Microconformal electrode-dielectric integration for flexible ultrasensitive robotic tactile sensing

Luo, Shi; Zhou, Xi; Tang, Xinyue; Li, Jialu; Wei, Dacheng; Tai, Guojun; Chen, Zongyong; Liao, Tingmao; Fu, Jianting; Wei, Dapeng; Yang, Jun

doi:10.1016/j.nanoen.2020.105580

Cited by 84 publications

(62 citation statements)

References 43 publications

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“…However, the fabrication of air gap with microporous structures requires complicated fabrication process such as lithography techniques and silicon-based molds/templates. Further, a reduction in the working pressure range was also observed for porous and microstructure-based sensors [32,33]. To enhance the sensitivity as well as the working pressure range, filler materials such as carbon nanotubes, graphene, graphite, silver nanowires, high dielectric nano particles etc.…”

mentioning

confidence: 94%

Soft Capacitive Pressure Sensor With Enhanced Sensitivity Assisted by ZnO NW Interlayers and Airgap

Kumaresan

Ozioko

et al. 2022

IEEE Sensors J.

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Highly sensitive capacitive pressure sensors with wide detection range are needed for applications such as humanmachine interfaces, electronic skin in robotics, and health monitoring. However, it is challenging to achieve high sensitivity and wide detection range at the same time. Herein, we present an innovative approach to obtain a highly sensitive capacitive pressure sensor by introducing a zinc oxide nanowire (ZnO NW) interlayer at the polydimethylsiloxane (PDMS)/electrodes interface in the conventional metal-insulator-metal architecture. The ZnO NW interlayer significantly enhanced the performance with ~7 times higher sensitivity (from 0.81 %kPa -1 to 5.6452 %kPa -1 at a low-pressure range (0-10 kPa)) with respect to conventional capacitive sensors having PDMS only as the dielectric. The improvement in sensitivity is attributed to the enhanced charge separation and electric dipole generation due to the displacement of Zn + and Ounder applied pressure. Further, the orientation of ZnO NWs and their placement between the electrodes were investigated which includes either vertical or horizontal NWs near the electrodes, placing a third ZnO NW interlayer in the middle of dielectric PDMS and introducing an air gap between the ZnO NWs/electrode. Among various combinations, the introduction of air gap between the electrode and ZnO NW interlayer revealed a significant improvement in the device performance with ~50 times enhancement at a low-pressure range (0-10 kPa) and more than 200 times increase at a high-pressure range (10-200 kPa), in comparison with the conventional PDMS-based pressure sensor.

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confidence: 94%

Soft Capacitive Pressure Sensor With Enhanced Sensitivity Assisted by ZnO NW Interlayers and Airgap

Kumaresan

Ozioko

et al. 2022

IEEE Sensors J.

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“…Rough surfaces can help improve the sensitivity of capacitive pressure sensors. [ 118,135,140 ] For instance, an AgNW/polymethylmethacrylate film was attached onto a pre‐strained PDMS film, which formed wrinkled structures after the PDMS film was released to its initial state ( Figure a). [ 135 ] An AgNW‐PEDOT:PSS/PDMS film was then placed on top of the wrinkled film to obtain the capacitive sensor.…”

Section: Nanowire‐based Wearable Skin Sensory Input Interfacesmentioning

confidence: 99%

Nanowire‐Based Soft Wearable Human–Machine Interfaces for Future Virtual and Augmented Reality Applications

