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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
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Deep Eutectic Solvents as Candidates for Lithium Isotope Enrichment

Nuclear fusion is a phenomenon that is well known within the nuclear physics community as a viable option for alternative energy as many natural gases and fossil fuels are phased out of commercial use. Deuterium and tritium fusion reactions are currently the leading candidates for nuclear fusion, with a major limiting factor being a means to produce tritium on an industrial scale. Lithium-6 is a well-known isotope that can produce tritium and helium following a fission reaction with a neutron. Unfortunately, the lithium-6 enrichment methods are limited to the COLEX process, which leaves behind an alarming amount of mercury waste as a potential environmental contaminant. Deep eutectic solvents are believed to be a potential alternative to lithium isotope separations due to the ease of generation, in addition to the minimum environmental waste generated when these solvents are employed. Previous studies have suggested that deep eutectic solvents are capable of separating lithium isotopes by utilizing a 2-thenoyltrifluoroacetone and trioctylphosphine oxide system that can biphasically react with a buffered solution containing lithium chloride. This system displays a separation factor of 1.068, which when compared to the 1.054 separation within the COLEX process, makes it a potential candidate for lithium-6/7 separation. Within this study, we investigate this system in comparison to two newly synthesized deep eutectic solvents and find that within these acetylacetone-based systems, little isotopic separation is observed. We investigate these systems both experimentally and computationally, showing the different lithium cation affinities, in addition to proposing how the electron-donating or -withdrawing nature can influence these systems.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS2003

Neuroprotection trek--the next generation: neuromodulation I. Techniques--deep brain stimulation, vagus nerve stimulation, and transcranial magnetic stimulation

Neuromodulation denotes controlled electrical stimulation of the central or peripheral nervous system. The three forms of neuromodulation described in this paper-deep brain stimulation, vagus nerve stimulation, and transcranial magnetic stimulation-were chosen primarily for their demonstrated or potential clinical usefulness. Deep brain stimulation is a completely implanted technique for improving movement disorders, such as Parkinson's disease, by very focal electrical stimulation of the brain-a technique that employs well-established hardware (electrode and pulse generator/battery). Vagus nerve stimulation is similar to deep brain stimulation in being well-established (for the treatment of refractory epilepsy), completely implanted, and having hardware that can be considered standard at the present time. Vagus nerve stimulation differs from deep brain stimulation, however, in that afferent stimulation of the vagus nerve results in diffuse effects on many regions throughout the brain. Although use of deep brain stimulation for applications beyond movement disorders will no doubt involve placing the stimulating electrode(s) in regions other than the thalamus, subthalamus, or globus pallidus, the use of vagus nerve stimulation for applications beyond epilepsy-for example, depression and eating disorders-is unlikely to require altering the hardware significantly (although stimulation protocols may differ). Transcranial magnetic stimulation is an example of an external or non-implanted, intermittent (at least given the current state of the hardware) stimulation technique, the clinical value of which for neuromodulation and neuroprotection remains to be determined.

Review, Tutorial↗
NASA NTRS1982

New CCIR papers on telecommunications for deep space research

Three draft reports submitted by JPL to the International Radio Consultative Committee (CCIR) for inclusion in their 1982 edition of reports and recommendations are presented. Potential interference between deep space telecommunications and the fixed satellite and broadcasting satellite services in harmonically related bands, protection criteria and sharing considerations relating to deep space research, and preferred frequencies and bandwidths for deep space research are considered.

Degroot, N. F.↗
NASA NTRS1992

Ozone production potential following convective redistribution of biomass burning emissions

The effects of deep convection on the potential for forming ozone in the free troposphere have been simulated for regions where the trace gas composition is influenced by biomass burning. Cloud photochemical and dynamic simulations based on observations in the 1980 and 1985 Brazilian campaigns form the basis of a sensitivity study of the ozone production potential under differing conditions. It is seen that there is considerably more ozone formed in the middle and upper troposphere when convection has redistributed hydrocarbons, NO(x), and CO compared to the example of no convection.

Pickering, Kenneth E.↗
NASA NTRS2013

Does Aspartic Acid Racemization Constrain the Depth Limit of the Subsurface Biosphere?

