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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 163 records · Page 9
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Mechanism-Resolved PFM of Ferroionic and Ferroelectric Responses in Thickness-Gradient Hf 0.5 Zr 0.5 O 2 Libraries

Resolving growth mechanisms and thickness evolution of functional properties is one of the key tasks in materials discovery and optimization involving thin-film materials, traditionally requiring significant experimental budgets. Here we introduce the combination of thickness-gradient libraries and automated scanning probe microscopy as a systematic pathway to elucidate growth modes and disentangle ferroelectric and electrochemical contributions in ferroelectric thin films. As a model system, we explore the Hf 0.5 Zr 0.5 O 2 (HZO) gradient thin films grown on La x Sr 1-x MnO 3 (LSMO) bottom electrode thin films. Automated piezoresponse force microscopy, spectroscopy, and lithography reveals that irreversible topographic deformation arises from electrochemical activity at the LSMO surface, whereas reversible phase inversion in HZO reflects ferroelectric switching. Automated topography height-map scans are used to further quantify nucleation density, particle-size evolution, and roughness correlations across the thickness-gradient, demonstrating that improved plume stabilization during growth suppresses interfacial reactions and promotes dense, fine-grained HZO conducive to ferroelectric phase formation. This combined materials-engineering and automated-SPM framework establishes a platform for high-throughput, mechanism-resolved characterization of ferroionic and ferroelectric responses in complex oxide films.

FOS: Physical sciences↗
DOE OSTI2021

Autonomous experimentation systems for materials development: A community perspective

Solutions to many of the world's problems depend upon materials research and development. However, advanced materials can take decades to discover and decades more to fully deploy. Humans and robots have begun to partner to advance science and technology orders of magnitude faster than humans do today through the development and exploitation of closed-loop, autonomous experimentation systems. This review discusses the specific challenges and opportunities related to materials discovery and development that will emerge from this new paradigm. Our perspective incorporates input from stakeholders in academia, industry, government laboratories, and funding agencies. We outline the current status, barriers, and needed investments, culminating with a vision for the path forward. We intend the article to spark interest in this emerging research area and to motivate potential practitioners by illustrating early successes. We also aspire to encourage a creative reimagining of the next generation of materials science infrastructure. To this end, we frame future investments in materials science and technology, hardware and software infrastructure, artificial intelligence and autonomy methods, and critical workforce development for autonomous research.

36 MATERIALS SCIENCE↗
DOE OSTI2024

An integrated high-throughput robotic platform and active learning approach for accelerated discovery of optimal electrolyte formulations

Solubility of redox-active molecules is an important determining factor of the energy density in redox flow batteries. However, the advancement of electrolyte materials discovery has been constrained by the absence of extensive experimental solubility datasets, which are crucial for leveraging data-driven methodologies. In this study, we design and investigate a highly automated workflow that synergizes a high-throughput experimentation platform with a state-of-the-art active learning algorithm to significantly enhance the solubility of redox-active molecules in organic solvents. Our platform identifies multiple solvents that achieve a remarkable solubility threshold exceeding 6.20 M for the archetype redox-active molecule, 2,1,3-benzothiadiazole, from a comprehensive library of more than 2000 potential solvents. Significantly, our integrated strategy necessitates solubility assessments for fewer than 10% of these candidates, underscoring the efficiency of our approach. Our results also show that binary solvent mixtures, particularly those incorporating 1,4-dioxane, are instrumental in boosting the solubility of 2,1,3-benzothiadiazole. Beyond designing an efficient workflow for developing high-performance redox flow batteries, our machine learning-guided high-throughput robotic platform presents a robust and general approach for expedited discovery of functional materials.

