Helium atom diffraction measurements of the surface structure and vibrational dynamics of CH_3-Si(111) and CD_3-Si(111) surfaces

The surface structure and vibrational dynamics of CH_3–Si(111) and CD_3–Si(111) surfaces were measured using helium atom scattering. The elastic diffraction patterns exhibited a lattice constant of 3.82 Å, in accordance with the spacing of the silicon underlayer. The excellent quality of the observed diffraction patterns, along with minimal diffuse background, indicated a high degree of long-range ordering and a low defect density for this interface. The vibrational dynamics were investigated by measurement of the Debye–Waller attenuation of the elastic diffraction peaks as the surface temperature was increased. The angular dependence of the specular (θ_i=θ_f) decay revealed perpendicular mean-square displacements of 1.0 x 10^(−5) Å^2 K^(−1) for the CH_3–Si(111) surface and 1.2 x 10^(−5) Å^2 K^(−1) for the CD_3–Si(111) surface, and a He-surface attractive well depth of ~7 meV. The effective surface Debye temperatures were calculated to be 983 K for the CH_3–Si(111) surface and 824 K for the CD_3–Si(111) surface. These relatively large Debye temperatures suggest that collisional energy accommodation at the surface occurs primarily through the Si–C local molecular modes. The parallel mean-square displacements were 7.1 x 10^(−4) and 7.2 x 10^(−4) Å^2 K^(−1) for the CH_3–Si(111) and CD_3–Si(111) surfaces, respectively. The observed increase in thermal motion is consistent with the interaction between the helium atoms and Si–CH_3 bending modes. These experiments have thus yielded detailed information on the dynamical properties of these robust and technologically interesting semiconductor interfaces

PinAPL-Py: A comprehensive web-application for the analysis of CRISPR/Cas9 screens.

Large-scale genetic screens using CRISPR/Cas9 technology have emerged as a major tool for functional genomics. With its increased popularity, experimental biologists frequently acquire large sequencing datasets for which they often do not have an easy analysis option. While a few bioinformatic tools have been developed for this purpose, their utility is still hindered either due to limited functionality or the requirement of bioinformatic expertise. To make sequencing data analysis of CRISPR/Cas9 screens more accessible to a wide range of scientists, we developed a Platform-independent Analysis of Pooled Screens using Python (PinAPL-Py), which is operated as an intuitive web-service. PinAPL-Py implements state-of-the-art tools and statistical models, assembled in a comprehensive workflow covering sequence quality control, automated sgRNA sequence extraction, alignment, sgRNA enrichment/depletion analysis and gene ranking. The workflow is set up to use a variety of popular sgRNA libraries as well as custom libraries that can be easily uploaded. Various analysis options are offered, suitable to analyze a large variety of CRISPR/Cas9 screening experiments. Analysis output includes ranked lists of sgRNAs and genes, and publication-ready plots. PinAPL-Py helps to advance genome-wide screening efforts by combining comprehensive functionality with user-friendly implementation. PinAPL-Py is freely accessible at http://pinapl-py.ucsd.edu with instructions and test datasets

[OII] emitters in the GOODS field at z~1.85: a homogeneous measure of evolving star formation

