe developments that eliminate the barrier between these two approaches - it is now possible to image polymer crystals with atomic resolution. The focus of this Review is on high-resolution imaging (30 Å and smaller) of unstained polymers. Recent advances in hardware allow for capturing numerous (as many as 105) low-dose images from an unperturbed specimen; beam damage is a significant barrier to high-resolution electron microscopy of polymers. Machine-learning-based software is then used to sort and average the images to retrieve pristine structural information from a collection of noisy images. Acknowledging the heterogeneity in polymer samples prior to averaging is essential. Molecular conformations in a wide range of amphiphilic block copolymers, polymerized ionic liquids, and conjugated polymers can be gleaned from two-dimensional projections (2D), three-dimensional (3D) tomograms, and four-dimensional (4D) scanning transmission electron microscopy (STEM) data sets where 2D diffraction patterns are taken as a function of position. Some methods such as phase contrast STEM have been used to image closely related materials such as metal-organic frameworks but not polymers. With improvements in hardware and software, such methods may soon be applied to polymers. Our goal is to provide a comprehensive understanding of the strategies toward the high-resolution imaging of radiation sensitive polymer materials at different length scales.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Advances in Multiscale Modeling of Lignocellulosic Biomass

Applications and associated processing technologies of lignocellulosic biomass are becoming more important as we endeavor to meet societal demand for fuels, chemicals, and materials from renewable resources. Meanwhile, the rapidly expanding availability and capabilities of high-performance computing present an unprecedented opportunity to accelerate development of technologies surrounding lignocellulose utilization. In order to realize this potential, suitable modeling frameworks must be constructed that effectively capture the multiscale complexity and tremendous variety exhibited by lignocellulosic materials. In our assessment of previous endeavors towards this goal, several important shortcomings have been identified: (1) the lack of multiscale integration strategies that capture emergent properties and behaviors spanning different length scales, and (2) the inability of many modeling approaches to effectively capture the variability and diversity of lignocellulose that arise from both natural and process-induced sources. In this perspective, we survey previous modeling approaches for lignocellulose and simulations processes involving its chemical and mechanical transformation, and suggest opportunities for future development to enhance the utility of computational tools to address barriers to widespread adoption of a renewable bioeconomy.

09 BIOMASS FUELS↗
DOE OSTI2022

Drivers of Future Physical Water Scarcity and Its Economic Impacts in Latin America and the Caribbean

Future water scarcity is a global concern with significant impacts on the energy, water, and land (EWL) sectors. Countries in Latin America and the Caribbean (LAC) are significant producers of agricultural goods consumed around the world, so disruptions resulting from land and water scarcity in LAC are a source of global supply chain risk. Understanding where water scarcity in LAC could occur and what could drive that scarcity to emerge is critical for strategic resource management and planning, both regionally and globally. Assessing future water scarcity impacts in LAC is challenging given the multisector dynamic interactions among the EWL sectors and the multiple uncertainties acting across different spatial scales. We use scenario discovery to explore the drivers of future water scarcity, considering assumptions related to climate, socioeconomics, and the EWL sectors. Understanding what factors drive uncertainty across outcomes can help stakeholders in targeting long-term solutions and future data collection efforts. To illuminate these dynamics, we use a database consisting of a large ensemble of scenarios representing diverse worlds simulated using the Global Change Analysis Model. We quantify future water scarcity and its economic impacts across the scenarios using three metrics: (1) physical water scarcity, (2) water price, and (3) crop profit. We find that physical water scarcity and water price are driven primarily by reservoir storage capacity assumptions, highlighting the importance of strategic water infrastructure development in maintaining future water availability and accessibility. Crop profit is driven by a combination of water supply and demand assumptions, which emphasizes the complex nature of EWL multisector dynamics. While most of LAC is poised to have abundant land and water resources available for development, water basins in Mexico and along the Pacific coast of South America experience high levels of severity and uncertainty across the future scenarios for at least one of the metrics. We find that the drivers of extreme scarcity vary spatially and across the metrics, further highlighting the heterogeneity of the region and the importance of considering multiple metrics to assess water scarcity vulnerability.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Testing Hidden Assumptions of Representativeness in Reach-Scale Studies of Hyporheic Exchange

