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DOE OSTI2023

Suppression of Parasitic Voltage Oscillation in Power Modules by Turn-Off Current Zero Placement

This paper presents a frequency-domain analysis of the turn-off equivalent switching network of a hard-switched semiconductor switching cell. This paper also proposes a novel method to suppress parasitic voltage ringing by placing the turn-off current zero at the parasitic resonant frequency. The turn-off current was decomposed into ramp functions and a constant-valued function to facilitate obtaining the frequency components of the turn-off current using the Laplace transform of standard signals. The proposed frequency-domain modeling of the parasitic voltage ringing and the suppression were validated by detailed circuit simulation using parameters corresponding to prior literature with experimental validation.

Galigekere, Veda Prakash↗

DOE OSTI2024

Fourier Analysis and Design of a Shielded 120kW Inductive Wireless System

High-power inductive wireless power transfer (WPT) systems for EVs are designed to meet specifications such as stray field, power level, efficiency, misalignment tolerance, and ground clearance. These metrics are all heavily influenced by the coil geometry. Herein this paper proposes a coil design method based on the Fourier Analysis Method (FAM) which is an analytical method for directly designing coil geometries to meet stray field and power level requirements through an optimization of Fourier basis function coefficients. In this work, two 120 kW WPT proof-of-concept demonstrators with low stray field and high efficiency are built from FAM optimization results to validate the models and show the impact of the FAM design process. Experimental validation of the Gen. 2 demonstrator at 120 kW output power resulted in a measured DC/DC efficiency of 97.2% at alignment with a 125 mm airgap. At the 120 kW test point, the stray fields 80 cm away from the center of the airgap between the coil assemblies were 3.4 µT(rms) on the X-axis and 3.5 µT(rms) on the Y-axis, much lower than the 27 µT(rms) ICNIRP limit.

42 ENGINEERING↗

DOE OSTI2026

GaN Core-shell Nanofin Vertical Transistor (CoNVerT): A New Direction for Power Electronics (Final Scientific/Technical Report)

A novel power transistor architecture, the GaN c ore-shell n anofin ver tical transistor (CoNVerT) to address fundamental challenges in realizing the ultimate limit of GaN power transistor performance was explored experimentally. This technology promises ultra-high-efficiency high voltage/high power applications (e.g. DC/DC converters, motor control, fast charging, actuation), as well as to operate in harsh environments. The device exploits a vertical superjunction structure based on an experimentally-validated core-shell nanofin growth process in which lateral p-n heterojunctions are formed in a single growth step, while still maintaining vertical current flow for compact die size and low cost. The concept leverages the best properties of GaN for mid-range voltage applications: high mobility, high breakdown voltage, and native heterojunction enhancement-mode operation. Due to the crystallographic nature of the nanofin growth by molecular beam epitaxy, the sidewall heterojunctions occur on non-polar planes, resulting in ultra-smooth interfaces for high mobility, no sidewall etch damage and related surface/interface states, and elimination of piezoelectric effects that can limit reliability in conventional structures. This also facilitates superjunction formation for maximum device performance, and the selective-area growth of the nanofin results in dislocation-free growth, even on low-cost Si (111) substrate. In this program, core-shell nanofins were grown by molecular beam epitaxy, test structures to evaluate the doping, resistivity, and other electrical properties were fabricated, and the material and test structures were characterized in detail. The work identified clear potential (e.g., the doping was well controlled as required for superjunction concepts), but also additional areas that require additional effort to resolve (some unexpected crystal defects were encountered that require additional engineering to overcome). Simulation studies of the proposed concept validate that the fundamental approach is very promising, but additional effort in experimental realization is needed.

42 ENGINEERING↗

DOE OSTI2023

Multiple Critical Unresolved Region Integral Experiment (MCURIE): A Proposed Integral Critical Experimental Framework for Unresolved Region and Intermediate Energies

