A novel method is presented for in situ quantification of living cell adhesion forces using a homemade nanorobotic system provided with two independently actuated probes that form a dual-probe nanotweezer capable of pick-and-place manipulation of a single living cell in an aqueous environment. Compared with single-cell force spectroscopy (SCFS) based on traditional atomic force microscopy (AFM), cell immobilization via chemical trapping is unnecessary and the test cell can be efficiently released using the nanotweezer to significantly enhance production of the SCFS. Benefiting from the accurate force sensing capability of AFM, the nanotweezer allows reliable force measurement ranging from picoNewtons to microNewtons and is sufficiently sensitive to characterize short- and long-term adhesion of cell-cell and cell-substrate adhesions. Capabilities of the nanotweezer have been validated through experimental qualification of cell-substrate and cell-cell adhesion events of C2C12 cells (mouse myoblast adherent) with different contact times.

Optical Tweezers are considered one of the most suitable techniques for biological tasks, however the lack of automation make this technology less accessible. We present here a new 3D force sensing method with high bandwidth (up to 10Khz) which can allow implementing complex robotic approaches. Proposed technique uses high speed image tracking with nano-metric resolution in 3 directions. Its capabilities are demonstrated in a teleoperated 3D manipulation scenario with a haptic user interface, where naive users performed direct in vitro haptic exploration of isolated Red Blood Cells inside a Petri dish.

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To simluate the effects of lunar dust environment veritably by using lunar dust effects simulator, a detection and control system based on singlechip microcomputer was developed. In this system, peripheral circuits with stepper motor driver, temperature sensor and rotary transformer were used to collect the signals and control the temperature as well as speed. Stepper motor speed up/down curve, digital convolution filter and error compensation were adhibited for improving the system accuracy. The results showed that this method was simple, reliable and had high control accuracy.