Wu Bin Ying

Flexible hybrid electronics (FHEs) have widespread potential applications in wearable sensing, in particular for practical monitoring of human pulses. However, FHEs suffer from insufficient conformation with complex and dynamic skin surfaces. Resulting motion artifacts severely limit the long‐term reliability of pulse monitoring under various exercise conditions. This underscores the importance of developing system‐level mechanical stretchability. Herein, we propose a technical route to fabricate intrinsically stretchable hybrid electronics via liquid metals (LMs) patterning. Taking advantage of the selective wettability and microoxidation of LMs, a semicured polymer is used as a soft transfer medium to achieve high‐fidelity LMs patterning at a device yield of 96.3%, with the resolution of 100 µm. Importantly, this method can be scaled up through compatibility with mature roll‐to‐roll flexible circuit printing technology. Our method enables the fabrication of stretchable FHEs, which can form a conformal interface with the skin and undergo adaptive deformation during motion (sitting, walking, and running), allowing real‐time accurate acquisition of pulse signals by photoplethysmographic sensing. Our work provides a robust technical pathway for advancing FHE systems and boosts the development of wearable pulse monitors.

GaIn NPs-TPU core/shell mesh electrode enables an EMG wristband that offers stretchability, and a high SNR. Combined with a machine learning algorithm, it interprets muscle activity to precise motion control of a robotic prosthetic hand device.

A sensitive and waterproof ciliated E-skin, inspired by epidermal hair and mechanoreceptors, is fabricated via vertically 3D printing. It enables real-time pulse monitoring under high humidity, applicable to health management in maritime personnel.

Sensorineural hearing loss is the most common form of deafness, typically resulting from the loss of sensory cells on the basilar membrane, which cannot regenerate and thus lose sensitivity to sound vibrations. Here, we report a reconfigurable piezo-ionotropic polymer membrane engineered for biomimetic sustainable multi-resonance acoustic sensing, offering exceptional sensitivity (530 kPa^-1) and broadband frequency discrimination (20 Hz to 3300 Hz) while remaining resistant to "dying". The acoustic sensing capability is driven by an ion hitching-in cage effect intrinsic to the ion gel combined with fluorinated polyurethane. In this platform, the engineered ionotropic polymer stretches under acoustic vibrations, allowing cations to penetrate the widened hard segments and engage in strong ion-dipole interactions (cation···F), thereby restricting ion flux and amplifying impedance changes. Additionally, the sensor's sustainability is ensured through its self-healing properties and hydrophobic components, which enable effective self-repair in both conventional and aqueous environments without ion leakage, achieving a room-temperature healing speed of 0.3-0.4 μm/min. This sustainable acoustic sensing technology enables the devices to reliably identify specific sounds in everyday environments (e.g., human voices, piano notes), demonstrating their potential application as artificial basilar membranes.

Sensorineural hearing loss is the most common form of deafness, typically resulting from the loss of sensory cells on the basilar membrane, which cannot regenerate and thus lose sensitivity to sound vibrations. Here, we report a reconfigurable piezo-ionotropic polymer membrane engineered for biomimetic sustainable multi-resonance acoustic sensing, offering exceptional sensitivity (530 kPa⁻¹) and broadband frequency discrimination (20 to 3300 Hz) while remaining resistant to "dying." The acoustic sensing capability is driven by an ion hitching-in cage effect intrinsic to the ion gel combined with fluorinated polyurethane. In this platform, the engineered ionotropic polymer stretches under acoustic vibrations, allowing cations to penetrate the widened hard segments and engage in strong ion-dipole interactions (cation···F), thereby restricting ion flux and amplifying impedance changes. Additionally, the sensor’s sustainability is ensured through its self-healing properties and hydrophobic components, which enable effective self-repair in both conventional and aqueous environments without ion leakage, achieving a room-temperature healing speed of 0.3–0.4 µm/min. Leveraging this newly developed sustainable acoustic sensing technology, our devices demonstrate high proficiency in identifying specific sounds in everyday environments (e.g., human voices, piano notes), underscoring their potential as artificial basilar membranes.

Retinomorphic systems that can see, recognize, and respond to real-time environmental information will extend the complexity and range of tasks that an exoskeleton robot can perform to better assist physically disabled people. However, the lack of ultrasensitive, reconfigurable, and large-scale integratable retinomorphic devices and advanced edge-processing algorithms makes it difficult to realize retinomorphic hardware. Here, we report the retinomorphic hardware prototype with a 4096-pixel perovskite image sensor array as core module to endow embodied intelligent vision functionalities. The retinomorphic sensor array, using a one photodetector–one transistor geometry to resemble retinal circuit with broadband, ultrahigh, and reconfigurable photoresponsivities, executes both adaptive imaging with a contrast enhancement of ~620% under a dim-lit intensity of 10 microwatts per square centimeter and an instantaneous one-dimensional feature extraction algorithm to decompose the origin visual scenarios into parsimoniously encoded spatiotemporal information. This retinomorphic system endows embodied intelligence with adaptive imaging, in situ processing, and decision-making capabilities and promises enormous potential for autonomous robot applications.

The rapid advancement of tactile electronic skin (E-skin) has highlighted the effectiveness of incorporating bionic, force-sensitive microstructures in order to enhance sensing performance. Among these, cilia-like microstructures with high aspect ratios, whose inspiration is mammalian hair and the lateral line system of fish, have attracted significant attention for their unique ability to enable E-skin to detect weak signals, even in extreme conditions. Herein, this review critically examines recent progress in the development of cilia-inspired bionic tactile E-skin, with a focus on columnar, conical and filiform microstructures, as well as their fabrication strategies, including template-based and template-free methods. The relationship between sensing performance and fabrication approaches is thoroughly analyzed, offering a framework for optimizing sensitivity and resilience. We also explore the applications of these systems across various fields, such as medical diagnostics, motion detection, human–machine interfaces, dexterous robotics, near-field communication, and perceptual decoupling systems. Finally, we provide insights into the pathways toward industrializing cilia-inspired bionic tactile E-skin, aiming to drive innovation and unlock the technology’s potential for future applications.

In the burgeoning field of bioinspired materials, the principles governing biological perception and self‐healing drive advancements in biomimetic mechano‐responsive materials, seamlessly integrating ionic signal sensing with self‐healing. While current research often emphasizes individual functionalities, the concurrent enhancement of both self‐healing and sensitivity in iontronic skins is often overlooked. Drawing inspiration from transmembrane proteins like TSP‐15, Piezo 1 and Piezo 2, renowned for recruiting repair factors, multifunctional molecular‐ionic regulatory sites are constructed within a polyurethane/ionic liquid composite system, leading to the development of a mechano‐responsive elastomer (i‐DAPU) that exhibited both rapid self‐healing (72 µm min⁻¹) and impressive sensitivity (7012.05 kPa⁻¹). Leveraging the dual functionalities of i‐DAPU in tandem with deep learning algorithms, a sophisticated system is devised for intelligently analyzing neural conditions in comatose patients based on muscle strength, achieving a remarkable 99.2% accuracy rate, holding significant promise for healthcare applications.