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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 865 records · Page 48
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Entropy-Driven Structural Evolution in Ceramic Oxides

High-entropy ceramics, with five or more elements randomly occupying the same cation crystallographic sites, offer vast compositional diversity and unique properties for material design and applications. However, for many dissimilar elements, entropic stabilization cannot overcome the enthalpic barrier to cation substitution. As a result, most high-entropy ceramics incorporate only a few similar elements, limiting the in-depth exploration of the effect of entropy on ceramic properties. Here, we first use density functional theory to model fluorite crystal structures composed of 1-10 elements and then experimentally present practical fluorite oxide nanostructures containing 1, 3, 8, and 15 metals, as well as a record-breaking 25-element high-entropy ceramic incorporating a diverse palette of rare-earth, transition, alkaline, p-block, and noble metals. As entropy increases, structural and configurational disorder in the solid solution rises, altering structural features such as lattice distortion, crystallinity, homogeneity, defect density, and thermal stability. This research provides new insights and understanding of the role of entropy in stabilizing compositionally complex ceramics.

Liu, Shuo↗
DOE OSTI2025

Isopotential Electron Titration of Ammonia Charge Transfer on Metal Catalysts

Electron transfer between adsorbates and surfaces determines the binding strength and reactivity of chemical moieties on materials designed for separations and catalysis. To quantify electron exchange, the extent of charge transfer resulting from ammonia adsorption on a Ru surface was measured by isopotential electron titration (IET) on a Ru catalytic condenser, where isopotential conditions were maintained between Ru and silicon separated by an insulating HfO 2 layer during gas phase ammonia adsorption. Charge transfer upon ammonia adsorption on a Ru catalytic condenser increased from 40 to 1200 nC/cm 2 at 75 and 225 °C, respectively. Charge transfer measurements provided a direct estimate of ammonia adsorption thermodynamics on Ru without knowing surface coverages a priori, revealing an adsorption enthalpy of −53 ± 10 kJ/mol and entropy of −61 ± 26 J/mol·K. Combining experimentally-measured charge transfer with kinetic Monte Carlo simulations informed adsorbate surface coverages determined that 0.058 electrons were transferred to the Ru surface for each molecular ammonia adsorption event (δ NH 3 = 0.058 ± 0.005 e – /NH 3 *), consistent with calculated Bader charges. The ability to measure the extent of charge transfer for adsorbed species provides a fundamental descriptor to understand existing and new chemically functional surfaces, providing a foundational method for the emerging field of thermochemical surface coulometry.

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

Thermodynamic approach for enhancing superconducting critical current performance

Abstract The addition of artificial pinning centers has led to an impressive increase in the critical current density ( J c ) of superconductors, enabling record-breaking all-superconducting magnets and other applications. The J c of superconductors has reached ~0.2–0.3 J d , where J d is the depairing current density, and the numerical factor depends on the pinning optimization. By modifying λ and/or ξ, the penetration depth and coherence length, respectively, we can increase J d . For (Y 0.77 Gd 0.23 )Ba 2 Cu 3 O y ((Y,Gd)123), we can achieve this by controlling the carrier density, which is related to λ and ξ. We can also tune λ and ξ by controlling the chemical pressure in Fe-based superconductors, i.e., BaFe 2 (As 1− x P x ) 2 films. The variation in λ and ξ leads to an intrinsic improvement in J c via J d , allowing extremely high values of J c of 130 MA/cm 2 and 8.0 MA/cm 2 at 4.2 K, consistent with an enhancement in J d of a factor of 2 for both incoherent nanoparticle-doped (Y,Gd)123 coated conductors (CCs) and BaFe 2 (As 1− x P x ) 2 films, showing that this new material design is useful for achieving high critical current densities in a wide array of superconductors. The remarkably high vortex-pinning force in combination with this thermodynamic and pinning optimization route for the (Y,Gd)123 CCs reached ~3.17 TN/m 3 at 4.2 K and 18 T ( H || c ), the highest values ever reported for any superconductor.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2020

Unraveling the influence of non-fullerene acceptor molecular packing on photovoltaic performance of organic solar cells

