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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 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Effect of microstructural and environmental variables on ductility of austenitic stainless steels

Austenitic stainless steels are used extensively in harsh environments, including for high-pressure gaseous hydrogen service. However, the tensile ductility of this class of materials is very sensitive to materials and environmental variables. While tensile ductility is generally insufficient to qualify a material for hydrogen service, ductility is an effective tool to explore microstructural and environmental variables and their effects on hydrogen susceptibility, to inform understanding of the mechanisms of hydrogen effects in metals, and to provide insight to microstructural variables that may improve relative performance. In this study, hydrogen precharging was used to simulate high-pressure hydrogen environments to evaluate hydrogen effects on tensile properties. Several austenitic stainless steels were considered, including both metastable and stable alloys. Room temperature and subambient temperature tensile properties were evaluated with three different internal hydrogen contents for type 304L and 316L austenitic stainless steels and one hydrogen content for XM-11. Significant ductility loss was observed for both metastable and stable alloys, suggesting the stability of the austenitic phase is not sufficient to characterize the effects of hydrogen. Internal hydrogen does influence the character of deformation, which drives local damage accumulation and ultimately fracture for both metastable and stable alloys. While a quantitative description of hydrogen-assisted fracture in austenitic stainless steels remains elusive, these observations underscore the importance of the hydrogen-defect interactions and the accumulation of damage at deformation length scales.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Uncertainty Quantification Framework for Predicting Material Response with Large Number of Parameters: Application to Creep Prediction in Ferritic-Martensitic Steels Using Combined Crystal Plasticity and Grain Boundary Models

This paper presents an uncertainty quantification (UQ) framework for the physics-based model prediction of material response with a large number of parameters. The application problem presented in this work is that of predicting creep in Grade 91 steel at 600°C. The material response is defined with a physically based microstructural model with constitutive equations emulating several observed phenomena in Grade 91 and embodied into an explicit geometry mesoscale finite element model for prior austenite grains and grain boundaries. Creep within the grains and in grain boundaries are represented by crystal plasticity for dislocation motion and a physics-based model for cavity growth and nucleation, respectively. The creep behavior of this material is influenced by several parameters, some of which have a wide range of variation based on experimental data. UQ combined with microstructural modeling can discover the core microstructural causes of experimental variability, leading to improved materials with lower variability in critical long-term material properties. In this study, we investigate the model's uncertainty to identify material properties that may be modified during production to increase creep life and analyze different components of the crystal plasticity model for improvements. For this purpose, a quantity of interest is defined as time to minimum creep rate, which correlates well to the creep failure of the material. A deep neural network model was trained and validated to be used as a surrogate for the finite element model. Then, a variance-based sensitivity analysis is performed on the surrogate model to find the Sobol indices of the input parameters in respect to the output quantity of interest. The Sobol indices are used to reduce the dimensionality of the model. Generalized polynomial chaos expansion is used on the reduced basis models to propagate the uncertainty from the input parameters to the quantity of interest using the deep neural network surrogate model. These results are benchmarked against uncertainty propagation using Monte Carlo simulations. In conclusion, the UQ performed through the reduced basis model captures almost all the uncertainty in the model with significantly fewer simulations, making it possible to perform the UQ directly via simulations with the finite element model rather than surrogate machine-learned models.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Development and Implementation of a Bag Degradation Predictive Tool at Los Alamos National Laboratory

Nuclear material packaging involves many complex variables derived from the stored material's properties, the storage environment, and the synergistic interaction of said material and its environment on the containment boundary. In the Plutonium Facility (PF-4) at Los Alamos National Laboratory (LANL) the typical containment boundary found, starting from the stored material itself to the outermost containment layer, consists of the material contained within a stainless steel (typically 304) slip-lid (or other equivalent stainless steel packaging layer) wrapped in an sPVC bag-out bag contained within a facility approved outer container stored within the boundaries of an approved facility (i.e., a building designed and approved to store nuclear material). This packaging configuration, though occasionally deviated from in the past, represents the current procedurally enforced, expected containment structure for all interim nuclear material containment outside of an engineered control barrier (e.g., a glovebox). Over the life of containerization research at LANL, packaging engineers have become increasingly concerned with the degradation of the sPVC bag-out bag, which can cause corrosion to the outermost packaging layer as well as possible exposure to alpha contamination if the bag-out bag layer is degraded to the point of containment failure. A bag-out bag degradation predictive tool (BDT) is therefore needed to assess the current and future nuclear material inventory for possible bag degradation in order to continue to ensure safe operations for the facility and its workers to deliver on the vital national security mission of 30 PPY.

