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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 145 records · Page 8
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Strategies for Superconducting Transmon Qubits with Millisecond T1 Relaxation Time

Enhancing coherence in superconducting transmon qubits requires suppressing dielectric loss and quasiparticle-mediated dissipation at metal–substrate, metal–vacuum, and substrate–vacuum interfaces. We recently demonstrated a five-fold enhancement of the energy relaxation time (T₁) by encapsulating Nb thin films with a low-loss passivation layer that inhibits NbOₓ formation, thereby reducing two-level system (TLS) participation at the metal surface. To extend T₁ into the millisecond regime, we are implementing a comprehensive materials- and process-level optimization strategy. This includes engineered substrate surface treatments, evaluation of alternative low-loss superconducting and dielectric material stacks, development of ultra-low-loss capping layers, and redesigns of transmon geometries to suppress electric-field participation ratio in lossy regions. Additionally, we are pioneering novel etching processes to further lower surface participation ratios. We are also exploring novel Josephson-junction materials and device layouts to enhance coherence further. We report T₁ measurements from these efforts, with the leading devices achieving relaxation times>1 ms.

Crisa, Francesco↗
DOE OSTI2026

Strategies for Superconducting Transmon Qubits with Millisecond T1 Relaxation Time

Enhancing coherence in superconducting transmon qubits requires suppressing dielectric loss and quasiparticle-mediated dissipation at metal–substrate, metal–vacuum, and substrate–vacuum interfaces. We recently demonstrated a five-fold enhancement of the energy relaxation time (T₁) by encapsulating Nb thin films with a low-loss passivation layer that inhibits NbOₓ formation, thereby reducing two-level system (TLS) participation at the metal surface. To extend T₁ into the millisecond regime, we are implementing a comprehensive materials- and process-level optimization strategy. This includes engineered substrate surface treatments, evaluation of alternative low-loss superconducting and dielectric material stacks, development of ultra-low-loss capping layers, and redesigns of transmon geometries to suppress electric-field participation ratio in lossy regions. Additionally, we are pioneering novel etching processes to further lower surface participation ratios. We are also exploring novel Josephson-junction materials and device layouts to enhance coherence further. We report T₁ measurements from these efforts, with the leading devices achieving relaxation times>1 ms.

Crisa, Francesco↗
DOE OSTI2022

Tin sensitization and silver activation on indium tin oxide surfaces

Adhesion of electrical contacts on electronic and optoelectronic devices is important for reliable device operation. Sn-sensitization is a widely used process that improves uniformity and adhesion of plated metals to glass and polymer surfaces. Electrically conductive substrates can significantly benefit from improved uniformity, adhesion, and contact resistance to metallic contact layers. In this study, we investigate the process of Sn-sensitization and Ag-activation on indium tin oxide (ITO) surfaces—a dominant transparent conducting oxide used in optoelectronic devices. Here, our results show that ITO films exposed to Sn-sensitization solutions with HCl concentrations below 10 mM effectively modify the surface termination through the process of Sn-sensitization (Sn-bonding at the surface hydroxide sites) without etching the ITO film. However, the subsequent Ag-activation on an ITO surface does not seem to follow the typical response on a glass substrate, suggesting that Ag-activation of an ITO surface is likely limited by the Ag ion size relative to the area density of surface hydroxide sensitization sites.

42 ENGINEERING↗
DOE OSTI2021

First-Principles Characterization of Surface Phonons of Halide Perovskite CsPbI 3 and Their Role in Stabilization

The stability of halide perovskites has been a long-standing issue for their real-world application. Approaches to improve stability include nanostructuring, dimensionality reduction, and strain engineering, where surfaces play an important role in the formation of a stable structure. To understand the mechanism we compute the lattice dynamics of the surface of CsPbI 3 using density functional theory. We demonstrate, for the first time, that CsPbI 3 crystals exhibit surface phonons that are localized on the outermost layers of the slabs, and we perform a complete symmetry characterization including an identification of the Raman/IR active modes. These surface phonons are present in the optically active cubic phase but are absent in the optically inactive "yellow" phase. Furthermore, we show that the surface suppresses bulk instabilities by hardening soft modes of the bulk cubic phase, resulting in phase stabilization and quenching of dynamical disorder. Finally, this study is fundamental for understanding the structural behavior of halide perovskite materials with high surface area-to-volume ratios, and for guiding stabilization strategies.

