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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 991 records · Page 55
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Crystal misorientation correlates with hardness in tooth enamels

The multi-scale hierarchical structure of tooth enamel enables it to withstand a lifetime of damage without catastrophic failure. While many previous studies have investigated structure-function relationships in enamel, the effects of crystal misorientation on mechanical performance have not been assessed. To address this issue, in the present study, we review previously published polarization-dependent imaging contrast (PIC) maps of mouse and human enamel, and parrotfish enameloid, in which crystal orientations were measured and displayed in every 60-nm-pixel. By combining those previous results with the PIC maps of sheep enamel presented here we discovered that, in all enamel(oid)s, adjacent crystals are slightly misoriented, with misorientation angles in the 0°-30° range, and mean 2°-8°. Within this limited range, misorientation is positively correlated with literature hardness values, demonstrating an important structure-property relation, not previously identified. At greater misorientation angles 8°30°, this correlation is expected to reverse direction, but data from different non-enamel systems, with more diverse crystal misorientations, are required to determine if and where this occurs.

36 MATERIALS SCIENCE↗
DOE OSTI2024

A dynamic volumetric heat source model for laser additive manufacturing

Melt pool scale models of laser powder bed fusion (LPBF) offer insights into the process-structure-property relationships in additive manufacturing (AM). These models often neglect physical phenomena such as vapor cavity formation and fluid mechanics to reduce computational demands. Instead, volumetric heat source models are used to represent the effects that these phenomena have on the predicted melt pool dimensions. Generally, the dimensions and effective absorption of the volumetric heat source are calibrated to reproduce melt pool dimensions observed in metallographic cross sections taken from single-track experiments on bare plate. However, the transient nature of LPBF often deviates the melt pool dimensions from the assumed steady-state conditions of single-track experiments, motivating the need for a volumetric heat source model that more generally considers the dynamic relationship between melt pool shape and laser-material interactions. Here, we introduce a two-parameter volumetric heat source model that integrates several existing models into a generalized mathematical expression, providing independent control over the radial heat distribution via the parameter k and the volumetric shape of the heat source via the parameter m. This parameterization enables the calibration of melt pool shape predictions through simultaneous adjustment of these parameters, while keeping the radial heat source dimensions consistent with the experimental spot size (D4σ) and constraining the heat source depth and absorption to physically derived expressions for cavities. Consequently, the proposed volumetric heat source model adapts to changes in the local melt pool conditions due to scanning strategy and part geometry by dynamically adjusting the heat source depth and absorption. We demonstrate the capabilities of the proposed model through comparisons with a collection of experiments from the Additive Manufacturing Benchmark (AMBench).

36 MATERIALS SCIENCE↗
DOE OSTI2020

Selective hydrogenation of CO 2 and CO over potassium promoted Co/ZSM-5

The utilization of CO 2 as a C 1 feedstock for synthesis of value-added chemicals and fuels could both mitigate the negative effects associated with increasing CO 2 emissions and decrease dependence on fossil fuels as part of a future circular carbon economy. Co-based catalysts have been well-developed for Fischer-Tropsch synthesis (FTS), but replacing the CO reactant with CO 2 (CO 2 -FTS) typically results in low selectivity toward desirable light olefins. To better understand the structure-property relationships of Co-based catalysts, and extend promising FTS results to CO 2 -FTS, we have studied the effect of a potassium promoter and acidic properties of ZSM-5 on catalytic performance. The selectivity of FTS and CO 2 -FTS is shown to be a strong function of Si/Al ratio in co-impregnated catalysts, with findings supported by in situ XAFS and FTIR, demonstrating light olefin selectivity can be tuned by Si/Al ratio and the method of introducing the K promoter.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Disentangling interlayer confinement and pore surface adsorption in functionalized smectites for tunable ethylene gas capture

Smectite-based adsorbents are increasingly being studied as sustainable packaging materials for scavenging ethylene, a plant hormone that accelerates fruit ripening. However, the mechanisms governing their uptake and retention remain poorly understood. Here, to tackle this question we systematically investigate ethylene gas-solid interactions in pristine, acid-activated, and choline-functionalized montmorillonites using a complementary combination of structural, gravimetric, and spectroscopic techniques, including inelastic neutron scattering. We experimentally distinguish ethylene populations associated with interlayer confinement, mesopore, and external surface adsorption. We show that chemical functionalization distinctly controls adsorption pathways: acid activation enhances total uptake by generating mesoporous adsorption sites and promoting partial interlayer intercalation, yielding capacities comparable to those of leading smectite-based adsorbents, while choline functionalization promotes preferential confinement and stabilization of guest molecules within the interlayer galleries. Advanced spectroscopic analysis provides molecular-level insight into confinement environments and interaction strengths. We also establish clear structure-property relationships by correlating uptake values derived from independent techniques, linking chemical modification, accessible adsorption domains, and retention behavior. These findings provide general design principles for tuning gas-solid interactions in functionalized layered silicates and highlight their potential as adaptable platforms for sustainable ethylene gas capture and selective adsorption technologies.

