Particle trapping and binding in optical potential wells provide a versatile platform for various biomedical applications. However, implementation systems to study multi-particle contact interactions in an optical lattice remain rare. By configuring an optofluidic lattice, we demonstrate the precise control of particle interactions and functions such as controlling aggregation and multi-hopping. The mean residence time of a single particle is found considerably reduced from 7 s, as predicted by Kramer’s theory, to 0.6 s, owing to the mechanical interactions among aggregated particles. The optofluidic lattice also enables single-bacteria-level screening of biological binding agents such as antibodies through particle-enabled bacteria hopping. The binding efficiency of antibodies could be determined directly, selectively, quantitatively and efficiently. This work enriches the fundamental mechanisms of particle kinetics and offers new possibilities for probing and utilising unprecedented biomolecule interactions at single-bacteria level.

Human‐machine interfaces (HMIs) play important role in the communication between humans and robots. Touchless HMIs with high hand dexterity and hygiene hold great promise in medical applications, especially during the pandemic of coronavirus disease 2019 (COVID‐19) to reduce the spread of virus. However, current touchless HMIs are mainly restricted by limited types of gesture recognition, the requirement of wearing accessories, complex sensing platforms, light conditions, and low recognition accuracy, obstructing their practical applications. Here, an intelligent noncontact gesture‐recognition system is presented through the integration of a triboelectric touchless sensor (TTS) and deep learning technology. Combined with a deep‐learning‐based multilayer perceptron neural network, the TTS can recognize 16 different types of gestures with a high average accuracy of 96.5%. The intelligent noncontact gesture‐recognition system is further applied to control a robot for collecting throat swabs in a noncontact mode. Compared with present touchless HMIs, the proposed system can recognize diverse complex gestures by utilizing charges naturally carried on human fingers without the need of wearing accessories, complicated device structures, adequate light conditions, and achieves high recognition accuracy. This system could provide exciting opportunities to develop a new generation of touchless medical equipment, as well as touchless public facilities, smart robots, virtual reality, metaverse, etc.

As an on-skin electronic device, artificial skin shows great potential in medical monitoring and personal electronics, which also holds promise to develop human-machine merging interfaces. However, merging artificial skins with human bodies is largely restricted by the dissimilarity of material compositions in existing artificial skins and biological tissues. Naturally conductive protein is a potential material candidate for artificial skins, nevertheless, it suffers from the critical issue of dehydration which harms its proton conductivity. Inspired by the sebum membrane of human skin, herein, a proteinbased bioprotonic hydrogel (PBH) with reliable water retention ability is reported for artificial skins. The bovine serum albumin with natural proton conductivity is utilized in the PBH, and the glycerol that originally presents on human skin surface is used as an artificial sebum membrane to retain water. The PBH can act as a bioprotonic skin (B-skin) for collecting electrophysiological signals and self-powered sensing. Based on the B-skin, intelligent robot and cellphone control systems are demonstrated. Compared with present artificial skins, this B-skin is all made out of biological materials that are consistent with material components of human skin tissues including proteins, endogenous glycerol, and water. Such a B-skin may enable the development of next-generation human-machine merging interfaces.

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A compact device integrating a smart chip with a reaction tube is used to prepare monodisperse droplets for ddPCR.