42 ENGINEERING↗
DOE OSTI2024

Report on the Integration of Experimental and Modeling Data for Initial Equivalence Study of Microstructural Evolution in Irradiated LPBF 316SS

Advanced materials and manufacturing technologies are poised to improve the safety and design characteristics of nuclear technologies and meet US energy, environmental, and economic needs. In particular, metal additive manufacturing (AM) provides an opportunity to produce novel materials and component geometries, but their use is not without hurdles arising from the inherent microstructure variability that can result from the layer-by-layer build approach. Given the greater possible microstructure variability in AM materials—and the dearth of materials test reactors—it is impractical to rely solely on neutron irradiation studies to produce data for materials qualification for every possibility. This work within the Advanced Materials and Manufacturing Technologies (AMMT) Environmental Effects technical area contributes to the rapid qualification framework by developing a science-driven framework for the accelerated qualification of materials for nuclear environments. A key product of the Environmental Effects technical area of the AMMT program is the Licensing Approach with Ions and Neutrons (LAIN). This approach recognizes that whether using existing materials in new environments, newly developed materials tailored for these environments, or new manufacturing methods, the traditional decades-long approach for materials qualification does not facilitate rapid deployment. In FY 2023, the AMMT program presented a conceptual framework of specific steps to fulfill several technical challenges associated with qualifying materials for performance in radiation environments on an accelerated time frame informed by the state of the art in materials science and a review of the current regulatory landscape. The objective of this section of the Environmental Effects technical area is to critically evaluate and refine the proposed qualification framework presented under AMMT by integrating the research results of the neutron irradiations, the ion irradiations, and modeling efforts. These ongoing efforts span across Argonne National Laboratory (ANL), Idaho National Laboratory (INL), and Oak Ridge National Laboratory (ORNL) and are closely coordinated.

36 MATERIALS SCIENCE↗
DOE OSTI2022

A compendium and meta-analysis of flatband potentials for TiO2, ZnO, and SnO2 semiconductors in aqueous media

Semiconductor/electrolyte interfaces are of great interest to numerous scientific fields including renewable energy, (photo)electrochemistry, and energy storage. The semiconductor flatband potential is a key parameter in locating the conduction band minimum or valence band maximum of the semiconductor material in electrolyte. Despite its importance for quantifying the energetic location of the semiconductor bands, literature reports for the same material demonstrate significant variability in the flatband potential. In this compendium and meta-analysis, reported flatband potentials of the common semiconductor materials TiO2, SnO2, and ZnO in aqueous electrolyte were compiled and assessed to quantify the spread in literature flatband potentials as well as determine the factors that lead to the significant spread. For TiO2, SnO2, and ZnO, literature flatband potentials referenced to the reversible hydrogen electrode span a range of nearly 2 V each. Flatband potential tabulations were separated by variables such as the solution pH, the crystalline polymorph, the crystal facet, the morphology, and the dimensions or combinations of these variables to assess the factors that contribute to the observed spread. Important and surprising findings from these categorizations are summarized: (1) Even for the narrowest categorizations, the spread in flatband potential is still large. (2) Flatband potentials of TiO2 and SnO2 follow the expected Nernstian dependence with solution pH. ZnO materials deviate from this Nernstian dependence. (3) In the aggregate, there is no statistically significant difference in the reported flatband potentials of anatase and rutile TiO2. Single crystal tabulations were the only distributions to have statistically significant differences in the flatband potential between anatase and rutile TiO2. (4) Anatase TiO2 materials with a nanotube morphology appear to have a +400 mV difference in mean flatband potential compared to all other morphologies, but we argue that this is likely due to widespread misuse of the Mott–Schottky analysis. Other interesting findings are revealed within the spread of literature flatband potentials, and possible explanations are provided to generate discussion. We also briefly review and discuss common techniques that were used to determine the flatband potential and the pitfalls/criticisms of these techniques. Last, we discuss some ways in which future research on the determination of the flatband potential can be performed to improve the reliability of reported values and the quality of the work. In total, the results from this meta-analysis suggest multiple factors can affect the measured flatband potential and that an abundance of caution should be applied when attempting to quantify the flatband potential of complex or nanostructured systems.

