Bioinspired kinesthetic system for human-machine interaction

Shan, Liuting; Liu, Yaqian; Zhang, Xianghong; Li, Enlong; Yu, Rengjian; Lian, Qiming; Chen, Xiang; Chen, Huipeng; Guo, Tailiang

doi:10.1016/j.nanoen.2021.106283

Cited by 51 publications

(35 citation statements)

References 56 publications

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“…46 A classifier of the multiclass support vector machine (SVM) assisted to analyze the extracted features and recognize the hand gesture. 46,47 As shown in Figure 4C, after dimensionality reduction, data of the testing set could be distinguished as five independent categories standing for five typical hand gestures of "A", "B", "1", "2", and "3". The final classification performance of hand gesture recognition could reach up to ∼100% (Figure 4D).…”

Section: ■ Results and Discussionmentioning

confidence: 99%

Interfacial Electrochemical Polymerization for Spinning Liquid Metals into Core–Shell Wires

Long

Che

Yao

et al. 2022

ACS Appl. Mater. Interfaces

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Metal wires are of great significance in applications such as three-dimensional (3D) printing, soft electronics, optics, and metamaterials. Ga-based liquid metals (e.g., EGaIn), though uniquely combining metallic conductivity, fluidity, and biocompatibility, remain challenging to be spun due to their low viscosity, high surface tension, and Rayleigh−Plateau instability. In this work, we showed that EGaIn as a working electrode could induce the oxidization of EGaIn and interfacial electrochemical polymerization of electroactive monomers (e.g., acrylic acid, dopamine, and pyrrole), thus spinning itself from an opening of a blunt needle. During the spinning process, the high surface tension of EGaIn was reduced by electrowetting and electrocapillarity and stabilized by polymer shells (tunable thickness of ∼0.6−30 μm on wires with a diameter of 90−300 μm), which were chelated with metal ions. The polymeric shells offered EGaIn wires with an enhanced endurance to mechanical force and acidity. By further encapsulating into elastomers through a facile impregnation process, the resultant elastic EGaIn wires showed a combination of high stretchability (up to 800%) and metallic conductivity (1.5 × 10 6 S m −1 ). When serving as wearable sensors, they were capable of sensing facial expressions, body movements, voice recognition, and spatial pressure distributions with high sensitivity, good repeatability, and satisfactory durability. Machine-learning algorithms further assisted to detect gestures with high accuracy.

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Section: ■ Results and Discussionmentioning

confidence: 99%

Interfacial Electrochemical Polymerization for Spinning Liquid Metals into Core–Shell Wires

Long

Che

Yao

et al. 2022

ACS Appl. Mater. Interfaces

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“…Shan et al designed a TENG-based artificial kinesthetic system with a field effect synaptic transistor which simulated the learning process of nerves. 259 The single-electrode TENG, with PDMS/MXene as the material of the triboelectric layer, exhibited a high sensitivity of 0.197 kPa À1 in the low-pressure region (6-30 kPa region). The proposed system achieved the assessment of fatigue driving risk by perceiving the motion state of muscles and joints.…”

Section: Gesture-based Interfacementioning

confidence: 99%

Human body IoT systems based on the triboelectrification effect: energy harvesting, sensing, interfacing and communication

Zhang

Xin

Fan

et al. 2022

Energy Environ. Sci.

134

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“…In a VOFET, the active layer is sandwiched between the mesh source and drain electrodes, and the charge is transmitted through the entire active layer, which is the bulk transmission. Thus, VOFETs aer able to achieve a large current density and high operating frequency under low working voltage, which has attracted widespread attention in the field of electronic devices and has been widely used in various applications, such as photoelectronic memories, artificial synapses, organic light-emitting transistors, and flexible electronic devices. − ,− …”

Section: Introductionmentioning

confidence: 99%

Heterostructured Vertical Organic Transistor for High-Performance Optoelectronic Memory and Artificial Synapse

Gao

Yang

et al. 2021

ACS Photonics

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Organic field-effect transistor (OFET) memory has received widespread attention due to its easy integration, precise charge modulation, and multi-level memory. However, the performance of organic memory still needs to be improved for its practical application, and the reported technologies are strongly dependent on an additional charge-trapping layer, which increases the complexity of the device. Here, we report a heterostructured vertical organic memory transistor, which uses a p/n semiconductor bulk heterojunction as a semiconductor layer without using any additional charge-trapping layers. The device exhibits a large memory window of 52 V, and the memory ratio reaches 10 5 through electrical operation. Benefiting from the formation of the p/n semiconductor interface and the nanometer-scale transmission length, under the stimulation of visible light, the device achieved a 58 V memory window, high memory ratio 10 5 , and retention characteristics of over 10 years, which is better than those of most reported optical organic memory devices. More interestingly, we found that as the level of the doping in the n-type semiconductor increased, the device could transform from nonvolatile memory to artificial synapse, which is associated with the morphology of a heterojunction structure. Hence, we demonstrate a novel technique to manufacture high-performance nonvolatile optoelectronic memory and artificial synapse, which shows great potential in OFET-based memory and neuromorphic devices.

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Bioinspired kinesthetic system for human-machine interaction

Cited by 51 publications

References 56 publications

Interfacial Electrochemical Polymerization for Spinning Liquid Metals into Core–Shell Wires

Interfacial Electrochemical Polymerization for Spinning Liquid Metals into Core–Shell Wires

Human body IoT systems based on the triboelectrification effect: energy harvesting, sensing, interfacing and communication

Heterostructured Vertical Organic Transistor for High-Performance Optoelectronic Memory and Artificial Synapse

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