Wearing masks has been a recommended protective measure due to the risks of coronavirus disease 2019 (COVID-19) even in its coming endemic phase. Therefore, deploying a "smart mask" to monitor human physiological signals is highly beneficial for personal and public health. This work presents a smart mask integrating an ultrathin nanocomposite sponge structure-based soundwave sensor (≈400 μm), which allows the high sensitivity in a wide-bandwidth dynamic pressure range, i.e., capable of detecting various respiratory sounds of breathing, speaking, and coughing. Thirty-one subjects test the smart mask in recording their respiratory activities. Machine/deep learning methods, i.e., support vector machine and convolutional neural networks, are used to recognize these activities, which show average macro-recalls of ≈95% in both individual and generalized models. With rich high-frequency (≈4000 Hz) information recorded, the two-/tri-phase coughs can be mapped while speaking words can be identified, demonstrating that the smart mask can be applicable as a daily wearable Internet of Things (IoT) device for respiratory disease identification, voice interaction tool, etc. in the future. This work bridges the technological gap between ultra-lightweight but high-frequency response sensor material fabrication, signal transduction and processing, and machining/deep learning to demonstrate a wearable device for potential applications in continual health monitoring in daily life.

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Nanomaterial-based flexible sensors (NMFSs) can be tightly attached to the human skin or integrated with clothing to monitor human physiological information, provide medical data, or explore metaverse spaces. Owing to their facile processing, material compatibility, and unique properties, nanomaterials have been widely incorporated into flexible sensors. This review highlights the recent advancements in NMFSs involving various nanomaterial frameworks such as nanoparticles, nanowires, and nanofilms. Different triggering interaction interfaces between NMFSs and metaverse/virtual reality (VR) applications, e.g., skin-mechanics-triggered, temperature-triggered, magnetically triggered, and neural-triggered interfaces, are discussed. In the context of interfacing physical and virtual worlds, machine learning (ML) has emerged as a promising tool for processing sensor data for controlling avatars in metaverse/VR worlds, and many ML algorithms have been proposed for virtual interaction technologies. The advantages, disadvantages, and prospects of NMFSs in metaverse/VR applications are discussed.

Herein, kitchen waste hydrolysis residue (KWHR) was utilized as the precursor to fabricate nitrogen/oxygen co-doped microporous biocarbons (NOMBs) with ultrahigh specific surface area via KOH activation. Activation temperature was found to be crucial for heteroatom doping and pore structure construction. Attractively, the obtained NOMB with high surface area (2417 m 2 / g) and microporosity (~90%) displayed an outstanding capacity of Cr(VI) removal (526.1 mg/g at pH 2). The kinetics and isotherm studies showed that the adsorption of Cr(VI) onto NOMB was well described by the pseudo-second-order kinetics and Langmuir isotherm. Moreover, it was found that Cr(VI) was partly reduced to Cr(III) during the removal process as the nitrogen/ oxygen functionalities and unsaturated carbon bond played crucial roles of electron-donors, which revealed the fact that the removal of Cr(VI) by NOMB was attributed to the coupling of adsorption and reduction reaction. Overall, this study has demonstrated the possibility of preparing microporous biocarbons using KWHR as a renewable material and the resultant NOMB is of great potential to detoxify Cr(VI).

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