Patel, Milan Y. (ORCID:0000000291948141)↗
DOE OSTI2023

Precursor recommendation for inorganic synthesis by machine learning materials similarity from scientific literature

Synthesis prediction is a key accelerator for the rapid design of advanced materials. However, determining synthesis variables such as the choice of precursor materials is challenging for inorganic materials because the sequence of reactions during heating is not well understood. In this work, we use a knowledge base of 29,900 solid-state synthesis recipes, text-mined from the scientific literature, to automatically learn which precursors to recommend for the synthesis of a novel target material. The data-driven approach learns chemical similarity of materials and refers the synthesis of a new target to precedent synthesis procedures of similar materials, mimicking human synthesis design. When proposing five precursor sets for each of 2654 unseen test target materials, the recommendation strategy achieves a success rate of at least 82%. Our approach captures decades of heuristic synthesis data in a mathematical form, making it accessible for use in recommendation engines and autonomous laboratories.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Physics-informed neural networks for heterogeneous poroelastic media

This study presents a novel physics-informed neural network (PINN) framework for modeling poroelasticity in heterogeneous media with material interfaces. The approach introduces a composite neural network (CoNN) where separate neural networks predict displacement and pressure variables for each material. While sharing identical activation functions, these networks are independently trained for all other parameters. To address challenges posed by heterogeneous material interfaces, the CoNN is integrated with the Interface-PINNs (I-PINNs) framework (Sarma et al., Comput. Methods Appl. Mech. Eng. 429: 117135, 2024), allowing different activation functions across material interfaces. Further, this ensures accurate approximation of discontinuous solution fields and gradients. Performance and accuracy of this combined architecture were evaluated against the conventional PINNs approach, a single neural network (SNN) architecture, and the eXtended PINNs (XPINNs) framework through two one-dimensional benchmark examples with discontinuous material properties. The results show that the proposed CoNN with I-PINNs architecture achieves an RMSE that is two orders of magnitude better than the conventional PINNs approach and is at least 40 times faster than the SNN framework. Compared to XPINNs, the proposed method achieves an RMSE at least one order of magnitude better and is 40% faster.

42 ENGINEERING↗
DOE OSTI2020

Monitoring of Unvented Roofs with Fibrous Insulation, Diffusion Vents, and Interior Vapor Control in a Cold Climate

This research involved construction of a conditioned test hut in a cold climate (Climate Zone 5A) with multiple side-by-side instrumented roof rafter bays. This work examined seven experimental unvented roofs and one code-compliant control hybrid ccSPF-cellulose roof over three winters and following summers. Examined variables included insulation material (fiberglass and cellulose), the presence or absence of a ridge diffusion vent (vapor-open material at the roof ridge to promote drying), the effect of various interior vapor control membranes (fixed and variable permeance), the effect of interior relative humidity, and the effect of interstitial airflow (from the interior into the cavity). This experiment was run over three winters, changing variables of interior conditions, roof configurations/materials, and the addition of intentional air leakage.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

Optical constants of polycrystalline Al 0.25 Ga 0.75 P and Al 0.9 Ga 0.1 As determined by variable-angle spectroscopic ellipsometry

