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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Biofouling and Corrosion Study for a Novel Linear Guided Wave Energy Converter (Post Access Report)

The overall objective of this project was to examine the reliability and performance of antibiofouling coatings used for a wave energy converter (WEC) developed by E-Wave Technologies. The particular coatings were selected for their low toxicity and potential compatibility with aquaculture. The aim of this work was to 1) test coating solutions to prevent biofouling growth and saltwater corrosion on the static (paddle and attachment frame surface) components of the WEC that are submerged, 2) determine adhesion of the coatings to system components, and 3) assess the ease and effectiveness of biofouling cleaning to insure long term performance of the system. An analysis of commercial coatings was performed using methods to examine the prevention of biofouling and coating adhesion properties on two key materials of the WEC, which were 316L low carbon marine grade stainless steel (SS) and Ultra High Molecular Weight Polyethylene (PE). Three marine antifouling paints were selected based on their unique properties to test how different paint styles perform on different materials. The selected paints were ePaint Ecominder self-polishing paint with Zinc Omadine for slime control, Pettit ECO HRT Copper-Free ablative antifouling with Econea biocide, and Intersleek 1100SR foul release. Pacific Northwest National Laboratory (PNNL) prepared PE and SS substrates coated with the three paints and compared the performance against uncoated substrates when submerged in raw seawater for 3-, 6-, and 9-month (m) time periods. Results in adhesion testing indicated that Pettit and ePaint materials clearly bonded strongly to SS, but did not bond comparably well to PE. It was noted during adhesion testing that the Intersleek surfaces were especially difficult to test as the paint highly resists bonding to the epoxy adhesives used with the adherence testing platform. The wear rate of the coatings was not measured under this study; however, based on adhesion testing, coatings in the sliding regions of the Ewave device are expected to wear rapidly. Sandia National Laboratories (SNL) evaluated the adhesion of three different paints to PE and SS substrates which were exposed to a marine environment for time intervals of 0, 3, 6, and 9 months. From qualitative visual analysis of the 3 in2 coupons when pulled from the tank, the 3 in2 coupons generally only appeared to have biofouling consisting of filamentous algae or diatoms, which all have relatively low mass and can be easily wiped from the surface of coupons. Qualitative visual analysis indicated that ECO HRT and Unpainted were consistently worse than Intersleek and Ecominder at all time points. Results provide insight to aid with down-selection of commercial coatings under static conditions to support reliability of the WEC and potential maintenance schedules. This investigation was conducted using small coupon samples suspended in seawater and the development of testing rigs for dynamic component level testing is needed for future work. In addition, the potential toxicity of these commercial coatings on aquaculture has not been determined by this study. One recommendation is to conduct toxicity investigations at the Environmental Toxicity Laboratory at Oak Ridge National Laboratory.

16 TIDAL AND WAVE POWER↗
DOE OSTI2022

Fluid-Kinetic Coupling: Advanced Discretizations for Simulations on Emerging Heterogeneous Architectures (LDRD FY20-0643)

