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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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NASA NTRS2000

Combinatorial Optimization of Heterogeneous Catalysts Used in the Growth of Carbon Nanotubes

Libraries of liquid-phase catalyst precursor solutions were printed onto iridium-coated silicon substrates and evaluated for their effectiveness in catalyzing the growth of multi-walled carbon nanotubes (MWNTs) by chemical vapor deposition (CVD). The catalyst precursor solutions were composed of inorganic salts and a removable tri-block copolymer (EO)20(PO)70(EO)20 (EO = ethylene oxide, PO = propylene oxide) structure-directing agent (SDA), dissolved in ethanol/methanol mixtures. Sample libraries were quickly assayed using scanning electron microscopy after CVD growth to identify active catalysts and CVD conditions. Composition libraries and focus libraries were then constructed around the active spots identified in the discovery libraries to understand how catalyst precursor composition affects the yield, density, and quality of the nanotubes. Successful implementation of combinatorial optimization methods in the development of highly active, carbon nanotube catalysts is demonstrated, as well as the identification of catalyst formulations that lead to varying densities and shapes of aligned nanotube towers.

Cassell, Alan M.↗

DOE OSTI2026

Designing the Protocols for Programmable Ammonia Catalysis

Programmable catalysis can provide a more energy-efficient and cost-effective route to enhancing commercial ammonia production, a key process in the advancement of renewable energy technologies and the manufacture of fertilizers and basic chemicals. This work explores the computational discovery of optimal forcing protocols to drive such dynamic catalysis models. By employing matrix-free time-stepper methods, coupled with an optimization approach, that integrates Bayesian optimization with a Bayesian continuation strategy to efficiently discover the periodic steady states of such periodically forced systems, we enable the discovery of complex optimal catalyst strain waveforms, while ensuring robust solver convergence. We demonstrate the flexibility of our approach to discover optimized forcing protocols under varying physical constraints on strain modulation or other catalyst operating parameters. We show that these can have a temporal structure more complex than simple step functions. In order to detect undesirable catalytic loops that may correlate with overall reduced performance, we perform a study using graph-theoretical analysis to investigate the dynamics of catalytic kinetic networks formed.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

NASA NTRS2008

Low-Pt-Content Anode Catalyst for Direct Methanol Fuel Cells

Combinatorial experiments have led to the discovery that a nanophase alloy of Pt, Ru, Ni, and Zr is effective as an anode catalyst material for direct methanol fuel cells. This discovery has practical significance in that the electronic current densities achievable by use of this alloy are comparable or larger than those obtained by use of prior Pt/Ru catalyst alloys containing greater amounts of Pt. Heretofore, the high cost of Pt has impeded the commercialization of direct methanol fuel cells. By making it possible to obtain a given level of performance at reduced Pt content (and, hence, lower cost), the discovery may lead to reduction of the economic impediment to commercialization.

Narayanan, Sekharipuram↗

DOE OSTI2025

Low-Temperature Direct Oxidation of Propane to Propylene Oxide Using Supported Subnanometer Cu Clusters

Propylene oxide, a key commodity of the chemical industry for a wide range of consumer products, is synthesized through sequential propane dehydrogenation and epoxidation reactions. However, the lack of a direct catalytic route from propane to propylene oxide reduces efficiency and represents a major challenge for catalysis science. Herein, we report the discovery of a highly active and selective catalyst, made of alumina-supported subnanometer copper clusters, which can directly convert propane to propylene oxide at temperatures as low as 150 °C. Moreover, at higher temperatures, on the same catalysts, the selectivity is switched to propylene. Accompanying theoretical calculations indicate that partially oxidized and/or hydroxylated clusters have low activation energies for both propane dehydrogenation and propylene epoxidation pathways, enabling direct conversion with very high selectivity for propylene oxide. The discovery of a low-temperature catalyst that can convert propane directly to propylene oxide provides an important opportunity for the development of energy-efficient and economic catalysts for this industrially critical process. Similarly, when operating at higher temperatures, these catalysts are posed as potent oxidative dehydrogenation catalysts.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2024

