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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 253 records · Page 14
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

Multiple Lines of Evidences Reveal Mechanisms Underpinning Mercury Resistance and Volatilization by Stenotrophomonas sp. MA5 Isolated from the Savannah River Site (SRS), USA

A largely understudied microbially mediated mercury (Hg) bioremediative pathway includes the volatilization of Hg 2+ to Hg 0 . Therefore, studies on Hg resistant bacteria (HgR), isolated from historically long-term contaminated environments, can serve as models to understand mechanisms underpinning Hg cycling. Towards this end, a mercury resistant bacterial strain, identified as Stenotrophomonas sp., strain MA5, was isolated from Mill Branch on the Savannah River Site (SRS); an Hg-impacted ecosystem. Minimum inhibitory concentration (MIC) analysis showed Hg resistance of up to 20 µg/mL by MA5 with 95% of cells retaining viability. Microcosm studies showed that the strain depleted more than 90% of spiked Hg 2+ within the first 24 h of growth and the detection of volatilized mercury indicated that the strain was able to reduce Hg 2+ to Hg 0 . To understand molecular mechanisms of Hg volatilization, a draft whole genome sequence was obtained, annotated and analyzed, which revealed the presence of a transposon-derived mer operon (merRTPADE) in MA5, known to transport and reduce Hg 2+ into Hg 0 . Based on the whole genome sequence of strain MA5, qRT-PCR assays were designed on merRTPADE, we found a ~40-fold higher transcription of mer T, P, A, D and E when cells were exposed to 5 µg/mL Hg 2+ . Interestingly, strain MA5 increased cellular size as a function of increasing Hg concentrations, which is likely an evolutionary response mechanism to cope with Hg stress. Moreover, metal contaminated environments are shown to co-select for antibiotic resistance. When MA5 was screened for antibiotic resistance, broad resistance against penicillin, streptomycin, tetracycline, ampicillin, rifampicin, and erythromycin was found; this correlated with the presence of multiple gene determinants for antibiotic resistance within the whole genome sequence of MA5. Overall, this study provides an in-depth understanding of the underpinnings of Stenotrophomonas-mercury interactions that facilitate cellular survival in a contaminated soil habitat.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS1984

Heat Transfer Characteristics of Regenerator Matrix (Case of Packed Wire Gauzes)

The average heat transfer coefficient in the matrix of laminated wire screens (10 to 250 mesh) for a Stirling engine heat exchanger was studied experimentally. The data are correlated by N sub ud = 0.42 R sub ed 0.56 (3 or = R sub ed or = 400), and R sub ed are the Nusselt and Reynolds nubmers based on the wire diameter. The pressure drop decreased and the heat transfer increased as the wire diameter was decreased.

Hamaguchi, K.↗
NASA NTRS1990

Multiple Access Trade Study

The Personal Access Satellite System (PASS) strawman design uses a hybrid Time Division Multiple Access (TDMA)/Frequency Division Multiple Access (FDMA) implementation. TDMA is used for the forward direction (from Suppliers to Users), and FDMA for the return direction (from Users to Suppliers). An alternative architecture is proposed that will require minimal real time coordination and yet provide a fast access method by using random access Code Division Multiple Access (CDMA). The CDMA system issues are addressed such as connecting suppliers and users, both of whom may be located anywhere in the CONUS, when the user terminals are constrained in size and weight; and providing efficient traffic routing under highly variable traffic requirements. It is assumed that bandwidth efficiency is not of paramount importance. CDMA or Spread Spectrum Multiple Access (SSMA) communication is a method in which a group of carriers operate at the same nominal center frequency but are separable from each other by the low cross correlation of the spreading codes used. Interference and multipath rejection capability, ease of selective addressing and message screening, low density power spectra for signal hiding and security, and high resolution ranging are among the benefits of spread spectrum communications.

