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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 199 records · Page 11
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Phase-space Properties and Chemistry of the Sagittarius Stellar Stream Down to the Extremely Metal-poor ([Fe/H] ≲ -3) Regime

In this work, we study the phase-space and chemical properties of the Sagittarius (Sgr) stream, the tidal tails produced by the ongoing destruction of the Sgr dwarf spheroidal (dSph) galaxy, focusing on its very metal-poor (VMP; [Fe/H] < -2) content. We combine spectroscopic and astrometric information from SEGUE and Gaia EDR3, respectively, with data products from a new large-scale run of the StarHorse spectrophotometric code. Our selection criteria yield ~1600 stream members, including >200 VMP stars. We find the leading arm (b > 0°) of the Sgr stream to be more metal-poor, by ~0.2 dex, than the trailing one (b < 0°). With a subsample of turnoff and subgiant stars, we estimate this substructure's stellar population to be ~1 Gyr older than the thick disk's. With the aid of an N-body model of the Sgr system, we verify that simulated particles stripped earlier (>2 Gyr ago) have present-day phase-space properties similar to lower metallicity stream stars. Conversely, those stripped more recently (<2 Gyr) are preferentially akin to metal-rich ([Fe/H] > -1) members of the stream. Such correlation between kinematics and chemistry can be explained by the existence of a dynamically hotter, less centrally concentrated, and more metal-poor population in Sgr dSph prior to its disruption, implying that this galaxy was able to develop a metallicity gradient before its accretion. Finally, we identified several carbon-enhanced metal-poor ([C/Fe] > +0.7 and [Fe/H] ≤ -1.5) stars in the Sgr stream, which might be in tension with current observations of its remaining core where such objects are not found.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Layered Transition Metal Oxides as Ca Intercalation Cathodes: A Systematic First-Principles Evaluation

Finding high-voltage Ca cathode materials is a critical step to unleashing the full potential of high-energy-density Ca-ion batteries. Therefore, first-principles calculations are used to demonstrate that P-type layered calcium transition metal (TM) oxide materials (CaTM 2 O 4 ) with a range of TM substitutions (TM = Ti, V, Cr, Mn, Fe, Co, and Ni) have excellent battery-related properties including thermodynamic stability, average voltage, energy density, synthesizability, ionic mobility, and electronic structure. However, the thermodynamic stability of the charged phase and TM redox activity are shown to be sensitive to TM selection, with CaCo 2 O 4 having the best balance of all considered properties. The utility of combining multiple TMs to expand the chemical search space for TM substitutions is demonstrated by mixing Co and Ni in layered CaTM 2 O 4 .

25 ENERGY STORAGE↗
DOE OSTI2025

Recent trends in all-organic polymer dielectrics for high-temperature electrostatic energy storage capacitors

Electrostatic energy storage (EES) capacitors are critical for renewable energy and high-power systems, driving the search for dielectric materials that combine superior electrical insulation, mechanical flexibility, low density, cost-effectiveness, and processability. Polymer-based dielectrics have emerged as leading candidates, particularly for high electric field applications. However, conventional polymers often fail to meet the demands of high-temperature environments due to increased electrical conductivity and reduced discharged energy density at elevated temperatures, resulting in energy loss and reduced performance. High glass transition temperature (T g) polymers show promise but require further optimization to enhance their energy storage capabilities under thermal and electrical stress. This review provides a comprehensive update on recent advancements in high-T g polymer-based dielectrics for EES capacitors, focusing on both intrinsic polymers and all-organic composites. It outlines key design principles, critical performance parameters, and innovative strategies—such as nanofiller doping, layered architectures, physical blending, and chemical crosslinking—to improve electrical, thermal, and mechanical properties. The review also highlights emerging trends, including the integration of machine learning algorithms to explore novel polymer structures and expand the chemical design space. By bridging the gap between academic research and industrial application, this review aims to accelerate the development of next-generation dielectric materials capable of balancing multiple performance metrics for high-temperature EES capacitors.

