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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 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Freestanding BaTiO 3 ‐Au Vertically Aligned Nanocomposite toward Flexible Multi‐Sensing Platform

Abstract Flexible and wearable sensors show enormous potential for personalized healthcare devices by real‐time monitoring of an individual's health. Typically, a single functional material is selected for one sensor to sense a particular physical signal while multiple materials will be selected for multi‐mode sensing. Vertically aligned nanocomposites (VANs) have recently demonstrated various material combinations and novel coupled multifunctionalities that are hard to achieve in any single‐phase material alone, including multiphase multiferroics, magneto‐optic coupling, and strong magnetic and optical anisotropy. Integrating these novel VANs into wearable sensors shows enormous potential in multi‐mode sensing owing to their multifunctional nature. In this work, the transfer of VANs onto polydimethylsiloxane as a novel flexible chemical and pressure sensor is demonstrated. For this demonstration, the classical BaTiO 3 ‐Au VAN with combined plasmonic and piezoelectric properties is used to demonstrate a multi‐sensing mechanism. A thin water‐soluble buffer of Sr 3 Al 2 O 6 serves as a buffer layer for the epitaxial growth and transfer process. The electrical output based on the piezoelectric responses and identifying 4‐mercaptobenzoic acid by surface‐enhanced Raman spectroscopy reveal great potential for free‐standing VANs in a wearable multifunctional sensing platform.

Tsai, Benson Kunhung [School of Materials Engineer↗
DOE OSTI2026

Rheinheimera sp . T2C2 Bacterial Biofilm for Bioremediation of Cobalt(II)

Toxic metals, including cobalt, are often the cause of the contamination of rivers and lakes in mining regions. Heavy metal water pollution has been linked to numerous human health problems, prompting the need for environmental remediation. Existing techniques for removing heavy metals from water, such as chemical precipitation and filtration, produce toxic waste, are costly, or require high power consumption for pumping. Biosorption is a potential alternative strategy that is cost-effective and uses readily available and naturally produced biomass and living material to absorb pollutants. Engineering living materials, such as biofilms, which consist of living cells and a secreted polymer matrix, offer the potential to integrate toxin sensing, sequestration, and metabolism capabilities of cells to improve pollution remediation strategies. Alternative biofilm producing candidates need to be explored to implement these material capabilities. Previous biosorption studies have primarily used bacterial biofilms from known pathogens and/or generated toxic waste in the form of the absorbent material combined with the heavy metal. Here, we describe a recently isolated bacterium called Rheinheimera sp. T2C2 that forms biofilms with promising biosorption characteristics. T2C2 is an aquatic bacterium with low nutrient requirements and high biofilm production that is not known to be pathogenic. We demonstrate (1) the efficacy of Rheinheimera sp. T2C2 as a biosorbent for cobalt bioremediation; (2) how biosorption is altered by water conditions to establish the efficacy of this strategy in different environments; and (3) how the metal can be released from the biofilm for metal recycling. Our findings will provide a living materials strategy that overcomes the existing barriers for bioremediation and improves the health of ecosystems and humans through heavy metal removal and recycling.

Rheinheimera↗
DOE OSTI2024

Femto-second Laser’s Enabling New Length Scale Fabrications for Rapid Post Irradiation Examination of Materials: Concluding LDRD Project Poster

Mechanical testing campaigns are required to qualify materials for advanced reactor conditions, yet economical and safety limitations restrict the number of standardized mechanical tests that can be performed. Reducing the size of the sample is one approach to addressing these challenges and to accelerating testing. Previous research has shown that smaller mechanical test samples produce higher yield and ultimate stress values compared to values measured from standard sample sizes: the “smaller is stronger” effect. Specimens used in accelerated material testing campaigns must reflect bulk material performance to enable engineering scale material property measurement. The objective of this research project was to determine if engineering scale mechanical behavior—the yield stress—could be measured with micro-tensile test samples smaller than traditional standard testing geometries. The relationship between yield stress and sample size was explored with two different nuclear-relevant structural materials: Zircaloy-4 and tungsten. Mechanical testing of both metals demonstrated decreasing yield stress values with increasing sample gauge size across three different sizes. Yield stress values from the largest gauge size, fabricated with a femto-second laser ablation system, approach bulk material yield stress values reported in published literature. Preliminary analysis of the tungsten samples indicates the yield stress value depends on the grain characteristics within the gauge section, in addition to the gauge size. Accompanying modeling efforts, including response surface generation and crystal plasticity approaches, further demonstrated that the size of the sample gauge section alone cannot explain the change in yield stress values.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Employing Machine Learning for New Particle Formation Identification and Mechanistic Analysis: Insights From a Six‐Year Observational Study in the Southern Great Plains