Ethylene adsorption mechanism↗
DOE OSTI2024

Altered excitation energy transfer between phycobilisome and photosystems in the absence of ApcG, a small linker peptide, in Synechocystis sp. PCC 6803, a cyanobacterium

Phycobilisome (PBS) is a large pigment-protein complex in cyanobacteria and red algae responsible for capturing sunlight and transferring its energy to photosystems (PS). Spectroscopic and structural properties of various PBSs have been widely studied, however, the nature of so-called complex-complex interactions between PBS and PSs remains much less explored. In this work, we have investigated the function of a newly identified PBS linker protein, ApcG, some domain of which, together with a loop region (PB-loop in ApcE), is possibly located near the PBS-PS interface. Using Synechocystis sp. PCC 6803, we generated an ApcG deletion mutant and probed its deletion effect on the energetic coupling between PBS and photosystems. Steady-state and time-resolved spectroscopic characterization of the purified ΔApcG-PBS demonstrated that ApcG removal weakly affects the photophysical properties of PBS that the spectroscopic properties of terminal energy emitters are comparable to those of PBS from wild-type. However, analysis of fluorescence decay imaging datasets reveals that ApcG deletion induces disruptions within the allophycocyanin (APC) core, resulting in the emergence (splitting) of two spectrally diverse subgroups with some short-lived APC. Profound spectroscopic changes of the whole ΔApcG mutant cell, however, emerge during state transition, a dynamic process of light scheme adaptation. The mutant cells in State I show a substantial increase in PBS-related fluorescence. On the other hand, global analysis of time-resolved fluorescence demonstrates that in general ApcG deletion does not alter or inhibit state transitions if it is interpreted only in terms of the changes of the PSII and PSI fluorescence emission intensity. Furthermore, the results revealed yet–to–be discovered mechanism of ApcG-docking induced excitation energy transfer regulation within PBS or to Photosystems.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Tailored mesoporous structures of lignin-derived nano-carbons for multiple applications

This work uses a one-step KOH activation for lignin precursors to produce ultra-high mesoporous activated carbons (ACs) with an unprecedented combination of the surface area of 3207 m 2 g -1 and mesopore ratio of 76%. The ACs are applied for supercapacitors (SCs) and methylene blue (MB) adsorption. The capacitance of the SCs in the three-electrode system reaches 812.3 F g -1 and demonstrates a remarkable maximum MB adsorption capacity of 1250 mg g -1 . By modifying the process conditions, the mesopore ratio of ACs could be controlled from 10% to 80%. Compared with one-step activation, a two-step method produced microporous carbons with a lower surface area of 1227 m 2 g -1 and a high micropore ratio of 73%. The capacitance of SCs with two-step ACs reached 228.1 F g -1 and the maximum adsorption capacity of 476.19 mg g -1 for MB adsorption. The two-step method limited the surface area but had a higher oxygen surface functionality, benefiting its electrochemical performance. A techno-economic analysis reveals that the one-step KOH activation-based process develops ACs with a minimum selling price of $7648/ton. In conclusion, this work demonstrates tuning the processing-structure-property-performance relationship of lignin-based ACs to make an economically viable domestic carbon source.

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

Mechanistic insights into low-temperature oxidation of carbon fibers: Influence of hydrogen defects and crystallite size

Although oxidation mechanisms have been exhaustively studied for graphite, similar analyses of carbon fibers are comparatively sparse. Most prior work has focused on quantifying weight loss or assessing protective surface coatings designed to slow oxidation. The use of optical spectroscopic techniques for oxidation analyses is comparatively unexplored, but such techniques could provide an early indicator of fiber oxidation that would undermine carbon fiber performance. Here, in this work, we applied Raman spectroscopy to study oxidation-induced spectral alterations in 16 carbon fiber types from 7 manufacturers oxidized at 300 °C for 72 h, 400 °C for 8 h, and 500 °C for 1 h. We connect these results with structural properties of the carbon fibers obtained through wide-angle X-ray scattering, identifying a linear dependence between the reactivity of carbon fibers and the crystallite size of the unperturbed fibers. We then demonstrate that substituted hydrogen defects are likely removed from the fiber surface during oxidation and use the relative defect concentration to predict the Raman spectral change as a function of temperature and time, assuming Arrhenius behavior.