25 ENERGY STORAGE↗
DOE OSTI2025

Synthesis, Stability, and Magnetic Properties of Antiperovskite Co 3 PdN

Experimental synthesis and characterization of theoretically predicted compounds are important steps in the materials discovery pipeline. Here, we report on the synthesis of Co 3 PdN, which was recently predicted to be a stable magnetic antiperovskite. The Co 3 PdN thin films were grown by reactive sputtering and were confirmed to form in an antiperovskite crystal structure. The thermal stability of the compound is demonstrated up to 600 K by in situ X-ray diffraction, though the phase persists at slightly higher temperatures (700 K) in an air-free magnetometer. Both ab initio calculations and magnetization measurements find Co 3 PdN to be ferromagnetic with an experimentally determined Curie temperature of T C = 560 ± 5 K. The saturation magnetization of 1.2 μ B /Co found in the experiment is slightly lower than the 1.7 μ B /Co value expected by theory. A narrow magnetic hysteresis loop with a coercive field of 100 Oe at low temperature suggests that Co 3 PdN might be useful in electronic applications requiring fast switching of the magnetization vector. While prior prediction of Co 3 PdN showed a gapped electronic band structure for each spin channel, we show that this was due to incomplete sampling of Brillouin zone paths and that band crossings exist along R-X|M and X|M-R paths. The metallic nature of Co 3 PdN is further confirmed by temperature-dependent transport measurements, which also show a considerable anomalous Hall effect. Altogether, this work represents an appreciable step toward understanding the synthesis, structure, stability, and properties of a new magnetic material.

14 SOLAR ENERGY↗
DOE OSTI2022

In‐Silico Device Performance Prediction of Cosensitizer Dye Pairs for Dye‐Sensitized Solar Cells

Abstract Endeavors in the field of dye‐sensitized solar cells (DSCs) have shown great promise when adopting a data‐driven approach to materials discovery, such as successful molecular‐scale predictions of light‐harvesting chromophores. However, predictions of DSC dyes would become much more sophisticated if a molecular‐to‐macroscopic DSC device prediction methodology existed. Thereby, a fully computational pipeline is presented that predicts device‐performance parameters of DSCs which contain varying dye combinations. Optimal pairing of complementary dyes is identified via a data‐driven workflow that affords cosensitized DSCs with maximum power‐conversion efficiencies. Six high‐performing DSC dyes are paired with partner dyes that are screened from a database of 8488 compounds using sequential heuristic filters. Existing models that predict short‐circuit‐current density ( J SC ) and open‐circuit voltage ( V OC ) parameters are adapted to predict singly sensitized and cosensitized DSC performance. The predictions for J sc values of singly sensitized devices match experimental literature values with comparable accuracy to more computationally costly methods. Five out of six dye pairings are predicted to have greater J SC values when cosensitized compared to their corresponding singly sensitized devices, including two pairs that show strong J sc boosts of +13% and +12% when cosensitized. Thus, the prospect of an entirely in‐silico prediction pipeline for DSC performance that can be used to realize the fully automated design of optimized cosensitized DSCs is demonstrated.

14 SOLAR ENERGY↗
DOE OSTI2026

Project Development of an Electrochemical Denitration and Caustic Generation System for HLW Pretreatment at Hanford - 26350

