Phonon plasmon interaction in ternary group-III-nitrides

This article may be downloaded for personal use only. Any other use requires prior permission of the author and AIP Publishing. This article appeared in Appl. Phys. Lett. 101, 041909 (2012) and may be found at https://doi.org/10.1063/1.4739415.Phonon-plasmon-coupling in the ternary group-III-nitrides InGaN and AlGaN is investigated experimentally and theoretically. Based on the observation of broadening and shifting of the A1(LO) mode in AlGaN upon Si-doping, a lineshape analysis was performed to determine the carrier concentration. The results obtained by this method are in excellent agreement to those from Hall measurements, confirming the validity of the employed model. Finally, neglecting phonon and plasmon damping, the Raman shift of the A1(LO) mode in dependence of the carrier concentration for AlGaN and InGaN is calculated. This enables a fast and contactless determination of carrier concentrations in the future.DFG, 43659573, SFB 787: Halbleiter - Nanophotonik: Materialien, Modelle, Bauelement

Estimating the Rate of Change of Stratospheric Ozone using Deep Neural Networks

Due to the intensive ozone research in recent decades, the processes that influence stratospheric ozone are well understood. The chemistry and transport model ATLAS was developed to simulate the chemistry and transport of stratospheric ozone globally. The chemical rate of change of ozone is calculated at each model point and time step of the model by solving a system of differential equations that requires 55 input parameters (chemical species, temperatures, ...). But the computational e!ort to solve this complex system of differential equations is very high, and with respect to the overall limited computation time, this prevents the inclusion of ozone chemistry into ESMs. This project proposes a data-driven machine learning approach to predict the rate of change of stratospheric ozone. To derive a data set from modelled data, ATLAS was run for several short model runs. The rate of change of ozone and 55 parameters were stored at each model point and time step. By observing the co-variances of the high-dimensional feature-space, a large data set with reduced dimensionality has been created. A supervised learning algorithm used this data set of input and output pairs to train a deep feed- forward neural network (NN). This involved the identification and optimisation of several hyperparameters and to find a well- functioning combination of depth (number of layers) and width (number of neurons per layer). In this way, the NN model capacity is optimised with respect to the data itself. To evaluate this approach, the results were compared with another data-driven approach called SWIFT. The SWIFT model employs a repro-modelling approach that uses polynomials to approximate the rate of change of ozone. The resulting NN model is not only capable of learning the context of an eleven-dimensional hyperplane, but also improves the RMSE by about one order of magnitude compared to SWIFT’s previous polynomial approach. In addition, the deviations of the predictions at the boundaries (altitude and latitude) are significantly lower, which is a challenge for the polynomial approach. Only fully coupled ozone climate set ups are able to consider the complex interactions of the stratospheric ozone layer and climate. This is a step towards a computationally very fast but accurate application of an interactive ozone scheme in climate models

Förderung von Vögeln der Agrarlandschaft durch die Neuanlage von Brut- und Nahrungshabitaten

