Bioinspired Young's Modulus-Hierarchical E-Skin with Decoupling Multimodality and Neuromorphic Encoding Outputs to Biosystems

Phytic Acid-Induced Gradient Hydrogels for Highly Sensitive and Broad Range Pressure Sensing

Ionic conductive hydrogels have emerged as an excellent option for constructing dielectric layers of interfacial iontronic sensors. Among these, gradient ionic hydrogels, due to the intrinsic gradient elastic modulus, can achieve a wide range of pressure responses. However, the fabrication of gradient hydrogels with optimal mechanical and sensing properties remains a challenge. In this study, it is discovered first that phytic acid (PA) interacts in remarkably distinct manners (i.e., plasticizing effects and phase separation) with different polymers (i.e., polyacrylamide and polyacrylic acid). This distinctive PA-polymer interacting mechanism is innovatively utilized to construct a modulus gradient ionic hydrogel through a simple precursor solution infiltration approach. The gradient hydrogel-based flexible pressure sensor not only achieves a high sensitivity (9.00 kPa-1, <15 kPa) and a broad sensing range (from ≈3.7 Pa to 1.2 MPa) simultaneously, but also exhibits superior low pressure sensing performance. It successfully recognizes the subtle pressure due to acoustic waves and airflow, as well as the moderate pressure due to speaking and finger pressing and the high magnitude of plantar pressure. In addition, the gradient hydrogel demonstrates remarkable antibacterial properties and biocompatibility. This functional hydrogel with excellent sensing performance and bioactivity exhibits exceptional potential for wearable sensing applications.

Self-Powered Artificial Neuron Devices: Towards the All-In-One Perception and Computation System

The increasing demand for energy supply in sensing units and the computational efficiency of computation units has prompted researchers to explore novel, integrated technology that offers high efficiency and low energy consumption. Self-powered sensing technology enables environmental perception without external energy sources, while neuromorphic computation provides energy-efficient and high-performance computing capabilities. The integration of self-powered sensing technology and neuromorphic computation presents a promising solution for an all-in-one system. This review examines recent developments and advancements in self-powered artificial neuron devices based on triboelectric, piezoelectric, and photoelectric effects, focusing on their structures, mechanisms, and functions. Furthermore, it compares the electrical characteristics of various types of self-powered artificial neuron devices and discusses effective methods for enhancing their performance. Additionally, this review provides a comprehensive summary of self-powered perception systems, encompassing tactile, visual, and auditory perception systems. Moreover, it elucidates recently integrated systems that combine perception, computing, and actuation units into all-in-one configurations, aspiring to realize closed-loop control. The seamless integration of self-powered sensing and neuromorphic computation holds significant potential for shaping a more intelligent future for humanity.

Bionic Recognition Technologies Inspired by Biological Mechanosensory Systems

Mechanical information is a medium for perceptual interaction and health monitoring of organisms or intelligent mechanical equipment, including force, vibration, sound, and flow. Researchers are increasingly deploying mechanical information recognition technologies (MIRT) that integrate information acquisition, pre-processing, and processing functions and are expected to enable advanced applications. However, this also poses significant challenges to information acquisition performance and information processing efficiency. The novel and exciting mechanosensory systems of organisms in nature have inspired us to develop superior mechanical information bionic recognition technologies (MIBRT) based on novel bionic materials, structures, and devices to address these challenges. Herein, first bionic strategies for information pre-processing are presented and their importance for high-performance information acquisition is highlighted. Subsequently, design strategies and considerations for high-performance sensors inspired by mechanoreceptors of organisms are described. Then, the design concepts of the neuromorphic devices are summarized in order to replicate the information processing functions of a biological nervous system. Additionally, the ability of MIBRT is investigated to recognize basic mechanical information. Furthermore, further potential applications of MIBRT in intelligent robots, healthcare, and virtual reality are explored with a view to solve a range of complex tasks. Finally, potential future challenges and opportunities for MIBRT are identified from multiple perspectives.

3D printable and myoelectrically sensitive hydrogel for smart prosthetic hand control

Surface electromyogram (sEMG) serves as a means to discern human movement intentions, achieved by applying epidermal electrodes to specific body regions. However, it is difficult to obtain high-fidelity sEMG recordings in areas with intricate curved surfaces, such as the body, because regular sEMG electrodes have stiff structures. In this study, we developed myoelectrically sensitive hydrogels via 3D printing and integrated them into a stretchable, flexible, and high-density sEMG electrodes array. This electrode array offered a series of excellent human-machine interface (HMI) features, including conformal adherence to the skin, high electron-to-ion conductivity (and thus lower contact impedance), and sustained stability over extended periods. These attributes render our electrodes more conducive than commercial electrodes for long-term wearing and high-fidelity sEMG recording at complicated skin interfaces. Systematic in vivo studies were used to investigate its efficacy to control a prosthetic hand by decoding sEMG signals from the human hand via a multiple-channel readout circuit and a sophisticated artificial intelligence algorithm. Our findings demonstrate that the 3D printed gel myoelectric sensing system enables real-time and highly precise control of a prosthetic hand.