The optical constants of single-crystal Al x Ga 1-x P and Al x Ga 1-x As have been widely studied at various Al compositions but have not yet been thoroughly investigated as polycrystalline materials. Using variable-angle spectroscopic ellipsometry, we estimate and analyze the optical constants of polycrystalline Al 0.25 Ga 0.75 P and Al 0.9 Ga 0.1 As that were non-epitaxially deposited at deposition temperatures of either 250 or 420 °C, and with various dopant species, dopant concentrations, and V/III flux ratios. Here, for wavelengths of 350–400 nm, decreasing the V/III ratio increases the refractive index of Be-doped polycrystalline Al 0.25 Ga 0.75 P. For wavelengths >500 nm, as the targeted Be dopant concentration increases for polycrystalline Al 0.25 Ga 0.75 P, the amount the extinction coefficient increases and the indirect bandgap decreases depends on the V/III ratio. Furthermore, Si-doped polycrystalline Al 0.25 Ga 0.75 P has significantly smaller extinction coefficient values than when it is Be-doped, for the same V/III ratio and targeted doping concentration, at wavelengths >500 nm. Based on our findings, we hypothesize that the dopant species, dopant concentration, and the V/III ratio dictate various types of defect concentrations, which then affect the optical constants and indirect bandgap of polycrystalline Al 0.25 Ga 0.75 P. Additionally, for wavelengths >500 nm, we find that polycrystalline Al 0.25 Ga 0.75 P and Al 0.9 Ga 0.1 As have the lowest extinction coefficient values when deposited at 420 °C, as opposed to 250 °C. Thus, we hypothesize that the deposition temperature is the most significant factor in dictating the optical constants of these polycrystalline III-Vs. Ultimately, this work demonstrates alternative methods to tune the optical constants of polycrystalline Al 0.25 Ga 0.75 P and Al 0.9 Ga 0.1 As, as opposed to tuning the Al composition.

42 ENGINEERING↗
DOE OSTI2025

HFIR Activity Workbook Generator (HAWK) User Guide

The HFIR Activity WorkbooK generator (HAWK) is a Python code that automates and streamlines the activity calculation of samples after irradiation in the High Flux Isotope Reactor (HFIR). HAWK’s results provide estimates of the activity and nuclide inventory of irradiated specimens before they are moved to hot cell facilities, where they undergo post-irradiation examination. The samples’ activity results guide the packing of shipping containers and inform the accountable inventories for the hot cell facilities. The toolkit was originally developed by Charles Daily, a former R&D staff member at Oak Ridge National Laboratory (ORNL). As of May 2025, HAWK is developed by the Radiation Transport & HPC Methods Group (Nuclear Energy and Fuel Cycle Division) at ORNL. Figure 1 presents HAWK’s workflow. To use HAWK, users need to: 1. Develop an Excel input workbook (i.e., XLSX extension) containing data from the experiment’s materials, irradiation history (cycles), and irradiation positions. 2. Make minor edits to an existing template JSON file (i.e., auxiliary_data.JSON) and to the Python driver. The driver sets the necessary environment variables, defines the material compositions, and ultimately calls HAWK. Once configured, HAWK runs the Oak Ridge Isotope Generation code (ORIGEN) to calculate the masses, activities, and heat load at the end of irradiation for each isotope in the specimen. ORIGEN is part of SCALE, ORNL’s in-house computational tool for performing nuclear safety and design calculations. Following this step, HAWK postprocesses the results and generates three output workbooks summarizing the activity calculations.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2025

Machine Learning-Based Process Control for Injection Molding of Recycled Polypropylene

The increased interest in artificial intelligence in manufacturing has driven the adoption of machine learning to optimize processes and improve efficiency. A key challenge in injection molding is the variability of recycled materials, which affects part quality and processing stability. This study presents a novel closed-loop process control approach for injection molding, leveraging machine learning to adaptively predict processing inputs and quality outcomes. The methodology was tested on five blends of recycled polypropylene (rPP), using artificial neural networks (ANNs), linear regression, and polynomial regression to model the relationships between material properties and process parameters. The dataset was split 80/20 into training and testing sets. The ANN model was implemented using TensorFlow and Keras, with six hidden layers of 32 neurons per layer, ReLU activation, and an Adam optimizer. Empirical tuning and early stopping were used to optimize performance and prevent overfitting. Predictions were evaluated based on mean absolute error (MAE), mean squared error (MSE), and percentage error. The results showed that yield stress, ultimate elongation, and part weight were accurately predicted within a 5% error for linear and polynomial regression models and within a 10% error for the ANN. However, modulus predictions were less reliable, with errors of ~11% for ANN and linear regression and ~40% for polynomial regression, reflecting the inherent variability of this property in rPP blends. Predictions of processing inputs had errors ranging from 3% to 25%, depending on the model and response variable. No single modeling approach was consistently superior across all responses, highlighting the complexity of the relationship between material properties, process parameters, and quality metrics. Overall, the work demonstrates that closed-loop process control, powered by machine learning, can effectively predict key quality parameters in injection molding of recycled materials. The proposed approach can improve process stability and material utilization, facilitating increased adoption of sustainable materials.

