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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 667 records · Page 37
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Directed self-assembly of block copolymers for high-precision patterning in the era of extreme ultraviolet lithography

Extreme ultraviolet (EUV) lithography enables unprecedented resolution in semiconductor patterning but faces critical challenges in developing resist materials that achieve high-precision at economically viable throughput. Directed self-assembly (DSA) of block copolymers (BCPs) offers a promising solution for pattern rectification by leveraging thermodynamically determined domain structures to decouple BCP pattern quality from the imperfect original lithographic pattern. This prospective presents an overview of recent progress on the EUV + DSA strategy, covering advances in BCP material design, processing, metrology, and pattern transfer. We highlight recent advances in high-χ BCPs with perpendicular orientation and domain spacings compatible with EUV dimensions, leveraging A-b-(B-r-C) architectures. We also discuss progress in chemical pre-pattern fabrication using both positive- and negative tone resists, along with processing strategies to minimize defects and roughness based on BCP thermodynamics and assembly kinetics. We further examine metrology platforms for characterizing the thermodynamics of BCP materials and quantifying the size and shape of BCP domains. Lastly, we review pattern transfer strategies for generating functional inorganic masks suitable for semiconductor manufacturing. Together, these advances highlight the potential of DSA to complement EUV lithography, offering a pathway to address critical challenges in achieving high-precision patterning for the semiconductor industry.

Lee, Kyunghyeon [Univ. of Chicago, IL (United Stat↗
DOE OSTI2025

Coal Enhanced PEEK Filament Production for Additive Manufacturing in Industrial Services

The project (Award DE-FE0032146), led by Baker Hughes in collaboration with the University of Wyoming, aims to develop composite PEEK (Polyether-ether-ketone) materials enhanced using coal-derived graphene-based additives suitable for additive manufacturing (3D printing). Coal char (CC), graphene oxide (GO), and reduced graphene oxide (rGO), derived from Powder River Basin (PRB) coal, were successfully integrated into commercial PEEK feedstock. The composite PEEK materials demonstrated tensile and flexural strengths and modulus, Shore hardness, and thermal properties similar to unfilled PEEK. Dielectric strength of 0.5% GO/PEEK is twice that of unfilled PEEK. However, tensile elongation and Izod impact toughness of the composite materials are lower than unfilled PEEK. The 10% CC/PEEK material also shows good recyclability albeit with slightly increased glass transition and cold crystallization temperatures. Filaments of the composite PEEK materials were fabricated using a Filabot system. 3D printing of the composite PEEK initially encountered feeding stoppage caused by the non-uniform diameter of the filament, which was resolved by reducing the nominal diameter from 1.75 to 1.65 mm. The printability of the composite PEEK filaments is limited in geometry and build time, driven by differences in base PEEK feedstock from the commercial PEEK filament at printing conditions. Prototype part printed using the composite filaments shows inconsistent bead width and bead interruption resulting in porosity. Preliminary process and economic evaluation using PRB coal char estimates the cost of GO to be $0.5/lb. Considering the small fraction expected in the composite PEEK, the GO cost is several orders of magnitude lower than the commercial PEEK filament. It is recommended that the printability of composite PEEK filaments to be improved by optimizing the composite PEEK material at the printing conditions and reducing the variations in filament extrusion. Also important is to identify the mechanisms of how coal-derived additives affect the composite PEEK performance and printing characteristics to allow customized material design and processing for target performance.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2021

Layer spacing gradient (NaLi) 1–x CoO 2 for electrochemical Li extraction

Designing materials with high Li to Na selectivity is the key for direct extraction of Li from unconventional sources, such as seawater, without complex separations. However, material discovery is hindered by an insufficient understanding on how a material's composition and structure relate to Li selectivity during electrochemical co-intercalation. Here, we synthesized a core-shell structured (NaLi) 1-x CoO 2 with a core Li phase (Li 0.94 CoO 2 ), a shell Na phase (Na 0.51 CoO 2 ), and a transition intermediate phase via Na ion-exchange of parent Li 1-x CoO 2 . We reveal that the chemical compositions and spatial relation of these phases play critical roles in governing high Li to Na selectivity. The core Li phase restricts large layer spacing expansion and inhibits Na intercalation. The shell Na phase serves to maintain the electrode stability against further Na ion exchange. With the layer spacing gradient design, we achieved a high Li selectivity of 1.5 x 10 5 with a recovery of similar to 7.6:1 Li to Na from 1:20,000 initial ratio.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Computational synthesis of a new generation of 2D-based perovskite quantum materials

Perovskite-based optoelectronic devices have emerged as a promising energy source due to their potential for scalable production. This study introduces “perovskene,” a novel class of 2D materials derived from the ABC3-like perovskites, synthesized via a data-driven, high-throughput computational strategy. We harness machine learning and multitarget deep neural networks to systematically investigate the structure–property relations, paving the way for targeted material design and optimization in fields such as renewable energy, electronics, and catalysis. The characterization of over 1500 synthesized structures shows that more than 500 structures are stable, revealing properties such as ultra-low work function and large magnetic moment, underscoring the potential for advanced technological applications.

