Modelling and manufacturing of a dragonfly wing as basis for bionic research

Working principles in nature have been optimised by evolution for millions of years. Today we try to understand how these principles work and how they could be used in technical applications. Prominent examples for solutions which are inspired by bionic research are the Velcro fastener (inspired by the plant \u27Arcticum lappa\u27) [Pahl et al., 2003], swim suits (inspired by shark skin) [Thilmany, 2004] and self-cleaning surfaces using the lotus effect [von Baeyer, 2000]. The topic of aerodynamics is another large area for research and innovation in which we still hope to be able to learn from nature. The dragonfly combines very light wing structures with amazing flying abilities [Okamoto, 1996]. In order to study the exact properties of the dragonfly wing and to understand how this properties can be achieved, it is necessary to reproduce the geometry of the wing at a larger scale. This large scale model can be used to conduct further aerodynamic tests in a wind tunnel. The results of such investigations can lead to new impulses for the development of aircraft and micro air vehicles. In this paper the authors will describe the modelling and building of an enlarged model of a dragonfly wing as base for further bionic research

Modelling and manufacturing of a dragonfly wing as basis for bionic research

Working principles in nature have been optimised by evolution for millions of years. Today we try to understand how these principles work and how they could be used in technical applications. Prominent examples for solutions which are inspired by bionic research are the Velcro fastener (inspired by the plant 'Arcticum lappa') [Pahl et al., 2003], swim suits (inspired by shark skin) [Thilmany, 2004] and self-cleaning surfaces using the lotus effect [von Baeyer, 2000]. The topic of aerodynamics is another large area for research and innovation in which we still hope to be able to learn from nature. The dragonfly combines very light wing structures with amazing flying abilities [Okamoto, 1996]. In order to study the exact properties of the dragonfly wing and to understand how this properties can be achieved, it is necessary to reproduce the geometry of the wing at a larger scale. This large scale model can be used to conduct further aerodynamic tests in a wind tunnel. The results of such investigations can lead to new impulses for the development of aircraft and micro air vehicles. In this paper the authors will describe the modelling and building of an enlarged model of a dragonfly wing as base for further bionic research

Dopamine, affordance and active inference.

The role of dopamine in behaviour and decision-making is often cast in terms of reinforcement learning and optimal decision theory. Here, we present an alternative view that frames the physiology of dopamine in terms of Bayes-optimal behaviour. In this account, dopamine controls the precision or salience of (external or internal) cues that engender action. In other words, dopamine balances bottom-up sensory information and top-down prior beliefs when making hierarchical inferences (predictions) about cues that have affordance. In this paper, we focus on the consequences of changing tonic levels of dopamine firing using simulations of cued sequential movements. Crucially, the predictions driving movements are based upon a hierarchical generative model that infers the context in which movements are made. This means that we can confuse agents by changing the context (order) in which cues are presented. These simulations provide a (Bayes-optimal) model of contextual uncertainty and set switching that can be quantified in terms of behavioural and electrophysiological responses. Furthermore, one can simulate dopaminergic lesions (by changing the precision of prediction errors) to produce pathological behaviours that are reminiscent of those seen in neurological disorders such as Parkinson's disease. We use these simulations to demonstrate how a single functional role for dopamine at the synaptic level can manifest in different ways at the behavioural level

Surface-Initiated Group Transfer Polymerization Mediated by Rare Earth Metal Catalysts

We present the first example of a surface-initiated group transfer polymerization (SI-GTP) mediated by rare earth metal catalysts for polymer brush synthesis. The experimentally facile method allows rapid grafting of polymer brushes with a thickness of >150 nm in <5 min at room temperature. We show the preparation of common poly­(methacrylate) brushes and demonstrate that SI-GTP is a versatile route for the preparation of novel polymer brushes. The method gives access to both thermoresponsive and proton-conducting brush layers

Organic-inorganic hybrid nanoparticles via photoinduced micellation and siloxane core cross-linking of stimuli-responsive copolymers