We present a supervised machine learning (ML) framework to automatically identify new particle formation (NPF) events and analyze key atmospheric factors associated with their occurrence and growth. We applied ML to detect NPF events using start time and particle concentrations across size ranges, while identifying atmospheric variables including ambient temperature, relative humidity, solar radiation intensity (SRI), wind speed, wind direction, boundary layer height, total organics, sulfate, nitrate, total surface area concentration, sulfur dioxide, and turbulent kinetic energy (TKE). We analyzed a 6-year data set from the Atmospheric Radiation Measurement at the Southern Great Plains (SGP) site in Oklahoma, USA. Using long-term ground-based measurements, we identified NPF events and applied Random Forest Classifiers, which achieved 90%–95% prediction accuracy. Feature importance analysis highlighted SRI, relative humidity, and ambient temperature as the most influential variables, contributing normalized importances of 28%, 17%, and 10%. Partial Dependence Plots (PDPs) indicated that higher SRI and lower relative humidity were critical in promoting NPF formation at SGP. Seasonally, NPF events were more frequent in winter (42.1%) and spring (35.5%), and least in summer (4.0%). Particle growth rates also exhibited a seasonal variation, with the lowest in winter (below 2 nm hr −1 ) and highest in late spring and early summer (exceeding 5 nm hr −1 ). Temperature, turbulent kinetic energy, and aerosol properties were the primary factors of growth rate variability. This study advances predictive modeling of NPF, offers insights for future campaign deployments, and demonstrates the effectiveness of ML in understanding the formation and growth of atmospheric aerosols.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

High strain-rate strength response of single crystal tantalum through in-situ hole closure imaging experiments

The properties of crystalline materials often depend on directionality and operating conditions. Specifically, the strength of materials can depend anisotropically on crystal direction and the loading condition. To probe these effects, a preliminary series of high strain-rate (> 105/s) strength plate-impact hole closure experiments were performed on high purity single crystal Tantalum cubes. The orientation of the single crystals with respect to impact/loading were varied to provide data to inform crystal plasticity modeling efforts. The experiments consist of in-situ high-resolution X-ray radiographic imaging of the hole collapse under dynamic compression conditions to infer the material strength via its resistance to closure at increasing levels of plastic strain. The experiments are compared against hydrocode simulation predictions. Here, a comparison with simple elastic perfectly plastic strength model predictions is presented to elucidate the response of the different crystal orientations at high strain-rate and large plastic strains.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Submerged Fatigue Testing of Marine Energy Advanced Materials

Marine energy structures are typically made of advanced composite materials and are subjected to extreme ocean environments in service. In extreme ocean environments, seawater currents and waves load structures repeatedly, which cause two environmental conditions: water intrusion and mechanical fatigue. In prior research, the two environmental conditions were applied sequentially, where composite specimens were aged and then mechanically tested. To understand the combined effects of dynamic loading and water intrusion on composite materials, the present study involves the static and fatigue four-point bend testing of composite coupons in a water tank. The water tank was designed and built to fit either a 100 kN or a 250 kN load frame. Flexural strength value, cycles to failure, and failure mode results from submerged fatigue testing will be used to inform marine energy structure designs. The coupon-scale test method will be used to scale up to and inform methods for subsequent subcomponent testing and standards development. Finally, the benefits of designing marine energy structures to informed standards are decreased lifetime costs and increased reliability and energy production, ultimately leading to a sustainable and low-carbon energy system.

13 HYDRO ENERGY↗
DOE OSTI2025

New Particle Formation Events Over the Southeast Atlantic Coincide With the African Biomass Burning Season

Abstract We investigated the occurrence and evolution of new particle formation (NPF) events over the southeast Atlantic. The studied region is under the influence of the long‐range transport of aerosols and gases during the southern African biomass burning season, from June to October every year. Interestingly, NPF was observed to coincide with the African biomass burning season, although wet removal of pre‐existing aerosols is needed during these NPF events. Surface and airborne measurements show that these NPF events likely occurred in the upper region of the marine boundary layer, and the newly formed aerosols were further transported to the surface via vertical air motions. Using a box model, we predicted that a large fraction of these particles could grow to sizes related to cloud condensation nuclei. Our study shows that NPF can occur over the southeast Atlantic, and the African biomass‐burning plume likely contributed to the NPF occurrence.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

