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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 1,243 records · Page 69
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Catalytic materials for lithium-sulfur batteries: mechanisms, design strategies and future perspective

Lithium-sulfur batteries (LSBs) are attractive candidates for post-lithium-ion battery technologies because of their ultrahigh theoretical energy density and low cost of active cathode materials. However, the commercialization of LSBs remains extremely challenging primarily due to poor cycling performance and safety concerns, which are inherently caused by low conductivity of S 8 and Li 2 S, severe polysulfide shuttling, and high polarization by solid Li 2 S 2 /Li 2 S deposition. Catalytic materials could facilitate the large-scale practical application of LSBs by overcoming all these challenges. In this review, we investigate the sulfur species evolution in LSBs and explore the roles of catalytic materials in charge/discharge processes, highlighting the catalysis of solid S 8 to liquid polysulfides and solid Li 2 S 2 to Li 2 S. Furthermore, we offer systematic strategies from atomic to macro levels, including defect engineering, morphology engineering and catalyst compositing, to enhance catalysis efficiency in terms of sulfur supercooling, fast charge transfer, thiosulfate generation, disulfide bond cleavage, tuneable Li 2 S growth and Li 2 S decomposition enhancement. Finally, the design and availability of the proposed catalytic materials will further advance LSB technology from coin cells and pouch cells to the subsequent commercialization scale.

25 ENERGY STORAGE↗
DOE OSTI2025

Irradiation-induced formation of G-phase precipitates and M 2 X carbides in self-ion irradiated HT-9

Ferritic-martensitic steels with high chromium content are a promising material group for advanced nuclear systems due to their high temperature strength and good irradiation tolerance. HT-9 is an optimized and often-studied alloy in this group, but additional studies are required on its radiation response under extreme conditions to be experienced in various types of nuclear reactors, especially with respect to phase stability under irradiation. Self-ion irradiation of HT-9 by 5 MeV Fe ions was used to simulate neutron-induced behavior reaching peak doses of 100 and 300 dpa at temperatures ranging from 450 to 550 °C. M 23 C 6 carbides that existed prior to irradiation were found to remain stable under all examined irradiation conditions. As irradiation progressed at 450 and 500 °C, however, formation of spherical-like G-phase precipitates and needle-like M 2 X carbides was observed. G-phase precipitates were found to be enriched in Ni, Si, and Mn, and show no interface segregation, whereas needle-like M 2 X carbides were rich in Cr and Mo and clearly displayed interface segregation of Ni and Si. M 2 X carbide formation is believed to be assisted by vacancies, while G-phase precipitation is thought to be assisted by interstitials. Finally, this difference in defect-mediated formation leads to a difference in distribution with depth. M 2 X carbides are distributed over shallower depths than that of G-phase precipitates, consistent with defect imbalance predictions that consider the influence of the injected interstitial effect.

atom probe tomography↗
DOE OSTI2024

Multidimensional modeling of fuel-cladding friction in an LWR fuel rod

Solving finite element problems with friction often increases the level of difficulty to obtain properly converged solutions. This type of challenge becomes more salient when advanced, possibly multiscale, material models are employed to capture the thermomechanical behavior of fuel and cladding materials. Here the present work details our recent developments in a nuclear fuel performance finite element code for the systematic consideration of friction in nuclear reactor finite element simulations. We show the application of friction and its effects on the mechanics of light-water reactor rods accounting for various fuel constitutive modeling techniques, model dimensionalities, pellet assumed geometries, and power conditions. In particular, we focus on the fuel rod mechanical behavior as it relates to fuel constitutive models, sensitivity to the coefficient of friction, pellet states of stress, and rod elongation. We discuss the trade-offs between the various multidimensional modeling options and highlight the relevance of frictional effects in the prediction of the fuel rod deformation and interfacial stresses. To relate our modeling results with actual reactor operation, simulations including frictional effects are compared with fuel rod elongation experimental data and some challenges for carrying out a full validation of the axial mechanics are discussed.

