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DDC: 620 × Subject: Decomposition ×

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    Efficient suboxide sources in oxide molecular beam epitaxy using mixed metal + oxide charges: The examples of SnO and Ga2O
    (Melville, NY : AIP Publ., 2020) Hoffmann, Georg; Budde, Melanie; Mazzolini, Piero; Bierwagend, Oliver
    Sources of suboxides, providing several advantages over metal sources for the molecular beam epitaxy (MBE) of oxides, are conventionally realized by decomposing the corresponding oxide charge at extreme temperatures. By quadrupole mass spectrometry of the direct flux from an effusion cell, we compare this conventional approach to the reaction of a mixed oxide + metal charge as a source for suboxides with the examples of SnO2 + Sn → 2 SnO and Ga2O3 + 4 Ga → 3 Ga2O. The high decomposition temperatures of the pure oxide charge were found to produce a high parasitic oxygen background. In contrast, the mixed charges reacted at significantly lower temperatures, providing high suboxide fluxes without additional parasitic oxygen. For the SnO source, we found a significant fraction of Sn2O2 in the flux from the mixed charge that was basically absent in the flux from the pure oxide charge. We demonstrate the plasma-assisted MBE growth of SnO2 using the mixed Sn + SnO2 charge to require less activated oxygen and a significantly lower source temperature than the corresponding growth from a pure Sn charge. Thus, the sublimation of mixed metal + oxide charges provides an efficient suboxide source for the growth of oxides by MBE. Thermodynamic calculations predict this advantage for further oxides as well, e.g., SiO2, GeO2, Al2O3, In2O3, La2O3, and Pr2O3 © 2020 Author(s).
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    Agricultural Monitoring Using Polarimetric Decomposition Parameters of Sentinel-1 Data
    (Basel : MDPI, 2021) Harfenmeister, Katharina; Itzerott, Sibylle; Weltzien, Cornelia; Spengler, Daniel
    The time series of synthetic aperture radar (SAR) data are commonly and successfully used to monitor the biophysical parameters of agricultural fields. Because, until now, mainly backscatter coefficients have been analysed, this study examines the potentials of entropy, anisotropy, and alpha angle derived from a dual-polarimetric decomposition of Sentinel-1 data to monitor crop development. The temporal profiles of these parameters are analysed for wheat and barley in the vegetation periods 2017 and 2018 for 13 fields in two test sites in Northeast Germany. The relation between polarimetric parameters and biophysical parameters observed in the field is investigated using linear and exponential regression models that are evaluated using the coefficient of determination (R2) and the root mean square error (RMSE). The performance of single regression models is furthermore compared to those of multiple regression models, including backscatter coefficients in VV and VH polarisation as well as polarimetric decomposition parameters entropy and alpha. Characteristic temporal profiles of entropy, anisotropy, and alpha reflecting the main phenological changes in plants as well as the meteorological differences between the two years are observed for both crop types. The regression models perform best for data from the phenological growth stages tillering to booting. The highest R2 values of the single regression models are reached for the plant height of wheat related to entropy and anisotropy with R2 values of 0.64 and 0.61, respectively. The multiple regression models of VH, VV, entropy, and alpha outperform single regression models in most cases. R2 values of multiple regression models of plant height (0.76), wet biomass (0.7), dry biomass (0.7), and vegetation water content (0.69) improve those of single regression models slightly by up to 0.05. Additionally, the RMSE values of the multiple regression models are around 10% lower compared to those of single regression models. The results indicate the capability of dual-polarimetric decomposition parameters in serving as meaningful input parameters for multiple regression models to improve the prediction of biophysical parameters. Additionally, their temporal profiles indicate phenological development dependent on meteorological conditions. Knowledge about biophysical parameter development and phenology is important for farmers to monitor crop growth variability during the vegetation period to adapt and to optimize field management.