Previous studies of the subsurface biosphere have deduced average cellular doubling times of hundreds to thousands of years based upon geochemical models. We have directly constrained the in situ average cellular protein turnover or doubling times for metabolically active micro-organisms based on cellular amino acid abundances, D/L values of cellular aspartic acid, and the in vivo aspartic acid racemization rate. Application of this method to planktonic microbial communities collected from deep fractures in South Africa yielded maximum cellular amino acid turnover times of approximately 89 years for 1 km depth and 27 C and 1-2 years for 3 km depth and 54 C. The latter turnover times are much shorter than previously estimated cellular turnover times based upon geochemical arguments. The aspartic acid racemization rate at higher temperatures yields cellular protein doubling times that are consistent with the survival times of hyperthermophilic strains and predicts that at temperatures of 85 C, cells must replace proteins every couple of days to maintain enzymatic activity. Such a high maintenance requirement may be the principal limit on the abundance of living micro-organisms in the deep, hot subsurface biosphere, as well as a potential limit on their activity. The measurement of the D/L of aspartic acid in biological samples is a potentially powerful tool for deep, fractured continental and oceanic crustal settings where geochemical models of carbon turnover times are poorly constrained. Experimental observations on the racemization rates of aspartic acid in living thermophiles and hyperthermophiles could test this hypothesis. The development of corrections for cell wall peptides and spores will be required, however, to improve the accuracy of these estimates for environmental samples.

cellular↗
NASA NTRS2012

Potential Applications of Modularity to Enable a Deep Space Habitation Capability for Future Human Exploration Beyond Low-Earth Orbit

Evaluating preliminary concepts of a Deep Space Habitat (DSH) enabling long duration crewed exploration of asteroids, the Moon, and Mars is a technically challenging problem. Sufficient habitat volumes and equipment, necessary to ensure crew health and functionality, increase propellant requirements and decrease launch flexibility to deliver multiple elements on a single launch vehicle; both of which increase overall mission cost. Applying modularity in the design of the habitat structures and subsystems can alleviate these difficulties by spreading the build-up of the overall habitation capability across several smaller parts. This allows for a more flexible habitation approach that accommodates various crew mission durations and levels of functionality. This paper provides a technical analysis of how various modular habitation approaches can impact the parametric design of a DSH with potential benefits in mass, packaging volume, and architectural flexibility. This includes a description of the desired long duration habitation capability, the definition of a baseline model for comparison, a small trade study to investigate alternatives, and commentary on potentially advantageous configurations to enable different levels of habitability. The approaches investigated include modular pressure vessel strategies, modular subsystems, and modular manufacturing approaches to habitat structure. The paper also comments upon the possibility of an integrated habitation strategy using modular components to create all short and long duration habitation elements required in the current exploration architectures.

Simon, Matthew A.↗
DOE OSTI2026

Patch-Based Convolutional Neural Networks for Multiple Microstructural Features Detection in FIB-SEM Micrographs of Irradiated Nuclear Fuel

Focused ion beam scanning electron microscopy (FIB-SEM) tomography has increasingly been utilized for acquiring three-dimensional (3D) microstructure features at the sub-micron scale in irradiated nuclear materials. This technique involves sequential ion beam slicing followed by electron beam imaging and compositional mapping using energy dispersive spectroscopy (EDS). Despite its growing use, several challenges persist. These include the time-intensive nature of data collection of EDS data, difficulties in distinguishing between various microstructures, and issues with image alignment. These challenges currently limit the broader application of FIB-SEM tomography in the field. To overcome these limitations, we propose using convolutional neural networks (CNNs) to automate microstructure identification in SEM images. Our study introduces a new framework for identifying microstructures in irradiated U-10Zr (wt. %) metallic fuel with limited annotated data. The framework includes the creation of a reliable annotated dataset with paired SEM and ground truth data from EDS maps, the applications of CNNs for microstructure identification, and the validation of model performance. Specifically, we employed the Segment Anything Model (SAM) to align SEM images with corresponding EDS maps and focused ion beam (FIB) tomography SEM data. We evaluate several models, including Patch-based U-Net, Attention U-Net, and Residual U-Net, finding that patch-based U-Net exhibits superior segmentation performance and consistency. This approach reduces reliance on EDS detectors and aids in accelerating nuclear material analysis process, highlighting the potential of advanced deep learning techniques to improve microstructural understanding in nuclear material. This is the first framework to integrate SAM and Patch-based CNN models for semantic segmentation of irradiated nuclear materials, with potential applicability to other tomography datasets.