Xie, Zongliang↗
DOE OSTI2025

Rb 4 CuSb 2 Cl 11 and Rb 2 In 0.91(0.2) Sb 0.09 Cl 5 ·H 2 O: Wide Band Gap 0D Metal Halide Semiconductors

Herein, we report the discovery, structural and photophysical characterization of a new zero-dimensional (0D) lead-free all-inorganic halide, Rb 4 CuSb 2 Cl 11 , which adopts a new structure type. Single-crystal X-ray diffraction (SCXRD) shows that the structure consists of isolated, distorted seesaw [SbCl 4 ] − and trigonal planar [CuCl 3 ] 2− units, separated by Rb + cations that provide charge balance. Optoelectronic measurements and density functional theory (DFT) calculations indicate an indirect band gap of 2.89 eV, making it a candidate for wide-bandgap optoelectronic applications. Electrical resistivity was measured at 1.29 × 10 10 Ω·cm, and the trap-state density (n trap ) was found to be 7.44 × 10 10 cm −3 . Attempts to synthesize substitution analogs of Rb 4 CuSb 2 Cl 11 led to the synthesis of Rb 2 In 0.91(0.2) Sb 0.09 Cl 5 ·H 2 O, which was erroneously reported as Rb 2 SbCl 5 O in a previous study. Rb 2 In 0.91(0.2) Sb 0.09 Cl 5 ·H 2 O adopts a vacancyordered perovskite structure and exhibits broad-band yellow emission under UV excitation. The measured photoluminescence quantum yield (PLQY) for Rb 2 In 0.91 (0.2)Sb 0.09 Cl 5 ·H 2 O is 18.2%. These findings add to the growing class of quaternary metal halides with multiple cation and anion compositions, expanding the chemical phase space for the discovery of new materials with functional properties.

Crystal structure↗
DOE OSTI2022

Pressure-induced suppression of charge density phases across the entire rare-earth tritellurides by optical spectroscopy

The rare-earth tritellurides (RTe 3 ) are a distinct class of 2D layered materials that recently gained significant attention due to hosting such quantum collective phenomena as superconductivity or charge density waves (CDWs). Many members of this van der Waals (vdW) family crystals exhibit CDW behavior at room temperature, i.e., RTe 3 compound where R = La, Ce, Pr, Nd, Sm, Gd, and Tb. Here, our systematic studies establish the CDW properties of RTe 3 when the vdW spacing/interaction strength between adjacent RTe 3 layers is engineered under extreme hydrostatic pressures. Using a non-destructive spectroscopy technique, pressure-dependent Raman studies first establish the pressure coefficients of phonon and CDW amplitude modes for a variety of RTe 3 materials, including LaTe 3 , CeTe 3 , PrTe 3 , NdTe 3 , SmTe 3 , GdTe 3 , and TbTe 3 . Results further show that the CDW phase is eventually suppressed at high pressures when the interlayer spacing is reduced and interaction strength is increased. Comparison between different RTe 3 materials shows that LaTe 3 with the largest thermodynamic equilibrium interlayer spacing (smallest chemical pressure) exhibits the most stable CDW phases at high pressures. In contrast, CDW phases in late RTe 3 systems with the largest internal chemical pressures are suppressed easily with applied pressure. Overall results provide comprehensive insights into the CDW response of the entire RTe 3 series under extreme pressures, offering an understanding of CDW formation/engineering in a unique class of vdW RTe 3 material systems.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Evidence for sub-Chandrasekhar Type Ia supernovae from the last major merger