Krantz, Joshua↗
DOE OSTI2026

A Bayesian inferencing framework for ultrasound wave speed measurements in metal additive manufacturing

Process-related changes during metal additive manufacturing introduce microstructural variability in the material properties of printed parts, directly affecting component reliability. Accurate estimation of these property variations with part performance are essential for quality assurance. Ultrasound testing offers a non-destructive means to estimate mechanical properties and detect defects; however, conventional analysis methods often neglect the influence of microstructural variability, limiting their effectiveness. Here, this research presents a Bayesian inference technique for quantifying wave speed uncertainty from ultrasound measurements of metal additive manufactured parts. By integrating prior ultrasound data with a Bayesian model, the proposed approach generates posterior density estimates of wave speed that systematically account for manufacturing-induced variability and uncertainty. The novelty of this research lies in applying a Bayesian framework to analyze experimental ultrasound measurements within the context of metal additive manufacturing variability. The method enhances the accuracy of wave speed estimation by 64%, defect position by 50% and increases confidence associated with wave speed variance by 30% across different porosity levels, thereby providing a robust foundation for improved decision-making and increased reliability in additively manufactured components.

Additive manufacturing↗
DOE OSTI2021

A probabilistic creep model incorporating test condition, initial damage, and material property uncertainty

Uncertainty is prevalent in the creep resistance of alloys, where at elevated temperature and low pressure, rupture can range across logarithmic decades. In this study, a probabilistic continuum-damage-mechanics (CDM)-based model is derived to capture the uncertainty of creep resistance. To meet this objective, creep data for alloy 304 Stainless Steel is gathered. A constitutive model, “Sinh”, is calibrated deterministically to determine the statistical variability of the material properties. Three sources of uncertainty are injected into the model: test condition (stress and temperature), initial damage, and material properties. Probabilistic simulations are carried out by (a) calibrating probability distribution functions (pdfs) for each source of uncertainty (b) randomly sampling the pdfs using Monte Carlo methods and (c) executing simulations to replicate the uncertain creep behavior. A sensitivity analysis is performed to evaluate the relative effect of each source of uncertainty. In full probabilistic simulations, the cumulative uncertainty of creep behavior is evaluated. The probabilistic model accurately predicts the creep deformation and rupture of the available experiments. The probabilistic model is validated for interpolation but lacks extrapolation ability. Several future works are proposed to further improve the model.

36 MATERIALS SCIENCE↗
DOE OSTI2020

A Bezier Curve Informed Melt Pool Geometry to Model Additive Manufacturing Microstructures Using SPPARKS

Additive manufacturing is a transformative technology with the potential to manufacture designs which traditional subtractive machining methods cannot. Additive manufacturing offers fast builds at near final desired geometry; however, material properties and variability from part to part remain a challenge for certification and qualification of metallic components. AM induced metallic microstructures are spatially heterogeneous and highly process dependent. Engineering properties such as strength and toughness are significantly affected by microstructure morphologies resulting from the manufacturing process Linking process parameters to microstructures and ultimately to the dynamic response of AM materials is critical to certifying and qualifying AM built parts and components and improving the performance of AM materials. The AM fabrication process is characterized by building parts layer by layer using a selective laser melt process guided by a computer. A laser selectively scans and melts metal according to a designated geometry. As the laser scans, metal melts, fuses, and solidifies forming the final geometry in a layerwise fashion. As the laser heat source moves away, the metal cools and solidifies forming metallic microstructures. This work describes a microstructure modeling application implemented in the SPPARKS kinetic Monte Carlo computational framework for simulating the resulting microstructures. The application uses Bzier curves and surfaces to model the melt pool surface and spatial temperature profile induced by moving the laser heat source; it simulates the melting and fusing of metal at the laser hot spot and microstructure formation and evolution when the laser moves away. The geometry of the melt pool is quite flexible and we explore effects of variances in model parameters on simulated microstructures.

