The rapid development of advanced nano-electronic technologies has prompted research into smart electronic skins (E-skins) that effectively mimic the distinctive features of the human dermis.
The skin-like sensors play a vital role in bridging the gap between human beings, machines and real/virtual environments, carrying tremendous potential for internet-of-things (IoTs), artificial intelligence, and human-machine interfaces.
Mainstream efforts have therefore focused on developing multifunctional integrated flexible sensor systems, such as sensor arrays with additional dimensions to observers and multimodal flexible sensors capable of synchronously detecting multiple modalities.
Our somatosensory systems rely on the transduction of abundant stimuli into action potentials by receptors and signal transmission through neurons, which are ultimately received and analyzed by the brain in order for timely decisions to be made.
This feedback closed-loop function allows human beings to shape their interactions with complex environments. Thus, an ideal artificial skin-like sensor system should be endowed with such smart feedback functionalities, which will afford promising applications in prosthetics, robotics, and augmented/virtual reality (AR/VR).
Additionally, in order to enhance the recognition reliability of datasets generated by sensor networks, the development of appropriate machine learning algorithms is essential to reveal their subtle differences and correlations owing to very similar information, such as triboelectric-based or speech-related signals.
In a recent review paper published in Advanced Functional Materials (« Flexible Hybrid Sensor Systems with Feedback Functions »), researchers from Osaka Prefecture University (OPU) present a timely overview of feedback-driven, skin-like multifunctional sensor systems, from the basic material/structural design to applications.First, emerging functional nanomaterials and innovative structures for constructing flexible sensors are highlighted, followed by an introduction of various integrated flexible sensor systems.
In the second section, several categories of feedback sensor systems are described in terms of prosthesis- and AR/VR-based human-machine interfaces, smartphone-based approaches for point-of-care (POC) detection, and smart wearable displays for direct signal visualization.
The profound significance of these innovations is also highlighted.
Additionally, the strategy of machine learning-based flexible sensors is briefly described.
Finally, the authors present possible future trends of feedback closed-loop flexible sensor platforms.
Scientists at Oak Ridge National Laboratory and the University of Nebraska have developed an easier way to generate electrons for nanoscale imaging and sensing, providing a useful new tool for material science, bioimaging and fundamental quantum research.Lire la suite