ABSTRACT We investigate the contribution of sub-Chandrasekhar mass Type Ia supernovae to the chemical enrichment of the Gaia Sausage galaxy, the progenitor of a significant merger event in the early life of the Milky Way. Using a combination of data from Nissen & Schuster, the GALactic Archaeology with HERMES (GALAH) Data Release 3 [with 1D non-local thermal equilibrium (NLTE) abundance corrections], and the Apache Point Observatory Galactic Evolution Experiment (APOGEE) Data Release 16, we fit analytic chemical evolution models to a nine-dimensional chemical abundance space (Fe, Mg, Si, Ca, Cr, Mn, Ni, Cu, and Zn) in particular focusing on the iron-peak elements, Mn and Ni. We find that low [Mn/Fe] $\sim -0.15\, \mathrm{dex}$ and low [Ni/Fe] $\sim -0.3\, \mathrm{dex}$ Type Ia yields are required to explain the observed trends beyond the [α/Fe] knee of the Gaia Sausage (approximately at [Fe/H] $=-1.4\, \mathrm{dex}$). Comparison to theoretical yield calculations indicates a significant contribution from sub-Chandrasekhar mass Type Ia supernovae in this system (from ${\sim} 60\, \mathrm{per\, cent}$ to $100\, \mathrm{per\, cent}$ depending on the theoretical model with an additional ${\pm} 10\, \mathrm{per\, cent}$ systematic from NLTE corrections). We compare to results from other Local Group environments including dwarf spheroidal galaxies, the Magellanic Clouds, and the Milky Way’s bulge, finding the Type Ia [Mn/Fe] yield must be metallicity dependent. Our results suggest that sub-Chandrasekhar mass channels are a significant, perhaps even dominant, contribution to Type Ia supernovae in metal-poor systems, whilst more metal-rich systems could be explained by metallicity-dependent sub-Chandrasekhar mass yields, possibly with additional progenitor mass variation related to star formation history, or an increased contribution from Chandrasekhar mass channels at higher metallicity.

Sanders, Jason L.↗
DOE OSTI2021

Phase transitions and potential ferroelectricity in noncentrosymmetric KNaNbOF 5

Increasing the number of known ferroelectrics requires expanding the chemical design space and mechanisms producing ferroelectricity. To that end, we examine the displacive, noncentrosymmetric-to-centrosymmetric phase transition in the oxyfluoride KNaNbOF 5 using ab initio calculations and Landau theory. Here we predict an intermediate Pnma phase occurs in the transition from the known low- and high-temperature phases, Pna2 1 and Cmcm, respectively, which we subsequently confirm using high-resolution and in situ 19 F magic-angle spinning nuclear magnetic resonance. Using the intermediate structure, we then calculate the monodomain polarization switching barrier in KNaNbOF 5 to be ~ 93 meV per formula unit, comparable to existing ferroelectrics. The reversal of the polarization is due to uncompensated antipolar displacements of oxygen and fluorine and does not require oxygen and fluorine site exchange as in other nonswitchable heteroanionic materials, which makes KNaNbOF 5 a viable oxyfluoride for a switchable electric polarization.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Atoms to fibers: Identifying novel processing methods in the synthesis of pitch-based carbon fibers

Understanding and optimizing the key mechanisms used in the synthesis of pitch-based carbon fibers (CFs) are challenging, because unlike polyacrylonitrile-based CFs, the feedstock for pitch-based CFs is chemically hetero- geneous, resulting in complex fabrication leading to inconsistency in the final properties. In this work, we use molecular dynamics simulations to explore the processing and chemical phase space through a framework of CF models to identify their effects on elastic performance. The results are in excellent agreement with experiments. We find that density, followed by alignment, and functionality of the molecular constituents dictate the CF mechanical properties more strongly than their size and shape. Last, we propose a previously unexplored fabrication route for high-modulus CFs. Unlike graphitization, this results in increased sp 3 fraction, achieved via generating high-density CFs. In addition, the high sp 3 fraction leads to the fabrication of CFs with isometric compressive and tensile moduli, enabling their potential applications for compressive loading.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2022

Unexpected features in the optical vibrational spectra of δ-UO 3

Uranium trioxide displays a complex chemical phase space, with at least six structurally distinct polymorphs accessible via different synthetic routes. Remarkably, despite its technological importance, full structural and electronic characterization of these polymorphs remains an open area of study. δ-UO 3 in particular has attracted significant theoretical attention due to its high point group and space group symmetries, having U (VI) in octahedral coordination with polyhedra interconnected through corner-sharing to build a 3-D cubic lattice with space group symmetry Pm-3m and Z = 1. Critical experimental information, such as its optical vibrational spectra, are not known. Here, we study the Raman and infrared (IR) spectra of δ-UO 3 together with the support of density functional theory (DFT) calculations for spectral interpretation. A symmetry analysis of the DFT-predicted phonon eigenmodes indicates that δ-UO 3 should have two IR active modes and no Raman active modes. Experimental results, however, indicate significant Raman scattering from δ-UO 3 . We therefore propose four potential explanations for this apparent contradiction: a possible tetragonal distortion to the cubic cell, the existence of a surface impurity layer, vacancy scattering, and structural activation of Raman signal. We use powder X-ray diffraction and confocal Raman spectroscopy with depth profiling to investigate these possibilities and suggest future experiments to explore this phenomenon in more detail. Understanding the lattice dynamics of δ-UO 3 is important for identification of technogenic U phases via Raman and infrared spectroscopy and our results indicate that the simple understanding of δ-UO 3 as a high-symmetry cubic structure should be reconsidered.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
DOE OSTI2023