The Critical Unresolved Region Integral Experiment (CURIE) critical experiment was performed at the National Criticality Experiments Research Center (NCERC) at the Device Assembly Facility (DAF) at the Nevada Nuclear Security Sites (NNSS) in 2020. The objective of CURIE was to improve the quality of integral nuclear data in the uranium-235 ( 235 U) unresolved resonance region (URR) by performing benchmark integral experiments that were sensitive to the URR energy ranges. The CURIE experiment was evaluated for the International Criticality Safety Benchmark Evaluation Project (ICSBEP) handbook and the benchmark evaluation was accepted in 2022. The URR is a region within the intermediate neutron energy range (the intermediate energy ranges from 0.7 eV to 100 keV). The observed resonance structure in neutron cross sections is due to discrete energy levels in the nucleus and are characterized by resonance parameters. In the URR region, the resonance parameters are only partially resolved as the resolution of the experiment techniques becomes comparable to the average width of the resonances themselves, and the resonances are so close to one another that the structure cannot be determined empirically. The ENDF/B-VIII.0 and JEFF-3.3 nuclear data libraries define the URR as beginning at 2.25 keV and continuing until 25 keV. There are minimal intermediate neutron energy benchmarks available in the ICSBEP benchmark handbook, and aside from CURIE there are none that are highly sensitive in the URR energy region. There is a current need for additional experiments sensitive to the URR energy region. There is a new proposed subgroup for the Organization for Economic Co-operation and Development and Nuclear Energy Agency (OECD NEA) Working Party on International Nuclear Data Evaluation Cooperation (WPEC), so any new models or information will need experimental validation and testing. The NEA Working Party on Nuclear Criticality Safety (WPNCS) recent experimental needs and priority list includes intermediate energy 235 U and 238 U experiments, as does the recent Integral Experiments to Address Nuclear Criticality Safety Needs Meeting (May 2023). A variant on the original CURIE experiment called the Multiple Critical Unresolved Region Integral Experiment (MCURIE) is proposed to provide further investigation of the intermediate and URR energy region for uranium. MCURIE will utilize existing fuels at NCERC but use alternative moderators and reflectors to modify the neutron absorption, scattering, and fission spectra of the experiments, allowing for precise targeting of nuclear data sensitivities. The overall goal of MCURIE is to develop a framework for designing and performing integral benchmark experiments with high sensitivities in the intermediate and URR energy regions for nuclear data validation.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2024

Development of multi-scale computational frameworks to solve fusion materials science challenges

Over the past two decades, the US-DOE has funded multiple projects that rely on high-performance computing and exascale computing platforms to accelerate scientific discoveries and address grand scientific challenges, such as harnessing fusion energy. In this article, we review in detail one of these efforts aimed at enhancing our capability to model plasma-facing materials subject to plasma and high-energy ion/neutron irradiation. The plasma surface interactions project has built a multi-scale modeling framework where many of the plasma- and high-energy ion/neutron irradiation-induced effects occurring in tungsten are explored. Here, this knowledge is used to develop atomistically-informed, high-fidelity continuum and meso-scale models that can be validated against experiments. We review the developments within this project, with attention to experimental validation efforts, and specifically highlight activities associated with: helium bubble bursting and equation of state, and hydrogen-helium interactions in tungsten; atomistically-informed model development for beryllium-tungsten material mixing; coupling of scrape-of-layer plasma, sheath and material models; and coupling of stochastic cluster-dynamics and crystal plasticity models to address radiation effects in tungsten under stress. Finally, we present how the project is preparing for future computational architectures, for instance through efforts to adapt atomistic methods to exascale computing.

36 MATERIALS SCIENCE↗

DOE OSTI2026

Evidence of topological charge polarization at active-passive interfaces in acoustically powered active liquid crystals

Theoretical and computational studies predict topological charge separation at active-passive interfaces in active nematics, but reliable experimental validation has been lacking. We utilize a synthetic acoustically energized liquid crystal to experimentally investigate the dynamics of topological defects across an active-passive interface. The interference pattern induced by the acoustic wave inside the experimental cell creates a spatial distribution of activity, resulting in the formation of active-passive interfaces with the liquid-crystalline director field aligned parallel to those interfaces. The activity gradient drives the reorientation of positively charged topological defects along its direction, causing them to migrate into the passive zone and form a layer with net topological charge. That layer “discharges” upon cessation of activity through annihilation of positive and negative topological charges, resembling the discharge of a capacitor.

