Cellular Automaton Study of Time-Dynamics of Avalanche Breakdown in IMPATT Diodes

Employing a recently developed efficient cellular automaton technique for solving Boltzmann's transport equation for realistic devices, we present a detailed study of the carrier dynamics in GaAs avalanche p-i-n (IMPATT) diodes. We find that the impact ionization in reverse bias p-i-n diodes with ultrathin (less than 50 nm) intrinsic regions is triggered by Zener tunneling rather than by thermal generation. The impact generation of hot carriers occurs mainly in the low-field junction regions rather than in the high field intrinsic zone. The calculations predict significantly more minority carriers on the n-side than on the p-side

Target‐oriented habitat and wildlife management: estimating forage quantity and quality of semi‐natural grasslands with Sentinel‐1 and Sentinel‐2 data

Semi‐natural grasslands represent ecosystems with high biodiversity. Their conservation depends on the removal of biomass, for example, through grazing by livestock or wildlife. For this, spatially explicit information about grassland forage quantity and quality is a prerequisite for efficient management. The recent advancements of the Sentinel satellite mission offer new possibilities to support the conservation of semi‐natural grasslands. In this study, the combined use of radar (Sentinel‐1) and multispectral (Sentinel‐2) data to predict forage quantity and quality indicators of semi‐natural grassland in Germany was investigated. Field data for organic acid detergent fibre concentration (oADF), crude protein concentration (CP), compressed sward height (CSH) and standing biomass dry weight (DM) collected between 2015 and 2017 were related to remote sensing data using the random forest regression algorithm. In total, 102 optical‐ and radar‐based predictor variables were used to derive an optimized dataset, maximizing the predictive power of the respective model. High R2 values were obtained for the grassland quality indicators oADF (R2 = 0.79, RMSE = 2.29%) and CP (R2 = 0.72, RMSE = 1.70%) using 15 and 8 predictor variables respectively. Lower R2 values were achieved for the quantity indicators CSH (R2 = 0.60, RMSE = 2.77 cm) and DM (R2 = 0.45, RMSE = 90.84 g/m²). A permutation‐based variable importance measure indicated a strong contribution of simple ratio‐based optical indices to the model performance. In particular, the ratios between the narrow near‐infrared and red‐edge region were among the most important variables. The model performance for oADF, CP and CSH was only marginally increased by adding Sentinel‐1 data. For DM, no positive effect on the model performance was observed by combining Sentinel‐1 and Sentinel‐2 data. Thus, optical Sentinel‐2 data might be sufficient to accurately predict forage quality, and to some extent also quantity indicators of semi‐natural grassland

Computational molecular characterization of the flavonoid rutin

In this work, we make use of a model chemistry within Density Functional Theory (DFT) recently presented, which is called M05-2X, to calculate the molecular structure of the flavonoid Rutin, as well as to predict the infrared (IR) and ultraviolet (UV-Vis) spectra, the dipole moment and polarizability, the free energy of solvation in different solvents as an indication of solubility, the HOMO and LUMO orbitals, and the chemical reactivity parameters that arise from Conceptual DFT. The calculated values are compared with the available experimental data for this molecule as a means of validation of the used model chemistry

Quantifying Vegetation Biophysical Variables from Imaging Spectroscopy Data: A Review on Retrieval Methods

An unprecedented spectroscopic data stream will soon become available with forthcoming Earth-observing satellite missions equipped with imaging spectroradiometers. This data stream will open up a vast array of opportunities to quantify a diversity of biochemical and structural vegetation properties. The processing requirements for such large data streams require reliable retrieval techniques enabling the spatiotemporally explicit quantification of biophysical variables. With the aim of preparing for this new era of Earth observation, this review summarizes the state-of-the-art retrieval methods that have been applied in experimental imaging spectroscopy studies inferring all kinds of vegetation biophysical variables. Identified retrieval methods are categorized into: (1) parametric regression, including vegetation indices, shape indices and spectral transformations; (2) nonparametric regression, including linear and nonlinear machine learning regression algorithms; (3) physically based, including inversion of radiative transfer models (RTMs) using numerical optimization and look-up table approaches; and (4) hybrid regression methods, which combine RTM simulations with machine learning regression methods. For each of these categories, an overview of widely applied methods with application to mapping vegetation properties is given. In view of processing imaging spectroscopy data, a critical aspect involves the challenge of dealing with spectral multicollinearity. The ability to provide robust estimates, retrieval uncertainties and acceptable retrieval processing speed are other important aspects in view of operational processing. Recommendations towards new-generation spectroscopy-based processing chains for operational production of biophysical variables are given

Melamine Acoustic Chemosensor Based on Molecularly Imprinted Polymer Film

A melamine piezomicrogravimetric (acoustic) chemosensor using a molecularly imprinted polymer (MIP) film has been devised and tested. The MIP films were prepared by electropolymerization of the melamine complexed by the functional monomer of the bis(bithiophene) derivative bearing an 18-crown-6 substituent 4. The structure of the MIP-melamine complex was visualized by the DFT B3LYP/3-21G(*) energy optimization calculations. The sensitivity and selectivity of the MIP film was improved by cross-linking the polymer with the bithianaphthene monomer 5 and the presence of the porogenic ionic liquid in the prepolymerization solution. After electropolymerization, the melamine template was extracted from the MIP film with an aqueous strong base solution. The measurements of UV-vis spectroscopy, X-ray photoelectron spectroscopy (XPS), DPV, and EIS as well as scanning electrochemical microscopy (SECM) imaging confirmed extraction of the melamine template from the MIP film and then rebinding of the melamine analyte while the film relative roughness and porosity was determined by atomic force microscopy (AFM) and scanning electron microscopy (SEM) imaging, respectively. The analytical as well as kinetic and thermodynamic parameters of the chemosensing were assessed under flow-injection analysis (FIA) conditions with piezoelectric microgravimetry (PM) detection. The linear concentration range for melamine detection was 5 nM to at least 1 mM with a limit of detection of 3c5 nM. The chemosensor successfully discriminated the cyanuric acid, cyromazine, and ammeline interfering agents