Improving the Resolution of Flexible Large-Area Tactile Sensors through Machine-Learning Perception

Industrial robots are the main piece of equipment of intelligent manufacturing, and array-type tactile sensors are considered to be the core devices for their active sensing and understanding of the production environment. A great challenge for existing array-type tactile sensors is the wiring of sensing units in a limited area, the contradiction between a small number of sensing units and high resolution, and the deviation of the overall output pattern due to the difference in the performance of each sensing unit itself. Inspired by the human somatosensory processing hierarchy, we combine tactile sensors with artificial intelligence algorithms to simplify the sensor architecture while achieving tactile resolution capabilities far greater than the number of signal channels. The prepared 8-electrode carbon-based conductive network achieves high-precision identification of 32 regions with 97% classification accuracy assisted by a quadratic discriminant analysis algorithm. Notably, the output of the sensor remains unchanged after 13,000 cycles at 60 kPa, indicating its excellent durability performance. Moreover, the large-area skin-like continuous conductive network is simple to fabricate, cost-effective, and can be easily scaled up/down depending on the application. This work may address the increasing need for simple fabrication, rapid integration, and adaptable geometry tactile sensors for use in industrial robots.

Optoelectronic Pressure Sensor Based on the Bending Loss of Plastic Optical Fibers Embedded in Stretchable Polydimethylsiloxane

We report the design and testing of a sensor pad based on optical and flexible materials for the development of pressure monitoring devices. This project aims to create a flexible and low-cost pressure sensor based on a two-dimensional grid of plastic optical fibers embedded in a pad of flexible and stretchable polydimethylsiloxane (PDMS). The opposite ends of each fiber are connected to an LED and a photodiode, respectively, to excite and measure light intensity changes due to the local bending of the pressure points on the PDMS pad. Tests were performed in order to study the sensitivity and repeatability of the designed flexible pressure sensor.

Ultralow-Cost, Highly Sensitive, and Flexible Pressure Sensors Based on Carbon Black and Airlaid Paper for Wearable Electronics

Flexible pressure sensors have attracted considerable attention because of their potential applications in healthcare monitoring and human-machine interactions. However, the complicated fabrication process and the cos of sensing materials limit their widespread applications in practice. Herein, a flexible pressure sensor with outstanding performances is presented through an extremely simple and cost-efficient fabrication process. The sensing materials of the sensor are based on low-cost carbon black (CB)@airlaid paper (AP) composites, which are just prepared by drop-casting CB solutions onto APs. Through simply stacking multiple CB@APs with an irregular surface and a fiber-network structure, the obtained pressure sensor demonstrates an ultrahigh sensitivity of 51.23 kPa^-1 and an ultralow detection limit of 1 Pa. Additionally, the sensor exhibits fast response time, wide working range, good stability, as well as excellent flexibility and biocompatibility. All the comprehensive and superior performances endow the sensor with abilities to precisely detect weak air flow, wrist pulse, phonation, and wrist bending in real time. In addition, an array electronic skin integrated with multiple CB@AP sensors has been designed to identify spatial pressure distribution and pressure magnitude. Through a biomimetic structure inspired by blooming flowers, a sensor with the open-petal structure has been designed to recognize the wind direction. Therefore, our study, which demonstrates a flexible pressure sensor with low cost, simple preparation, and superior performances, will open up for the exploration of cost-efficient pressure sensors in wearable devices.

Multistimuli-Responsive Microstructured Superamphiphobic Surfaces with Large-Range, Reversible Switchable Wettability for Oil

The switchable wettability is essential for widespread applications in droplet manipulation, rewritable liquid patterning, fluid carrying, and so forth. However, it remains difficult to achieve the multistimuli-responsive, large-range, and reversible wetting switching especially for liquids with low surface tensions through surface topographical management. Here, we apply a simple and effective template-free self-assembly strategy to fabricate microstructured superamphiphobic surfaces that can reversibly switch the wetting performance for oil by transforming the surface morphology in response to multiple stimuli of magnetic fields and mechanical strains. Notably, the noticeably different wetting switching of oil triggered by different stimuli is demonstrated. The contact angles of hexadecane droplets on the as-prepared surfaces can be reversibly switched between 150 ± 1° and 38 ± 2° in response to mechanical strains. Furthermore, the underlying mechanism of wetting switching has been further elucidated using mathematical models. Interestingly, these switchable surfaces dramatically demonstrate the ability to transport oil droplets, without requiring lubricating liquid films. This work not only achieves the large-range and reversible wetting switching for oil but also opens new avenues for fabricating tunable superamphiphobic surfaces with transformable mushroom-like microstructures that can be easily extended to microstructure-dependent friction or adhesion control and used in other fields.