Active defects↗

DOE OSTI2024

High-throughput protein characterization by complementation using DNA barcoded fragment libraries

Abstract Our ability to predict, control, or design biological function is fundamentally limited by poorly annotated gene function. This can be particularly challenging in non-model systems. Accordingly, there is motivation for new high-throughput methods for accurate functional annotation. Here, we used co mplementation of aux otrophs and DNA barcode seq uencing (Coaux-Seq) to enable high-throughput characterization of protein function. Fragment libraries from eleven genetically diverse bacteria were tested in twenty different auxotrophic strains of Escherichia coli to identify genes that complement missing biochemical activity. We recovered 41% of expected hits, with effectiveness ranging per source genome, and observed success even with distant E. coli relatives like Bacillus subtilis and Bacteroides thetaiotaomicron . Coaux-Seq provided the first experimental validation for 53 proteins, of which 11 are less than 40% identical to an experimentally characterized protein. Among the unexpected function identified was a sulfate uptake transporter, an O-succinylhomoserine sulfhydrylase for methionine synthesis, and an aminotransferase. We also identified instances of cross-feeding wherein protein overexpression and nearby non-auxotrophic strains enabled growth. Altogether, Coaux-Seq’s utility is demonstrated, with future applications in ecology, health, and engineering.

59 BASIC BIOLOGICAL SCIENCES↗

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 OSTI2020

Determining Multi-Component Phase Diagrams with Desired Characteristics Using Active Learning

Herein, we demonstrate how to predict and experimentally validate phase diagrams for multi-component systems from a high-dimensional virtual space of all possible phase diagrams involving several elements based on small existing experimental data. The experimental data for bulk phases for known systems represents a sampling from this space, and screening the space allows multi-component phase diagrams with given design criteria to be built. This approach uses machine learning methods to predict phase diagrams and Bayesian experimental design to minimize experiments for refinement and validation, all within an active learning loop. The approach is proven by predicting and synthesizing the ferroelectric ceramic system (1-ω)(Ba 0.61 Ca 0.28 Sr 0.11 TiO 3 )-ω(BaTi 0.888 Zr 0.0616 Sn 0.0028 Hf 0.0476 O 3 ) with a relatively high transition temperature and triple point, as well as the NiTi-based pseudo-binary phase diagram (1-ω)(Ti 0.309 Ni 0.485 Hf 0.20 Zr 0.006 )-ω(Ti 0.309 Ni 0.485 Hf 0.07 Zr 0.068 Nb 0.068 ) designed for high transition temperature (ω ≤ 1). Each phase diagram is validated and optimized through only three new experiments. The complexity of these compounds is beyond the reach of today’s computational methods.

36 MATERIALS SCIENCE↗

DOE OSTI2022

Bayesian-based response expansion and uncertainty quantification using sparse measurement sets

Systems subjected to dynamic loads often require monitoring of their vibrational response, but limitations on the total number and placement of the measurement sensors can hinder the data-collection process. Here, we present an indirect approach to estimate a system’s full-field dynamic response, including all uninstrumented locations, using response measurements from sensors sparsely located on the system. This approach relies on Bayesian inference that utilizes a system model to estimate the full-field response and quantify the uncertainty in these estimates. By casting the estimation problem in the frequency domain, this approach utilizes the modal frequency response functions as a natural, frequency-dependent weighting scheme for the system mode shapes to perform the expansion. This frequency-dependent weighting scheme enables an accurate expansion, even with highly correlated mode shapes that may arise from spatial aliasing due to the limited number of sensors, provided these correlated modes do not have natural frequencies that are closely spaced. Furthermore, the inherent regularization mechanism that arises in this Bayesian-based procedure enables the utilization of the full set of system mode shapes for the expansion, rather than any reduced subset. This approach can produce estimates when considering a single realization of the measured responses, and with some modification, it can also produce estimates for power spectral density matrices measured from many realizations of the responses from statistically stationary random processes. A simply supported beam provides an initial numerical validation, and a cylindrical test article excited by acoustic loads in a reverberation chamber provides experimental validation.