Motamedi, Masoud↗
DOE OSTI2025

Machine Learning-Driven Solvent Screening for Biobased 2,3-Butanediol Extraction

Biobased 2,3-butanediol (2,3-BDO) is a valuable biomass-derived chemical due to its versatility in being transformed into a wide variety of products. However, the separation and purification of 2,3-BDO from fermentation broth remain a significant challenge owing to its high boiling point and hydrophilic nature. Herein, we developed a machine learning (ML)-based screening workflow that uses molecular calculations as training data and requires only a small number of experimental measurements for validation to identify alternative solvent candidates for the liquid–liquid extraction (LLE) of 2,3-BDO from aqueous solution. In particular, 130 density functional theory (DFT) calculations with the implicit solvation method not only built a correlation between the computational partition coefficient and the experimental distribution coefficient of 2,3-BDO but also parameterized an Extra-Trees ML model to screen the distribution coefficient for a wider range of 6717 organic solvents. The experimental measurements of only 24 solvents were needed to validate the computational results. A list of 50 prioritized solvents was proposed for 2,3-BDO LLE, and seven additional experimental measurements were conducted to further verify our selected solvents. The impact of the extraction temperature and solvent-to-feed ratio was also investigated for selected solvents in experiments. Furthermore, this work suggested alternative solvents for 2,3-BDO LLE and proposed a versatile workflow that requires fewer experiments and can be applied to a broader range of LLE studies.

Extraction↗
DOE OSTI2016

A marked correlation function for constraining modified gravity models

Future large scale structure surveys will provide increasingly tight constraints on our cosmological model. These surveys will report results on the distance scale and growth rate of perturbations through measurements of Baryon Acoustic Oscillations and Redshift-Space Distortions. It is interesting to ask: what further analyses should become routine, so as to test as-yet-unknown models of cosmic acceleration? Models which aim to explain the accelerated expansion rate of the Universe by modifications to General Relativity often invoke screening mechanisms which can imprint a non-standard density dependence on their predictions. This suggests density-dependent clustering as a `generic' constraint. This paper argues that a density-marked correlation function provides a density-dependent statistic which is easy to compute and report and requires minimal additional infrastructure beyond what is routinely available to such survey analyses. We give one realization of this idea and study it using low order perturbation theory. We encourage groups developing modified gravity theories to see whether such statistics provide discriminatory power for their models.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Extending Orbital-Optimized Density Functional Theory to L-Edge XPS and Beyond: Spin–Orbit Coupling via Nonorthogonal Quasi-Degenerate Perturbation Theory

Quantum mechanical calculations of core electron binding energies (CEBEs) are relevant to interpreting X-ray photoelectron spectroscopy (XPS). Orbital-optimized density functional theory (OO-DFT) accurately predicts K-edge CEBEs but is challenged by the presence of significant spin–orbit coupling (SOC) at L- and higher edges involving inner-shell orbitals with nonzero angular momentum. Here, to extend OO-DFT to L-edges and higher, our method utilizes scalar-relativistic, spin-restricted open-shell OO-DFT to construct a minimal, quasi-degenerate basis of core-hole states corresponding to a chosen inner-shell (e.g., ionizing all six possible 2p spin orbitals). Nonorthogonal configuration interaction (NOCI) is then used to obtain the matrix elements of the full Hamiltonian including SOC in this quasi-degenerate model space of determinants. Using a screened 1-electron SOC operator parametrized with the Dirac-Coulomb-Breit (DCB) Hamiltonian results in doublet splitting (DS) values for third row elements that are nearly in quantitative agreement with experiment. The resulting NOCI eigenvalues are shifted by the average of the (scalar) OO-DFT CEBEs to yield CEBEs (split by SOC) corrected for dynamic correlation. Comparing calculations on gas phase molecules with experimental results establishes that NO-QDPT with the SCAN functional (NO-QDPT/SCAN), using the DCB screened 1-electron SOC operator is accurate to about 0.2 eV for L-edge CEBEs of molecules containing third row atoms. However, this NO-QDPT approach becomes less accurate for fourth-row elements starting in the middle of the 3d transition metal series, with errors increasing as atomic number increases.

