Preferred Orientation of a Physisorbed Molecular Catalyst and Implications for Selectivity
Explore the source record for details and available documents.
Laurent, Guillaume P. [Division of Chemical and Bi↗
SEARCH · Engineering Papers
Results for “neighbor selection”
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.
Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.
Single Entity Electrochemistry in Nanopore Electrode Arrays: Ion Transport Meets Electron Transfer in Confined Geometries
Electrochemical measurements conducted in confined volumes provide a powerful and direct means to address scientific questions at the nexus of nanoscience, biotechnology, and chemical analysis. How are electron transfer and ion transport coupled in confined volumes and how does understanding them require moving beyond macroscopic theories? Also, how do these coupled processes impact electrochemical detection and processing? We address these questions by studying a special type of confined-volume architecture, the nanopore electrode array, or NEA, which is designed to be commensurate in size with physical scaling lengths, such as the Debye length, a concordance that offers performance characteristics not available in larger scale structures. The experiments described here depend critically on carefully constructed nanoscale architectures that can usefully control molecular transport and electrochemical reactivity. We begin by considering the experimental constraints that guide the design and fabrication of zero-dimensional nanopore arrays with multiple embedded electrodes. These zero-dimensional structures are nearly ideal for exploring how permselectivity and unscreened ion migration can be combined to amplify signals and improve selectivity by enabling highly efficient redox cycling. Our studies also highlight the benefits of arrays, in that molecules escaping from a single nanopore are efficiently captured by neighboring pores and returned to the population of active redox species being measured, benefits that arise from coupling ion accumulation and migration. These tools for manipulating redox species are well-positioned to explore single molecule and single particle electron transfer events through spectroelectrochemistry, studies which are enabled by the electrochemical zero-mode waveguide (ZMW), a special hybrid nanophotonic/nanoelectronic architecture in which the lower ring electrode of an NEA nanopore functions both as a working electrode to initiate electron transfer reactions and as the optical cladding layer of a ZMW. Furthermore, while the work described here is largely exploratory and fundamental, we believe that the development of NEAs will enable important applications that emerge directly from the unique coupled transport and electron-transfer capabilities of NEAs, including in situ molecular separation and detection with external stimuli, redox-based electrochemical rectification in individually encapsulated nanopores, and coupled sorters and analyzers for nanoparticles.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Atomically Ordered PdCu Electrocatalysts for Selective and Stable Electrochemical Nitrate Reduction
Electrochemical nitrate reduction (NO 3 RR) has attracted attention as an emerging approach to mitigate nitrate pollution in groundwater. Here, we report that a highly ordered PdCu alloy-based electrocatalyst exhibits selective (91% N 2 ), stable (480 h), and near complete (94%) removal of nitrate without loss of catalyst. In situ and ex situ XAS provide evidence that structural ordering between Pd and Cu improves long-term catalyst stability during NO 3 RR. In contrast, we also report that a disordered PdCu alloy-based electrocatalyst exhibits non-selective (44% N 2 and 49% NH 4 + ), unstable, and incomplete removal of nitrate. The copper within disordered PdCu alloy is vulnerable to accepting electrons from hydrogenated neighboring Pd atoms. This resulted in copper catalyst losses which were 10× greater than that of the ordered catalyst. The design of stable catalysts is imperative for water treatment because loss of the catalyst adds to the system cost and environmental impacts.
36 MATERIALS SCIENCE↗
Hot carrier extraction from 2D semiconductor photoelectrodes
Hot carrier-based energy conversion systems could double the efficiency of conventional solar energy technology or drive photochemical reactions that would not be possible using fully thermalized, “cool” carriers, but current strategies require expensive multijunction architectures. Using an unprecedented combination of photoelectrochemical and in situ transient absorption spectroscopy measurements, we demonstrate ultrafast (<50 fs) hot exciton and free carrier extraction under applied bias in a proof-of-concept photoelectrochemical solar cell made from earth-abundant and potentially inexpensive monolayer (ML) MoS 2 . Our approach facilitates ultrathin 7 Å charge transport distances over 1 cm 2 areas by intimately coupling ML-MoS 2 to an electron-selective solid contact and a hole-selective electrolyte contact. Our theoretical investigations of the spatial distribution of exciton states suggest greater electronic coupling between hot exciton states located on peripheral S atoms and neighboring contacts likely facilitates ultrafast charge transfer. Our work delineates future two-dimensional (2D) semiconductor design strategies for practical implementation in ultrathin photovoltaic and solar fuel applications.
