Surfactant‐Free Shape Control of Gold Nanoparticles Enabled by Unified Theoretical Framework of Nanocrystal Synthesis

Gold nanoparticles have unique properties that are highly dependent on their shape and size. Synthetic methods that enable precise control over nanoparticle morphology currently require shape‐directing agents such as surfactants or polymers that force growth in a particular direction by adsorbing to specific crystal facets. These auxiliary reagents passivate the nanoparticles' surface, and thus decrease their performance in applications like catalysis and surface‐enhanced Raman scattering. Here, a surfactant‐ and polymer‐free approach to achieving high‐performance gold nanoparticles is reported. A theoretical framework to elucidate the growth mechanism of nanoparticles in surfactant‐free media is developed and it is applied to identify strategies for shape‐controlled syntheses. Using the results of the analyses, a simple, green‐chemistry synthesis of the four most commonly used morphologies: nanostars, nanospheres, nanorods, and nanoplates is designed. The nanoparticles synthesized by this method outperform analogous particles with surfactant and polymer coatings in both catalysis and surface‐enhanced Raman scattering.     

Benefit of Modern Arc Management in MF Dual Magnetron Sputtering Power Supplies

For modern large area coatings reactive dual magnetron sputtering (DMS) is a widespread technology. Economic constraints and advanced technology calls for increasing process flexibility regarding target utilization as well as the use of new materials. This in turn raises demands to the arc handling capabilities of the power supplies. On the one hand, a stable process at high arcing rates demands short arc blanking times and fast recovery to full output power. On the other hand, some materials tend to the formation of so‐called “hard” arcs, with hot cathode spots. In this case, longer treatment times are needed. The benefit of modern arc handling capabilities is demonstrated for reactive SiO₂ deposition from heavily used targets. For other demanding materials with low melting point, favourable operating modes are investigated.     

Real-time optimization and nonlinear model predictive control of processes governed by differential-algebraic equations

Optimization problems in chemical engineering often involve complex systems of nonlinear DAE as the model equations. The direct multiple shooting method has been known for a while as a fast off-line method for optimization problems in ODE and later in DAE. Some factors crucial for its fast performance are briefly reviewed. The direct multiple shooting approach has been successfully adapted to the specific requirements of real-time optimization. Special strategies have been developed to effectively minimize the on-line computational effort, in which the progress of the optimization iterations is nested with the progress of the process. They use precalculated information as far as possible (e.g. Hessians, gradients and QP presolves for iterated reference trajectories) to minimize response time in case of perturbations. In typical real-time problems they have proven much faster than fast off-line strategies. Compared with an optimal feedback control computable upper bounds for the loss of optimality can be established that are small in practice. Numerical results for the Nonlinear Model Predictive Control (NMPC) of a high-purity distillation column subject to parameter disturbances are presented.     

Spatial and temporal variations of summer surface temperatures of wet polygonal tundra in Siberia - implications for MODIS LST based permafrost monitoring

The surface temperature of permafrost soils in remote arctic areas is accessible by satellite land surface temperature (LST) detection. However, the spatial resolution of satellite measurements such as the MODIS LST products is limited and does not detect the heterogeneities of the wet polygonal tundra landscape where surface wetness varies over distances of several meters. This paper examines the spatial and temporal variability of summer surface temperatures of a polygonal tundra site in northern Siberia using a ground based high resolution thermal imaging system. Thermal infrared images were taken of a 1000 m² polygonal tundra area in 10 min intervals from July to September 2008. Under clear sky conditions, the individual measurements indicate temperature differences of up to 6 K between dry and wet tundra surfaces and which can exceed 12 K when dry tundra and water surfaces are compared. These differences disappear when temperature averages are considered for intervals longer than the diurnal cycle; for weekly averages the spatial temperature variability decreases below 1 K. The exception is the free water surface of a shallow polygonal pond where weekly averaged temperature differences of 2.5 K are sustained compared to the tundra surface.The ground based thermal infrared images are upscaled to MODIS sized pixels and compared to available MODIS LST data for individual measurements and weekly averages. The comparisons show generally good agreement for the individual measurements under clear sky conditions, which exist during 20% of the studied time period. However, several erroneous measurements and large data gaps occur in the MODIS LST data during cloudy conditions, leading to biased weekly temperature averages inferred from the satellite observations. Based on these results the following recommendations are given for future permafrost temperature monitoring based on MODIS LST products: (i) high resolution surface water masks for the quality assessment in landscapes where lakes and ponds are frequent and (ii) reliable cloud cover detection in conjunction with a gap filling procedure for accurate temporal averages.     

