This study presents the design, fabrication, and test of a micro accelerometer with intrinsic processing capabilities, that integrates the functions of sensing and computing in the same MEMS. The device consists of an inertial mass electrostatically coupled to an oscillating beam through a gap of 8 µm. The motion of the inertial mass modulates an AC electrostatic field that drives the beam in its non-linear regime. This non-linearity is used to implement machine learning in the mechanical domain, using reservoir computing with delayed feedback to process the acceleration information provided by the inertial mass. The device is microfabricated on a silicon-on-insulator substrate using conventional MEMS processes. Dynamic characterization showed good accelerometer functionalities, with an inertial mass sensitivity on the order of 100 mV/g from 250 to 1300 Hz and a natural frequency of 1.7 kHz. In order to test the device computing capabilities, two different machine learning benchmarks were implemented, with the inputs fed to the device as accelerations. The neuromorphic MEMS accelerometer was able to accurately emulate non-linear autoregressive moving average models and compute the parity of random bit streams. These results were obtained in a test system with a non-trivial transfer function, showing a robustness that is well-suited to anticipated applications.
A MEMS squeezer able to compress single living cells underwater until rupture was designed and tested. The relatively large motion range of the device in aqueous media (~2.5 µm) allows provoking cell disruption while measuring cell mechanical properties before and after membrane rupture. An AC driven electrothermal micro actuator with mechanical amplification pressed single cells against a reference back spring. Deformations of the cell and the reference spring were measured with nanoscale resolution using optical Fourier transform techniques. The motion of the reference spring divided by the cell deformation provides the cell stiffness relative to the reference spring constant. An abrupt change in the cell stiffness and the appearance of cracks indicated the cell wall rupture force was reached. A total of 22 baker’s yeast cells (Saccharomyces cerevisiae) were squeezed with the micro device. The average force necessary to rupture the cell membrane was 0.47 ± 0.1 µN. Before rupture the cells had an average stiffness of 9.3 ± 3.1 N m−1; the post-rupture stiffness dropped to 0.94 ± 0.57 N m−1. Cell hysteresis was also measured: cells squeezed and released before reaching the rupture force showed residual deformations below 100 nm, while cells squeezed past the rupture force and then released showed residual deformations between 490 and 990 nm.
This study investigated the rehydration of active dried yeast and the impact of temperature and wort density on the strength and stiffness of individual cells using a microelectromechanical system. Dried yeast was rehydrated using a variety of methods, including direct pitching into wort (13.6°P) at 12, 22 and 30°C, as well as propagation using YEPD media (4.2°P). Cell viability was found to broadly correlate with measurement of cell strength and stiffness. Both wort density and temperature affected viability and physical characteristics of the cells after 1 h of rehydration. Yeast cells rehydrated at low temperature and high wort density burst at a lower force (0.26 ± 0.02 μN) than cells rehydrated using high temperature and low density media (0.50 ± 0.10 μN). Cells rehydrated at higher temperatures or using low density media showed no significant difference in strength and stiffness when compared with high viability, actively fermenting yeast. Changes in yeast physiology, owing to stress responses, may contribute to the observed differences in mechanical properties. These findings have application in brewery design, as pumping, centrifugation, storage and associated shear impart mechanical stress upon yeast cells.
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