A Scalable Reduced‐Order Model for the Steady Navier–Stokes Equations

Scaling up new scientific technologies from laboratory to industry often involves demonstrating performance on a larger scale. Computer simulations can accelerate design and predictions in the deployment process, though traditional numerical methods are computationally intractable even for intermediate pilot plant scales. Recently, the component reduced order modeling method has been developed to tackle this challenge by combining projection reduced order modeling and discontinuous Galerkin domain decomposition. However, while many scientific or engineering applications involve nonlinear physics, this method has only been demonstrated for various linear systems. In this work, the component reduced order modeling method is extended to steady Navier–Stokes flow, with application to general nonlinear physics in view. The large‐scale, global domain is decomposed into a combination of small‐scale unit component. Linear subspaces for flow velocity and pressure are identified via proper orthogonal decomposition over sample snapshots collected from each small‐scale unit component. Velocity bases are augmented with a pressure supremizer to satisfy the inf–sup condition for stable pressure prediction. Two different nonlinear reduced order modeling methods are employed and compared for efficient evaluation of nonlinear advection: A third‐order tensor projection operator and the empirical quadrature procedure. The proposed method is demonstrated on the flow over arrays of five different unit objects, achieving a 23‐fold speedup with less than 4% relative error in domains up to 256 times larger than the unit components. Furthermore, a numerical experiment with the pressure supremizer strongly indicates the need for a supremizer for stable pressure prediction. A comparison between the tensorial approach and the empirical quadrature procedure revealed a slight advantage of the empirical quadrature procedure. The framework is compared with an alternating Schwarz‐based reduced‐order approach, demonstrating improved efficiency and robustness for the DG‐based global solver while retaining flexibility for sub‐scale iterative solvers. The method is further extended to a coupled advection–diffusion and Navier–Stokes system, illustrating its applicability to multi‐physics problems and its potential for more general, inter‐coupled nonlinear systems.

42 ENGINEERING↗
DOE OSTI2025

AI‐Driven Defect Engineering for Advanced Thermoelectric Materials

Thermoelectric materials offer a promising pathway to directly convert waste heat to electricity. However, achieving high performance remains challenging due to intrinsic trade-offs between electrical conductivity, the Seebeck coefficient, and thermal conductivity, which are further complicated by the presence of defects. This review explores how artificial intelligence (AI) and machine learning (ML) are transforming thermoelectric materials design. Advanced ML approaches including deep neural networks, graph-based models, and transformer architectures, integrated with high-throughput simulations and growing databases, effectively capture structure-property relationships in a complex multiscale defect space and overcome the “curse of dimensionality”. This review discusses AI-enhanced defect engineering strategies such as composition optimization, entropy and dislocation engineering, and grain boundary design, along with emerging inverse design techniques for generating materials with targeted properties. Finally, it outlines future opportunities in novel physics mechanisms and sustainability, highlighting the critical role of AI in accelerating the discovery of thermoelectric materials.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Analcime-wairakite formation during experimental cement-bentonite alteration at 200–300 °C

Engineered barrier system materials in nuclear waste repositories may undergo hydrothermal alteration in response to groundwater saturation and heating events over their long operational timescales. Hydrothermal interactions between engineered materials (e.g., bentonite buffers, cements, waste canister materials) and the host rock environment may drive alteration processes that affect the advantageous properties of some barrier materials. However, such alteration will also promote the formation of zeolites, hydrothermally formed minerals that may themselves act to isolate radionuclides. Understanding the environmental conditions that control radionuclide-sorbing properties of zeolites, such as Si/Al ratio or Na content, will be valuable for assessing the changing properties of engineered barrier materials in the case of an in situ heating event. Here, we present experimental work characterizing the formation of zeolites during hydrothermal interactions between generic clay and cement barrier materials, with a focus on the analcime-wairakite zeolite series, which has known radionuclide sorption and exchange properties. We present the results of hydrothermal experiments combining uncured ordinary Portland cement powder with steel and Wyoming bentonite in the presence of Opalinus clay and a synthesized Opalinus clay groundwater to simulate water-saturated conditions in an argillaceous rock repository. The experiments were conducted isothermally at 200 or 300 °C for 8 to 24 weeks. In contrast to analogous studies that did not include cement reactants, we observed the formation of analcime-group minerals in all experiments. The addition of cement resulted in lower Si/Al ratios in the zeolites compared to similar studies that did not include cement. The presence of cement as a reactant was interpreted to promote analcime formation at 200 °C. At 300 °C, we observed higher calcium and silica concentrations in the aqueous solutions as well as increased wairakite formation and decreased analcime formation compared to the experiments at 200 °C. These results show a fully realized analcime-wairakite solid solution that falls between Si/Al = 2 (ideal) and a trend of analcime minerals that have increasing Si/Al ratios with increasing Na/(Na+Ca). Our results show predictable relationships between the Si/Al ratio and the analcime-wairakite content under hydrothermal conditions and illustrate that repository material interactions may promote the formation of zeolites in the analcime-wairakite solid-solution series during heating events in the subsurface.

