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All sourcesNASA NTRSDOE OSTI
NASA NTRS2012

Adhesion Between Volcanic Glass and Spacecraft Materials in an Airless Body Environment

The successful exploration of airless bodies, such as the Earth s moon, many smaller moons of the outer planets (including those of Mars) and asteroids, will depend on the development and implementation of effective dust mitigation strategies. The ultrahigh vacuum environment (UHV) on the surfaces of these bodies, coupled with constant ion and photon bombardment from the Sun and micrometeorite impacts (space weathering), makes dust adhesion to critical spacecraft systems a severe problem. As a result, the performance of thermal control surfaces, photovoltaics and mechanical systems can be seriously degraded even to the point of failure. The severe dust adhesion experienced in these environments is thought to be primarily due to two physical mechanisms, electrostatic attraction and high surface energies, but the dominant of these has yet to be determined. The experiments presented here aim to address which of these two mechanisms is dominant by quantifying the adhesion between common spacecraft materials (polycarbonate, FEP and PTFE Teflon, (DuPont) Ti-6-4) and a synthetic noritic volcanic glass, as a function of surface cleanliness and triboelectric charge transfer in a UHV environment. Adhesion force has been measured between pins of spacecraft materials and a plate of synthetic volcanic glass by determining the pull-off force with a torsion balance. Although no significant adhesion is observed directly as a result of high surface energies, the adhesion due to induced electrostatic charge is observed to increase with spacecraft material cleanliness, in some cases by over a factor of 10, although the increase is dependent on the particular material pair. The knowledge gained by these studies is envisioned to aid the development of new dust mitigation strategies and improve existing strategies by helping to identify and characterize mechanisms of glass to spacecraft adhesion for norite volcanic glass particles. Furthermore, the experience of the Apollo missions revealed that dust mitigation strategies must be subjected to high fidelity tests. To facilitate the effectiveness of ground-based testing of mitigation strategies, the issue of a pressure limit for high fidelity tests will be addressed.

Berkebile, Stephen↗
NASA NTRS2018

Advances in Hyperspectral Image Classification Methods for Vegetation and Agricultural Cropland Studies

Hyperspectral data are becoming more widely available via sensors on airborne and unmanned aerial vehicle (UAV) platforms, as well as proximal platforms. While space-based hyperspectral data continue to be limited in availability, multiple spaceborne Earth-observing missions on traditional platforms are scheduled for launch, and companies are experimenting with small satellites for constellations to observe the Earth, as well as for planetary missions. Land cover mapping via classification is one of the most important applications of hyperspectral remote sensing and will increase in significance as time series of imagery are more readily available. However, while the narrow bands of hyperspectral data provide new opportunities for chemistry-based modeling and mapping, challenges remain. Hyperspectral data are high dimensional, and many bands are highly correlated or irrelevant for a given classification problem. For supervised classification methods, the quantity of training data is typically limited relative to the dimension of the input space. The resulting Hughes phenomenon, often referred to as the curse of dimensionality, increases potential for unstable parameter estimates, overfitting, and poor generalization of classifiers. This is particularly problematic for parametric approaches such as Gaussian maximum likelihood–based classifiers that have been the backbone of pixel-based multispectral classification methods. This issue has motivated investigation of alternatives, including regularization of the class covariance matrices, ensembles of weak classifiers, development of feature selection and extraction methods, adoption of nonparametric classifiers, and exploration of methods to exploit unlabeled samples via semi-supervised and active learning. Data sets are also quite large, motivating computationally efficient algorithms and implementations. This chapter provides an overview of the recent advances in classification methods for mapping vegetation using hyperspectral data. Three data sets that are used in the hyperspectral classification literature (e.g., Botswana Hyperion satellite data and AVIRIS airborne data over both Kennedy Space Center and Indian Pines) are described in Section 3.2 and used to illustrate methods described in the chapter. An additional high-resolution hyperspectral data set acquired by a SpecTIR sensor on an airborne platform over the Indian Pines area is included to exemplify the use of new deep learning approaches, and a multiplatform example of airborne hyperspectral data is provided to demonstrate transfer learning in hyperspectral image classification. Classical approaches for supervised and unsupervised feature selection and extraction are reviewed in Section 3.3. In particular, nonlinearities exhibited in hyperspectral imagery have motivated development of nonlinear feature extraction methods in manifold learning, which are outlined in Section 3.3.1.4. Spatial context is also important in classification of both natural vegetation with complex textural patterns and large agricultural fields with significant local variability within fields. Approaches to exploit spatial features at both the pixel level (e.g., co-occurrence–based texture and extended morphological attribute profiles [EMAPs]) and integration of segmentation approaches (e.g., HSeg) are discussed in this context in Section 3.3.2. Recently, classification methods that leverage nonparametric methods originating in the machine learning community have grown in popularity. An overview of both widely used and newly emerging approaches, including support vector machines (SVMs), Gaussian mixture models, and deep learning based on convolutional neural networks is provided in Section 3.4. Strategies to exploit unlabeled samples, including active learning and metric learning, which combine feature extraction and augmentation of the pool of training samples in an active learning framework, are outlined in Section 3.5. Integration of image segmentation with classification to accommodate spatial coherence typically observed in vegetation is also explored, including as an integrated active learning system. Exploitation of multisensor strategies for augmenting the pool of training samples is investigated via a transfer learning framework in Section 3.5.1.2. Finally, we look to the future, considering opportunities soon to be provided by new paradigms, as hyperspectral sensing is becoming common at multiple scales from ground-based and airborne autonomous vehicles to manned aircraft and space-based platforms.

