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Abstract Drawing inspiration from cohesive integration of skeletal muscles and sensory skins in vertebrate animals, we present a design strategy of soft robots, primarily consisting of an electronic skin (e-skin) and an artificial muscle. These robots integrate multifunctional sensing and on-demand actuation into a biocompatible platform using an in-situ solution-based method. They feature biomimetic designs that enable adaptive motions and stress-free contact with tissues, supported by a battery-free wireless module for untethered operation. Demonstrations range from a robotic cuff for detecting blood pressure, to a robotic gripper for tracking bladder volume, an ingestible robot for pH sensing and on-site drug delivery, and a robotic patch for quantifying cardiac function and delivering electrotherapy, highlighting the application versatilities and potentials of the bio-inspired soft robots. Our designs establish a universal strategy with a broad range of sensing and responsive materials, to form integrated soft robots for medical technology and beyond. 

A microfolding strategy realizes kirigami at nanoscale to form 3D shape-morphable microelectronic systems in freestanding forms. 

Abstract Breath ammonia is an essential biomarker for patients with many chronic illnesses, such as chronic kidney disease (CKD), chronic liver disease (CLD), urea cycle disorders (UCD), and hepatic encephalopathy. However, existing breath ammonia sensors fail to compensate for the impact of breath humidity and complex breathing motions associated with a human breath sample. Here, a multimodal breath sensing system is presented that integrates an ammonia sensor based on a thermally cleaved conjugated polymer, a humidity sensor based on reduced graphene oxide (rGO), and a breath dynamics sensor based on a 3D folded strain‐responsive mesostructure. The miniaturized construction and module‐based configuration offer flexible integration with a broad range of masks. Experimental results present the capabilities of the system in continuously detecting diagnostic ranges of breath ammonia under real, humid breath conditions with sufficient sensing accuracy and selectivity over 3 weeks. A machine‐learning algorithm based on K‐means clustering decodes multimodal signals collected from the breath sensor to differentiate between healthy and diseased breath concentrations of ammonia. The on‐body test highlights the operational simplicity and practicality of the system for noninvasively tracing ammonia biomarkers.