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

Complex-valued universal linear transformations and image encryption using spatially incoherent diffractive networks

As an optical processor, a diffractive deep neural network (D2NN) utilizes engineered diffractive surfaces designed through machine learning to perform all-optical information processing, completing its tasks at the speed of light propagation through thin optical layers. With sufficient degrees of freedom, D2NNs can perform arbitrary complex-valued linear transformations using spatially coherent light. Similarly, D2NNs can also perform arbitrary linear intensity transformations with spatially incoherent illumination; however, under spatially incoherent light, these transformations are nonnegative, acting on diffraction-limited optical intensity patterns at the input field of view. Here, we expand the use of spatially incoherent D2NNs to complex-valued information processing for executing arbitrary complex-valued linear transformations using spatially incoherent light. Through simulations, we show that as the number of optimized diffractive features increases beyond a threshold dictated by the multiplication of the input and output space-bandwidth products, a spatially incoherent diffractive visual processor can approximate any complex-valued linear transformation and be used for all-optical image encryption using incoherent illumination. The findings are important for the all-optical processing of information under natural light using various forms of diffractive surface-based optical processors.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Phase Transformation and Water Adsorption Behavior of As‐Deposited and Annealed Ru Metal Thin Films Prepared by Atomic Layer Deposition

ABSTRACT Surfaces play a central role in catalytic processes, and understanding the transformation of ruthenium metal into ruthenium oxide during annealing is essential for tailoring functional catalytic interfaces. In this study, we systematically investigate ≈22 nm thick Ru metal films deposited by atomic layer deposition (ALD) at 300°C, focusing on their chemical composition, structural evolution, and surface hydration behavior following post‐deposition annealing in air from 400 to 600°C. Lab‐based and synchrotron X‐ray photoelectron spectroscopy (XPS) reveal a gradual conversion from metallic Ru to fully oxidized Ru 4+ with increasing annealing temperature, accompanied by a corresponding increase in lattice oxygen. X‐ray diffraction (XRD) shows amorphous Ru oxide phases at 400°C and 500°C that evolve into crystalline RuO 2 at 600°C, while atomic force microscopy (AFM) indicates enhanced grain growth and surface roughening upon annealing. Ambient‐pressure XPS (AP‐XPS) under controlled H 2 O vapor environments (1–17 Torr) demonstrates that samples annealed at 400°C and 500°C exhibit initially high hydroxyl coverage that decreases with increasing water vapor pressure, concurrent with a rise in molecular H 2 O adsorption. In contrast, the crystalline RuO 2 surface formed at 600°C maintains stable hydroxylation and supports increased water uptake. Overall, this work provides fundamental insight into Ru oxide–H 2 O interactions and establishes design principles for engineering oxide surfaces optimized for electrocatalytic applications.

APXPS↗
DOE OSTI2021

Understanding Flow Characteristics in Metal Additive Manufacturing

In fluid flow, the frictional effects from interior surfaces of flow components create pressure loss across the surface. Understanding this inherent pressure loss due to friction and other phenomena is important when designing a flow system. For conventional manufacturing, friction loss has been empirically studied and is well known in the engineering community. However, newer manufacturing techniques, such as laser powder bed fusion (LPBF), an additive manufacturing technique, need to be understood. The inherent flow conditions resulting from the additive processes were studied. Test samples were printed with the flow path oriented in the vertical direction with different channel diameters to characterize the inherent fundamental flow properties of the process. Moody diagrams were replicated for a greater understanding of possibilities and shortfalls when implementing LPBF into design applications. Experimental flow testing revealed that greater effective sand grain roughness resulted from this process when compared to conventional machining methods. This roughness did not correlate well to the average roughness measured with a laser scanning microscope but was consistent with the average particle size of the powder metal feedstock.

42 ENGINEERING↗
DOE OSTI2021

Nanoscale Design of Pd-Based Electrocatalysts for Oxygen Reduction Reaction Enhancement in Alkaline Media

Palladium (Pd)-based electrocatalysts have recently emerged as one class of the foremost promising candidates for the oxygen reduction reaction (ORR) in alkaline media due to their excellent ORR activity and durability and lower costs compared with platinum. Insightful design of Pd-based nano-architectures with optimized active surface sites and maximal intrinsic performance is central to promoting the ORR applications. To further accelerate the sluggish ORR kinetics at the cathode of fuel cells and substantially decrease the overall cost of the electrocatalysts, various strategies, including controlled sizes and shapes with selected crystallographic facets, crystal-phase engineering, heteroatom doping, tailored surface strains, and surface engineering by de-alloying, have been extensively developed in the past decade. Here in this review, a brief introduction to the fundamental ORR mechanisms of Pd-based electrocatalysts in alkaline media is presented, followed by a thorough discussion on various strategies for delicately designing high-performance Pd-based catalysts with corresponding examples. Thereafter, the perspectives and new insights into the challenges are outlined, and some emerging research directions related to the rational design and controlled synthesis of Pd-based ORR electrocatalysts are also proposed.