Photoacid-induced siloxane cross-linking of stimuli-responsive copolymer micelles allows the synthesis of well-defined organic-inorganic hybrid nanoparticles. Two conceptually different synthetic approaches are presented, both via photoinduced cross-linking of poly(4-hydroxystyrene-block-styrene) micelles and via one-pot photoacid-catalyzed micelle formation and siloxane cross-linking of poly(4-tert-butoxystyrene-block-styrene). The multistep synthetic route showed intermicellar cross-linking leading to agglomerates. In contrast to this, the formation of the nanoparticles via the one-pot synthesis yielded well-defined structures. The use of different siloxane cross-linking agents and their effects on the properties of the cross-linked micellar structures have been evaluated. Scanning electron microscopy and differential scanning calorimetry indicate rigid core cross-linked nanoparticles. Their size, molar mass, and swelling behavior were analyzed by dynamic and static light scattering. Cyclic siloxane cross-linking agents lead to residual C\uee - C double bonds within the nanoparticle core that allow postsynthetic modification by, e.g., thiol-ene click reactions. \ua9 2013 American Chemical Society.Peer reviewed: YesNRC publication: N

Graphene Transistors with Multifunctional Polymer Brushes for Biosensing Applications

Exhibiting a combination of exceptional structural and electronic properties, graphene has a great potential for the development of highly sensitive sensors. To date, many challenging chemical, biochemical, and biologic sensing tasks have been realized based on graphene. However, many of these sensors are rather unspecific. To overcome this problem, for instance, the sensor surface can be modified with analyte-specific transducers such as enzymes. One problem associated with the covalent attachment of such biomolecular systems is the introduction of crystal defects that have a deleterious impact on the electronic properties of the sensor. In this work, we present a versatile platform for biosensing applications based on polymer-modified CVD-grown graphene transistors. The functionalization method of graphene presented here allows one to integrate several functional groups within surface-bound polymer brushes without the introduction of additional defects. To demonstrate the potential of this polymer brush functionalization scaffold, we modified solution-gated graphene field-effect transistors with the enzyme acetylcholinesterase and a transducing group, allowing the detection of the neurotransmitter acetylcholine. Taking advantage of the transducing capability of graphene transistors and the versatility of polymer chemistry and enzyme biochemistry, this study presents a novel route for the fabrication of highly sensitive, multipurpose transistor sensors that can find application for a multitude of biologically relevant analytes

Organic–Inorganic Hybrid Nanoparticles via Photoinduced Micellation and Siloxane Core Cross-Linking of Stimuli-Responsive Copolymers

Photoacid-induced siloxane cross-linking of stimuli-responsive copolymer micelles allows the synthesis of well-defined organic–inorganic hybrid nanoparticles. Two conceptually different synthetic approaches are presented, both via photoinduced cross-linking of poly­(4-hydroxystyrene-<i>block</i>-styrene) micelles and via <i>one-pot</i> photoacid-catalyzed micelle formation and siloxane cross-linking of poly­(4-<i>tert</i>-butoxystyrene-<i>block</i>-styrene). The <i>multistep</i> synthetic route showed intermicellar cross-linking leading to agglomerates. In contrast to this, the formation of the nanoparticles via the one-pot synthesis yielded well-defined structures. The use of different siloxane cross-linking agents and their effects on the properties of the cross-linked micellar structures have been evaluated. Scanning electron microscopy and differential scanning calorimetry indicate rigid core cross-linked nanoparticles. Their size, molar mass, and swelling behavior were analyzed by dynamic and static light scattering. Cyclic siloxane cross-linking agents lead to residual CC double bonds within the nanoparticle core that allow postsynthetic modification by, e.g., thiol–ene click reactions

Associative Learning and Derived Attention in Humans

Attention describes the collection of cognitive mechanisms that act to preferentially allocate mental resources to the processing of certain aspects of sensory input. This chapter describes important advances that have been made in recent years in elucidating the nature and operation of derived attention in studies of human learning. A dysfunction of the relationship between learning and attention has been implicated in the development of psychotic symptoms that are a characteristic feature of schizophrenia. The chapter explains the new techniques for assessing derived attention, which potentially provide a more selective demonstration of an abnormal relationship between learning and the effective salience of stimuli in psychotic patients. The concept of derived attention, first introduced by William James over a century ago, describes how associative learning can produce changes in the effective salience of stimuli. The chapter discusses the influence of learning on the attentional processing of stimuli that predict outcomes