Electrochemical Behavior of Single CuO Nanoparticles: Implications for the Assessment of their Environmental Fate

The electrochemical behavior of copper oxide nanoparticles is investigated at both the single particle and at the ensemble level in neutral aqueous solutions through the electrode‐particle collision method and cyclic voltammetry, respectively. The influence of Cl^? and NO₃^? anions on the electrochemical processes occurring at the nanoparticles is further evaluated. The electroactivity of CuO nanoparticles is found to differ between the two types of experiments. At the single‐particle scale, the reduction of the CuO nanoparticles proceeds to a higher extent in the presence of chloride ion than of nitrate ion containing solutions. However, at the multiparticle scale the CuO reduction proceeds to the same extent regardless of the type of anions present in solution. The implications for assessing realistically the environmental fate and therefore the toxicity of metal‐based nanoparticles in general, and copper‐based nanoparticles in particular, are discussed.     

An efficient microfluidic sorter: implementation of double meandering micro striplines for magnetic particles switching

The ability to trap, manipulate, and separate magnetic beads has become one of the key requirements in realizing an integrated magnetic lab-on-chip biosensing system. In this article, we present the design and fabrication of an integrated magneto-fluidic device for sorting magnetic particles with a sorting efficiency of up to 95%. The actuation and manipulation of magnetic beads are realized using microfabricated square meandering current-carrying micro striplines. The current is alternated between two neighboring micro striplines to switch the magnetic beads to either one of the two outlets. We performed a series of parametric study to investigate the effect of applied current, flow rate, and switching frequency on the sorting efficiency. Experimental results reveal that the sorting efficiency is proportional to the square of current applied to the stripline, and decreases with increasing buffer flow rate and switching frequency. Such phenomena agree well with our theoretical analysis and simulation result. The fastest switching rate, which is limited by the microchannel geometry and bead velocity, is 2 Hz.     

Shifts in sensory dominance between various stages of user-product interactions

In the area of product design, sensory dominance can be defined as the relative importance of different sensory modalities for product experience. It is often assumed that vision dominates the other senses. In the present study, we asked 243 participants to describe their experiences with consumer products in various situations: while buying a product, after the first week, the first month, and the first year of usage. The data suggest that the dominant sensory modality depends on the period of product usage. At the moment of buying, vision is the most important modality, but during the usage the other sensory modalities gain importance. The roles of the different modalities during usage are product-dependent. Averaged over 93 products analyzed in this study, after one month of usage touch becomes more important than vision, and after one year vision, touch and audition appear to be equally important. We conclude that to create a long-lasting positive product experience, designers need to consider user-product interaction at different stages of product usage and to determine which sensory modality dominates product experience at each stage.     

Textures on Rank‐1 Lattices

Storing textures on orthogonal tensor product lattices is predominant in computer graphics, although it is known that their sampling efficiency is not optimal. In two dimensions, the hexagonal lattice provides the maximum sampling efficiency. However, handling these lattices is difficult, because they are not able to tile an arbitrary rectangular region and have an irrational basis. By storing textures on rank‐1 lattices, we resolve both problems: Rank‐1 lattices can closely approximate hexagonal lattices, while all coordinates of the lattice points remain integer. At identical memory footprint texture quality is improved as compared to traditional orthogonal tensor product lattices due to the higher sampling efficiency. We introduce the basic theory of rank‐1 lattice textures and present an algorithmic framework which easily can be integrated into existing off‐line and real‐time rendering systems.     

Predicting the unexpected

Dagstuhl seminar no. 10102 on discrete event logistic systems recognized a network of persistent models to be a “Grand Challenge.” Such on-line model network will offer an infrastructure that facilitates the management of logistic operations. This ambition to create a network of persistent models implies a radical shift for model design activities as the objective is an infrastructure rather than application-specific solutions. In particular, model developers can no longer assume that they know what their model will be used for. It is no longer possible to design for the expected.This paper presents insights in model development and design in the absence of precise knowledge concerning a model's usage. Basically, model developers may solely rely on the presence of the real-world counterpart mirrored by their model and a general idea about the nature of the application (e.g. coordination of logistic operations). When the invariants of their real-world counterpart suffice for models to be valid, these models become reusable and integrate-able. As these models remain valid under a wide range of situations, they become multi-purpose and durable resources rather than single-purpose short-lived components or legacy, which is even worse.Moreover and more specifically, the paper describes how to build models that allow their users to generate predictions in unexpected situations and atypical conditions. Referring to previous work, the paper concisely discusses how these predictions can be generated starting from the models. This prediction-generating technology is currently being transferred into an industrial MES.     

