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Results for “Structural properties”

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 271 records · Page 15
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Structure property correlations in wet-spun textile PAN fibers for cost-effective carbon fibers applicable in H 2 storage tank manufacturing

The economic viability of high-strength carbon fibers is pivotal for their integration into Type IV compressed hydrogen storage tanks. Oak Ridge National Laboratory is advancing this objective by developing cost-effective, textile-grade polyacrylonitrile (PAN) precursor fibers through an optimized wet spinning-process to garner more microstructural control over the precursor fibers. Here, this study examines the microstructural characteristics of PAN fibers, particularly the crystalline orientation factor, and their impact on mechanical properties in both precursor and thermally converted carbon fibers (CFs). Three wet-spun PAN fibers, all having diameters of 12-13 µm but varying orientation factors were assessed. Comprehensive characterization using X-ray scattering and microscopy revealed that fibers with higher orientation factors exhibited enhanced crystallinity and optimized crystallite sizes. Mechanical testing demonstrated that the PAN fiber with the highest orientation factor achieved the greatest tensile strength (98 ksi) and modulus (1.60 Msi). Additionally optical and scanning electron microscopy confirmed smooth and defect-free surfaces across all samples. These findings indicate that manipulating spinning parameters to maximize orientation factor is more impactful in achieving high strength and modulus over factors attributed to crystallinity and crystallite sizes. Here, highest performing CFs, thermally processed in lab from textile-grade precursor averaged 558 ksi, a 10% increase over previous results.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Structure–Property Linkage in Alloys Using Graph Neural Network and Explainable Artificial Intelligence

Deep learning tools have recently shown significant potential for accelerating the prediction of microstructure–property linkage in materials. While deep neural networks like convolution neural networks (CNNs) can extract physics information from 3D microstructure images, they often require a large network architecture and substantial training time. In this research, we trained a graph neural network (GNN) using phase field generated microstructures of Ni-Al alloys to predict the evolution of mechanical properties. We found that a single GNN is capable of accurately predicting the strengthening of Ni-Al alloys with microstructures of varying sizes and dimensions, which cannot otherwise be done with a CNN. Additionally, GNN requires significantly less GPU utilization than CNN and offers more interpretable explanation of predictions using saliency analysis as features are manually defined in the graph. We also utilize explainable artificial intelligence tool Bayesian Inference to determine the coefficients in the power law equation that governs coarsening of precipitates. Overall, our work demonstrates the ability of the GNN to accurately and efficiently extract relevant information from material microstructures without having restrictions on microstructure size or dimension and offers an interpretable explanation.

Chemistry↗
DOE OSTI2024

Development of Structure–Property Relationships for Ammonium Transport through Charged Organogels

Ammonia is a promising carbon-free fuel, but current methods to produce ammonia are energy intensive. New methods are thereby needed, with one promising method being electrochemical nitrogen reduction cells. Efficient cell operation requires robust catalysts but also efficient membrane separators that permit the selective transport of ions while minimizing the transport of the products across the cell. Commercial membranes have an unknown morphology which makes designing improved cells challenging. To address this problem, we synthesized a series of membranes with controlled crosslinking density and chemical composition to understand their impact on ammonium transport. Higher crosslinking density led to lower ammonium permeability. At the highest crosslinking density, similar ammonium permeability was observed independent of the water volume fraction and hydrophobicity of the monomers. These results suggest new directions to develop membranes with reduced ammonium crossover to improve the efficiency of these electrochemical cells.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS1974

Investigation of Structure-Property Relationships in Systematic Series of Novel Polymers

The results are discussed of the on-line interface of the Torsional Braid Analysis experiment to an Hierarchical Computer System for data acquisition, data reduction and control of experimental variables. Some experimental results are demonstrated and the data reduction procedures are outlined. Several modes of presentation of the final computer-reduced data are discussed in an attempt to elucidate possible interrelations between the thermal variation of the rigidity and loss parameters.

Gillham, J. K.↗
NASA NTRS1975

Structure-property relationships in thermomechanically treated beryllia dispersed nickel alloys

BeO dispersed nickel alloys, produced by powder metallurgy techniques, were studied extensively in stress rupture at 815, 982, and 1093 C (1088, 1255, and 1366 K) and by transmission electron microscopy. The alloys were subjected to a variety of thermomechanical treatments (TMT) to determine the benefits of TMT on properties. It is shown that the use of intermediate annealing treatments after 10 pct reduction steps is highly beneficial on both low and high temperature properties. It is indicated that the high temperature strength is not primarily dependent on the grain aspect ratio or texture but depends strongly on the dislocation density and distribution of dislocations in a stable substructure which is pinned by the fine oxide dispersion.

Grewal, M. S.↗
NASA NTRS1976

Investigation of structure-property relationships in systematic series of novel polymers

Solid state transitions in polymeric materials was associated with the onset of sub-molecular motions of the polymer chains. Although these were considered to be intramolecular in general, the local environment of the polymer molecule exerts a strong influence through, for example, the effects of crystallinity, polarity and diluents. The manner of specimen preparation and previous history also affect transitions. The transitions are considered to arise when sufficient free volume is available to permit the occurrence of these side chain and backbone reorientations. The glass transition is the principal transition of amorphous polymeric materials and is associated with the onset of long range segmental motion of the polymer backbone. The various types of shorter range motion occurring below the glass transition have been catalogued.

Gillham, J. K.↗
NASA NTRS1976

Structure-property relationships in block copolymers

Block copolymers are a class of relatively new materials which contain long sequences of two (or more) chemically different repeat units. Unlike random copolymers, each segment may retain some properties which are characteristic of its homopolymer. It is well known that most physical blends of two different homopolymers are incompatible on a macro-scale. By contrast most block copolymers display only a microphase (eg. 100-200 A domains) separation. Complete separation is restricted because of a loss in configurational entropy. The latter is due to presence of chemical bond(s) between the segments. Novel physical properties can be obtained because it is possible to prepare any desired combination of rubber-like, glassy, or crystalline blocks. The architecture and sequential arrangement of the segments can strongly influence mechanical behavior.

Mcgrath, J. E.↗