Pasolli, Edoardo↗
DOE OSTI2025

2D Vacancy Confinement in Anatase TiO 2 for Enhanced Photocatalytic Activities

Abstract Light‐driven energy conversion devices call for the atomic‐level manipulation of defects associated with electronic states in solids. However, previous approaches to produce oxygen vacancy (V O ) as a source of sub‐bandgap energy levels have hampered the precise control of the distribution and concentration ofV O . Here, a new strategy to spatially confineV O at the homo‐interfaces is demonstrated by exploiting the sequential growth of anatase TiO 2 under dissimilar thermodynamic conditions. Remarkably, metallic behavior with high carrier density and electron mobility is observed after sequential growth of the TiO 2 films under low pressure and temperature (L‐TiO 2 ) on top of high‐quality anatase TiO 2 epitaxial films (H‐TiO 2 ), despite the insulating properties of L‐TiO 2 and H‐TiO 2 single layers. Multiple characterizations elucidate that theV O layer is geometrically confined within 4 unit cells at the interface, along with low‐temperature crystallization of upper L‐TiO 2 films; this 2DV O layer is responsible for the formation of in‐gap states, promoting photocarrier lifetime (≈300%) and light absorption. These results suggest a synthetic strategy to locally confine functional defects and emphasize how sub‐bandgap energy levels in the confined imperfections influence the kinetics of light‐driven catalytic reactions.

Chemistry↗
DOE OSTI2025

An iterative CMB lensing estimator minimizing instrumental noise bias

Noise maps from cosmic microwave background (CMB) experiments are generally statistically anisotropic, due to scanning strategies, atmospheric conditions, or instrumental effects. Any mismodeling of this complex noise can bias the reconstruction of the lensing potential and the measurement of the lensing power spectrum from the observed CMB maps. We introduce a new CMB lensing estimator based on the maximum (MAP) reconstruction that is minimally sensitive to these instrumental noise biases. By modifying the likelihood to rely exclusively on correlations between CMB map splits with independent noise realizations, we minimize autocorrelations that contribute to biases. In the regime of many independent splits, this maximum closely approximates the optimal MAP reconstruction of the lensing potential. In simulations, we demonstrate that this method is able to determine lensing observables that are immune to any noise mismodeling with a negligible cost in signal-to-noise ratio. Our estimator enables unbiased and nearly optimal lensing reconstruction for next-generation CMB surveys.

Legrand, Louis [University of Cambridge (United Ki↗
NASA NTRSDate not supplied

The Emphasis of Design Patterns in Expressing Expert Knowledge From A Technical Solution – A Framework for Continued Research

Digital Engineering is a transformative strategy that leverages an integrated model-based approach to improve communication, decision making, design understanding, and acquisition efficiency of system development. As modern systems are derived from pre-existing systems, harvesting expert knowledge from proven systems in a useful, model-based way will reduce the experiential learning and cognition required for new system development, contributing to a Digital Engineering transformation. Motivated by performance gains observed during a multi-year, sequential development activity, this survey reviews knowledge, architecture, and pattern literature to establish a framework for research of architectural methods for expert knowledge identification and description using Model Based System Engineering. The multi-year sequential development activity is offered as the experimental system of interest for this research. This work aims to enable a digital engineering strategy that improves concept phase decision making, accelerates knowledge acquisition from lessons learned repositories, and eases the burden of generational knowledge loss.