Die zunehmende Verschlechterung von Brut- und Nahrungshabitaten für Vögel der Agrarlandschaft zählt heute zu den wichtigsten Ursachen für deren steten Rückgang. Mit der Bildung großflächiger Bewirtschaftungseinheiten mit einseitigen Fruchtfolgen verschlechtern sich die Nahrungsbedingungen für die Vögel auf dem Feld. Nach der politischen Wende bestand schon Anfang der 1990er Jahre Bedarf an der Neustrukturierung ausgeräumter Agrarflächen, besonders in Ostdeutschland. 1993 wurde auf einer Ackerfläche am südlichen Stadtrand von Berlin die sogenannte “Brandenburger Schichtholzhecke” als Modellprojekt angelegt, um einen ökologischen und ökonomischen Weg zur Neuanlage von Hecken und Feldrainen aufzuzeigen. Diese naturnahen Kleinstrukturen bieten in ihrer Kombination sowohl Brut- als auch ganzjährig Nahrungshabitate und zeigen insbesondere unter den heutigen Bedingungen Möglichkeiten auf, um die Lebensbedingungen der Agrarvögel zu verbessern. Dabei ist das Konzept der „Benjeshecke“ modifiziert worden, indem zwischen zwei parallel zueinander verlaufenden Gestrüppwällen aus Totholz heimische Bäume und Sträucher einreihig gepflanzt wurden. Im Frühjahr 1994 ist ein mindestens 5 m breiter Wildkräuterstreifen zwischen buhnenförmigen Querwällen aus Totholz entlang der 575 m langen Hecke etabliert worden und beendete damit die Gestaltungsphase. Regelmäßige Vogelbestandserfassungen von 1995 bis 1998 und im Jahr 2004 zeigten die kontinuierliche Nutzung der Hecke als Lebensraum durch Agrarvögel. Schon 1994 konnten die ersten Neuntöter an den Totholzwällen beobachtet werden. 1995 diente der Saum als Brutrevier für Goldammer, Neuntöter, Steinschmätzer und Schafstelze. 1998 waren insbesondere die Gehölzstrukturen schon so weit entwickelt, dass die Dorngrasmücke erstmalig in der Hecke nistete. Im Jahr 2004, zehn Jahre nach Anlage der Hecke, brüteten 7 Vogelarten mit 13 Brutpaaren im Saum (Goldammer, Neuntöter, Steinschmätzer, Schafstelze, Dorngrasmücke, Stieglitz, Rotkehlchen). Die Goldammer mit fünf Brutpaaren war der häufigste Brutvogel. Insgesamt erreichte die Zahl der Brutreviere einen Wert von 2,3 je 100 lfd. Meter Hecke. Untersuchungen zum Auftreten von Schwebfliegen im Saum und dem angrenzenden Feld haben gezeigt, dass deren Aktivität auf den Krautstreifen 1995 fünf mal und 2004 sieben mal höher war als im 5 m Bereich der angrenzenden Ackerkultur. Da Schwebfliegen den Hauptbestandteil der Nestlingsnahrung z. B. für die Goldammer darstellen, belegen diese Zahlen die Bedeutung des Saumes als Nahrungshabitat.Stichwörter: Vögel, Hecken, Feldraine, NahrungPromotion of farmland birds by recreation of nesting and feeding habitatsAbstractThe increasing degradation of nesting and feeding habitats for farmland birds, is one of the main causes of their steady decline. With the increasing formation of large-scale fields with uniform crops, the food conditions deteriorate for the birds on the field. After the political turn in the beginning of the 90ies, there was also a need for the restructuring of cleared agricultural landscape in East Germany. In 1993, the so-called ‚Brandenburg stackedwood hedge‘ was created on a fi eld south to Berlin as a model to show an ecological and economic way for the replanting of hedges and fi eld margins. These small structures providing in their combination of both breeding and foraging habitat throughout the year. Under the current condition they are showing a way to improve the living conditions for birds on farmland. The ‚Brandenburg stacked-wood hedge‘ consists of two brush barriers of dead wood arranged in parallel. Between them, there is one row of native-species trees and shrubs planted. The 575 m long stacked hedge is adjoined by a five-metre-wide herbaceous strip established by sowing a suitable mixture of seed in 1994. Regular recording of birds from 1995 to 1998 and in 2004 shows the continuous use of the hedge as a habitat for birds. Already in 1994, the first red-backed shrikes were observed in the dead wood. In 1995, the dead wood provided a breeding ground for yellow-hammer, red-backed shrike, northern wheatear und yellow wagtail. In 1998, the shrubs had grown to a suitable size to shelter for the fi rst time white throat. In 2004, ten years after establishment of the hedgerow, 13 bird pairs of 7 species nested in the hedgerow - yellowhammer, red-backed shrike, northern wheatear, yellow wagtail, whitethroat, eurasian goldfi nch, european robin. The yellow-hammer occurred most frequently with fi ve pairs. Density of breeding birds totalled 2.3 pairs per 100 m hedgerow. Investigations on the occurrence of hoverflies in the edge biotop and the adjacent field have shown, that there activity on the wild herb strips were five times higher in 1995 and seven times higher in 2004 than in the 5 m region of the adjacent arable crop. Since hoverflies representing the main part of the nestling food (yellowhammer), these figures demonstrate the importance of the edge biotop as foraging habitat.Keywords: birds, hedges, field margins, food sourc

