Induction plasma synthesis of fullerenes and nanotubes using carbon black–nickel particles

The existence of fullerenes (as allotropes of carbon) was established in the mid-1980s and during the last 15–18 years, systematic efforts have been devoted to improve the methods of their synthesis, including plasma-based system methods. The work presented here is focused on the investigation of fullerenes synthesis, using a radio frequency plasma reactor. The main objectives were to explore the use of induction plasma technology for the synthesis in-continuo of carbon fullerenes and to predict their formation conditions through conduct of theoretical studies. Thus, a thermodynamic study was carried out to predict the equilibrium composition of fullerenes produced at several combinations of operating conditions. Additionally, a statistical factorial design experiment, employing four factors at two levels, was also developed, in order to study the influence of the system’s operating parameters on the eventual C₆₀ fullerene yield. The results obtained showed that the reactor pressure, the electrical power and the raw material feed rates all have an important effect on the synthesis of fullerenes. The highest C₆₀ concentration in the products was found to be about 7.7 wt.%. Various other carbon nanostructures, such as nanotubes and nano-onions, were also successfully produced.     

A Distributed O(1)-Approximation Algorithm for the Uniform Facility Location Problem

We investigate a metric facility location problem in a distributed setting. In this problem, we assume that each point is a client as well as a potential location for a facility and that the opening costs for the facilities and the demands of the clients are uniform. The goal is to open a subset of the input points as facilities such that the accumulated cost for the whole point set, consisting of the opening costs for the facilities and the connection costs for the clients, is minimized. We present a randomized distributed algorithm that computes in expectation an ${\mathcal {O}}(1)$ -approximate solution to the metric facility location problem described above. Our algorithm works in a synchronous message passing model, where each point is an autonomous computational entity that has its own local memory and that communicates with the other entities by message passing. We assume that each entity knows the distance to all the other entities, but does not know any of the other pairwise distances. Our algorithm uses three rounds of all-to-all communication with message sizes bounded to $\mathcal{O}(\log(n))$ bits, where n is the number of input points. We extend our distributed algorithm to constant powers of metric spaces. For a metric exponent ?≥1, we obtain a randomized ${\mathcal {O}}(1)$ -approximation algorithm that uses three rounds of all-to-all communication with message sizes bounded to $\mathcal{O}(\log(n))$ bits.     

Unmanned Ground Vehicle Navigation in Coordinate-Free and Localization-Free Wireless Sensor and Actuator Networks

We present a set of algorithms for the navigation of Unmanned Ground Vehicles (UGVs) towards a set of pre-identified target nodes in coordinate-free and localization-free wireless sensor and actuator networks. The UGVs are equipped with a set of wireless listeners that provide sensing information about the potential field generated by the network of actuators. Two main navigation scenarios are considered: single-UGV, single-destination navigation and multi-UGV, multi-destination navigation. For the single-UGV, single-destination case, we present both centralized and distributed navigation algorithms. Both algorithms share a similar two-phase concept. In the first phase, the system assigns level numbers to individual nodes based on their hop distance from the target nodes. In the second phase, the UGV uses the potential field created by the network of actuators to move towards the target nodes, requiring cooperation between triplets of actuator nodes and the UGV. The hop distance to the target nodes is used to control the main moving direction while the potential field, which can be measured by listeners on the UGV, is used to determine the UGV’s movement. For the multi-UGV, multi-destination case, we present a decentralized allocation algorithm such that multiple UGVs avoid converging to the same destination. After each UGV determines its destination, the proposed navigation scheme is applied. The presented algorithms do not attempt to localize UGVs or sensor nodes and are therefore suitable for operating in GPS-free/denied environments. We also present a study of the communication complexity of the algorithms as well as simulation examples that verify the proposed algorithms and compare their performances.     

Ordering Partial Partitions for Image Segmentation and Filtering: Merging,Creating and Inflating Blocks

The refinement order on partitions corresponds to the operation of merging blocks in a partition, which is relevant to image segmentation and filtering methods. Its mathematical extension to partial partitions, that we call standard order, involves several operations, not only merging, but also creating new blocks or inflating existing ones, which are equally relevant to image segmentation and filtering techniques. These three operations correspond to three basic partial orders on partial partitions, the merging, inclusion and inflating orders. There are three possible combinations of these three basic orders, one of them is the standard order, the other two are the merging-inflating and inclusion-inflating orders. We study these orders in detail, giving in particular their minimal and maximal elements, covering relations and height functions. We interpret hierarchies of partitions and partial partitions in terms of an adjunction between (partial) partitions (possibly with connected blocks) and scalars. This gives a lattice-theoretical interpretation of edge saliency, hence a typology for the edges in partial partitions. The use of hierarchies in image filtering, in particular with component trees, is also discussed. Finally, we briefly mention further orders on partial partitions that can be useful for image segmentation.     

Characterization of Maximum Likelihood Solutions to Image Reconstruction in Photon Emission Tomography

For photon emission tomography, the maximum likelihood (ML) estimator for image reconstruction is generally solution to a nonlinear equation involving the vector of measured data. No explicit closed-form solution is known in general for such a nonlinear ML equation, and numerical resolution is usually implemented, with a very popular iterative method formed by the expectation-maximization algorithm. The numerical character of such resolutions usually makes it difficult to obtain a general characterization of the performance of the ML solution. We show that the nonlinear ML equation can be replaced by an equivalent system of two dual linear equations nonlinearly coupled. This formulation allows us to exhibit explicit (to some extent) forms for the solutions to the ML equation, in general conditions corresponding to the various possible configurations of the imaging system, and to characterize their performance with expressions for the mean-squared error, bias and Cramér-Rao bound. The approach especially applies to characterize the ML solutions obtained numerically, and offers a theoretical framework to contribute to better appreciation of the capabilities of ML reconstruction in photon emission tomography.     

Bestimmung des molekulargewichts von „mikrogelhaltigem”︁ polystyrol mit der ultrazentrifuge (methode archibald)

The weight average molecular weight M_w of a mixture of two polystyrenes with very high and relatively low molecular weights (M_w = 16.1 · 10⁶ and 98 · 10³ g/mole), as a model for polymers containing microgel, was determined by ultracentrifugation with the ARCHIBALD-methode. M_w decreases with increasing number of rotation N. The exact molecular weight can only be determined by extrapolation to N = 0. This value depends nearly completely on the molecular weight and weight percentage of the “microgel”. It is not suitable for the characterization of the whole sample.     

Ceramic Composites with Three-Dimensional Architectures Designed to Produce a Threshold Strength—II. Mechanical Observations

Finite element modeling and linear elastic fracture mechanics are used to model the residual stresses and failure stress of ceramic composites consisting of polyhedral alumina cores surrounded by thin alumina/mullite layers in residual compression. This type of composite architecture is expected to exhibit isotropic threshold strength behavior, in which the strength of the composite for a particular assumed flaw will be constant and independent of the orientation of tensile loading. The results of the modeling indicate that the strengths of such architectures will be higher than those of laminates of similar architectural dimensions that were previously found to exhibit threshold strength behavior for a particular flaw type. Flexural testing of the polyhedral architectures reveals that failure is dominated by processing defects found at junctions between the polyhedra. Fractography revealed the interaction of these defects with the residual stresses in the compressive layers that separate the polyhedra.