High-Pressure Reverse Osmosis for Industrial Water Recycling: Permeate-Sided Pressure Drop as Performance-Limiting Factor

In the chemical industry large amounts of saline wastewater occur. Its disposal into rivers is a considerable burden to the ecosystem. To strive for a circular economy and enable a viable raw material recycling, energy-efficient concentration processes are requisite. High-pressure reverse osmosis meets this criterion, but its industrial application demands suitable membrane elements that withstand the exceptional operation conditions and provide sufficient performance. Hence, new requirements regarding the design of spiral-wound elements arise. To identify those, specific performance-limiting effects need a better understanding.     

Chemical Recycling of Carbon Fiber Reinforced Epoxy Composites Using Mild Conditions

The increased use of carbon fiber reinforced thermosets generates more waste and end-of-life products. However, an efficient recycling method for the expensive carbon fibers has not yet been developed. The selective decomposition of amine-cured epoxy resin under mild conditions is presented. A two-step method was investigated to decompose the epoxy resin. The optimum parameters were initially determined using a model compound. By analysis of the reaction products, a cleavage of the C–N bond according to the Cope elimination could be proven. Therefore, the Cope elimination is suggested as the main step of the decomposition of amine-cured epoxy resins in presence of hydrogen peroxide. By dissolving the resin, it is possible to recover resin-free fibers with unimpaired mechanical properties.     

Automated design of error-resilient and hardware-efficient deep neural networks

Applying deep neural networks (DNNs) in mobile and safety-critical systems, such as autonomous vehicles, demands a reliable and efficient execution on hardware. The design of the neural architecture has a large influence on the achievable efficiency and bit error resilience of the network on hardware. Since there are numerous design choices for the architecture of DNNs, with partially opposing effects on the preferred characteristics (such as small error rates at low latency), multi-objective optimization strategies are necessary. In this paper, we develop an evolutionary optimization technique for the automated design of hardware-optimized DNN architectures. For this purpose, we derive a set of inexpensively computable objective functions, which enable the fast evaluation of DNN architectures with respect to their hardware efficiency and error resilience. We observe a strong correlation between predicted error resilience and actual measurements obtained from fault injection simulations. Furthermore, we analyze two different quantization schemes for efficient DNN computation and find one providing a significantly higher error resilience compared to the other. Finally, a comparison of the architectures provided by our algorithm with the popular MobileNetV2 and NASNet-A models reveals an up to seven times improved bit error resilience of our models. We are the first to combine error resilience, efficiency, and performance optimization in a neural architecture search framework.

     

Resources and reserves of European gravel deposits

Contrary to public opinion, gravel and sand are mineral raw materials that are not available in unlimited quantities, and, at any place. In quantity and economic value they belong to the top-ranking resources on a world scale. Supplies are not unlimited: lifetime is very short in some regions. Being very vulnerable to changes in transport costs and sensitive with respect to conflicts between environmental protection and mining, sand and gravel require scientific effort in order that new deposits be found and the lifetime of reserves increased.     

Fully automated single-molecule force spectroscopy for screening applications

With the introduction of single-molecule force spectroscopy (SMFS) it has become possible to directly access the interactions of various molecular systems. A bottleneck in conventional SMFS is collecting the large amount of data required for statistically meaningful analysis. Currently, atomic force microscopy (AFM)-based SMFS requires the user to tediously 'fish' for single molecules. In addition, most experimental and environmental conditions must be manually adjusted. Here, we developed a fully automated single-molecule force spectroscope. The instrument is able to perform SMFS while monitoring and regulating experimental conditions such as buffer composition and temperature. Cantilever alignment and calibration can also be automatically performed during experiments. This, combined with in-line data analysis, enables the instrument, once set up, to perform complete SMFS experiments autonomously.     

Transmitted-light microscopy - a new method for surface structure analysis of cleanable non-woven dust filter media

A series of CeO(2)-ZrO(2) mixed oxides were prepared using coprecipitation method and characterized by BET, oxygen storage capacity (OSC), X-ray diffraction (XRD) and H(2)-temperature-programmed reduction (H(2)-TPR). The catalytic activities toward toluene combustion were investigated in a micro-reactor. The results demonstrate that the catalytic activity of Pt/gamma-Al(2)O(3)/Ce(0.50)Zr(0.50)O(2) monolithic catalyst can be greatly improved by doping metal into Ce(0.50)Zr(0.50)O(2). When doping Y and Mn into Ce(0.50)Zr(0.50)O(2) simultaneously, the catalyst Pt/gamma-Al(2)O(3)/Ce(0.40)Zr(0.40)Y(0.10)Mn(0.10)O(X) shows the highest activity. The T(10) (the temperature of 10% toluene conversion) and the complete conversion temperature (the temperature of 90% toluene conversion) of toluene are 443 and 489K, respectively. Gas hourly space velocity (GHSV) results show that the prepared catalyst can be applied in a wide range of GHSV (from 12,000 to 20,000h(-1)). The catalyst prepared shows great potential for practical application.     

Kinetic gas-water transfer and gas accumulation in porous media during pulsed oxygen sparging

Gas-water mass transfer and the transport of dissolved gases in variably saturated porous media are key processes for in-situ remediation by pulsed gas sparging. In this context, gas dissolution tests were conducted during pulsed oxygen gas injection into sand columns. The columns were recharged with anoxic water, effluents were analyzed for dissolved O2, and tracer tests were performed to detect accumulation of trapped gas. In a second series oxygen gas was blended with sulfur hexafluoride (SF6), and O2 and SF6 breakthrough curves were recorded. To interpret experimental results, a numerical model was applied that simulates multi-species kinetic mass transfer during gas dissolution. The model predicted breakthrough curves of dissolved gas species and delivered spatially resolved values for gas phase accumulation and composition, which are not directly accessible experimentally. It was shown how dissolved nitrogen accumulates increasingly in trapped gas phase and inhibits its complete dissolution, in case the pulsed gas injections were operated based on O2 breakthrough only. Accumulation of nitrogen also retarded dissolved oxygen transport and thus oxygen breakthrough. Experiments plus modeling demonstrated that SF6 measurements are highly sensitive to the gas dissolution processes, and provide a more sensitive criterion for determining gas injection frequencies during pulsed biosparging.