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Here we present the highly sensitive detection of dopamine using gold nanogap IDAs with redox-cycling amplification. Through the combination with a facile electrochemical activation and a chronoamperometric multistep protocol fouling of the gold electrode surface can be prevented and a sensitivity of 14 nA μM -1 with excellent linearity up to 10 μM is achieved. The low-cost and reproducible wafer level fabrication process of the nanogap IDAs plays a key role. Electrode and substrate materials can be nearly arbitrarily chosen. Also the gap sizes could be adjusted down to sub-100 nm dimensions with this versatile approach, allowing for very high signal amplification. In comparison to the current gold standard, fastscan cyclic voltammetry (FSCV) with carbon fiber microelectrodes (CFMEs), which suffers from high background currents, no elaborate data processing and high-end electronic equipment is needed. Employing our flexible, easy and inexpensive method, DA monitoring with a short acquisition period and a detection limit less than 200 nM is successfully demonstrated.
The electricity demand due to the increasing number of EVs presents new challenges for the operation of the electricity network, especially for the distribution grids. The existing grid infrastructure may not be sufficient to meet the new demands imposed by the integration of EVs. Thus, EV charging may possibly lead to reliability and stability issues, especially during the peak demand periods. Demand side management (DSM) is a potential and promising approach for mitigation of the resulting impacts. In this work, we developed an autonomous DSM strategy for optimal charging of EVs to minimize the charging cost and we conducted a simulation study to evaluate the impacts to the grid operation. The proposed approach only requires a one way communicated incentive. Real profiles from an Austrian study on mobility behavior are used to simulate the usage of the EVs. Furthermore, real smart meter data are used to simulate the household base load profiles and a real low voltage grid topology is considered in the load flow simulation. Day-ahead electricity stock market prices are used as the incentive to drive the optimization. The results for the optimum charging strategy is determined and compared to uncontrolled EV charging. The results for the optimum charging strategy show a potential cost saving of about 30.8% compared to uncontrolled EV charging. Although autonomous DSM of EVs achieves a shift of load as pursued, distribution grid operation may be substantially affected by it. We show that in the case of real time price driven operation, voltage drops and elevated peak to average powers result from the coincident charging of vehicles during favourable time slots.
In contrast to fossil energy sources, the supply by renewable energy sources likewind and photovoltaics can not be controlled. Therefore, flexibilities on the demandside of the electric power grid, like electro-chemical energy storage systems, are usedincreasingly to match electric supply and demand at all times. To control those flex-ibilities, we consider two algorithms that both lead to linear programming problems.These are solved autonomously on the demand side, i.e., by household computers.In the classic approach, an energy price signal is sent by the electric utility to thehouseholds, which, in turn, optimize the cost of consumption within their constraints.Instead of an energy price signal, we claim that an appropriate power signal that istracked in L1-norm as close as possible by the household has favorable character-istics. We argue that an interior point of the household’s feasibility region is neveran optimal price-based point but can result in a L1-norm optimal point. Thus, pricesignals can not parametrize the complete feasibility region which may not lead to anoptimal allocation of consumption.We compare the price and power tracking algorithms over a year on the base ofone-day optimizations regarding different information settings and using a large dataset of daily household load profiles. The computational task constitutes an embarrassingly parallel problem. To this end, the performance of the two parallel computation frameworks DEF [1] and Ray [2] are investigated. The Ray framework is used to run the Python applications locally on several cores. With the DEF frameworkwe execute our Python routines parallelly in a cloud. All in all, the results providean understanding of when which computation framework and autonomous algorithmwill outperform the other.
On the extension of digital ecosystems for SCM and customs with distributed ledger technologies
(2019)
Global supply chains represent the backbone of the modern manufacturing industry. Planning of global supply chains still represents a major hurdle, mainly because of the high complexity and unforeseen disruptions that have to be mastered for meeting the different logistics windows in a globally distributed production environment. Trust in supply chains is an additional challenge. A major – albeit sometimes overlooked - part of Supply Chain Management (SCM) is the management and integration of customs processes, clearing of tariffs, (re-)billing of customers, and fulfilling other legal requirements related to crossing borders, ranging from environmental standards over goods inspection to general paper work. With the exception of work offered by the World Customs Organization (WCO) the issue of customs and blockchain is still underrepresented in research and practice. In this paper, we look at innovations that drive the current ICTenabled SCM research and how these can be combined with smart customs management. After a literature review and introduction to the state-of-the-art, we list potential trust-based innovations for SCM and customs in digital business ecosystems. Based upon the innovations we also describe a requirements analysis of existing distributed ledger technologies (requirements for system layout, system configuration, system governance). A description of the prototype for the Lake Constance region – on which we are currently working – concludes the paper.
Comparison of silicon nitride based 1x8 Y-branch splitters applying different waveguide structures
(2019)
This paper presents design, simulation and optimization of 1x8 Y-branch power splitters based on Si/SiO2/SiN/SiOx material platform. For the designs, two different waveguide structures were used, i.e. ridge and rib waveguides. The splitters were designed for 850 nm spectral optical window and the simulations were performed applying FEM and BPM methods in RSoft photonic tool. The aim of this work was to find minimum physical dimensions of the designed splitters occupying minimal space on PIC chip. The optimization was done with regards to high symmetrical splitting ratio and low insertion loss. Finally, the optical properties of both splitters were studied and compared with each other.
Compact and high-resolution 256-channel silicon nitride based AWG-spectrometer for OCT on a chip
(2019)
We present design, simulation and technological verification of a compact 256-channel, 42-GHz silicon nitride based AWG-spectrometer. The spectrometer was designed for TM-polarized light with a central wavelength of 850 nm, applying “AWG-Parameters” tool. This design is based on a previous study of various AWG designs (8-channel, 100-GHz; 20-channel, 50-GHz; 40-channel, 50-GHz, 80-channel, 50-GHz and 160-channel, 50-GHz AWGs), which were all technologically verified. The spectrometer features small size and high resolution. It is integrated on OCT chip using standard CMOS processes. The SD-OCT system is developed to operate in a wavelength range from 800 nm to 900 nm, having 0.1 nm resolution.