Nano-Micro Letters最新文献

Highlights

Liquid leakage of pipeline networks not only results in considerable resource wastage but also leads to environmental pollution and ecological imbalance. In response to this global issue, a bioinspired superhydrophobic thermoplastic polyurethane/carbon nanotubes/graphene nanosheets flexible strain sensor (TCGS) has been developed using a combination of micro-extrusion compression molding and surface modification for real-time wireless detection of liquid leakage. The TCGS utilizes the synergistic effects of Archimedean spiral crack arrays and micropores, which are inspired by the remarkable sensory capabilities of scorpions. This design achieves a sensitivity of 218.13 at a strain of 2%, which is an increase of 4300%. Additionally, it demonstrates exceptional durability by withstanding over 5000 usage cycles. The robust superhydrophobicity of the TCGS significantly enhances sensitivity and stability in detecting small-scale liquid leakage, enabling precise monitoring of liquid leakage across a wide range of sizes, velocities, and compositions while issuing prompt alerts. This provides critical early warnings for both industrial pipelines and potential liquid leakage scenarios in everyday life. The development and utilization of bioinspired ultrasensitive flexible strain sensors offer an innovative and effective solution for the early wireless detection of liquid leakage.

Highlights

Structural reconstruction of electrocatalysts plays a pivotal role in catalytic performances for CO₂ reduction reaction (CO₂RR), whereas the behavior is by far superficially understood. Here, we report that CO₂ accessibility results in a universal self-adaptive structural reconstruction from Cu₂O to Cu@Cu_xO composites, ending with feeding gas-dependent microstructures and catalytic performances. The CO₂-rich atmosphere favors reconstruction for CO₂RR, whereas the CO₂-deficient one prefers that for hydrogen evolution reaction. With the assistance of spectroscopic analysis and theoretical calculations, we uncover a CO₂-induced passivation behavior by identifying a reduction-resistant but catalytic active Cu(I)-rich amorphous layer stabilized by *CO intermediates. Additionally, we find extra CO production is indispensable for the robust production of C₂H₄. An inverse correlation between durability and FE_CO/FE_C2H4 is disclosed, suggesting that the self-stabilization process involving the absorption of *CO intermediates on Cu(I) sites is essential for durable electrolysis. Guided by this insight, we design hollow Cu₂O nanospheres for durable and selective CO₂RR electrolysis in producing C₂H₄. Our work recognizes the previously overlooked passivation reconstruction and self-stabilizing behavior and highlights the critical role of the local atmosphere in modulating reconstruction and catalytic processes.

Highlights

Highlights

The complex wiring, bulky data collection devices, and difficulty in fast and on-site data interpretation significantly limit the practical application of flexible strain sensors as wearable devices. To tackle these challenges, this work develops an artificial intelligence-assisted, wireless, flexible, and wearable mechanoluminescent strain sensor system (AIFWMLS) by integration of deep learning neural network-based color data processing system (CDPS) with a sandwich-structured flexible mechanoluminescent sensor (SFLC) film. The SFLC film shows remarkable and robust mechanoluminescent performance with a simple structure for easy fabrication. The CDPS system can rapidly and accurately extract and interpret the color of the SFLC film to strain values with auto-correction of errors caused by the varying color temperature, which significantly improves the accuracy of the predicted strain. A smart glove mechanoluminescent sensor system demonstrates the great potential of the AIFWMLS system in human gesture recognition. Moreover, the versatile SFLC film can also serve as a encryption device. The integration of deep learning neural network-based artificial intelligence and SFLC film provides a promising strategy to break the “color to strain value” bottleneck that hinders the practical application of flexible colorimetric strain sensors, which could promote the development of wearable and flexible strain sensors from laboratory research to consumer markets.

Highlights

Spike-based neural networks, which use spikes or action potentials to represent information, have gained a lot of attention because of their high energy efficiency and low power consumption. To fully leverage its advantages, converting the external analog signals to spikes is an essential prerequisite. Conventional approaches including analog-to-digital converters or ring oscillators, and sensors suffer from high power and area costs. Recent efforts are devoted to constructing artificial sensory neurons based on emerging devices inspired by the biological sensory system. They can simultaneously perform sensing and spike conversion, overcoming the deficiencies of traditional sensory systems. This review summarizes and benchmarks the recent progress of artificial sensory neurons. It starts with the presentation of various mechanisms of biological signal transduction, followed by the systematic introduction of the emerging devices employed for artificial sensory neurons. Furthermore, the implementations with different perceptual capabilities are briefly outlined and the key metrics and potential applications are also provided. Finally, we highlight the challenges and perspectives for the future development of artificial sensory neurons.