Lithium Ion↗
NASA NTRSDate not supplied

Inferring Collaboration Strategies and Usability from Remote Observations in a Spaceflight Analog Environment

Collecting usability and user evaluations of novel software in spaceflight analog missions is challenging. For one such analog mission, we collected audio recordings of crew members as they completed their assigned self-scheduling task using novel software. Conversation analysis provided insight about collaborative nature of task and potential new evaluation measures.

human computer interaction↗
DOE OSTI2023

Reliable extrapolation of deep neural operators informed by physics or sparse observations

Deep neural operators can learn nonlinear mappings between infinite-dimensional function spaces via deep neural networks. As promising surrogate solvers of partial differential equations (PDEs) for real-time prediction, deep neural operators such as deep operator networks (DeepONets) provide a new simulation paradigm in science and engineering. Pure data-driven neural operators and deep learning models, in general, are usually limited to interpolation scenarios, where new predictions utilize inputs within the support of the training set. However, in the inference stage of real-world applications, the input may lie outside the support, i.e., extrapolation is required, which may result to large errors and unavoidable failure of deep learning models. Here, we address this challenge of extrapolation for deep neural operators. First, we systematically investigate the extrapolation behavior of DeepONets by quantifying the extrapolation complexity, via the 2-Wasserstein distance between two function spaces and propose a new strategy of bias–variance trade-off for extrapolation with respect to model capacity. Subsequently, we develop a complete workflow, including extrapolation determination, and we propose five reliable learning methods that guarantee a safe prediction under extrapolation by requiring additional information—the governing PDEs of the system or sparse new observations. The proposed methods are based on either fine-tuning a pre-trained DeepONet or multifidelity learning. We demonstrate the effectiveness of the proposed framework for various types of parametric PDEs. Furthermore, our systematic comparisons provide practical guidelines for selecting a proper extrapolation method depending on the available information, desired accuracy, and required inference speed.

42 ENGINEERING↗
DOE OSTI2022

Search for rare 3-$α$ decays in the region of the Hoyle state of 12 C

We performed a refined analysis of the data collected with the CHIMERA detector with the aim of searching rare 3-$α$ decay modes near the Hoyle state of 12 C. In particular we investigated the direct and sequential decay modes of the Hoyle state including also the recently suggested Efimov state. The comparison of selected events with simulations constrains the precision achievable with measurements in different experimental conditions. We observe good sensitivities to different decay scenarios also in the CHIMERA spherical region notwithstanding its limited angular resolution. Furthermore, the search for the decay of the Efimov state in 12 C was performed by using new selection strategies as suggested by simulations. The obtained results are not conclusive, however, we are able to exclude the presence of a sequential decay of this state, while our data could be compatible with its direct decay.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2026

Comparative physiological and genomic characterization of a novel Nitrobacter vulgaris strain from a nitrate-contaminated subsurface

Nitrite-oxidizing bacteria (NOB) represent a crucial node in the global nitrogen cycle. By catalyzing the second step of nitrification—the oxidation of nitrite to nitrate to generate energy for growth—NOB activity controls the fate of nitrite (NO 2 - ) in aerobic environments. Despite thriving in diverse environments, including soils, freshwater, marine ecosystems, subsurface habitats, and water treatment systems, organisms capable of nitrite oxidation are confined to Nitrobacter, Nitrospira, Nitrospina, Nitrotoga, and a few other specific lineages. The genus Nitrobacter, recognized for its facultative heterotrophic metabolism, is often associated with high-nitrogen environments. Here, we report the physiological characterization of a novel strain, Nitrobacter vulgaris strain MLSD-S22, isolated from a nitrate- and heavy-metal-contaminated subsurface. Growth inhibition experiments revealed that strain MLSD-S22 and the N. vulgaris type strain Z exhibited similar sensitivities to nitrite and nitrate, with nitrite being the most inhibitory. Microrespirometry demonstrated that the two N. vulgaris strains and Nitrobacter winogradskyi Nb-255 possessed higher affinities for nitrite and oxygen than previously reported for Nitrobacter, suggesting potential to compete in low-substrate environments. Long-read DNA sequencing provided a complete genome for strain MLSD-S22, revealing two plasmids and an intact nitrous oxide (N 2 O) reduction operon—an unexpected feature for Nitrobacter. While N 2 O reduction activity was not observed under the tested conditions, this discovery raises questions about the contribution of Nitrobacter NOB to the N 2 O sink. These findings broaden the physiological and genomic diversity of Nitrobacter, offering new insights into their adaptation strategies and providing a framework for future evaluation of their potential roles in nitrogen loss.

