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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 991 records · Page 55
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Electrostatic‐Attraction‐Driven Self‐Assembled Graphene‐Disordered Rocksalt Composite Cathode for Lithium‐Ion Batteries

Disordered rocksalt cathodes hold promise for achieving high-capacity lithium-ion batteries while using low-cost, earth-abundant elements. However, their electrochemical performance remains critically limited by their poor electronic conductivity. Conventional strategies such as high-energy ball milling with excess carbon additives can improve conductivity but remain challenging to scale and often produce defects and increase surface area, thereby accelerating capacity degradation. Herein, we report an alternative approach of electrostatic-attraction-driven self-assembly to fabricate Li 1.2 Mn 0.6 Ti 0.2 O 1.8 F 0.2 (LMTOF) particles uniformly wrapped with electronically conductive graphene sheets without associated materials degradation. The graphene-wrapped LMTOF demonstrates significantly improved cycling stability (89% capacity retention after 100 cycles) and superior rate capability compared with an LMTOF-carbon composite electrode fabricated using the conventional high-energy ball-milling process. Post-cycling analysis reveals reduced oxygen evolution, suppressed unwanted side reactions, and improved structural integrity for the graphene-LMTOF composite. This work highlights the advantages of solution-based carbon wrapping and offers a scalable strategy to prepare high-performance DRX cathodes for lithium-ion batteries.

carbon composite↗
DOE OSTI2026

Ionic Precursors Transformed Into Vinyl Acetate Synthesis Catalyst via Reaction-Driven Restructuring

Conventional preparation of supported bimetallic catalysts relies on solution-mediated metal salt immobilization and pre-formation of alloy nanoparticles before reaction. Here, we report a fundamentally different synthesis strategy of using a physical mixture of salt precursors to generate an active catalyst during reaction. The catalytic structure is generated in situ from Pd3(OAc)6, Au(OH)3, and KOAc through H2 treatment and reaction-driven restructuring under vinyl acetate monomer (VAM) synthesis conditions. Ascertained from in situ X-ray diffraction and operando infrared spectroscopy analyses, reduction treatment produces segregated Pd and Au domains, and subsequent exposure to a VAM reaction mixture triggers dynamic extraction of Pd from the metal surface. This latter process, mediated by acetate-assisted redox cycles, facilitates Pd migration toward Au domains to form a near-surface localized Pd50Au50 alloy phase. Monometallic Pd domains serve as a reservoir of Pd to the alloy phase, leading to and sustaining a more Pd-enriched active surface and a higher population of accessible Pd sites, compared to a conventionally prepared K-PdAu/SiO2 catalyst. Consequently, this leads to a twofold increase in the VAM formation rate, demonstrating highly active bimetallic catalysts can be generated through the gas-phase treatment of physically mixed ionic precursors.

Cha, Byeong Jun [Rice University]↗
DOE OSTI2021

Novel Ultrabright and Air-Stable Photocathodes Discovered from Machine Learning and Density Functional Theory Driven Screening

The high brightness, low emittance electron beams achieved in modern X-ray free-electron lasers (XFELs) have enabled powerful X-ray imaging tools, allowing molecular systems to be imaged at picosecond time scales and sub-nanometer length scales. One of the most promising directions for increasing the brightness of XFELs is through the development of novel photocathode materials. Whereas past efforts aimed at discovering photocathode materials have typically employed trial-and-error-based iterative approaches, this work represents the first data-driven screening for high brightness photocathode materials. Through screening over 74 000 semiconducting materials, a vast photocathode dataset is generated, resulting in statistically meaningful insights into the nature of high brightness photocathode materials. This screening results in a diverse list of photocathode materials that exhibit intrinsic emittances that are up to 4x lower than currently used photocathodes. In a second effort, multiobjective screening is employed to identify the family of M 2 O (M = Na, K, Rb) that exhibits photoemission properties that are comparable to the current state-of-the-art photocathode materials, but with superior air stability. This family represents perhaps the first intrinsically bright, visible light photocathode materials that are resistant to reactions with oxygen, allowing for their transport and storage in dry air environments.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Strain‐Driven Mixed‐Phase Domain Architectures and Topological Transitions in Pb 1− x Sr x TiO 3 Thin Films

