Flexible tactile sensors have broad applications in human physiological monitoring,robotic operation and human-machine interaction.However,the research of wearable and flexible tactile sensors with high sensitivity,wi...Flexible tactile sensors have broad applications in human physiological monitoring,robotic operation and human-machine interaction.However,the research of wearable and flexible tactile sensors with high sensitivity,wide sensing range and ability to detect three-dimensional(3D)force is still very challenging.Herein,a flexible tactile electronic skin sensor based on carbon nanotubes(CNTs)/polydimethylsiloxane(PDMS)nanocomposites is presented for 3D contact force detection.The 3D forces were acquired from combination of four specially designed cells in a sensing element.Contributed from the double-sided rough porous structure and specific surface morphology of nanocomposites,the piezoresistive sensor possesses high sensitivity of 12.1 kPa?1 within the range of 600 Pa and 0.68 kPa?1 in the regime exceeding 1 kPa for normal pressure,as well as 59.9 N?1 in the scope of<0.05 N and>2.3 N?1 in the region of<0.6 N for tangential force with ultra-low response time of 3.1 ms.In addition,multi-functional detection in human body monitoring was employed with single sensing cell and the sensor array was integrated into a robotic arm for objects grasping control,indicating the capacities in intelligent robot applications.展开更多
Benefitting from the interlaced networking structure of carbon nanotubes(CNTs),the composites of CNTs/polydimethylsiloxane(PDMS)have found extensive applications in wearable electronics.While hierarchical multiscale s...Benefitting from the interlaced networking structure of carbon nanotubes(CNTs),the composites of CNTs/polydimethylsiloxane(PDMS)have found extensive applications in wearable electronics.While hierarchical multiscale simulation frameworks exist to optimize the structure parameters,their wide applications were hindered by the high computational cost.In this study,a machine learning model based on the artificial neural networks(ANN)embedded graph attention network,termed as AGAT,was proposed.The datasets collected from the micro-scale and the macro-scale simulations are utilized to train the model.The ANN layer within the model framework is trained to pass the information from micro-scale to macro-scale,while the whole model is aimed to predict the electro-mechanical behavior of the CNTs/PDMS composites.By comparing the AGAT model with the original multiscale simulation results,the data-driven strategy is shown to be promising with high accuracy,demonstrating the potential of the machine-learning-enabled approach for the structure optimization of CNT-based composites.展开更多
基金funding from National Natural Science Foundation of China(NSFC Nos.61774157,81771388,61874121,and 61874012)Beijing Natural Science Foundation(No.4182075)the Capital Science and Technology Conditions Platform Project(Project ID:Z181100009518014).
文摘Flexible tactile sensors have broad applications in human physiological monitoring,robotic operation and human-machine interaction.However,the research of wearable and flexible tactile sensors with high sensitivity,wide sensing range and ability to detect three-dimensional(3D)force is still very challenging.Herein,a flexible tactile electronic skin sensor based on carbon nanotubes(CNTs)/polydimethylsiloxane(PDMS)nanocomposites is presented for 3D contact force detection.The 3D forces were acquired from combination of four specially designed cells in a sensing element.Contributed from the double-sided rough porous structure and specific surface morphology of nanocomposites,the piezoresistive sensor possesses high sensitivity of 12.1 kPa?1 within the range of 600 Pa and 0.68 kPa?1 in the regime exceeding 1 kPa for normal pressure,as well as 59.9 N?1 in the scope of<0.05 N and>2.3 N?1 in the region of<0.6 N for tangential force with ultra-low response time of 3.1 ms.In addition,multi-functional detection in human body monitoring was employed with single sensing cell and the sensor array was integrated into a robotic arm for objects grasping control,indicating the capacities in intelligent robot applications.
基金supported by the National Key R&D Program of China(2022ZD0117501)the National Natural Science Foundation of China(62201441)
文摘Benefitting from the interlaced networking structure of carbon nanotubes(CNTs),the composites of CNTs/polydimethylsiloxane(PDMS)have found extensive applications in wearable electronics.While hierarchical multiscale simulation frameworks exist to optimize the structure parameters,their wide applications were hindered by the high computational cost.In this study,a machine learning model based on the artificial neural networks(ANN)embedded graph attention network,termed as AGAT,was proposed.The datasets collected from the micro-scale and the macro-scale simulations are utilized to train the model.The ANN layer within the model framework is trained to pass the information from micro-scale to macro-scale,while the whole model is aimed to predict the electro-mechanical behavior of the CNTs/PDMS composites.By comparing the AGAT model with the original multiscale simulation results,the data-driven strategy is shown to be promising with high accuracy,demonstrating the potential of the machine-learning-enabled approach for the structure optimization of CNT-based composites.