Plasma physics simulations are vital for a host of Sandia mission concerns, for fundamental science, and for clean energy in the form of fusion power. Sandia's most mature plasma physics simulation capabilities come in the form of particle-in-cell (PIC) models and magnetohydrodynamics (MHD) models. MHD models for a plasma work well in denser plasma regimes when there is enough material that the plasma approximates a fluid. PIC models, on the other hand, work well in lower-density regimes, in which there is not too much to simulate; error in PIC scales as the square root of the number of particles, making high-accuracy simulations expensive. Real-world applications, however, almost always involve a transition region between the high-density regimes where MHD is appropriate, and the low-density regimes for PIC. In such a transition region, a direct discretization of Vlasov is appropriate. Such discretizations come with their own computational costs, however; the phase-space mesh for Vlasov can involve up to six dimensions (seven if time is included), and to apply appropriate homogeneous boundary conditions in velocity space requires meshing a substantial padding region to ensure that the distribution remains sufficiently close to zero at the velocity boundaries. Moreover, for collisional plasmas, the right-hand side of the Vlasov equation is a collision operator, which is non-local in velocity space, and which may dominate the cost of the Vlasov solver. The present LDRD project endeavors to develop modern, foundational tools for the development of continuum-kinetic Vlasov solvers, using the discontinuous Petrov-Galerkin (DPG) methodology, for discretization of Vlasov, and machine-learning (ML) models to enable efficient evaluation of collision operators. DPG affords several key advantages. First, it has a built-in, robust error indicator, allowing us to adapt the mesh in a very natural way, enabling a coarse velocity-space mesh near the homogeneous boundaries, and a fine mesh where the solution has fine features. Second, it is an inherently high-order, high-intensity method, requiring extra local computations to determine so-called optimal test functions, which makes it particularly suited to modern hardware in which floating-point throughput is increasing at a faster rate than memory bandwidth. Finally, DPG is a residual-minimizing method, which enables high-accuracy computation: in typical cases, the method delivers something very close to the $L^2$ projection of the exact solution. Meanwhile, the ML-based collision model we adopt affords a cost structure that scales as the square root of a standard direct evaluation. Moreover, we design our model to conserve mass, momentum, and energy by construction, and our approach to training is highly flexible, in that it can incorporate not only synthetic data from direct-simulation Monte Carlo (DSMC) codes, but also experimental data. We have developed two DPG formulations for Vlasov-Poisson: a time-marching, backward-Euler discretization and a space-time discretization. We have conducted a number of numerical experiments to verify the approach in a 1D1V setting. In this report, we detail these formulations and experiments. We also summarize some new theoretical results developed as part of this project (published as papers previously): some new analysis of DPG for the convection-reaction problem (of which the Vlasov equation is an instance), a new exponential integrator for DPG, and some numerical exploration of various DPG-based time-marching approaches to the heat equation. As part of this work, we have contributed extensively to the Camellia open-source library; we also describe the new capabilities and their usage. We have also developed a well-documented methodology for single-species collision operators, which we applied to argon and demonstrated with numerical experiments. We summarize those results here, as well as describing at a high level a design extending the methodology to multi-species operators. We have released a new open-source library, MLC, under a BSD license; we include a summary of its capabilities as well.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

Accelerating defect predictions in semiconductors using graph neural networks

First-principles computations reliably predict the energetics of point defects in semiconductors but are constrained by the expense of using large supercells and advanced levels of theory. Machine learning models trained on computational data, especially ones that sufficiently encode defect coordination environments, can be used to accelerate defect predictions. Here, we develop a framework for the prediction and screening of native defects and functional impurities in a chemical space of group IV, III–V, and II–VI zinc blende semiconductors, powered by crystal Graph-based Neural Networks (GNNs) trained on high-throughput density functional theory (DFT) data. Using an innovative approach of sampling partially optimized defect configurations from DFT calculations, we generate one of the largest computational defect datasets to date, containing many types of vacancies, self-interstitials, anti-site substitutions, impurity interstitials and substitutions, as well as some defect complexes. We applied three types of established GNN techniques, namely crystal graph convolutional neural network, materials graph network, and Atomistic Line Graph Neural Network (ALIGNN), to rigorously train models for predicting defect formation energy (DFE) in multiple charge states and chemical potential conditions. We find that ALIGNN yields the best DFE predictions with root mean square errors around 0.3 eV, which represents a prediction accuracy of 98% given the range of values within the dataset, improving significantly on the state-of-the-art. We further show that GNN-based defective structure optimization can take us close to DFT-optimized geometries at a fraction of the cost of full DFT. The current models are based on the semi-local generalized gradient approximation-Perdew–Burke–Ernzerhof (PBE) functional but are highly promising because of the correlation of computed energetics and defect levels with higher levels of theory and experimental data, the accuracy and necessity of discovering novel metastable and low energy defect structures at the PBE level of theory before advanced methods could be applied, and the ability to train multi-fidelity models in the future with new data from non-local functionals. The DFT-GNN models enable prediction and screening across thousands of hypothetical defects based on both unoptimized and partially optimized defective structures, helping identify electronically active defects in technologically important semiconductors.

Rahman, Md Habibur (ORCID:000000027705984X)↗
DOE OSTI2020

Studies of surface adsorbate electronic structure and femtochemistry at the fundamental length and time scales. Final report