Abstract The potential for creating hierarchical domain structures, or mixtures of energetically degenerate phases with distinct patterns that can be modified continually, in ferroelectric thin films offers a pathway to control their mesoscale structure beyond lattice‐mismatch strain with a substrate. Here, it is demonstrated that varying the strontium content provides deterministic strain‐driven control of hierarchical domain structures in Pb 1− x Sr x TiO 3 solid‐solution thin films wherein two types, c / a and a 1 / a 2 , of nanodomains can coexist. Combining phase‐field simulations, epitaxial thin‐film growth, detailed structural, domain, and physical‐property characterization, it is observed that the system undergoes a gradual transformation (with increasing strontium content) from droplet‐like a 1 / a 2 domains in a c / a domain matrix, to a connected‐labyrinth geometry of c / a domains, to a disconnected labyrinth structure of the same, and, finally, to droplet‐like c / a domains in an a 1 / a 2 domain matrix. A relationship between the different mixed‐phase modulation patterns and its topological nature is established. Annealing the connected‐labyrinth structure leads to domain coarsening forming distinctive regions of parallel c / a and a 1 / a 2 domain stripes, offering additional design flexibility. Finally, it is found that the connected‐labyrinth domain patterns exhibit the highest dielectric permittivity.

Kavle, Pravin↗
DOE OSTI2023

Hybrid Data‐Driven Discovery of High‐Performance Silver Selenide‐Based Thermoelectric Composites

Optimizing material compositions often enhances thermoelectric performances. However, the large selection of possible base elements and dopants results in a vast composition design space that is too large to systematically search using solely domain knowledge. To address this challenge, a hybrid data-driven strategy that integrates Bayesian optimization (BO) and Gaussian process regression (GPR) is proposed to optimize the composition of five elements (Ag, Se, S, Cu, and Te) in AgSe-based thermoelectric materials. Data is collected from the literature to provide prior knowledge for the initial GPR model, which is updated by actively collected experimental data during the iteration between BO and experiments. Within seven iterations, the optimized AgSe-based materials prepared using a simple high-throughput ink mixing and blade coating method deliver a high power factor of 2100 µW m −1 K −2 , which is a 75% improvement from the baseline composite (nominal composition of Ag 2 Se 1 ). In conclusion, the success of this study provides opportunities to generalize the demonstrated active machine learning technique to accelerate the development and optimization of a wide range of material systems with reduced experimental trials.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Multilaminate Energy Storage Films from Entropy‐Driven Self‐Assembled Supramolecular Nanocomposites

Abstract Composite materials comprising polymers and inorganic nanoparticles (NPs) are promising for energy storage applications, though challenges in controlling NP dispersion often result in performance bottlenecks. Realizing nanocomposites with controlled NP locations and distributions within polymer microdomains is highly desirable for improving energy storage capabilities but is a persistent challenge, impeding the in‐depth understanding of the structure–performance relationship. In this study, a facile entropy‐driven self‐assembly approach is employed to fabricate block copolymer‐based supramolecular nanocomposite films with highly ordered lamellar structures, which are then used in electrostatic film capacitors. The oriented interfacial barriers and well‐distributed inorganic NPs within the self‐assembled multilaminate nanocomposites effectively suppress leakage current and mitigate the risk of breakdown, showing superior dielectric strength compared to their disordered counterparts. Consequently, the lamellar nanocomposite films with optimized composition exhibit high energy efficiency (>90% at 650 MV m −1 ), along with remarkable energy density and power density. Moreover, finite element simulations and statistical modeling have provided theoretical insights into the impact of the lamellar structure on electrical conduction, electric field distribution, and electrical tree propagation. This work marks a significant advancement in the design of organic–inorganic hybrids for energy storage, establishing a well‐defined correlation between microstructure and performance.

Li, He↗
DOE OSTI2026

Oscillatory and Collective Dynamics of Gold‐Nanoparticle‐Laden Droplets Driven by Photothermal‐Induced Thermocapillarity

Droplets have long intrigued researchers due to their ability to exhibit complex and fascinating behavior when subjected to external stimuli. Here, a coupled oscillatory behavior of gold-nanoparticle-surfactant-laden aqueous droplets is investigated at an oil-oil interface stimulated by light. This study shows that the interaction between light and the droplets gives rise to a range of oscillatory modes, including bouncing and clustering, where droplets exhibit collective movement. From experiment and numerical simulations, this study elucidates the underlying mechanism: upon laser irradiation, gold nanoparticles convert light into heat, generating asymmetric thermal gradients that drive upward thermocapillary flows and a hydrodynamic force from photothermal convection. These forces compete with gravity and buoyancy to induce droplet bouncing, while the resulting asymmetric flow fields bias neighboring droplets toward the illuminated droplet, leading to clustering. These findings not only expand the library of dynamic droplet behavior but also offer insights into the potential applications of light-driven systems in materials science, soft robotics, and microfluidics.

