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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 253 records · Page 14
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Heterogeneous Morphologies and Hardness of Co-Sputtered Thin Films of Concentrated Cu-Mo-W Alloys

Heterogeneous microstructures in Cu-Mo-W alloy thin films formed by magnetron co-sputtering immiscible elements with concentrated compositions are characterized using scanning transmission electron microscopy (STEM) and nanoindentation. In this work, we modified the phase separated structure of a Cu-Mo immiscible system by adding W, which impedes surface diffusion during film growth. The heterogeneous microstructures in the Cu-Mo-W ternary system exhibited bicontinuous matrices and agglomerates composed of Mo(W)-rich phase. This is unique, as these are the slower-diffusing species, contrasting past reports of binary Cu-Mo thin films that exhibited Cu-rich agglomerates. The bicontinuous matrices comprised of Cu-rich and Mo(W)-rich phases exhibited bilayer thicknesses of less than 5 nm. The hardness of these thin films measured using nanoindentation is reported and compared to similar multilayers and nanocomposites in binary systems.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Embedded Ferroelectric Nanoclusters can drive Polarization Reversal in a Non-Ferroelectric Polar Film via the Proximity Effect

Heterogeneous nucleation from defects dominates the electric field required for polarization switching of ferroelectrics. Here, we consider the switching of a nominally non-switchable polar thin film of AlN due to the proximity effect arising from embedded ferroelectric nanoclusters of Al 1-x Sc x N. Using a Landau-Ginzburg-Devonshire thermodynamic approach and finite element modeling, we study the influence of nanocluster shape on polarization switching and domain nucleation emerging in AlN. The ferroelectric nanocluster boundary is modeled as a thin layer transitioning from Al 1-x Sc x N to AlN. We analyze the conditions under which polarization switching in the AlN film occurs at coercive fields significantly lower than its dielectric breakdown field. In the presence of spike-like Al 1-x Sc x N nanoclusters, the proximity effect enables switching of the spontaneous polarization in AlN and significantly reduces the corresponding coercive field. The internal field, which is depolarizing inside the AlN (due to its larger spontaneous polarization) and polarizing within the ferroelectric Al 1-x Sc x N nanoclusters (due to its smaller spontaneous polarization), lowers the potential barrier in the clusters and nucleates nanodomains at the Al 1-x Sc x N-AlN interface, forming localized regions of reversed polarization. Proximity effect can thus provide a pathway towards "thawing" previously "frozen" ferroelectrics through engineered nucleation for memory, actuation and optical technologies.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Milestone report for MRT 8504: Commission EXAFS spectrometer with spectral range of 17.95-18.6 keV design to obtain EXAFS data for Pu L shell experiments

This milestone describes the development and fielding of EXAFS Phase II spectrometer designed to provide high quality (SNR>=5) EXAFS spectra for Plutonium measurements at the National Ignition facility (NIF). Benefits: the EXAFS data will be used by the High Energy Density (HED) Physics and more specifically the Focused Material Science program to measure the thermal state of compressed materials with good temperature sensitivity (~100-1000 Kelvin). Data with good signal to noise ratio (SNR>=5) and good spectral resolution allows the FMS program to fill a gap in diagnosing temperature to completely constrain material properties for weapon physics and Stockpile Stewardship applications.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2025

Sandwiching of MOF nanoparticles between graphene oxide nanosheets among ice grains

Current strategies to tailor the formation of nanoparticle clusters require specificity and directionality built into the surface functionalization of the nanoparticles by involved chemistries that can alter their properties. Here, we describe a non-disruptive approach to place nanomaterials of different shapes between nanosheets, i.e., nano-sandwiches, absent any pre-modification of the components. We demonstrate this with metal-organic frameworks (MOFs) and silicon oxide (SiO 2 ) nanoparticles sandwiched between graphene oxide (GO) nanosheets, MOF-GO and SiO 2 -GO, respectively. For the MOF-GO, the MOF shows significantly enhanced conductivity and retains its original crystallinity, even after one-year exposure to aqueous acid/base solutions, where the GO effectively encapsulates the MOF, shielding it from polar molecules and ions. The MOF-GOs are shown to effectively capture CO 2 from a high-humidity flue gas while fully maintaining their crystallinities and porosities. Similar behavior is found for other MOFs, including water-sensitive HKUST-1 and MOF-5, promoting the use of MOFs in practical applications. The nanoparticle sandwich strategy provides opportunities for materials science in the design of nanoparticle clusters consisting of different materials and shapes with predetermined spatial arrangements.

