Voyager capsule phase B. Volume III - Surface laboratory system. Part C1 - Subsystem functional description Final report
Voyager capsule surface laboratory system - subsystem functional descriptions
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Voyager capsule surface laboratory system - subsystem functional descriptions
Voyager capsule surface laboratory system - subsystem functional descriptions
Remote sensing of aerosol optical thickness from space is difficult over continental surfaces. There are two retrieval algorithms, one based on the use of dark targets and a second based on contrast reduction between selected pixels. Improvements in the contrast reduction method are reported. A procedure is developed for using the satellite image to evaluate whether conditions for applying the structure method are met. The theoretical background is discussed, and the usefulness of the structure functions is demonstrated. The method is applied to NOAA Advanced Very High Resolution Radiometer (AVHRR) imagery where simultaneous ground measurements are used for validation.
We introduce an approach to risk modeling that we call functional modeling , which we have developed to estimate the capabilities of a lunar base. The functional model tracks the availability of functions provided by systems, in addition to the operational state of those systems constituent strings. By tracking functions, we are able to identify cases where identical functions are provided by elements (rovers, habitats, etc.) that are connected together on the lunar surface. We credit functional diversity in those cases, and in doing so compute more realistic estimates of operational mode availabilities. The functional modeling approach yields more realistic estimates of the availability of the various operational modes provided to astronauts by the ensemble of surface elements included in a lunar base architecture. By tracking functional availability the effects of diverse backup, which often exists when two or more independent elements are connected together, is properly accounted for.
In this work, we present a combination of machine-learned models that predicts the surface elastic properties of general free surfaces in face-centered cubic (FCC) metals. These models are built by combining a semi-analytical method based on atomistic simulations to calculate surface properties with the artificial neural network (ANN) method or the boosted regression tree (BRT) method. The latter is also used to link bulk properties and surface orientation to surface properties. The surface elastic properties are represented by their invariants considering plane elasticity within a polar method. The resulting models are shown to accurately predict the surface elastic properties of seven pure FCC metals (Cu, Ni, Ag, Au, Al, Pd, Pt). The BRT model reveals the correlations between bulk and corresponding surface properties in terms of invariants, which can be used to guide the design of complex nano-sized particles, wires and films. Finally, by expressing the surface excess energy density as a function of surface elastic invariants, fast predictions of surface energy as a function of in-plane deformations can be made from these model constructs.
Visual sensor responses may be used to classify objects on the basis of their surface reflectance functions. In a color image, the image data are represented as a vector of sensor responses at each point in the image. This vector depends both on the surface reflectance functions and on the spectral power distribution of the ambient illumination. Algorithms designed to classify objects on the basis of their surface reflectance functions typically attempt to overcome the dependence of the sensor responses on the illuminant by integrating sensor data collected from multiple surfaces. In machine vision applications, it is shown that it is often possible to design the sensor spectral responsivities so that the vector direction of the sensor responses does not depend upon the illuminant. The conditions under which this is possible are given and an illustrative calculation is performed. In biological systems, where the sensor responsivities are fixed, it is shown that some changes in the illumination cause no change in the sensor responses. Such changes in illuminant are called black illuminants. It is possible to express any illuminant as the sum of two unique components. One component is a black illuminant. The second component is called the visible component. The visible component of an illuminant completely characterizes the effect of the illuminant on the vector of sensor responses.
Calculations of the surface tensions of liquid Al and Na are described using the full ionic density functional formalism of Wood and Stroud (1983). Surface tensions are in good agreement with experiment in both cases, with results substantially better for Al than those found previously in the gradient approximation. Preliminary minimization with respect to surface profile leads to an oscillatory profile superimposed on a nearly steplike ionic density disribution; the oscillations have a wavellength of about a hardsphere diameter.
NASA's Advanced Technology Development (ATD) program is sponsoring the development of a new generation of surface light scattering hardware. This instrument is designed to non-invasively measure the surface response function of liquids over a wide range of operating conditions while automatically compensating for a sloshing surface. The surface response function can be used to compute surface tension, properties of monolayers present, viscosity, surface tension gradient and surface temperature. The instrument uses optical and electronic building blocks developed for the laser light scattering program at NASA Lewis along with several unique surface light scattering components. The emphasis of this paper is the compensation for bulk surface motion (slosh). Some data processing background information is also included.
It is desirable that a surface layer scheme in an atmospheric numerical model is consistent with an atmospheric boundary layer scheme incorporated in the same model. In this study, stability functions based on Monin–Obukhov similarity theory for momentum and heat, φ m and φ h , in the stable surface layer are derived from the Mellor–Yamada–Nakanishi–Niino (MYNN) scheme. The resulting stability functions are approximated by φ m = 1 + 4.8z/L and φ h = 0.74 + 6.0z/L, which can be analytically integrated with respect to height z to obtain momentum and heat fluxes, where L is the Obukhov length. The fluxes thus, obtained are compared with those acquired from stability functions in four previous studies: they are nearly the same as those from two of them, and show better agreement with observational data of the Surface Heat Budget of the Arctic Ocean experiment (SHEBA) over sea ice than those from the other two studies. Detailed comparisons of the results of the MYNN scheme with the SHEBA data suggest that significant variations of the fluxes observed during “winter” when the ice was covered with dry snow may have been caused by those of the surface roughness around the observational site. The stability functions obtained from the MYNN scheme predict that the bulk and flux Richardson numbers approach critical values of 0.26 and 0.21, respectively, in the limit of z/L → ∞. These critical values result from the Kolmogorov hypothesis applied to the turbulent dissipation in the MYNN scheme and are considered to correspond to a transition from Kolmogorov to non-Kolmogorov turbulence.