Marangoni effect↗
DOE OSTI2025

Constructing Highly Durable Fuel Cell Catalysts Through Integrating Graphitic Shell‐Protected Composite Carbon Support with Gaseous Co Deposition‐Driven PtCo Intermetallics

Metal dissolution, nanoparticle agglomeration, and carbon support corrosion cause significant performance degradation of current PtCo catalysts under acidic and oxidative oxygen reduction reaction. Here, in this study, an integrated strategy is presented to design high-performance Pt 3 Co intermetallic catalysts by regulating gaseous Co deposition-driven diffusion into Pt nanoparticles supported on a composite carbon. The composite carbon is derived from ZIF-8/polyaniline, consisting of a high-surface-area (HSC) core and a protective graphitic shell (GS), which is employed to design 40 wt.% Pt/HSC@GS catalyst. The corresponding membrane electrode assemblies (MEAs) can maintain 1.09 A cm −2 at 0.7 V (20. 7% loss) after 10 000 cycles (1.0–1.5 V for carbon stability) and 1.15 A cm −2 (16.1% loss) after 150 000 cycles (0.60–0.95 V for catalyst stability). A ordered Pt 3 Co intermetallic is synthesized via a gaseous Co-deposition process, which yields a homogeneous Co-rich layer onto the Pt nanoparticles on the composite support, thereby facilitating Co diffusion into Pt crystal during subsequent ordering annealing to form the ordered intermetallic structure. This gaseous deposition leads to a thin carbon layer on PtCo nanoparticles, inhibiting particle growth during the annealing and mitigating Co dissolution under dynamic electrochemical conditions. The PtCo catalyst achieves impressive MEA performance and long-term durability (1.45 A cm −2 at 0.7 V after 120 000 cycles) under heavy-duty conditions.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2023

Polarity‐Driven Atomic Displacements at the 2D Mg 2 TiO 4 ‐MgO (001) Oxide Interface for Hosting Potential Interlayer Excitons

Abstract Interlayer excitons in solid‐state systems have emerged as candidates for realizing novel platforms ranging from excitonic transistors and optical qubits to exciton condensates. Interlayer excitons have been discovered in 2D transition metal dichalcogenides, with large exciton binding energies and the ability to form various van der Waals heterostructures. Here, an oxide system consisting of a single unit cell of Mg 2 TiO 4 on MgO (001) is proposed as a platform for hosting interlayer excitons. Using a combination of density functional theory (DFT) calculations, molecular beam epitaxy growth, and in situ crystal truncation rod measurements, it is shown that the Mg 2 TiO 4 ‐MgO interface can be precisely controlled to yield an internal electric field suitable for hosting interlayer excitons. The atoms in the polar Mg 2 TiO 4 layers are observed to be displaced to reduce polarity at the interface with the non‐polar MgO (001) surface. Such polarity‐driven atomic displacements strongly affect electrostatics of the film and the interface, resulting in localization of filled and empty band‐edge states in different layers of the Mg 2 TiO 4 film. The DFT calculations suggest that the electronic structure is favorable for localization of photoexcited electrons in the bottom layer and holes in the top layer, which may bind to form interlayer exciton states.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Oxidation Driven Thin‐Film Solid‐State Metal Dealloying Forming Bicontinuous Nanostructures

Abstract Solid‐state metal dealloying (SSMD) is a promising method for fabricating nanoscale metallic composites and nanoporous metals across a range of materials. Thin‐film SSMD is particularly attractive due to its ability to create fine features via solid‐state interfacial reactions within a thin‐film geometry, which can be integrated into devices for various applications. This work examines a new dealloying couple, namely the Nb–Al alloy with the dealloying agent Sc, as previously predicted in the machine‐learning (ML) models. Prior ML predictions aimed to guide the design of nanoarchitectured materials through dealloying, relying on intuition‐driven discovery within a large parameter space. However, this work reveals that at the nanoscale, the involvement of oxygen in thin film processing may instead drive the dealloying process, resulting in the formation of bicontinuous nanostructures similar to those formed by metal‐agent dealloying. The phase evolution, as well as chemical and morphological changes, are closely analyzed using a combination of X‐ray absorption spectroscopy, diffraction, and scanning transmission electron microscopy to understand the mechanisms behind nanostructure formation. The findings suggest a potential pathway for utilizing oxygen to drive the formation of bicontinuous metal–metal oxide nanocomposites, paving the way for further development of functional nanoporous materials in diverse fields.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Engineering Ultrathin Alloy Shell in Au@AuPd Core-Shell Nanoparticles for Efficient Plasmon-Driven Photocatalysis