42 ENGINEERING↗

DOE OSTI2022

Ultra-Wideband Microwave Imaging System for Root Phenotyping

The roots are a vital organ for plant growth and health. The opaque surrounding environment of the roots and the complicated growth process means that in situ and non-destructive root phenotyping face great challenges, which thus spur great research interests. The existing methods for root phenotyping are either unable to provide high-precision and high accuracy in situ detection, or they change the surrounding root environment and are destructive to root growth and health. Thus,we propose and develop an ultra-wideband microwave scanning method that uses time reversal to achieve in situ root phenotyping nondestructively. To verify the method’s feasibility, we studied an electromagnetic numerical model that simulates the transmission signal of two ultra-wideband microwave antennas. The simulated signal of roots with different shapes shows the proposed system’s capability to measure the root size in the soil. Experimental validations were conducted considering three sets of measurements with different sizes, numbers and locations, and the experimental results indicate that the developed imaging system was able to differentiate root sizes and numbers with high contrast. The reconstruction from both simulations and experimental measurements provided accurate size estimation of the carrots in the soil, which indicates the system’s potential for root imaging.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2023

Mixed Ionic Electronic Conducting Quaternary Perovskites: Materials by Design for Solar Thermochemical Hydrogen

The innovative research conducted by Arizona State University and Princeton University in the project "Mixed Ionic-Electronic Conducting Quaternary Perovskites: Materials by Design for Solar Thermochemical Hydrogen" marks a significant stride forward in thermochemical water splitting. Through an intricate blend of computational design and experimental validation, the project delved into the promising potential of Mixed Ionic Electronic Conducting (MIEC) perovskites. These complex materials, characterized by their unique redox-active nature and adaptability in stoichiometry, present a promising frontier for efficient solar thermochemical hydrogen production. Firstly, the research enhanced the science by utilizing state-of-the-art computational methodologies to unravel the nuanced chemical potentials of MIEC perovskites. By simulating various off-stoichiometric scenarios and redox conditions, the team was able to predict material behaviors under diverse environmental conditions, a feat unachievable through conventional experimental methodologies alone. This approach not only fast-tracks the material screening process, significantly reducing the time from laboratory re-search to practical application, but also uncovers trends and correlations that are pivotal for future materials innovation. Regarding technical effectiveness, the project stands out in its economic feasibility. Traditional methods of materials discovery are often marred by high costs and extensive timeframes, owing to the iterative nature of experimental processes. However, by employing theoretical computations and validating these findings with targeted experiments, the project introduced a cost-effective paradigm for materials discovery and the first ever prediction, synthesis, and preliminary validation of a material solely from computational and theoretical considerations. This synergy between computation and experimentation expedites the discovery of optimal materials conducive to high-efficiency solar-to-hydrogen conversion processes. Furthermore, the public stands to benefit substantially from this research. The success of MIEC perovskites in solar thermochemical applications heralds a shift towards lower cost and lower electricity input for clean hydrogen production, hence potentially impacting climate and energy resilience. By improving the efficiency of solar-to-hydrogen conversions, the research paves the way for reduced dependency on fossil fuels, addressing the urgent global need for accessible and renewable energy sources. Moreover, the project's advancements contribute to scientific literacy in renewable energy technologies, empowering society through knowledge and spurring future innovations. In essence, this research project demonstrates significant progress in the realm of advanced water splitting through solar thermochemistry. Through its groundbreaking approaches in computational materials science and its implications for real-world applications, it holds the promise of a cleaner, more energy-resilient future.

08 HYDROGEN↗

DOE OSTI2022

Simulation and Optimization of Volatile Fatty Acid Upgrading Strategies for Sustainable Transportation Fuel Production

Underutilized wet waste is a promising feedstock for production of carbon-neutral or carbon-negative liquid transportation fuels. Arrested methanogenesis of wet wastes by microbes, which produces mixtures of C2-C8 carboxylic acids (volatile fatty acids, VFAs), is practiced industrially at a pilot scale and the VFA products can be catalytically upgraded to molecules suitable for use as transportation fuels (alcohols and alkanes) via sequential ketonization and hydrogenation, both steps of which have been demonstrated with high (>90%) yield at the lab scale. We present in this work a simulation-based decision-making framework which evaluates upgrading strategies of VFAs to alcohol- and hydrocarbon-based neat and blended diesel, jet, and gasoline fuels. The processes utilize sequential upgrading steps (ketonization and hydrogenation) accompanied by one distillation step (either separating VFAs before ketonization or alcohols after ketone hydrogenation) to form two liquid streams: a light alcohol stream best suited as a light-duty fuel and a heavy alcohol or hydrocarbon stream best suited as a diesel or jet fuel. Suitability of the liquid products as transportation fuels and maximum blending levels with petrofuels comes from simulation of critical fuel properties (boiling point, flash point, lower heating value, viscosity, melting point, water solubility, and cetane/octane number). Catalytic upgrading steps and fuel property mixing models used by the simulation are experimentally validated. We demonstrate the flexibility of the decision-making algorithm by evaluating processing scenarios for experimentally-observed VFA profiles with varied carbon chain length distributions. Processing scenarios are optimized for either maximum total renewable carbon utilization or production of heavy-duty (jet or diesel) fuels. We evaluate the economic viability and CO2 emissions of each proposed upgrading scenario using techno-economic and life cycle analyses. The decision-making framework developed in this work can also be used to down-select promising strategies for upgrading of other bio-based feedstocks to sustainable fuels and chemicals.