The electronic structure and ultrafast (10-15 s-femtosecond timescale) electron dynamics were investigated for clean and atom/molecule covered metal surfaces. The studies were performed by scanning tunneling microscopy (STM) to measure the structure of adsorbed atoms and molecules on metal surfaces, and to investigate their electronic properties. The electronic structure of the observed molecular networks was calculated by electronic structure theory in collaboration with Prof. Jin Zhao, who is a long-time collaborator, a Professor at the University of Science and Technology of China, and holds an Adjunct Professorship at the University of Pittsburgh. We also investigated the electronic properties of C60 molecules when they are templated by corrugated black phosphorous surfaces. We found unexpected charge delocalization that is enabled by the templating. This research was done in collaboration with Professor Min Feng at the Wuhan University, and who also holds an Adjunct Professorship at the University of Pittsburgh. Moreover, the electronic structure and electron dynamics in metal surfaces were investigated by time-resolved photoemission electron spectroscopy. The focus of ultrafast spectroscopy has been on the plasmonic response of silver surfaces. One direction has been to develop multidimensional (energy, momentum, and time) photoelectron spectroscopy of the coherent response of solid surfaces. This method was applied to study the collective electron excitations known generally as plasmons, which screen optical fields from penetration into metals. Although this collective response has been known for more than 60 years and is used extensively to deposit optical energy into metals, how this happens is poorly known. We investigated the plasmonic response of silver at the point where the dielectric response passes through zero and bulk plasmon is excited by light. We discovered that the plasmon excitation decays by exciting electrons from the Fermi level of a metal, which is contrary to what is believed in the plasmonic science community. This research has been performed in collaboration with Dr. Marcel Reutzel, who was a postdoctoral fellow working on this research at the University of Pittsburgh, and now has a faculty position at the University of Göttingen in Germany. Prof. Branko Gumhalter from the Institute of Physics in Zagreb contributed on the theory of plasmonic decay processes. Furthermore, we investigated the Floquet engineering of electronic bands in metals leading to multiphoton photoemission and above threshold photoemission. Finally, we demonstrated that it is possible to change the electronic structure of metals by application of optical fields. Our studies indicated that this happens on subfemtosecond time scale and could potentially be used in ultrafast information processing and quantum computation. Related document information

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2025

On-demand formation of Lewis bases for efficient and stable perovskite solar cells

In the fabrication of FAPbI 3 -based perovskite solar cells, Lewis bases play a crucial role in facilitating the formation of the desired photovoltaic α-phase. However, an inherent contradiction exists in their role: they must strongly bind to stabilize the intermediate δ-phase, yet weakly bind for rapid removal to enable phase transition and grain growth. To resolve this conflict, we introduced an on-demand Lewis base molecule formation strategy. This approach utilized Lewis-acid-containing organic salts as synthesis additives, which deprotonated to generate Lewis bases precisely when needed and could be reprotonated back to salts for rapid removal once their role is fulfilled. This method promoted the optimal crystallization of α-phase FAPbI 3 perovskite films, ensuring the uniform vertical distribution of A-site cations, larger grain sizes and fewer voids at buried interfaces. Perovskite solar cells incorporating semicarbazide hydrochloride achieved an efficiency of 26.1%, with a National Renewable Energy Laboratory-certified quasi-steady-state efficiency of 25.33%. These cells retained 96% of their initial efficiency after 1,000 h of operation at 85 °C under maximum power point tracking. Additionally, mini-modules with an aperture area of 11.52 cm 2 reached an efficiency of 21.47%. This strategy is broadly applicable to all Lewis-acid-containing organic salts with low acid dissociation constants and offers a universal approach to enhance the performance of perovskite solar cells and modules.

14 SOLAR ENERGY↗
DOE OSTI2024

A soft departure from jamming: the compaction of deformable granular matter under high pressures

Here, the high-pressure compaction of three-dimensional granular packings is simulated using a bonded particle model (BPM) to capture linear elastic deformation. In the model, grains are represented by a collection of point particles connected by bonds. A simple multibody interaction is introduced to control Poisson's ratio and the arrangement of particles on the surface of a grain is varied to model both high- and low-frictional grains. At low pressures, the growth in packing fraction and coordination number follows the expected behavior near jamming and exhibit friction dependence. As the pressure increases, deviations from the low-pressure power-law scaling emerge after the packing fraction grows by approximately 0.1 and results from simulations with different friction coefficients converge. These results are compared to predictions from traditional discrete element method simulations which, depending on the definition of packing fraction and coordination number, may only differ by a factor of two. As grains deform under compaction, the average volumetric strain and asphericity, a measure of the change in the shape of grains, are found to grow as power laws and depend heavily on the Poisson's ratio of the constituent solid. Larger Poisson's ratios are associated with less volumetric strain and more asphericity and the apparent power-law exponent of the asphericity may vary. The elastic properties of the packed grains are also calculated as a function of packing fraction. In particular, we find the Poisson's ratio near jamming is 1/2 but decreases to around 1/4 before rising again as systems densify.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Production of polyacrylonitrile/ionic covalent organic framework hybrid nanofibers for effective removal of chromium(VI) from water

