Precise Engineering of Conductive Pathway by Frictional Direct-Writing for Ultrasensitive Flexible Strain Sensors

Nonintrusive Monitoring of Mental Fatigue Status Using Epidermal Electronic Systems and Machine-Learning Algorithms

Mental fatigue, characterized by subjective feelings of "tiredness" and "lack of energy", can degrade individual performance in a variety of situations, for example, in motor vehicle driving or while performing surgery. Thus, a method for nonintrusive monitoring of mental fatigue status is urgently needed. Recent research shows that physiological signal-based fatigue-classification methods using wearable electronics can be sufficiently accurate; by contrast, rigid, bulky devices constrain the behavior of those wearing them, potentially interfering with test signals. Recently, wearable electronics, such as epidermal electronics systems (EES) and electronic tattoos (E-tattoos), have been developed to meet the requirements for the comfortable measurement of various physiological signals. However, comfortable, effective, and nonintrusive monitoring of mental fatigue levels remains to be fulfilled. In this work, an EES is established to simultaneously detect multiple physiological signals in a comfortable and nonintrusive way. Machine-learning algorithms are employed to determine the mental fatigue levels and a predictive accuracy of up to 89% is achieved based on six different kinds of physiological features using decision tree algorithms. Furthermore, EES with the trained predictive model are applied to monitor in situ human mental fatigue levels when doing several routine research jobs, as well as the effect of relaxation methods in relieving fatigue.

Understanding the Cycling Performance Degradation Mechanism of a Graphene-Based Strain Sensor and an Effective Corresponding Improvement Solution

Graphene-based strain sensors have attracted tremendous interest due to their potential application as intelligent wearable sensing devices. However, for graphene-based strain sensors, it is found that the sensing property at the beginning of the tensile cycle is not stable. Concretely, the peak resistance value gradually declines in the first dozens of cycles in every cyclic test. This is a problem that obviously affects the measurement accuracy but is rarely investigated so far. In this paper, this phenomenon is for the first time systematically studied. According to the reliable experimental results, it can be concluded that the decline of resistance is caused by the evolution of wrinkle morphologies in the graphene layer, which is essentially attributed to the temporary slippage of the graphene sheets under external stress. Based on the analyzed mechanism, a targeted improvement solution was proposed and verified. By the combined effects of polydopamine and Ni²⁺, the slippage among the rGO sheets was suppressed and a strain sensor with excellent sensing stability was obtained as expected. Furthermore, the sensitivity of the modified sensor was six times higher than that of the pristine one due to the change in the crack form, demonstrating it to be an effective method to obtain a graphene-based strain sensor with comprehensively high performance.

Strain Sensor with Both a Wide Sensing Range and High Sensitivity Based on Braided Graphene Belts

Recent years have witnessed significant development of flexible strain sensors in a variety of fields. Nevertheless, the challenge of integrating a broad sensing range (>50%) with high sensitivity [gauge factor (GF) value > 100 over the entire sensing strain] in one single flexible strain sensor still exists. Herein, we prepared a flexible strain sensor based on braided graphene belts (BGBs) and dragon skin. Such a BGB strain sensor exhibits an integration of a wide sensing range (up to 55.55%) and high sensitivity (GF value > 175.16 through the entire working range). Besides, this BGB strain sensor also demonstrates a minute monitoring limit (0.01%), low hysteresis and overshoot behaviors, and reliable cycling repeatability (>6000 cycles). The SEM microscopy observations reveal that the skew angle and intersection regions of graphene belts are mainly responsible for the desirable sensing performance. Finally, the successful detection of full-range human motions, from subtle actions to vigorously joint-related movements, reflects great potential of the BGB strain sensor in the application of wearable instruments.

Recent advances in nanomaterial-enabled acoustic devices for audible sound generation and detection

Acoustic devices are widely applied in telephone communication, human-computer voice interaction systems, medical ultrasound examination, and other applications. However, traditional acoustic devices are hard to integrate into a flexible system and therefore it is necessary to fabricate light weight and flexible acoustic devices for audible sound generation and detection. Recent advances in acoustic devices have greatly overcome the limitations of conventional acoustic sensors in terms of sensitivity, tunability, photostability, and in vivo applicability by employing nanomaterials. In this review, light weight and flexible nanomaterial-enabled acoustic devices (NEADs) including sound generators and sound detectors are covered. Additionally, the fundamental concepts of acoustic as well as the working principle of the NEAD are introduced in detail. Also, the structures of future acoustic devices, such as flexible earphones and microphones, are forecasted. Further exploration of flexible acoustic devices is a key priority and will have a great impact on the advancement of intelligent robot-human interaction and flexible electronics.

High-Performance and Multifunctional Skinlike Strain Sensors Based on Graphene/Springlike Mesh Network

The development of skinlike strain sensors that are integrated with multiple sensing functions has attracted tremendous attention in recent years. To mimic human skin, strain sensors should have the abilities to detect various deformations such as pressing, stretching, bending, and even subtle vibrations. Here, we developed a facile, cost-effective, and scalable method for fabrication of high-performance strain sensors based on a graphene-coated springlike mesh network. This composite-based sensor exhibits an incorporation of low detection limit (LOD) for minute deformation (LOD of 1.38 Pa for pressure, 0.1% for tensile strain, and 10 μm for vibration), multiple sensing functions, long-term stability, and wide maximal sensing range (up to 80 kPa for pressure and 110% for tensile strain). On the basis of its superior performance, it can be applied for in situ monitoring of human motions ranging from subtle physiological signals (e.g., pulse, respiration, and phonation) to substantial movements (e.g., finger bending).