Impact of lattice dynamics on the phase stability of metamagnetic FeRh: Bulk and thin films

We present phonon dispersions, element-resolved vibrational density of states (VDOS) and corresponding thermodynamic properties obtained by a combination of density functional theory (DFT) and nuclear resonant inelastic X-ray scattering (NRIXS) across the metamagnetic transition of B2 FeRh in the bulk material and thin epitaxial films. We see distinct differences in the VDOS of the antiferromagnetic (AF) and ferromagnetic (FM) phase which provide a microscopic proof of strong spin-phonon coupling in FeRh. The FM VDOS exhibits a particular sensitivity to the slight tetragonal distortions present in epitaxial films, which is not encountered in the AF phase. This results in a notable change in lattice entropy, which is important for the comparison between thin film and bulk results. Our calculations confirm the recently reported lattice instability in the AF phase. The imaginary frequencies at the $X$-point depend critically on the Fe magnetic moment and atomic volume. Analyzing these non vibrational modes leads to the discovery of a stable monoclinic ground state structure which is robustly predicted from DFT but not verified in our thin film experiments. Specific heat, entropy and free energy calculated within the quasiharmonic approximation suggest that the new phase is possibly suppressed because of its relatively smaller lattice entropy. In the bulk phase, lattice degrees of freedom contribute with the same sign and in similar magnitude to the isostructural AF-FM phase transition as the electronic and magnetic subsystems and therefore needs to be included in thermodynamic modeling.Comment: 15 pages, 12 figure

Antibiotic use during pregnancy increases offspring asthma severity in a dose‐dependent manner

Background: The use of antibiotics during pregnancy is associated with increased allergic asthma risk in the offspring, and given that approximately 25% of pregnant women are prescribed antibiotics, it is important to understand the mechanisms contributing to this phenomenon. Currently, there are no studies that directly test this association experimentally. Our objective was to develop a mouse model in which antibiotic treatment during pregnancy results in increased offspring asthma susceptibility. Methods: Pregnant mice were treated daily from gestation day 8-17 with an oral solution of the antibiotic vancomycin, and three concentrations were tested. At weaning, offspring were subjected to an adjuvant-free experimental asthma protocol using ovalbumin as an allergen. The composition of the gut microbiome was determined in mothers and offspring with samples collected from five different time points; shortchain fatty acids were also analyzed in allergic offspring. Results: We found that maternal antibiotic treatment during pregnancy was associated with increased offspring asthma severity in a dose-dependent manner. Furthermore, maternal vancomycin treatment during pregnancy caused marked changes in the gut microbiome composition in both mothers and pups at several different time points. The increased asthma severity and intestinal microbiome changes in pups were also associated with significantly decreased cecal short-chain fatty acid concentrations. Conclusion: Consistent with the "Developmental Origins Hypothesis," our results confirm that exposure to antibiotics during pregnancy shapes the neonatal intestinal environment and increases offspring allergic lung inflammation