Hexavalent Cr(VI) found in industrial wastewater is a proven carcinogen which causes serious health issues in humans around the world. This study presents a novel method to enhance the Cr(VI) oxyanion removal from wastewater by polyacrylonitrile (PAN) nanofibers through incorporation of a guanidinium-based ionic covalent organic framework (BT-DG) in the nanofibers structure. In this work, simple electrospinning technique was employed to produce PAN nanofibers and BT-DG was synthesized through condensation between benzene-1,3,5-tricarbaldehyde and N,N'-diaminoguanidine monohydrochloride. In-situ polymerization of BT-DG onto PAN nanofibers resulted in generation of hybrid PAN-BT-DG nanofibers. This modified PAN-BT-DG was characterized by obtaining its point of zero charge (PZC), differential scanning calorimeter (DSC), scanning electron microscopy (SEM) morphology and surface elements and oxidation states by X-ray photoelectron spectroscopy (XPS). PAN-BT-DG exhibited positive surface charge below pH 4, making it an outstanding adsorbent, for Cr(VI) removal. Cr(VI) adsorption onto PAN-BT-DG followed pseudo second order kinetics and adsorption data fitted well to Freundlich isotherm model. Highest Cr(VI) removal was obtained at 55 °C with a maximum Langmuir adsorption capacity of 173 mg/g at pH 3. Kinetic studies revealed that Cr(VI) adsorption onto PAN-BT-DG is endothermic and thermodynamically feasible. Desorption studies were conducted on PAN-BT-DG using 1 M NaOH as the stripping solvent and PAN-BT-DG exhibited excellent regeneration after five consecutive cycles.

36 MATERIALS SCIENCE↗
DOE OSTI2026

A Tutorial on Bayesian analysis of linear shock compression data

Gas gun and other shock compression experiments often produce shock wave velocity measurements that are linearly associated with particle velocity. Traditionally, this empirical relationship is quantified with a single Hugoniot curve that is estimated using least squares regression. However, for downstream modeling and simulation tasks, it is often more useful to have multiple Hugoniot curves in the pressure–volume plane that are consistent with the data. We employ Bayesian uncertainty quantification methods as a framework for propagating measurement uncertainty through to model parameters and predictions. Specifically, this Tutorial shows how to sample multiple Hugoniot curves in the pressure–volume plane that are consistent with the shock wave-particle velocity measurements in a two-step Bayesian approach. First, we obtain an analytical expression for the posterior distribution of the linear model parameters using Bayesian linear regression. Second, we propagate samples from the posterior distribution through the Rankine–Hugoniot equations to yield Hugoniot curves in the pressure–volume plane. The procedure is demonstrated with publicly available data on argon, copper, and nickel, and compared against bootstrapping and linear regression. The Bayesian procedure is shown to be interpretable, computationally inexpensive, and less sensitive than an alternative bootstrapping approach to the removal of the point in the copper dataset that has the largest particle velocity. As a Tutorial on Bayesian methodology for the shock compression community, we provide several derivations and explanations that make this paper self-contained, and make all code and data available at github.com/llnl/BALSCD.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2025

Development of an interatomic potential for the Ta–Li system

A new interatomic potential for the Ta–Li system is introduced to facilitate the study of phase stability, mechanical properties and non-equilibrium dynamics after Li implantation in Ta. Here, this potential is based on a generalization of the embedded atom method (GEAM) and includes contributions from embedding energy, explicit two- and three-body interactions, and nonlocal many-body interaction terms. The parameters of the potential are optimized using energies and atomic forces for a wide range of configurations obtained from ab initio density functional theory (DFT) calculations. The potential is rigorously validated across a range of physical properties, including elastic constants, equations of state, phonon dispersion curves, point defect properties, and melting temperatures for different compositions. Although our potential is trained on a small dataset, its accuracy is comparable to that of available machine learning potentials for Li and Ta. Our simulations show that at temperatures below 500 K, Li atoms in Ta–Li alloys form clusters separated by Ta-rich domains, and we find no evidence of ordered phase formation. For Li concentrations below a few percent, Li atoms preferentially segregate to surfaces and grain boundaries. However, in alloys containing more than ~10% Li, the accumulation of Li in symmetric-tilt grain boundaries can lead to one of the following effects: formation of amorphous-like regions, changes in grain boundary structural units, or lateral movement of the grain boundary.