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

Assessing the hygrothermal performance of bio-based materials in building wall systems

Building envelope systems are crucial in regulating thermal and moisture exchange between interior and exterior environments, accounting for approximately 28 % of building energy consumption in the United States with walls being the primary contributors. Improper selection of building envelope materials can lead to moisture-related issues, reduced resilience, and compromised durability. Hygrothermal performance assessment is a key factor in efficient building design. As such, improving the energy and hygrothermal performance of opaque wall materials, through careful assessment of material choices, is essential to enhancing building resilience, lowering energy costs, and improving occupant comfort. As the building industry seeks new strategies to reduce material energy intensity, bio-based materials emerge as a promising solution. However, their long-term hygrothermal performance in building envelope systems remains underexplored. To fill this gap, this study evaluates the hygrothermal behavior of 13 bio-based materials in residential wall systems across three U.S. climate zones. Laboratory experiments were performed to measure material properties such as density, thermal conductivity, moisture transmission, and sorption isotherms. These data were integrated into the WUFI® simulation tool to assess wall hygrothermal performance in Houston, Baltimore, and Chicago. A three-phase modeling approach was used: (1) baseline residential walls with oriented strand board (OSB) and gypsum board; (2) replacing OSB with bio-based materials; and (3) replacing drywall with bio-based materials. Results showed that the evaluated bio-based materials maintained acceptable moisture thresholds of ≤ 16 % across all climates, confirming their viability as an alternative for current sheathing materials. Furthermore, this study provides a foundation for future research and innovation in material science on the use of certain bio-based materials in high-performance, low energy use residential construction. Ultimately, providing critical data, offering a database of bio-based material properties, and supplying a simulation-based approach will help designers make informed decisions for future efficient building practices.

Bio-based materials↗
DOE OSTI2025

An unsupervised machine learning based approach to identify efficient spin-orbit torque materials

Materials with large spin–orbit torque (SOT) hold considerable significance for many spintronic applications because of their potential for energy-efficient magnetization switching. Unfortunately, most of the existing materials exhibit an SOT efficiency factor that is much less than unity, requiring a large current for magnetization switching. The search for new materials that can exhibit an SOT efficiency much greater than unity is a topic of active research, and only a few such materials have been identified using conventional approaches. In this paper, we present a machine learning-based approach using a word embedding model that can identify new results by deciphering non-trivial correlations among various items in a specialized scientific text corpus. We show that such a model can be used to identify materials likely to exhibit high SOT and rank them according to their expected SOT strengths. The model captured the essential spintronics knowledge embedded in scientific abstracts within various materials science, physics, and engineering journals and identified 97 new materials to exhibit high SOT. Among them, 16 candidate materials are expected to exhibit an SOT efficiency greater than unity, and one of them has recently been confirmed with experiments with quantitative agreement with the model prediction.

Sayed, Shehrin↗
DOE OSTI2024

SQMS Nanofabrication Taskforce: Towards Fabrication of High Coherence Superconducting Qubits

SQMS Nanofabrication Taskforce, which brings together experts in nanofabrication and materials science at the SQMS Center, has been launched to implement novel materials, substrates, and fabrication techniques for high coherence superconducting quantum devices. In a first coordinated effort, the Nanofabrication Taskforce developed fabrication processes to eliminate the lossy materials at surfaces and interfaces of superconducting qubits to enhance qubit coherence. The initial results of this study demonstrated T1 enhancement by almost an order of magnitude with best T1 s reaching ~ 600 $\mu$s. [1] We attribute this improvement to the replacement of lossy native Nb oxide layer with native Ta oxide, which is thinner and less disordered. we are now currently working on strategies with an aim towards moving qubit coherence times to millisecond timescales and beyond. The results of a systematic study will be presented to address substrate preparation, alternative materials as low loss platforms (Nb, Ta, and Re), novel non-oxide forming low loss capping layers such as proximitized Au, optimized qubit designs, and optimized Josephson junction materials, processing, and design.

