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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 109 records · Page 6

Development of Antimicrobial PLA Composites for Fused Filament Fabrication

In addition to possessing the desirable properties of being a biodegradable and biocompatible polymer fabricated from renewable resources, poly (lactic acid) (PLA) has useful mechanical and thermal attributes that has enabled it to be one of the most widely-used plastics for medicine, manufacturing, and agriculture. Yet, PLA composites have not been heavily explored for use in 3D-printing applications, and the range of feasible materials for the technology is limited, which inhibits its potential growth and industry adoption. In this study, tunable, multifunctional antimicrobial PLA composite filaments for 3D-printing have been fabricated and tested via chemical, thermal, mechanical, and antimicrobial experiments. Thermally stable antimicrobial ceramics, ZnO and TiO2, were used as fillers up to 30 wt%, and poly (ethylene glycol) (PEG) was used as a plasticizer to tune the physical material properties. Results demonstrate that the PLA composite filaments exhibit the thermal phase behaviors and thermal stability suitable for 3D-printing. Additionally, PEG can be used to tune the mechanical properties while not affecting the antimicrobial efficacy that ZnO and TiO2 imbue.

TiO2↗

Understanding the Electronic Behavior of f-Element Intermetallics

Due to their partially filled f-orbitals, lanthanide and actinide intermetallic materials exhibit intriguing electronic, structural, and magnetic properties. These materials provide a unique platform to explore exotic magnetic and quantum phenomena that arise from their strongly correlated f-electrons, offering insights into f-electron behavior. Incorporating light elements such as boron and carbon can significantly impact the electronic, chemical, and physical properties of these materials, with varying influence between lanthanides and actinides due to the more localized f-electrons in lanthanides. In this work, lanthanide-based intermetallic single crystals containing light atoms were synthesized using the molten metal flux growth method. In this method, one or more low-melting metals such as aluminum, gallium, tin, and bismuth are used in excess as the reaction medium. This solution-state approach allows for lower reaction temperatures compared to solid-state reactions and enables the isolation of kinetic products rather than thermodynamically stable compounds. The synthesized crystals were analyzed using an extensive array of analytical and computational characterization techniques, including single crystal X-ray diffraction, scanning electron microscopy, energy-dispersive spectroscopy, magnetization measurements, and density functional theory calculations. These analyses provided detailed insights into the structural and magnetic properties of the synthesized materials.

37 - INORGANIC, ORGANIC, PHYSICAL AND ANALYTICAL C↗

Spatiotemporal Reaction Dynamics Control in Two‐Photon Polymerization for Enhancing Writing Characteristics

Since 2001, 3D microfabrication based on two‐photon polymerization (TPP) has drawn extensive attention and interest in biology, optics, photonics, material science, and high‐energy physics. The in‐volume fabrication capability due to the threshold behavior of two‐photon absorption enables TPP higher flexibility compared with other nanofabrication techniques. However, as determined by the in‐volume fabrication feature as well as various reaction dynamics, the writing characteristics of TPP, such as throughput, accuracy, surface quality, and fabrication capability, are still limited. Herein, a comprehensive study is performed on the spatiotemporal behavior of reaction dynamics during TPP fabrication, mainly focusing on spatiotemporal characteristics of radical diffusion, photothermal effect, microscale mechanics, and voxel stacking process. Based on the study, a nonsequential fabrication method is established to simultaneously improve key writing characteristics of TPP and realize sharp features, high speeds, large overhang structure, and smooth surfaces. The method established in this work can be applied to improve the performance of functional devices for various fields.

36 MATERIALS SCIENCE↗

Towards Automated Design of Corrosion Resistant Alloy Coatings with an Autonomous Scanning Droplet Cell

We present an autonomous scanning droplet cell platform designed for on-demand alloy electrodeposition and real-time electrochemical characterization. Automation and machine learning are currently driving rapid innovation in high-throughput and autonomous materials design and discovery. We present two alloy design vignettes: one focusing on a multi-objective corrosion resistant alloy optimization and a study highlighting the complexity of the multimodal characterization needed to provide insight into the underlying structural and chemical factors that drive observed material behavior. This motivates a close coupling between autonomous research platforms and scientific machine learning methodology that blends mechanistic physical models and black box machine learning models. Lastly, we reflect on our early efforts in on-demand alloy deposition, highlighting some of the challenges. This emerging research area presents new opportunities to accelerate materials synthesis, evaluation, and hence discovery and design.