Carbon fiber↗
DOE OSTI2021

Microstructural refinement of cement paste internally cured by polyacrylamide composite hydrogel particles containing silica fume and nanosilica

Supplementary cementitious materials were incorporated into hydrogel-based internal curing agents to improve the hydration, microstructure, and ultimately strength of internally cured high-performance cement paste. Polyacrylamide composite hydrogel particles containing amorphous silica – either silica fume or nanosilica – and two different polymer network crosslink densities were synthesized and incorporated into cement paste. The presence of silica and low crosslink density increased the absorption capacity of the particles in pore solution. Micrographs of internally cured paste indicated a significant improvement in hydrogel-related void-filling ability and an increase in void size for low crosslink density particles containing silica. Compressive strength and electrical resistivity increased at later ages for paste samples containing particles with higher silica dosage. The relationship between extent of hydration, void size, and void-filling activity was found to strongly influence the paste's long-term strength and is thus an important structure-property relationship to consider when selecting hydrogels for internal curing purposes.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Molecular-level solvation and selectivity behavior of Na + , K + , and Li + within glycerol-derived solvents

Ether-based acyclic solvents have a tendency to confine positively charged atoms, forming structures called podands. Due to the stability of these structures, podands have been identified as promising candidates for several different electrochemical applications. In this work, we use molecular dynamics simulations to investigate the solvation and selectivity of Na + , K + , and Li + by different glycerol-derived solvents. The structural properties suggest that the solvent-cation association is stable over a very wide temperature range. Further, a strong correlation is observed between the more electronegative oxygen sites and preferential interaction with the cations, which suggests that the electrostatic oxygen-ion interactions dominate the coordination between these species. Significantly more intense interactions are observed between the solvents and Li + , versus Na + or K + . These molecular-level thermodynamic insights provide fundamental support for further exploring the performance of glycerol-derived podand complexes in Li + capture applications and/or electrolyte formulations.

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

Reinforced double-threaded slide-ring networks for accelerated hydrogel discovery and 3D printing

Traditionally, slide-ring gels are stretchable but soft as a result of an elasticity-stretchability trade-off. Herein, we introduce a new approach to breaking this trade-off and creating reinforced slide-ring networks with mobile crosslinkers. Our approach involves the construction of a polyethylene glycol double-threaded γ-cyclodextrin-based pro-slide-ring crosslinker that serves as a modular component for 3D printing and copolymerization. The resulting crystalline-domain-reinforced slide-ring hydrogels, or CrysDoS-gels, exhibit both high elasticity and high stretchability. The modular synthesis allows for high-throughput synthesis of CrysDoS-gels, generating a large amount of data for structure-property analysis. Here, by employing data science techniques, such as machine learning and linear regression, not only were we able to identify which chemical components influence the mechanical properties of CrysDoS-gels, but this analysis also aided in the discovery of better-performing CrysDoS-gels. Finally, we demonstrate the potential application of the newly discovered CrysDoS-gels as sensing devices by 3D printing them as stress sensors with high sensitivity and a broad detection range.

3D-printing↗
DOE OSTI2026

Understanding and mitigating degradation in amine-based sorbents for CO 2 direct air capture

The success of direct air capture (DAC) of CO 2 depends on sorbents that combine high capacity, low energy requirements, and long-term durability. Amine-based sorbents-including solid-supported aminopolymers, grafted amines, and amine-functionalized resins-remain the leading candidates, but their limited lifetimes drive up costs and constrain deployment. In this review, we outline the current understanding of amine-based sorbent degradation with an emphasis on clearly identifying what is known about structure-property-performance relationships, as well as important knowledge gaps. More specifically, we discuss how polymer chemistry, sorbent design variables, and environmental and process conditions contribute to performance loss. In parallel, we outline how advances in spectroscopy, modeling, and accelerated testing are beginning to illuminate chemical and physical degradation mechanisms. Looking forward, we identify future research directions that will be critical for gaining a deeper understanding of degradation, as well as opportunities for developing innovative mitigation strategies for improving the lifetime of amine-based sorbents.

