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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 181 records · Page 10
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Group-Additivity–Embedded Multiscale Modeling for Electric Field-Enhanced Nanocatalysis

Elucidating structure-performance relationships remains a central challenge in field-enhanced catalysis, where nanoparticles exhibit nonuniform surface sites with site-dependent responses to electric fields. Low-coordination sites (edges, corners, and tips) are particularly electric field-sensitive (EF), leading to nonuniform charge distribution, adsorption energies, and catalytic activity. Here, using ammonia decomposition on a ruthenium cluster as a model system, we develop a transferable multiscale framework integrating density functional theory, group additivity (GA), Brønsted-Evans-Polanyi scaling, and microkinetic modeling to predict EF-dependent activity across nonuniform cluster sites. Across sites and fields, the nitrogen adsorption energy (E N ) emerges as the governing descriptor, yielding robust volcano relationships whose optimum shifts systematically with field: negative fields strengthen N binding via electron accumulation, while positive fields weaken N binding via charge depletion, moving the optimal E N toward weaker binding. Microkinetic analysis shows that N≡N bond formation remains the key kinetic bottleneck over most conditions; positive fields lower the effective barrier and, critically, increase the fraction of near-optimal active sites, leading to a net enhancement in overall activity relative to zero-field and negative-field cases. By capturing EF- and site-dependent energetics with high accuracy and low computational cost, this GA-embedded multi-scale simulation workflow provides a physically interpretable route to predict and design field-enhanced nanocatalysis.

ammonia decomposition↗
DOE OSTI2024

Revealing Structural Evolution of Single Atom Catalysts during Electrochemical CO 2 Reduction by in Situ X-ray Absorption Spectroscopy

Investigating the structural variation of single-atom catalysts (SACs) is crucial to reveal the reaction mechanism under working conditions. Different in situ techniques, especially X-ray absorption spectroscopy (XAS), have been reported to study the structural changes in active sites. However, a systematic study of the relationship between the coordination environment and the catalytic ability of different kinds of SACs is still lacking. Herein, we established isolated transition metal atoms (Fe, Co, Ni, Cu) on N-doped carbon (M-N-C) and employed them in electrocatalytic CO 2 reduction reaction (CO 2 RR). Significantly, Ni-N-C exhibits the highest selectivity (similar to 97.9%) toward CO at -0.8 V vs RHE. In situ XAS characterization discloses the coordination number dependent catalytic performance. Further, the decreased average coordination number of Ni in Ni-N-C at the voltage point with maximum Faradaic efficiency was observed. Density functional theory further provides the possible mechanism of CO 2 -to-CO over the undercoordinated Ni-N-C structure.

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

Switchable Charge Storage Mechanism via in Situ Activation of MXene Enables High Capacitance and Stability in Aqueous Electrolytes

The need for reliable renewable energy storage devices has become increasingly important. However, the performance of current electrochemical energy storage devices is limited by either low energy or power densities and short lifespans. Herein, we report the synthesis and characterization of multilayer Ti 4 N 3 T x MXene in various aqueous electrolytes. We demonstrate that Ti 4 N 3 T x can be electrochemically activated through continuous cation intercalation over a 10 day period using cyclic voltammetry. A wide operating window of 2 V is maintained throughout activation. After activation, capacitance at 2 mV s -1 increases by 300%, 140%, and 500% in 1 M H2SO4, 1 M MgSO4, and 1 M KOH, respectively, while maintaining ~600 F g -1 at 2 mV s–1 after 50000 cycles in 1 M H 2 SO 4 . This activation process is possibly attributed to the unique morphology of the multilayered material, allowing cation intercalation to increase access to redox-active sites between layers. This work adds to the growing repository of electrochemically stable MXenes reported for aqueous energy storage applications. These findings offer a reliable option for reliable energy storage devices with potential applications in large-scale grid storage and electric vehicles.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Dual-Band Electrochromism in Hydrous Tungsten Oxide

The independent modulation of visible and near-infrared light by a single material, termed dualband electrochromism, is highly desirable for smart windows to enhance the energy efficiency of buildings. Tungsten oxides are commercially important electrochromic materials, exhibiting reversible visible and near-infrared absorption when electrochemically reduced in an electrolyte containing small cations or protons. The presence of structural water in tungsten oxides has been associated with faster electrochromic switching speeds. Here, we find that WO 3 ·H 2 O, a crystalline hydrate, exhibits dual-band electrochromism unlike the anhydrous WO 3 providing a heretofore unexplored route to tune the electrochromic response of tungsten oxides. Absorption of near-infrared light is achieved at low Li + /e - injection, followed by the absorption of visible light at higher Li + /e - injection as a result of an electrochemically-induced phase transition. We propose that this dual-band modulation is possible due to the more open structure of WO 3 ·H 2 O as compared to WO 3 . This facilitates a more extended solid solution Li + insertion regime that benefits the modulation of near-infrared radiation via plasmon absorption. Higher degrees of Li + /e - insertion lead to polaronic absorption associated with localized charge storage. Furthermore, these results inform how structural factors influence the electrochemically induced spectral response of transition metal oxides and the important role of structural water beyond optical switching speed.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