36 MATERIALS SCIENCE↗

Structural metamaterials with innate capacitive and resistive sensing

Interpenetrating lattices consist of two or more interwoven but physically separate sub-lattices with unique behaviors derived from their multi-body construction. If the sublattices are constructed or coated with an electrically conducting material, the close proximity and high surface area of the electrically isolated conductors allow the two lattices to interact electromagnetically either across the initial dielectric filled gap or through physical contact. Furthermore, changes in the size of the dielectric gap between the sub-lattices induced by deformation can be measured via capacitance or resistance, allowing a structurally competent lattice to operate as a force or deformation sensor. In addition to resistive and capacitive deformation sensing, this work explores capacitance as a fundamental metamaterial property and the environmental sensing behaviors of interpenetrating lattices.

Computational Solid Mechanics↗

Calibrating cryogenic temperature of TEM specimens using EELS

Cryogenic Scanning/Transmission Electron Microscopy has been established as a leading method to image sensitive biological samples and is now becoming a powerful tool to understand materials' behavior at low temperatures. However, achieving precise local temperature calibration at low temperatures remains a challenge, which is especially crucial for studying phase transitions and emergent physical properties in quantum materials. In this study, we employ electron energy loss spectroscopy (EELS) to measure local cryogenic specimen temperatures. Here we use the temperature-dependent characteristics of aluminum's bulk plasmon peak in EEL spectra, which shifts due to changes in electron density caused by thermal expansion and contraction. We successfully demonstrate the versatility of this method by calibrating different liquid nitrogen cooling holders in various microscopes, regardless of whether a monochromated or non-monochromated electron beam is used. Temperature discrepancies between the actual temperature and the setpoint temperatures are identified across a range from room temperature to 100 K. This work demonstrates the importance of temperature calibrations at intermediate temperatures and presents a straightforward, robust method for calibrating local temperatures of cryogenically-cooled specimens in electron microscopes.

36 MATERIALS SCIENCE↗

Automated Experiment in 4D-STEM: Exploring Emergent Physics and Structural Behaviors

Automated experiments in 4D scanning transmission electron microscopy (STEM) are implemented for rapid discovery of local structures, symmetry-breaking distortions, and internal electric and magnetic fields in complex materials. Deep kernel learning enables active learning of the relationship between local structure and 4D-STEM-based descriptors. With this, efficient and “intelligent” probing of dissimilar structural elements to discover desired physical functionality is made possible. This approach allows effective navigation of the sample in an automated fashion guided by either a predetermined physical phenomenon, such as strongest electric field magnitude, or in an exploratory fashion. Therefore, we verify the approach first on preacquired 4D-STEM data and further implement it experimentally on an operational STEM. The experimental discovery workflow is demonstrated using graphene and subsequently extended toward a lesser-known layered 2D van der Waals material, MnPS 3 . This approach establishes a pathway for physics-driven automated 4D-STEM experiments that enable probing the physics of strongly correlated systems and quantum materials and devices, as well as exploration of beam-sensitive materials.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Effects of fabric architectures on mechanical and damage behaviors in carbon/epoxy woven composites under multiaxial stress states

The mechanical properties and damage evolutions of carbon/epoxy woven fabric composites with three different fabric architectures, including one plain weave and two twill weave patterns, are experimentally investigated under multiaxial stress states. In particular, the effects of weave patterns are investigated by monotonic and cyclic off-axis tension tests. Both elastic modulus and strength degrade remarkably with increasing off-axis loading angle, while Poisson's ratio is much higher than that measured from on-axis tests and increases with loading strain gradually. Different fabric architectures show limited effects on the modulus and strength under multiaxial stress states, and they are well predicted by transformation equation and Tsai-Wu failure criteria, respectively. However, significantly different failure behaviors are observed in three fabric composites, and microstructure observation shows that fabric architecture affects the stress concentration and the damage development. Smaller crimp ratio and compacted structure postpone the damage development but result in more abrupt failure under multiaxial stress states.

36 MATERIALS SCIENCE↗

Thermodynamics of order and randomness in dopant distributions inferred from atomically resolved imaging

Abstract Exploration of structure-property relationships as a function of dopant concentration is commonly based on mean field theories for solid solutions. However, such theories that work well for semiconductors tend to fail in materials with strong correlations, either in electronic behavior or chemical segregation. In these cases, the details of atomic arrangements are generally not explored and analyzed. The knowledge of the generative physics and chemistry of the material can obviate this problem, since defect configuration libraries as stochastic representation of atomic level structures can be generated, or parameters of mesoscopic thermodynamic models can be derived. To obtain such information for improved predictions, we use data from atomically resolved microscopic images that visualize complex structural correlations within the system and translate them into statistical mechanical models of structure formation. Given the significant uncertainties about the microscopic aspects of the material’s processing history along with the limited number of available images, we combine model optimization techniques with the principles of statistical hypothesis testing. We demonstrate the approach on data from a series of atomically-resolved scanning transmission electron microscopy images of Mo x Re 1- x S 2 at varying ratios of Mo/Re stoichiometries, for which we propose an effective interaction model that is then used to generate atomic configurations and make testable predictions at a range of concentrations and formation temperatures.