Interpretable, extensible linear and symbolic regression models for charge density prediction using a hierarchy of many-body correlation descriptors

Here, density functional theory (DFT) is routinely used to make electronic structure predictions for high-throughput screening of materials and molecules for technologically relevant areas, like the identification of better catalysts, electronic materials, and drug discovery. However, the DFT formalism is limited by (a) its poor (quadratic-to-quartic) scaling, and (b) the need to perform repeated eigenvalue computations of the electronic Hamiltonian as part of its self-consistent field (SCF) iteration procedure to obtain the converged ground state electron density, ρ (r). Approaches that directly predict ρ (r) of a structure with high accuracy can accelerate conventional SCF calculations and can also be used in linearly scaling methods such as orbital-free DFT. To this end, we present a procedure to predict the ground state electron density of molecular and periodic three-dimensional systems directly from the atomic structure with a particular emphasis on physical interpretability. In our framework, ρ (r) is modeled using many-body correlation descriptors that accurately capture the effects of local atomic arrangements in the neighborhood of a grid point. Our use of a linear regression scheme to fit to charge density data enables transparent analysis of the relative contributions of various types of local atomic correlations. By systematically including increasingly complex correlations, our model is shown to accurately predict ρ (r) for a variety of chemically and electronically diverse systems — amorphous Ge, Al(001) slab, crystalline Ga 2 O 3 , molecular benzene, and polyethylene. We then demonstrate a symbolic regression-based protocol to construct easily computable, interpretable features from lower-order correlations that significantly improves our electron density predictions with effectively no increase in the computational cost.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2026

Interpretable Deep Learning for Advancing Field-Enhanced Catalysis

This DOE Early Career project developed a physics-informed, interpretable AI-and-modeling framework to understand and exploit electric-field effects in heterogeneous catalysis, with ammonia cracking and synthesis as a representative pathway. The team built and validated methods to map local electric fields on metal surfaces and nanoparticles, showing that low-coordination features (tips/edges/corners) can concentrate fields by several-fold relative to flat facets. Using DFT-generated datasets, the project created physics-guided machine learning models that rapidly predict local electric fields and field-dependent adsorption energetics with near-DFT accuracy while reducing computational cost by orders of magnitude. These predictions were integrated with microkinetic modeling to quantify how field-dipole interactions reshape reaction energetics and mechanisms, enabling large increases in predicted catalytic rates and substantial reductions in operating temperature under favorable field conditions. To accelerate discovery of earth-abundant catalysts, the project combined interpretable ML screening (with electronic-structure descriptors identified as key drivers) with a generative inverse-design workflow based on diffusion models and physics constraints. The resulting closed-loop approach, linking simulation, mechanistic modeling, and AI, provides reusable tools and datasets for designing catalysts and operating conditions in field-enhanced catalysis, with broad relevance to electrostatic catalysis, plasma catalysis, electrocatalysis, and other energy-related chemical transformations.

30 DIRECT ENERGY CONVERSION↗

NASA NTRS1975

Development of gold alloy catalyst cathode for alkaline electrolyte fuel cells

A program for the development of improved catalyst and Teflon-bonded electrode structures using this improved catalyst is described, for use in fuel cell cathodes. It was found that Au-Pt was superior to the traditional platinum black as a catalyst. The impetus to the program was provided by the discovery that a life-limiting mechanism on the old catalyst was the gradual dissolution of platinum from the cathode and subsequent redeposition in the electrolyte-containing matrix.

