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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 19 records
All sourcesNASA NTRSDOE OSTI
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 OSTI2024

Shock compression experiments using the DiPOLE 100-X laser on the high energy density instrument at the European x-ray free electron laser: Quantitative structural analysis of liquid Sn

X-ray free electron laser (XFEL) sources coupled to high-power laser systems offer an avenue to study the structural dynamics of materials at extreme pressures and temperatures. The recent commissioning of the DiPOLE 100-X laser on the high energy density (HED) instrument at the European XFEL represents the state-of-the-art in combining x-ray diffraction with laser compression, allowing for compressed materials to be probed in unprecedented detail. Here, we report quantitative structural measurements of molten Sn compressed to 85(5) GPa and ~3500 K. The capabilities of the HED instrument enable liquid density measurements with an uncertainty of ~1% at conditions which are extremely challenging to reach via static compression methods. We discuss best practices for conducting liquid diffraction dynamic compression experiments and the necessary intensity corrections which allow for accurate quantitative analysis. We also provide a polyimide ablation pressure vs input laser energy for the DiPOLE 100-X drive laser which will serve future users of the HED instrument.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2023

Artificial Neural Network Models for Octane Number and Octane Sensitivity: A Quantitative Structure Property Relationship Approach to Fuel Design

Octane sensitivity (OS), defined as the research octane number (RON) minus the motor octane number (MON) of a fuel, has gained interest among researchers due to its effect on knocking conditions in internal combustion engines. Compounds with a high OS enable higher efficiencies, especially within advanced compression ignition engines. RON/MON must be experimentally tested to determine OS, requiring time, funding, and specialized equipment. Thus, predictive models trained with existing experimental data and molecular descriptors (via quantitative structure-property relationships (QSPRs)) would allow for the preemptive screening of compounds prior to performing these experiments. Here, the present work proposes two methods for predicting the OS of a given compound: using artificial neural networks (ANNs) trained with QSPR descriptors to predict RON and MON individually to compute OS (derived octane sensitivity (dOS)), and using ANNs trained with QSPR descriptors to directly predict OS. Twenty-five ANNs were trained for both RON and MON and their test sets achieved an overall 6.4% and 5.2% error, respectively. Twenty-five additional ANNs were trained for both dOS and OS; dOS calculations were found to have 15.3% error while predicting OS directly resulted in 9.9% error. A chemical analysis of the top QSPR descriptors for RON/MON and OS is conducted, highlighting desirable structural features for high-performing molecules and offering insight into the inner mathematical workings of ANNs; such chemical interpretations study the interconnections between structural features, descriptors, and fuel performance showing that connectivity, structural diversity, and atomic hybridization consistently drive fuel performance.

09 BIOMASS FUELS↗
DOE OSTI2020

On single-crystal neutron-diffraction in DACs: quantitative structure refinement of light elements on SNAP and TOPAZ

Quantitative single crystal neutron-diffraction in diamond anvil cells has so far been limited by the neutron flux available at the various neutron sources. As a result, highly precise measurements of the exact position of light elements have not been possible preventing, for example, structural studies of hydrogen and hydrogen bonds under pressure. Here we report experiments carried out on SNAP at the Spallation Neutron Source (ORNL, TN, USA) to explore the possibility and current limits of such studies. Furthermore, we benchmarked the obtained data quality with reference experiments carried out on TOPAZ, a dedicated single-crystal instrument.We show that measuring single-crystal diffraction intensities on SNAP is possible to such a precision that we are able to resolve the hydrogen bonds in potassium dideuterium phosphate (DKDP) as well as in ice VI.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

Quantitative Structure Determination from Experimental Four-Dimensional Scanning Transmission Electron Microscopy via the Scattering Matrix

Considerable inroads have recently been made on algorithms to determine the sample potential from four-dimensional scanning transmission electron microscopy data from thick samples where multiple scattering cannot be neglected. This paper further develops the scattering matrix approach to such structure determination. Through simulation, we demonstrate how this approach can be modified to better handle partial spatial coherence, unknown probe defocus, and information from the dark field region. By combining these developments we reconstruct the electrostatic potential of a monolithic SrTiO 3 crystal showing good quantitative agreement with the expected structure.

4D STEM↗
NASA NTRS1989

Investigation of structural behavior of candidate Space Station structure

Quantitative evaluations of the structural loads, stiffness and deflections of an example Space Station truss due to a variety of influences, including manufacturing tolerances, assembly operations, and operational loading are reported. The example truss is a dual-keel design composed of 5-meter-cube modules. The truss is 21 modules high and 9 modules wide, with a transverse beam 15 modules long. One problem of concern is the amount of mismatch which will be expected when the truss is being erected on orbit. Worst-case thermal loading results in less than 0.5 inch of mismatch. The stiffness of the interface is shown to be less than 100 pounds per inch. Thus, only moderate loads will be required to overcome the mismatch. The problem of manufacturing imperfections is analyzed by the Monte Carlo approach. Deformations and internal loads are obtained for ensembles of 100 example trusses. All analyses are performed on a personal computer. The necessary routines required to supplement commercially available programs are described.