GrGen.NET

GrGen.NET is a generative programming system for graph rewriting, transforming intuitive and expressive rewrite rule specifications into highly efficient .NET code. The user is supported by a convenient environment consisting of a graph viewer, an interactive shell with integrated debugging support, and an elegant domain-specific language for the combination of rewrite rules. After rapid prototyping with these tools, the resulting graph transformation programmes can be easily integrated into arbitrary .NET applications to serve as the algorithmic kernel. Expressiveness, convenience, and speed are exemplified by GrGen-solutions to the case studies AntWorld, Refactoring, and Conference Scheduling—besides others.     

LSTM-Modeling of continuous emotions in an audiovisual affect recognition framework

Automatically recognizing human emotions from spontaneous and non-prototypical real-life data is currently one of the most challenging tasks in the field of affective computing. This article presents our recent advances in assessing dimensional representations of emotion, such as arousal, expectation, power, and valence, in an audiovisual human–computer interaction scenario. Building on previous studies which demonstrate that long-range context modeling tends to increase accuracies of emotion recognition, we propose a fully automatic audiovisual recognition approach based on Long Short-Term Memory (LSTM) modeling of word-level audio and video features. LSTM networks are able to incorporate knowledge about how emotions typically evolve over time so that the inferred emotion estimates are produced under consideration of an optimal amount of context. Extensive evaluations on the Audiovisual Sub-Challenge of the 2011 Audio/Visual Emotion Challenge show how acoustic, linguistic, and visual features contribute to the recognition of different affective dimensions as annotated in the SEMAINE database. We apply the same acoustic features as used in the challenge baseline system whereas visual features are computed via a novel facial movement feature extractor. Comparing our results with the recognition scores of all Audiovisual Sub-Challenge participants, we find that the proposed LSTM-based technique leads to the best average recognition performance that has been reported for this task so far.     

Pathophysiological In Vitro Profile of Neuronal Differentiated Cells Derived from Niemann-Pick Disease Type C2 Patient-Specific iPSCs Carrying the NPC2 Mutations c.58G>T/c.140G>T

Niemann-Pick type C2 (NP-C2) disease is a rare hereditary disease caused by mutations in the NPC2 gene. NPC2 is a small, soluble protein consisting of 151 amino acids, primarily expressed in late endosomes and lysosomes (LE/LY). Together with NPC1, a transmembrane protein found in these organelles, NPC2 accomplishes the exclusion of cholesterol; thus, both proteins are essential to maintain cellular cholesterol homeostasis. Consequently, mutations in the NPC2 or NPC1 gene result in pathophysiological accumulation of cholesterol and sphingolipids in LE/LY. The vast majority of Niemann-Pick type C disease patients, 95%, suffer from a mutation of NPC1, and only 5% display a mutation of NPC2. The biochemical phenotype of NP-C1 and NP-C2 appears to be indistinguishable, and both diseases share several commonalities in the clinical manifestation. Studies of the pathological mechanisms underlying NP-C2 are mostly based on NP-C2 animal models and NP-C2 patient-derived fibroblasts. Recently, we established induced pluripotent stem cells (iPSCs), derived from a donor carrying the NPC2 mutations c.58G>T/c.140G>T. Here, we present a profile of pathophysiological in vitro features, shared by NP-C1 and NP-C2, of neural differentiated cells obtained from the patient specific iPSCs. Profiling comprised a determination of the NPC2 protein level, detection of cholesterol accumulation by filipin staining, analysis of oxidative stress, and determination of autophagy. As expected, the NPC2-deficient cells displayed a significantly reduced amount of NPC2 protein, and, accordingly, we observed a significantly increased amount of cholesterol. Most notably, NPC2-deficient cells displayed only a slight increase of reactive oxygen species (ROS), suggesting that they do not suffer from oxidative stress and express catalase at a high level. As a site note, comparable NPC1-deficient cells suffer from a lack of catalase and display an increased level of ROS. In summary, this cell line provides a valuable tool to gain deeper understanding, not only of the pathogenic mechanism of NP-C2, but also of NP-C1.