2D materials↗
On-Orbit Spatial Characterization of MODIS with ASTER Aboard the Terra Spacecraft
This letter presents a novel approach for on-orbit characterization of MODerate resolution Imaging Spectroradiometer (MODIS) band-to-band registration (BBR) using Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) aboard the Terra spacecraft. The spatial resolution of ASTER spectral bands is much higher than that of MODIS, making it feasible to characterize MODIS on-orbit BBR using their simultaneous observations. The ground target selected for on-orbit MODIS BBR characterization in this letter is a water body, which is a uniform scene with high signal contrast relative to its neighbor areas. A key step of this approach is to accurately localize the measurements of each MODIS band in an ASTER measurement plane coordinate (AMPC). The ASTER measurements are first interpolated and aggregated to simulate the measurements of each MODIS band. The best measurement match between ASTER and each MODIS band is obtained when the measurement difference reaches its weighted minimum. The position of each MODIS band in the AMPC is then used to calculate the BBR. The results are compared with those derived from MODIS onboard Spectro-Radiometric Calibration Assembly. They are in good agreement, generally less than 0.1 MODIS pixel. This approach is useful for other sensors without onboard spatial characterization capability. Index Terms Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), band-to-band registration (BBR), MODerate resolution Imaging Spectroradiometer (MODIS), spatial characterization.
Xie, Yong↗
Aerosol and Surface Parameter Retrievals for a Multi-Angle, Multiband Spectrometer
This software retrieves the surface and atmosphere parameters of multi-angle, multiband spectra. The synthetic spectra are generated by applying the modified Rahman-Pinty-Verstraete Bidirectional Reflectance Distribution Function (BRDF) model, and a single-scattering dominated atmosphere model to surface reflectance data from Multiangle Imaging SpectroRadiometer (MISR). The aerosol physical model uses a single scattering approximation using Rayleigh scattering molecules, and Henyey-Greenstein aerosols. The surface and atmosphere parameters of the models are retrieved using the Lavenberg-Marquardt algorithm. The software can retrieve the surface and atmosphere parameters with two different scales. The surface parameters are retrieved pixel-by-pixel while the atmosphere parameters are retrieved for a group of pixels where the same atmosphere model parameters are applied. This two-scale approach allows one to select the natural scale of the atmosphere properties relative to surface properties. The software also takes advantage of an intelligent initial condition given by the solution of the neighbor pixels.
Broderick, Daniel↗
Bridgeport Urban Development: Leveraging NASA Earth Observations and Sociodemographic Data to Assess Urban Heat Vulnerability and Inform Cool Corridors in Bridgeport, Connecticut
Urban environments face hotter temperatures than suburban and rural areas due to higher concentrations of impervious surfaces, heat-retaining buildings, and lack of green space. Bridgeport, Connecticut, which was formerly a national manufacturing hub, is now the densest and most populous city in the state. Bridgeport experiences hotter temperatures, exposing its residents to more extreme temperatures than the surrounding affluent suburbs. Extreme heat affects the health of those exposed to it and intensifies energy demands. Understanding temperature differences is the first step in effectively directing mitigation efforts. Our partner, Groundwork Bridgeport, along with the Yale Urban Design Workshop, are planning a “cool corridors” project, implementing cooling infrastructure to combat urban heat. We used Landsat 8 Thermal Infrared Sensor and Landsat 9 Thermal Infrared Sensor-2 data to conduct a Land Surface Temperature analysis in Google Earth Engine for the county of Fairfield. A Principal Component Analysis was performed to identify indicators of social vulnerability in Bridgeport. We used the SOlar and LongWave Environmental Irradiance Geometry model to identify felt heat on the block level to inform where the partner should locate their cooling interventions to ensure they are most effective and equitable. We focused on the East Side of Bridgeport, which we found was 10 degrees hotter than other areas of Bridgeport and the neighboring town of Fairfield. We integrated our findings using Earth observations and additional sociodemographic and climate data into final communication products for our partners which will facilitate their selection of candidate locations for their Cool Corridors project.
