Bending Sensor Based on Controlled Microcracking Regions for Application toward Wearable Electronics and Robotics

All-Light Remote Driving and Programming of Soft Actuator Based on Selective Laser Stimulation and Modification

Soft robots are advantageous due to their flexibility, ability to interact with humans, and multifunctional adaptability. However, developing soft robots that are unrestrained and can be reprogrammed for reversible control without causing damage remains a significant challenge. The majority of soft robots have a bilayer structure with internal stress, which limits their motion to pre-programmed anisotropic structures. Taking inspiration from pillworms found in nature, we propose an approach for controlling and reprogramming the motion of actuators using infrared light as the driver and a laser-melted paraffin wax (PW) shell as the controller. The dual-purpose shell can not only protect the actuator but can also alter its initial motion behavior to achieve multiple programming, profile modeling, object grasping, and directional crawling tasks, thereby enabling active changes to the motion strategy in response to external stimuli. This method can also be extended to other materials with similar properties and multi-stimulus responses, offering a new pathway for developing unconstrained, autonomous soft robots and intelligent devices.

Electroactive soft actuators utilizing PEDOT:PSS and 3D lithium-ion-conducting phosphate columnar liquid crystals embedded in a porous polyethylene membrane

This study introduces a novel supramolecular thermotropic columnar liquid-crystalline (LC) electrolyte tailored for high-performance ionic electroactive polymer (iEAP) actuators. The electrolyte is designed by integrating lithium salts into a taper-shaped molecule with bisphosphate moieties (BPO), which self-assembles into a columnar hexagonal (Colh) phase, forming 3D continuous ion-conductive pathways. This architecture achieves high ionic conductivity of up to 2 × 10-4 S cm-1 at room temperature. An actuator was fabricated by embedding this electrolyte into a microporous polyethylene membrane, sandwiched between PEDOT:PSS electrodes. The resulting device exhibits exceptional performance, achieving a bending strain of 0.52% and a force output of 0.5 mN under a ± 2 V, along with outstanding durability, retaining its performance over 9000 cycles. These results underscore the potential of 3D ion-conductive LC electrolytes in advancing iEAP actuator technologies, paving the way for innovative applications in tactile interfaces and soft robotics.

High Performance KNN-Based Macro Fiber Composites for Human Motion Monitoring Applications

Piezoelectric materials are increasingly used in portable smart electronics and Internet of Things sensors. Among them, piezoelectric macro fiber composites (MFCs) have attracted much attention due to their architectural simplicity, scalability, and high-power density. However, most MFCs currently use toxic lead-based piezoelectric materials, hindering their applications for bio-friendly intelligent electronics. Here, a lightweight, thin, and high-performance lead-free MFC for human motion monitoring is developed using multilevel structure engineered (K,Na)NbO3-based ceramics as eco-friendly piezoelectric matrix based on delicate simulation analysis. The variation of the effective electric field and piezo-potential inside the KNN-based MFC during polarization and under different stress distributions is analyzed by using the finite element analysis method. The electrical output signals of the KNN-based MFC are tested under different deformation modes, achieving high output voltage (≈25 V), as well as high output current (≈25 µA) and instantaneous output power density of 80.42 µW cm-2. When the MFC is attached to the human body, it can sensitively convert even tiny body motion into a noticeable electrical response, with remarkable motion monitoring capabilities. This research provides a fundamental methodology for the development of lead-free MFCs, as well as an alternative material for next-generation smart sensing devices.

Meso Hybridized Silk Fibroin Watchband for Wearable Biopotential Sensing and AI Gesture Signaling

Human biopotential signals, such as electrocardiography, are closely linked to health and chronic conditions. Electromyography, corresponds to muscle actions and is pertinent to human-machine interactions. Here, we present a type of smart and flexible watchband that includes a mini flexible electrode array based on Mo-Au filament mesh, combined with mesoscopic hybridized silk fibroin films. As the layer in contact with the skin, waterborne polyurethane and SF create a highly flexible and permeable meso-hybridized SF/WPU layer, ensuring skin-friendliness and comfortable wearing. The flexible FM electrodes are created by integrating Mo-Au FM into 2D-interconnected networks. Molybdenum filaments provide high rigidity and are coated with Aurum to enhance conductivity. The use of Mo-Au FMs in warp-knitted patterns results in high SNR (43.22 dB), high sensitivity (44.43 mV/kg), and significant motion noise reduction due to the pattern's elastic deformability and skin-gripping properties. Leveraging these unique technologies, these smart watchbands excel in prolonged sensing operation, grasping force detection, and gesture recognition. Through smart raining via deep learning, we achieved an unparalleled recognition rate (96% across 20 volunteers of different genders) among other EMG sensing devices. These results have significant implications for human-machine interaction, including applications in underwater robot control, drone operation, and autonomous vehicle control.

Architectural design and affecting factors of MXene-based textronics for real-world application

Today, textile-based wearable electronic devices (textronics) have been developed by taking advantage of nanotechnology and textile substrates. Textile substrates offer flexibility, air permeability, breathability, and wearability, whereas, using nanomaterials offers numerous functional properties, like electrical conductivity, hydrophobicity, touch sensitivity, self-healing properties, joule heating properties, and many more. For these reasons, textronics have been extensively used in many applications. Recently, new emerging two-dimensional (2D) transition metal carbide and nitride, known as MXene, nanomaterials have been highly considered for developing textronics because the surface functional groups and hydrophilicity of MXene nanoflakes allow the facile fabrication of MXene-based textronics. In addition, MXene nanosheets possess excellent electroconductivity and mechanical properties as well as large surface area, which also give numerous opportunities to develop novel functional MXene/textile-based wearable electronic devices. Therefore, this review summarizes the recent advancements in the architectural design of MXene-based textronics, like fiber, yarn, and fabric. Regarding the fabrication of MXene/textile composites, numerous factors affect the functional properties (e.g. fabric structure, MXene size, etc.). All the crucial affecting parameters, which should be chosen carefully during the fabrication process, are critically discussed here. Next, the recent applications of MXene-based textronics in supercapacitors, thermotherapy, and sensors are elaborately delineated. Finally, the existing challenges and future scopes associated with the development of MXene-based textronics are presented.

Machine-learned wearable sensors for real-time hand-motion recognition: toward practical applications

Soft electromechanical sensors have led to a new paradigm of electronic devices for novel motion-based wearable applications in our daily lives. However, the vast amount of random and unidentified signals generated by complex body motions has hindered the precise recognition and practical application of this technology. Recent advancements in artificial-intelligence technology have enabled significant strides in extracting features from massive and intricate data sets, thereby presenting a breakthrough in utilizing wearable sensors for practical applications. Beyond traditional machine-learning techniques for classifying simple gestures, advanced machine-learning algorithms have been developed to handle more complex and nuanced motion-based tasks with restricted training data sets. Machine-learning techniques have improved the ability to perceive, and thus machine-learned wearable soft sensors have enabled accurate and rapid human-gesture recognition, providing real-time feedback to users. This forms a crucial component of future wearable electronics, contributing to a robust human-machine interface. In this review, we provide a comprehensive summary covering materials, structures and machine-learning algorithms for hand-gesture recognition and possible practical applications through machine-learned wearable electromechanical sensors.