Field studies of hyporheic exchange in mountain systems are often conducted using short study reaches and a limited number of observations. It is common practice to assume these study reaches represent hyporheic exchange at larger scales or different sites and to infer general relationships among potential causal mechanisms from the limited number of observations. However, these assumptions of representativeness are rarely tested. In this study, we develop numerical models from four segments of mountain streams in different geomorphologic settings and extract shorter reaches to test how representative exchange metrics are in shorter reaches compared to their reference segments. We also map the locations of the representative reaches to determine if a pattern exists based on location. Finally, we compare variance of these shorter within-site reaches to 29 additional reaches across the same basin to understand the impacts of inferring causal mechanisms, for example, the expectation that wide and narrow valley bottoms will yield different hyporheic exchange patterns. Our results show that the location and length strategy of the study reach must be considered before assuming an exchange metric to be representative of anything other than the exact segment studied. Further, it is necessary to quantify within and between site variations before making causal inferences based on observable characteristics, such as valley width or stream morphology. Our findings have implications for future field practices and how those practices are translated into models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Evaluating the Effects of Precipitation and Evapotranspiration on Soil Moisture Variability Within CMIP5 Using SMAP and ERA5 Data

=The effects of precipitation (Pr) and evapotranspiration (ET) on surface soil moisture (SSM) play an essential role in the land-atmosphere system. Here we evaluate multimodel differences of these effects within the Coupled Model Intercomparison Project Phase 5 (CMIP5) compared to Soil Moisture Active Passive (SMAP) products and ECMWF Reanalysis v5 (ERA5) as references in a frequency domain. The variability of SSM, Pr, and ET within three frequency bands (1/7 ~ 1/30 days -1 , 1/30 ~ 1/90 days -1 , and 1/90 ~ 1/365 days -1 ) after normalization is quantified using Fourier transform. We analyze the impact of ET and Pr on SSM variability based on a transfer function assuming that these variables form a linear time-invariant (LTI) system. For the total effects of ET and Pr on SSM variability, the CMIP5 estimations are smaller than the reference data in the two higher frequency bands and are larger than the reference data in the lowest frequency band. Besides, the effects on SSM by Pr and ET are found to be different across the three frequency bands. In each frequency band, the variability of the factor that dominates SSM (i.e., Pr or ET) from CMIP5 is smaller than that from the references. This study identifies the spatiotemporal distribution of differences between CMIP5 models and references (SMAP and ERA5) in simulating ET and Pr effects on SSM within three frequency bands. This study provides insightful information on how soil moisture variability is affected by varying precipitation and evapotranspiration at different time scales within Earth System Models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Closing the methane gap in US oil and natural gas production emissions inventories

Methane (CH 4 ) emissions from oil and natural gas (O&NG) systems are an important contributor to greenhouse gas emissions. In the United States, recent synthesis studies of field measurements of CH 4 emissions at different spatial scales are ~1.5–2× greater compared to official greenhouse gas inventory (GHGI) estimates, with the production-segment as the dominant contributor to this divergence. Based on an updated synthesis of measurements from component-level field studies, we develop a new inventory-based model for CH 4 emissions, for the production-segment only, that agrees within error with recent syntheses of site-level field studies and allows for isolation of equipment-level contributions. We find that unintentional emissions from liquid storage tanks and other equipment leaks are the largest contributors to divergence with the GHGI. If our proposed method were adopted in the United States and other jurisdictions, inventory estimates could better guide CH 4 mitigation policy priorities.

03 NATURAL GAS↗
DOE OSTI2022

Cas9-induced large deletions and small indels are controlled in a convergent fashion

Repair of Cas9-induced double-stranded breaks results primarily in formation of small insertions and deletions (indels), but can also cause potentially harmful large deletions. While mechanisms leading to the creation of small indels are relatively well understood, very little is known about the origins of large deletions. Using a library of clonal NGS-validated mouse embryonic stem cells deficient for 32 DNA repair genes, we have shown that large deletion frequency increases in cells impaired for non-homologous end joining and decreases in cells deficient for the central resection gene Nbn and the microhomology-mediated end joining gene Polq. Across deficient clones, increase in large deletion frequency was closely correlated with the increase in the extent of microhomology and the size of small indels, implying a continuity of repair processes across different genomic scales. Furthermore, by targeting diverse genomic sites, we identified examples of repair processes that were highly locus-specific, discovering a role for exonuclease Trex1. Finally, we present evidence that indel sizes increase with the overall efficiency of Cas9 mutagenesis. These findings may have impact on both basic research and clinical use of CRISPR-Cas9, in particular in conjunction with repair pathway modulation.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Biomolecular condensates form spatially inhomogeneous network fluids