GEAM potential↗
DOE OSTI2020

Hybridizing pseudo-Hamiltonians and non-local pseudopotentials in diffusion Monte Carlo

An accurate treatment of effective core potentials (ECPs) requires care in continuum quantum Monte Carlo (QMC) methods. While most QMC studies have settled on the use of familiar non-local (NL) pseudopotentials with additional localization approximations, these approaches have been shown to result in moderate residual errors for some classes of molecular and solid state applications. Here, we revisit an idea proposed early in the history of QMC ECPs that does not require localization approximations, namely, a differential class of potentials referred to as pseudo-Hamiltonians. We propose to hybridize NL potentials and pseudo-Hamiltonians to reduce residual non-locality of existing potentials. We derive an approach to recast pseudopotentials for 3d elements as hybrid pseudo-Hamiltonians with optimally reduced NL energy. We demonstrate the fidelity of the hybrid potentials by studying atomic ionization potentials of Ti and Fe and the binding properties of TiO and FeO molecules with diffusion Monte Carlo (DMC). We show that localization errors have been reduced relative to potentials with the same NL channels for Sc–Zn by considering the DMC energy change with respect to the choice of approximate localization. While localization error decreases proportionate to the reduced NL energy without a Jastrow, with a Jastrow, the degree of reduction decreases at higher filling of the d-shell. Our results suggest that a subset of existing ECPs may be recast in this hybrid form to reduce the DMC localization error. They also point to the prospect of further reducing this error by generating ECPs within this hybrid form from the start.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Calibration of RAFM Micromechanical Model for Creep Using Bayesian Optimization for Functional Output

A Bayesian optimization procedure is presented for calibrating a multimechanism micromechanical model for creep to experimental data of F82H steel. Reduced activation ferritic martensitic (RAFM) steels based on Fe(8–9)%Cr are the most promising candidates for some fusion reactor structures. Although there are indications that RAFM steel could be viable for fusion applications at temperatures up to 600°C, the maximum operating temperature will be determined by the creep properties of the structural material and the breeder material compatibility with the structural material. Due to the relative paucity of available creep data on F82H steel compared to other alloys such as Grade 91 steel, micromechanical models are sought for simulating creep based on relevant deformation mechanisms. As a point of departure, this work recalibrates a model form that was previously proposed for Grade 91 steel to match creep curves for F82H steel. Due to the large number of parameters (9) and cost of the nonlinear simulations, an automated approach for tuning the parameters is pursued using a recently developed Bayesian optimization for functional output (BOFO) framework (Huang et al., 2021, “Bayesian optimization of functional output in inverse problems,” Optim. Eng., 22, pp. 2553–2574). Incorporating extensions such as batch sequencing and weighted experimental load cases into BOFO, a reasonably small error between experimental and simulated creep curves at two load levels is achieved in a reasonable number of iterations. In conclusion, validation with an additional creep curve provides confidence in the fitted parameters obtained from the automated calibration procedure to describe the creep behavior of F82H steel.

42 ENGINEERING↗
DOE OSTI2022

Dynamical exponent of a quantum critical itinerant ferromagnet: A Monte Carlo study

Here we consider the effect of the coupling between two-dimensional (2D) quantum rotors near an XY ferromagnetic quantum critical point and spins of itinerant fermions. We analyze how this coupling affects the dynamics of rotors and the self-energy of fermions. A common belief is that near a q = 0 ferromagnetic transition, fermions induce an Ω/q Landau damping of rotors (i.e., the dynamical critical exponent is z = 3) and Landau overdamped rotors give rise to non-Fermi liquid fermionic self-energy Σ∝ω 2/3 . This behavior has been confirmed in previous quantum Monte Carlo (QMC) studies. Here we show that for the XY case the behavior is different. We report the results of large-scale quantum Monte Carlo simulations, which show that at small frequencies z = 2 and Σ∝ω 1/2 . We argue that the new behavior is associated with the fact that a fermionic spin is by itself not a conserved quantity due to spin-spin coupling to rotors, and a combination of self-energy and vertex corrections replaces 1/q in the Landau damping by a constant. We discuss the implication of these results to experiments.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Thermal and electric field driven rf breakdown precursor formation on metal surfaces