36 - MATERIALS SCIENCE↗
NASA NTRS1995

Long-wavelength Radar Studies of the Lunar Maria

Radar measurements at 70 cm and 7.5 m wavelengths provide insight into the structure and chemical properties of the upper 5-100 m of the lunar regolith and crust. Past work has identified a number of anomalous regions and changes in echo strength, some attributed to differences in titanium content. There has been little opportunity, however, to compare calibrated long-wavelength backscatter among different units or to theoretical model results. We combine recent high-resolution (3-5 km) 70-cm radar data for the nearside with earlier calibrated full-disk observations to provide a reasonable estimate of the true lunar backscatter coefficient. These data are tested against models for quasi-specular scattering from the surface, echoes from a buried substrate, and Mie scattering from surface and buried rocks. We find that 70 cm echoes likely arise from Mie scattering by distributed rocks within the soil, consistent with earlier hypotheses. Returns from a buried substrate would provide a plausible fit to the observations only if the regolith depth were ~3 m or less and varied little across the maria. Depolarized echoes are due to some combination of single and multiple scattering events, but it appears that single scattering alone could account for the observed echo power, based on comparisons with terrestrial rocky surfaces. Backscatter strength from the regolith is most strongly affected by the loss tangent, whose variation with mineral content is still poorly defined. We compared the backscatter values for the mare deposits to the oxide contents inferred from spectral ratio methods, and found that in general the unit boundaries evident in radar images closely follow those seen in color difference images. The 70-cm data are not well correlated with TiO2 values found using the Charette relationship nor with Fe abundances derived from Clementine observations. The lack of a relationship between radar echo and Fe content is reasonable given the distribution of iron among various mineral phases, but ilmenite content (FeTiO3) has typically been cited as the dominant cause of changes in loss tangent (and thus the radar absorption). The lack of correlation between the radar data and TiO2 estimates may arise from uncertainties in the Charette technique, subtle differences in the upper surface and bulk properties of the regolith, mineralogic effects on the radar not linked to titanium content, or to some combination of these factors. Dark crater haloes in the mare and highlands, and low radar returns from apparent cryptomare regions, are used to illustrate the role radar data can play in identifying changes in regolith composition; low-return haloes around craters such as Petavius may indicate 5-25% contamination of the highlands soil by excavated mare material or a layer of rock-poor ejecta at least several meters deep. The 7.5-m data were shown to correlate to a reasonable degree with estimates of Fe abundance, suggesting that this component of the mare basalts is primarily responsible for attenuation losses at very long wavelengths. The different sensitivities of the two radar wavelengths and multispectral data offers the potential for future deep mapping of the mare lava flows and regolith.