42 ENGINEERING↗
DOE OSTI2021

Damage detection through Förster Resonance Energy Transfer in mechanoresponsive polymer nanocomposites

Polymer nanocomposites offer design solutions to control and tune optical, conductive, topological, and thermomechanical properties of advanced and multifunctional materials. Because of their ubiquitous nature, methodologies to diagnose failure or structural changes in the nanocomposites are of significant interest. Herein, we report a nanocomposite system loaded with quantum dots and coumarin-modified carbon nanotubes that transduce mechanical force into fluorescence at a strain, for the first time, as low as 7.5%. Our comprehensive studies detail the optical, morphological, and thermomechanical properties of these nanocomposites to establish the fundamental reason behind the activation of fluorescence. Here our results indicate that bare carbon nanotubes can irreversibly quench the fluorescence from quantum dots and that the coumarin-modified carbon nanotubes mitigate the quenching through Förster Resonance Energy Transfer. Next, the application of force to the sample changes the quantum dot-carbon nanotube spacing as well as the carbon nanotube morphology to activate fluorescence in the nanocomposite. Overall, this force activation of fluorescence can serve as a general strategy for the development of a new class of mechano-responsive nanocomposites that impart polymeric materials with desirable functionalities including damage sensing and mechanical strength.

36 MATERIALS SCIENCE↗
DOE OSTI2025

RLMolLM: Reinforcement Learning-Enhanced Language Model Framework for Inverse Molecular Design

Inverse molecular design faces significant challenges due to vast chemical space and complex property requirements. While language models show promise for molecular generation, they struggle with validity, multi-property optimization, and structural constraints. This work presents RLMolLM, a reinforcement learning framework combining Proximal Policy Optimization (PPO) with genetic algorithms to address these limitations. Our approach optimizes multiple user-specified properties including quantitative estimates of drug-likeness (QED), synthetic accessibility (SA), and ADMET (absorption, distribution, metabolism, excretion, and toxicity) endpoints without requiring complete model retraining, while maintaining capability for scaffold-constrained generation where specific substructures must be preserved. We outperform state-of-the-art methods for molecular optimization, achieving best QED scores across GDB13, Moses, and Zinc datasets with up to 31% improvement over previous methods while maintaining excellent validity, uniqueness, and novelty metrics. For simultaneous multi-property optimization, our framework achieves substantial improvements in ADMET properties including 4.5-fold reduction in hERG toxicity and enhanced Caco-2 permeability compared to Moses dataset. Under structural constraints, the framework significantly improves molecular validity while preserving scaffolds and effectively optimizing properties. In conclusion, this versatile solution advances pharmaceutical and materials molecular design through effective integration of reinforcement learning and genetic algorithms with multi-property optimization and scaffold preservation.

Genetic algorithms↗
DOE OSTI2026

Enhanced Water Interaction at Dual Cu Sites Within the Defects on a Copper Sulfide Layer

Electrochemical transformations of stable molecules and water into fuels and value-added chemicals require efficient catalyst surfaces. Introducing controlled defects at atomic scales can offer promising routes to enhance catalyst performance. In this study, we found novel dual copper site (-Cu-Cu-) defects within a copper sulfide (Cu-S) layer supported on Cu(111). Using scanning tunneling microscopy (STM) and density functional theory (DFT), we found these dual copper sites enhance molecular adsorption strength, specifically for water molecules, compared to intact Cu-S layer or pristine copper surfaces. This discovery highlights the potential of engineered dual-site copper defects to advance electrochemical catalytic materials, particularly for reactions involving water activation.

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

Role of Polysulfide Anions in Solid-Electrolyte Interphase Formation at the Lithium Metal Surface in Li–S Batteries

Delineating intricate interactions between highly reactive Li-metal electrodes and the diverse constituents of battery electrolytes has been a long-standing scientific challenge in materials design for advanced energy storage devices. Here, we isolated lithium polysulfide anions (LiS 4 - ) from an electrolyte solution based on their mass-to-charge ratio and deposited them on Li-metal electrodes under clean vacuum conditions using ion soft landing (ISL), a highly controlled interface preparation technique. The molecular level precision in the construction of these model interfaces with ISL, coupled with in situ x-ray photoelectron spectroscopy and ab initio theoretical calculations, allowed us to obtain unprecedented insight into the parasitic reactions of well-defined polysulfides on Li-metal electrodes. Our study reveals that the oxide-rich surface layer, which is amenable to direct electron exchange, drives multielectron sulfur oxidation (S o → S 6+ ) processes. Furthermore, our results have substantial implications for the rational design of future Li-S batteries with improved efficiency and durability.