Nanoscale Spatial Distribution of Supported Nanoparticles Controls Activity and Stability in Powder Catalysts for CO Oxidation and Photocatalytic H 2 Evolution

Here we discuss, supported metal nanoparticles are essential components of high-performing catalysts, and their structures are intensely researched. In comparison, nanoparticle spatial distribution in powder catalysts is conventionally not quantified, and the influence of this collective property on catalyst performance remains poorly investigated. Here, we demonstrate a general colloidal self-assembly method to control uniformity of nanoparticle spatial distribution on common industrial powder supports. We quantify distributions on the nanoscale using image statistics and show that the type of nanospatial distribution determines not only the stability, but also the activity of heterogeneous catalysts. Widely investigated systems (Au–TiO 2 for CO oxidation thermocatalysis and Pd–TiO 2 for H 2 evolution photocatalysis) were used to showcase the universal importance of nanoparticle spatial organization. Spatially and temporally resolved microkinetic modeling revealed that nonuniformly distributed Au nanoparticles suffer from local depletion of surface oxygen, and therefore lower CO oxidation activity, as compared to uniformly distributed nanoparticles. Nanoparticle spatial distribution also determines the stability of Pd–TiO 2 photocatalysts, because nonuniformly distributed nanoparticles sinter while uniformly distributed nanoparticles do not. This work introduces new tools to evaluate and understand catalyst collective (ensemble) properties in powder catalysts, which thereby pave the way to more active and stable heterogeneous catalysts.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Enhanced CO 2 Reactive Capture and Conversion Using Aminothiolate Ligand–Metal Interface

Metallic catalyst modification by organic ligands is an emerging catalyst design in enhancing the activity and selectivity of electrocatalytic carbon dioxide (CO 2 ) reactive capture and reduction to value-added fuels. However, a lack of fundamental science on how these ligand–metal interfaces interact with CO 2 and key intermediates under working conditions has resulted in a trial-and-error approach for experimental designs. With the aid of density functional theory calculations, we provided a comprehensive mechanism study of CO 2 reduction to multicarbon products over aminothiolate-coated copper (Cu) catalysts. Our results indicate that the CO 2 reduction performance was closely related to the alkyl chain length, ligand coverage, ligand configuration, and Cu facet. The aminothiolate ligand–Cu interface significantly promoted initial CO 2 activation and lowered the activation barrier of carbon–carbon coupling through the organic (nitrogen (N)) and inorganic (Cu) interfacial active sites. Experimentally, the selectivity and partial current density of the multicarbon products over aminothiolate-coated Cu increased by 1.5-fold and 2-fold, respectively, as compared to the pristine Cu at –1.16 V RHE , consistent with our theoretical findings. Furthermore, this work highlights the promising strategy of designing the ligand–metal interface for CO 2 reactive capture and conversion to multicarbon products.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

Electrochemical C–N Bond Formation within Boron Imidazolate Cages Featuring Single Copper Sites

Electrocatalysis expands the ability to generate industrially relevant chemicals locally and on-demand with intermittent renewable energy, thereby improving grid resiliency and reducing supply logistics. Herein, we report the feasibility of using molecular copper boron-imidazolate cages, BIF-29(Cu), to enable coupling between the electroreduction reaction of CO 2 (CO 2 RR) with NO 3 – reduction (NO 3 RR) to produce urea with high selectivity of 68.5% and activity of 424 μA cm –2 . Remarkably, BIF-29(Cu) is among the most selective systems for this multistep C–N coupling to-date, despite possessing isolated single-metal sites. The mechanism for C–N bond formation was probed with a combination of electrochemical analysis, in situ spectroscopy, and atomic-scale simulations. We found that NO 3 RR and CO 2 RR occur in tandem at separate copper sites with the most favorable C–N coupling pathway following the condensation between *CO and NH 2 OH to produce urea. In conclusion, this work highlights the utility of supramolecular metal–organic cages with atomically discrete active sites to enable highly efficient coupling reactions.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

Diffusion Model-Guided Inverse Design of Bimetallic Catalysts for Ammonia Decomposition