organic↗
DOE OSTI2023

Operando methods: A new era of electrochemistry

One of the grand challenges facing electrochemistry is to directly resolve the complex nature of (electro)catalyst active sites and capture real-time “movies” of reaction dynamics, i.e. “watching chemistry in action”. The need for such fundamental understanding has stimulated the development of operando/in situ methods, which have greatly enhanced our ability to identify activity descriptors of electrocatalysts and establish structure–property relationships of energy materials. Here, this review summarizes the frontiers of operando electrochemical liquid-cell scanning transmission electron microscopy and correlative synchrotron X-ray methods, which are complementary tools to comprehensively investigate reaction dynamics across multiple spatiotemporal scales. In an effort to encourage greater adoption of advanced operando methods by the general electrochemistry community, this review points out the need to benchmark electrochemistry in confined and heterogenous liquid environment with minimal beam-induced damage. We anticipate that multimodal operando methods will become indispensable for understanding interfacial reaction mechanisms for the broad chemistry and energy materials communities.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Machine learning-guided design, synthesis, and characterization of atomically dispersed electrocatalysts

The recent integration of machine learning into materials design has revolutionized the understanding of structure–property relationships and optimization of material properties beyond the trial-and-error paradigm. On one hand, machine learning has significantly accelerated the development of atomically dispersed metal-nitrogen-carbon (M-N-C) electrocatalysts, which traditionally heavily relied on heuristic approaches. On the other hand, the primary challenge of leveraging machine learning to expedite M-N-C materials discovery lies in the cost associated with data collection. Here, we review recent machine learning integration strategies for M-N-C catalyst development, including discussions on the typical algorithms such as symbolic regression and convolutional neural networks employed for the theoretical design, synthesis optimization via active learning, and advanced microscopy characterization. Subsequently, we provide our perspective on potential near-future directions for furthering machine learning-assisted development of new M-N-C catalysts and elucidating the complex physicochemical mechanisms governing the selectivity, activity, and durability in this class of materials.

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

Multisublattice cluster expansion study of short-range ordering in iron-substituted strontium titanate

Owing to the challenges in obtaining realistic atomic configurations in large chemical phase spaces, it is not straightforward to describe structure–property relations in materials exhibiting configurational disorder. One example is iron-substituted strontium titanate (SrTi 1–x Fe x O 3–d , STF), a promising perovskite-derivative cathode material in solid oxide fuel cells that exhibits full solid solubility 0 ≤ x ≤ 1 and a tendency to exhibit short-range order. Here we demonstrate a multisublattice cluster expansion (CE) framework and apply it to STF across the full composition range. The CE approach is distinct from more traditional CE formulations in that clusters are defined explicitly by the chemical species distributed among multiple sublattices, rather than via cluster functions of occupation variables with decoration. The modified CE approach makes it easy to distinguish meaningful chemical interactions that are harder to extract from conventional CE, since for the latter chemical identity in a cluster is expressed as a product of site occupations. The least absolute shrinkage and selection operator (LASSO) is implemented as a regression analysis tool to select key clusters and avoid overfitting. We demonstrate this formulation on STF, and show that it can accurately predict configurational energies in comparison to conventional CE. From the key clusters, we identify that short-range ordering between substitutional Fe and oxygen vacancies (V O ) results in the formation of Fe–VO strings. In addition, we consider the stability of STF through CE-based Monte Carlo (MC) simulations and confirm the presence of superstructures that were previously observed in transmission electron microscopy. In this work, analysis of atomic configurations from MC samples reveals variations in the oxidation state of Fe atoms, which can be explained by the ordering tendency of Fe and V O . The cluster description and selection formalism described here may be applied to other disordered multisublattice systems for accurate and efficient material modeling.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Calibrating uncertain parameters in melt pool simulations of additive manufacturing

