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Determination of the frictional coefficient of the implant-antler interface : experimental approach
(2012)
The similar bone structure of reindeer antler to human bone permits studying the osseointegration of dental implants in the jawbone. As the friction is one of the major factors that have a significant influence on the initial stability of immediately loaded dental implants, it is essential to define the frictional coefficient of the implant-antler interface. In this study, the kinetic frictional forces at the implant-antler interface were measured experimentally using an optomechanical setup and a stepping motor controller under different axial loads and sliding velocities. The corresponding mean values of the static and kinetic frictional coefficients were within the range of 0.5–0.7 and 0.3–0.5, respectively. An increase in the frictional forces with increasing applied axial loads was registered. The measurements showed an evidence of a decrease in the magnitude of the frictional coefficient with increasing sliding velocity. The results of this study provide a considerable assessment to clarify the suitable frictional coefficient to be used in the finite element contact analysis of antler specimens.
Due to the decarbonization of the energy sector, the electric distribution grids are undergoing a major transformation, which is expected to increase the load on the operating resources due to new electrical loads and distributed energy resources. Therefore, grid operators need to gradually move to active grid management in order to ensure safe and reliable grid operation. However, this requires knowledge of key grid variables, such as node voltages, which is why the mass integration of measurement technology (smart meters) is necessary. Another problem is the fact that a large part of the topology of the distribution grids is not sufficiently digitized and models are partly faulty, which means that active grid operation management today has to be carried out largely blindly. It is therefore part of current research to develop methods for determining unknown grid topologies based on measurement data. In this paper, different clustering algorithms are presented and their performance of topology detection of low voltage grids is compared. Furthermore, the influence of measurement uncertainties is investigated in the form of a sensitivity analysis.
The capacitive electrolyte–insulator–semiconductor (EIS) structure is a typical device based on a field-effect sensor platform. With a simple silicon-based structure, EIS have been useful for several sensing applications, especially with incorporation of nanostructured films to modulate the ionic transport and the flat-band potential. In this paper, we report on ion transport and changes in flat-band potential in EIS sensors made with layer-by-layer films containing poly(amidoamine) (PAMAM) dendrimer and single-walled carbon nanotubes (SWNTs) adsorbed on p-Si/SiO 2 /Ta 2 O 5 chips with an Al ohmic contact. The impedance spectra were fitted using an equivalent circuit model, from which we could determine parameters such as the double-layer capacitance. This capacitance decreased with the number of bilayers owing to space charge accumulated at the electrolyte–insulator interface, up to three PAMAM/SWNTs bilayers, after which it stabilized. The charge-transfer resistance was also minimum for three bilayers, thus indicating that this is the ideal architecture for an optimized EIS performance. The understanding of the influence of nanostructures and the fine control of operation parameters pave the way for optimizing the design and performance of new EIS sensors.
Booster stations can fulfill a varying pressure demand with high energy-efficiency, because individual pumps can be deactivated at smaller loads. Although this is a seemingly simple approach, it is not easy to decide precisely when to activate or deactivate pumps. Contemporary activation controls derive the switching points from the current volume flow through the system. However, it is not measured directly for various reasons. Instead, the controller estimates the flow based on other system properties. This causes further uncertainty for the switching decision. In this paper, we present a method to find a robust, yet energy-efficient activation strategy.