2D materials↗
NASA NTRS1997

Robotic Materials Handling in Space: Mechanical Design of the Robot Operated Materials Processing System HitchHiker Experiment

The Goddard Space Flight Center has developed the Robot Operated Materials Processing System (ROMPS) that flew aboard STS-64 in September, 1994. The ROMPS robot transported pallets containing wafers of different materials from their storage racks to a furnace for thermal processing. A system of tapered guides and compliant springs was designed to deal with the potential misalignments. The robot and all the sample pallets were locked down for launch and landing. The design of the passive lockdown system, and the interplay between it and the alignment system are presented.

Voellmer, George↗
DOE OSTI2024

Experimental Considerations for Estimating Degradation in PV Modules

Carefully controlled laboratory experiments and measurements can enable the determination of acceleration factors suitable for extrapolation to durability and performance of a fielded PV module. Ideally, a single mechanism can be identified with appropriate acceleration factors for extrapolation to the field. However, even with a single mechanism, the inherent uncertainty in these factors leads to uncertainty in the extrapolation which is greater the higher the acceleration factor. This course will explain how because of the wide range of acceleration factors for a given degradation mode, utilizing acceleration factors greater than about 10x will typically lead to unacceptable uncertainty in the results. Therefore, if even just a rank ordering of materials is desired, acceleration factors must be minimized which requires a good general understanding of the scale of the different acceleration factors for the degradation mode of interest. In this tutorial we will discuss what the different purposes are for many of the accelerated stress tests used today. E.g., what is a qualification test, a highly accelerated stress test, a rank ordering test, or a service life prediction test. We will discuss how one can understand the relationship between test results and expected field performance. A single accelerated stress test condition cannot duplicate outdoor exposure for all possible degradation pathways; therefore, one must use targeted evaluation of material properties at different stress levels to determine the relevant acceleration factors and fit it to a model. We will also discuss how to interpret the results of experiments understanding what is relevant/not relevant, or not e valuated in a test. There are many common error people make in their test interpretations because they push the stress levels to be too harsh. This creates biases and can mask the relevant failure modes and mechanisms or will erroneously lead one to over design materials against things that aren't relevant. Several case studies will be presented to illustrate appropriate interpretation of accelerated stress testing results.

degradation↗
DOE OSTI2024

High–Performance NiCo 2 O 4 /Graphene Quantum Dots for Asymmetric and Symmetric Supercapacitors with Enhanced Energy Efficiency

For the sustainable growth of future generations, energy storage technologies like supercapacitors and batteries are becoming more and more common. However, reliable and high-performance materials’ design and development is the key for the widespread adoption of batteries and supercapacitors. Quantum dots with fascinating and unusual properties are expected to revolutionize future technologies. However, while the recent discovery of quantum dots honored with a Nobel prize in Chemistry, their benefits for the tenacious problem of energy are not realized yet. In this context, herein, chemical-composition tuning enabled exceptional performance of NiCo 2 O 4 (NCO)/graphene quantum dots (GQDs) is reported, which outperform the existing similar materials, in supercapacitors. A comprehensive study is performed on the synthesis, characterization, and electrochemical performance evaluation of highly functional NCO/GQDs in supercapacitors delivering enhanced energy efficiency. The high-performance, functional NCO/GQDs electrode materials are synthesized by the incorporation of GQDs into NCO. The effect of variable amount of GQDs on the energy performance characteristics of NCO/GQDs in supercapacitors is studied systematically. In-depth structural and chemical bonding analyses using X-ray diffraction (XRD) and Raman spectroscopic studies indicate that all the NCO/GQDs composites crystallize in the spinel cubic phase of NiCo 2 O 4 while graphene integration evident in all the NCO/GQDs. The scanning electron microscopy imaging analysis reveals homogeneously distributed spherical particles with a size distribution of 5–9 nm validating the formation of QDs. The high-resolution transmission electron microscopy analyses reveal that the NCOQDs are anchored on graphene sheets, which provide a high surface area of 42.27 m 2 g –1 and high mesoporosity for the composition of NCO/GQDs-10%. In addition to establishing reliable electrical connection to graphene sheets, the NCOQDs provide reliable 3D-conductive channels for rapid transport throughout the electrode as well as synergistic effects. Chemical-composition tuning, and optimization yields NCO/GQDs-10% to deliver the best specific capacitance of 3940 Fg –1 at 0.5 Ag –1 , where the electrodes retain ≈98% capacitance after 5000 cycles. The NCO/GQD-10%//AC asymmetric supercapacitor device demonstrates outstanding energy density and power density values of 118.04 Wh kg –1 and 798.76 W kg –1 , respectively. The NCO/GQDs-10%//NCO/GQDs-10% symmetric supercapacitor device delivers excellent energy and power density of 24.30 Wh kg –1 and 500 W kg –1 , respectively. These results demonstrate and conclude that NCO/GQDs are exceptional and prospective candidates for developing next-generation high-performance and sustainable energy storage devices.

