Wearing face masks has been widely recommended to contain respiratory virus diseases, yet the improper use of masks poses a threat of jeopardizing the protection effect. We here identified the bacteria viability on common face masks and found that the majority of bacteria (90%) remain alive after 8 h. Using laser-induced graphene (LIG), the inhibition rate improves to ∼81%. Combined with the photothermal effect, 99.998% bacterial killing efficiency could be attained within 10 min. For aerosolized bacteria, LIG also showed superior antibacterial capacity. The LIG can be converted from a diversity of carbon precursors including biomaterials, which eases the supply stress and environmental pressure amid an outbreak. In addition, self-reporting of mask conditions is feasible using the moisture-induced electricity from gradient graphene. Our results improve the safe use of masks and benefit the environment.

Despite its emerging significant public health concern, the presence of antibiotic resistance genes (ARGs) in urban air has not received significant attention. Here, we profiled relative abundances (as a fraction, normalized by 16S rRNA gene) of 30 ARG subtypes resistant to seven common classes of antibiotics, which are quinolones, β-lactams, macrolides, tetracyclines, sulfonamides, aminoglycosides, and vancomycins, in ambient total particulate matter (PM) using a novel protocol across 19 world cities. In addition, their longitudinal changes in PM samples in Xi'an, China as an example were also studied. Geographically, the ARGs were detected to vary by nearly 100-fold in their abundances, for example, from 0.07 (Bandung, Indonesia) to 5.6 (San Francisco, USA). The β-lactam resistance gene blaTEM was found to be most abundant, seconded by quinolone resistance gene qepA; and their corresponding relative abundances have increased by 178% and 26%, respectively, from 2004 to 2014 in Xi'an. Independent of cities, gene network analysis indicates that airborne ARGs were differentially contributed by bacterial taxa. Results here reveal that urban air is being polluted by ARGs, and different cities are challenged with varying health risks associated with airborne ARG exposure. This work highlights the threat of urban airborne transmission of ARGs and the need of redefining our current air quality standards in terms with public health.

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With the rapid advances in wearable electronics and photonics, self-sustainable wearable systems are desired to increase service life and reduce maintenance frequency. Triboelectric technology stands out as a promising versatile technology due to its flexibility, self-sustainability, broad material availability, low cost, and good scalability. Various triboelectric−human−machine interfaces (THMIs) have been developed including interactive gloves, eye blinking/body motion-triggered interfaces, voice/breath monitors, and self-induced wireless interfaces. Nonetheless, THMIs conventionally use electrical readout and produce pulse-like signals due to the transient charge flows, leading to unstable and lossy transfer of interaction information. To address this issue, we propose a strategy by equipping THMIs with robust nanophotonic aluminum nitride (AlN) modulators for readout. The electrically capacitive nature of AlN modulators enables THMIs to work in the open-circuit condition with negligible charge flows. Meanwhile, the interaction information is transduced from THMIs' voltage to AlN modulators' optical output via the electro-optic Pockels effect. Thanks to the negligible charge flow and the high-speed optical information carrier, stable, information-lossless, and real-time THMIs are achieved. Leveraging the design flexibility of THMIs and nanophotonic readout circuits, various linear sensitivities independent of force speeds are achieved in different interaction force ranges. Toward practical applications, we develop a smart glove to realize continuous real-time robotics control and virtual/augmented reality interaction. Our work demonstrates a generic approach for developing selfsustainable HMIs with stable, information-lossless, and real-time features for wearable systems.

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