25 ENERGY STORAGE↗

Unraveling fundamental mechanisms of silicon nitride crystallization in microelectronics manufacturing

This research project investigates the fundamental mechanisms of silicon nitride (SiN) crystallization, aiming to enhance the understanding of this critical material in microelectronics manufacturing. Through a collaborative effort between Sandia National Laboratories, the University of Tennessee, and the University of Florida, we developed a comprehensive framework that integrates experimental techniques, atomistic modeling, meso-scale simulations, and an integrated multi-scale model to capture this physical phenomenon on multiple time and length scales . The project developed a new machine learning based atomistic potential and utilized advanced phase field modeling to capture the complexities of polycrystalline growth and the influence of mechanical stresses on crystallization dynamics. By employing a grain tracker algorithm, the meso-scale model effectively identified and tracked individual crystal grains, enabling the simulation of anisotropic growth behaviors reflective of SiN’s physical properties. The integration of atomistic simulations with meso-scale modeling created a powerful multi-scale framework that validated atomistic inputs and enhanced predictive accuracy for crystallization dynamics at larger scales, validated experimentally. This adaptable modeling capability not only accelerates development times by informing manufacturing processes but also serves as a valuable starting point for understanding crystallization physics in similar materials. The insights gained from this research unlock new opportunities for the development of advanced materials tailored for future microelectronics and photonics applications. Overall, this project represents a significant advancement in understanding of fundamental physics of SiN and establishes a foundation for future research in material science, bridging the gap between atomic-level phenomena and macroscopic material behaviors for practical applications.

36 MATERIALS SCIENCE↗

Lessons Learned in Employing Data Analytics to Predict Oxidation Kinetics and Spallation Behavior of High-Temperature NiCr-Based Alloys

Machine learning (ML) can offer many advantages in predicting material properties over traditional materials development methods based solely on limited experimental investigations or physical-based simulations with the capability to reduce development cost, risk, and time. However, so far, limited efforts have been made to predict alloy oxidation kinetics and spallation behavior via ML due to the lack of consistently measured and sufficient experimental data and the inherent complexity in oxidation behavior of multicomponent high-temperature alloys. A previous study reported the ability of ML to predict oxidation kinetics of NiCr-based alloys as a function of alloy composition and operating conditions. Here, the performance of a ML model in predicting rate constants and spallation probability was evaluated in light of the roles of the data distribution of the experimental dataset (data analytics), the alloy composition, the exposure environment and the chosen oxidation approach to extracting kinetic values from the measured mass changes (but using either a simple parabolic law or a statistical cyclic oxidation model). Potential strategies to improve the predictions and enhance the extrapolative capability of the previously trained model will be discussed.

36 MATERIALS SCIENCE↗

Molecular-Dynamics Analysis of the Mechanical Behavior of Plasma-Facing Tungsten

We report a systematic computational analysis of the mechanical behavior of plasma-facing component (PFC) tungsten focusing on the impact of void and helium (He) bubble defects on the mechanical response beyond the elastic regime. Specifically, we explore the effects of porosity and He atomic fraction on the mechanical properties and structural response of PFC tungsten, at varying temperature and bubble size. We find that the Young modulus of defective tungsten undergoes substantial softening that follows an exponential scaling relation as a function of matrix porosity and He atomic content. Beyond the elastic regime, our high strain rate simulations reveal that the presence of nanoscale spherical defects (empty voids and He bubbles) reduces the yield strength of tungsten in a monotonically decreasing fashion, obeying an exponential scaling relation as a function of tungsten matrix porosity and He concentration. Our detailed analysis of the structural response of PFC tungsten near the yield point reveals that yielding is initiated by emission of dislocation loops from bubble/matrix interfaces, mainly 1/2<111> shear loops, followed by gliding and growth of these loops and reactions to form <100> dislocations. Furthermore, dislocation gliding on the <111>{211} twin systems nucleates 1/6<111> twin regions in the tungsten matrix. These dynamical processes reduce the stress in the matrix substantially. Subsequent dislocation interactions and depletion of the twin phases via nucleation and propagation of detwinning partials lead the tungsten matrix to a next deformation stage characterized by stress increase during applied straining. Furthermore, our structural analysis reveals that the depletion of twin boundaries (areal defects) is strongly impacted by the density of He bubbles at higher porosities. After the initial stress relief upon yielding, increase in the dislocation density in conjunction with decrease in the areal defect density facilitates the initiation of dislocation-driven deformation mechanisms in the PFC crystal.