Lunar MariaRadarLunar Studies↗
NASA NTRS1995

Long-wavelength Radar Studies of the Lunar Maria

Radar measurements at 70 cm and 7.5 m wavelengths provide insight into the structure and chemical properties of the upper 5-100 m of the lunar regolith and crust. Past work has identified a number of anomalous regions and changes in echo strength, some attributed to differences in titanium content. There has been little opportunity, however, to compare calibrated long-wavelength backscatter among different units or to theoretical model results. We combine recent high-resolution (3-5 km) 70-cm radar data for the nearside with earlier calibrated full-disk observations to provide a reasonable estimate of the true lunar backscatter coefficient. These data are tested against models for quasi-specular scattering from the surface, echoes from a buried substrate, and Mie scattering from surface and buried rocks. We find that 70 cm echoes likely arise from Mie scattering by distributed rocks within the soil, consistent with earlier hypotheses. Returns from a buried substrate would provide a plausible fit to the observations only if the regolith depth were approx.3 m or less and varied little across the maria. Depolarized echoes are due to some combination of single and multiple scattering events, but it appears that single scattering alone could account for the observed echo power, based on comparisons with terrestrial rocky surfaces. Backscatter strength from the regolith is most strongly affected by the loss tangent, whose variation with mineral content is still poorly defined. We compared the backscatter values for the mare deposits to the oxide contents inferred from spectral ratio methods, and found that in general the unit boundaries evident in radar images closely follow those seen in color difference images. The 70-cm data are not well correlated with TiO2 values found using the Charette relationship nor with Fe abundances derived from Clementine observations. The lack of a relationship between radar echo and Fe content is reasonable given the distribution of iron among various mineral phases, but ilmenite content (FeTiO3) has typically been cited as the dominant cause of changes in loss tangent (and thus the radar absorption). The lack of correlation between the radar data and TiO2 estimates may arise from uncertainties in the Charette technique, subtle differences in the upper surface and bulk properties of the regolith, mineralogic effects on the radar not linked to titanium content, or to some combination of these factors. Dark crater haloes in the mare and highlands, and low radar returns from apparent cryptomare regions, are used to illustrate the role radar data can play in identifying changes in regolith composition; low-return haloes around craters such as Petavius may indicate 5-25% contamination of the highlands soil by excavated mare material or a layer of rock-poor ejecta at least several meters deep. The 7.5-m data were shown to correlate to a reasonable degree with estimates of Fe abundance, suggesting that this component of the mare basalts is primarily responsible for attenuation losses at very long wavelengths. The different sensitivities of the two radar wavelengths and multispectral data offers the potential for future deep mapping of the mare lava flows and regolith.

Lunar Studies↗
NASA NTRS2014

Design Evolutuion of Hot Isotatic Press Cans for NTP Cermet Fuel Fabrication

Nuclear Thermal Propulsion (NTP) is under consideration for potential use in deep space exploration missions due to desirable performance properties such as a high specific impulse (> 850 seconds). Tungsten (W)-60vol%UO2 cermet fuel elements are under development, with efforts emphasizing fabrication, performance testing and process optimization to meet NTP service life requirements [1]. Fuel elements incorporate design features that provide redundant protection from crack initiation, crack propagation potentially resulting in hot hydrogen (H2) reduction of UO2 kernels. Fuel erosion and fission product retention barriers include W coated UO2 fuel kernels, W clad internal flow channels and fuel element external W clad resulting in a fully encapsulated fuel element design as shown.

Mireles, O. R.↗
DOE OSTI2024

Untapped Potential of Fluoride Ions in Maximizing the Electrochemical Stability of Deep Eutectic Solvents

Advancing the batteries is pivotal to propelling our society toward a sustainable, electrified future. The stability of electrolytes forms the backbone of energy storage systems. This is particularly the case for redox flow batteries (RFBs). Their deployability depends on their longevity and dependability. The presence of unstable electrolytes can trigger undesirable reactions, degrade performance, and lead to battery failure. Aqueous electrolytes, with limited electrochemical stability window (ESW), are prone to hydrogen and oxygen evolution. Conversely, non-aqueous electrolytes offer enhanced stability. In this study, we unveil the ESW of a non-aqueous eutectic solvent, comprising choline fluoride and ethylene glycol—a composition that has eluded experimental investigation until now. Our findings show that the stability window, reduction and oxidation potential limits of deep eutectic solvents, are sensitive to variations in the halide component of the ammonium salt. Furthermore, this work not only highlights the benefits of novel deep eutectic solvents but also sets the stage for their strategic use in future battery electrolytes.

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

Deep learning forecasts the spatiotemporal evolution of fluid-induced microearthquakes

Microearthquakes generated by subsurface fluid injection record the evolving stress state and permeability of reservoirs. Forecasting their spatiotemporal evolution is therefore critical for applications such as enhanced geothermal systems, carbon dioxide sequestration and other geoengineering applications. Here we propose a transformer neural network model that ingests hydraulic stimulation history and prior microearthquake observations to forecast four key quantities: cumulative microearthquake count, cumulative logarithmic seismic moment, and the 50th- and 95th-percentile extents of the microearthquake cloud. Applied to the EGS Collab Experiment 1 dataset, the model achieves R2 > 0.98 for the 1-s forecast horizon and R2 > 0.88 for the 15-s forecast horizon across all targets, and supplies uncertainty estimates through a learned standard deviation term. These accurate, uncertainty-quantified forecasts enable real-time inference of fracture propagation and permeability evolution, demonstrating the strong potential of deep-learning approaches to improve seismic-risk assessment and guide mitigation strategies in future fluid-injection operations.