An engineering-scale electrochemical processing skid is proposed to perform the denitration of Hanford tank waste, which would help to mitigate a key process concern with the direct feed processing of the Hanford Tank Waste Treatment and Immobilization Plant (WTP). The reduction of nitrates and organic compounds in the waste feed will directly reduce hazardous NOx and ammonia gases generated during the vitrification process, which in turn will aid in addressing potential regulatory and safety challenges associated with processing large volumes of tank waste. This paper highlights the past legacy work, project layout, accomplishments from Phase 1 and research and development envisioned for Phase 2. An innovative electrochemical denitration and caustic generation (EDCGe) process was demonstrated for the pretreatment of tank waste at the Savannah River Site (SRS) in the early 2000s. The denitration electrolyzer, off-gas abatement system, and caustic generator electrolyzer are being developed with the intent that the denitration electrolyzer will convert nitrate and nitrite anions to nitrogen gas while also yielding other gaseous byproducts, which may include N2O, NH3, VOCs, and H2. The gaseous byproducts will be managed via a tandem off-gas catalyst-bed treatment system. The caustic generation electrolyzer will recycle NaOH from the feed to produce a clean caustic stream for use within the batching tanks at Hanford, aiding in the preparation of waste for WTP. The reduction in hazardous emissions and improved waste treatment processes provides a robust solution for nuclear waste management, contributing to environmental safety and regulatory compliance. The EDCGe technology is being adapted, modified, and updated for the preparation of the Direct Feed-High Level Waste (DF-HLW) flowsheet at Hanford. Phase 1 demonstrated a bench-scale proof-of-concept for reactions involving the denitration electrolyzer and gas phase abatement of ammonia. The electrochemical technology is drawing on the scientific outcomes that were reported in the legacy work. The results from Phase 1 demonstrated the viability of the EDCGe system in reducing the nitrogen species of simple non-radioactive waste simulants. Commercially available alloys used as electrode materials and membranes are being studied for the denitration and caustic generation electrolyzers. The continuation of this project holds promise for broader applications, such as energy-efficient ammonia production, and contributes significant advancements in nuclear waste management. Additional material discovery has been investigated into ceramic Na super ion conductive (NaSICON) materials and off-gas abatement catalyst discovery. NaSICON is of interest for selective transport of Na within the electrolyzers to make a clean caustic stream. Future integration and optimization efforts, informed by Phase 1 results and ongoing research, will continue to drive advancements in nuclear waste management technology. The technology developed for the EDCGe treatment of tank waste will also have broader potential to inform other fields, such as energy-efficient ammonia production, as well as ammonia abatement catalysis through the lessons learned in electrochemical nitrate reduction. The applications and benefits of this research extend beyond Hanford and the Savannah River Site, supported by a collaborative team of scientists and engineers from national labs, academia, and industry, ensuring a comprehensive approach to solving complex waste treatment challenges. The team is leveraging advanced electrochemical technologies, machine learning, novel catalysts tailored for gaseous nitrogen species, and cutting-edge reactor systems to enhance the process efficiency and effectiveness of the denitration process.

Rodene, Dylan [Savannah River National Laboratory ↗
DOE OSTI2022

Use of Refractory-Volatile Element Deep Eutectic Regions to Grow Single Crystalline Intermetallic Compounds

Compounds containing both refractory and volatile elements present a unique challenge for crystal growth, given the conflicting realities of high melting temperatures and high vapor pressures. Nevertheless, the discovery of superconductivity in FeAs and FeSe based materials and a Weyl semimetal state in TaP are motivations to explore compounds containing such pairs. Here, we discuss use of the low-melting single phase liquid regions above deep M-X eutectics (M=transition metal, X=P, S) as the basis for high temperature solutions for growing intermetallic compounds containing volatile-refractory pairs. We show that Ni-P, Pd-P, Pt-P, and Pd-S compositions form single phase melts at moderate temperatures below 1000 °C and with minimal vapor pressure. We first present the simple case of growing Ni 2 P from Ni-P and next discuss the more complicated growth of RPd 3 S 4 (R=La, Ce, Nd, Eu) from a Pd-S melt. We show how frit-disc alumina crucible sets allow for contamination-free capture of decanted liquid and its reuse in subsequent experiments, demonstrating a fractionation of the CePd 3 S 4 growth to determine the optimal conditions for crystal growth. We conclude by using the single phase liquid regions above the Pt-P and Pd-P eutectics to grow single crystals of MPt 5 P (M=Mn, Fe) and MnPd 5 P. As these materials have primarily been studied in polycrystalline form, we give an overview of the magnetic and transport properties of our single crystals. The examples outlined here illustrate the utility of using the single phase liquid above deep metal-X eutectics for solution growth and materials discovery.