Bal, Mustafa↗
DOE OSTI2024

Covalent integration of polymers and porous organic frameworks

Covalent integration of polymers and porous organic frameworks (POFs), including metal-organic frameworks (MOFs), covalent organic frameworks (COFs) and hydrogen-bonded organic frameworks (HOFs), represent a promising strategy for overcoming the existing limitations of traditional porous materials. This integration allows for the combination of the advantages of polymers, i.e., flexibility, processability and chemical versatility etc., and the superiority of POFs, like the structural integrity, tunable porosity and the high surface area, creating a type of hybrid materials. These resulting polymer-POF hybrid materials exhibit enhanced mechanical strength, chemical stability and functional diversity, thus opening up new opportunities for applications across a large variety of fields, such as gas separation, catalysis, biomedical applications, environmental remediation and energy storage. In this review, an overview of synthetic routes and strategies on how to covalently integrate different polymers with various POFs is discussed, especially with a particular focus on methods like polymerization within, on and among POF structures. To investigate the unique properties and functions of these resultant hybrid materials, the characterization techniques, including nuclear magnetic resonance spectroscopy (NMR), Fourier transform infrared spectroscopy (FTIR), X-ray diffraction (XRD), thermogravimetric analysis (TGA), transmission electron microscopy (TEM) and scanning electron microscopy (SEM), gas adsorption analysis (BET) and computational modeling and machine learning, are also presented. The ability of polymer-POFs to manipulate the pore environments at the molecular level affords these materials a wide range of applications, providing a versatile platform for future advancements in material science. Looking forward, to fully realize the potential of these hybrid materials, the authors highlight the scalability, green synthesis methods, and potential for stimuli-responsive polymer-POF materials as critical areas for future research.

Hossain, Md Amjad↗
DOE OSTI2024

Computational Approaches for Clean Energy Materials

Currently, 80% of the global final energy consumption occurs in form of fuels and only 20% as electricity. On the other hand, renewable energy additions come almost exclusively in the form of electricity (dominantly photovoltaics and wind). Thus, a successful energy transition will require enormous growth in renewables, sufficient to convert excess electricity into fuels, as well as the development of non-electricity based solar fuel technologies. As much as photovoltaic capacities have grown over the past 20 years, it is far from clear that current technologies and materials are up to the task to grow from here by yet another factor 100 until 2050. Therefore, sustained research efforts on emerging inorganic semiconductors for solar electricity and fuels are essential for facing the double challenge of climate change and energy security. Computational materials science can make important contributions, guiding and supporting research activities through both materials search and discovery and through detailed studies that help to develop a mechanistic understanding of materials performance and bottlenecks. This presentation will highlight three recent computational projects with relevance for photovoltaics and solar fuels (1) Defect graph neural networks (dGNN) for materials discovery in solar thermochemical hydrogen (STCH) [1]. The dGNN approach facilitates broad and fast materials screening for defect properties. (2) Modeling highly off-stoichiometric systems by evaluating the free energy of defect interaction [2]. This approach allows quantitative prediction of H2 production in complex STCH oxides. (3) First-principles atomic structure prediction for interfaces [3]. This work showed how an atomically thin CdCl2 interlayer phase enables in principle ideal electron transport across the incommensurate SnO2/CdTe interface. [1] M.D. Witman, A. Goyal, T. Ogitsu, A.H. McDaniel, S. Lany, Nat. Comput. Sci. (2023). https://doi.org/10.1038/s43588-023-00495-2. [2] A. Goyal, M.D. Sanders, R.P. O'Hayre, S. Lany, PRX Energy 3, 013008 (2024). https://doi.org/10.1103/PRXEnergy.3.013008. [3] A. Sharan, M. Nardone, D. Krasikov, N. Singh, S. Lany, Appl. Phys. Rev. 9, 041411 (2022). https://doi.org/10.1063/5.0104008.

density functional theory↗
DOE OSTI2025

MechBERT: Language Models for Extracting Chemical and Property Relationships about Mechanical Stress and Strain

Language models are transforming materials-aware naturallanguage processing by enabling the extraction of dynamic, context-rich information from unstructured text, thus, moving beyond the limitations of traditional information-extraction methods. Moreover, small language models are on the rise because some of them can perform better than large language models (LLMs) when given domain-specific questionanswer tasks, especially about an application area that relies on a highly specialized vernacular, such as materials science. We therefore present a new class of MechBERT language models for understanding mechanical stress and strain in materials. These employ Bidirectional Encoder Representations for transformer (BERT) architectures. We showcase four MechBERT models, all of which were pretrained on a corpus of documents that are textually rich in chemicals and their stress–strain properties and were fine-tuned on question-answering tasks. We evaluated the level of performance of our models on domain-specific as well as general English-language question-answer tasks and also explored the influence of the size and type of BERT architectures on model performance. We find that our MechBERT models outperform BERT-based models of the same size and maintain relevancy better than much larger BERT-based models when tasked with domain-specific question-answering tasks within the stress–strain engineering sector. These small language models also enable much faster processing and require a much smaller fraction of data to pretrain them, affording them greater operational efficiency and energy sustainability than LLMs.