25 ENERGY STORAGE↗
DOE OSTI2023

Design of Multicomponent Peptide Fibrils with Ordered and Programmable Compositional Patterns

Advanced applications of biomacromolecular assemblies require a stringent degree of control over molecular arrangement, which is a challenge to current synthetic methods. Here we used a neighbor-controlled patterning strategy to build multicomponent peptide fibrils with an unprecedented capacity to manipulate local composition and peptide positions. Eight peptides were designed to have regulable nearest neighbors upon co-assembly, which, by simulation, afforded 412 different patterns within fibrils, with varied compositions and/or peptide positions. The fibrils with six prescribed patterns were experimentally constructed with high accuracy. The controlled patterning also applies to functionalities appended to the peptides, as exemplified by arranging carbohydrate ligands at nanoscale precision for protein recognition. Importantly, this study offers a route to molecular editing of inner structures of peptide assemblies, prefiguring the uniqueness and richness of patterning-based material design.

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

Doping Metallic Grain Boundaries to Control Atomic Structure and Damage Tolerance (Final Report)

The interfaces between crystals, known as grain boundaries, often act as sites for crack and void nucleation during plastic deformation of metallic materials. While it is known that interfacial character and structural state can greatly influence this damage nucleation process, the current level of control over such details is extremely limited. The objective of this project was to obtain a fundamental understanding of how metallic grain boundary structure can be controlled through intelligent doping, with the idea of inducing planned grain boundary phases or complexions. The effect of complexion structure on dislocation accommodation mechanisms was studied, to improve the field’s understanding of damage nucleation at interfaces. While mechanical damage was the primary focus, the project showed that amorphous complexions can also improve a material’s resistance to radiation damage, providing a single materials design concept that can address multiple important technological areas. This research used a combination of computational, experimental, and characterization techniques to isolate and understand the importance of nanoscale grain boundary structure and interfacial chemistry. The fundamental insights provided by this project enable the creation of advanced engineering metals with improved damage tolerance.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Durable Module Materials Consortium (DuraMAT) FY 2021 Annual Report: New Results and a Renewed Consortium

The Durable Module Materials Consortium (DuraMAT) is a multi-lab consortium led by the National Renewable Energy Laboratory, with Sandia National Laboratories, SLAC National Accelerator Laboratory, and Lawrence Berkeley National Laboratory as core research labs. DuraMAT's overarching goal is to discover, develop, de-risk, and enable the rapid commercialization of improved materials, designs, predictive tests, and models for photovoltaic (PV) modules that increase performance, extend lifetime, and enable new applications. Technical results are highlighted throughout this report, and the new projects awarded for FY 2022 address many of the challenges to making 50-year, high-energy-yield modules that were identified in these working groups.

backsheet↗
DOE OSTI2020

Exploiting ultra-large linear elasticity over a wide temperature range in nanocrystalline NiTi alloy

Many shape memory alloys can support large recoverable strains of a few percent by reversible stress-induced martensite transformation, yet they behave non-linear within a narrow operating temperature range. Developing the bulk metallic materials with ultra-large linear elasticity over a wide temperature range has proven to be difficult. In this work, a material design concept was proposed, that is true elastic deformation and reversible twinning-detwinning deformation run in parallel to overcome this challenge. By engineering the residual internal stress to realize the concurrency of true elastic deformation and twinning-detwinning deformation, a bulk nanocrystalline NiTi that possesses an ultra-large linear elastic strain up to 5.1 % and a high yield stress of 2.16 GPa over a wide temperature range of 270 °C was developed. This study offers a new avenue for developing the metallic materials with ultra-large linear elasticity over a wide temperature range of 270 °C (from 70 °C to -197 °C).