Reactor Network Analysis with Various Reaction Mechanisms to Investigate Hydrogen vs. Methane Fuel at Varying Flame Temperatures with Experimental Data

Emissions data were evaluated for a set of test hardware that adapts Collins Aerospace’s aeroengine liquid fueled injector technology to a ground-based turbine. The hardware was specifically developed to operate on 100% hydrogen; however it has the ability to operate on both pure methane and hydrogen and mixtures in between. 16 injector configurations and seven factors (air split, fuel and air swirl, pressure drop, preheat temperature, fuel composition, and flame temperature) were investigated based on a Box Behnken statistical model. Of the 16, configuration 3 was selected for further discussion being in the middle of the statistical design space. A chemical reactor network was developed based on the experimental data obtained and was assessed with three different mechanisms: GRI Mech 3.0, UCSD, and Galway. All factors were held constant except the fuel composition to directly compare chemical pathways for methane and hydrogen. This study was conducted across different flame temperatures of 1500K, 1675K, and 1850K for both hydrogen and methane. Additional lower flame temperatures of 1100K and 1300K were evaluated for hydrogen. Moreover, results for fully premixed and non-premixed results were compared to obtain information on the implications of mixedness on emissions. These results were compared with the Leonard and Stegmaier plot for methane and a similar plot was constructed for hydrogen.

08 HYDROGEN↗
DOE OSTI2023

COMPUTATIONAL MODELING OF IGNITION AND PREMIXED FLAME PROPAGATION INITIATED BY A PRE-CHAMBER TURBULENT JET

Addressing the pressing need for reduced carbon emissions, Turbulent Jet Ignition (TJI) emerges as a promising technology for ultra-lean combustion, offering enhanced thermal efficiencies and minimized cyclic variability in spark-ignited engines. To facilitate rapid testing and integration of this technology, a robust computational modeling framework is crucial. This study delves into the predictive capabilities of computational models for main-chamber ignition and premixed flame propagation using a single-cycle TJI rig measured by Biswas et al. (Applied Thermal Engineering, vol 106, 2016). Employing an open-source compressible flow simulation solver with Large Eddy Simulation (LES) for turbulence modeling, the investigation integrates the conventional Laminar Finite Rate Chemistry (LFRC) model alongside the transported Probability Density Method (PDF) for turbulence-chemistry interaction. A fully-consistent Eulerian Monte-Carlo Fields (EMCF) method is utilized to approximate the transported PDF, while Interaction by Exchange with Mean is employed to close micro-mixing terms in stochastic differential equations. A reduced chemical reaction mechanism with 21 species and 84 reactions (DRM-19) is used for solving chemical kinetics, and a double Gaussian energy deposition model is used to approximate the spark ignition in the pre-chamber. An unstructured O-grid mesh with 0.3 million cells in the prechamber and 1 million cells in the main chamber is employed. Results are divided into two phases: pre-chamber initialization and full TJI simulations. Validation of the predicted pre-chamber flame propagation and the lean ignition in the main-chamber is carried out by using available experimental data. Under quiescent conditions, both the LFRC and transported PDF methods largely underestimate the flame speed and subsequent pressure growth in the pre-chamber. A linear momentum forcing technique is applied to investigate the impact of initial turbulence in the pre-chamber, demonstrating a notable influence on flame propagation. Fine-tuning of the forcing coefficient reproduces the sudden pressure growth observed in the experiment. The experimentally validated pre-chamber simulation serves as the initial condition for the full TJI simulations. It is found that the LFRC model fails to predict lean-ignition in the main-chamber, resulting in a misfiring event. Incorporation of turbulence-chemistry interaction using the transported PDF method substantially improves the prediction of the ignition event in the main-chamber, achieving fair qualitative agreement and quantitative validation of combustion parameters within ±10% of the reported experimental data. The rich simulation results consisting of a full set of statistical description of the thermo-chemical states enable us to gain deep insights into the ignition mechanisms in the main chamber, which is limited when done experimentally. A novel dual ignition phenomenon is revealed in the TJI rig for the first time. Initially, a primary ignition kernel is formed at a downstream location which eventually detaches from the main jet. As the jet momentum decreases, a secondary ignition event follows, this time at a more upstream location which eventually combines with the primary ignition kernel to form a single connected flame front. Investigation of these ignition sequences in chemical composition space reveal distinct differences between the two. The primary ignition event in the main-chamber is followed by a large concentration of active radicals from the pre-chamber jet, accelerating the chain-branching steps, characterizing what has been referred to as flame ignition. In contrast, the secondary ignition occurs in the absence of active radicals in the pre-chamber jet, hence characterized as jet ignition. Further analysis of the effect of pre-chamber jet characteristics on lean ignition in the main-chamber is conducted by setting up cases with different initial pressure ratios (por) between the two chambers, a non-dimensional parameter, ranging from 1.2 to 3.2. As the initial pressure ratio increases, jet momentum increases, with dual ignition observed in cases above por= 2.2. Case with por= 3.2 lead to misfiring. The effect of ignition sequence on global combustion characteristics of TJI is analyzed. Dual ignition events lead to non-monotonicity in combustion characteristics such as global reaction progress variable, flame penetration, and global heat release rate. In dual ignition events, although the rate of fuel consumption and global heat release rate is initially lower, the secondary ignition leads to a sudden increase in flame surface area, resulting in a sudden jump and promoting the overall performance of the TJI system.