Bimetallic core-shell nanoparticles (NPs) possessing a synergetic coupling of plasmonic and catalytic metals have emerged as promising prototypes for efficient plasmon-driven photocatalysis. Here, Au@AuPd core-shell NPs composed of Au core NP and ultrathin AuPd shell are introduced to acquire improved light utilization efficiency in photocatalytic selective oxidation. With systematical control of the composition of the ultrathin alloy shell, it reveals that the Au@AuPd core-shell NPs with 10 at% Pd (in the single-atom alloy regime) is an effective nanostructure capable of maximizing the quantum yield of plasmon-induced light absorption and optimizing the surface electronic structure for catalytic reactions. This controlled system provides new insights into shell engineering for enhancing photocatalytic performance via the regulation of energy funneling processes in core-shell nanocatalysts.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Root Cause Analysis of Degradation in Protonic Ceramic Electrochemical Cell with Interfacial Electrical Sensors Using Data‐Driven Machine Learning

Abstract Protonic ceramic electrochemical cells (PCECs) offer promising paths for energy storage and conversion. Despite considerable achievements made, PCECs still face challenges such as physiochemical compatibility between componenets and suboptimal solid–solid contact at the interfaces between the electrolytes and electrodes. In this study, a novel approach is proposed that combines in situ electrochemical characterization of interfacial electrical sensor embedded PCECs and machine learning to quantify the contributions of different cell components to total degradation, as well as to predict the remaining useful life. The experimental results suggest that the overpotential induced by the oxygen electrode is 48% less than that of oxygen electrode/electrolyte interfacial contact for up to 1171 h. The data‐driven machine learning simulation predicts the RUL of up to 2132 h. The root cause of degradation is overpotential increase induced by oxygen electrode, which accounts for 82.9% of total cell degradation. The success of the failure diagnostic model is demonstrated by its consistency with degradation modes that do not manifest in electrolysis fade during early real operations. This synergistic approach provides valuable insights into practical failure diagnosis of PCECs and has the potential to revolutionize their development by enabling improved performance prediction and material selection for enhanced durability and efficiency.

25 ENERGY STORAGE↗
DOE OSTI2022

Integrated Solar-Driven Device with a Front Surface Semitransparent Catalysts for Unassisted CO 2 Reduction

Monolithic integrated photovoltaic-driven electrochemical (PV-EC) artificial photosynthesis is reported for unassisted CO 2 reduction. The PV-EC structures employ triple junction photoelectrodes with a front mounted semitransparent catalyst layer as a photocathode. The catalyst layer is comprised of an array of microscale triangular metallic prisms that redirect incoming light toward open areas of the photoelectrode to reduce shadow losses. Full wave electromagnetic simulations of the prism array (PA) structure guide optimization of geometries and length scales. An integrated device is constructed with Ag catalyst prisms covering 35% of the surface area. The experimental device has close to 80% of the transmittance with a catalytic surface area equivalent 144% of the glass substrate area. Experimentally this photocathode demonstrates a direct solar-to-CO conversion efficiency of 5.9% with 50 h stability. Selective electrodeposition of Cu catalysts onto the surface of the Ag triangular prisms allows CO2 conversion to higher value products enabling demonstration of a solar-to-C 2+ product efficiency of 3.1%. This design featuring structures that have a semitransparent catalyst layer on a PV-EC cell is a general solution to light loss by shadowing for front surface mounted metal catalysts, and opens a route for the development of artificial photosynthesis based on this scalable design approach.

14 SOLAR ENERGY↗
DOE OSTI2025

Data‐Driven Insights into Rare Earth Mineralization: Machine Learning Applications Using Functional Material Synthesis Data

Understanding rare‐earth element (REE) mineralization mechanisms is essential for developing efficient separation strategies. Although the geochemical pathways that generate REE deposits are qualitatively known, quantitative links between specific conditions and mineralization outcomes remain limited. Herein, the repurpose laboratory REE hydrothermal synthesis data—originally collected for functional‐materials fabrication—as a surrogate for studying mineralization with data‐driven methods. The compiled 1,200+ hydrothermal reaction records and trained three machine‐learning models—K‐nearest neighbors (KNN), random forest (RF), and extreme gradient boosting (XGB)—to predict product elements and phases from precursors, additives, reaction conditions, and engineered features. Validation shows XGB achieves the highest accuracy. Feature importance indicates thermodynamic properties of cations and anions dominate model decisions. Correlations reveal positive relationships among precursor concentration, reaction time, pH, and temperature, consistent with classical crystallization behavior. XGB‐based regressors are built to predict crystallization temperature and pH from precursor/product attributes. Performance is strongest when similar training examples exist, while accuracy declines for underrepresented reactions, notably REE carbonates and heavy‐REE systems. Overall, the study shows that functional‐materials datasets can illuminate REE mineralization and provide priors for exploration and processing. Expanding datasets with less‐studied chemistries and conditions will improve generality and support deposit discovery and more efficient REE recovery.