36 MATERIALS SCIENCE↗
DOE OSTI2024

Chapter 4: Physically informed deep learning networks for simulating microstructure evolution of 3D polycrystals

As discussed in the previous chapter, high energy diffraction microscopy (HEDM) is used to study the micromechanical evolution of a material during in situ loading. HEDM experiments have been used to verify crystal plasticity (CP) simulations [119, 91, 90, 120], for experimental planning, material design, and to further analyze experimental results. However, Fast Fourier transform-based CP (CP-FFT) or finite element-based CP (CP-FE) methods are often too slow to be used in real-time during an experiment. CP-FFT is faster than CP-FE simulations due to the absence of meshing, but can still take hours to simulate the response of a single volume depending on the size and number of strain steps [127]. Reducing computation time would create a larger exploration space in planning and design, and enable faster analysis of experimental results and real-time feedback during an experiment. This research expands upon previous works to develop a workflow for predicting the full-field evolution of a 3D polycrystal. The workflow is simplified from previous works to predict only orientation and elastic strain tensors (from which stress tensors are calculated). The network is physically informed through loss functions and network architecture for a more robust model. The orientation predictions are informed about the cubic crystal symmetry of the material by incorporating disorientation and misorientation information into the network architecture and loss. The Von Mises stress is used to enforce the correct stress-strain trends in the strain tensor predictions. Additional total strain steps from the elastic and elastoplastic region are included to better capture the stress-strain evolution at smaller total strain steps. Material and hardening parameters are additional inputs into the networks to further inform the network and to study the network’s ability to predict different materials other than those used for training.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Colossal Cryogenic Electro‐Optic Response Through Metastability in Strained BaTiO 3 Thin Films

The search for thin film electro-optic materials that can retain superior performance under cryogenic conditions has become critical for quantum computing. Barium titanate thin films show large linear electro-optic coefficients in the tetragonal phase at room temperature, which is severely degraded down to ≈200 pm V −1 in the rhombohedral phase at cryogenic temperatures. There is immense interest in manipulating these phase transformations and retaining superior electro-optic properties down to liquid helium temperature. Utilizing the thermodynamic theory of optical properties, a large low-temperature electro-optic response is designed by engineering the energetic competition between different ferroelectric phases, leading to a low-symmetry monoclinic phase with a massive electro-optic response. The existence of this phase is demonstrated in a strain-tuned BaTiO 3 thin film that exhibits a linear electro-optic coefficient of 2516 ± 100 pm V −1 at 5 K, which is an order of magnitude higher than the best reported performance thus far. Importantly, the electro-optic coefficient increases by 100 × during cooling, unlike the conventional films, where it degrades. Further, at the lowest temperature, significant higher order electro-optic responses also emerge. These results represent a new framework for designing materials with property enhancements by stabilizing highly tunable metastable phases with strain.

barium titanate↗
DOE OSTI2026

Enhanced accuracy through ensembling of randomly initialized auto-regressive models for dynamical systems

Computational mechanics simulations using traditional finite element methods (FEM) require prohibitively expensive computational resources for real-time engineering applications, design optimization, and digital twin implementations. While machine learning (ML) surrogate models offer significant computational speedups, autoregressive ML models for time-dependent mechanical systems suffer from error accumulation that compromises long-term prediction reliability - a critical concern for engineering applications where accuracy over extended time horizons is essential for safety and performance assessments. Here, we propose a deep ensemble framework specifically designed to address this challenge in computational mechanics applications, where multiple ML surrogate models with random weight initializations are trained in parallel and their predictions aggregated during inference. This approach leverages statistical diversity to maximize information gain from a fixed set of training data and to mitigate error propagation, while maintaining the computational efficiency that makes ML surrogates attractive for engineering practice. We validate the framework on three representative problems spanning critical areas of computational mechanics: stress field evolution in heterogeneous microstructures under complex loading (relevant to advanced materials design and composite analysis), planetary-scale shallow water dynamics (applicable to environmental and geotechnical engineering), and Gray-Scott reaction-diffusion systems (relevant to mass transport and chemical process engineering). Across all test cases, the ensemble approach demonstrates consistent error reduction of 15-33% compared to individual models. The codes for this work are available on GitHub (https://github.com/Graham-Brady-Research-Group/AutoregressiveEnsemble_SpatioTemporal_Evolution).