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

High throughput structure–property relationship for additively manufactured 316L/IN625 alloy mixtures leveraging 2-step Bayesian estimation

While the fabrication of graded materials by directed energy deposition (DED) has led to accelerated materials discovery, the ability to rapidly explore sufficiently large material composition spaces is limited due to the time-intensive nature of conventional materials characterization techniques. The present study investigates the viability of small punch test (SPT) protocols for rapidly evaluating DED-fabricated alloy mixtures of stainless steel 316L (316L) and Inconel 625 (IN625). The SPT protocols evaluated in this study include both the recently established two-step Bayesian estimation framework as well as the empirical relationships established in prior literature. It is shown that these protocols are capable of reliably and quantitatively tracking the changes in the mechanical properties of the alloy mixtures studied. Enhancement of mechanical properties was observed with the addition of IN625 to 316L, which is attributed to the austenite stabilization in the matrix and the formation of fine δ - Ni3Nb precipitates. It is shown that CALPHAD-based Scheil model simulations predicted the formation of different precipitate phases for each composition. The novel protocols presented in this paper open new avenues for high throughput material explorations for additive manufacturing.

2-step Bayesian estimation↗
DOE OSTI2021

Bias free multiobjective active learning for materials design and discovery

The design rules for materials are clear for applications with a single objective. For most applications, however, there are often multiple, sometimes competing objectives where there is no single best material and the design rules change to finding the set of Pareto optimal materials. In this work, we leverage an active learning algorithm that directly uses the Pareto dominance relation to compute the set of Pareto optimal materials with desirable accuracy. We apply our algorithm to de novo polymer design with a prohibitively large search space. Using molecular simulations, we compute key descriptors for dispersant applications and drastically reduce the number of materials that need to be evaluated to reconstruct the Pareto front with a desired confidence. This work showcases how simulation and machine learning techniques can be coupled to discover materials within a design space that would be intractable using conventional screening approaches.

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

Pressure-driven density match nucleates metastable r8 phases from amorphous Si and Ge

The pressure–temperature phase behavior of covalent disordered solids such as amorphous silicon and germanium is complex. Questions remain on possible glass transitions, on polyamorphism via amorphous–amorphous transitions, on connections with liquid–liquid transitions, on structure-behavior relationships, and on their potential as precursor for novel methods for material discovery. Here we demonstrate experimentally the nucleation of a metastable, four-fold coordinated rhombohedral r8 phase from pure amorphous silicon and germanium upon room temperature compression at pressures below 10 GPa. Accompanying theory reveals a strong pressure-driven distortion of the bond angle transforming the starting tetrahedral low-density amorphous network to a distorted four-fold coordinated medium-density state. This state is of lower density than metallic high-density networks, resembles the crystalline r8 phase and initiates its nucleation. Our finding shows that polyamorphism is not the only possible transformation mode for these amorphous solids and that instead nucleation of interesting functional phases at potentially useful pressures is possible. Such novel access modes to metastable structures are critical for future exploitability and could be useful for other tetrahedral materials including carbon, where the related (bc8) post-diamond phase remains elusive. Our observed density match between an amorphous and a metastable crystalline phase clearly allows for a new phase transition pathway, while corresponding theory demonstrates how carefully validated atomistic simulations can guide prediction, discovery and synthesis of novel material structures.

Materials discovery↗
DOE OSTI2021

Synthesis and Processing by Design of High-Nickel Cathode Materials

The high demand of lightweight, high energy density batteries for energy storage promotes new materials discovery and development. Despite the large number of battery materials being discovered, very few of them have been commercially deployed, mostly bottlenecked by synthesis and processing – namely, making certain phases with the desired structure and properties to meet the multifaceted performance requirements. Alternative to the traditional trial and error, we present here an in situ study aided synthesis- and processing-by-design approach. With specific examples, we illustrate how to use the approach to identify reaction pathways in synthesis and processing of high-Nickel (Ni) cathode materials for next-generation lithium-ion batteries, thereby ensuring precise control of their structure, morphology, and surface properties. Furthermore, perspectives are provided on the wide applicability of the approach to solving critical issues inherent to high-Ni cathodes and the new directions and opportunities in the area.