feature importance analysis↗
DOE OSTI2022

Sensor selection and tool wear prediction with data‐driven models for precision machining

Abstract Estimation of tool wear in precision machining is vital in the traditional subtractive machining industry to reduce processing cost, improve manufacturing efficiency and product quality. In this vein, fusion of time and frequency‐domain features of commonly sensed signals can provide an early indication of tool wear and improve its prediction accuracy for prognostics and health management. This paper presents a data‐driven methodology and a complete tool chain for the inference of precision machining tool wear from fused machine measurements, such as cutting force, power, audio and vibration signals, and quantify the usefulness of each measurement. Indicators of tool wear are extracted from time‐domain signal statistics, frequency‐domain analysis, and time‐frequency domain analysis. Correlation coefficients between the extracted features (indicators) and the tool wear are used to select the most informative features. Principal Component Analysis and Partial Least‐Squares are used to reduce the dimensionality of the feature space. Regression models, including linear regression, support vector regression, Decision tree regression, neural network regression and Gaussian process regression, are used to predict the tool wear using data from a Haas milling machine performing spiral boss face milling. The performance of the regression models based on subsets of sensors validates the preliminary estimates about the saliency of the sensors. The experimental results show that the proposed methods can predict the machine tool wear precisely, with readily available sensor measurements. Neural network and Gaussian process regression were able to achieve good estimates of tool wear at different machine operating conditions. The most informative signal in predicting tool wear was shown to be the vibration signal. Time‐frequency domain features were the most informative features among the combination of features of three domains. In addition, using partial least squares components extracted from the original features of signals led to higher prediction accuracy.

Han, Seulki↗
DOE OSTI2019

Entropy‐Driven Mechanochemical Synthesis of Polymetallic Zeolitic Imidazolate Frameworks for CO 2 Fixation

Abstract High‐entropy materials refer to a kind of materials in which five or more metal species were incorporated deliberately into a single lattice with random occupancy. Up to now, such a concept has been only restricted to hard materials, such as high‐entropy alloys and ceramics. Herein we report the synthesis of hybrid high‐entropy materials, polymetallic zeolitic imidazolate framework (also named as high‐entropy zeolitic imidazolate framework, HE‐ZIF), via entropy‐driven room‐temperature mechanochemistry. HE‐ZIF contains five metals including Zn II , Co II , Cd II , Ni II , and Cu II which are dispersed in the ZIF structure randomly. Moreover, HE‐ZIF shows enhanced catalytic conversion of CO 2 into carbonate compared with ZIF‐8 presumably a result of the synergistic effect of the five metal ions as Lewis acid in epoxide activation.

Xu, Wei↗
DOE OSTI2019

Entropy-Driven Mechanochemical Synthesis of Polymetallic Zeolitic Imidazolate Frameworks for CO 2 Fixation

High-entropy materials refer to a kind of materials in which five or more metal species were incorporated deliberately into a single lattice with random occupancy. Up to now, such a concept has been only restricted to hard materials, such as high-entropy alloys and ceramics. Herein we report the synthesis of hybrid high-entropy materials, polymetallic zeolitic imidazolate framework (also named as high-entropy zeolitic imidazolate framework, HE-ZIF), via entropy-driven room-temperature mechanochemistry. HE-ZIF contains five metals including Zn II , Co II , Cd II , Ni II , and Cu II which are dispersed in the ZIF structure randomly. Moreover, HE-ZIF shows enhanced catalytic conversion of CO 2 into carbonate compared with ZIF-8 presumably a result of the synergistic effect of the five metal ions as Lewis acid in epoxide activation.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2019

Orthogonal Halogen-Bonding-Driven 3D Supramolecular Assembly of Right-Handed Synthetic Helical Peptides

Peptide-mediated self-assembly is a prevalent method for creating highly ordered supramolecular architectures. Herein, we report the first example of orthogonal C-X∙∙∙X-C/C-X∙∙∙π halogen bonding and hydrogen bonding driven crystalline architectures based on synthetic helical peptides bearing hybrids of l-sulfono-γ-AApeptides and natural amino acids. The combination of halogen bonding, intra-/intermolecular hydrogen bonding, and intermolecular hydrophobic interactions enabled novel 3D supramolecular assembly. The orthogonal halogen bonding in the supramolecular architecture exerts a novel mechanism for the self-assembly of synthetic peptide foldamers and gives new insights into molecular recognition, supramolecular design, and rational design of biomimetic structures.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