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

Mechanical Behavior of Neutron Irradiated Refractory Multi-Principal Element Alloys Processed via Spark Plasma Sintering

The search for advanced materials capable of withstanding the extreme conditions of Generation IV reactors is a critical area in materials science research. These reactors operate under severe environments, including high temperatures, corrosion, stress, and irradiation damage. Consequently, there is a need for innovative alloy systems to ensure the reliability and longevity of proposed Generation IV reactor components. Refractory multi-principal-element alloys (RMPEA) have emerged as a promising candidate due to their exceptional properties. These alloys, characterized by their composition of multiple principal elements in near-equiatomic ratios, exhibit superior resistance to irradiation damage, reduced void swelling, enhanced microstructural stability, and minimal irradiation-induced hardening. While initial studies on RMPEAs have shown promising results, most research has been limited to thin films, nanocrystalline microstructures, and ion irradiation, which do not accurately represent the behavior of bulk materials. To address this gap, our research focused on the neutron irradiation of bulk RMPEAs. We aim to conduct comprehensive post-irradiation examinations (PIE) of RMPEAs irradiated at the Advanced Test Reactor at Idaho National Laboratory. The RMPEAs were synthesized using spark plasma sintering (SPS) with mechanically alloyed metallurgical powder. The RMPEA specimens are a MoNbTi alloy system with additions of -Zr, and -ZrV. Furthermore, PIE consisted of mechanical testing and advanced materials characterization. The mechanical testing consisted of sub-sized tensile testing, micro- and nano- indentation. Microstructural characterization included scanning electron microscopy and transmission electron microscopy. Mechanical testing coupled with advanced microscopy techniques provides insight into phase morphology and its effects on the mechanical properties of the RMPEA specimens. The results indicate that both pristine and irradiated RMPEA specimens exhibited brittle behavior during tensile testing, which can be attributed to their heterogeneous microstructure. The SPS manufacturing process did not include any post treatment, which resulted in a heterogeneous microstructure. Energy-dispersive X-ray spectroscopy revealed the presence of intermetallic such as laves phases within the microstructure. Specifically, Ti-rich precipitates were observed in the MoNbTi specimen, while Mo-rich precipitates were found in the MoNbTiZrV specimen. Nano-hardness testing of pristine samples showed that the laves phases exhibited higher hardness values compared to the matrix phase, suggesting that precipitate hardening is likely the dominant hardening mechanism in these specimens. The results from this work will be used to build a finite element model to predict mechanical behavior of future MPEA compositions. Thus, enabling for a streamlined approach to developing novel MPEAs for the nuclear industry.

36 - MATERIALS SCIENCE↗
DOE OSTI2025

Out-of-distribution detection with non-parametric density estimation for models predicting processing history of uranium ore concentrates

The rapid advancement in machine learning (ML) and computer vision (CV) coincides with the growth of interest in deploying these ML/CV models in numerous fields from medicine to social science. Similar to those areas, we have witnessed a great number of works in materials science employing ML/CV models – neural networks in particular – in their studies in recent years. These models have proven to obtain accurate performance in various tasks. However, these models struggle to attain a similar performance when encountering test samples coming from a distribution that is different from the training set. More importantly, they fail without providing any warning to the users. Therefore, we propose a framework for detecting out-of-distribution (OOD) samples to alert users when a human intervention might be necessary in this work. Specifically, we explore the use of a non-parametric density estimation method to detect OOD samples. Here, we assess OOD detection capability of the proposed framework on ML models developed for categorizing precipitation routes of U 3 O 8 when encountering OOD datasets that contain samples (1) undergone different imaging acquisition process, (2) undergone different material synthesis process, and (3) different materials than ID set. Through those experiments, we achieve an average area under the receiver operating characteristic (AUROC) of at least 91% on average in detecting OOD samples. With minimal overhead cost and superior performance, the proposed framework enables a reliable and safe system when deploying in real-world scenarios.

