It is a challenge to manufacture pressure‐sensing materials that possess flexibility, high sensitivity, large‐area compliance, and capability to detect both tiny and large motions for the development of artificial intelligence products. Herein, a very simple and low‐cost approach is proposed to fabricate versatile pressure sensors based on microcrack‐designed carbon black (CB)@polyurethane (PU) sponges via natural polymer‐mediated water‐based layer‐by‐layer assembly. These sensors are capable of satisfying the requirements of ultrasmall as well as large motion monitoring. The versatility of these sensors benefits from two aspects: microcrack junction sensing mechanism for tiny motion detecting (91 Pa pressure, 0.2% strain) inspired by the spider sensory system and compressive contact of CB@PU conductive backbones for large motion monitoring (16.4 kPa pressure, 60% strain). Furthermore, these sensors exhibit excellent flexibility, fast response times (<20 ms), as well as good reproducibility over 50 000 cycles. This study also demonstrates the versatility of these sensors for various applications, ranging from speech recognition, health monitoring, bodily motion detection to artificial electronic skin. The desirable comprehensive performance of our sensors, which is comparable to the recently reported pressure‐sensing devices, together with their significant advantages of low‐cost, easy fabrication, especially versatility, makes them attractive in the future of artificial intelligence.
Despite its widespread use in signal collection, flexible sensors have been rarely used in human-machine interactions owing to its indistinguishable signal, poor reliability, and poor stability when inflicted with unavoidable scratches and/or mechanical cuts. A highly sensitive and self-healing sensor enabled by multiple hydrogen bonding network and nanostructured conductive network is demonstrated. The nanostructured supramolecular sensor displays extremely fast (ca. 15 s) and repeatable self-healing ability with high healing efficiency (93 % after the third healing process). It can precisely detect tiny human motions, demonstrating highly distinguishable and reliable signals even after cutting-healing and bending over 20 000 cycles. Furthermore, a human-machine interaction system is integrated to develop a facial expression control system and an electronic larynx, aiming to control the robot to assist the patient's daily life and help the mute to realize real-time speaking.
Ultra-robust nanomembranes possessing high mechanical strength combined with excellent stiffness and toughness rarely achieved in nanocomposite materials are presented. These are fabricated by alternately depositing 1D cellulose nanocrystals and 2D graphene oxide nanosheets by using a spin assisted layer-by-layer assembly technique. Such a unique combination of 1D and 2D reinforcing nanostructures results in layered nanomaterials.
Soft
actuation materials are highly desirable in flexible electronics,
soft robotics, etc. However, traditional bilayered
actuators usually suffer from poor mechanical properties as well as
deteriorated performance reliability. Here, inspired by the delicate
architecture of natural bamboo, we present a hierarchical gradient
structured soft actuator via mesoscale assembly of
micro–nano-scaled two-dimentional MXenes and one-dimentional
cellulose nanofibers with molecular-scaled strong hydrogen bonding.
The resultant actuator integrates high tensile strength (237.1 MPa),
high Young’s modulus (8.5 GPa), superior toughness (10.9 MJ/m3), and direct and fast hygroscopic actuation within a single
body, which is difficult to achieve by traditional bilayered actuators.
The proof-of-concept prototype robot is demonstrated to emphasize
its high mechanical robustness even after bending 100 000 times,
kneading, or being trampled by an adult (7 500 000 times
heavier than a crawling robot). This bioinspired mesoscale assembly
strategy provides an approach for soft materials to the application
of next-generation robust robotics.
Strain sensors play an important role in the next generation of artificially intelligent products. However, it is difficult to achieve a good balance between the desirable performance and the easy-to-produce requirement of strain sensors. In this work, we proposed a simple, cost-efficient, and large-area compliant strategy for fabricating highly sensitive strain sensor by coating a polyurethane (PU) yarn with an ultrathin, elastic, and robust conductive polymer composite (CPC) layer consisting of carbon black and natural rubber. This CPC@PU yarn strain sensor exhibited high sensitivity with a gauge factor of 39 and detection limit of 0.1% strain. The elasticity and robustness of the CPC layer endowed the sensor with good reproducibility over 10,000 cycles and excellent wash- and corrosion-resistance. We confirmed the applicability of our strain sensor in monitoring tiny human motions. The results indicated that tiny normal physiological activities (including pronunciation, pulse, expression, swallowing, coughing, etc.) could be monitored using this CPC@PU sensor in real time. In particular, the pronunciation could be well parsed from the recorded delicate speech patterns, and the emotions of laughing and crying could be detected and distinguished using this sensor. Moreover, this CPC@PU strain-sensitive yarn could be woven into textiles to produce functional electronic fabrics. The high sensitivity and washing durability of this CPC@PU yarn strain sensor, together with its low-cost, simplicity, and environmental friendliness in fabrication, open up new opportunities for cost-efficient fabrication of high performance strain sensing devices.
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