Nitrobacter↗
NASA NTRS2022

ExoVista: A Suite of Planetary System Models for Exoplanet Studies

Studies of future space- and ground-based exoplanet surveys often rely on models of planetary systems to simulate instrument response, estimate scientific yields, perform trade analyses, and study efficient observation strategies. Until now, no planetary system models contained all of the basic physics necessary to enable study with all of the major exoplanet detection methods. Here we introduce a suite of such models generated by a new tool, exoVista. The exoVista tool quickly generates thousands of models of quasi-self-consistent planetary systems around known nearby stars at scattered light wavelengths and efficiently records the position, velocity, spectrum, and physical parameters of all bodies as functions of time. The modeled planetary systems can be used to simulate surveys using the direct imaging, transit, astrometric, and radial velocity techniques, as well as the overlap of these different methods.

Christopher C Stark↗
NASA NTRS2021

Yonkers Urban Development: Utilizing NASA Earth Observations to Identify Environmental and Social Drivers of Urban Heat Vulnerability and Model Urban Cooling Interventions in Yonkers, New York

The City of Yonkers, New York, is located directly north of the Bronx in Westchester County and currently hosts a population of nearly 200,000. In response to increasing hot-weather episodes, the risk of heat-related illnesses and mortality is disproportionately affecting neighborhoods in Yonkers historically subjected to race-based housing segregation. NASA DEVELOP collaborated with Groundwork Hudson Valley to determine regions within Yonkers that are experiencing the most intense urban heat island effects, identify and rank sociodemographic and environmental determinants of increasing community-level vulnerability, map these vulnerabilities as a combined vulnerability index, complete a proximity analysis of walkability to local cooling centers and health facility locations, and model potential cooling strategies. The study area consisted of Yonkers, NY and the analyses used data from 2015-2020 (June through August). The project utilized NASA Earth observation products including Landsat 4 and 5 Thematic Mapper (TM), Landsat 7 Enhanced Thematic Mapper Plus (ETM+), Landsat 8 Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS), Terra Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), and ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS). We assessed the benefits of different heat-mitigation scenarios by utilizing the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) urban cooling model. Results from these analyses can be used by Groundwork Hudson Valley, supporting the New York State’s Climate Safe Communities Certifiable Planning Actions, expanding knowledge on the relationship between historic redlining and environmental equity, and informing their Climate Safe Neighborhoods initiative to identify and prioritize mitigation efforts to abate the worst impacts of extreme heat.

Jillian Walechka↗
NASA NTRS2016

A New Tropical Cyclone Dynamic Initialization Technique Using High Temporal and Spatial Density Atmospheric Motion Vectors and Airborne Field Campaign Data

Background: Initialization of tropical cyclones in numerical weather prediction (NWP) systems is a great challenge: Mass-wind field balance; Secondary circulation and heating; Asymmetries. There can be large adjustments in structure and intensity in the first 24 hours if the initial vortex is not in balance: Spurious gravity waves; Spin-up (model and physics). Existing mesoscale NWP model TC (Tropical Cyclone) initialization strategies: Bogus vortex, cold start from global analyses; 3DVAR or 4DVAR, possibly with synthetic observations; EnKF (Ensemble Kalman Filter); Dynamic initialization. Dynamic initialization allows vortex to have improved balance and physics spin-up at the initial time (e.g., Hendricks et al. 2013, 2011; Nguyen and Chen 2011; Fiorino and Warner 1981; Hoke and Anthes 1976). Himawari-8 geostationary satellite has capability of continuous imagery (10-minutes) over the full disk: New GOES-R satellites will have same capability. This will allow for unprecedented observations of tropical cyclones. However, current data assimila1on systems are not capable of ingesting such high temporal observations (Atmospheric Mo1on Vectors - AMVs). Hourly AMVs are produced, and thinned to 100-kilometer spacing in the horizontal. An entirely new data assimilation concept is required to utilize these observations.

