10 research outputs found

    Sensing applications of micro- and nanoelectromechanical resonators

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    The sensitivity of micro- and nanoscale resonator beams for sensing applications in ambient conditions was investigated. Micro-electromechanical (MEMS) and nanoelectromechanical systems (NEMS) were realized using silicon carbide (SiC) and polycrystalline aluminium nitride (AlN) as active layers on silicon substrates. Resonant frequencies and quality factors in vacuum as well as in air were measured. The sensitivity behaviour under ambient conditions with a mass loading in the range of picogram (pg) was verified and measurements with biological mass loading were performed. In addition, the sensitivity to pressure variations was analysed

    Nanomechanics of Single Crystalline Tungsten Nanowires

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    Single crystalline tungsten nanowires were prepared from directionally solidified NiAl-W alloys by a chemical release from the resulting binary phase material. Electron back scatter diffraction (EBSD) proves that they are single crystals having identical crystallographic orientation. Mechanical investigations such as bending tests, lateral force measurements, and mechanical resonance measurements were performed on 100–300 nm diameter wires. The wires could be either directly employed using micro tweezers, as a singly clamped nanowire or in a doubly clamped nanobridge. The mechanical tests exhibit a surprisingly high flexibility for such a brittle material resulting from the small dimensions. Force displacement measurements on singly clamped W nanowires by an AFM measurement allowed the determination of a Young's modulus of 332 GPa very close to the bulk value of 355 GPa. Doubly clamped W nanowires were employed as resonant oscillating nanowires in a magnetomotively driven resonator running at 117 kHz. The Young's modulus determined from this setup was found to be higher 450 GPa which is likely to be an artefact resulting from the shift of the resonance frequency by an additional mass loading

    Modelling the Exhaust Gas Aftertreatment System of a SI Engine Using Artificial Neural Networks

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