In the past decade, artificial intelligence and deep learning have played increasingly prominent roles in materials design and discovery. Among these, generative AI models, known for their ability to create unique and complex structures, have emerged as state-of-the-art tools for materials screening due to their high efficiency and low computational cost. In catalysis, one of the major challenges is identifying promising material candidates within an immense chemical space. This challenge can be addressed using generative approaches, such as diffusion-based inverse design models. In this study, we present a machine learning-guided workflow that employed a diffusion model for the inverse design of bimetallic alloy catalysts for low-carbon ammonia decomposition, a key reaction for ammonia emission control and sustainable hydrogen production. Catalyst candidates were evaluated using nitrogen adsorption energy as the key descriptor, inspired by multiscale modeling. The proposed workflow identified low-cost, environmentally friendly catalysts with excellent catalytic performance, which have been validated theoretically and experimentally. Our framework decoupled the generative and property-prediction components, enhancing both flexibility and accuracy in the catalytic material design process.

Adsorption↗
DOE OSTI2024

Grass Evolutionary Lineages Can Be Identified Using Hyperspectral Leaf Reflectance

Hyperspectral remote sensing has the potential to map numerous attributes of the Earth’s surface, including spatial patterns of biological diversity. Grasslands are one of the largest biomes on Earth. Accurate mapping of grassland biodiversity relies on spectral discrimination of endmembers of species or plant functional types. We focused on spectral separation of grass lineages that dominate global grassy biomes: Andropogoneae (C 4 ), Chloridoideae (C 4 ), and Pooideae (C 3 ). We examined leaf reflectance spectra (350–2,500 nm) from 43 grass species representing these grass lineages from four representative grassland sites in the Great Plains region of North America. Here, we assessed the utility of leaf reflectance data for classification of grass species into three major lineages and by collection site. Classifications had very high accuracy (94%) that were robust to site differences in species and environment. We also show an information loss using multispectral sensors, that is, classification accuracy of grass lineages using spectral bands provided by current multispectral satellites is much lower (accuracy of 85.2% and 61.3% using Sentinel 2 and Landsat 8 bands, respectively). Our results suggest that hyperspectral data have an exciting potential for mapping grass functional types as informed by phylogeny. Leaf-level hyperspectral separability of grass lineages is consistent with the potential increase in biodiversity and functional information content from the next generation of satellite-based spectrometers.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Three Decades of Wetland Methane Surface Flux Modeling by Earth System Models–Advances, Applications, and Challenges

Earth System Models (ESMs) simulate the exchange of mass and energy between the land surface and the atmosphere, with a key focus on modeling natural greenhouse gas feedbacks. Methane is the second most important greenhouse gas after carbon dioxide. There are growing concerns over the rapidly increasing methane concentration in the atmosphere, underscoring the need for accurate global modeling of its emissions using ESMs. Of the multitude of sources of methane globally, wetlands are the largest natural emitters for methane, leading to significant efforts targeting their representation in ESMs with a special focus on their methane emissions. In this review, we first provide a historical overview of including wetland–methane components in ESMs and how methane modeling approaches have evolved over time. Second, we discuss recent modeling advancements that show promise for improvements in methane emissions predictions, namely the coupling of surface and atmospheric modules of ESMs, the representation of microtopography and transport mechanisms, the resolution of microbial processes at different spatial–temporal scales, and the improved mapping of wetland area extent across the different wetland types. Third, we shed light on the different challenges hindering accurate estimations of wetland–methane emissions, as shown by the consistent discrepancy between bottom–up and top–down models' predictions. Finally, we emphasize that more detailed representation of biogeochemistry and dynamic hydrology while resolving the within–wetland vegetation heterogeneity should improve model predictions, especially when coupled with expanding ground–based measurement networks and high–resolution remote sensing mapping of methane–relevant variables, such as water elevation, water table depth, and methane concentration.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Broadband unidirectional visible imaging using wafer-scale nano-fabrication of multi-layer diffractive optical processors

We present a broadband and polarization-insensitive unidirectional imager that operates at the visible part of the spectrum, where image formation occurs in one direction, while in the opposite direction, it is blocked. This approach is enabled by deep learning-driven diffractive optical design with wafer-scale nano-fabrication using high-purity fused silica to ensure optical transparency and thermal stability. Our design achieves unidirectional imaging across three visible wavelengths (covering red, green, and blue parts of the spectrum), and we experimentally validated this broadband unidirectional imager by creating high-fidelity images in the forward direction and generating weak, distorted output patterns in the backward direction, in alignment with our numerical simulations. This work demonstrates wafer-scale production of diffractive optical processors, featuring 16 levels of nanoscale phase features distributed across two axially aligned diffractive layers for visible unidirectional imaging. This approach facilitates mass-scale production of ~0.5 billion nanoscale phase features per wafer, supporting high-throughput manufacturing of hundreds to thousands of multi-layer diffractive processors suitable for large apertures and parallel processing of multiple tasks. Beyond broadband unidirectional imaging in the visible spectrum, this study establishes a pathway for artificial-intelligence-enabled diffractive optics with versatile applications, signaling a new era in optical device functionality with industrial-level, massively scalable fabrication.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Detection and imaging of chemicals and hidden explosives using terahertz time-domain spectroscopy and deep learning