36 MATERIALS SCIENCE↗
DOE OSTI2021

A linked-scale coupled model of mass erosion and redistribution in plasma-exposed micro-foam surfaces

Surface evolution due to exposure to harsh environments is of importance in many scientific and technological applications. In plasma-exposed materials, the surface receives charged particles from the plasma, leading to a series of processes that drive the system far from equilibrium and may lead to the severe deterioration of the surface properties. Although surface morphological changes are driven by atomic collisions taking place over picoseconds and nanometers, these processes result in mass loss and redistribution of matter over much larger length and time scales. This necessitates a multi-scale approach capable of capturing the range of processes linking primary atomic collision events with engineering-level surface geometry changes. In this paper, we develop a computational model to simulate the morphological evolution and effective erosion rate of micro-architected tungsten foams during low-energy plasma ion bombardment. Furthermore, the model acts on several length scales, with the energy and angular dependence of the sputtering yield of flat tungsten surfaces determined using the SRIM code based on the binary collision approximation. This information is introduced into a low-fluence, short-term Monte Carlo raytracing model, and further into a high-fluence, long-term particle transport model. In the latter, material particles representing billions of atoms are described in a digitized 3-D representation of the foam structure from X-ray tomography data, and are sputtered off and redeposited using the atomistic information. We show that the redeposition of sputtered atoms leads to partial self-healing in the bottom layers with a sharp reduction in the sputtering coefficient of low density foams, roughly 25% of the solid W value. This is in qualitative agreement with recent experiments on low porosity W structures. At high fluence, the foam structure degrades considerably as there are fewer ligaments available to recapture sputtered atoms and, consequently, the sputtering rate increases again.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Manipulating Surface Termination of Perovskite Manganate for Oxygen Activation

For ABO 3 perovskite oxides, one of the key issues limiting their utilization in heterogeneous catalysis is the dominant presence of catalytically inactive A-site cations at the surface. The engineering of B-site terminated perovskites is considered as an effective method to address this issue, especially when dealing with Mn/Co-based perovskite catalysts. However, to date, such a strategy has not been fully successful and remains a major challenge in the field. Herein, a Mn-terminated La 0.45 Sr 0.45 MnO 3 (B-LSM) is successfully synthesized via a one-pot hydrothermal method, in which low-valence Mn ions partially occupy the A site to form the active Mn-excess phase. Additionally, experimental results and theoretical calculations reveal that the presence of the surface Mn termination in B-LSM optimizes the hybrid orbitals of Mn 3d-O 2p and promotes the activation of surface lattice oxygen, where the pristine inert lattice O 2- is evolved into active and stable lattice O 2-x . Such structural optimization significantly reduces the activation energy barriers on going from O 2 ⁻ species to important intermediate O - species during O 2 activation. Moreover, this results in good stability and Pt-like activity for the B-LSM during CO oxidation. This work offers a new chemical route for the design of advanced perovskite-type oxides possessing novel functions.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Machine learning overcomes human bias in the discovery of self-assembling peptides

Peptide materials have a wide array of functions, from tissue engineering and surface coatings to catalysis and sensing. Tuning the sequence of amino acids that comprise the peptide modulates peptide functionality, but a small increase in sequence length leads to a dramatic increase in the number of peptide candidates. Traditionally, peptide design is guided by human expertise and intuition and typically yields fewer than ten peptides per study, but these approaches are not easily scalable and are susceptible to human bias. Here, in this work, we introduce a machine learning workflow—AI-expert—that combines Monte Carlo tree search and random forest with molecular dynamics simulations to develop a fully autonomous computational search engine to discover peptide sequences with high potential for self-assembly. We demonstrate the efficacy of the AI-expert to efficiently search large spaces of tripeptides and pentapeptides. The predictability of AI-expert performs on par or better than our human experts and suggests several non-intuitive sequences with high self-assembly propensity, outlining its potential to overcome human bias and accelerate peptide discovery.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Low Temperature Activation of Methane on Metal-Oxides and Complex Interfaces: Insights from Surface Science