Silas Kirsch↗
Localized keyhole pore prediction during laser powder bed fusion via multimodal process monitoring and X-ray radiography
Systematic fault detection and control during laser powder bed fusion (L-PBF) has been a long-standing objective for system manufacturers and researchers in the additive manufacturing (AM) industry. This manuscript investigates a data fusion approach for detection of keyhole porosity formation during laser irradiation of Ti-6Al-4V substrates by concurrent recording of thermally induced optical emission measured using both off-axis and coaxial photodiode sensors, and acoustic emission. Subsurface defect formation was monitored via high-speed synchrotron X-ray imaging at 20,000 frames per second, enabling temporal registration of keyhole pore formation events to the monitoring signals at a resolution of 50 µs. We developed data fusion machine learning (ML) models for localized prediction of keyhole pore formation at various time scales ranging from 0.5 ms to 2 ms. The signal segments were featurized using two independent approaches: (1) power spectral density (PSD) and (2) highly comparative time series analysis (HCTSA) framework. The extracted features from different sensor modalities were fused together to construct a multimodal feature space and sequential feature selection was used to determine the most informative features for training the ML models. The predictive performance was evaluated for three classifying algorithms: Support Vector Machine (SVM), K-Nearest Neighbor (KNN), and Gaussian Naive Bayes (GNB). As a result, pore formation events were predicted with up to 0.95 F1-score, 1.0 recall and 0.94 accuracy. The most heavily weighted features indicate that model performance is chiefly governed by the acoustic monitoring signal, with a secondary contribution from the optical emission sensors.
36 MATERIALS SCIENCE↗
Lithium-Ion Battery Diagnostics Using Electrochemical Impedance via Machine-Learning
Diagnosing battery states such as health, state-of-charge, or temperature is crucial for ensuring the safety and reliability of electrochemical energy storage systems. While some states, such as temperature, may be measured using cheap sensors, accurate diagnosis of battery health metrics usually requires time-consuming performance measurements, making them infeasible for use in real-world operation. These health metrics can be measured during lab-testing and then estimated on-line using predictive life models or via state observer algorithms such as Kalman filters, but these predictive methods should be supplemented by actual measurement of battery health whenever possible to ensure reliability. Rapid measurement of battery health may be done by various types of fast diagnostic techniques such as electrochemical impedance spectroscopy (EIS), which can be performed in only a few minutes and require only a fraction of the energy and power needed for a full charge and discharge measurement. But there is a substantial challenge for estimating battery health using EIS data, as EIS is sensitive to cell temperature, state-of-charge, current, and resting time in addition to health. Thus, utilizing EIS data to predict battery capacity requires correcting for all these additional variables, a task that is extremely difficult to handle analytically. This talk utilizes machine-learning methods to estimate the effectiveness of battery capacity prediction from EIS data, leveraging a data set of hundreds of EIS measurements recorded at varying temperature and state-of-charge throughout a 500-day aging study of 32 commercial, large-format NMC-Graphite lithium-ion batteries. Using EIS as input to machine-learning models is complicated by the nonlinear response of impedance to battery health, temperature, and state-of-charge, as well as the collinearity between the impedance response at neighboring frequencies, which can easily lead to overfit models. To train robust models, features from EIS data need to be extracted from the data or some subset of critical frequencies selected. Many approaches for extracting and selecting features from EIS data from electrochemical analysis and machine-learning fields were identified for analysis: using the entire raw spectra; selection of one, two, or many frequencies from the entire spectra; selecting interesting points from the EIS measurement using domain knowledge; fitting EIS with an equivalent-circuit model; calculating statistics on the raw impedance values; and reducing the dimensionality of the data using unsupervised linear (principal component analysis) and non-linear (uniform manifold approximation and projection) methods. These approaches were rigorously compared using a machine-learning pipeline approach, training linear, Gaussian process, and random forest regression models and quantifying performance using cross-validation as well as a held-out test set. An artificial neural network model trained on the raw spectra was also tested. Promising pipelines were fine-tuned via Bayesian hyperparameter optimization using cross-validation loss and training with class-specific weights to counter data set imbalance. The most reliable method for utilizing impedance in this work was the selection of two optimal frequencies through an exhaustive search, resulting in about 2% mean absolute error on test data for both Gaussian process and random forest model architectures. Interrogation of a variety of models reveals critical frequencies of 100 Hz and 103 Hz for this data set, though the optimal set of frequencies is not necessarily intuitive, i.e., the best performing models are not simply those that use impedance at frequencies that have the highest correlation to the relative discharge capacity. The best performing model is an ensemble model, which is able to predict battery capacity with 1.9% mean absolute error for unseen cells using impedance recorded at a variety of temperatures and states-of-charge.
