Zutao Zhang

Flexible wearable sensors are increasingly used for long-duration monitoring of body-state information, but they can degrade silently during operation while continuing to output physiologically plausible signals, making it difficult to determine whether an incoming measurement remains trustworthy. Here, we report a self-prognostic electronic skin platform that couples multimodal body-state sensing with autonomous sensor-health assessment. The device monolithically co-integrates piezoelectric and triboelectric transduction by origami folding into an ultrathin, six-channel wrist-mounted array (∼0.1 mm, <1 g), so that a single mechanoelectrical stream simultaneously encodes cardiovascular pulsation, wrist activity, and the device’s own degradation state. A two-stage self-diagnostic framework uses an autoencoder-CUSUM pipeline to establish device-specific failure labels from degradation trajectories, followed by an LSTM-Transformer model for remaining useful lifetime prediction. Across a twenty-device cohort with a nearly sevenfold variation in lifetime, the model predicts remaining useful lifetime across the cohort’s degradation trajectories with R2 = 0.95 and captures uncertainty that increases as degradation becomes more severe. In single-participant daily-use proof-of-concept testing, the system estimates physiological parameters and sensor remaining lifetime from the same stream. The predicted lifetime of a newly fabricated device remained near 100% during a representative 1.5 h segment and declined modestly to 97.2% over approximately 12 h of wear distributed across multiple days; this single-device demonstration illustrates the operation of the diagnostic channel during daily use rather than validated cross-device predictive accuracy. These results establish reliability-aware wearable sensing, in which each body-state measurement is interpreted together with the operational health of the sensor itself, with potential value for long-duration applications such as health monitoring and embodied intelligence.

Aerial robots operate in complex three‐dimensional environments, demonstrating high agility and maneuverability. Because aerial robots must continuously overcome gravitational and aerodynamic resistance, lightweight design is critical to flight efficiency, endurance, and adaptability. As next‐generation aerial robots evolve toward intelligence, miniaturization, and swarm operation, lightweight design faces increasing challenges in balancing low density, high mechanical performance, and multifunctional integration. To ensure survival and efficiency, biological organisms have evolved structural strategies that combine low mass, mechanical robustness, and functional integration, offering valuable inspiration for metastructure‐based lightweight design in aerial robots. In this Review, we propose a nature‐inspired metastructure design paradigm for next‐generation aerial robots to enable sustainable systems with exceptional mechanical performance and multifunctional integration. We investigate natural metastructure prototypes through the lens of adaptive evolution, providing a biomimicry blueprint for sustainable aerial systems. We also provide a systematic overview of emerging applications of nature‐inspired metastructures across frame, actuation, sensing, power, and system‐level design. Finally, we outline the remaining fundamental and technical challenges and identify future research directions and potential applications for sustainable aerial robots.

Manual Material Handling (MMH) involves repetitive and sustained lifting, lowering, and walking with loads. Driven by fatigue, these activities are prone to inducing awkward postures and accumulating ergonomic risks. Long-term exposure can lead to Work-related Musculoskeletal Disorders (WMSDs) and shorten career lifespan. Although existing assessment methods can identify risks, they remain limited in terms of real-time capability, portability, and cost. To address this, this paper proposes a safe, low-cost Wearable Ergonomic Multi-node Observation System (WEMOS) that continuously and accurately captures critical posture information during handling operations. The system comprises multiple Triboelectric-Electromagnetic Nanosensor (TEN) nodes that convert the mechanical energy of human motion into electrical signals that characterize postures, enabling robust motion characterization and early risk warning through multi-node fusion. Experiments based on typical handling motions demonstrate that WEMOS enables user identification, motion recognition, and early warning of risky postures. This performance was validated on 15 human subjects. The hardware design and placement of the nodes balance accuracy and comfort. Furthermore, the rationality of the multi-node distribution and information complementarity is validated via a Multi-level Context Refinement Network (MCRnet). Furthermore, by integrating digital twin technology, the system's potential for safeguarding occupational health in continuous smart logistics operations is demonstrated. Overall, WEMOS provides a novel pathway for early warning and behavioral feedback regarding ergonomic risks during handling operations, and can be extended to other labor-intensive scenarios to enhance operational safety and worker well-being.