Autonomous use of electrical energy by an artificial molecular machine

The ability to exploit energy autonomously is one of the hallmarks of Life. Mastering such processes in artificial nanosystems can open unforeseen technological opportunities. In the last decades, light- and chemically-driven autonomous systems have been developed in relation to conformational motion and self-assembly. On the contrary, the autonomous exploitation of electrical energy remains essentially unexplored, despite being an attractive energy source. Herein we demonstrate the autonomous operation of an electrochemically-powered self-assembling nanomachine. Threading and dethreading motions of a pseudorotaxane take place autonomously in solution, between the electrodes of a scanning electrochemical microscope. This innovative actuation mode allows operating a molecular machine with an energy efficiency of 9%, unprecedented in autonomous systems. The strategy is general and can be applied to any redox-driven system, including molecular pumps that perform work repetitively. Ultimately, our study brings molecular nanoscience one step closer to everyday technology.

A Low Cost Inkjet-Printed Mass Sensor Using a Frequency Readout Strategy

The development of low-cost mass sensors is of unique interest for the scientific community due to the wide range of fields requiring these kind of devices. In this paper, a full inkjet-printed mass sensor is proposed. The device is based on a PolyEthylene Terephthalate (PET) cantilever beam (operating in its first natural frequency) where a strain-sensor and a planar coil have been realized by a low-cost InkJet Printing technology to implement the sensing and actuation strategies, respectively. The frequency readout strategy of the sensor presents several advantages, such as the intrinsic robustness against instabilities of the strain sensor, the residual stress of the cantilever beam, the target mass material, and the distance between the permanent magnet and the actuation coil (which changes as a function of the target mass values). However, the frictionless actuation mode represents another shortcoming of the sensor. The paper describes the sensor design, realization, and characterization while investigating its expected behavior by exploiting dedicate models. The working span of the device is 0–0.36 g while its resolution is in the order of 0.001 g, thus addressing a wide range of potential applications requiring very accurate mass measurements within a narrow operating range.

A Polymeric Bilayer Multi-Legged Soft Millirobot with Dual Actuation and Humidity Sensing

There are numerous works that report wirelessly controlling the locomotion of soft robots through a single actuation method of light or magnetism. However, coupling multiple driving modes to improve the mobility of robots is still in its infancy. Here, we present a soft multi-legged millirobot that can move, climb a slope, swim and detect a signal by near-infrared irradiation (NIR) light or magnetic field dual actuation. Due to the design of the feet structure, our soft millirobot incorporates the advantages of a single actuation mode of light or magnetism. Furthermore, it can execute a compulsory exercise to sense a signal and analyze the ambience fluctuation in a narrow place. This work provides a novel alternative for soft robots to achieve multimode actuation and signal sensing.

Material properties identification of a piezoelectric beam using inverse method

This paper presents an inverse method for material properties identification of a piezoelectric beam (piezoelectric charge and relative dielectric coefficients) using a wavelet-based neural network as an inverse tool. The identification analysis is carried out by using two approaches. In the first approach, i.e. sensor mode analysis, the input data for wavelet-based neural network training are measured voltages at several specific points on the beam's top surface resulting from the applied beam tip deflection. In the second approach, i.e. actuation mode analysis, the input data are values of the beam tip deflection caused by applying voltage on the beam's top surface. In this study, the input parameters employed to train the wavelet-based neural network are obtained using the finite element method. The identification results are compared with those of some conventional neural networks including radial basis function and multilayer perceptron. The results show that the proposed neural network is an efficient tool in the material properties identification problem.

Dynamic Analysis of a Micro Beam-Based Tactile Sensor Actuated by Fringing Electrostatic Fields

A new kind of fringing electrostatic actuation mode is developed. In this new actuation mode, the expression of fringing electrostatic force is found. The nonlinear dynamic analysis of this new actuation mode is presented by using the Method of Multiple Scales. An experiment is designed to observe the dynamic behaviors of this structure. It is observed that the resonance frequency rises with the increase of the initial displacement and the decrease of the slit gap; a smaller slit gap makes marked change of the resonance frequency in the same range of the initial displacement; the increase of the vibration amplitude is linear with the increase of the initial displacement; the fringing electrostatic force has a larger impact on the frequency response of the nonlinear vibration when the initial displacement, the beam length and the actuated voltage are larger. This new fringing electrostatic actuation mode can be used in a micro tactile sensor. The results of dynamic analysis can provide support for sensor design. Based on the dynamic investigations into the micro cantilevered beam actuated by fringing electrostatic force; three usage patterns of the sensor are introduced as follows. Firstly, measuring resonance frequency of the micro beam can derive the initial displacement. Second, the initial displacement can be derived from vibration amplitude measurement. Third, jump phenomenon can be used to locate the initial displacement demand.