Flexible mechano-optical dual-responsive perovskite molecular ferroelectric composites for advanced anticounterfeiting and encryption

Hybrid organic-inorganic molecular ferroelectrics have emerged as promising materials for multifunctional piezoelectric devices. However, they present challenges in practical applications because of their inherent brittleness and poor ductility. Herein, we present a flexible mechano-optical dual-responsive molecular ferroelectric composite by incorporating trimethylchloromethyl ammonium (TMCM)-MnCl3 into styrene ethylene butylene styrene (SEBS) matrix. The SEBS/TMCM-MnCl3 exhibits excellent stretchable mechanical properties (tensile strain >1300%, thickness of 30 μm), piezoelectricity, and photoluminescence, enabling advanced visual-tactile-fused anticounterfeiting and encryption applications. Anticounterfeiting and antitampering tags are developed to judge whether the valued items are true or tampered with based on pattern recognition and piezoelectric response, respectively. Additionally, high-security password keyboards featuring triple-layer encryption are designed, offering more password combinations (524,288 times greater than those of traditional password devices relying solely on digital encryption) and enhanced security reliability against cracking attempts. This work can inspire designs of multifunctional optoelectronic materials and enable visual-tactile-fused intelligent applications in human-machine interfaces, information security, and advanced robotics.

Directional Characteristic Enhancement of an Omnidirectional Detection Sensor Enabled by Strain Partitioning Effects in a Periodic Composite Hole Substrate

An omnidirectional stretchable strain sensor with high resolution is a critical component for motion detection and human-machine interaction. It is the current dominant solution to integrate several consistent units into the omnidirectional sensor based on a certain geometric structure. However, the excessive similarity in orientation characteristics among sensing units restricts orientation recognition due to their closely matched strain sensitivity. In this study, based on strain partition modulation (SPM), a sensitivity anisotropic amplification strategy is proposed for resistive strain sensors. The stress distribution of a sensitive conductive network is modulated by structural parameters of the customized periodic hole array introduced underneath the elastomer substrate. Meanwhile, the strain isolation structures are designed on both sides of the sensing unit for stress interference immune. The optimized sensors exhibit excellent sensitivity (19 for 0-80%; 109 for 80%-140%; 368 for 140%-200%), with nearly a 7-fold improvement in the 140%-200% interval compared to bare elastomer sensors. More importantly, a sensing array composed of multiple units with different hole configurations can highlight orientation characteristics with amplitude difference between channels reaching up to 29 times. For the 48-class strain-orientation decoupling task, the recognition rate of the sensitivity-differentiated layout sensor with the lightweight deep learning network is as high as 96.01%, superior to that of 85.7% for the sensitivity-consistent layout. Furthermore, the application of the sensor to the fitness field demonstrates an accurate recognition of the wrist flexion direction (98.4%) and spinal bending angle (83.4%). Looking forward, this methodology provides unique prospects for broader applications such as tactile sensors, soft robotics, and health monitoring technologies.

Twistable and Stretchable Nasal Patch for Monitoring Sleep-Related Breathing Disorders Based on a Stacking Ensemble Learning Model

Obstructive sleep apnea syndrome disrupts sleep, destroys the homeostasis of biological systems such as metabolism and the immune system, and reduces learning ability and memory. The existing polysomnography used to measure sleep disorders is executed in an unfamiliar environment, which may result in sleep patterns that are different from usual, reducing accuracy. This study reports a machine learning-based personalized twistable patch system that can simply measure obstructive sleep apnea syndrome in daily life. The stretchable patch attaches directly to the nose in an integrated form factor, detecting sleep-disordered breathing by simultaneously sensing microscopic vibrations and airflow in the nasal cavity and paranasal sinuses. The highly sensitive multichannel patch, which can detect airflow at the level of 0.1 m/s, has flexibility via a unique slit pattern and fabric layer. It has linearity with an R2 of 0.992 in the case of the amount of change according to its curvature. The stacking ensemble learning model predicted the degree of sleep-disordered breathing with an accuracy of 92.9%. The approach demonstrates high sleep disorder detection capacity and proactive visual notification. It is expected to help as a diagnostic platform for the early detection of chronic diseases such as cerebrovascular disease and diabetes.

Venus Flytrap-Inspired Data-Center-Free Fast-Responsive Soft Robots Enabled by 2D Ni3(HITP)2 MOF and Graphite

The rapid and responsive capabilities of soft robots in perceiving, assessing, and reacting to environmental stimuli are highly valuable. However, many existing soft robots, designed to mimic humans and other higher animals, often rely on data centers for the modulation of mechanoelectrical transduction and electromechanical actuation. This reliance significantly increases system complexity and time delays. Herein, drawing inspiration from Venus flytraps, a soft robot employing a power modulation strategy is presented for active stimulus reaction, eliminating the need for a data center. This robot achieves mechanoelectrical transduction through Ni3(2,3,6,7,10,11-hexaiminotriphenylene)2 (Ni3(HITP)2) metal-organic framework (MOF) with an ultralow time delay (256 ns) and electromechanical actuation via graphite. The Joule heating effect in graphite is effectively modulated by Ni3(HITP)2 before and after the presence of pressure, thus enabling the stimulus reaction of soft robots. As demonstrated, three soft robots are created: low-level edge tongue robots, Venus flytrap robots, and high-level nerve-center-controlled dragonfly robots. This power modulation strategy inspires designs of edge soft robots and high-level robots with a human-like effective fusion of conditioned and unconditioned reflexes.