Hedgepeth, John M.↗
DOE OSTI2024

Prediction of Dielectric Constant in Series of Polymers by Quantitative Structure-Property Relationship (QSPR)

This work is devoted to the investigation of dielectric permittivity which is influenced by electronic, ionic, and dipolar polarization mechanisms, contributing to the material’s capacity to store electrical energy. In this study, an extended dataset of 86 polymers was analyzed, and two quantitative structure–property relationship (QSPR) models were developed to predict dielectric permittivity. From an initial set of 1273 descriptors, the most relevant ones were selected using a genetic algorithm, and machine learning models were built using the Gradient Boosting Regressor (GBR). In contrast to Multiple Linear Regression (MLR)- and Partial Least Squares (PLS)-based models, the gradient boosting models excel in handling nonlinear relationships and multicollinearity, iteratively optimizing decision trees to improve accuracy without overfitting. The developed GBR models showed high R2 coefficients of 0.938 and 0.822, for the training and test sets, respectively. An Accumulated Local Effect (ALE) technique was applied to assess the relationship between the selected descriptors—eight for the GB_A model and six for the GB_B model, and their impact on target property. ALE analysis revealed that descriptors such as TDB09m had a strong positive effect on permittivity, while MLOGP2 showed a negative effect. These results highlight the effectiveness of the GBR approach in predicting the dielectric properties of polymers, offering improved accuracy and interpretability.

Ascencio-Medina, Estefania↗
DOE OSTI2021

Unveiling decaying mechanism through quantitative structure-activity relationship in electrolytes for lithium-ion batteries

Understanding the decaying mechanism in lithium-ion batteries (LIBs) is critical to establishing a stable electrolyte system. Despite the advent of various novel electrolyte solvents designated for high-voltage LIBs, their working principles are not fully understood. Currently, oxidative decomposition of electrolytes is believed to be the major cause of capacity fade, and tremendous effort has been devoted to discovering a new electrolyte with enhanced anodic stability. However, the oxidative decomposition process cannot solely explain the rapid decay of some electrolyte systems with intrinsic high anodic stability when used with a high-nickel layered oxide cathode such as LiNi 0.6 Mn 0.2 Co 0.2 O 2 (NMC622). In this report, a study of the quantitative structure-activity relationship was conducted to deepen the mechanistic understanding of the decay in high-voltage LIBs. Here, the results obtained from the newly introduced molecular pair analysis and linear free-energy relationship (LFER) studies were highly consistent with the solvation-involved decaying mechanism in a high-nickel layered oxide cathode cycling at high voltage (> 4.5 V vs. Li/Li + ). There was no evidence correlating the solvation ability of electrolyte solvents with the decay of a high-nickel layered oxide cathode cycling at a relatively low voltage (< 4.3 V vs. Li/Li + ), nor with the high-voltage spinel cathode LiNi 0.5 Mn 1.5 O 4 (LNMO). Undoubtedly, the unveiled mechanistic insight provides a critical guideline for the development of an appropriate electrolyte system targeting different high-voltage cathode materials.

25 ENERGY STORAGE↗
DOE OSTI2025

A simple and highly efficient protocol for 13 C-labeling of plant cell wall for structural and quantitative analyses via solid-state nuclear magnetic resonance

Plant cell walls are made of a complex network of interacting polymers that play a critical role in plant development and responses to environmental changes. Thus, improving plant biomass and fitness requires the elucidation of the structural organization of plant cell walls in their native environment. The 13 C-based multi-dimensional solid-state nuclear magnetic resonance (ssNMR) has been instrumental in revealing the structural information of plant cell walls through 2D and 3D correlation spectral analyses. However, the requirement of enriching plants with 13 C limits the applicability of this method. To our knowledge, there is only a very limited set of methods currently available that achieve high levels of 13 C-labeling of plant materials using 13 CO 2 , and most of them require large amounts of 13 CO 2 in larger growth chambers. In this study, a simplified protocol for 13C-labeling of plant materials is introduced that allows ca 60% labeling of the cell walls, as quantified by comparison with commercially labeled samples. This level of 13 C-enrichment is sufficient for all conventional 2D and 3D correlation ssNMR experiments for detailed analysis of plant cell wall structure. The protocol is based on a convenient and easy setup to supply both 13 C-labeled glucose and 13 CO 2 using a vacuum-desiccator. The protocol does not require large amounts of 13 CO 2 . This study shows that our 13 C-labeling of plant materials can make the accessibility to ssNMR technique easy and affordable. The derived high-resolution 2D and 3D correlation spectra are used to extract structural information of plant cell walls. This helps to better understand the influence of polysaccharide-polysaccharide interaction on plant performance and allows for a more precise parametrization of plant cell wall models.

09 BIOMASS FUELS↗
DOE OSTI2021

Predicting the Dissolution Rate of Borosilicate Glasses using QSPR analysis based on molecular dynamics simulations

Quantitative Structure Property Relationship (QSPR) analysis based on molecular dynamics (MD) simulations is a promising approach for establishing the composition-property relationships of glass and other materials with complex structures. A series of 20 borosilicate and boroaluminosilicate glasses have been modeled by using MD simulations with recently developed effective potentials. Short- and medium-range structures of these glasses were analyzed and, based on these structural information, QSPR analysis of the initial dissolution rate (r0) was made and compared with measured r0 at 90°C and pH 9 using various structural descriptors such as percentage of bridging oxygen species, network connectivity and average ring size. The structural descriptors, Fnet, containing energetic information such as single bond strength and other structural information were also used. It was found that overall network connectivity, average ring size and Fnet give reasonable predictions of the r0 of studied glasses, given the conditions that the glasses are homogeneous and dissolve congruently. Modifying glass compositions to account preferential release of modifiers gives a better prediction for incongruently dissolving glasses. The results were compared with our recent work of predicting glass dissolution behavior from compositions using the topological-constraints-based models.

Du, Jincheng↗