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1. Characterization of oscillation amplitude of contact angle during AC electrowetting of water droplets

   https://doi.org/10.1088/2399-6528/ab9ea1  

   Wikramanayake, Enakshi; Bahadur, Vaibhav (June 2020, Journal of Physics Communications)

   Abstract Most studies on electrowetting (EW) involve the use of AC electric fields, which cause droplets to oscillate in response to the sinusoidal waveform. Oscillation-driven mixing in droplets is the basis for multiple microfluidic applications. Presently, we study the voltage and AC frequency-dependent oscillations of electrowetted water droplets on a smooth, hydrophobic surface. We introduce a new approach towards analyzing droplet oscillations, which involves characterization of the oscillation amplitude of the contact angle (CA). An experimentally validated, fundamentals-based model to predict voltage and frequency-dependent CA oscillations is developed, which is analogous to the Lippmann’s equation for predicting voltage-dependent CAs. It is seen that this approach can help estimate the threshold voltage more accurately, than from experimental measurements of CA change. Additionally, we use a coplanar electrode configuration with high voltage and ground electrodes arranged on the substrate. This configuration eliminates measurement artefacts in the classical EW configuration associated with a wire electrode protruding into the droplet. An interesting consequence of this configuration is that the system capacitance is reduced substantially, compared to the classical configuration. The coplanar electrode configuration shows a reduced rate of CA change with voltage, thereby increasing the voltage range over which the CA can be modulated. 

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2. Capacitance Effects of a Hydrophobic-Coated Ion Gel Dielectric on AC Electrowetting

   https://doi.org/10.3390/mi12030320  

   Lee, Taewoo; Park, Sung-Yong (March 2021, Micromachines)

   We present experimental studies of alternating current (AC) electrowetting dominantly influenced by several unique characteristics of an ion gel dielectric in its capacitance. At a high-frequency region above 1 kHz, the droplet undergoes the contact angle modification. Due to its high-capacitance characteristic, the ion gel allows the contact angle change as large as Δθ = 26.4°, more than 2-fold improvement, compared to conventional dielectrics when f = 1 kHz. At the frequency range from 1 to 15 kHz, the capacitive response of the gel layer dominates and results in a nominal variation in the angle change as θ ≈ 90.9°. Above 15 kHz, such a capacitive response of the gel layer sharply decreases and leads to the drastic increase in the contact angle. At a low-frequency region below a few hundred Hz, the droplet’s oscillation relying on the AC frequency applied was mainly observed and oscillation performance was maximized at corresponding resonance frequencies. With the high-capacitance feature, the ion gel significantly enlarges the oscillation performance by 73.8% at the 1st resonance mode. The study herein on the ion gel dielectric will help for various AC electrowetting applications with the benefits of mixing enhancement, large contact angle modification, and frequency-independent control. 

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3. Threshold Pressure Sensing Using Parametric Resonance in Electrostatic MEMS

   https://doi.org/10.1109/SENSORS43011.2019.8956679  

   Hasan, Md Nahid; Pallay, Mark; Towfighian, Shahrzad (October 2019, 2019 IEEE SENSORS)

   This study illustrates the concept of threshold pressure sensing using the parametric resonance of an electrostatic levitation mechanism. The electrostatic levitation allows the oscillations in the opposite direction of the substrate, thereby not limited to small gaps. The pressure sensor detects the pressure drop below a threshold value by triggering the parametric resonance with significant peak to peak dynamic amplitude changes (~ 25 𝝁𝒎). This detection relies on the fact that the instability region expands when the pressure drop forces the amplitude jump up to the higher oscillation branch. This significant change in the resonator amplitude can be related to a large capacitance variation indicating the threshold pressure. A mathematical model of the resonator is presented to show the working principle of the sensor through frequency response. Our experimental results show that the threshold pressure the sensor detects, can be adjusted by the AC voltage it receives. 

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4. Epidermis‐Inspired Wearable Piezoresistive Pressure Sensors Using Reduced Graphene Oxide Self‐Wrapped Copper Nanowire Networks

   https://doi.org/10.1002/smtd.202100900  

   Zhu, Yangzhi; Hartel, Martin C.; Yu, Ning; Garrido, Pamela Rosario; Kim, Sanggon; Lee, Junmin; Bandaru, Praveen; Guan, Shenghan; Lin, Haisong; Emaminejad, Sam; et al (December 2021, Small Methods)

   Abstract Wearable piezoresistive sensors are being developed as electronic skins (E‐skin) for broad applications in human physiological monitoring and soft robotics. Tactile sensors with sufficient sensitivities, durability, and large dynamic ranges are required to replicate this critical component of the somatosensory system. Multiple micro/nanostructures, materials, and sensing modalities have been reported to address this need. However, a trade‐off arises between device performance and device complexity. Inspired by the microstructure of the spinosum at the dermo epidermal junction in skin, a low‐cost, scalable, and high‐performance piezoresistive sensor is developed with high sensitivity (0.144 kPa^‐1), extensive sensing range ( 0.1–15 kPa), fast response time (less than 150 ms), and excellent long‐term stability (over 1000 cycles). Furthermore, the piezoresistive functionality of the device is realized via a flexible transparent electrode (FTE) using a highly stable reduced graphene oxide self‐wrapped copper nanowire network. The developed nanowire‐based spinosum microstructured FTEs are amenable to wearable electronics applications. 

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5. Distributed Proprioception of 3D Configuration in Soft, Sensorized Robots via Deep Learning

   Ryan L. Truby, Cosimo Della (April 2020, IEEE robotics automation letters)

   Creating soft robots with sophisticated, autonomous capabilities requires these systems to possess reliable, on-line proprioception of 3D configuration through integrated soft sensors. We present a framework for predicting a soft robot’s 3D configuration via deep learning using feedback from a soft, proprioceptive sensor skin. Our framework introduces a kirigami-enabled strategy for rapidly sensorizing soft robots using off-the-shelf materials, a general kinematic description for soft robot geometry, and an investigation of neural network designs for predicting soft robot configuration. Even with hysteretic, non-monotonic feedback from the piezoresistive sensors, recurrent neural networks show potential for predicting our new kinematic parameters and, thus, the robot’s configuration. One trained neural network closely predicts steady-state configuration during operation, though complete dynamic behavior is not fully captured. We validate our methods on a trunk-like arm with 12 discrete actuators and 12 proprioceptive sensors. As an essential advance in soft robotic perception, we anticipate our framework will open new avenues towards closed loop control in soft robotics. 

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