We present the results of a deep, near-infrared, narrow band imaging survey at a central wavelength of 1.062 microns (FWHM=0.01 microns) in the GOODS-South field using the ESO VLT instrument, HAWK-I. The data are used to carry out the highest redshift search for [OII]3727 emission line galaxies to date. The images reach an emission line flux limit (5 sigma) of 1.5 x 10^-17 erg cm^-2 s^-1, additionally making the survey the deepest of its kind at high redshift. In this paper we identify a sample of [OII]3727 emission line objects at redshift z~1.85 in a co-moving volume of ~4100 Mpc^3. Objects are selected using an observed equivalent width (EW_obs) threshold of EW_obs = 50 angstroms. The sample is used to derive the space density and constrain the luminosity function of [OII] emitters at z=1.85. We find that the space density of objects with observed [OII] luminosities in the range log(L_[OII]) > 41.74 erg s^-1 is log(rho)=-2.45+/-0.14 Mpc^-3, a factor of 2 greater than the observed space density of [OII] emitters reported at z~1.4. After accounting for completeness and assuming an internal extinction correction of A_Halpha=1 mag (equivalent to A_[OII]=1.87), we report a star formation rate density of rho* ~0.38+/-0.06 Msun yr^-1 Mpc^-3. We independently derive the dust extinction of the sample using 24 micron fluxes and find a mean extinction of A_[OII]=0.98+/-0.11 magnitudes (A_Halpha=0.52). This is significantly lower than the A_Halpha=1 (A[OII]=1.86) mag value widely used in the literature. Finally we incorporate this improved extinction correction into the star formation rate density measurement and report rho*~0.24+/-0.06 Msun yr^-1 Mpc^-3.Comment: 11 pages, 10 figures, accepted for publication in MNRA

Anomalous solute transport in saturated porous media:linking transport model parameters to electrical and nuclear magnetic resonance properties

The advection-dispersion equation (ADE) fails to describe commonly observed non-Fickian solute transport in saturated porous media, necessitating the use of other models such as the dual-domain mass-transfer (DDMT) model. DDMT model parameters are commonly calibrated via curve fitting, providing little insight into the relation between effective parameters and physical properties of the medium. There is a clear need for material characterization techniques that can provide insight into the geometry and connectedness of pore spaces related to transport model parameters. Here, we consider proton nuclear magnetic resonance (NMR), direct-current (DC) resistivity, and complex conductivity (CC) measurements for this purpose, and assess these methods using glass beads as a control and two different samples of the zeolite clinoptilolite, a material that demonstrates non-Fickian transport due to intragranular porosity. We estimate DDMT parameters via calibration of a transport model to column-scale solute tracer tests, and compare NMR, DC resistivity, CC results, which reveal that grain size alone does not control transport properties and measured geophysical parameters; rather, volume and arrangement of the pore space play important roles. NMR cannot provide estimates of more-mobile and less-mobile pore volumes in the absence of tracer tests because these estimates depend critically on the selection of a material-dependent and flow-dependent cutoff time. Increased electrical connectedness from DC resistivity measurements are associated with greater mobile pore space determined from transport model calibration. CC was hypothesized to be related to length scales of mass transfer, but the CC response is unrelated to DDMT

High-performance Si microwire photovoltaics

Crystalline Si wires, grown by the vapor–liquid–solid (VLS) process, have emerged as promising candidate materials for lowcost, thin-film photovoltaics. Here, we demonstrate VLS-grown Si microwires that have suitable electrical properties for high-performance photovoltaic applications, including long minority-carrier diffusion lengths (L_n » 30 µm) and low surface recombination velocities (S « 70 cm·s^(-1)). Single-wire radial p–n junction solar cells were fabricated with amorphous silicon and silicon nitride surface coatings, achieving up to 9.0% apparent photovoltaic efficiency, and exhibiting up to ~600 mV open-circuit voltage with over 80% fill factor. Projective single-wire measurements and optoelectronic simulations suggest that large-area Si wire-array solar cells have the potential to exceed 17% energy-conversion efficiency, offering a promising route toward cost-effective crystalline Si photovoltaics

Si microwire-array solar cells

Si microwire-array solar cells with Air Mass 1.5 Global conversion efficiencies of up to 7.9% have been fabricated using an active volume of Si equivalent to a 4 μm thick Si wafer. These solar cells exhibited open-circuit voltages of 500 mV, short-circuit current densities (J_(sc)) of up to 24 mA cm^(-2), and fill factors >65% and employed Al_2O_3 dielectric particles that scattered light incident in the space between the wires, a Ag back reflector that prevented the escape of incident illumination from the back surface of the solar cell, and an a-SiN_x:H passivation/anti-reflection layer. Wire-array solar cells without some or all of these design features were also fabricated to demonstrate the importance of the light-trapping elements in achieving a high J_(sc). Scanning photocurrent microscopy images of the microwire-array solar cells revealed that the higher J_(sc) of the most advanced cell design resulted from an increased absorption of light incident in the space between the wires. Spectral response measurements further revealed that solar cells with light-trapping elements exhibited improved red and infrared response, as compared to solar cells without light-trapping elements