aviation fuel↗

DOE OSTI2026

Heat Transfer Experiments of a 1st Stage Blade Cascade for Supercritical CO2 Oxy-Combustion Turbine Application

The results of internally cooled 1st stage blade (S1B) cascade testing in a supercritical CO2 environment is presented. The turbine blade design has been previously established for the end application of an oxy-combustion turbine operating in the Allam-Fetvedt cycle with turbine inlet conditions of 305 bar and 1150°C. The internally cooled blade features leading edge (LE) region impingement cooling, mid-section ribbed serpentine passages, and a pin-finned trailing edge (TE) region before cooling ejection holes. The geometry for the tested blade cascade has a cooled central blade with un-cooled blades on either side to match flowpath areas of the actual turbine. The flowpath reuses internal components previously employed for mid-section region ribbed serpentine passage experiments that established Nusselt number enhancement ratios over a range of Reynolds numbers from 100,000-400,000. New components include flow conditioning plates upstream and downstream of the blade cascade to adequately represent the flow field and blade external heat transfer coefficient profiles for the actual turbine. The cooled central blade utilizes uniform crystal temperature sensors (UCTS) with six sensors each on the blade pressure and suction surfaces distributed radially and from LE to TE. The post-processed UCTS quantified the maximum wall temperature seen at each installed sensor location. The test procedure consisted of establishing supercritical CO2 cooling flow temperature and flow rate and maintaining it throughout the test. The flow rate aims to match that for the actual in-service turbine blade design and is maintained through an orifice restriction to keep the pressure differential between internal cooling flow and external hot flow nearly constant. For the sCO2 flow path external to the blade, temperatures were ramped throughout the test via control of the test loop’s natural gas burner heater. The maximum temperature seen was 468°C and held constant for a duration of 10 minutes at which the blade metal temperature was predicted to be at its maximum before ramping down. For the turbine blade design for service inlet conditions, external flow path computational fluid dynamics (CFD) results and an internal cooling 1-D thermal and hydraulic flow network model using experimentally validated correlations served as thermal finite element (FE) boundary conditions to predict blade metal temperatures. These predicted temperatures were subsequently utilized in a structural FE model to predict blade life ratings dictated by Haynes 282 creep strength data, having a strong dependence on temperature. The boundary conditions experienced during testing are used in the same workflow and compared to the experimental results, with the goal of validating the analysis methodology and providing insight on the uncertainty in local metal temperature predictions.

20 FOSSIL-FUELED POWER PLANTS↗

DOE OSTI2025

Framework for assessment of magnetic equilibrium controller performance on the MAST upgrade spherical tokamak

Here, in this work we present the assessment framework for magnetic equilibrium controllers on MAST Upgrade spherical tokamak (MAST-U) spherical tokamak. Such controllers are essential for the MAST-U since exhaust physics and core-edge integration studies require advanced divertor plasma configurations. The developed framework is based on the TokSys suite of plasma control codes, which was adapted and upgraded for MAST-U. However, extra capabilities were added on top of TokSys to support the development of new control algorithms, deployment of controllers to the plasma control system (PCS) and evaluation of their performance. The controller assessment was realized via closed-loop integrated control simulations with the actual MAST-U PCS and different physics-based plasma models. Since all components of the assessment chain were experimentally validated, these simulations provide qualified controllers applicable for direct use in the experiment. This resulted in the successful experimental demonstration of advanced plasma shape control on MAST-U with minimal on-machine development time. A similar methodology would be beneficial to other tokamaks, both existing and future.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2024