Anode work functions from cesiated metal surfaces as parameters for thermionic converter systems analysis
Spacecraft material behavior plays a very important role in space missions. Spacecraft immersed in plasma get charged by absorbing plasma particles and by emitting electrons from spacecraft surfaces via photoelectron and secondary electron emission. Spacecraft charging depends heavily on material properties such as work function, secondary electron yield, dielectric constant, and electric conductivity among other. Material properties are typically assumed to be static in charging models. However, it is well known that this is not the case in space. This makes spacecraft charging predictions very challenging. Material properties are well characterized before the spacecraft is put in orbit through characterization in the lab under clean conditions. However, once in space, material properties change due to the harsh and very dynamic space environment. We present a new capability to predict material behavior in space from first-principles modeling. The ongoing effort seeks to couple material models, density functional theory (DFT) and molecular dynamic (MD) codes, with environment models, plasma kinetic codes. This preliminary study will show results of surface charging as a function of material work function, dielectric constant, and conductivity.
Work function and surface chemistries of SiC-polished, electropolished, and nitrogen-doped niobium coupons were analyzed before and after plasma processing using a neon-oxygen gas mixture. These studies represent an initial enquiry into the feasibility of applying the plasma processing technique designed at ORNL for the Spallation Neutron Source (SNS) to the nitrogen-doped Nb cavities for the Coherent Light Source II (LCLS-II). Work function of all measured samples was increased after plasma processing, which indicates the strong potential of the plasma processing technique as a tool for increasing the accelerating gradient of nitrogen-doped cavities.
The effects of various surface preparation procedures, including chemical treatment and anodic or chemical oxidation, closed-ampoule diffusion conditions, and post-diffusion surface preparation and annealing conditions, on the passivating properties of InP have been investigated in order to optimize the fabrication procedures of n(+)p InP solar cells made by closed-ampoule diffusion of sulfur into p-type InP. The InP substrates used were p-type Cd-doped to a level of 1.7 x 10 to the 16th/cu cm, Zn-doped to levels of 2.2 x 10 to the 16th and 1.2 x 10 to the 18th/cu cm, and n-type S-doped to 4.4 x 10 to the 18th/cu cm. The passivating properties have been evaluated from photoluminescence (PL) and conductance-voltage (G-V) data. Good agreement was found between the level of surface passivation and the composition of different surface layers as revealed by X-ray photoelectron spectroscopy (XPS) analysis.
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This study investigates the use of radio frequency air plasma as an eco-friendly method to rapidly and reversibly tailor the surface properties of graphene oxide (GO) films. We observed a transition from hydrophilic (contact angle ∼55°) to superhydrophilic (<10°) with short plasma exposure, attributed to a synergistic combination of surface modification and etching. Spectroscopic analyses (FTIR, XPS) revealed early stage formation of carbonyl groups and reduction of hydroxyls, while longer treatments induced atomic-level etching (AFM) and structural changes (XRD). This surface engineering enhanced the dielectric properties of GO films but led to reduced aqueous stability. The elucidated interplay between plasma-induced functionalization and etching provides valuable insights for the controlled modification of GO surfaces for various applications, including advanced dielectrics.
An equation is derived to compute the amount of diffuse light reflected by a particulate surface, such as on Mars or an asteroid. The method traces the paths of rays within an ensemble of randomly shaped grains and finds the eventual probability of emission. The amount of diffuse, unpolarized emitted light is obtained in terms of the real index of refraction, the imaginary index, and the average diameter of particles making up the surface. The equation is used to compute the empirical rule for obtaining the planetary albedo from the slope of its polarization curve. Accuracy of the equation, estimated at + or - 4%, seems justified because of quantitative agreement with experimental measures of the empirical rule. It is also shown that the equation can be applied to bubble-enclosing surfaces such as volcanic foams. Results for the indices of the moon, Mars, Io, and Europa are obtained and compared with other data.
Chemoresistive sensors based on multiwalled carbon nanotubes (MWCNTs) functionalized with SnO{sub 2} nanocrystals (NCs) have great potential for detecting trace gases at low concentrations (single ppm levels) at room temperature, because the SnO{sub 2} nanocrystals act as active sites for the chemisorption of gas molecules, and carbon nanotubes (CNTs) act as an excellent current carrying platform, allowing the adsorption of gas on SnO{sub 2} to modulate the resistance of the CNTs. However, uniform conjugation of SnO{sub 2} NCs with MWCNTs is challenging. An effective atomic layer deposition based approach to functionalize the surface of MWCNTs with SnO{sub 2} NCs, resulting in a novel CH{sub 4} sensor with 10 ppm sensitivity, is presented in this paper. Scanning electron microscopy, transmission electron microscopy (TEM), energy dispersive x-ray spectroscopy, and Raman spectroscopy were implemented to study the morphology, elemental composition, and the crystal quality of SnO{sub 2} functionalized MWCNTs. High resolution TEM images showed that the crystal quality of the functionalizing SnO{sub 2} NCs was of high quality with clear lattice fringes and the dimension almost three times smaller than shown thus far in literature. A lift-off based photolithography technique comprising bilayer photoresists was optimized to fabricate SnO{sub 2} functionalized MWCNTs-based chemoresistor sensor, which at room temperature can reliably sense below 10 ppm of CH{sub 4} in air. Such low level gas sensitivity, with significant reversible relative resistance change, is believed to be the direct result of the successful functionalization of the MWCNT surface by SnO{sub 2} NCs.