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

Analysis of correlations between intrinsic ductility and electronic density of states in refractory alloys

High entropy alloys (HEAs) correspond to a new and emerging class of materials that allows us to explore a large composition space to tune mechanical strength and thermal stability. Therefore, to design better alloys, it is important to scan the high-dimensional space of chemistry, composition and temperature. Here, to facilitate this search, we present a method to screen intrinsically ductile body centered cubic (BCC) refractory alloys from electronic structure calculations by using the density of states (DOS) at the Fermi level, g(μ F ). This correlation between intrinsic ductility and g(μ F ) is tested by analyzing group V (V, Nb, Ta) and VI (Mo, W) refractory metals, binary alloys, such as W-Nb, W-V, Mo-Nb and Mo-V, and refractory alloys for which experimental stress-strain measurements are available. In addition, we perform a high-throughput exploration of the entire composition space of a recently proposed alloy system, CrMoNbV, and identify compositions that exhibit high intrinsic ductility.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Electrochemical implications of modulating the solvation shell around redox active organic species in aqueous organic redox flow batteries

Significance The development of cost-effective batteries for long-duration grid scale energy storage will be accelerated using frameworks to rapidly screen and select battery components. Herein, we show that the solvent reorganization energy calculated from the Born equation (with reference to an electrolyte’s composition) is predictive of the electrolytes’ device level performance. This descriptor was found to correlate with key transport and kinetic properties over a range of electrolyte compositions and pH values, succinctly capturing the multicomponent interactions between the electrolyte salts and solvent. This enables the initial high-throughput screening of electrolyte candidates with minimal experimentation. Applied to aqueous redox flow batteries employing organic redox active species, we predict high-performance electrolyte compositions, enabling significantly enhanced device performance.

Sharma, Kritika↗
DOE OSTI2018

Circular RNA hsa{sub c}ircRNA{sub 1}03809 promotes lung cancer progression via facilitating ZNF121-dependent MYC expression by sequestering miR-4302

Lung cancer characterized with malignant cell growth is the leading cause of cancer-related deaths. In recent years, several circular RNAs (circRNAs) have been reported to participate in lung cancer progression. However, the correlation between circular RNA (circRNA) and lung cancer still remains to be further investigated. In this study, we screened out a highly expressed circular RNA hsa{sub c}ircRNA{sub 1}03809 in lung cancer tissues. We showed hsa{sub c}ircRNA{sub 1}03809 could serve as a prognostic biomarker for patients with lung cancer. Furthermore, we found that hsa{sub c}ircRNA{sub 1}03809 knockdown significantly suppressed lung cancer cell proliferation and invasion in vitro and delayed tumor growth in vivo. In mechanism, we identified hsa{sub c}ircRNA{sub 1}03809 as a sponge of miR-4302 targeting ZNF121. By sequestering miR-4302, hsa{sub c}ircRNA{sub 1}03809 promoted the expression of ZNF121 which consequently enhanced MYC protein level in lung cancer cells. Through rescue assays, we demonstrated hsa{sub c}ircRNA{sub 1}03809 contributed to lung cancer cell proliferation and invasion via facilitating ZNF121-dependent MYC expression by sponging miR-4302. In conclusion, our findings illustrated a novel hsa{sub c}ircRNA{sub 1}03809/miR-4302/ZNF121/MYC regulatory signaling pathway in lung cancer progression.

60 APPLIED LIFE SCIENCES↗
NASA NTRS1972

Experimental investigation of a throttlable 15 cm hollow cathode ion thruster

The use of dished high perveance grids on a 15 cm modified SERT 2 thruster is shown to facilitate throttled operation over a beam current range from 60 to 600 mA. Effects of increasing the radial component of the magnetic field in the main discharge chamber and decreasing the dimensions of the cathode discharge region are examined and found to degrade performance to the extent that primary electrons are forced in toward the center-line of the thruster. Studies of the baffle aperture region of two thrusters indicate that the electric potential gradient vector is perpendicular to the local magnetic field lines when the thruster is operating properly. The correlation between the shape of the ion beam current density and that of the ion density at the screen grid within the thruster is shown to be 94%. Additional experimental studies on maximum propellant utilization, plasma ion production cost, neutral density in the cathode discharge region, double ion production in hollow cathode thrusters and thermal flow meter performance are discussed.