42 ENGINEERING↗
DOE OSTI2021

Identification of bioprivileged molecules: expansion of a computational approach to broader molecular space

As interest in biobased chemicals grows, and their application space expands, computational tools to navigate molecule space as a complement to experimental approaches are imperative. This work expands upon previous work that identified candidate bioprivileged molecules from the C 6 H x O y (C6) subspace. It refines the framework that was developed previously to better refine the molecules according to their biological origin and applies it to three new subspaces of chemical structure: C 4 H x O y (C4), C 5 H x O y (C5), and C 7 H x O y (C7). For C5 and C7, roughly the top 100 bioprivileged candidates were identified, and the enhanced framework was applied to recast slightly the previous list of the top 100 C6 molecules. In addition, all top candidates were analyzed for their key functional moieties using a random forest model, and this algorithm was applied to compare the functional group space occupied by bioprivileged molecules of various databases of molecules with a focus on evaluating how closely the molecules were aligned with those known to biology. Furthermore, with the present work's focus on automation and data science principles, the framework can be easily expanded to include other chemical formulae to screen for bioprivileged candidates. As a result, this in turn facilitates the retrosynthesis process inherent in the framework to identify those bioprivileged intermediates in other subspaces that lead to target molecules.

42 ENGINEERING↗
DOE OSTI2025

A Variational Autoencoder Model Toward Molecular Structure Representation Learning of Fuels

Here, in this work, a Variational Autoencoder (VAE)-based data-driven modeling framework is developed with the overarching goal of enabling fuel design. The VAE model is trained on a large dataset with several chemical species to learn a compressed latent space molecular representation. Chemical structure in the form of Simplified Molecular Input Line Entry System (SMILES) string is fed as input, encoded into the VAE latent space, and decoded back to the SMILES string using Long Short-Term Memory (LSTM) networks. Complexities of the VAE training loss function are thoroughly examined by varying the weightage (beta (𝜷) parameter) of the latent space regularization term, thereby assessing the balance between reconstruction accuracy and validity, and focusing on both accurate molecular structure reconstruction and latent space consistency. Two different strategies for 𝜷 variation are evaluated: linear annealing and cyclic annealing. In addition, the impact of total correlation adjustment and hierarchical priors is also studied with regard to the balance between reconstruction fidelity and latent space regularization, and potential issues such as posterior collapse, over-regularization, and poor disentanglement of latent variables. Overall, the best performance of the model is achieved with hierarchical priors and incrementally increasing 𝜷 from 0 to a threshold value of 0.25 over 75 epochs. The generative VAE model can be readily coupled with Quantitative Structure–Property Relationship (QSPR) analysis to develop an integrated end-to-end framework for fuel-property prediction and molecular design of novel promising fuels.

