Abstract: Remote sensing of global ocean color is a valuable tool for understanding the ecology and biogeochemistry of the worlds oceans, and provides critical input to our knowledge of the global carbon cycle and the impacts of climate change. Ocean polarized reflectance contains information about the constituents of the upper ocean euphotic zone, such as colored dissolved organic matter (CDOM), sediments, phytoplankton, and pollutants. In order to retrieve the information on these constituents, remote sensing algorithms typically rely on radiative transfer models to interpret water color or remote-sensing reflectance; however, this can be resource-prohibitive for operational use due to the extensive CPU time involved in radiative transfer solutions. In this work, we report a fast model based on machine learning techniques, called Neural Network Reflectance Prediction Model (NNRPM), which can be used to predict ocean bidirectional polarized reflectance given inherent optical properties of ocean waters. This supervised model is trained using a large volume of data derived from radiative transfer simulations for coupled atmosphere and ocean systems using the successive order of scattering technique (SOS-CAOS). The performance of the model is validated against another large independent test dataset generated from SOS-CAOS. The model is able to predict both polarized and unpolarized reflectances with an absolute error (AE) less than 0.004 for 99% of test cases. We have also shown that the degree of linear polarization (DoLP) for unpolarized incident light can be predicted with an AE less than 0.002 for 99% of test cases. In general, the simulation time of SOS-CAOS depends on optical depth, and required accuracy. When comparing the average speeds of the NNRPM against the SOS-CAOS model for the same parameters, we see that the NNRPM is able to predict the Ocean BRDF 6000 times faster than SOS-CAOS. Both ultraviolet and visible wavelengths are included in the model to help differentiate between dissolved organic material and chlorophyll in the study of the open ocean and the coastal zone. The incorporation of this model into the retrieval algorithm will make the retrieval process more efficient, and thus applicable for operational use with global satellite observations.

Abstract: Clouds play a large role in the way our Earth system distributes energy. The measurement of cloud droplet size distribution (DSD) is one way to connect small-scale cloud processes to scattered radiation. Our small satellite instrument, the Airborne Hyper-Angular Rainbow Polarimeter, is the first to infer DSDs over a wide spatial cloud field using polarized light. This study improves the way we interpret cloud properties and shows that high-quality space science does not require a large taxpayer cost.

Abstract: We report a detailed experimental and theoretical study of the effects of V substitution for Fe atom on the structural, magnetic, transport, electronic and mechanical properties of an off-stoichiometric Fe_3-xV_xGe intermetallic alloy series (0 ≤ x ≤ 1). Single phase microstructures are observed for x < 0.75, whereas higher V content alloys x ≥ 0.75 are multi-phased. Vanadium substitution is observed to induce a diffusionless martensitic phase transformation from a Heusler-like L2₁ structure to hexagonal DO₁₉ structure, as corroborated by Differential Scanning Calorimetry results. The vanadium substitution is also found to decreases the grain size, inhibiting the grain growth by pinning the grain boundary migration. All the alloys in the series are found to be soft ferromagnets at 5 K with saturation magnetic moment and Curie temperature decreasing as V concentration increases. The low temperature saturation magnetic moment is in close agreement with the expected Slater-Pauling values for the L2₁ phases, while the hexagonal samples have markedly higher values of saturation moments. First-principle calculations agree with the experimental findings and reveal that V substitution energetically favours one of the Fe sites in Fe₃Ge. The electrical resistivity measured over the temperature range from 5 K to 400 K shows negative temperature coefficient of resistivity at high temperatures with increasing the V concentration. Relatively high mechanical hardness values are also observed, with the values increasing with increasing V content. Vanadium substitution is found to play a central role in tuning the mechanical properties, stabilizing the L2₁ structure, and shifting the martensitic transformation temperature to higher values from that of parent Fe₃Ge.

Abstract: Recently, 2D tellurene (Te) structures have been experimentally synthesized. These structures possess high carrier mobility and stability which make them ideal candidates for applications in electronics, optoelectronics and energy devices. We performed density functional theory (DFT) and molecular dynamics (MD) simulations to investigate the stability and electronic structure of 2D α- and β-Te sheets, and hydrogen, oxygen, and fluorine functionalized counterparts, including spin–orbit coupling effects. Our calculations show that bare α and β-Te sheets are stable with band gaps of 0.44 eV and 1.02 eV respectively. When functionalized, α and β monolayers exhibit metallic properties, except for hydrogenated β-Te, which exhibits semiconducting properties with a band gap of 1.37 eV. We see that H, O and F destabilize the structure of α-Te. We also find that F and H cause β-Te layers to separate into functionalized atomic chains and O causes β-Te to transform into a Te₃O₂-like structure. We also studied single atom and molecule binding on the Te surface, the effects of adatom coverage, and the effects of functionalized Te on a GaSe substrate. Our results indicate that tellurene monolayers and functionalized counterparts are not only suitable for future optoelectronic devices, but can be used as metallic contacts in nanoscale junctions.

Image: Can Ataca, second from left, meets with some of his students. Photo by Marlayna Demond ’11 for UMBC.