25 ENERGY STORAGE↗
DOE OSTI2025

Cold-Sprayed NMC622 Composite as a Cathode for Lithium-Ion Batteries

The growing demand for high-energy, low-cost lithium-ion batteries (LIBs) to power electric vehicles (EVs) necessitates advances in both materials and manufacturing processes. Conventional cathode fabrication methods, such as slurry casting and drying, are energy-intensive and pose challenges for scalability and environmental compliance. In this study, we propose cold-spray (CS) deposition as a solvent-free approach for fabricating LiNi 0.6 Mn 0.2 Co 0.2 O 2 (NMC622) composite cathodes. Powder blends of NMC622, poly(vinylidene fluoride) (PVDF), and carbon black (CB) are directly deposited onto stainless steel and Inconel substrates under varied gas temperatures, pressures (and thus velocities), and standoff distances. The effects of temperature on the deposit morphology, coating density, and volume are systematically investigated. Computational fluid dynamics simulations reveal that increasing the gas temperature enhances the particle velocity, narrows the spray angle, and reduces the mass concentration radially at the nozzle outlet. CS deposition results in a dense cathode microstructure, accompanied by fracture in polycrystalline NMC622 particles. X-ray diffraction analysis further verifies that there are no phase changes during the deposition process. The electrochemical performance of the cold-sprayed cathodes reveals an initial capacity of approximately 96 mAh g –1 for single-crystal NMC622 and 167 mAh g –1 for polycrystalline NMC622. While these values are modest compared to state-of-the-art slurry-cast cathodes, which typically exhibit 180–200 mAh g –1 under optimized conditions. The results demonstrate a competitive performance given the solvent-free nature of the CS process and compare favorably with tape-cast samples made from identical feedstock. In conclusion, he CS process enables the formation of dense, binder-integrated cathode coatings without the need for solvent processing, offering a promising pathway for scalable, energy-efficient dry electrode manufacturing of next-generation LIBs.

Batteries↗
DOE OSTI2026

Hole-Transport Layer for High Current Density and Stability of Sn-Pb Perovskites and All-Perovskite Tandem Solar Cells

Sn-Pb perovskites are essential for achieving efficient single-junction solar cells and all-perovskite tandem solar cells (APTSCs). Although Sn oxidation and defective surfaces were once major limitations, recent advances in intrinsic material quality have largely mitigated these issues. As a result, the HTL-related interface is now regarded as the primary bottleneck for device performance. To address the intrinsic drawbacks of PEDOT:PSS, chemical surface modification and additive strategies have been widely applied, and alternative HTLs, like polymeric, inorganic, or small-molecule HTLs, have also gained attention. These approaches offer improved energy-level alignment, high transparency, and enhanced chemical durability, leading to higher short-circuit current density and longer operational lifetime in both single-junction and tandem devices. In this Perspective, we highlight the key criteria and practical effects of HTL materials suitable for Sn-Pb perovskites.

14 SOLAR ENERGY↗
DOE OSTI2025

Autonomous Multistate Nanoencoding Using Combinatorial Ferroelectric Closure Domains in BiFeO 3

Recent advances in ferroic materials have identified topological defects as promising candidates for enabling additional functionalities in future electronic systems. The generation of stable and customizable polar topologies is needed to achieve multistates that enable beyond-binary device architectures. Here, in this study, we show how to autonomously pattern on-demand highly tunable striped closure domains in pristine rhombohedral-phase BiFeO 3 thin films through precise scanning of a biased atomic force microscopy tip along carefully designed paths. By employing this strategy, we generate and manipulate closed-loop structures with high spatial resolution in an automated manner, allowing the creation of highly tunable and intricate topological domain structures that exhibit distinct polarization configurations without the need for electrode deposition or complex heterostructure growth. As a proof-of-concept for ferroelectric beyond-binary memory devices, we use such topological domains as multistates, engineering an alphabet and automating the symbolic writing/reading process using autonomous microscopy. The resulting information density is compared with that of current commercially available memory devices, demonstrating the potential of ferroelectric topological domains for multistate information storage applications.

