In-Memory Tactile Sensor with Tunable Steep-Slope Region for Low-Artifact and Real-Time Perception of Mechanical Signals

Recent Advances in Tactile Sensory Systems: Mechanisms, Fabrication, and Applications

Flexible electronics is a cutting-edge field that has paved the way for artificial tactile systems that mimic biological functions of sensing mechanical stimuli. These systems have an immense potential to enhance human-machine interactions (HMIs). However, tactile sensing still faces formidable challenges in delivering precise and nuanced feedback, such as achieving a high sensitivity to emulate human touch, coping with environmental variability, and devising algorithms that can effectively interpret tactile data for meaningful interactions in diverse contexts. In this review, we summarize the recent advances of tactile sensory systems, such as piezoresistive, capacitive, piezoelectric, and triboelectric tactile sensors. We also review the state-of-the-art fabrication techniques for artificial tactile sensors. Next, we focus on the potential applications of HMIs, such as intelligent robotics, wearable devices, prosthetics, and medical healthcare. Finally, we conclude with the challenges and future development trends of tactile sensors.

Ultralow-cost piezoelectric sensor constructed by thermal compression bonding for long-term biomechanical signal monitoring in chronic mental disorders

Wearable bioelectronic devices, which circumvent issues related to the large size and high cost of clinical equipment, have emerged as powerful tools for the auxiliary diagnosis and long-term monitoring of chronic psychiatric diseases. Current devices often integrate multiple intricate and expensive devices to ensure accurate diagnosis. However, their high cost and complexity hinder widespread clinical application and long-term user compliance. Herein, we developed an ultralow-cost poly(vinylidene fluoride)/zinc oxide nanofiber film-based piezoelectric sensor in a thermal compression bonding process. Our piezoelectric sensor exhibits remarkable sensitivity (13.4 mV N-1), rapid response (8 ms), and exceptional stability over 2000 compression/release cycles, all at a negligibly low fabrication cost. We demonstrate that pulse wave, blink, and speech signals can be acquired by the sensor, proposing a single biomechanical modality to monitor multiple physiological traits associated with bipolar disorder. This ultralow-cost and mass-producible piezoelectric sensor paves the way for extensive long-term monitoring and immediate feedback for bipolar disorder management.

A Discolorable Flexible Synaptic Transistor for Wearable Health Monitoring

Multifunctional intelligent wearable electronics, providing integrated physiological signal analysis, storage, and display for real-time and on-site health status diagnosis, have great potential to revolutionize health monitoring technologies. Advanced wearable systems combine isolated digital processor, memory, and display modules for function integration; however, they suffer from compatibility and reliability issues. Here, we introduce a flexible multifunctional electrolyte-gated transistor (EGT) that integrates synaptic learning, memory, and autonomous discoloration functionalities for intelligent wearable application. This device exhibits synergistic light absorption coefficient changes during voltage-gated ion doping that modulate the electrical conductance changes for synaptic function implementation. By adaptively changing color, the EGT can differentiate voltage pulse inputs with different frequency, amplitude, and duration parameters, exhibiting excellent reversibility and reliability. We developed a smart wearable monitoring system that incorporates EGT devices and sensors for respiratory and electrocardiogram signal analysis, providing health warnings through real-time and on-site discoloration. This study represents a significant step toward smart wearable technologies for health management, offering health evaluation through intelligent displays.

A gas-permeable, durable, and sensitive wearable strain sensor through thermal-radiation-promoted in situ welding

A convenient strategy for fabricating a wearable sensor with favorable durability and sensitivity is reported. This approach exploits the reconstructed hydrogen bonds within the thermoplastic polyurethane (TPU) during the heating evaporation of metal to form robust welding of the fibers in the substrate. The sensor can steadily monitor pulse waves and facilitate real-time human-machine interaction.

Bioinspired Luminescent HOF-Based Foam as Ultrafast and Ultrasensitive Pressure and Acoustic Bimodal Sensor for Human-Machine Interactive Object and Information Recognition

Bionic sensors have extensively served smart robots, medical equipment, and flexible wearable devices. The luminescent pressure-acoustic bimodal sensor can be treated as a remarkable, multifunctional, integrated bionic device. Here, a blue-emitting hydrogen-bonded organic framework (HOF-TTA) as luminogen combines with melamine foam (MF), generating the flexible and elastic HOF-TTA@MF (1 and 2) as a pressure-auditory bimodal sensor. In the luminescent pressure sensing process, 1 has excellent maximum sensitivity (132.02 kPa-1 ), low minimum detection limit (0.0 1333 Pa), fast response time (20 ms), high precision and great recyclability. 2 as a luminescent auditory sensor exhibits the highest response to the 520 Hz sound at 255-1453 Hz. In the process of sensing sound at 520 Hz, 2 possesses high sensitivity (1 648 441.3 cps Pa-1  cm-2 ), low detection limit (0.36 dB) and ultrafast response time (10 ms) within 11.47-91.77 dB. The sensing mechanisms toward pressure and auditory are analyzed in detail by finite element simulation. Furthermore, 1 and 2, as a human-machine interactive bimodal sensor, can recognize nine different objects and word information of "Health", "Phone", and "TongJi" with high accuracy and strong robustness. This work provides a facile fabricated method of luminescent HOF-based pressure-auditory bimodal sensors and endows them with new recognition functions and dimensions.