Defining Resizable Virtual Canvases using 3D Printed Objects

When working in an augmented reality (AR) environment, a key component for successful interactions is close integration of the real and virtual world. To that end, this project sought to further current research into the 3D printing of easily trackable objects which could then be used to influence actions in an AR environment. By using these objects to dynamically generate virtual canvases or objects, one can control the virtual world in a manner which is more natural and tactile than what purely virtual objects and controls can provide. The project was broken down into 3 basic components—3D printing, object tracking, and virtual canvas generation—and was a collaborative project between Ryan Lewis and Nolan Cooper. This abstract primarily addresses the 3D printing component of the project, research on which was completed by Ryan Lewis. To ascertain the optimal properties of 3D-printed trackable objects, several prototypes were designed, printed, and tested. Size, shape, object complexity, color, and surface contrast were all tested traits. To test the impacts of size on trackability, 3 sizes of 3 different types of simple objects were printed: cylinders, cubes, and spheres, with cross-axis dimensions measuring 20mm, 50mm, and 100mm across. We found that, while the size improved the distance at which the objects could be detected by our AR application to an appreciable degree, they had no obvious impact on the quality of detection itself, as the increased size did not add any additional features for detection. To test the effect of shape, 5 different 3D models were used, 3 of which were primitives—a cylinder, cube, and sphere—and 2 of which were complex shapes—a Klein bottle (a non-orientable 3D projection of a 4-dimensional object) and a custom designed amalgamation of uniquely positioned polyhedral shapes, dubbed a “Reticle”. Unsurprisingly, the shape of the object had a large impact on its detectability, with the simple shapes performing much poorer than the more complicated Klein Bottle and Reticle. Since the tracking software used edge and feature detection, the Sphere was virtually un-trackable, while the reticle performed superbly with its numerous distinct faces and edges. In the context of 3D printing, the color of an object is almost always determined by the material in which it is printed. A few materials and colors, including white, blue, gray, yellow, and green, were tested using the Reticle model. While the color itself only made a slight difference, mostly in so far as it provided contrast against a given background, a slightly different property was of far greater interest. While testing color, a slightly translucent, glossy yellow material was used, and it was discovered that this made the target entirely invisible to our tracking software. Because the appearance of the object changes based on lighting and position due to the material, it became impossible to accurately track. This suggests that for optimal results, extremely matte, opaque materials are preferred. To test complexity, a Klein bottle printed with a regular smooth surface was compared to a Klein bottle printed with a Voronoi Tessellation applied to its surface, creating numerous holes and edges. Applying the Voronoi Tesselation lead to a marked increasing in detectable features, suggesting that such algorithmic complexity-increasing approaches could help improve the detectability of otherwise difficult to detect objects. Surface Contrast Lastly, surface contrast was tested by printing a multi-material Reticle, using two different colors. This also proved effective at increasing the detectable feature count of the object, effectively adding additional edges to the object where the colors meet on the surface of the object. This suggest another effective method for making difficult to detect models more trackable is by printing it with a zebra-like multi-color surface. Ultimately, the 3D printing component of the project determined that, of the properties tested, the optimal design involved a complex shape, printed with a non-trivial surface using multiple materials, with a matte material and a size based on the distance at which tracking is required by a given application. While trackable objects printed without some of these characteristics could often work to some degree or another, incorporating at least one of these characteristics is generally required. For more information on the Tracking and Virtual Canvas Generation components of the project, see Nolan Cooper’s Capstone Abstract of the same name in the Honors Capstone Library. A copy of the source code, complete with a working Augmented Reality (AR) Demo Project, copies of the 3D meshes used in the project, and recorded Video Demonstrations, will be uploaded to the Huskie Commons library, in addition to the live GitHub repository publicly available at https://github.com/barrelmaker97/VirtualCanvas