BiFeO3↗
DOE OSTI2021

Mimicking associative learning using an ion-trapping non-volatile synaptic organic electrochemical transistor

Associative learning, a critical learning principle to improve an individual’s adaptability, has been emulated by few organic electrochemical devices. However, complicated bias schemes, high write voltages, as well as process irreversibility hinder the further development of associative learning circuits. Here, by adopting a poly(3,4-ethylenedioxythiophene):tosylate/Polytetrahydrofuran composite as the active channel, we present a non-volatile organic electrochemical transistor that shows a write bias less than 0.8 V and retention time longer than 200 min without decoupling the write and read operations. By incorporating a pressure sensor and a photoresistor, a neuromorphic circuit is demonstrated with the ability to associate two physical inputs (light and pressure) instead of normally demonstrated electrical inputs in other associative learning circuits. To unravel the non-volatility of this material, ultraviolet-visible-near-infrared spectroscopy, X-ray photoelectron spectroscopy and grazing-incidence wide-angle X-ray scattering are used to characterize the oxidation level variation, compositional change, and the structural modulation of the poly(3,4-ethylenedioxythiophene):tosylate/Polytetrahydrofuran films in various conductance states. The implementation of the associative learning circuit as well as the understanding of the non-volatile material represent critical advances for organic electrochemical devices in neuromorphic applications.

42 ENGINEERING↗
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 OSTI2022

Topological kagome magnets and superconductors

A kagome lattice naturally features Dirac fermions, flat bands and van Hove singularities in its electronic structure. The Dirac fermions encode topology, flat bands favour correlated phenomena such as magnetism, and van Hove singularities can lead to instabilities towards long-range many-body orders, altogether allowing for the realization and discovery of a series of topological kagome magnets and superconductors with exotic properties. Recent progress in exploring kagome materials has revealed rich emergent phenomena resulting from the quantum interactions between geometry, topology, spin and correlation. Here we review these key developments in this field, starting from the fundamental concepts of a kagome lattice, to the realizations of Chern and Weyl topological magnetism, to various flat-band many-body correlations, and then to the puzzles of unconventional charge-density waves and superconductivity. We highlight the connection between theoretical ideas and experimental observations, and the bond between quantum interactions within kagome magnets and kagome superconductors, as well as their relation to the concepts in topological insulators, topological superconductors, Weyl semimetals and high-temperature superconductors. In conclusion, these developments broadly bridge topological quantum physics and correlated many-body physics in a wide range of bulk materials and substantially advance the frontier of topological quantum matter.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2022

A reusable neural network pipeline for unidirectional fiber segmentation

Abstract Fiber-reinforced ceramic-matrix composites are advanced, temperature resistant materials with applications in aerospace engineering. Their analysis involves the detection and separation of fibers, embedded in a fiber bed, from an imaged sample. Currently, this is mostly done using semi-supervised techniques. Here, we present an open, automated computational pipeline to detect fibers from a tomographically reconstructed X-ray volume. We apply our pipeline to a non-trivial dataset by Larson et al . To separate the fibers in these samples, we tested four different architectures of convolutional neural networks. When comparing our neural network approach to a semi-supervised one, we obtained Dice and Matthews coefficients reaching up to 98%, showing that these automated approaches can match human-supervised methods, in some cases separating fibers that human-curated algorithms could not find. The software written for this project is open source, released under a permissive license, and can be freely adapted and re-used in other domains.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Absolute band-edge energies are over-emphasized in the design of photoelectrochemical materials