Convolutional neural networks↗
DOE OSTI2024

Numerical simulation projects in micromagnetics with Jupyter

We report a case study where an existing materials science course was modified to include numerical simulation projects on the micromagnetic behavior of materials. The Ubermag micromagnetic simulation software package is used in order to solve problems computationally. The simulation software is controlled through the Python code in Jupyter notebooks. Our experience is that the self-paced problem-solving nature of the project work can facilitate a better in-depth exploration of the course contents. We discuss which aspects of the Ubermag and the project Jupyter ecosystem have been beneficial for the students' learning experience and which could be transferred to similar teaching activities in other subject areas.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Overview and Current Status of Neutron Imaging at Oak Ridge National Laboratory

Neutron imaging is a non-destructive technique used to study the internal structure and composition of a wide range of materials. At Oak Ridge National Laboratory (ORNL), there are two neutron imaging beamlines that provide complementary capabilities that serve a diverse community, from materials science and engineering to biomedical and plant sciences. This report provides an overview of the current ORNL imaging capabilities that support materials research for a broad user community.

Torres, James [ORNL] (ORCID:0000000289407610)↗
DOE OSTI2025

Designing protein–material interfaces

This article addresses recent advances in using de novo protein design to create coherent interfaces between proteins and inorganic materials, either through protein self-assembly on crystal lattices or through directed nucleation and growth of crystals by protein scaffolds. Inspired by natural protein-crystal interfaces, we focus on a class of designed helical repeat proteins that present a repeating pattern of charged amino acid residues. We describe the use of in situ imaging and spectroscopic methods to investigate both the assembly of these proteins and their ability to direct crystal nucleation and growth. Furthermore, the findings reveal the importance of surface charge, facet-specific binding, solvent organization, and, more generally, the balance of protein-substrate-solvent interactions in determining how organized protein-materials interfaces emerge. Moreover, the results demonstrate the vast potential of protein design in materials science and elucidate the mechanisms by which interactions between biomolecules and inorganic surfaces lead to unique materials and morphologies.

Biomaterials-Proteins↗
DOE OSTI2025

33 Unresolved Questions in Nanoscience and Nanotechnology

Significant advances in science and engineering often emerge at the intersections of disciplines. Nanoscience and nanotechnology are inherently interdisciplinary, uniting researchers from chemistry, physics, biology, medicine, materials science, and engineering. This convergence has fostered novel ways of thinking and enabled the development of materials, tools, and technologies that have transformed both basic and applied research, as well as how we address critical societal challenges. In this Nano Focus, we pose and explore 33 questions whose answers could profoundly impact fields such as energy, electronics, the environment, optics, and medicine. These questions highlight the need for deeper foundational understanding, improved tools and techniques, and innovative applications─each with significant societal relevance. Together, they represent a global call-to-action for the scientific community.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2025

Micrometer: Micromechanics transformer for predicting full field mechanical responses of heterogeneous materials

Predicting mechanical responses of heterogeneous materials across scales remains a significant challenge. Traditional computational methods often struggle with complex and multiscale nature of these materials, limiting their effectiveness in real-world applications. Here, in this paper, we introduce Micrometer, a vision transformer based deep learning model designed to predict full field mechanical responses of heterogeneous materials, bridging the gap between computer vision and solid mechanics problems. We show that Micrometer, trained on a large-scale high-resolution dataset of 2D fiber-reinforced composites, can achieve state-of-the-art performance in predicting microscale strain fields across a wide range of material properties and loading conditions. Our model demonstrates accuracy and computational efficiency in applications such as computational homogenization and multiscale modeling, reducing computational time by up to two orders of magnitude compared to conventional numerical solvers while maintaining less than 1 % errors in predicting macroscale stress fields. Furthermore, we showcase Micrometer’s adaptability through transfer learning experiments on new materials with limited data, highlighting its potential to tackle diverse scenarios in computational solid mechanics. These results represent a significant step towards AI-driven innovation in materials science, addressing the limitations of traditional numerical methods and paving the way for more efficient simulations of heterogeneous materials across various industrial applications.

Composite materials↗
DOE OSTI2025

Science acceleration and accessibility with self-driving labs

In the evolving landscape of scientific research, the complexity of global challenges demands innovative approaches to experimental planning and execution. Self-Driving Laboratories (SDLs) automate experimental tasks in chemical and materials sciences and the design and selection of experiments to optimize research processes and reduce material usage. This perspective explores improving access to SDLs via centralized facilities and distributed networks. We discuss the technical and collaborative challenges in realizing SDLs’ potential to enhance human–machine and human–human collaboration, ultimately fostering a more inclusive research community and facilitating previously untenable research projects.

Canty, Richard B. [North Carolina State University↗