The phenomenon of electric breakdown poses serious challenges to the design of devices that operate in high electric field environments. Experimental evidence points toward breakdown events that are accompanied by elevated temperatures and dark current spikes, which is attributed to high-asperity nanostructure formation that enhances the local electric field and triggers a runaway process. However, the exact mechanistic origin of such nanostructures under typical macroscopic operational conditions of electric field and magnetic-field-mediated heating remains poorly understood. In this work, we simulate the evolution of a copper surface under the combined action of the electric fields and elevated temperatures. Using a mesoscale curvature-driven surface evolution model, we show how a copper surface can undergo a type of dynamical instability that naturally leads to the formation of sharp asperities in realistic experimental conditions. Exploring the combined effect of fields and temperature rise, we identify the critical regimes that allow for the formation of breakdown precursors. The results show that thermoelastic stresses, while not essential, can significantly lower the critical electric field required for runaway surface instability, which is consistent with experimental observations that thermal effects can increase breakdown rates. Published by the American Physical Society 2024

43 PARTICLE ACCELERATORS↗
DOE OSTI2022

Band gap and electronic structure of defects in the ternary nitride BP 3 N 6 : experiment and theory

Recent advances in methods to access nitride systems by a high-pressure high-temperature approach have made possible the one-step synthesis of mixed ternary non-metal nitrides. As a prerequisite to use in a practical device, it is important to understand important bulk electronic properties, such as the band gap, as well as characterizing the presence and effect of defects that are present. In this work, the novel ternary nitride BP 3 N 6 is studied using techniques sensitive to the partial electronic density of states, specifically X-ray absorption spectroscopy and X-ray emission spectroscopy. Complementary full-potential all-electron density functional theory (DFT) calculations allow important bulk electronic parameters, such as the band gap, to be elucidated. The band gap of BP 3 N 6 has been determined to be 3.9 ± 0.2 eV and 4.1 ± 0.4 eV at the B K- and N K-edges, respectively. This is close to a theoretical value of 4.3 eV predicted by the PBEsol exchange–correlation functional and considerably less than a value of 5.8 eV predicted by the modified Becke–Johnson exchange–correlation functional. X-Ray excited optical luminescence (XEOL) measurements are performed to interrogate the presence of point defects in this system. Together with DFT calculations, these measurements reveal the presence of nitrogen vacancies which lead to multiple mid-gap trap states.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Machine learning for impurity charge-state transition levels in semiconductors from elemental properties using multi-fidelity datasets

Quantifying charge-state transition energy levels of impurities in semiconductors is critical to understanding and engineering their optoelectronic properties for applications ranging from solar photovoltaics to infrared lasers. While these transition levels can be measured and calculated accurately, such efforts are time-consuming and more rapid prediction methods would be beneficial. Here, we significantly reduce the time typically required to predict impurity transition levels using multi-fidelity datasets and a machine learning approach employing features based on elemental properties and impurity positions. We use transition levels obtained from low-fidelity (i.e., local-density approximation or generalized gradient approximation) density functional theory (DFT) calculations, corrected using a recently proposed modified band alignment scheme, which well-approximates transition levels from high-fidelity DFT (i.e., hybrid HSE06). Further, the model fit to the large multi-fidelity database shows improved accuracy compared to the models trained on the more limited high-fidelity values. Crucially, in our approach, when using the multi-fidelity data, high-fidelity values are not required for model training, significantly reducing the computational cost required for training the model. Our machine learning model of transition levels has a root mean squared (mean absolute) error of 0.36 (0.27) eV vs high-fidelity hybrid functional values when averaged over 14 semiconductor systems from the II–VI and III–V families. As a guide for use on other systems, we assessed the model on simulated data to show the expected accuracy level as a function of bandgap for new materials of interest. Finally, we use the model to predict a complete space of impurity charge-state transition levels in all zinc blende III–V and II–VI systems.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Enhanced biomechanical performance of additively manufactured Ti-6Al-4V bone plates

As the global trauma fixation devices market expands rapidly, it is imperative to improve the production of fixation devices through enhanced design accuracy and fit for best performance and maximum patient comfort. Selective laser melting (SLM) is one of the mature additive manufacturing methods, which provides a viable route for the rapid production of such devices. In this work, the ability of SLM to produce near-net-shape parts, as desired for medical implants, was utilized for the fabrication of bone plates from Ti-6Al-4V alloy powder. Martensitic microstructure obtained after the printing of alloy resulted in poor ductility, limiting its application in the field of orthopedics. A specially designed repeated cyclic heating and cooling close to but below the β-transus was used to transform from acicular to a bimodal microstructure without the need for plastic deformation prior to heat treatment for improving the ductility. Bone plates subjected to this heat treatment were mechanically tested by means of tensile and 3-point bend tests and demonstrated large improvement in ductility, and the values were comparable to those similar plates prepared from wrought alloy. Other important properties required for implants were assessed, such as corrosion resistance in simulated body fluid and cytocompatibility in vitro using MC3T3-E1 cells. These results for the bone plate after heat treatment were excellent and similar to those of the additively manufactured and wrought plates. Taken together, the performance of the additively manufactured bone plates after subjecting to heat treatment was similar to those of bone plate manufactured using wrought alloy. These results have important implications for the fabrication of patient-specific metallic orthopedic devices using SLM without compromising their biomechanical performance by subjecting them to a tailored heat treatment.