36 MATERIALS SCIENCE↗

Understanding the effect of crystal anisotropy on grain growth, texturing and transport via the orthorhombic ?-U system [Slides]

The alpha phase of uranium exhibits the orthorhombic crystal structure, resulting in significant physical property anisotropy. These complex properties make the material challenging to work with in engineering settings but provide a rich arena to investigate the fundamental, multi-scale effects of anisotropy on physical behaviors such as grain growth and microstructure evolution under irradiation. We apply molecular dynamics and phase field modeling to study mass transport and grain growth in alpha-uranium. We characterize the mobilities of crystalline defects and the interfacial energy, and We find that the grain boundary energy is highly variable depending on the interface structure and that the relative rates of defect transport change with temperature. We also find that anisotropic thermal expansion has a significant impact on grain growth kinetics and texture development. Our results are used to explain experimental observations and to provide a basis for further hypotheses into the complex physical behavior of alpha-uranium.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

Tailoring electrophotonic capabilities of atomically thin GeS through controlled organometallic intercalation

The unique structure of van der Waals materials facilitates a robust mechanism for precise control of physical properties. In this study, we present a comprehensive analysis based on the intercalation of organometallics to modulate the optoelectronic behavior of two-dimensional germanium sulfide (GeS). Advanced computational exploration reveals significant and tunable features in the intercalated material. Additionally, the weak chemical interactions between organometallics and GeS support the electric-field-mediated drift and charge–discharge processes in intercalants. Controlling the concentration of organometallics in this manner enables the dynamic emergence of novel characteristics post-intercalation. These include flatbands near the Fermi level, significant enhancement of carrier mobility, and a magnetic ground state that is atypical for pristine GeS. Our findings demonstrate that organometallic intercalation offers a powerful strategy for tailoring the optoelectronic and magnetic characteristics of GeS, paving the way for harnessing emerging features for applications in next-generation devices.

Physics↗

Atomic-Scale Surface Studies of Bulk Metallic Glasses. Final Report

Bulk metallic glasses (BMGs) are of both scientific and technological interest because the absence of periodic atomic arrangements provides them with unique physical, chemical, and mechanical properties. Their high strength, superior elasticity, and an ability to be easily formed into virtually unlimited shapes with feature sizes from centimeters to Angstroms by blow molding and thermoplastic forming makes them an attractive choice for more and more practical applications and products. Due to their complex internal structure, however, experiments that yield insight into their exact atomic arrangements have been scarce. As a result, glass physics is one of the last remaining unexplored fields of materials science despite the scientific and technological importance of glasses in our daily lives, and the question how to characterize and control matter away from equilibrium, as glasses are, was listed as one of five Grand Challenges in a recent DoE report. The main reason for the slow progress in glass physics is the lack of experimental tools that enable access to atomic-scale structural and behavioral information for disordered materials. Such atomic-scale knowledge is mandatory to establish structure-property relationships that ultimately could allow to custom-design alloys featuring specific desired characteristics. With no such relationships available, theory development in glass remains basic and the few that exist are often untested. The aim of this research was to enable progress in our understanding of BMGs by developing a new approach that will allow a meaningful application of local surface science methods to specially prepared BMG samples to obtain a wealth of quantitative information on their atomic arrangements. Key was the availability of specially prepared samples whose surfaces feature large atomically flat terraces despite being entirely amorphous, which we have produced from a Pt 57.5 Cu 14.7 Ni 5.3 P 22.5 alloy (‘Pt-BMG’) both under ambient conditions as well as in ultrahigh vacuum using a unique setup that has been specially developed within this grant. Our approach starts with the in-situ preparation of oxide crystals that are terminated by large terraces, from which exact mirror images out of BMG will be produced using thermoplastic forming (TPF); for the research within this grant, we have successfully used (001)-oriented SrTiO 3 single crystal surfaces as well as (100)-, (110)-, and (111)-oriented single crystals made from LaAlO 3 . Since the resulting BMG replicas display all features of the original crystal with sub-Angstrom fidelity, thereby mimicking the original crystal’s termination by atomically flat terraces without being crystalline themselves, they are ideally suited for further investigation. The following atomic-scale local studies were then carried within this proposal: (i) high-resolution surface imaging and local spectroscopy using scanning probe microscopy, which showed disordered atom-like features and revealed changes in the gradient of the local surface potential on a 1-2 nm length scale; (ii) characterization of atomic-scale plastic flow with affected volumes as low as 1000 atoms, which showed local hardness near or above the theoretically predicted maximum and, once plastic deformation was initiated, homogeneous flow of the atoms involved; (iii) characterization of surface relaxation processes and the onset of crystallization induced by annealing, which showed that upon heating over the material’s glass transition temperature, the surface rearranges and relaxes towards a more stable, denser packed glass, which increases on-terrace surface roughness, while surface tension smoothens step edges; and (iv) studies that investigate the dependence of the material’s mechanical properties and structure on processing parameters, revealing that relaxed glasses get denser, harder, and more elastic. In combination, this information allows to combine structural models with mechanical properties and preparation history, thereby facilitating the development of preparation-structure-property relationships for metallic glasses. With the availability of such information, bulk metallic glasses can be further optimized to be used in more and more applications in industry.