The absolute band-edge potentials of semiconductors, i.e., the conduction-band minimum, valence-band maximum, and their relative positions to solution redox potentials, are often invoked as design principles for photoelectrochemical (PEC) devices, especially for particulate photocatalysts. Here we show that reliance on these criteria is not necessary and limits the exploration of materials that will advance the fields of photoelectrochemistry, photochemistry, and photocatalysis. We discuss how i) band-edge energies are not singular parameters and instead shift with pH, electrolyte type, and surface chemistry; ii) the free energy of electrons and holes in comparison to that of solution redox couples dictates overall reaction spontaneity and thus reactivity; and iii) favorable charge-transfer kinetics can occur even when the relevant electrolyte redox potential(s) appear ‘outside’ the bandgap, enabled by the inversion or accumulation of electronic charge at the semiconductor surface. As a result, this discussion informs design principles for photocatalyst systems engineering for both one-electron redox reactions as well as for more complex multi-electron transfer reactions (e.g, H 2 evolution, H 2 O oxidation, CO 2 reduction).

14 SOLAR ENERGY↗
DOE OSTI2023

Superior metal storage behavior of Zn-containing porous carbon nanostructures for Na and Li metal batteries

Na and Li metals are ideal anodes for high-energy rechargeable batteries. However, their poor coulombic efficiency and the associated safety issues due to dendritic metal deposition and large volume changes during metallic plating/stripping are the major causes that persistently hinder their practical energy-storage applications. Herein, Zn-containing carbon nanostructures derived from typical zeolitic imidazolate framework-8 (ZIF-8) are proposed as advanced metal anode materials for both Na and Li metal batteries. In this study; the three-dimensional interconnected porous surfaces confine metallic Na and Li within the nanostructures, filling the carbon scaffold, and covering the electrode surface to form stable solid electrolyte interphases. Based on the metal nucleation and growth results obtained through various electrochemical tests, Zn incorporation and pore morphology have been revealed to be the key factors that regulate the metal nucleation and metallic plating/stripping during cycling. Thus, the incorporation of Zn in the closed pores of a carbon nanostructure improves the cycle performance, even in carbonate electrolytes, and produces stable Na and Li metallic anodes for next-generation rechargeable batteries.

25 ENERGY STORAGE↗
DOE OSTI2026

Finite-element-based simulations of electrodes for CO 2 cascade reduction reactions

The multielectron reduction of CO 2 to liquid fuels could be a path to scalable energy storage, but reaching this goal requires major advances in catalysis and systems engineering. Cascade catalysis, which couples sequential reactions without isolating intermediates, has emerged as a promising route to enhance selectivity and efficiency in CO 2 reduction (CO 2 R). In this review, we examine how finite-element-based simulations of continuum model [finite element method (FEM)] approaches are being used to analyze and guide CO 2 R cascade systems. We first outline the fundamentals of cascade catalysis and recent advances in catalytic materials (metallic, molecular, and hybrid architectures). We then focus on FEM developments at the electrode and device scales, emphasizing how these models capture transport phenomena, local microenvironments, and geometry-dependent effects. To clarify design principles, we present case studies of cascade electrodes organized in systems without and with integrated semiconductors. We further emphasize the integration of FEM with multiscale frameworks (density functional theory, molecular dynamics, kinetic Monte Carlo) and its role in bridging atomic-level insights with device-level performance. Finally, we identify current limitations and future prospects, including improved boundary conditions, coupling with operando experiments, and machine learning-accelerated model development. Together, these insights provide design principles for next-generation CO 2 R cascade systems for efficient solar fuel production.

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

Ion correlations drive charge overscreening and heterogeneous nucleation at solid–aqueous electrolyte interfaces

Significance Ion distributions at charged solid–water interfaces, referred to as the electrical double layer (EDL), are poorly understood at high ion concentrations, in part due to the lack of molecular-scale descriptions of the interactions between adsorbed hydrated ions. Here, direct visualization of the salinity-dependent evolution of EDL structure reveals molecular origins of nonclassical transformation of the EDL, in which charge overscreening and heterogeneous nucleation are driven by ion–ion correlations at the interfaces. This manifestation of the atomistic basis of nonclassical behaviors provides a much-needed understanding of the impact of ion cooperativity at charged interfaces for the development of predictive models for element transport in natural environments and advanced technologies for material growth and synthesis in saline environments.

36 MATERIALS SCIENCE↗