Enhanced Thermal Oxidation Stability of Reduced Graphene Oxide by Nitrogen Doping

Nitrogen‐doped reduced graphene oxide (N‐doped RGO) samples with a high level of doping, up to 13 wt. %, have been prepared by annealing graphene oxide under a flow of pure ammonia. The presence of nitrogen within the structure of RGO induces a remarkable increase in the thermal stability against oxidation by air. The thermal stability is closely related with the temperature of synthesis and the nitrogen content. The combustion reaction of nitrogen in different coordination environments (pyridinic, pyrrolic, and graphitic) is analyzed against a graphene fragment (undoped) from a thermodynamic point of view. In agreement with the experimental observations, the combustion of undoped graphene turns out to be more spontaneous than when nitrogen atoms are present.     

Segmentation of Stochastic Images using Stochastic Extensions of the Ambrosio-Tortorelli and the Random Walker Model

We discuss methods based on stochastic PDEs for the segmentation of images with uncertain gray values resulting from measurement errors and noise. Our approach yields a reliable precision estimate for the segmentation result, and it allows us to quantify the robustness of edges in noisy images and under gray value uncertainty. The ansatz space for such images identifies gray values with random variables. For their discretization we utilize generalized polynomial chaos expansions and the generalized spectral decomposition method. This leads to the stochastic generalization of the Ambrosio-Tortorelli approximation of the Mumford-Shah functional. Moreover, we present the extension of the random walker segmentation for our stochastic images, which is based on an identification of the graph weights with random variables. We demonstrate the performance of the methods on a data set obtained from a digital camera as well as real medical ultrasound data. (© 2011 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim)     

Gyroscopic Stabilization of 2nd-Order-Systems with Indefinite Damping

We consider the gyroscopic stabilization of the unstable system ẍ + D ẋ + Kx = 0 with positive definite stiffness matrix K. The indefinite damping matrix D is responsible for the instability of the system. The modelling of sliding bearings can lead to negative damping, see [6]. A gyroscopic stabilization of an unstable mechanical system with indefinite damping matrix was investigated in [4] in the case of matrix order n = 2 using the Routh-Hurwitz criterion. The question was raised whether an unstable system can be stabilized by adding a gyroscopic term Gẋ with a suitable skew-symmetric matrix G = −G^T . (© 2011 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim)     

P9]+ [Al(OR(F))4)]-, the salt of a homopolyatomic phosphorus cation

Positive at last: The first condensed-phase homopolyatomic phosphorus cation [P(9)](+) was prepared using a combination of the oxidant [NO](+) and weakly coordinating anion, [Al{OC(CF(3))(3)}(4)](-). [P(9)](+) consists of two P(5) cages linked by a phosphonium atom to give a D(2d)-symmetric Zintl cluster. NMR (see picture), Raman, and IR spectroscopy, mass spectrometry, and quantum-chemical calculations confirmed the structure.     

How to confirm identified toxicants in effect-directed analysis

Due to the production and use of a multitude of chemicals in modern society, waters, sediments, soils and biota may be contaminated with numerous known and unknown chemicals that may cause adverse effects on ecosystems and human health. Effect-directed analysis (EDA), combining biotesting, fractionation and chemical analysis, helps to identify hazardous compounds in complex environmental mixtures. Confirmation of tentatively identified toxicants will help to avoid artefacts and to establish reliable cause–effect relationships. A tiered approach to confirmation is suggested in the present paper. The first tier focuses on the analytical confirmation of tentatively identified structures. If straightforward confirmation with neat standards for GC–MS or LC–MS is not available, it is suggested that a lines-of-evidence approach is used that combines spectral library information with computer-based structure generation and prediction of retention behaviour in different chromatographic systems using quantitative structure–retention relationships (QSRR). In the second tier, the identified toxicants need to be confirmed as being the cause of the measured effects. Candidate components of toxic fractions may be selected based, for example, on structural alerts. Quantitative effect confirmation is based on joint effect models. Joint effect prediction on the basis of full concentration–response plots and careful selection of the appropriate model are suggested as a means to improve confirmation quality. Confirmation according to the Toxicity Identification Evaluation (TIE) concept of the US EPA and novel tools of hazard identification help to confirm the relevance of identified compounds to populations and communities under realistic exposure conditions. Promising tools include bioavailability-directed extraction and dosing techniques, biomarker approaches and the concept of pollution-induced community tolerance (PICT). Figure Toxicity confirmation in EDA as a tiered approach