battery↗
Geometric Frustration on the Trillium Lattice in a Magnetic Metal-Organic Framework
In the dense metal-organic framework Na [ Mn ( HCOO ) 3 ] , Mn 2 + ions ( S = 5 2 ) occupy the nodes of a “trillium” net. In this work, we show that the system is strongly magnetically frustrated: the Néel transition is suppressed well below the characteristic magnetic interaction strength; short-range magnetic order persists far above the Néel temperature; and the magnetic susceptibility exhibits a pseudo-plateau at 1 3 -saturation magnetization. A simple model of nearest-neighbor Heisenberg antiferromagnetic and dipolar interactions accounts quantitatively for all observations, including an unusual 2- k magnetic ground state. We show that the relative strength of dipolar interactions is crucial to selecting this particular ground state. Geometric frustration within the classical spin liquid regime gives rise to a large magnetocaloric response at low applied fields that is degraded in powder samples as a consequence of the anisotropy of dipolar interactions.
71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
Measurements of nonequilibrium interatomic forces using time-domain x-ray scattering
Here, we demonstrate an experimental approach to determining the excited-state interatomic forces using femtosecond x-ray pulses from an x-ray free-electron laser. We determine experimentally the excited-state interatomic forces that connect photoexcited carriers to the nonequilibrium lattice dynamics in the prototypical Peierls-distorted material, bismuth. The forces are obtained by a constrained least-squares fit of a pairwise interatomic force model to the excited-state phonon dispersion relation as measured by the time- and momentum-resolved x-ray diffuse scattering. We find that photoexcited carriers weaken predominantly the nearest-neighbor forces, which drives the measured softening of the transverse acoustic modes throughout the Brillouin zone as well as the zone-center A 1g optical mode. This demonstrates a bond-selective approach to measuring electron-phonon coupling relevant to a broad range of photoinduced phase transitions and transient light-driven states in quantum materials.
75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
The Additive Manufacturing Moment Measure - A Parallel Computation Technique for Determining Build Variance in the Laser Powder Bed Fusion Process
Additive manufacturing (AM) has unique process attributes that facilitate the creation of optimized, complex, and unique parts for aerospace applications. However, the multi-scale and complicated building process for AM parts can cause unexpected build conditions that result in microstructural variability within the deposited material. The need for richer datasets and computational modeling capabilities to improve AM process reliability is a consequence of this microstructural variability. A computational approach, referred to as the Additive Manufacturing Moment Measure (AM3), addresses this need by leveraging the way AM processes fuse material in precise, incremental steps. During the layering sequence, each previous step contributes to the condition of the current step at the center of the heat source. The precise steps can be selected to systematically create a time-space point field with co-located machine input and in-situ sensor data. The sequential nature of the AM process, coupled with nearest neighbor calculations, allows for a fully parallel computation for part-scale build profile analysis. The AM3 concept introduced here enables part-scale assessment directly from build files and in-situ process monitoring sensors alike. The AM moment measures were calculated for build point fields and compared with co-located in-situ and ex-situ nondestructive evaluation and optical microscopy observations. These comparisons permit a better understanding of how the sequential process actions can affect the quality of a laser powder bed fusion (LPBF) build. Details of the AM3technique will be discussed and compared to measured LPBF part characteristics. The AM3 results indicate a strong potential to advance the qualification process for aerospace applications.
