Publication | Open Access
Bioinspired Bimodal Mechanosensors with Real‐Time, Visualized Information Display for Intelligent Control
97
Citations
42
References
2023
Year
Haptic FeedbackArtificial Sensory SystemsEngineeringNeural ControlBioroboticsElectronic SkinMicroelectromechanical SystemsHaptic TechnologyBiomedical EngineeringPressure VisualizationSensing (Management Information Systems)KinesiologySensing (Sensor Engineering)BiomechanicsBio-inspired RoboticsBioinspired Bimodal MechanosensorsHealth SciencesSensorimotor ControlWearable BiosensorsAbstract ImitatingHigh SensitivityRobotic SensingIntelligent ControlSensorimotor IntegrationSensing MechanismBiomedical SensorsVisualized Information DisplayMultimodal SensingSensorsBioelectronicsRobotics
Imitating the mechano‑sensing luminescence of organisms has long been envisioned to enable dynamic, real‑time visualized information display, yet reproducing skin‑like visual sensation in artificial sensors remains difficult. The study presents a bioinspired mechanosensor that can perceive pressure and display patterns in real time. The sensor combines a mechanoluminescent/triboelectric hierarchical structure with an image‑based machine‑learning control system to achieve pressure perception and pattern display. The device demonstrates a self‑powered, bimodal response with a 0.082 N detection limit, 9.69 a.u.
Abstract Imitating mechano‐sensing luminescence of organisms has long been envisaged to achieve dynamic and real‐time visualized information display, however, it remains challenging to recreate the skin‐like visual sensation functions in artificial sensors. Here, a bioinspired mechanosensor is presented with mechanoluminescent/triboelectric hierarchical structure that is capable of pressure perception and patterning display in real time. Interestingly, a facilitative effect of interfacial triboelectric field on luminescent output and pressure visualization was found. The developed mechanosensor shows self‐powered, bimodal, and real‐time patterning sensation behavior with a low force detection limit (0.082 N), high sensitivity (9.69 a.u. N −1 ), fast response (35 ms), and good reliability (5000 cycles). On this basis, an intelligent control system is further constructed via combining image machine learning approach. This study not only addresses the long‐lasting challenge of real‐time pressure visualized display, but also boosts the further development of untethered, small‐scale, and efficient intelligent systems.
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