In non-fullerene organic solar cells, the long-range structure ordering induced by end-group π–π stacking of fused-ring non-fullerene acceptors is considered as the critical factor in realizing efficient charge transport and high power conversion efficiency. Here, we demonstrate that side-chain engineering of non-fullerene acceptors could drive the fused-ring backbone assembly from a π–π stacking mode to an intermixed packing mode, and to a non-stacking mode to refine its solid-state properties. Different from the above-mentioned understanding, we find that close atom contacts in a non-stacking mode can form efficient charge transport pathway through close side atom interactions. The intermixed solid-state packing motif in active layers could enable organic solar cells with superior efficiency and reduced non-radiative recombination loss compared with devices based on molecules with the classic end-group π–π stacking mode. Our observations open a new avenue in material design that endows better photovoltaic performance.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Tuning the electronic structure of Ag-Pd alloys to enhance performance for alkaline oxygen reduction

Alloying is a powerful tool that can improve the electrocatalytic performance and viability of diverse electrochemical renewable energy technologies. Herein, we enhance the activity of Pd-based electrocatalysts via Ag-Pd alloying while simultaneously lowering precious metal content in a broad-range compositional study focusing on highly comparable Ag-Pd thin films synthesized systematically via electron-beam physical vapor co-deposition. Cyclic voltammetry in 0.1 M KOH shows enhancements across a wide range of alloys; even slight alloying with Ag (e.g. Ag 0.1 Pd 0.9 ) leads to intrinsic activity enhancements up to 5-fold at 0.9 V vs. RHE compared to pure Pd. Based on density functional theory and x-ray absorption, we hypothesize that these enhancements arise mainly from ligand effects that optimize adsorbate–metal binding energies with enhanced Ag-Pd hybridization. This work shows the versatility of coupled experimental-theoretical methods in designing materials with specific and tunable properties and aids the development of highly active electrocatalysts with decreased precious-metal content.

25 ENERGY STORAGE↗
DOE OSTI2021

Reaction inhomogeneity coupling with metal rearrangement triggers electrochemical degradation in lithium-rich layered cathode

High-energy density lithium-rich layered oxides are among the most promising candidates for next-generation energy storage. Unfortunately, these materials suffer from severe electrochemical degradation that includes capacity loss and voltage decay during long-term cycling. Present research efforts are primarily focused on understanding voltage decay phenomena while origins for capacity degradation have been largely ignored. Here, we thoroughly investigate causes for electrochemical performance decline with an emphasis on capacity loss in the lithium-rich layered oxides, as well as reaction pathways and kinetics. Advanced synchrotron-based X-ray two-dimensional and three-dimensional imaging techniques are combined with spectroscopic and scattering techniques to spatially visualize the reactivity at multiple length-scales on lithium- and manganese-rich layered oxides. These methods provide direct evidence for inhomogeneous manganese reactivity and ionic nickel rearrangement. Coupling deactivated manganese with nickel migration provides sluggish reaction kinetics and induces serious structural instability in the material. Our findings provide new insights and further understanding of electrochemical degradation, which serve to facilitate cathode material design improvements.

25 ENERGY STORAGE↗
DOE OSTI2023

Toward the design of ultrahigh-entropy alloys via mining six million texts

Abstract It has long been a norm that researchers extract knowledge from literature to design materials. However, the avalanche of publications makes the norm challenging to follow. Text mining (TM) is efficient in extracting information from corpora. Still, it cannot discover materials not present in the corpora, hindering its broader applications in exploring novel materials, such as high-entropy alloys (HEAs). Here we introduce a concept of “context similarity" for selecting chemical elements for HEAs, based on TM models that analyze the abstracts of 6.4 million papers. The method captures the similarity of chemical elements in the context used by scientists. It overcomes the limitations of TM and identifies the Cantor and Senkov HEAs. We demonstrate its screening capability for six- and seven-component lightweight HEAs by finding nearly 500 promising alloys out of 2.6 million candidates. The method thus brings an approach to the development of ultrahigh-entropy alloys and multicomponent materials.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Mapping the pathways of photo-induced ion migration in organic-inorganic hybrid halide perovskites

Abstract Organic-inorganic hybrid perovskites exhibiting exceptional photovoltaic and optoelectronic properties are of fundamental and practical interest, owing to their tunability and low manufacturing cost. For practical applications, however, challenges such as material instability and the photocurrent hysteresis occurring in perovskite solar cells under light exposure need to be understood and addressed. While extensive investigations have suggested that ion migration is a plausible origin of these detrimental effects, detailed understanding of the ion migration pathways remains elusive. Here, we report the characterization of photo-induced ion migration in perovskites using in situ laser illumination inside a scanning electron microscope, coupled with secondary electron imaging, energy-dispersive X-ray spectroscopy and cathodoluminescence with varying primary electron energies. Using methylammonium lead iodide and formamidinium lead iodide as model systems, we observed photo-induced long-range migration of halide ions over hundreds of micrometers and elucidated the transport pathways of various ions both near the surface and inside the bulk of the samples, including a surprising finding of the vertical migration of lead ions. Our study provides insights into ion migration processes in perovskites that can aid perovskite material design and processing in future applications.