Freed, M. S.↗

NASA NTRS1992

Microgravity fluid physics research in the Space Station Freedom era

Microgravity fluid physics covers an exciting range of established and potential fields of scientific research. Areas in which the Microgravity Science and Applications Division of NASA's Office of Space Science and Applications is currently supporting research include: multiphase flow and phase change heat transfer, behavior of granular media and colloids; and interface dynamics, morphological stability, and contact line phenomena. As they contribute to our knowledge of fluid behavior, advances in these areas will enhance our understanding of materials processing on Earth and in space, and will contribute to technologies as diverse as chemical extraction, the prediction of soil behavior in earthquakes, and the production of oil reservoirs. NASA' s primary platform for research in microgravity fluid physics will soon be the Fluid Physics/Dynamics Facility on Space Station Freedom. This facility shares a rack for control and utilities with the Modular Combustion Facility, and has one rack for experiment-unique instruments. It is planned to change out the content of the experiment-unique rack at intervals on the order of one year. In order to obtain a maximum return on the operation of the facility during these intervals, the research community must carefully plan and coordinate an effort that brings the efforts of many investigators to bear on problems of particular importance. NASA is currently working with the community to identify research areas in which microgravity can make a unique and valuable contribution, and to build a balanced program of research around these areas or thrusts. Selections will soon be made from our first solicitation for research in fluid dynamics and transport phenomena. These solicitations will build the research community that will make Space Station Freedom a catalyst for scientific and technological discovery, and offer U.S. scientists in many disciplines a unique opportunity to participate in space science.

Carpenter, Bradley M.↗

DOE OSTI2025

Photon counting with intensified charge coupled device (ICCD) – I. In-depth detector characterization

While the adsorption properties of transition metal catalysts have been widely studied, leading to the discovery of various scaling relations, descriptors of catalytic activity, and well-established computational models, a similar understanding of semiconductor catalysts has not yet been achieved. In this work, we present a high-throughput density functional theory investigation into the adsorption properties of 5 oxides of interest to the photocatalytic CO 2 reduction reaction: TiO 2 (rutile and anatase), SrTiO 3 , NaTaO 3 , and CeO 2 . Using a systematic approach, we exhaustively identify unique surfaces and construct adsorption structures to undergo geometry optimizations. We then perform a data-driven analysis, which reveals the presence of weak adsorption energy scaling relations, the propensity of adsorbates of interest to interact with oxygen surface sites, and the importance of slab deformation upon adsorption. Our findings are presented in the context of experimental observations and in comparison to previously studied classes of catalysts, such as pure metals and tellurium-containing semiconductors, and reinforce the need for a comprehensive approach to the study of site-specific surface phenomena on semiconductors.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2025

Systematic computational study of oxide adsorption properties for applications in photocatalytic CO 2 reduction

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

NASA NTRS2007

Why We Explore: The Value of Space Exploration for Future Generations

The National Aeronautics and Space Administration (NASA) and its industry partners are making measurable progress toward delivering new human space transportation capabilities to serve as the catalyst for a new era of discovery, as directed by the U.S. Vision for Space Exploration. In the interest of ensuring prolonged support, the Agency encourages space advocates of all stripes to accurately portray both the tangible and intangible benefits of space exploration, especially its value for future generations. This may be done not only by emphasizing the nation's return on its aerospace investment, but also by highlighting enabling security features and by promoting the scientific and technological benefits that accrue from the human exploration of space. As America embarks on a new era of leadership and international partnership on the next frontier, we are poised to master space by living off-planet on the Moon to prepare astronauts for longer journeys to Mars. These and other relevant facts should be clearly in the view of influential decision-makers and the American taxpayers, and we must increasingly involve those on whom the long-term sustainability of space exploration ultimately depends: America's youth. This paper will examine three areas of concrete benefits for future generations: fundamental security, economic enterprise, and high-technology advancements spurred by the innovation that scientific discovery demands.

Cook, Stephen A.↗

DOE OSTI2026

Oxidation, Oligomerization, Isomerization of Hydrocarbons Using Metal–Organic Frameworks

The selective conversion of hydrocarbons into higher-value fuels and feedstocks is essential to the global energy and chemistry landscape. While porous inorganic materials have enabled significant progress in these transformations, achieving high activity, selectivity, and stability under industrially relevant conditions remains challenging. Metal–organic frameworks (MOFs) are a promising platform to precisely control active-site environments and interrogate structure–function relationships due to their crystallinity, tunability, and porosity. This review highlights relevant hydrocarbon transformations and outlines the general mechanisms for oxidation, oligomerization, and isomerization. Metal node acidity, confinement effects, and active site dispersion are analyzed for their impact on reactivity and selectivity across the three reactions. Lastly, we discuss current limitations in catalyst stability and offer a perspective on integrating reticular chemistry with high-throughput experimentation and machine learning to accelerate the discovery and design of robust, next-generation MOF catalysts.