The abundance of inexpensive, natural gas has transformed the energy landscape, whereby revealing new possibilities for sustainable chemical technologies or impacting those that have relied on traditional fossil fuels. The primary component, methane, is underutilized and wastefully exhausted, leading to anthropogenic global warming. Historically, the manipulation of methane remained “ clavis aurea ,” an insurmountable yet rewarding challenge and thus the focus of intense research. This is primarily due to an inability to dissociate C–H bonds in methane selectively, which requires a high energy penalty and is an essential prerequisite for the direct conversion of methane into a large set of value-added products. The discovery of such processes would promise an energy gainful use of natural gas benefiting several essential chemical processes associated with C1 chemistry. This first C–H bond dissociation step of the methane molecule appears in numerous catalytic mechanisms as the rate-determining step or most essential barrier sequence for all subsequent steps that follow in the production of C–C, C–O, or C x –H y –O z bonds found in value added products. A main goal is to catalytically reduce the energy barrier for the first C–H bond dissociation to be able to achieve the activation of methane at low or moderate temperatures. As such there is great value in understanding the fundamental nature of the active sites responsible for bond breaking or formation and thus be able to facilitate better control of this chemistry, leading to the development of new technologies for fuel production and chemical conversion. Surface science studies offer enhanced perspectives for a careful manipulation of bonds over the last layer atoms of catalyst surfaces, an essential factor for the design of atomically precise catalysts and unravelling of the reaction mechanism. With the advent of new surface imaging, spectroscopy, and in situ tools, it has been possible to decipher the surface chemistry of complex materials systems and further our understanding of atomic active sites on the surfaces of metals, oxides, and carbides or metal–oxide and metal–carbide interfaces. The once considered near impossible step of C–H bond activation is now observed at low temperatures with high propensity over a collection of oxide, metal–oxide, and metal–carbide systems in a conventional or inverse configuration (oxide or carbide on metal). The enabling of C–H activation at low temperature has opened interesting possibilities for the specific production of chemicals such as methanol directly from methane, a step toward facile synthesis of liquid fuels. We highlight the most recent of these results and present the key aspects of active site configurations engineered from surface science studies which enable such a simple reactive event through careful manipulation of the last surface layer of atoms found in the catalyst structure. New concepts which help in the activation and conversion of methane are discussed.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Pairing a Global Optimization Algorithm with EXAFS to Characterize Lanthanide Structure in Solution

Ensemble-average sampling of structures from ab initio molecular dynamics (AIMD) simulations can be used to predict theoretical extended X-ray absorption fine structure (EXAFS) signals that closely match experimental spectra. However, AIMD simulations are time-consuming and resource-intensive, particularly for solvated lanthanide ions, which often form multiple nonrigid geometries with high coordination numbers. Here, to accelerate the characterization of lanthanide structures in solution, we employed the Northwest Potential Energy Surface Search Engine (NWPEsSe), an adaptive-learning global optimization algorithm, to efficiently screen first-shell structures. As case studies, we examine two systems: Eu(NO 3 ) 3 dissolved in acetonitrile with a terpyridine ligand (terpyNO 2 ), and Nd(NO 3 ) 3 dissolved in acetonitrile. The theoretical spectra for structures identified by NWPEsSe were compared to both experimental and AIMD-derived EXAFS spectra. The NWPEsSe algorithm successfully identified the proper solvation structure for both Eu(NO 3 ) 3 (terpyNO 2 ) and Nd(NO 3 )(acetonitrile) 3 , with the calculated EXAFS signals closely matching the experimental spectra for the Eu-ligand complex and showing good similarity for the Nd salt; the better agreement with the ligand-containing structure is attributed to a less dynamic coordination environment due to the rigid ligand. The key advantage of the global optimization algorithm lies in its ability to sample the coordination environment across the potential energy surface and reduce the time required to identify structures from generally a month to within a week. Additionally, this approach is versatile and can be adapted to characterize main-group metal complexes.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Enhancing the Multifunctional Photocatalytic Activity of Sustainable Magnetic Nanoparticles