Conductive and Enhanced Mechanical Strength of Mo2Ti2C3 MXene-Based Hydrogel Promotes Neurogenesis and Bone Regeneration in Bone Defect Repair

Bone defects are common with increasing high-energy fractures, tumor bone invasion, and implantation revision surgery. Bone is an electroactive tissue that has electromechanical interaction with collogen, osteoblasts, and osteoclasts. Hydrogel provides morphological plasticity and extracellular matrix (ECM) 3D structures for cell survival, and is widely used as a bone engineering material. However, the hydrogels have poor mechanical intensity and lack of cell adhesion, slow gelation time, and limited conductivity. MXenes are novel nanomaterials with hydrophilic groups that sense cell electrophysiology and improve hydrogel electric conductivity. Herein, gelatin had multiple active groups (NH2, OH, and COOH) and an accelerated gelation time. Acrylamide has Schiff base bonds to cross-link with gelatin and absorb metal ions. Deacetylated chitosan improved cell adhesion and active groups to connect MXene and acrylamide. We constructed Mo2Ti2C3 MXene hydrogel with improved elastic modulus and viscosity, chemical cross-linking structure, electric conductivity, and good compatibility. Mo2Ti2C3 MXene hydrogel exhibits outstanding osteogenesis in vitro. Mo2Ti2C3 MXene hydrogel promotes osteogenesis via alkaline phosphatase (ALP) and alizarin red S (ARS) staining, improving osteogenic marker genes and protein expressions in vitro. Mo2Ti2C3 MXene hydrogel aids new bone formation in the in vivo calvarial bone defect model via micro-CT and histology. Mo2Ti2C3 MXene hydrogel facilitates neurogenesis factors nerve growth factor (NGF) and brain-derived neurotrophic factor (BDNF) expression, and aids newly born neuron marker Tuj-1 and sensory neuron marker serotonin (5-HT) and osteogenesis pathway proteins, runt-related transcription factor 2 (Runx2), osteocalcin (OCN), SMAD family member 4 (SMAD4), and bone morphogenetic protein-2 (BMP2) in the bone defect repair process. Mo2Ti2C3 MXene hydrogel promotes osteogenesis and neurogenesis, which extends its biomedical application in bone defect reconstruction.

A double-crack structure for bionic wearable strain sensors with ultra-high sensitivity and a wide sensing range

Flexible strain sensors are crucial in fully monitoring human motion, and they should have a wide sensing range and ultra-high sensitivity. Herein, inspired by lyriform organs, a flexible strain sensor based on the double-crack structure is designed. An MXene layer and an Au layer with cracks are constructed on both sides of the insulated polydimethylsiloxane (PDMS) film, forming an equivalent parallel circuit that guarantees the integrity of the conductive path under a large strain. The rapid disconnection of the crack junctions causes a significant change in the resistance value. Due to the effect of cracks on the conductive path, the sensitivity of the sensor is largely improved. Benefiting from the double-crack structure, the as-obtained sensor shows ultra-high sensitivity (maximum gauge factor of up to 14 373.6), a wide working range (up to 21%), a fast response time (183 ms) and excellent dynamical stability (almost no performance loss after 1000 stretching cycles and different frequency cycles). In practical applications, the sensor is applied to different parts of the human body to sense the deformation of the skin, demonstrating its great potential application value in human physiological detection and the human-machine interaction. This study can provide new ideas for preparing high-performance flexible strain sensors.

Insights into Materials, Physics, and Applications in Flexible and Wearable Acoustic Sensing Technology

Sound plays a crucial role in the perception of the world. It allows to communicate, learn, and detect potential dangers, diagnose diseases, and much more. However, traditional acoustic sensors are limited in their form factors, being rigid and cumbersome, which restricts their potential applications. Recently, acoustic sensors have made significant advancements, transitioning from rudimentary forms to wearable devices and smart everyday clothing that can conform to soft, curved, and deformable surfaces or surroundings. In this review, the latest scientific and technological breakthroughs with insightful analysis in materials, physics, design principles, fabrication strategies, functions, and applications of flexible and wearable acoustic sensing technology are comprehensively explored. The new generation of acoustic sensors that can recognize voice, interact with machines, control robots, enable marine positioning and localization, monitor structural health, diagnose human vital signs in deep tissues, and perform organ imaging is highlighted. These innovations offer unique solutions to significant challenges in fields such as healthcare, biomedicine, wearables, robotics, and metaverse. Finally, the existing challenges and future opportunities in the field are addressed, providing strategies to advance acoustic sensing technologies for intriguing real-world applications and inspire new research directions.