Autonomic information flow improves prognostic impact of task force HRV monitoring

Heart rate variability (HRV) represents the cardiovascular control mediated by the autonomic nervous system and other mechanisms. In the established task force HRV monitoring different cardiovascular control mechanisms can approximately be identified at typical frequencies of heart rate oscillations by power spectral analysis. HRV measures assessing complex and fractal behavior partly improved clinical risk stratification. However, their relationship to (patho-)physiology is not sufficiently explored. Objective of the present work is the introduction of complexity measures of different physiologically relevant time scales. This is achieved by a new concept of the autonomic information flow (AIF) analysis which was designed according to task force HRV. First applications show that different time scales of AIF improve the risk stratification of patients with multiple organ dysfunction syndrome and cardiac arrest patients in comparison to standard HRV. Each group's significant time scales correspond to their respective pathomechanisms.     

Theoretical and Experimental Damage Prediction in Cold and Semi‐Hot Bulk Forming of Ductile Steels

Bulk forging is among the most important manufacturing methods in metal forming, due to its wide applicability from some ounces to several tons of steel in a high diversity of shapes and forming conditions. Economical constraints demand for further optimisation and cost‐effective production. This requires the application of suitable finite elements simulation software, in order to support the already digitalised construction processes. Ductile damage is one of the most severe problems to arise during the production sequences, not only in cold but also in semi‐hot forging operations. Mathematical approaches exist for the modelling and simulation of ductile fracture in steel. In this paper some widespread used damage models are introduced and discussed. Their damage prediction quality has been verified by experiments, the tensile test and the collar specimen upsetting with several different steels under cold and semi‐hot forging conditions. The methods for the experimental fracture detection are introduced as well. In cold forging the passive ultrasonic testing with integrated statistical filtering algorithms is used. As this method is not applicable to semi‐hot forging experiments, optical fracture detection by means of a high‐speed camera is used instead. A very interesting material behaviour of the steels tested has been identified in the semi‐hot upsetting of collar specimen. For every steel a distinct temperature crossover interval exists, in which the forging process abruptly changes from damaged to undamaged state. This interval amounts to some degrees Celsius only for each of the seven materials investigated. Among the damage models proposed, the Model of Effective Stresses by Lemaitre is chosen for the application to a cold and a semi‐hot forging operation. These industrial processes of an axle end (cold) and a journal bearing (semi‐hot) are susceptible to damage for reasons to be discussed in this paper. It will be shown that the internal fracture of the axle end (chevrons) and the surface fissures of the journal bearing can be predicted with high accuracy. Moreover, the application of the damage model in the finite element software MSC.SuperForm 2004 offers a promising approach for process optimisation. Several possibilities could be tested for their suitability of reducing the calculated damage: geometry variation of the forming tools, process annealing, different materials. The use of damage models in finite element simulation can be regarded as a further step towards an optimal process design.     

Generalized ordering-search for learning directed probabilistic logical models

Recently, there has been an increasing interest in directed probabilistic logical models and a variety of formalisms for describing such models has been proposed. Although many authors provide high-level arguments to show that in principle models in their formalism can be learned from data, most of the proposed learning algorithms have not yet been studied in detail. We introduce an algorithm, generalized ordering-search, to learn both structure and conditional probability distributions (CPDs) of directed probabilistic logical models. The algorithm is based on the ordering-search algorithm for Bayesian networks. We use relational probability trees as a representation for the CPDs. We present experiments on a genetics domain, blocks world domains and the Cora dataset. Editors: Stephen Muggleton, Ramon Otero, Simon Colton.