Wilbur, P. J.↗
NASA NTRSDate not supplied

Downfolding Complex Materials Problems Onto Model Hamiltonians for Quantum Computers

Simulating the properties of quantum materials is expected to be one of the exciting applications for quantum computers and where we hope to see advantages over classical hardware. The complexity of ab initio Hamiltonians describing the physics of application-relevant materials places them beyond the realm of possibility for solution on near-term hardware with a limited number of qubits. Various Hamiltonian approximations, including Hamiltonian downfolding, offers a possibility towards simulating complex materials on near-term hardware. This is accomplished by approximating the relevant physics of a given material through their representation by simpler model Hamiltonians, such as the Hubbard Hamiltonian or extensions of it. Here we employ a well-defined first-principles methodology for deriving downfolded multi-band extended Hubbard Hamiltonians of materials, capturing strong electronic correlation and electron-phonon coupling, based on the formalism of Wannier functions and the calculation of the screened Coulomb interaction. We demonstrate for a variety of systems that quantum simulation of these downfolded Hamiltonians reproduces key properties, thus establishing downfolding as a promising route to achieve near-term simulation of application-relevant systems on quantum hardware.

Antonios Markos Alvertis↗
NASA NTRSDate not supplied

Downfolding complex materials problems onto model Hamiltonians for quantum computers

Simulating the properties of quantum materials is expected to be one of the exciting applications for quantum computers and where we hope to see advantages over classical hardware. The complexity of ab initio Hamiltonians describing the physics of application-relevant materials places them beyond the realm of possibility for solution on near-term hardware with a limited number of qubits. Various Hamiltonian approximations, including Hamiltonian downfolding, offers a possibility towards simulating complex materials on near-term hardware. This is accomplished by approximating the relevant physics of a given material through their representation by simpler model Hamiltonians, such as the Hubbard Hamiltonian or extensions of it. Here we employ a well-defined first-principles methodology for deriving downfolded multi-band extended Hubbard Hamiltonians of materials, capturing strong electronic correlation and electron-phonon coupling, based on the formalism of Wannier functions and the calculation of the screened Coulomb interaction. We demonstrate for a variety of systems that quantum simulation of these downfolded Hamiltonians reproduces key properties, thus establishing downfolding as a promising route to achieve near-term simulation of application-relevant systems on quantum hardware.

Antonios M. Alvertis↗
DOE OSTI2022

Image Analysis for Rapid Assessment and Quality-Based Sorting of Corn Stover

Imaging in the visible spectrum is a low-cost tool that can be readily deployed for in-field or over-belt monitoring of biomass quality for bio-refining operations. Rapid image analysis coupled with innovative preprocessing may reduce the impacts of feedstock variability through identification of contaminants or other material attributes to guide selective sorting and quality management. Image analysis was employed to evaluate the quality of corn stover in red-green-blue (RGB) chromatic space. This study used controlled, bench-scale imaging as a proof-of-concept for rapid quality assessment of corn stover based on variations in material attributes, including chemical and physical attributes, that relate to biological degradation and soil contamination. Additionally, logistic regression-based classification algorithms were used to develop a method for biomass screening as a function of biological degradation or soil contamination. This study demonstrated the use of image analysis to extract features from RGB color space to investigate variations in critical material attributes from chemical composition of corn stover. Fourier transform infrared (FT-IR) suggested a correlation between red band intensity and biological degradation, while detailed surface texture analysis was found to distinguish among variations in ash. These insights offer promise for development of a rapid screening tool that could be deployed by farmers for in-field assessment of biomass quality or biorefinery operators for in-line sorting and process optimization.

09 BIOMASS FUELS↗
DOE OSTI2015

Use of XRF to characterize pre-Hanford orchards in the 100-OL-1 operable unit - 15717