36 MATERIALS SCIENCE↗
DOE OSTI2022

System and technoeconomic analysis of solar thermochemical hydrogen production

Hydrogen is a promising energy carrier that can be obtained from various feedstocks using renewable energy sources. Direct solar thermochemical hydrogen (STCH) production by water splitting can utilize the full spectrum of solar radiation and has the potential to achieve high solar energy conversion efficiencies. Currently STCH research areas focus on material discovery. This paper evaluates the performance of various STCH materials in the context of a system platform to assess techno-economic benefits and gaps in the path to STCH scale-up. Additionally, to analyze the hydrogen production cost, a concentrating solar thermal (CST) system is introduced as a platform for integrating STCH materials and accommodating generalized thermochemical processes. The thermochemical process is based on a two-step STCH cycle using metal oxide that consists of a high temperature step for metal oxide reduction, followed by an oxidation step for water splitting at a lower temperature. A preferred configuration is to have the high temperature step occurring in a directly irradiated solar receiver reactor. To this end, we conceptualized a receiver design and associated solar field layout and investigated STCH operational boundaries, component costs and sensitivity parameters on the $2/kgH 2 goal of hydrogen production. The study explored system-related variables and factors associated with scaling up. The CST platform allows more comprehensive studies that encompass aspects of STCH materials and systems such as cost, hydrogen productivity and replacement frequency, alongside other system components like heliostat field, tower, and potential receiver costs.

08 HYDROGEN↗
DOE OSTI2025

Induction ultrafast sintering

This study proposes and demonstrates induction ultrafast sintering (IUS), which enables rapid densification of refractory and other materials via two contactless modalities: direct IUS (d-IUS), where heating occurs through electromagnetic coupling with the sample, and susceptor IUS (s-IUS), where heating is achieved indirectly via an induction-heated metal case. Ultrahigh heating rates of ∼75 to >450 °C/s and temperatures exceeding 2500 °C are readily achieved. Both d-IUS and s-IUS densify molybdenum to high densities within 120 s, with only ∼1–3 % porosity observed by image analysis. Similarly, 3 mol % yttria-stabilized zirconia (3YSZ) reaches ∼97 % relative density in 30 s via s-IUS. This study further demonstrates ultrafast reactive sintering of two difficult-to-sinter materials: a refractory compositionally complex alloy–carbide (RCCA–CCC) composite, NbMoTaW–(Nb 0.37 Mo 0.11 Ta 0.39 W 0.13 ) 2 C, using d-IUS, and a compositionally complex silicide (CCS), (Mo 1/3 Nb 1/3 Zr 1/3 )Si 2 , using s-IUS. This IUS platform offers a versatile route for high-throughput materials discovery and energy-efficient fabrication of bulk refractory materials.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Accelerating amorphous polymer electrolyte screening by learning to reduce errors in molecular dynamics simulated properties

Polymer electrolytes are promising candidates for the next generation lithium-ion battery technology. Large scale screening of polymer electrolytes is hindered by the significant cost of molecular dynamics (MD) simulation in amorphous systems: the amorphous structure of polymers requires multiple, repeated sampling to reduce noise and the slow relaxation requires long simulation time for convergence. Here, we accelerate the screening with a multi-task graph neural network that learns from a large amount of noisy, unconverged, short MD data and a small number of converged, long MD data. We achieve accurate predictions of 4 different converged properties and screen a space of 6247 polymers that is orders of magnitude larger than previous computational studies. Further, we extract several design principles for polymer electrolytes and provide an open dataset for the community. Our approach could be applicable to a broad class of material discovery problems that involve the simulation of complex, amorphous materials.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Learning Molecular Mixture Property Using Chemistry-Aware Graph Neural Network