36 MATERIALS SCIENCE↗
DOE OSTI2023

AN ACID BAKING APPROACH TO ENHANCE RARE EARTH ELEMENT RECOVERY FROM BITUMINOUS COAL SOURCES

Rare earth elements (REE) are a group of 17 elements typically classified as light and heavy rare earth elements, which play a crucial role in developing the latest technologies for energy, defense, and medical sectors. Even though REEs have been found in more than 200 minerals, only bastnaesite, monazite and xenotime are commercially exploited for REE extraction. However, the recent exponential increase in REE demand has spurred countries such as the United States into research for the extraction of REEs from secondary sources such as coal, acid mine drainage, and coal ash. Several coal sources (e.g., Fire Clay seam coal) across the United States have been identified to contain elevated concentrations of rare earth elements (>600 ppm), and various researchers have investigated the feasibility of both physical and hydrometallurgical extraction techniques for rare earth concentration and subsequent extraction. However, both physical and direct leaching were concluded to be inefficient for RE beneficiation and extraction due to low recoveries. Alternatively, thermal treatment provides promising means for RE recovery from bituminous coal sources. The positive impact of thermal treatment/calcination was established to be due to the decarbonization and dehydroxylation of the clays, which released entrapped rare earth elements within the dominant minerals and converted them into an acid-soluble form. Nonetheless, the improvement in recovery was limited to the light REEs (LREEs) with an insignificant increase in the heavy REEs (HREE). It was demonstrated that the light and heavy REEs in the material were associated with difficult-to-leach minerals such as monazite, xenotime, and zircon, which were not decomposed by simple calcination due to their high thermal stability. Hence, roasting in the presence of chemicals was necessary to ensure the decomposition of those REE-containing minerals. As such, this study was focused on the acid baking treatment of bituminous coals with an aim to enhance REE recovery, especially HREEs. Based on the presence of REE minerals like monazite and xenotime, three pre-leach treatment methods, i.e., 1) roasting, 2) direct acid baking, and 3) acid baking after roasting were investigated. Roasting tests at 600 ⁰C revealed that the recovery of light REEs (LREEs) was enhanced while the recovery of HREEs remained relatively unaffected. LREE and HREE recovery values of 38.3% and 21.3%, respectively, were achieved using a 50 g/L sulfuric acid solution at 5% solid concentration and a solution temperature of 75 ⁰C for 2 hours. Comparatively, direct acid baking at 250 °C provided substantial increased LREE and HREE recovery values to approximately 49.4% and 53.0%, respectively, using an equivalent acid dosage. Recoveries were maximized to 77.0% and 79.6% for LREE and HREE, respectively, by roasting followed by acid baking. Similar results were obtained from the treatment of a second bituminous coal source. Due to strong correlations between REE and Al recovery values, tests were performed on kaolinite and illite, which were prominent clay minerals within the source coals. These experiments revealed that the REE recovery improvements were likely a result of dehydroxylation of clays and subsequent release and decomposition of REE-bearing minerals such as monazite, xenotime and zircon. Subsequently, a parametric study was conducted to identify the impact of acid baking parameters on rare earth element recovery. The factors investigated using a three-level statistical experimental program were acid baking time, acid solution concentration, baking temperature, and acid solution-to-solids ratio which were found to significantly impact REE and contaminant element (Al, Fe, and Ca) recovery. An increase in baking temperature up to around 250 ⁰C improved the light and heavy REE recovery values by more than 50 absolute percentage points relative to performances achieved when direct leaching. As aforementioned, acid baking was needed to both decompose the clay minerals and liberate the REE minerals, which allowed access for the acid to solubilize the REEs. Acid concentration of the solution used for acid baking was studied as a means of minimizing the amount of acid needed to achieve a target REE recovery. However, thermo-gravimetric and differential scanning calorimetry analysis (TGA-DSC) of sulfuric acid under oxidizing atmosphere revealed that the addition of water decreased the evaporation temperature, which explains the lower REE recovery values obtained when using acid concentrations less than 100%. Using pure sulfuric acid at an acid-to-solid ratio of 0.8:1 resulted in recovery values of around 70% for both LREEs and HREEs. The decomposition reaction time was relatively quick with 65% of the TREEs recovered within the first 10 minutes. Following the identification of optimum operating conditions through the parametric study, a systematic leaching study was carried out to examine the impact of leaching parameters, such as solid-to-liquid (S/L) ratio, temperature, and time, on REE recovery using acid baking conditions of 1:1 acid to solids ratio at 250 °C for 30 minutes. The solid-to-liquid ratio was varied from 1-20% by weight at 25 °C, 50 °C, and 75 °C solution temperatures. The results indicated that reaction time and solution temperature considerably impacted the recovery of heavy and light REEs. Interestingly, LREE recovery reduced from 68% at 5% S/L to 58% at 20%S/L, whereas HREE recovery of 78% remained unaffected. The decrease in the LREE recovery was determined to be due to La and Ce precipitation, likely through isomorphic substitution with calcium in gypsum. The kinetic data indicated that 67% LREE and 77% HREE recovery could be obtained within the first 15 minutes of the reaction, suggesting fast reaction kinetics. Furthermore, raising the solution temperature from 25 °C to 75 °C increased the LREE and HREE recovery from 60% and 32% to 67% and 79%, respectively. The kinetic modeling results demonstrated that the rate-limiting step in the LREE dissolution was diffusion and chemical reaction, whereas the HREE extraction was controlled by only chemical reaction. The leaching study concluded that using 20% S/L at 75 °C for 15 minutes maximized LREE and HREE recovery. The lab-scale precipitation study showed that Fe and Al in solution could be removed at pH 4.5 followed by REE precipitation at pH 6.0 using 6 mol/L NaOH. Finally, the bench-scale data was used to develop a process flowsheet for REE recovery from low-grade bituminous coal sources using acid baking. Finally, based on the proposed flowsheet, a concentrated RE-cake obtained through selective precipitation at pH 6.5 was re-leached using HCl at pH 1.5. The resultant leachate was used to identify the impact of various operating parameters on REE recovery and purity with an aim to maximize REE precipitation efficiency while minimizing the oxalic acid dosage. The operating parameters for this investigation were oxalic acid dosage, iron (III) contamination, solution pH and temperature. The resultant model suggested that oxalic acid dosage and reaction pH are the most significant factors for the REE precipitation efficiency, followed by the interaction of oxalic dosage and Fe concentration. Test results indicated that increasing the oxalic acid concentration from 0g/L to 80g/L improved the REE precipitation efficiency from approximately 4.2% to 95.0%. Furthermore, raising the solution pH from 0.5 to 2.5 considerably enhanced the precipitation efficiency from 0.0% to 98.9%. A solution temperature elevation decreased REE recovery, which indicated an exothermic reaction between REEs and oxalate anions. Finally, a high level of Fe contamination adversely impacted REE precipitation efficiency. The speciation analysis revealed that the dominant iron species in the solution system were Fe-(C₂O₄)₃³⁻, Fe-(C₂O₄)²⁻, and Fe-(C₂O₄)⁺, which consumed the majority of the oxalate anions