LPBF Processability of NiTiHf Alloys: Systematic Modeling and Single-Track Studies

Research into the processability of NiTiHf high-temperature shape memory alloys (HTSMAs) via laser powder bed fusion (LPBF) is limited; nevertheless, these alloys show promise for applications in extreme environments. This study aims to address this limitation by investigating the printability of four NiTiHf alloys with varying Hf content (1, 2, 15, and 20 at. %) to assess their suitability for LPBF applications. Solidification cracking is one of the main limiting factors in LPBF processes, which occurs during the final stage of solidification. To investigate the effect of alloy composition on printability, this study focuses on this defect via a combination of computational modeling and experimental validation. To this end, solidification cracking susceptibility is calculated as Kou’s index and Scheil–Gulliver model, implemented in Thermo-Calc/2022a software. An innovative powder-free experimental method through laser remelting was conducted on bare NiTiHf ingots to validate the parameter impacts of the LPBF process. The result is the processability window with no cracking likelihood under diverse LPBF conditions, including laser power and scan speed. This comprehensive investigation enhances our understanding of the processability challenges and opportunities for NiTiHf HTSMAs in advanced engineering applications.

36 MATERIALS SCIENCE↗

DOE OSTI2023

Towards Pareto optimal high entropy hydrides via data-driven materials discovery

The ability to rapidly screen material performance in the vast space of high entropy alloys is of critical importance to efficiently identify optimal hydride candidates for various use cases. Given the prohibitive complexity of first principles simulations and large-scale sampling required to rigorously predict hydrogen equilibrium in these systems, we turn to compositional machine learning models as the most feasible approach to screen on the order of tens of thousands of candidate equimolar high entropy alloys (HEAs). Critically, we show that machine learning models can predict hydride thermodynamics and capacities with reasonable accuracy (e.g. a mean absolute error in desorption enthalpy prediction of ~5 kJ mol H 2 –1 ) and that explainability analyses capture the competing trade-offs that arise from feature interdependence. We can therefore elucidate the multi-dimensional Pareto optimal set of materials, i.e., where two or more competing objective properties can't be simultaneously improved by another material. This provides rapid and efficient down-selection of the highest priority candidates for more time-consuming density functional theory investigations and experimental validation. Various targets were selected from the predicted Pareto front (with saturation capacities approaching two hydrogen per metal and desorption enthalpy less than 60 kJ mol H 2 –1 ) and were experimentally synthesized, characterized, and tested amongst an international collaboration group to validate the proposed novel hydrides. Finally, additional top-predicted candidates are suggested to the community for future synthesis efforts, and we conclude with an outlook on improving the current approach for the next generation of computational HEA hydride discovery efforts.

36 MATERIALS SCIENCE↗

DOE OSTI2026

Assessing Metal Ion Assignment Accuracy in Protein Data Bank Models via Elemental Spectroscopy

Accurate representation of metal ions in macromolecular structures is critical for chemical interpretation, computational modeling, and machine-learning methods that rely on Protein Data Bank (PDB) entries. However, the elemental identity of metals modeled in crystallographic structures is often inferred indirectly and rarely validated experimentally. Here, we combine Particle Induced X-ray Emission (PIXE) and X-ray Fluorescence Spectroscopy (XRFS) to determine the elemental composition of protein samples used to generate 70 deposited metalloprotein crystal structures. By analyzing the original protein material employed for crystallization, but before the addition of crystallization buffer solutions, we assess whether the modeled metal ions in deposited structures are consistent with experimentally detectable elemental content. We find that in a majority of cases, the metals modeled in the corresponding PDB entries are inconsistent with the metals present in the protein samples before crystallization, or that additional metals are present but not represented in the structural models. Spectroscopic results were integrated with automated crystallographic validation metrics, including real-space Z-difference (RSZD) analysis and systematic rerefinement, to evaluate atomic-number mismatch at metal sites. PIXE and XRFS show strong agreement for dominant elemental signals and provide complementary, scalable approaches for identifying suspect metal assignments. This work does not address physiological or functional metalation but instead highlights a widespread data integrity issue in deposited macromolecular structures, PDB-wide. These results establish an experimentally corroborated link between elemental identity and crystallographic validation metrics, enabling the large-scale detection of chemically inconsistent annotations in structural databases used for computational modeling and machine learning.

Crystallization↗