Detecting concealed chemicals and explosives remains a critical challenge in global security. Terahertz time-domain spectroscopy (THz-TDS) offers a promising non-invasive and stand-off detection technique owing to its ability to penetrate optically opaque materials without causing ionization damage. While many chemicals exhibit distinct spectral features in the terahertz range, conventional terahertz-based detection methods often struggle in real-world environments, where variations in sample geometry, thickness, and packaging can lead to inconsistent spectral responses. In this study, we present a chemical imaging system that integrates THz-TDS with deep learning to enable accurate pixel-level identification and classification of different explosives. Operating in reflection mode and enhanced with plasmonic nanoantenna arrays, our THz-TDS system achieves a peak dynamic range of 96 dB and a detection bandwidth of 4.5 THz, supporting practical, stand-off operation. By analyzing individual time-domain pulses with deep neural networks, the system exhibits strong resilience to environmental variations and sample inconsistencies. Blind testing across eight chemicals—including pharmaceutical excipients and explosive compounds—resulted in an average classification accuracy of 99.42% at the pixel level. Notably, the system maintained an average accuracy of 88.83% when detecting explosives concealed under opaque paper coverings, demonstrating its robust generalization capability. These results highlight the potential of combining advanced terahertz spectroscopy with neural networks for highly sensitive and specific chemical and explosive detection in diverse and operationally relevant scenarios.

Imaging and sensing↗
DOE OSTI2020

Nanobody-enabled monitoring of kappa opioid receptor states

Recent studies show that GPCRs rapidly interconvert between multiple states although our ability to interrogate, monitor and visualize them is limited by a relative lack of suitable tools. We previously reported two nanobodies (Nb39 and Nb6) that stabilize distinct ligand- and efficacy-delimited conformations of the kappa opioid receptor. Here, we demonstrate via X-ray crystallography a nanobody-targeted allosteric binding site by which Nb6 stabilizes a ligand-dependent inactive state. As Nb39 stabilizes an active-like state, we show how these two state-dependent nanobodies can provide real-time reporting of ligand stabilized states in cells in situ. Significantly, we demonstrate that chimeric GPCRs can be created with engineered nanobody binding sites to report ligand-stabilized states. Our results provide both insights regarding potential mechanisms for allosterically modulating KOR with nanobodies and a tool for reporting the real-time, in situ dynamic range of GPCR activity.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Silica-copper catalyst interfaces enable carbon-carbon coupling towards ethylene electrosynthesis

Membrane electrode assembly (MEA) electrolyzers offer a means to scale up CO 2 -to-ethylene electroconversion using renewable electricity and close the anthropogenic carbon cycle. To date, excessive CO 2 coverage at the catalyst surface with limited active sites in MEA systems interferes with the carbon-carbon coupling reaction, diminishing ethylene production. With the aid of density functional theory calculations and spectroscopic analysis, here we report an oxide modulation strategy in which we introduce silica on Cu to create active Cu-SiO x interface sites, decreasing the formation energies of OCOH* and OCCOH*—key intermediates along the pathway to ethylene formation. We then synthesize the Cu-SiO x catalysts using one-pot coprecipitation and integrate the catalyst in a MEA electrolyzer. By tuning the CO 2 concentration, the Cu-SiO x catalyst based MEA electrolyzer shows high ethylene Faradaic efficiencies of up to 65% at high ethylene current densities of up to 215 mA cm -2 ; and features sustained operation over 50 h.

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

Self-mixing in microtubule-kinesin active fluid from nonuniform to uniform distribution of activity

Active fluids have applications in micromixing, but little is known about the mixing kinematics of systems with spatiotemporally-varying activity. To investigate, UV-activated caged ATP is used to activate controlled regions of microtubule-kinesin active fluid and the mixing process is observed with fluorescent tracers and molecular dyes. At low Péclet numbers (diffusive transport), the active-inactive interface progresses toward the inactive area in a diffusion-like manner that is described by a simple model combining diffusion with Michaelis-Menten kinetics. At high Péclet numbers (convective transport), the active-inactive interface progresses in a superdiffusion-like manner that is qualitatively captured by an active-fluid hydrodynamic model coupled to ATP transport. Results show that active fluid mixing involves complex coupling between distribution of active stress and active transport of ATP and reduces mixing time for suspended components with decreased impact of initial component distribution. This work will inform application of active fluids to promote micromixing in microfluidic devices.

59 BASIC BIOLOGICAL SCIENCES↗