14 SOLAR ENERGY↗
DOE OSTI2026

Distributed quantum approximate optimization algorithm on a quantum-centric supercomputing architecture

Quantum approximate optimization algorithm (QAOA) has shown promise in solving combinatorial optimization problems by providing quantum speedup on near-term gate-based quantum computing systems. However, QAOA faces challenges for high-dimensional problems due to the large number of qubits required and the complexity of deep circuits, limiting its scalability for real-world applications. In this study, we present a distributed QAOA (DQAOA), which leverages distributed computing strategies to decompose a large computational workload into smaller tasks that require fewer qubits and shallower circuits than are necessary to solve the original problem. These sub-problems are processed using a combination of high-performance and quantum computing resources. The global solution is iteratively updated by aggregating sub-solutions, allowing convergence toward the optimal solution. We demonstrate that DQAOA can handle considerably large-scale optimization problems (e.g., 1000-bit problem), achieving a high solution quality and short time-to-solution, outperforming existing strategies. Furthermore, we realize DQAOA on a quantum-centric supercomputing architecture, paving the way for practical applications of gate-based quantum computers in real-world optimization tasks. To extend DQAOA’s applicability to materials science, we further develop an active learning algorithm integrated with our DQAOA (AL-DQAOA), which involves machine learning, DQAOA, and active data production in an iterative loop. We successfully optimize photonic structures using AL-DQAOA, indicating that solving real-world optimization problems using gate-based quantum computing is feasible. We expect the proposed DQAOA to be applicable to a wide range of optimization problems and AL-DQAOA to find broader applications in material design.

Kim, Seongmin [ORNL] (ORCID:0000000159063004)↗
DOE OSTI2023

The role of ionic blockades in controlling the efficiency of energy recovery in forward bias bipolar membranes

Limited understanding exists about the operation of bipolar membranes (BPMs) in forward bias to convert protonic gradients into electrical work, despite their emerging role in many electrochemical devices. In these device contexts, the BPM is typically exposed to complex electrolyte mixtures, but their impact on polarization remains poorly understood. Here, for this work, we develop a mechanistic model explaining the forward bias polarization behaviour of BPMs in mixed electrolytes with different acidities/basicities. This model invokes that weak acids/bases accumulate in the BPM and impose an ionic blockade that inhibits the recombination of stronger acids/bases, resulting in a substantial neutralization overpotential. We demonstrate the utility of our model for fuel cells and redox flow batteries and introduce two materials design strategies for mitigating this inhibition. Lastly, we apply our findings to enhance the energy efficiency of carbonate management in CO 2 electrolysers. This work highlights how non-equilibrium local environments at membrane–membrane interfaces can define the efficiency of protonic-to-electrical energy conversion.

25 ENERGY STORAGE↗
DOE OSTI2026

Achieving high tensile strength and ductility in refractory alloys by tuning electronic structure

The energy efficiency of heat engines (gas and steam turbines) for electricity production and propulsion is determined by the Carnot cycle and scales with operating temperature. Commercial nickel- and cobalt-based superalloys melt near 1,500 °C and rapidly lose mechanical strength beyond 1,000 °C. Refractory metals melt well above 2,000 °C but have inherent manufacturability challenges that are barriers to adoption, such as high ductile-to-brittle transition temperatures. Using density functional theory-guided design, we demonstrate tailored local lattice distortions that promote phase-stable, non-equiatomic refractory concentrated solid solutions with both high ductility and strength. Here, we exemplify this for single-phase, body-centred cubic Nb 4 Ta 4 V 3 Ti that exhibits castability, excellent room-temperature tensile yield strength (∼1 GPa) and ductility (approaching 20% uniform strain), and exceptional high-temperature tensile strength (500 MPa at 1,000 °C). These findings illustrate a path for designing materials that hold great potential for advancing next-generation technologies such as Generation IV fission reactors, first-generation fusion-plasma reactors, and more efficient gas turbines for electricity generation and propulsion.