The functions of biomolecular condensates are thought to be influenced by their material properties, and these will be determined by the internal organization of molecules within condensates. However, structural characterizations of condensates are challenging, and rarely reported. Here, we deploy a combination of small angle neutron scattering, fluorescence recovery after photobleaching, and coarse-grained molecular dynamics simulations to provide structural descriptions of model condensates that are formed by macromolecules from nucleolar granular components (GCs). We show that these minimal facsimiles of GCs form condensates that are network fluids featuring spatial inhomogeneities across different length scales that reflect the contributions of distinct protein and peptide domains. The network-like inhomogeneous organization is characterized by a coexistence of liquid- and gas-like macromolecular densities that engenders bimodality of internal molecular dynamics. These insights suggest that condensates formed by multivalent proteins share features with network fluids formed by systems such as patchy or hairy colloids.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Cholesterol modulates membrane elasticity via unified biophysical laws

Cholesterol and lipid unsaturation underlie a balance of opposing forces that features prominently in adaptive cell responses to diet and environmental cues. These competing factors have resulted in contradictory observations of membrane elasticity across different measurement scales, requiring chemical specificity to explain incompatible structural and elastic effects. Here, we demonstrate that – unlike macroscopic observations – lipid membranes exhibit a unified elastic behavior in the mesoscopic regime between molecular and macroscopic dimensions. Using nuclear spin techniques and computational analysis, we find that mesoscopic bending moduli follow a universal dependence on the lipid packing density regardless of cholesterol content, lipid unsaturation, or temperature. Our observations reveal that compositional complexity can be explained by simple biophysical laws that directly map membrane elasticity to molecular packing associated with biological function, curvature transformations, and protein interactions. The obtained scaling laws closely align with theoretical predictions based on conformational chain entropy and elastic stress fields. These findings provide unique insights into the membrane design rules optimized by nature and unlock predictive capabilities for guiding the functional performance of lipid-based materials in synthetic biology and real-world applications.

Kumarage, Teshani [Virginia Polytechnic Inst. and ↗
DOE OSTI2025

Hierarchically ordered porous transition metal compounds from one-pot type 3D printing approaches

Solution-based soft matter self-assembly (SA) promises unique material structures and properties from approaches including additive manufacturing/three-dimensional (3D) printing. The 3D printing of periodically ordered porous functional inorganic materials through SA unfolding during printing remains a major challenge, however, due to the often vastly different ordering kinetics of separate processes at different length scales. Here, we report a “one-pot” direct ink writing process to produce hierarchically porous transition metal nitrides and precursor oxides from block copolymer (BCP) SA. Heat treatment protocols identified in various environments enable mesostructure retention in the final crystalline materials with periodic lattices on three distinct length scales. Moreover, embedded printing enables the first BCP directed mesoporous non-self-supporting helical oxides and nitrides. Resulting nitrides are superconducting, with record nanoconfinement-induced upper critical fields correlated with BCP molar mass and record surface areas for compound superconductors. Results suggest scalable porous functional inorganic material formation approaches for applications including catalysis, sensing, and microelectronics.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Coupling physics in machine learning to predict properties of high-temperatures alloys

Abstract High-temperature alloy design requires a concurrent consideration of multiple mechanisms at different length scales. We propose a workflow that couples highly relevant physics into machine learning (ML) to predict properties of complex high-temperature alloys with an example of the 9–12 wt% Cr steels yield strength. We have incorporated synthetic alloy features that capture microstructure and phase transformations into the dataset. Identified high impact features that affect yield strength of 9Cr from correlation analysis agree well with the generally accepted strengthening mechanism. As a part of the verification process, the consistency of sub-datasets has been extensively evaluated with respect to temperature and then refined for the boundary conditions of trained ML models. The predicted yield strength of 9Cr steels using the ML models is in excellent agreement with experiments. The current approach introduces physically meaningful constraints in interrogating the trained ML models to predict properties of hypothetical alloys when applied to data-driven materials.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Leveraging unlabeled SEM datasets with self-supervised learning for enhanced particle segmentation