Chung, Jaehong↗
DOE OSTI2024

Improved deep learning prediction of antigen–antibody interactions

Identifying antibodies that neutralize specific antigens is crucial for developing effective immunotherapies, but this task remains challenging for many target antigens. The rise of deep learning–based computational approaches presents a promising avenue to address this challenge. Here, we assess the performance of a deep learning approach through two benchmark tests aimed at predicting antibodies for the receptor-binding domain of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spike protein. Three different strategies for constructing input sequence alignments are employed for predicting structural models of antigen–antibody complexes. In our initial testing set, which comprises known experimental structures, these strategies collectively yield a significant top-ranked prediction for 61% of cases and a success rate of 47%. Notably, one strategy that utilizes the sequences of known antigen binders outperforms the other two, achieving a precision of 90% in a subsequent test set of ~1,000 antibodies, balanced between true and control antibodies for the antigen, albeit with a lower recall of 25%. Our results underscore the potential of integrating deep learning methods with single B cell sequencing techniques to enhance the prediction accuracy of antigen–antibody interactions.

Science & Technology - Other Topics↗
NASA NTRS2016

The Deep-Space Positioning System Concept: Automating Complex Navigation Operations Beyond the Earth

There is no equivalent of GPS in “deep space,” that is beyond the Earth Moon system (and use of GPS beyond LEO and MEO is already problematic.) NASA has been awarded a patent on the concept of the “Deep-space Positioning System” (DPS) which is a self-contained instrument concept that would use in-situ observations to provide an automated and autonomous means of determining its position in the Solar System, and at least as important, its position relative to a target, whether that target is a planet, asteroid, moon or comet. In addition, the DPS system computes trajectory change parameters, as necessary to keep a spacecraft on a planned trajectory. Implementation options include the use of a radio receiver to receive a frequency beacon from a non-in-situ source for additional navigation information over and above that which can be provided by the cameras that are the principal data source of DPS. This paper discusses the DPS instrument concept, and its potential use in deep space.

Guinn, Joseph R.↗
NASA NTRS2011

Large Aperture "Photon Bucket" Optical Receiver Performance in High Background Environments

The potential development of large aperture groundbased "photon bucket" optical receivers for deep space communications, with acceptable performance even when pointing close to the sun, is receiving considerable attention. Sunlight scattered by the atmosphere becomes significant at micron wavelengths when pointing to a few degrees from the sun, even with the narrowest bandwidth optical filters. In addition, high quality optical apertures in the 10-30 meter range are costly and difficult to build with accurate surfaces to ensure narrow fields-of-view (FOV). One approach currently under consideration is to polish the aluminum reflector panels of large 34-meter microwave antennas to high reflectance, and accept the relatively large FOV generated by state-of-the-art polished aluminum panels with rms surface accuracies on the order of a few microns, corresponding to several-hundred micro-radian FOV, hence generating centimeter-diameter focused spots at the Cassegrain focus of 34-meter antennas. Assuming pulse-position modulation (PPM) and Poisson-distributed photon-counting detection, a "polished panel" photon-bucket receiver with large FOV will collect hundreds of background photons per PPM slot, along with comparable signal photons due to its large aperture. It is demonstrated that communications performance in terms of PPM symbol-error probability in high-background high-signal environments depends more strongly on signal than on background photons, implying that large increases in background energy can be compensated by a disproportionally small increase in signal energy. This surprising result suggests that large optical apertures with relatively poor surface quality may nevertheless provide acceptable performance for deep-space optical communications, potentially enabling the construction of cost-effective hybrid RF/optical receivers in the future.

photon bucket detection↗
DOE OSTI2025

Improved guided-wave acoustic defect detection and localization in pipes under varying temperature conditions using deep learning