DFT↗
DOE OSTI2025

Enhancing nanoscale charged colloid crystallization near a metastable liquid binodal

Achieving predictive control over crystallization using non-classical nucleation while avoiding kinetic traps would be a step towards designing materials with new functionalities. We address these challenges by inducing the bottom-up assembly of nanocrystals into ordered arrays, or superlattices. Using electrostatics—rather than density—to tune the interactions between particles, we watch self-assembly proceed through a metastable liquid phase. Here, we systematically investigate the phase behaviour as a function of quench conditions in situ and in real time using small-angle X-ray scattering. By fitting to colloid, liquid and superlattice models, we extract the time evolution of each phase and the system phase diagram, which we find to be consistent with short-range attractive interactions. Using the predictive power of the phase diagram, we establish control of the self-assembly rate over three orders of magnitude, and we identify one- and two-step self-assembly regimes, with only the latter implicating the metastable liquid as an intermediate. The presence of the metastable liquid increases the superlattice formation rate relative to the equivalent one-step pathway, and the superlattice order increases with the rate, revealing a generalizable kinetic strategy for promoting and enhancing ordered assembly.

Tanner, Christian P. N. [University of California,↗
DOE OSTI2021

Adaptive hyperparameter updating for training restricted Boltzmann machines on quantum annealers

Restricted Boltzmann Machines (RBMs) have been proposed for developing neural networks for a variety of unsupervised machine learning applications such as image recognition, drug discovery, and materials design. The Boltzmann probability distribution is used as a model to identify network parameters by optimizing the likelihood of predicting an output given hidden states trained on available data. Training such networks often requires sampling over a large probability space that must be approximated during gradient based optimization. Quantum annealing has been proposed as a means to search this space more efficiently which has been experimentally investigated on D-Wave hardware. D-Wave implementation requires selection of an effective inverse temperature or hyperparameter (β) within the Boltzmann distribution which can strongly influence optimization. Here, we show how this parameter can be estimated as a hyperparameter applied to D-Wave hardware during neural network training by maximizing the likelihood or minimizing the Shannon entropy. We find both methods improve training RBMs based upon D-Wave hardware experimental validation on an image recognition problem. Neural network image reconstruction errors are evaluated using Bayesian uncertainty analysis which illustrate more than an order magnitude lower image reconstruction error using the maximum likelihood over manually optimizing the hyperparameter. The maximum likelihood method is also shown to out-perform minimizing the Shannon entropy for image reconstruction.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Ion-specific phenomena limit energy recovery in forward-biased bipolar membranes

The ability for bipolar membranes (BPMs) to interconvert voltage and pH makes them attractive materials for use in energy conversion and storage. Reverse-biased BPMs, which use electrical voltage to dissociate water into acid and base, have become increasingly well studied. However, forward-biased BPMs (FB-BPMs), in which voltage is extracted from pH gradients through recombination, require further study. Here physics-based modeling elucidates how the complex coupling of transport and kinetics dictates the performance of FB-BPMs in electrochemical devices. Simulations reveal that the open-circuit potential of FB-BPMs is dictated by the balance of ion recombination and crossover, where recombination of buffering counter-ions attenuates the open-circuit potential. Counter-ion mass-transport limitations and uptake of ionic impurities limit achievable current densities by reducing the applied pH gradient or the available fixed-charge sites that mediate recombination. Further, the model highlights the importance of selective ion management in mitigating energy losses and provides insight into the rational material design of FB-BPMs for energy applications.