autoregressive prediction↗
DOE OSTI2021

Metallic surface doping of metal halide perovskites

Abstract Intentional doping is the core of semiconductor technologies to tune electrical and optical properties of semiconductors for electronic devices, however, it has shown to be a grand challenge for halide perovskites. Here, we show that some metal ions, such as silver, strontium, cerium ions, which exist in the precursors of halide perovskites as impurities, can n-dope the surface of perovskites from being intrinsic to metallic. The low solubility of these ions in halide perovskite crystals excludes the metal impurities to perovskite surfaces, leaving the interior of perovskite crystals intrinsic. Computation shows these metal ions introduce many electronic states close to the conduction band minimum of perovskites and induce n-doping, which is in striking contrast to passivating ions such as potassium and rubidium ion. The discovery of metallic surface doping of perovskites enables new device and material designs that combine the intrinsic interior and heavily doped surface of perovskites.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Nanoparticle self-assemblies with modern complexity

Thanks to decades of tireless efforts, nanoparticle assemblies have reached at an extremely high level of controllability, sophistication, and complexity, with new insights provided by integration with graph theory, cutting-edge characterization, and machine learning (ML)-based computation and modeling, as well as with ever-diversifying applications in energy, catalysis, biomedicine, optics, electronics, magnetics, organic biosynthesis, and quantum technology. Nanoparticle assemblies can be crystalline, known as superlattices or supracrystals. Their assembly entails a transition from disorder—dispersed nanoparticles—to order, which can be achieved through classical nucleation pathways or nonclassical pathways via prenucleation precursors or particle aggregation. Further, the periodic lattices allow facile manipulations of electrons, phonons, photons, and even spins, leading to advanced device components and metamaterials. Meanwhile, aperiodic assemblies out of nanoparticles, such as gels, networks, and amorphous solids, also start to attract attentions. Despite the loss of periodicity, symmetry-lowering or symmetry-breaking three-dimensional (3D) structures emerge with unique properties, such as chiroptical activity, topological mechanical strength, and quantum entanglement. Real-space imaging such as electron microscopy and X-ray based tomography methods are utilized to characterize these complex structures, while mathematical tools such as graph theories are in need to describe such complex structures. This issue aims to provide a timely review of the efforts in this greatly broadened materials design space including experiment, simulation, theory, and applications. Nine top experts (and their teams) from four countries deliver six review papers, summarizing fundamental mechanistic understandings of nanoparticle assemblies, highlighted with the developments of state-of-the-art in situ characterization tools and ML-assisted reverse engineering, and newly emergent applications of nanoarchitectures.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Autonomous Synthesis of Metastable Materials Using a Modular Mixed-Flow Reactor

Understanding and controlling atomic-level processes at solid-liquid interfaces is key to advancing technologies in energy storage, carbon capture, critical element recovery, and materials synthesis. Many of these processes are dominated by the formation of short-lived intermediate precipitates that determine the final properties of synthesized materials. However, studying these intermediates is challenging due to their sensitivity and the reliance on trial-and-error methods. To address this, we developed an automated variable-volume mixed-flow reactor (MFR) to optimize metastable material synthesis and investigate rapid kinetic processes. This state-of-the-art MFR system, paired with an automated modeling framework, enables efficient synthesis and real-time analysis of transient phases. Benchmarking with advanced capabilities, such as wide-/small-angle X-ray scattering, allows us to resolve fast nucleation and growth dynamics that were previously inaccessible. By combining automation, ML-guided optimization, and tailored kinetic modeling, this approach provides a robust platform for improving material design and achieving precise control over solid-liquid reactions.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Boric acid removal with polyol-functionalized polyether membranes

Poor selectivity of conventional desalination membranes for boron often necessitates multi-stage treatment trains to achieve desired boron removal for end uses such as irrigation. One approach to membrane design for improved single-pass boric acid removal is via incorporation of chelating ligands that selectively sorb boron. In this study, membranes based on epoxy-amine cross-linked poly(glycidyl glycidyl ether) (PGGE) were synthesized and functionalized with N-methyl-d-glucamine (NMDG), a polyol known to interact selectively with boron. PGGE and PGGE-NMDG membranes exhibited boron sorption isotherms that were well-described by dual mode isotherms. PGGE-NMDG sorbed 2.5 mmol B/g dry polymer from a neutral aqueous solution containing 100 mmol B/L, which was almost three times the adsorption density of a commercial boron selective resin, Amberlite IRA743. Further, the membranes were regenerated in acid without a significant loss of boron sorption capacity over four cycles. Interactions between boron and NMDG and PGGE sites (e.g., epoxides) impacted boron diffusion in both membranes. The use of ligand functionalized membranes to capture target solutes such as boron requires high loading of interacting sites to maximize uptake capacity. Establishing fundamental structure/property rules for boron selectivity could lead to new material designs with improved boron separation properties for water purification.

36 MATERIALS SCIENCE↗