Catalysts↗

DOE OSTI2026

Final Technical Report: In-Silico Heterogeneous Catalyst Design for GHG Reduction via Bulk Chemicals

This project has advanced the understanding of ammonia synthesis by developing novel thermochemical catalysts, setting a new benchmark for energy and carbon efficiency in large-scale bulk chemicals production. This research leverages a proprietary computational discovery platform and high-throughput experimental validation, significantly accelerating catalyst optimization and enabling ammonia synthesis at lower temperature and pressure conditions than conventional processes. As a result, green ammonia can be produced at a cost that meets or exceeds established targets, supporting a viable pathway toward decarbonized ammonia for fuel, fertilizers, and hydrogen supply applications.

Grose, Jacob E [Copernic Catalysts, Inc.]↗

NASA NTRSDate not supplied

TPSAS-NF1676L-33911-DND

Future NASA missions for space exploration require key technological elements that must provide sustainability, survivability and operational envelope in extreme environments, such as the high and low extremes in pressures and temperatures, ionizing radiations, chemical and/or physical corrosion, and hypervelocity particles. Advanced multifunctional materials enable revolutionary design schemes for future aerospace vehicles and structures for the extreme environments of NASA missions. Recent studies of nanocomposite materials have shown the potential for both structural integrity and multifunctional capabilities, such as sensing, actuating, health monitoring, radiation shielding, energy harvesting, thermal management, and thermal protection in extreme environments. After the advent of carbon nanotube (CNT) in 1991, scientists predicted that boron and nitrogen, carbons immediate neighbors on the periodic chart, might also form perfect nanotubes, namely boron nitride nanotubes (BNNTs). The discovery and progress of a new catalyst-free method for synthesizing highly crystalline, very long, and small diameter BNNTs under a high temperature and pressure (HTP) environment have enabled new applications for multifunctional materials. The white color BNNTs offer extraordinary properties including neutron radiation shielding, piezoelectricity, electrostriction, high thermal oxidative stability (>800?C in air), not present in CNTs, as well as excellent mechanical strength and toughness, equivalent to CNTs. The characteristics of the novel BNNTs and their composites along with CNT composites are discussed in this presentation along with their potential aerospace applications in extreme environments.

Cheol Park↗

NASA NTRSDate not supplied

TPSAS-NF1676L-35440-DND

Future NASA missions for space exploration require key technological elements that must provide sustainability, survivability and operational envelope in extreme environments, such as the high and low extremes in pressures and temperatures, ionizing radiations, chemical and/or physical corrosion, and hypervelocity particles. Advanced multifunctional materials enable revolutionary design schemes for future aerospace vehicles and structures for the extreme environments of NASA missions. Recent studies of nanocomposite materials have shown the potential for both structural integrity and multifunctional capabilities, such as sensing, actuating, health monitoring, radiation shielding, energy harvesting, thermal management, and thermal protection in extreme environments. After the advent of carbon nanotube (CNT) in 1991, scientists predicted that boron and nitrogen, carbon's immediate neighbors on the periodic chart, might also form perfect nanotubes, namely boron nitride nanotubes (BNNTs). The discovery and progress of a new catalyst-free method for synthesizing highly crystalline, very long, and small diameter BNNTs under a high temperature and pressure (HTP) environment have enabled new applications for multifunctional materials. The white color BNNTs offer extraordinary properties including neutron radiation shielding, piezoelectricity, electrostriction, high thermal oxidative stability(>800 C in air), not present in CNTs, as well as excellent mechanical strength and toughness, equivalent to CNTs. The characteristics of the BNNTs and their composites along with CNT composites are discussed in this presentation along with their potential aerospace applications in extreme environments.