rare earth elements, acid baking, high-temperature↗
DOE OSTI2020

Thermodynamic signatures of an antiferromagnetic quantum critical point inside a superconducting dome

Recent experiments in unconventional superconductors, and in particular iron-based materials, have reported evidence of an antiferromagnetic quantum critical point (AFM-QCP) emerging inside the superconducting dome of the phase diagram. Fluctuations associated with such an AFM-QCP are expected to promote unusual temperature dependencies of thermodynamic quantities. Here, we compute the T dependence of the specific heat C(T) deep inside a fully gapped s +– superconducting state as the AFM-QCP is approached. We find that at the AFM-QCP, the specific heat C(T) vanishes quadratically with temperature, as opposed to the typical exponential suppression seen in fully gapped BCS superconductors. This robust result is due to a nonanalytic contribution to the free energy arising from the general form of the bosonic (AFM) propagator in the SC state. Away from the AFM-QCP, as temperature is lowered, C(T) shows a crossover from a T2 behavior to an exponential behavior, with the crossover temperature scale set by the value of the superconducting gap and the distance to the QCP. As a result, we argue that these features in the specific heat can be used to unambiguously determine the existence of AFM-QCPs inside the superconducting domes of iron-based and other fully gapped unconventional superconductors.

36 MATERIALS SCIENCE↗