Nano-Micro Letters最新文献

The rising flexible and intelligent electronics greatly facilitate the noninvasive and timely tracking of physiological information in telemedicine healthcare. Meticulously building bionic-sensitive moieties is vital for designing efficient electronic skin with advanced cognitive functionalities to pluralistically capture external stimuli. However, realistic mimesis, both in the skin’s three-dimensional interlocked hierarchical structures and synchronous encoding multistimuli information capacities, remains a challenging yet vital need for simplifying the design of flexible logic circuits. Herein, we construct an artificial epidermal device by in situ growing Cu₃(HHTP)₂ particles onto the hollow spherical Ti₃C₂T_x surface, aiming to concurrently emulate the spinous and granular layers of the skin’s epidermis. The bionic Ti₃C₂T_x@Cu₃(HHTP)₂ exhibits independent NO₂ and pressure response, as well as novel functionalities such as acoustic signature perception and Morse code-encrypted message communication. Ultimately, a wearable alarming system with a mobile application terminal is self-developed by integrating the bimodular senor into flexible printed circuits. This system can assess risk factors related with asthmatic, such as stimulation of external NO₂ gas, abnormal expiratory behavior and exertion degrees of fingers, achieving a recognition accuracy of 97.6% as assisted by a machine learning algorithm. Our work provides a feasible routine to develop intelligent multifunctional healthcare equipment for burgeoning transformative telemedicine diagnosis.

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The incidence of large bone defects caused by traumatic injury is increasing worldwide, and the tissue regeneration process requires a long recovery time due to limited self-healing capability. Endogenous bioelectrical phenomena have been well recognized as critical biophysical factors in bone remodeling and regeneration. Inspired by bioelectricity, electrical stimulation has been widely considered an external intervention to induce the osteogenic lineage of cells and enhance the synthesis of the extracellular matrix, thereby accelerating bone regeneration. With ongoing advances in biomaterials and energy-harvesting techniques, electroactive biomaterials and self-powered systems have been considered biomimetic approaches to ensure functional recovery by recapitulating the natural electrophysiological microenvironment of healthy bone tissue. In this review, we first introduce the role of bioelectricity and the endogenous electric field in bone tissue and summarize different techniques to electrically stimulate cells and tissue. Next, we highlight the latest progress in exploring electroactive hybrid biomaterials as well as self-powered systems such as triboelectric and piezoelectric-based nanogenerators and photovoltaic cell-based devices and their implementation in bone tissue engineering. Finally, we emphasize the significance of simulating the target tissue’s electrophysiological microenvironment and propose the opportunities and challenges faced by electroactive hybrid biomaterials and self-powered bioelectronics for bone repair strategies.

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Monitoring minuscule mechanical signals, both in magnitude and direction, is imperative in many application scenarios, e.g., structural health monitoring and robotic sensing systems. However, the piezoelectric sensor struggles to satisfy the requirements for directional recognition due to the limited piezoelectric coefficient matrix, and achieving sensitivity for detecting micrometer-scale deformations is also challenging. Herein, we develop a vector sensor composed of lead zirconate titanate-electronic grade glass fiber composite filaments with oriented arrangement, capable of detecting minute anisotropic deformations. The as-prepared vector sensor can identify the deformation directions even when subjected to an unprecedented nominal strain of 0.06%, thereby enabling its utility in accurately discerning the 5 μm-height wrinkles in thin films and in monitoring human pulse waves. The ultra-high sensitivity is attributed to the formation of porous ferroelectret and the efficient load transfer efficiency of continuous lead zirconate titanate phase. Additionally, when integrated with machine learning techniques, the sensor’s capability to recognize multi-signals enables it to differentiate between 10 types of fine textures with 100% accuracy. The structural design in piezoelectric devices enables a more comprehensive perception of mechanical stimuli, offering a novel perspective for enhancing recognition accuracy.

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