remote sensing↗
NASA NTRS1999

A Polarimeter for SOFIA: Summary of Research

The purpose of this research was to study designs for a far-infrared polarimeter for SOFIA that would greatly surpass the performance of the earlier polarimeter, Stokes, used on the Kuiper Airborne Observatory. Specifically we wished to gain the sensitivity to observe much fainter objects, to provide a choice of passbands, to reduce systematic errors, and to improve the efficiency. All of these objectives were successfully addressed. The gain in sensitivity will be achieved in part by the superior capabilities of SOFIA but to an even greater extent by the incorporation of new-technology detector arrays. We are developing superconducting transition-edge detectors using the "pop-up" design conceived by Dr. Harvey Moseley. The choice of passbands is achieved by providing three alternative optical paths, each with its own spectral filter, half-wave plate, and pupil tens. A new investigation has shown that multiwavelength observations provide an essential feature of far-infrared polarimetry. The principal source of systematic errors in observations of extended objects has been unknown polarization in the reference beams. We have developed a strategy for estimating these errors.

Hildebrand, Roger H.↗
NASA NTRS2000

Visual motion integration for perception and pursuit

To examine the relationship between visual motion processing for perception and pursuit, we measured the pursuit eye-movement and perceptual responses to the same complex-motion stimuli. We show that humans can both perceive and pursue the motion of line-figure objects, even when partial occlusion makes the resulting image motion vastly different from the underlying object motion. Our results show that both perception and pursuit can perform largely accurate motion integration, i.e. the selective combination of local motion signals across the visual field to derive global object motion. Furthermore, because we manipulated perceived motion while keeping image motion identical, the observed parallel changes in perception and pursuit show that the motion signals driving steady-state pursuit and perception are linked. These findings disprove current pursuit models whose control strategy is to minimize retinal image motion, and suggest a new framework for the interplay between visual cortex and cerebellum in visuomotor control.

NASA Center ARC↗
NASA NTRS1991

NASA SETI microwave observing project: Sky Survey element

The SETI Sky Survey Observing Program is one of two complimentary strategies that NASA plans to use in its microwave Search for Extraterrestrial Intelligence (SETI). The primary objective of the sky survey is to search the entire sky over the frequency range of 1.0 to 10.0 GHz for evidence of narrow band signals of extraterrestrial intelligent origin. Frequency resolutions of 30 Hz or narrower will be used across the entire band. Spectrum analyzers with upwards of ten million channels are required to keep the survey time approximately 6 years. Data rates in excess of 10 megabits per second will be generated in the data taking process. Sophisticated data processing techniques will be required to determine the ever changing receiver baselines, and to detect and archive potential SETI signals. Existing radio telescopes, including several of NASA's Deep Space Network (DSN) 34 meter antennas located at Goldstone, CA and Tidbinbilla, Australia will be used for the observations. The JPL has the primary responsibility to develop and carry out the sky survey. In order to lay the foundation for the full scale SETI Sky Survey, a prototype system is being developed at the JPL. The system will be installed at the new 34-m high efficiency antenna at the Deep Space Station (DSS) 13 research and development station, Goldstone, CA, where it will be used to initiate the observational phase of the NASA SETI Sky Survey. It is anticipated that the early observations will be useful to test signal detection algorithms, scan strategies, and radio frequency interference rejection schemes. The SETI specific elements of the prototype system are: (1) the Wide Band Spectrum Analyzer (WBSA); a 2-million channel fast Fourier transformation (FFT) spectrum analyzer which covers an instantaneous bandpass of 40 MHz; (2) the signal detection processor; and (3) the SETI Sky Survey Manager, a network-based C-language environment that provides observatory control, performs data acquisition and analysis algorithms. A high level description of the prototype hardware and software systems will be given and the current status of the system development will be reported.