Prior to 1943, Hanford site included several small towns with approximately 5,000 acres of orchards; at the time lead arsenate (PbHAsO{sub 4}) was the most commonly used pesticide. High concentration of lead and arsenic have been recorded where trees and stumps are still visible. Pre-Hanford orchards have been designated as 100-OL-1 Operable Unit. An Optimization study evaluated optimal counting time and position of the XRF from soils collected at the site. A Pilot Study included the evaluation of a field portable x-ray fluorescence (XRF) analyzer determining if performance results meet quality assurance criteria for cleanup. XRF has only previously been used as a screening tool. Four Decision Units (DUs) are to be analyzed: Ol-14. OL-32. OL-IU6-4 and OL-FR2-1. The DUs vary in size and have had distinct historic activities; such as 46.4 acres OL-14 having a military camp in the 1950's unlike OL-FR2-1 having been disturbed as it is near the F area reactor. Together the DUs provide an adequate representation of the Operable Unit. Screening criteria for lead (250 mg/kg) and arsenic (20 mg/kg). Six samples were collected ranging above, at, and below the screening criteria of 250 mg/kg for Pb and 20 mg/kg for As at the Decision Units OL-14 and OL-IU6-4, and labeled respectively. Samples were hand sieved, homogenized and placed in sample cups. Three replicates were prepared for each sample. Each sample cup was analyzed 3 times for 30 sec. A Standard Reference Material (SRM) was analyzed after every 20 readings. Three samples were selected for 15, 30, 45, 60, 90, 120, 150 and 180 second fixed and variable analysis. Two transects at OL-14, and one at OL-IU6-4 were created by selecting locations 90. around ground zero at distances of 0.5, 1, 2, 4, 8 and 12 feet (16 ft for OL-14). Locations were scanned in triplicate for 60 seconds each. 4 Decision Units spanning roughly 370 acres were chosen to represent 100-OL-1 Operable Unit. 40 locations within each DU were selected using Visual Sample Plan (VSP) with a random start and systematic grid pattern. Each location would be analyzed with 3 replicates and a 60 second count time. The Standard Reference Material (SRM) and blank are analyzed after every 20 counts to ensure accuracy. Data is extracted from XRF to be implemented in GIS for visual representation of site with 1943 and 2013 aerial imagery. Discussion/Conclusion: - XRF provided a good indication of precision and accuracy relative to ICP with a linear correlation R{sup 2} value higher than anticipated at .9735; - MDL proved the detection limit is significantly lower than the screening criteria; - When comparing results from the first 40 locations of OL-14 to the total 80 locations of OL-14, the same variability was seen; - We expect high concentrations of Pb and As to be where past orchards are present though this does not occur as all sites. Significance: - XRF is competent for decision making as values fall within ±20% ICP data; - The highest concentrations are not where expected, which may be an issue for sites outside of Hanford that have had more development; - The size of the DU does not affect the number of samples measured. Future Work: - Verification and validation of the XRF Data (QA/QC); - Revise conceptual site model; - Statistical evaluations to investigate: Estimation of variability in surface soils, appropriate size for DU, and sample size per DU; - Coordinating sample results through CORE; - Displaying sample results in PHOENIX (GIS tool)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2018

miR-126-5p targets Malate Dehydrogenase 1 in non-small cell lung carcinomas

Highlights: • A microRNA (miR) screen identified 3 binding sites of miR-126-5p in the 3′UTR of MDH1. • miR-126 and MDH1 are inversely correlated in large primary cohorts of NSCLCs. • miR-126 can reduce the protein level of MDH1, while sparing its isoenzyme MDH2. • miR-126-5p inhibits the MDH1 enzymatic activity and mitochondrial respiration. • miR-126-5p exerts cytostatic and cytotoxic effects in NSCLCs. Malate Dehydrogenase (MDH) 1 has recently been shown to be highly expressed and display prognostic value in non-small cell lung carcinomas (NSCLCs). However, it is not known how MDH1 expression is regulated and there is no current molecular or chemical strategy that specifically targets MDH1. This may be due to structural and enzymatic similarities with its isoenzyme, malate dehydrogenase 2 (MDH2). However, MDH1 and MDH2 are encoded by distinct genes and this opens up the possibility for modulation at the expression level. Here, we screened in silico for microRNAs (miRs) that selectively targets the 3′UTR region of MDH1. These analyses revealed that mir-126-5p has three binding sites in the 3′UTR region of MDH1. Additionally, we show that expression of miR-126-5p suppresses the enzymatic activity of MDH1, mitochondrial respiration and caused cell death in NSCLC cell lines.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2017

A perturbative approach to the redshift space correlation function: beyond the Standard Model