Additive Manufacturing↗
The Hourly GHI, DHI and DNI: Intercomparison of the CERES-Based Data and the NSRDB Data through Comparison with the Ground-Based BSRN Data
The NASA Clouds and the Earth’s Radiant Energy System (CERES) SYN1deg(Ed4.1) provides hourly global horizontal irradiance (GHI), diffuse horizontal irradiance (DHI) and direct horizontal irradiance (DirHI) at 1-degree latitude by 1-degree longitude resolution, and the time span is from March 2000 to near present. While the GHI agrees well with the ground-based Baseline Surface Radiation Network (BSRN) data, the DHI and DirHI are, respectively, positively and negatively biased against the BSRN data, and so is the direct normal irradiance (DNI) derived simply by dividing the DirHI by the cosine of the hourly solar zenith angle (SZA). We found that the biases tend to be well-defined functions of cos(SZA) and the cloud fraction (CLFR). We thus performed a bias-correction on the hourly DHI and DNI in the latitude-cos(SZA)-CLFR phase space, and therefrom, we derived the hourly global tilted irradiance (GTI) at a number of tilt angles using the isotropic diffuse irradiance model, and the results agree well with the GTI derived from the original BSRN near-instantaneous records. Meanwhile, the National Renewable Energy Laboratory (NREL) has produced the National Solar Radiation Database (NSRDB) with finer spatiotemporal resolution using its Physical Solar Model (PSM) and surface-based measured data, model results and satellite-based data as inputs. Now the NSRDB data covers not only the United States and its neighboring regions, but Europe, Africa, Asia and Oceania. The available temporal resolutions are 5, 10, 15, 30 and 60 minutes and the spatial resolutions are 2, 4 and 10 km, depending on the selected region. In this presentation, we compare both the CERES-based and NSRDB GHI, DHI and DNI with their BSRN counterparts and show how finer spatial resolution may give us an advantage in data accuracy and usability.
Taiping Zhang↗
Faint-object spectrograph optical bench
The Faint-Object Spectrograph (FOS) is one of five scientific instruments under development for use with the Optical Telescope Assembly (OTA) of NASA's Space Telescope. It is a dual-channel spectrograph operating with two independent 512-channel pulse-counting Digicons. The FOS will be employed in connection with the study of scientific questions associated with quasars, active galaxies, normal distant and local group galaxies, a wide class of objects within the Milky Way Galaxy and neighboring galaxies, and objects within the solar system. The FOS contains an optical bench which supports all optical elements. Dimensional stability was the primary design requirement for the optical bench. This led to the selection of a graphite/epoxy structure using laminates with very low coefficients of thermal expansion and high stiffness. The technical requirements are considered and details of fabrication are discussed.
Toth, J. M., Jr.↗
Rule groupings in expert systems using nearest neighbour decision rules, and convex hulls
Expert System shells are lacking in many areas of software engineering. Large rule based systems are not semantically comprehensible, difficult to debug, and impossible to modify or validate. Partitioning a set of rules found in CLIPS (C Language Integrated Production System) into groups of rules which reflect the underlying semantic subdomains of the problem, will address adequately the concerns stated above. Techniques are introduced to structure a CLIPS rule base into groups of rules that inherently have common semantic information. The concepts involved are imported from the field of A.I., Pattern Recognition, and Statistical Inference. Techniques focus on the areas of feature selection, classification, and a criteria of how 'good' the classification technique is, based on Bayesian Decision Theory. A variety of distance metrics are discussed for measuring the 'closeness' of CLIPS rules and various Nearest Neighbor classification algorithms are described based on the above metric.
