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Synthetic mimics of natural high-performance structural materials have shown great and partly unforeseen opportunities for the design of multifunctional materials. For nacre-mimetic nanocomposites, it has remained extraordinarily challenging to make ductile materials with high stretchability at high fractions of reinforcements, which is however of crucial importance for flexible barrier materials. Here, highly ductile and tough nacre-mimetic nanocomposites are presented, by implementing weak, but many hydrogen bonds in a ternary nacre-mimetic system consisting of two polymers (poly(vinyl amine) and poly(vinyl alcohol)) and natural nanoclay (montmorillonite) to provide efficient energy dissipation and slippage at high nanoclay content (50 wt%). Tailored interactions enable exceptional combinations of ductility (close to 50% strain) and toughness (up to 27.5 MJ m⁻³). Extensive stress whitening, a clear sign of high internal dynamics at high internal cohesion, can be observed during mechanical deformation, and the materials can be folded like paper into origami planes without fracture. Overall, the new levels of ductility and toughness are unprecedented in highly reinforced bioinspired nanocomposites and are of critical importance to future applications, e.g., as barrier materials needed for encapsulation and as a printing substrate for flexible organic electronics.
As with most high-velocity free-surface flows, stepped spillway flows become self-aerated when the drop height exceeds a critical value. Due to the step-induced macro-roughness, the flow field becomes more turbulent than on a similar smooth-invert chute. For this reason, cascades are oftentimes used as re-aeration structures in wastewater treatment. However, for stepped spillways as flood release structures downstream of deoxygenated reservoirs, gas transfer is also of crucial significance to meet ecological requirements. Prediction of mass transfer velocities becomes challenging, as the flow regime differs from typical previously studied flow conditions. In this paper, detailed air-water flow measurements are conducted on stepped spillway models with different geometry, with the aim to estimate the specific air-water interface. Re-aeration performances are determined by applying the absorption method. In contrast to earlier studies, the aerated water body is considered a continuous mixture up to a level where 75% air concentration is reached. Above this level, a homogenous surface wave field is considered, which is found to significantly affect the total air-water interface available for mass transfer. Geometrical characteristics of these surface waves are obtained from high-speed camera investigations. The results show that both the mean air concentration and the mean flow velocity have influence on the mass transfer. Finally, an empirical relationship for the mass transfer on stepped spillway models is proposed.
The chemical imaging sensor is a semiconductor-based chemical sensor capable of visualizing pH and ion distributions. The spatial resolution depends on the lateral diffusion of photocarriers generated by illumination of the semiconductor substrate. In this study, two types of optical setups, one based on a bundle of optical fibers and the other based on a binocular tube head, were developed to project a hybrid illumination of a modulated light beam and a ring-shaped constant illumination onto the sensor plate. An improved spatial resolution was realized by the ring-shaped constant illumination, which suppressed lateral diffusion of photocarriers by enhanced recombination due to the increased carrier concentration.
Sleep scoring is a necessary and time-consuming task in sleep studies. In animal models (such as mice) or in humans, automating this tedious process promises to facilitate long-term studies and to promote sleep biology as a data-driven f ield. We introduce a deep neural network model that is able to predict different states of consciousness (Wake, Non-REM, REM) in mice from EEG and EMG recordings with excellent scoring results for out-of-sample data. Predictions are made on epochs of 4 seconds length, and epochs are classified as artifactfree or not. The model architecture draws on recent advances in deep learning and in convolutional neural networks research. In contrast to previous approaches towards automated sleep scoring, our model does not rely on manually defined features of the data but learns predictive features automatically. We expect deep learning models like ours to become widely applied in different fields, automating many repetitive cognitive tasks that were previously difficult to tackle.
In this work, we report on our attempt to design and implement an early introduction to basic robotics principles for children at kindergarten age. One of the main challenges of this effort is to explain complex robotics contents in a way that pre-school children could follow the basic principles and ideas using examples from their world of experience. What sets apart our effort from other work is that part of the lecturing is actually done by a robot itself and that a quiz at the end of the lesson is done using robots as well. The humanoid robot Pepper from Softbank, which is a great platform for human–robot interaction experiments, was used to present a lecture on robotics by reading out the contents to the children making use of its speech synthesis capability. A quiz in a Runaround-game-show style after the lecture activated the children to recap the contents they acquired about how mobile robots work in principle. In this quiz, two LEGO Mindstorm EV3 robots were used to implement a strongly interactive scenario. Besides the thrill of being exposed to a mobile robot that would also react to the children, they were very excited and at the same time very concentrated. We got very positive feedback from the children as well as from their educators. To the best of our knowledge, this is one of only few attempts to use a robot like Pepper not as a tele-teaching tool, but as the teacher itself in order to engage pre-school children with complex robotics contents.