Recent advances in machine learning (ML) are expediting materials discovery and design. One significant challenge facing ML for materials is the expansive combinatorial space of potential materials formed by diverse constituents and their flexible configurations. This complexity is particularly evident in molecular mixtures, a frequently explored space for materials, such as battery electrolytes. Owing to the complex structures of molecules and the sequence-independent nature of mixtures, conventional ML methods have difficulties in modeling such systems. Here, we present MolSets, a specialized ML model for molecular mixtures, to overcome the difficulties. Representing individual molecules as graphs and their mixture as a set, MolSets leverages a graph neural network and the deep sets architecture to extract information at the molecular level and aggregate it at the mixture level, thus addressing local complexity while retaining global flexibility. We demonstrate the efficacy of MolSets in predicting the conductivity of lithium battery electrolytes and highlight its benefits in the virtual screening of the combinatorial chemical space. Published by the American Physical Society 2024

Zhang, Hengrui (ORCID:0000000231831654)↗
DOE OSTI2025

Materials laboratories of the future for alloys, amorphous, and composite materials

In alignment with the Materials Genome Initiative and as the product of a workshop sponsored by the US National Science Foundation, we define a vision for materials laboratories of the future in alloys, amorphous materials, and composite materials; chart a roadmap for realizing this vision; identify technical bottlenecks and barriers to access; and propose pathways to equitable and democratic access to integrated toolsets in a manner that addresses urgent societal needs, accelerates technological innovation, and enhances manufacturing competitiveness. Spanning three important materials classes, this article summarizes the areas of alignment and unifying themes, distinctive needs of different materials research communities, key science drivers that cannot be accomplished within the capabilities of current materials laboratories, and open questions that need further community input. Here, we provide a broader context for the workshop, synopsize the salient findings, outline a shared vision for democratizing access and accelerating materials discovery, highlight some case studies across the three different materials classes, and identify significant issues that need further discussion.

36 MATERIALS SCIENCE↗
DOE OSTI2026

SynthEsizing Novel H2 Sensors for Operational Resilience in Pipeline Infrastructure (SENSOR)

A flexible and extensible computational framework acts as a black-box materials discovery engine, capable of screening, predicting, and designing advanced materials with minimal manual intervention was developed. While developed for hydrogen sensing, the approach can be readily adapted to other materials challenges, offering a powerful tool for data-driven materials innovation.

08 HYDROGEN↗
DOE OSTI2023

Pulsed Light Synthesis of High Entropy Nanocatalysts with Enhanced Catalytic Activity and Prolonged Stability for Oxygen Evolution Reaction

The ability to synthesize compositionally complex nanostructures rapidly is a key to high-throughput functional materials discovery. In addition to being time-consuming, a majority of conventional materials synthesis processes closely follow thermodynamics equilibria, which limit the discovery of new classes of metastable phases such as high entropy oxides (HEO). Herein, a photonic flash synthesis of HEO nanoparticles at timescales of milliseconds is demonstrated. By leveraging the abrupt heating and cooling cycles induced by a high-power-density xenon pulsed light, mixed transition metal salt precursors undergo rapid chemical transformations. Hence, nanoparticles form within milliseconds with a strong affinity to bind to the carbon substrate. Oxygen evolution reaction (OER) activity measurements of the synthesized nanoparticles demonstrate two orders of magnitude prolonged stability at high current densities, without noticeable decay in performance, compared to commercial IrO 2 catalyst. This superior catalytic activity originates from the synergistic effect of different alloying elements mixed at a high entropic state. It is found that Cr addition influences surface activity the most by promoting higher oxidation states, favoring optimal interaction with OER intermediates. The proposed high-throughput method opens new pathways toward developing next-generation functional materials for various electronics, sensing, and environmental applications, in addition to renewable energy conversion.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