Anastasiadis, Stergios↗
Decomposition of Sources of Errors in Seasonal Streamflow Forecasting over the U.S. Sunbelt
Seasonal streamflow forecasts, contingent on climate information, can be utilized to ensure water supply for multiple uses including municipal demands, hydroelectric power generation, and for planning agricultural operations. However, uncertainties in the streamflow forecasts pose significant challenges in their utilization in real-time operations. In this study, we systematically decompose various sources of errors in developing seasonal streamflow forecasts from two Land Surface Models (LSMs) (Noah3.2 and CLM2), which are forced with downscaled and disaggregated climate forecasts. In particular, the study quantifies the relative contributions of the sources of errors from LSMs, climate forecasts, and downscaling/disaggregation techniques in developing seasonal streamflow forecast. For this purpose, three month ahead seasonal precipitation forecasts from the ECHAM4.5 general circulation model (GCM) were statistically downscaled from 2.8deg to 1/8deg spatial resolution using principal component regression (PCR) and then temporally disaggregated from monthly to daily time step using kernel-nearest neighbor (K-NN) approach. For other climatic forcings, excluding precipitation, we considered the North American Land Data Assimilation System version 2 (NLDAS-2) hourly climatology over the years 1979 to 2010. Then the selected LSMs were forced with precipitation forecasts and NLDAS-2 hourly climatology to develop retrospective seasonal streamflow forecasts over a period of 20 years (1991-2010). Finally, the performance of LSMs in forecasting streamflow under different schemes was analyzed to quantify the relative contribution of various sources of errors in developing seasonal streamflow forecast. Our results indicate that the most dominant source of errors during winter and fall seasons is the errors due to ECHAM4.5 precipitation forecasts, while temporal disaggregation scheme contributes to maximum errors during summer season.
Decomposition↗
Enhanced Selectivity for C 2 H 4 Production from C 2 H 6 on Partially Chlorinated IrO 2 (110) Surfaces
Modifying metal oxide surfaces to limit their oxidizing activity can provide a means of improving catalytic selectivity toward the partial oxidation of light alkanes. Here, in this study, we investigated the oxidation of C 2 H 6 on Cl-modified IrO 2 (110) surfaces using temperature-programmed reaction spectroscopy (TPRS) and first-principles microkinetic modeling. We find that substituting Cl for O in the IrO 2 (110) surface enhances the selectivity for C 2 H 6 conversion to C 2 H 4 during TPRS by suppressing extensive oxidation to CO x products, while also either enhancing C 2 H 4 production or altering it to a lesser extent, depending on the initial C 2 H 6 coverage. The C 2 H 4 selectivity increased with increasing C 2 H 6 and Cl coverage, but reached a limiting value below 50%. The Cl coverage changed negligibly during C 2 H 6 oxidation, and the surface reactivity decreased only marginally for Cl coverages up to 0.5 ML (monolayer). TPRS simulations using a microkinetic model predict C 2 H 4 and CO x product yields as a function of the Cl coverage that agree closely with the experimental results. According to the simulations, C 2 H 6 conversion to C 2 H 4 occurs on Cl-IrO 2 (110) by the hydrogenation of C 2 H 3 * species adsorbed in blocked states, in which neighboring sites are occupied only by unreactive HO and Cl species. The microkinetic modeling shows that H-hopping away from surface HO groups provides a relatively efficient route for C 2 H 3 * to escape blocked configurations and dehydrogenate, and that this process can limit the C 2 H 4 selectivity on Cl-IrO 2 (110) under the conditions studied. Overall, our results demonstrate that Cl-substitution into IrO 2 (110) enhances the selectivity for C 2 H 4 production from C 2 H 6 and provides insights into the reaction mechanism that can guide strategies to further improve the C 2 H 4 selectivity.
IrO2↗
An investigation to improve selenodetic control through surface and orbital lunar photography
The use of lunar surface photography to achieve the photogrammetric transfer of available selenographic coordinates from future lunar landing sites to neighboring, photoidentifiable features was investigated. It can be implied from the procedures developed that overhead photography, were it available, could be utilized and would provide a material strengthening of the total solution. By the methodic selection of features and confirmation that they can in reality be identified from orbital photography, a modest selenodetic control system can be expanded into a net that could ultimately control all future, manned or unmanned, orbital photographic missions.
Sweet, H. J., III↗