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

Atomic/molecular layer deposition for energy storage and conversion

Energy storage and conversion systems, including batteries, supercapacitors, fuel cells, solar cells, and photoelectrochemical water splitting, have played vital roles in the reduction of fossil fuel usage, addressing environmental issues and the development of electric vehicles. The fabrication and surface/interface engineering of electrode materials with refined structures are indispensable for achieving optimal performances for the different energy-related devices. Atomic layer deposition (ALD) and molecular layer deposition (MLD) techniques, the gas-phase thin film deposition processes with self-limiting and saturated surface reactions, have emerged as powerful techniques for surface and interface engineering in energy-related devices due to their exceptional capability of precise thickness control, excellent uniformity and conformity, tunable composition and relatively low deposition temperature. In the past few decades, ALD and MLD have been intensively studied for energy storage and conversion applications with remarkable progress. In this work, we give a comprehensive summary of the development and achievements of ALD and MLD and their applications for energy storage and conversion, including batteries, supercapacitors, fuel cells, solar cells, and photoelectrochemical water splitting. Moreover, the fundamental understanding of the mechanisms involved in different devices will be deeply reviewed. Furthermore, the large-scale potential of ALD and MLD techniques is discussed and predicted. Finally, we will provide insightful perspectives on future directions for new material design by ALD and MLD and untapped opportunities in energy storage and conversion.

25 ENERGY STORAGE↗
DOE OSTI2020

Nanomorphology of eco-friendly colloidal inks, relating non-fullerene acceptor surface energy to structure formation

Nanoengineered, eco-friendly, solution-processable electroactive materials are in demand for the growing field of printed electronics, and these material requirements can be achieved by the development of waterborne colloidal dispersions. Functionality in these composite materials can be tuned by thermodynamically modifying the material nanomorphology, often by creation of kinetically stabilized aqueous nanoparticle dispersions. In this work we demonstrate that the internal structure of organic nanoparticles is controlled by the surface energy difference between the polymeric donor material and the non-fullerene acceptor (NFA) material. Nanoparticles of the following donor–acceptor combinations, suitable for printed organic photovoltaics, have been synthesized: TQ1:N2200, TQ1:PNDIT10, P3HT:N2200, P3HT:o-IDTBR and P3HT:eh-IDTBR. Advanced synchrotron-based X-ray spectroscopy and microscopy are used to correlate the formation of core–shell nanoparticle morphology to the material surface energy. We subsequently present a viable avenue for customizing the blended nanoparticle structure into (i) core–shell, (ii) molecularly intermixed, or (iii) inverted shell–core structures. Our results showed that TQ1:PNDIT10 and P3HT:o-IDTBR nanoparticles were comprised of a donor-rich shell and an NFA-rich core, however, interestingly we show a reversal to the inverse NFA shell/donor core structure for TQ1:N2200, P3HT:N2200 and P3HT:eh-IDTBR nanoparticles, driven by the low surface energy of N2200 (23.7 mJ m –2 ) and eh-IDTBR (18.3 mJ m –2 ). This article is the first report of a flipped nanoparticle core–shell morphology comprising an NFA-rich shell for the miniemulsion synthesis route. The composition of the shells and cores was able to be controlled by the differential mismatch in the surface energy of the donor and acceptor materials, with ΔG surface > 0, ΔG surface = 0, and ΔG surface < 0 for acceptor core–donor shell, molecularly intermixed, and acceptor shell–donor core, respectively. Accordingly, we introduce an entirely overlooked new figure of merit (FoM) for customizing nanoparticulate colloidal inks: tunable surface energy of non-fullerene-based semiconductors. Finally, the establishment of this FoM opens up electroactive material design to a wide range of functional printing applications with varying device and ink structure requirements, thereby reshaping the nanoengineering toolkit for waterborne colloidal dispersions and hence printed electronics.

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

Quantum machine learning for chemistry and physics

Machine learning (ML) has emerged as a formidable force for identifying hidden but pertinent patterns within a given data set with the objective of subsequent generation of automated predictive behavior. In recent years, it is safe to conclude that ML and its close cousin, deep learning (DL), have ushered in unprecedented developments in all areas of physical sciences, especially chemistry. Not only classical variants of ML, even those trainable on near-term quantum hardwares have been developed with promising outcomes. Such algorithms have revolutionized materials design and performance of photovoltaics, electronic structure calculations of ground and excited states of correlated matter, computation of force-fields and potential energy surfaces informing chemical reaction dynamics, reactivity inspired rational strategies of drug designing and even classification of phases of matter with accurate identification of emergent criticality. In this review we shall explicate a subset of such topics and delineate the contributions made by both classical and quantum computing enhanced machine learning algorithms over the past few years. We shall not only present a brief overview of the well-known techniques but also highlight their learning strategies using statistical physical insight. The objective of the review is not only to foster exposition of the aforesaid techniques but also to empower and promote cross-pollination among future research in all areas of chemistry which can benefit from ML and in turn can potentially accelerate the growth of such algorithms.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