Early defect detection in pipelines is critical across industries, particularly in the oil and gas sector, where failures result in significant maintenance costs and operational disruptions. Acoustic guided-wave techniques are widely used for nondestructive evaluation of pipeline defects due to their long-distance propagation capability. However, environmental variations, sensitivity limitations, and complex signal interpretation challenges limit the effectiveness of traditional signal processing approaches with guided-wave signals. Recent advances in deep learning methods have demonstrated remarkable success in solving complex real-world problems in many fields. In particular, deep-learning-based signal processing holds substantial promise to overcome limitations and challenges of conventional signal processing. This study presents a deep learning framework for pipeline inspection using acoustic guided-wave signals under temperature varying environments. The proposed framework employs a dual-path one-dimensional convolutional autoencoder that combines defect detection, localization, and temperature prediction functions. The proposed system utilizes multi-mode and broadband acoustic waves with an optimized number of sensors that provide high accuracy while retaining practical simplicity. Experimental validation is performed on a carbon steel pipe. The results indicate exceptional defect detection accuracy and precise defect localization with a mean absolute error of 66 mm. The proposed technique also predicts the effective average temperature of the pipe with a mean absolute error of 0.2°C. Comparative analysis shows superior performance of the proposed method over a traditional method previously developed by the authors' team. These results highlight the potential of integrating deep learning methods into guided-wave pipeline inspection systems to improve reliability under varying environmental conditions.

42 ENGINEERING↗
DOE OSTI2026

Deep Spectroscopy with DESI for Photometric Redshift Training and Calibration

Deep spectroscopic samples can improve photometric redshift (photo-z) estimates and reduce uncertainties on redshift distributions. Such improvements can increase the cosmological constraining power of large imaging-based experiments such as the Vera C. Rubin Observatory’s Legacy Survey of Space and Time (LSST) and mitigate what may be a limiting systematic effect. We present results from the “DESI-Deep pilot” program, which was designed to assess the capability of the Dark Energy Spectroscopic Instrument (DESI) on the 4m Mayall telescope to measure redshifts of galaxies as faint as expected lensing samples for early LSST data (m i ≤ 24.5). We find that DESI is remarkably efficient at this task, with redshift success rates comparable to the results of observations from 10 m class telescopes with only ∼2 × longer integration time (rather than ∼8 × longer as would be expected from aperture-area scaling), while simultaneously achieving ∼30 times larger multiplexing. We also find that the signal-to-noise ratio of the spectra scales as expected for background-limited observations even for the longest exposure times (∼7 hr) and faintest targets in the program. These results demonstrate that DESI could provide the definitive redshift sample for the early years of LSST with a modest investment of observing time. Based upon the results of this program, we provide updated predictions for the time required to collect benchmark samples for photo-z training and calibration using a variety of spectroscopic facilities. Finally, we describe a potential “DESI-Deep” survey designed to train and calibrate photo-z’s for imaging experiments, and provide forecasts of its impact on cosmological inference.

Dey, Biprateep [University of Toronto; University ↗
DOE OSTI2024

Bottom-up design of actinide materials from molecular clusters: Demonstration of a general-purpose simulation capability leveraging machine-learned atomic potentials

Actinide thin-film coatings such as uranium dioxide (UO 2 ) play an important role in nuclear reactors and other mission-relevant applications, but realization of their potential requires a deep fundamental understanding of the chemical vapor deposition (CVD) processes used for their growth. The slow experimental progress can be attributed, in part, to the standard safety guidelines associated with handling uranium byproducts, which are often corrosive, toxic, and radioactive. Accurate simulation techniques, when used in concert with experiment, can improve laboratory safety, material durability, and deliverable timeframes. However, state-of-the-art computational methods are either insufficiently accurate or intractably expensive. To remedy this situation, in this project we suggested a machine-learning (ML) accelerated workflow for simulating molecular clustering toward deposition. As a benchmark test case, we considered molecular clustering in steam and assessed independent components of our workflow by comparing with measured thermodynamic properties of water. After analyzing each component individually and finding no fundamental barrier to realization of the workflow, we attempted to integrate the ML component, a Sandia-developed tool called FitSNAP. As this was the first application of FitSNAP to atoms and molecules in the gas phase at Sandia, the method required more fitting data than was originally anticipated. Systematic improvements were made by including in the fit data diatomic potentials, molecular single-bond-breaking curves, and symmetry-constrained intermolecular potentials. We concluded that our strategy provides a feasible pathway toward modeling CVD and related processes, but that extensive training data must be generated before it can be of practical use.

36 MATERIALS SCIENCE↗