36 MATERIALS SCIENCE↗
DOE OSTI2026

The Influence of Residual Copper Catalyst on the Polymer-to-Ceramic Conversion of a Click-Derived Polycarbosilane

Preceramic polymers (PCPs) are a key class of materials for the preparation of advanced ceramic components. Following conventional polymer processing and subsequent pyrolysis, polymer-derived ceramic (PDC) fibers, monoliths, and composites offer novel engineering opportunities for aerospace applications. Despite the rapidly emerging commercial applications of PDCs, many basic scientific questions remain regarding the structural transformation of the polymer to the final ceramic and how processing variables affect PDC materials properties. PCPs prepared using a mild and scalable copper (Cu) catalyzed azide/alkyne “click” reaction have been demonstrated as a unique class of material that can bind with transition metals, ultimately producing ultrahigh temperature ceramics (UHTCs) upon pyrolysis. Unexpectedly, it was found that the removal of residual Cu catalyst from a model click-derived polycarbosilane PCP significantly reduced the ceramic yield during pyrolysis. Herein, we report the influence of Cu impurities on polycarbosilane-derived SiC ceramics and present a mechanistic understanding of the unintended role of Cu in the polymer-to-ceramic conversion process. Synchrotron-based scattering and spectroscopy methods were implemented to track the evolution of different atomic species and local structures as well as the influence of Cu toward atomic-scale structures in the final ceramics. It was determined that Cu impurities catalyze the formation of silicon carbide through the formation of an intermediate molten copper/copper silicide, ultimately increasing the ceramic yield. Overall, this contribution highlights the need to carefully assess the interplay between the chemistries needed for PCP synthesis and their potential influence on final PDCs following processing.

Ponder, Jr., James F. [Air Force Research Laborato↗
DOE OSTI2020

IGA-MPM: The Isogeometric Material Point Method

In this work, we propose the use of Isogeometric Analysis (IGA) within the context of the Material Point Method (MPM), and refer to the approach as IGA-MPM. We use the idea of IGA, and its instantiation based on Non-Uniform Rational B-Splines (NURBS), to build higher-order accurate and smooth approximation for MPM. Higher-order smoothness yields a continuous representation of the strain rate, and, as a result, prevents jumps in the stress and other history variables as the material points cross the element boundaries. Furthermore, NURBS can exactly represent all conic sections and the corresponding symmetries in the solution, which may be important in some applications. Several numerical examples of increasing complexity are presented, and show the ability of IGA-MPM to eliminate the well known cell-crossing instability of the conventional MPM. In addition, the examples presented demonstrate improved accuracy, convergence, and symmetry preservation of IGA-MPM compared to the conventional MPM, both for rectilinear and curved geometries.

42 ENGINEERING↗
DOE OSTI2022

Smart material based multilayered microbeam structures for spatial self-deployment and reconfiguration: A residual stress approach

Alleviation of the potentially damaging effects induced by residual stresses was comprehensively investigated in previous research. Here, this paper, however, presents a spatially self-deployable and reconfigurable multilayered microbeam which takes advantage of residual stresses and shape memory effects. Reconfigurable mechanism of a typical four-layered microbeam composed of Pt\Ni 50 Ti 50 \Ni 50 Ti 50 \Pt is introduced, followed by analytical modeling of the maximum distance of the self-deployed gap as functions of variable structural and material parameters, including compressive residual stress in Pt layers and tensile residual stress in Ni 50 Ti 50 layers. Analytical solutions given by the static model agree well with the results obtained via finite element models (FEMs). Fabrication, characterization, and in-situ experiments were carried out to validate the feasibility of deployment of the as-released four-layered microbeam. The maximum distance of the gap was measured to be 41.39 μm at 20 °C, which could be increased to 51.73 μm thanks to controllable reconfiguration driven by shape memory effects. Theoretical analysis of such self-deployment and reconfiguration suggested a tensile residual stress increase by 52 MPa in Ni 50 Ti 50 layers. The multilayered microbeam structure with capabilities of self-deployment and reconfiguration offers great potential for various emerging applications, such as micro robotics, medical drug delivery devices, and intelligent chip scale spacecraft.

36 MATERIALS SCIENCE↗