Scanning Electron Microscopes (SEMs) are widely used in experimental science laboratories, often requiring cumbersome and repetitive user analysis. Automating SEM image analysis processes is highly desirable to address this challenge. In particle sample analysis, Machine Learning (ML) has emerged as the most effective approach for particle segmentation. However, the time-intensive process of manually annotating thousands of SEM images limits the applicability of supervised learning approaches. Self-Supervised Learning (SSL) offers a promising alternative by enabling knowledge extraction from raw, unlabeled data. This study presents a framework for evaluating SSL techniques in SEM image analysis, focusing on novel methods leveraging the ConvNeXtV2 architecture for particle detection. A dataset comprising 25,000 SEM images is curated to benchmark these proposed SSL methods. The results demonstrate that ConvNeXtV2 models, with varying parameter counts, consistently outperform other techniques in particle detection across different length scales, achieving up to a 34% reduction in relative error compared to established SSL methods. Furthermore, an ablation study explores the relationship between dataset size and SSL performance, providing actionable insights for practitioners regarding model selection and resource efficiency. This research advances the integration of SSL into autonomous analysis pipelines and supports its application in accelerating materials science discovery.

Rettenberger, Luca↗
DOE OSTI2022

Mapping of the mechanical response in Si/SiGe nanosheet device geometries

Abstract The performance of next-generation, nanoelectronic devices relies on a precise understanding of strain within the constituent materials. However, the increased flexibility inherent to these three-dimensional device geometries necessitates direct measurement of their deformation. Here we report synchrotron x-ray diffraction-based non-destructive nanoscale mapping of Si/SiGe nanosheets for gate-all-around structures. We identified two competing mechanisms at different length scales contributing to the deformation. One is consistent with the in-plane elastic relaxation due to the Ge lattice mismatch with the surrounding Si. The second is associated with the out-of-plane layering of the Si and SiGe regions at a length scale of film thickness. Complementary mechanical modeling corroborated the qualitative aspects of the deformation profiles observed across a variety of nanosheet sample widths. However, greater deformation is observed in the SiGe layers of the nanosheets than the predicted distributions. These insights could play a role in predicting carrier mobilities of future devices.

Murray, Conal E. (ORCID:0000000199014290)↗
DOE OSTI2021

Transfer learning of memory kernels for transferable coarse-graining of polymer dynamics

The present work concerns the transferability of coarse-grained (CG) modeling in reproducing the dynamic properties of the reference atomistic systems across a range of parameters. In particular, we focus on implicit-solvent CG modeling of polymer solutions. The CG model is based on the generalized Langevin equation, where the memory kernel plays the critical role in determining the dynamics in all time scales. Thus, we propose methods for transfer learning of memory kernels. The key ingredient of our methods is Gaussian process regression. By integration with the model order reduction via proper orthogonal decomposition and the active learning technique, the transfer learning can be practically efficient and requires minimum training data. Through two example polymer solution systems, we demonstrate the accuracy and efficiency of the proposed transfer learning methods in the construction of transferable memory kernels. The transferability allows for out-of-sample predictions, even in the extrapolated domain of parameters. Built on the transferable memory kernels, the CG models can reproduce the dynamic properties of polymers in all time scales at different thermodynamic conditions (such as temperature and solvent viscosity) and for different systems with varying concentrations and lengths of polymers.

Ma, Zhan↗
DOE OSTI2021

Sodium dodecyl sulfate modulates the structure and rheological properties of Pluronic F108–poly(acrylic acid) coacervates)

Micelles formed within coacervate phases can impart functional properties, but it is unclear if this micellization provides mechanical reinforcement of the coacervate whereby the micelles act as high functionality crosslinkers. Here, we examine how sodium dodecyl sulfate (SDS) influences the structure and properties of Pluronic F108–polyacrylic acid (PAA) coacervates as SDS is known to decrease the aggregation number of Pluronic micelles. Increasing the SDS concentration leads to larger water content in the coacervate and an increase in the relative concentration of PAA to the other solids. Rheological characterization with small angle oscillatory shear (SAOS) demonstrates that these coacervates are viscoelastic liquids with the moduli decreasing with the addition of the SDS. The loss factor (tan δ) initially increases linearly with the addition of SDS, but a step function increase in the loss factor occurs near the reported CMC of SDS. However, this change in rheological properties does not appear to be correlated with any large scale structural differences in the coacervate as determined by small angle X-ray scattering (SAXS) with no signature of Pluronic micelles in the coacervate when SDS concentration is >4 mM during formation of the coacervate, which is less than that observed (6 mM SDS) in initial Pluronic F108 solution despite the higher polymer concentration in the coacervate. Finally, these results suggest that the mechanical properties of polyelectrolyte-non-ionic surfactant coacervates are driven by the efficicacy of binding between the complexing species driving the coacervate, which can be disrupted by competitive binding of the SDS to the Pluronic.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