Klein, M. J.↗
NASA NTRS2018

Airborne and Shipborne Polarimetric Measurements over Open Ocean and Coastal Waters: Intercomparisons and Implications for Spaceborne Observations

Comprehensive polarimetric closure is demonstrated using observations from two in-situ polarimeters and Vector Radiative Transfer (VRT) modeling. During the Ship-Aircraft Bio-Optical Research (SABOR) campaign, the novel CCNY HyperSAS-POL polarimeter was mounted on the bow of the R/V Endeavor and acquired hyperspectral measurements from just above the surface of the ocean, while the NASA GISS Research Scanning Polarimeter was deployed onboard the NASA LaRC's King Air UC-12B aircraft. State-of-the-art, ancillary measurements were used to characterize the atmospheric and marine contributions in the VRT model, including those of the High Spectral Resolution Lidar (HSRL), the AErosol RObotic NETwork for Ocean Color (AERONET-OC), a profiling WETLabs ac-9 spectrometer and the Multi-spectral Volume Scattering Meter (MVSM). An open-ocean and a coastal scene are analyzed, both affected by complex aerosol conditions. In each of the two cases, it is found that the model is able to accurately reproduce the Stokes components measured simultaneously by each polarimeter at different geometries and viewing altitudes. These results are mostly encouraging, considering the different deployment strategies of RSP and HyperSAS-POL, which imply very different sensitivities to the atmospheric and ocean contributions, and open new opportunities in above-water polarimetric measurements. Furthermore, the signal originating from each scene was propagated to the top of the atmosphere to explore the sensitivity of polarimetric spaceborne observations to changes in the water type. As expected, adding polarization as a measurement capability benefits the detection of such changes, reinforcing the merits of the full-Stokes treatment in modeling the impact of atmospheric and oceanic constituents on remote sensing observations.

Matteo Ottaviani↗
NASA NTRSDate not supplied

Climate Information and Lessons Learned from the NASA Earth eXchange (NEX), Short-term Prediction Research and Transition Center (NASA SPoRT), and the NASA Regional Climate Model Evaluation System (RCMES)

Climate resilience can emerge from a combination of (1) climate projections that enable future-oriented planning and (2) contemporary observations that can be used for adaptation in the face of realized climate. NASA is uniquely positioned to provide global climate projections and satellite-based observations to users who must take a global perspective on climate risk (e.g., supply chain managers). Here, we will share the capabilities and experiences of three NASA projects. The first project, the NASA Earth eXchange (NEX) provides two downscaled climate projections, NEX-GDDP-CMIP6, a global 25 km resolution product, and NEX-DCP30-CMIP6, an 800 m resolution product covering the coterminous U.S. specifically designed for U.S. National Climate Assessment (NCA). NEX also offers land surface products that mirror the twice-daily products provided by the Moderate Resolution Imaging Spectroradiometer (MODIS) but are derived from operational geostationary weather satellite data collected at 5-10 minutes intervals. The second project, the Short-term Prediction Research and Transition Center (SPoRT), was established in 2002 to transition unique NASA satellite products and capabilities to stakeholders to improve decision-making that benefits society. SPoRT provides products and capabilities derived from NASA observations and models to end users in government, academia, and the private sector. SPoRT translates NASA Earth science into actionable solutions reaching over 400 stakeholders across 80 organizations. Finally, the Regional Climate Model Evaluation System (RCMES) is designed to facilitate regional-scale evaluations of climate models by providing standardized access to a vast and comprehensive set of observations, such as satellite, in-situ, and reanalyses, and modeling resources such as CMIP & CORDEX. These three projects form part of the core capabilities at the NASA Centers deliver climate information under the new NASA Earth Science to Action (ES2A) strategy.

NEX↗
DOE OSTI2020

Designing a Nanoscale Three-phase Electrochemical Pathway to Promote Pt-catalyzed Formaldehyde Oxidation

Gas-phase heterogeneous catalysis is a process spatially constrained on the two-dimensional surface of a solid catalyst. Here, we introduce a new toolkit to open up the third dimension. We discovered that the activity of a solid catalyst can be dramatically promoted by covering its surface with a nanoscale-thin layer of liquid electrolyte while maintaining efficient delivery of gas reactants, a strategy we call three-phase catalysis. Introducing the liquid electrolyte converts the original surface catalytic reaction into an electrochemical pathway with mass transfer facilitated by free ions in a three-dimensional space. Furthermore, we chose the oxidation of formaldehyde as a model reaction and observed a 25000-times enhancement in the turnover frequency of Pt in three-phase catalysis as compared to conventional heterogeneous catalysis. We envision three-phase catalysis as a new dimension for catalyst design and anticipate its applications in more chemical reactions from pollution control to the petrochemical industry.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