58 GEOSCIENCES↗
DOE OSTI2024

Microscopy modality transfer of steel microstructures: Inferring scanning electron micrographs from optical microscopy using generative AI

Scanning electron microscopy (SEM) is resource intensive, which limits its throughput in some applications. As an alternative, we propose applying computer vision and machine learning to generate high-quality synthetic SEM micrographs from micrographs obtained using light optical microscopy (LOM). Working with a correlated LOM/SEM dataset of dual-phase steel images, we test generative models of various architectures, including encoder-decoder networks, generative adversarial networks (GANs), and diffusion-based models. We find that the diffusion models significantly outperform other methods on both qualitative and quantitative assessments, while preserving key metallurgical meaning. This work establishes diffusion as the state-of-the-art for microscopy modality transfer and demonstrates the potential of AI-powered microscopy to enhance LOM with micron scale structural recreation.

Computer vision↗
DOE OSTI2025

Large Non‐Resonant Infrared Optical Second Harmonic Generation in Bulk Crystals of Van der Waals Semiconductor, SnP 2 Se 6

2D van der Waals (vdW) materials have emerged as a highly promising candidates for nonlinear optical (NLO) applications. This study presents the synthesis, comprehensive linear optical, and optical second harmonic generation (SHG) characterization of a novel 2D vdW semiconductor SnP2Se6 in its bulk single crystal form. It exhibits an indirect bandgap of ≈1.47 eV and an exceptional non-resonant SHG coefficient of d 33 ∼ −222 ± 30 pm V −1 at a fundamental wavelength of 2 µm, which is ≈7 times larger than that of the commercial AgGaSe 2 with a comparable bandgap. Density functional theory (DFT) calculations of the linear and nonlinear optical properties exhibit reasonable agreement with the experimental measurements, revealing the chemical origin of the enhanced properties. Moreover, SnP 2 Se 6 can exhibit both type-I and type-II phase matching over a wide spectral range, fulfilling one of the key criteria for an ideal NLO crystal. These exceptional properties position SnP 2 Se 6 as a highly promising candidate for NLO applications.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Critical Review of Brazil Disk Techniques for Tensile Strength Characterization With an Emphasis on High Explosive Materials

Mechanical properties are a critical performance metric for many high explosive (HE) materials and tensile strength properties are particularly important. Direct tensile measurements using dogbone shaped samples are the gold standard but they have the disadvantage that they are fairly large and require samples machined from billets. Diametral compression, more commonly known as Brazil disk (BD) testing, is an indirect method for measuring tensile strength on smaller and more easily fabricated samples. A review of the BD literature is presented with an emphasis on tensile strength measurements in high explosive materials. BD literature is reviewed in three primary areas: (i) rocks and concrete, (ii) pharmaceutical materials, and (iii) high explosive materials. The literature for rocks/concrete is extensive and dates back over 80 years; despite this there is no consensus on the validity/accuracy of the BD technique or the optimal variant of the BD technique to employ. The pharmaceutical literature is the opposite, being limited in scope and quantity of studies. BD literature on high explosive materials falls in between, not as impressive as in the rocks/concrete community but more substantiative than in the pharmaceutical community. After the review of the literature practical parameters for HE BD testing and recommended future work is discussed.

Brazil disk↗
DOE OSTI2025

Elucidating texture and grain morphology contributions to the micromechanical response of additively manufactured Inconel 625

Microstructural variation of additively manufactured (AM) metal components in comparison to wrought counterparts makes certification for critical applications a challenge. Microscale simulations leveraging modern computational tools may be used to supplement testing of AM microstructures, thus accelerating certification by reducing the number of experiments needed. However, as micromechanical response is closely tied to critical properties like fatigue-life and fracture, utilization of these simulations with macroscale experimental data alone is insufficient. One means to attain microscale experimental data is in situ diffraction data collected from synchrotron X-ray sources. In this work, such data were collected during in situ compression of AM Inconel 625 superalloy. Interpretation of experimental results was assisted by massive (8M element) complementary micromechanical simulations performed on sets of virtual microstructures generated using cellular automata. Together, micromechanical data from diffraction experiments and simulations were used to probe the effects of textured “track” microstructures generated during laser powder bed fusion and directional strength-to-stiffness on micromechanical response. Though fiber-averaged directional strength-to-stiffness ratios were expected to dominate given the high elastic anisotropy of the material, the combination of small variations in texture and specific grain configurations unique to AM microstructures lead to significant variability in micromechanical response after yield. The findings emphasize the importance of high-fidelity microstructural representation that captures key texture components and AM-specific morphology for property prediction of AM metals.

36 MATERIALS SCIENCE↗