The development of efficient photocatalytic materials has intensified in response to increasing emphasis on sustainable energy conversion and environmental restoration. However, the excessive use and indiscriminate release of heavy metals from photocatalytic nanoparticles pose potential environmental risks. This study provides insights into optimizing visible-light-active photocatalysts to enhance their photocatalytic properties and stability. Specifically, cobalt doping and controlled pH modulation are employed on the modified Fe 3 O 4 , forming two distinct samples: Co@Fe 3 O 4 -B (base-treated) and Co@Fe 3 O 4 -A (acid-treated) nanoparticles. Microscopic characterization reveals that Co@Fe 3 O 4 -A undergoes a phase transition to hematite, whereas Co@Fe 3 O 4 -B retains its mixed-phase configuration. Spectroscopic analyses confirm that the cobalt dopants decorate the outskirts of each nanoparticle, forming a core–shell structure. However, Co@Fe 3 O 4 -B exhibit Co 2+ states with a high oxygen vacancy content, whereas Co@Fe 3 O 4 -A contain mixed Co 2+/3+ states. The high density of defect states in the Co@Fe 3 O 4 -B results in superior photocatalytic efficiency, achieving near-complete oxidation of furfuraldehyde (97% conversion) and 5-hydroxymethylfurfural (94% conversion), as well as effective degradation of toluene (78% conversion). This study demonstrates that the combined approach of doping and pH treatment is promising for the surface-defect engineering of photocatalysts, enhancing their multifunctional performance and reusability for sustainable energy conversion.

Fe3 O4 nanoparticles↗
DOE OSTI2022

Facet Impact of CuMn 2 O 4 Spinel Nanocatalysts on Enhancement of the Oxygen Reduction Reaction in Alkaline Media

Surface-structure engineering represents an attractive strategy to optimize the energy conversion performance of nanocatalysts using their deliberately controlled exposed facets. To further exploit the potential of non-PGM-based spinel catalysts for alkaline oxygen reduction reaction (ORR), a cathodic fuel cell reaction, we hereby report a strategy of ORR improvement by controlling the crystallographic facets of ultra-small CuMn 2 O 4 spinel nanocatalysts through a developed colloidal synthesis approach. The synthesis of CuMn 2 O 4 nanocrystals with morphological control relies on the design and selection of the Cu-/Mn-precursors with striking discrepancies in reaction kinetics. Following carbon loading and an annealing post-treatment of the as-synthesized nanocatalysts, the exclusively {101} facet-exposed CuMn 2 O 4 spinel nano-octahedra exhibit improved electrocatalytic activity toward ORR in 1 M KOH, when compared to their spherical counterparts, exhibiting a mass activity (MA) of 37.6 A/g at 0.85 V. After 10,000 cycles of ORR durability test, the nano-octahedra still retain an MA of 24.5 A/g, which is twice that of the CuMn 2 O 4 spinel nanospheres. Structural characterizations after durability testing indicate that the MA decay is likely associated with a decrease in the Mn 3+ fraction and the emergence of Cu + on CuMn 2 O 4 nano-octahedral surfaces. Finally, as a paradigm, this synthesis approach could be extended to other Mn-based spinel nanocatalysts with precise shape control, enabling us to understand and establish the relationship between the surface lattice/valence state and electrocatalytic properties.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Time‐Lapse Image Classification Using a Diffractive Neural Network

Diffractive deep neural networks (D 2 NNs), comprised of spatially engineered passive surfaces, collectively process optical input information at the speed of light propagation through a thin diffractive volume, without any external computing power. Diffractive networks were demonstrated to achieve all‐optical object classification and perform universal linear transformations. Herein, a “time‐lapse” image classification scheme using a diffractive network is demonstrated for the first time, significantly advancing its classification accuracy and generalization performance on complex input objects by using the lateral movements of the input objects and/or the diffractive network, relative to each other. In a different context, such relative movements of the objects and/or the camera are routinely being used for image super‐resolution applications; inspired by their success, a time‐lapse diffractive network is designed to benefit from the complementary information content created by controlled or random lateral shifts. The design space and performance limits of time‐lapse diffractive networks are numerically explored, revealing a blind testing accuracy of 62.03% on the optical classification of objects from the CIFAR‐10 dataset. This constitutes the highest inference accuracy achieved so far using a single diffractive network on the CIFAR‐10 dataset. Time‐lapse diffractive networks will be broadly useful for the spatiotemporal analysis of input signals using all‐optical processors.

36 MATERIALS SCIENCE↗