Wang

Yap

Gong

et al. 2021

Adv Funct Materials

104

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A virtual world has now become a reality as augmented reality (AR) and virtual reality (VR) technology become commercially available. Similar to how humans interact with the physical world, AR and VR systems rely on human–machine interface (HMI) sensors to interact with the virtual world. Currently, this is achieved via state of‐the‐art wearable visual and auditory tools that are rigid, bulky, and burdensome, thereby causing discomfort during practical application. To this end, a skin sensory interface has the potential to serve as the next‐generation AR/VR technology because skin‐like wearable sensors have advantages in that they can be ultrathin, ultra‐soft, conformal, and imperceptible, which provides the ultimate comfort and immersive experience for users. In this progress report, nanowire‐based soft wearable HMI sensors including acoustic, strain, pressure sensors, and physiological sensors are reviewed that may be adopted as skin sensory inputs in future AR/VR systems. Further, nanowire‐based soft contact lenses, haptic force, and thermal and vibration actuators are covered as potential means of feedback for future AR/VR systems. Considering the possible effects of the virtual world on human health, skin‐like wearable artery pulses, glucose, and lactate sensors are also described, which may enable imperceptible health monitoring during future AR/VR practices.

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“…Recently, tremendous efforts have been made to develop artificial tactile sensors based on different working mechanisms, including piezoelectricity, [ 8–10 ] piezoresistance, [ 11–16 ] capacitance, [ 17,18 ] strain gauges, [ 19 ] optics, [ 20–22 ] and magnetics, [ 23–25 ] within which machine learning technologies have played a critical role to extract useful information from raw tactile data for pattern recognition. Mami et al [ 10 ] reported a tactile sensor system that is based on a piezoelectric film for Braille characters recognition, where a 91.45% recognition accuracy was achieved by using the support vector machine (SVM) and multilayer perceptron (MLP).…”

Section: Introductionmentioning

confidence: 99%

“…To further improve the recognition accuracy, researchers have recently developed tactile sensor arrays using a wide variety of advanced materials. [ 11,13,17,18 ] However, when reading continuous lines of Braille texts, most of these sensors have to discontinuously press each individual Braille character and use each of the six taxels in the sensor array to check whether a dot is raised or not in a six‐dot Braille cell. This leads to a high device complexity and low reading efficiency.…”

Section: Introductionmentioning

confidence: 99%

“…By using the proposed method, we have achieved a recognition accuracy of 99% for 60 types of fabrics. A real‐time recognition accuracy of 97% is also achieved for Braille characters by natural and continuous sliding (rather than discontinuous pressing of existing tactile sensors [ 11,13,17,18 ] ), which is comparable with that of proficient human readers. [ 28 ]…”

Section: Introductionmentioning

confidence: 99%

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Surface Texture Recognition by Deep Learning‐Enhanced Tactile Sensing

Yan

Shen

et al. 2021

Advanced Intelligent Systems

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Tactile perception is a primary sensing channel for both humans and robots to be conscious of the surface properties of an object. Due to the unique functionalities of mechanoreceptors in human skin, humans can easily distinguish materials with different surface characteristics (e.g., compressibility, roughness, etc.) by simply pressing and sliding the fingertip over the samples. However, how to achieve such delicate texture recognition for robots remains an open challenge due to the lack of skin‐comparable tactile sensing systems and smart pattern recognition algorithms. Herein, a novel texture recognition method is proposed by designing an arc‐shaped soft tactile sensor and a bidirectional long short‐term memory (LSTM) model with the attention mechanism. By using the proposed method, a respective recognition accuracy of 97% for Braille characters and 99% for 60 types of fabrics have been achieved, revealing the effectiveness of our method in surface texture recognition and the potential benefit to various applications, such as Braille reading for visually impaired people and defect detection in the textile industry.

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Microconformal electrode-dielectric integration for flexible ultrasensitive robotic tactile sensing

Cited by 84 publications

References 43 publications

Soft Capacitive Pressure Sensor With Enhanced Sensitivity Assisted by ZnO NW Interlayers and Airgap

Soft Capacitive Pressure Sensor With Enhanced Sensitivity Assisted by ZnO NW Interlayers and Airgap

Nanowire‐Based Soft Wearable Human–Machine Interfaces for Future Virtual and Augmented Reality Applications

Surface Texture Recognition by Deep Learning‐Enhanced Tactile Sensing

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