We extend our previous redshift space power spectrum code to the redshift space correlation function. Here we focus on the Gaussian Streaming Model (GSM). Again, the code accommodates a wide range of modified gravity and dark energy models. For the non-linear real space correlation function used in the GSM we use the Fourier transform of the RegPT 1-loop matter power spectrum. We compare predictions of the GSM for a Vainshtein screened and Chameleon screened model as well as GR. These predictions are compared to the Fourier transform of the Taruya, Nishimichi and Saito (TNS) redshift space power spectrum model which is fit to N-body data. We find very good agreement between the Fourier transform of the TNS model and the GSM predictions, with ≤ 6% deviations in the first two correlation function multipoles for all models for redshift space separations in 50Mpc h ≤ s ≤ 180Mpc/ h . Excellent agreement is found in the differences between the modified gravity and GR multipole predictions for both approaches to the redshift space correlation function, highlighting their matched ability in picking up deviations from GR. We elucidate the timeliness of such non-standard templates at the dawn of stage-IV surveys and discuss necessary preparations and extensions needed for upcoming high quality data.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2022

Prospects and Limitations of Predicting Fuel Ignition Properties from Low-Temperature Speciation Data

Using chemical kinetic modeling and statistical analysis, we investigate the possibility of correlating key chemical “markers”–typically small molecules–formed during very lean (φ ~ 0.001) oxidation experiments with near-stoichiometric (φ ~ 1) fuel ignition properties. One goal of this work is to evaluate the feasibility of designing a fuel-screening platform, based on small laboratory reactors that operate at low temperatures and use minimal fuel volume. Buras et al. [Combust. Flame2020,216, 472–484] have shown that convolutional neural net (CNN) fitting can be used to correlate first-stage ignition delay times (IDTs) with OH/HO2 measurements during very lean oxidation in low-T flow reactors with better than factor-of-2 accuracy. In this work, we test the limits of applying this correlation-based approach to predict the low-temperature heat release (LTHR) and total IDT, including the sensitivity of total IDT to the equivalence ratio, φ. We demonstrate that first-stage IDT can be reliably correlated with very lean oxidation measurements using compressed sensing (CS), which is simpler to implement than CNN fitting. LTHR can also be predicted via CS analysis, although the correlation quality is somewhat lower than for first-stage IDT. In contrast, the accuracy of total IDT prediction at φ = 1 is significantly lower (within a factor of 4 or worse). Furthermore, these results can be rationalized by the fact that the first-stage IDT and LTHR are primarily determined by low-temperature chemistry, whereas total IDT depends on low-, intermediate-, and high-temperature chemistry. Oxidation reactions are most important at low temperatures, and therefore, measurements of universal molecular markers of oxidation do not capture the full chemical complexity required to accurately predict the total IDT even at a single equivalence ratio. As a result, we find that φ-sensitivity of ignition delay cannot be predicted at all using solely correlation with lean low-T chemical speciation measurements.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Towards inverse microstructure-centered materials design using generative phase-field modeling and deep variational autoencoders

The field of Integrated Computational Materials Engineering (ICME) combines a broad range of methods to study materials’ responses over a spectrum of length scales. A relatively unexplored aspect of microstructure-sensitive materials design is uncertainty propagation and quantification (UP/UQ) of materials’ microstructure, as well as establishing process-structure–property (PSP) relationships for inverse material design. In this study, an efficient UP technique built on the idea of changing probability measures and a deep generative unsupervised representative machine learning method for microstructure-based design of thermal conductivity of materials is proposed. Probability measures are used to represent microstructure space, and Wasserstein metrics are used to test the efficiency of the UP method. By using deep Variational AutoEncoder (VAE), we identify the correlations between the material/process parameters and the thermal conductivity of heterogeneous dual-phase microstructures. Through high-throughput screening, UP, and the deep-generative VAE method, PSP relationships that are too complex can be revealed by exploiting the materials’ design space with an emphasis on microstructures. As a last point, we demonstrate generative machine learning serves as a useful tool for inverse microstructure-centered materials design, and we demonstrate this by examining the inverse design of thermal conductivity in nano-structured materials. Here, the results reveal the effects of morphology, volume fraction, characteristic length scale, and the individual thermal diffusivity of phases on the thermal conductivity of dual-phase alloys. Our findings emphasize the advantages of high-throughput phase-field modeling and generative deep learning for linking PSP and inverse microstructure-centered materials design.

36 MATERIALS SCIENCE↗