36 MATERIALS SCIENCE↗

On-the-fly autonomous control of neutron diffraction via physics-informed Bayesian active learning

We demonstrate the first live, autonomous control over neutron diffraction experiments by developing and deploying ANDiE: the autonomous neutron diffraction explorer. Neutron scattering is a unique and versatile characterization technique for probing the magnetic structure and behavior of materials. However, instruments at neutron scattering facilities in the world is limited, and instruments at such facilities are perennially oversubscribed. We demonstrate a significant reduction in experimental time required for neutron diffraction experiments by implementation of autonomous navigation of measurement parameter space through machine learning. Prior scientific knowledge and Bayesian active learning are used to dynamically steer the sequence of measurements. We show that ANDiE can experimentally determine the magnetic ordering transition of both MnO and Fe 1.09 Te all while providing a fivefold enhancement in measurement efficiency. Furthermore, in a hypothesis testing post-processing step, ANDiE can determine transition behavior from a set of possible physical models. ANDiE's active learning approach is broadly applicable to a variety of neutron-based experiments and can open the door for neutron scattering as a tool of accelerated materials discovery.

36 MATERIALS SCIENCE↗

Internal Microstructure Dictates Interactions of Polymer-grafted Nanoparticles in Solution

Understanding the effects of polymer brush architecture on particle interactions in solution is requisite to enable the development of functional materials based on self-assembled polymer-grafted nanoparticles (GNPs). Static and dynamic light scattering of polystyrene-grafted silica particle solutions in toluene reveals that the pair interaction potential, inferred from the second virial coefficient, A 2 , is strongly affected by the grafting density, σ, and degree of polymerization, N, of tethered chains. In the limit of intermediate σ (~0.3 to 0.6 nm -2 ) and high N, A 2 is positive and increases with N. This confirms the good solvent conditions and can be qualitatively rationalized on the basis of a pair interaction potential derived for grafted (brush) particles. In contrast, for high σ > 0.6 nm -2 and low N, A 2 displays an unexpected reversal to negative values, thus indicating poor solvent conditions. These findings are rationalized by means of a simple analysis based on a coarse-grained brush potential, which balances the attractive core-core interactions and the excluded volume interactions imparted by the polymer grafts. The results suggest that the steric crowding of polymer ligands in dense GNP systems may fundamentally alter the interactions between brush particles in solution and highlight the crucial role of architecture (internal microstructure) on the behavior of hybrid materials. The effect of grafting density also illustrates the opportunity to tailor the physical properties of hybrid materials by altering geometry (or architecture) rather than a variation of the chemical composition.

36 MATERIALS SCIENCE↗

Imaging from Macro to Nanoscale: Multimodal Advances in Chemical and Biomedical Imaging

Imaging increasingly serves as a multiscale framework for linking molecular mechanisms to cellular behavior, tissue architecture, and organ phenotypes in biology and unraveling fundamental processes in chemistry, physics and materials science. This Perspective highlights recent advances in chemical and biomedical imaging across macro-, micro-, and nanoscales, using representative examples published in Chemical and Biomedical Imaging (CBMI). At the macroscale, we discuss chemically selective MRI, including endogenous and exogenous CEST strategies, together with photoacoustic imaging as a hybrid modality with functional and chemical contrast. At the microscale, we consider fluorescence, label-free optical and vibrational imaging, and selected X-ray approaches that expand sensitivity, specificity, and temporal resolution in biological and materials systems. At the nanoscale, we highlight super-resolution fluorescence microscopy, single-molecule methods, tip-enhanced Raman spectroscopy, and correlative imaging strategies that resolve local heterogeneity and molecular organization. Across scales, a common theme emerges that advances in probes, contrast mechanisms, instrumentation, and sample handling are enabling chemically informed imaging that connects molecular specificity with biological context.

multiscale imaging↗