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The quantitative nanomechanical characterization of soft materials using the nanoindentation technique requires further improvements in the performances of instruments, including their force Resolution in particular. A micro-machined silicon nanoforce transducer based upon electrostatic comb drives featuring the force and depth resolutions down to 1 nN and 0.2 nm, respectively, is described.
At the end of theMEMStransducer’s main shaft, a pyramidal tip is fabricated using a focused ion beam facility. A proof-of-principle setup with this MEMS nanoindenter has been established to measure the mechanical properties of soft polydimethylsiloxane. First measurement results demonstrate that the prototype measurement system is able to quantitatively characterize soft materials with elastic moduli down to a few MPa.
Today, short- and long-term structural health monitoring (SHM) of bridge infrastructures and their safe, reliable and cost-effective maintenance has received considerable attention. From a surveying or civil engineer’s point of view, vibration-based SHM can be conducted by inspecting the changes in the global dynamic behaviour of a structure, such as natural frequencies (i. e. eigenfrequencies), mode shapes (i. e. eigenforms) and modal damping, which are known as modal parameters. This research work aims to propose a robust and automatic vibration analysis procedure that is so-called robust time domain modal parameter identification (RT-MPI) technique. It is novel in the sense of automatic and reliable identification of initial eigenfrequencies even closely spaced ones as well as robustly and accurately estimating the modal parameters of a bridge structure using low numbers of cost-effective micro-electro-mechanical systems (MEMS) accelerometers. To estimate amplitude, frequency, phase shift and damping ratio coefficients, an observation model consisting of: (1) a damped harmonic oscillation model, (2) an autoregressive model of coloured measurement noise and (3) a stochastic model in the form of the heavy-tailed family of scaled t-distributions is employed and jointly adjusted by means of a generalised expectation maximisation algorithm. Multiple MEMS as part of a geo-sensor network were mounted at different positions of a bridge structure which is precalculated by means of a finite element model (FEM) analysis. At the end, the estimated eigenfrequencies and eigenforms are compared and validated by the estimated parameters obtained from acceleration measurements of high-end accelerometers of type PCB ICP quartz, velocity measurements from a geophone and the FEM analysis. Additionally, the estimated eigenfrequencies and modal damping are compared with a well-known covariance driven stochastic subspace identification approach, which reveals the superiority of our proposed approach. We performed an experiment in two case studies with simulated data and real applications of a footbridge structure and a synthetic bridge. The results show that MEMS accelerometers are suitable for detecting all occurring eigenfrequencies depending on a sampling frequency specified. Moreover, the vibration analysis procedure demonstrates that amplitudes can be estimated in submillimetre range accuracy, frequencies with an accuracy better than 0.1 Hz and damping ratio coefficients with an accuracy better than 0.1 and 0.2 % for modal and system damping, respectively.
Nanoscale building blocks impart added functionalities to microelectromechanical systems (MEMS). The integration of silicon nanowires with MEMS-based sensors leading to miniaturization with improved sensitivity and higher noise immunity is one example highlighting the advantages of this multiscale approach. The accelerated pace of research in this area gives rise to an urgent need for batch-compatible solutions for scaling to nano. To address this challenge, a monolithic fabrication approach of silicon nanowires with 10-lm-thick silicon-on-insulator (SOI) MEMS is developed in this work. A two-step Si etching approach is adopted, where the first step creates a shallow surface protrusion and the second step releases it in the form of a nanowire. It is during this second deep etching step that MEMS—with at least a 2-order-of-magnitude scale difference - is formed as well. The technique provides a pathway for preserving the lithographic resolution and transforming it into a very high mechanical precision in the assembly of micro- and nanoscales with an extreme topography. Validation of the success of integration is carried out via in situ actuation of MEMS inside an electron microscope loading the nanowire up to its fracture. The technique yields nanowires on the top surface of MEMS, thereby providing ease of access for the purposes of carrying out surface processes such as doping and contact formation as well as in situ observation. As the first study demonstrating such monolithic integration in thick SOI, the work presents a pathway for scaling down to nano for future MEMS combining multiple scales.