Melt pool scale numerical modeling of additive manufacturing (AM) processes can provide predictive capabilities and theoretical insight into the process-property-structure-performance relationships for AM parts. Despite capabilities of numerical models to solve complex multi-physics problems, it is often important to consider a tradeoff between detailed physics and computational cost. Therefore, sources of uncertainty in both experimental conditions and the parameters needed for modeling require models to be validated against empirical evidence. Here, a method is proposed to calibrate uncertain parameters used in continuum-scale melt pool models for powder bed fusion (PBF) AM. Both a simplified heat transfer model and a heat transfer and fluid flow model were investigated. A surrogate model and Markov chain-based optimization algorithm calibrated melt pool geometry for models within experimental variation of the target melt pool width and depth from the NIST AM-Bench 2018-02 dataset. The melt pool temperature distributions, solidification parameters, and simulated multi-layer solidification microstructures were compared between the two models. Similar results from both models indicate that calibrated, lower fidelity numerical models may be used in place of higher fidelity models to generate melt pool solidification data. Finally, these calibrated models therefore enable lower computational cost melt pool simulations without a noticeable decrease in simulation accuracy for grain-scale microstructure simulations.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Myna: Connecting powder bed fusion build data to simulation tools for digital twin applications

Additive manufacturing (AM), as a digital process, can generate a detailed digital thread linking a part’s design and manufacturing to its operational performance. As AM systems advance, an increasing amount of process data is stored in manufacturing databases. In principle, this data can be utilized by simulation-based digital twin approaches, such as real-time process control and asynchronous post-processing guidance. However, few tools currently exist for systematically integrating digital thread data with computational tools. Here, in this study, we propose a software package, called Myna, for connecting data from powder bed fusion processes to simulation tools. The utility of such a platform is demonstrated using build data from the Oak Ridge National Laboratory Manufacturing Demonstration Facility “Peregrine v2023-10” public dataset to automatically configure and run 54 semi-analytical 3DThesis melt pool simulations, 78 numerical Additive FOAM melt pool simulations, and 3 ExaCA microstructure simulations. The simulated, spatially registered microstructures are then compared directly with electron backscatter diffraction characterization of the corresponding as-built part locations. The resulting simulated microstructure showed variation as a function of process parameters, particularly stripe width; however, the experimental data had little variation between the microstructure texture and grain size resulting from different processing conditions. Analysis of the discrepancies suggest that it is possible a two-phase ferritic-austenitic solidification model is needed to accurately predict grain size and texture for certain stainless steel 316L feedstock compositions under powder bed fusion conditions, providing direction for future research. As illustrated here, due to the number and complexity of the simulations involved in AM process-structure–property predictions, automated methods to connect process data and simulations will remain necessary tools for testing hypotheses and implementing digital twin applications.

Knapp, Gerald L. [Oak Ridge National Laboratory (O↗
DOE OSTI2026

Inverse design of hypoeutectoid pearlite steel microstructures using a deep learning and genetic algorithm optimization framework

Goal-oriented microstructure design in metallic materials is a challenging task due to complex structure-property relationships. Traditional experimental and computational approaches are time-intensive and economically inefficient, limiting their applicability for large-scale design space exploration. Here, in this work, we propose an end-to-end framework that integrates deep learning models with genetic optimization to design microstructures with targeted mechanical properties. Deep learning models enable accurate forward design, while their integration with genetic optimization enables efficient inverse design within a few hours, compared to days or weeks using conventional finite element simulations. The framework combines experimental characterization and finite element modeling to analyze the influence of microstructural features on the mechanical behavior of hypoeutectoid steels. Data from both experiments and simulations are used to train the deep learning models. To demonstrate its effectiveness, we apply the framework to 0.63% carbon steel with proeutectoid ferrite and pearlite phases, commonly used in industrial applications. In this study, 2D microstructures were used for modeling, selected primarily for computational efficiency and to establish proof of concept. The framework successfully optimizes microstructures for targeted yield strength, ultimate strength, and stress concentration factors while significantly reducing computational time. Beyond hypoeutectoid steels, this scalable framework can be extended to other material systems and integrated with additive manufacturing, offering an efficient approach for accelerating microstructure design for specific engineering applications.

ConvLSTM↗
DOE OSTI2024

Quest for environmentally sustainable materials: A case for animal-based fillers and fibers in polymeric biocomposites

This review explores the potential of animal-based fillers and fibers as eco-friendly alternatives to conventional synthetic ones. Examining materials such as wool, silk, feather, hair and beak, the review elucidates their chemistry, structure, properties and sources, emphasizing biodegradability and renewability. It also discusses the compatibility of these materials with polymer matrices and their mechanical, acoustic and thermal performances. Further, the review critically analyzes environmental and ethical implications, presenting challenges and opportunities in the emerging field. By addressing ecological and performance aspects, it contributes to global efforts in fostering sustainability in materials science. Future research to address gaps and enhance the design, manufacture and application of animal-based reinforcements in various industries are clearly outlined at the end of the review.

36 MATERIALS SCIENCE↗