Cheol Park↗

NASA NTRSDate not supplied

High Temperature Pressure (HTP) Boron Nitride Nanotube (BNNT): Discovery and Commercialization

Future NASA missions for space exploration require key technological elements that must provide sustainability, survivability and operational envelope in extreme environments, such as the high and low extremes in pressures and temperatures, ionizing radiations, chemical and/or physical corrosion, and hypervelocity particles. Advanced multifunctional materials enable revolutionary design schemes for future aerospace vehicles and structures for the extreme environments of NASA missions. Recent studies of nanocomposite materials have shown the potential for both structural integrity and multifunctional capabilities, such as sensing, actuating, health monitoring, radiation shielding, energy harvesting, thermal management, and thermal protection in extreme environments. After the advent of carbon nanotube (CNT) in 1991, scientists predicted that boron and nitrogen, carbons immediate neighbors on the periodic chart, might also form perfect nanotubes, namely boron nitride nanotubes (BNNTs). The discovery and progress of a new catalyst-free method for synthesizing highly crystalline, very long, and small diameter BNNTs under a high temperature and pressure (HTP) environment have enabled new applications for multifunctional materials. The white color BNNTs offer extraordinary properties including neutron radiation shielding, piezoelectricity, electrostriction, high thermal oxidative stability (>800C in air), not present in CNTs, as well as excellent mechanical strength and toughness, equivalent to CNTs. The characteristics of the BNNTs and their composites along with CNT composites are discussed in this presentation along with their potential aerospace applications in extreme environments.

Cheol Park↗

DOE OSTI2026

Enzymatic Nylon Deconstruction: Enzyme Discovery, Engineering, and Opportunities

Nylons are widely used synthetic polyamides valued for their strength, versatility, and durability across diverse applications. However, their petrochemical origin and energy-intensive production underscore the need for efficient, circular solutions. Conventional recycling methods remain limited by incomplete recovery, material degradation, and costly sorting requirements. Enzymatic depolymerization offers a selective, low-energy alternative capable of processing mixed waste streams under mild conditions. While significant progress has been achieved for polyesters, enzymatic degradation of polyamides is still at an early stage. The discovery of nylon hydrolases demonstrated the potential of biological systems to evolve catalysts for synthetic polyamides, yet reported depolymerization yields remain low. These limitations reflect both the structural complexity of nylons and the need for improved enzyme discovery and engineering. In conclusion, this review highlights recent advances, key challenges, and future directions for enzymatic nylon recycling, outlining its potential role enabling mixed polymer waste to be used as a green feedstock for remanufacturing.

Amides↗

DOE OSTI2025

Machine Learning‐Guided Discovery of High‐Entropy Perovskite Oxide Electrocatalysts via Oxygen Vacancy Engineering

Abstract High‐entropy perovskite oxides (HEPOs) have recently emerged as multifunctional catalysts. However, the HEPOs’ structural and compositional complexity hinders the easy and accurate extrapolation of activity indicators, which are essential for establishing structure‐property correlations. Here, OxiGraphX, is introduced as a novel graph neural network (GNN) model designed to capture the complex relationships among structure, composition, and atomic chemical environments for accurate prediction of oxygen vacancy formation energies (OVFEs) in HEPOs. By integrating machine learning (ML), density functional theory (DFT), and experimental validation, this work demonstrates an efficient framework for rapidly and accurately screening HEPO electrocatalysts for oxygen evolution reaction (OER). The OxiGraphX predicts OVFEs with a precision exceeding existing data, enabling the identification of compositions of higher oxygen vacancy content (OVC) and, thus, higher catalytic activity. Furthermore, the model explores latent spaces that translate effectively into experimental domains, bridging computational predictions with real‐world applications. This approach accelerates the discovery of high‐performance HEPO catalysts while providing deeper insights into their catalytic mechanisms.

Chemistry↗