fuel design↗
DOE OSTI2022

Computing diffraction patterns of microstructures from phase-field simulations

The diffraction pattern of a material contains information not only on the crystal structures of its constituting phases, but also on its mesoscale spatial distributions of phases, grains, and ferroelastic, ferroelectric, and ferromagnetic domains. While diffraction patterns from experiments such as X-ray diffraction are presented in the reciprocal or Fourier space, mesoscale microstructure models such as the phase-field method naturally produce real-space images of spatial distribution of chemical composition, structural, and ferroic domains. Although one could rather readily compute the Fourier amplitudes of chemical and structural domain distributions generated by mesoscale simulations, they only contain information about the length scale and alignment of the real-space chemical and structure domains. Therefore, a direct comparison between diffraction experiments and mesoscale microstructure simulations is not possible. Here, we develop a theoretical approach to directly compute the crystal diffraction patterns of microstructures predicted by phase-field simulations. In particular, we consider five representative examples of microstructure patterns involving purely compositional domains, a single pair of tetragonal twin structures, multiple twin variants in a hexagonal system, ferroelectric polar vortices, and polycrystalline grains. The results are compared with previous experimental observations as well as X-ray diffraction experiments performed in the present study. Furthermore, the theoretical framework allows one to directly connect material microstructures and diffraction patterns predicted from phase-field simulations and the corresponding diffraction patterns from experiments, and thus providing guidance to experimental diffraction characterization and interpretation of microstructures.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Representations and strategies for transferable machine learning improve model performance in chemical discovery

Strategies for machine-learning (ML)-accelerated discovery that are general across material composition spaces are essential, but demonstrations of ML have been primarily limited to narrow composition variations. By addressing the scarcity of data in promising regions of chemical space for challenging targets such as open-shell transition-metal complexes, general representations and transferable ML models that leverage known relationships in existing data will accelerate discovery. Over a large set (~1000) of isovalent transition-metal complexes, we quantify evident relationships for different properties (i.e., spin-splitting and ligand dissociation) between rows of the Periodic Table (i.e., 3d/4d metals and 2p/3p ligands). We demonstrate an extension to the graph-based revised autocorrelation (RAC) representation (i.e., eRAC) that incorporates the group number alongside the nuclear charge heuristic that otherwise overestimates dissimilarity of isovalent complexes. To address the common challenge of discovery in a new space where data are limited, we introduce a transfer learning approach in which we seed models trained on a large amount of data from one row of the Periodic Table with a small number of data points from the additional row. We demonstrate the synergistic value of the eRACs alongside this transfer learning strategy to consistently improve model performance. Analysis of these models highlights how the approach succeeds by reordering the distances between complexes to be more consistent with the Periodic Table, a property we expect to be broadly useful for other material domains.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

A combined machine learning and density functional theory study of binary Ti-Nb and Ti-Zr alloys: Stability and Young’s modulus

The multicomponent Ti alloys, specifically the -phase, have experienced a strong growth over the last decades, due to their outstanding properties of ultra-high strength and low Young’s modulus. These properties play a significant role in many aerospace and biomedical applications. Selection and optimization of multicomponent alloys is challenging due to the vast chemical and compositional space. Here we investigate the use of machine learning techniques informed by density functional calculations to guide the selection of Nb- and Zr-based Ti binary alloys. From the cubic structures obtained from high throughput calculations and literature, we identify several structures with Young’s moduli below 40 GPa. The multivariant decision tree methods provide efficient surrogate models to identify structure variables have high influences on the energetic stability and Young’s modulus. Furthermore, we implement a workflow of incorporating DFT provided results and machine learning method to explore the chemical and composition space of other binary and multicomponent alloys, to eventually accelerate the material design via taking advantages of identified key variables.

36 MATERIALS SCIENCE↗