CpG-Methylation Regulates a Class of Epstein-Barr Virus Promoters

DNA methylation is the major modification of eukaryotic genomes and plays an essential role in mammalian gene regulation. In general, cytosine-phosphatidyl-guanosine (CpG)-methylated promoters are transcriptionally repressed and nuclear proteins such as MECP2, MBD1, MBD2, and MBD4 bind CpG-methylated DNA and contribute to epigenetic silencing. Methylation of viral DNA also regulates gene expression of Epstein-Barr virus (EBV), which is a model of herpes virus latency. In latently infected human B cells, the viral DNA is CpG-methylated, the majority of viral genes is repressed and virus synthesis is therefore abrogated. EBV's BZLF1 encodes a transcription factor of the AP-1 family (Zta) and is the master gene to overcome viral gene repression. In a genome-wide screen, we now identify and characterize those viral genes, which Zta regulates. Among them are genes essential for EBV's lytic phase, which paradoxically depend on strictly CpG-methylated promoters for their Zta-induced expression. We identified novel DNA recognition motifs, termed meZRE (methyl-Zta-responsive element), which Zta selectively binds in order to ‘read’ DNA in a methylation- and sequence-dependent manner unlike any other known protein. Zta is a homodimer but its binding characteristics to meZREs suggest a sequential, non-palindromic and bipartite DNA recognition element, which confers superior DNA binding compared to CpG-free ZREs. Our findings indicate that Zta has evolved to transactivate cytosine-methylated, hence repressed, silent promoters as a rule to overcome epigenetic silencing

Degradation of haloaromatic compounds

An ever increasing number of halogenated organic compounds has been produced by industry in the last few decades. These compounds are employed as biocides, for synthetic polymers, as solvents, and as synthetic intermediates. Production figures are often incomplete, and total production has frequently to be extrapolated from estimates for individual countries. Compounds of this type as a rule are highly persistent against biodegradation and belong, as "recalcitrant" chemicals, to the class of so-called xenobiotics. This term is used to characterise chemical substances which have no or limited structural analogy to natural compounds for which degradation pathways have evolved over billions of years. Xenobiotics frequently have some common features. e.g. high octanol/water partitioning coefficients and low water solubility which makes for a high accumulation ratio in the biosphere (bioaccumulation potential). Recalcitrant compounds therefore are found accumulated in mammals, especially in fat tissue, animal milk supplies and also in human milk. Highly sophisticated analytical techniques have been developed for the detection of organochlorines at the trace and ultratrace level

Benchmark dataset for daily stratospheric ozone tendencies

State-of-the art white-box models for stratospheric chemistry require enormous computational power. Our research is focused on developing much faster black-box models to determine 24-hour tendencies of stratospheric ozone. To apply machine learning methods, our project requires a high number of labeled data samples. Our data is derived from simulation runs of the Lagrangian chemistry and transport model ATLAS using the numerical full chemistry model. This numerical full chemistry model solves a system of ordinary differential equations for variable time steps at every model point. By storing these calculations it becomes feasible to train black-box models, which in turn require a very much lower computation time. The used chemistry and transport model is initialized with satellite data and ozone probes and enables a data-product that tracks the chemical change in-place of air-parcels. The result is a dateset offering global and daily data, containing input and output pairs to determine 24-hour tendencies of stratospheric ozone

Neural representation of the stratospheric ozone chemistry

Abstract In climate modeling, the stratospheric ozone layer is typically only considered in a highly simplified form due to computational constraints. For climate projections, it would be of advantage to include the mutual interactions between stratospheric ozone, temperature, and atmospheric dynamics to accurately represent radiative forcing. The overarching goal of our research is to replace the ozone layer in climate models with a machine-learned neural representation of the stratospheric ozone chemistry that allows for a particularly fast, but accurate and stable simulation. We created a benchmark data set from pairs of input and output variables that we stored from simulations of the ATLAS Chemistry and Transport Model. We analyzed several variants of multilayer perceptrons suitable for physical problems to learn a neural representation of a function that predicts 24-h ozone tendencies based on input variables. We performed a comprehensive hyperparameter optimization of the multilayer perceptron using Bayesian search and Hyperband early stopping. We validated our model by replacing the full chemistry module of ATLAS and comparing computation time, accuracy, and stability. We found that our model had a computation time that was a factor of 700 faster than the full chemistry module. The accuracy of our model compares favorably to the full chemistry module within a 2-year simulation run, also outperforms a previous polynomial approach for fast ozone chemistry, and reproduces seasonality well in both hemispheres. In conclusion, the neural representation of stratospheric ozone chemistry in simulation resulted in an ozone layer that showed a high accuracy, significant speed-up, and stability in a long-term simulation.</jats:p