Publications

Abstract Wearable electrochemical biosensors for sweat analysis present a promising means for noninvasive biomarker monitoring. However, sweat‐based sensing still poses several challenges, including easy degradation of enzymes and biomaterials with repeated testing, limited detection range and sensitivity of enzyme‐based biosensors caused by oxygen deficiency in sweat, and poor shelf life of sensors using all‐in‐one working electrodes patterned by traditional techniques (e.g., electrodeposition and screen printing). Herein, a stretchable, wearable, and modular multifunctional biosensor is developed, incorporating a novel MXene/Prussian blue (Ti 3 C 2 T x /PB) composite designed for durable and sensitive detection of biomarkers (e.g., glucose and lactate) in sweat. A unique modular design enables a simple exchange of the specific sensing electrode to target the desired analytes. Furthermore, an implemented solid–liquid–air three‐phase interface design leads to superior sensor performance and stability. Typical electrochemical sensitivities of 35.3 µA m m −1 cm −2 for glucose and 11.4 µA m m −1 cm −2 for lactate are achieved using artificial sweat. During in vitro perspiration monitoring of human subjects, the physiochemistry signals (glucose and lactate level) can be measured simultaneously with high sensitivity and good repeatability. This approach represents an important step toward the realization of ultrasensitive enzymatic wearable biosensors for personalized health monitoring.

Aiming at harvesting ambient mechanical energy for self-powered systems, triboelectric nanogenerators (TENGs) have been recently developed as a highly efficient, cost-effective and robust approach to generate electricity from mechanical movements and vibrations on the basis of the coupling between triboelectrification and electrostatic induction. However, all of the previously demonstrated TENGs are based on vertical separation of triboelectric-charged planes, which requires sophisticated device structures to ensure enough resilience for the charge separation, otherwise there is no output current. In this paper, we demonstrated a newly designed TENG based on an in-plane charge separation process using the relative sliding between two contacting surfaces. Using Polyamide 6,6 (Nylon) and polytetrafluoroethylene (PTFE) films with surface etched nanowires, the two polymers at the opposite ends of the triboelectric series, the newly invented TENG produces an open-circuit voltage up to ∼1300 V and a short-circuit current density of 4.1 mA/m2 with a peak power density of 5.3 W/m2, which can be used as a direct power source for instantaneously driving hundreds of serially connected light-emitting diodes (LEDs). The working principle and the relationships between electrical outputs and the sliding motion are fully elaborated and systematically studied, providing a new mode of TENGs with diverse applications. Compared to the existing vertical-touching based TENGs, this planar-sliding TENG has a high efficiency, easy fabrication, and suitability for many types of mechanical triggering. Furthermore, with the relationship between the electrical output and the sliding motion being calibrated, the sliding-based TENG could potentially be used as a self-powered displacement/speed/acceleration sensor.

Fault diagnosis is critical for improving product yield and reducing manufacturing cost. However, it is very challenging to identify the root cause of failures on a complex circuit board. Ambiguous diagnosis results lead to long debug times and even wrong repair actions, which significantly increases the repair cost. We propose an automatic diagnostic system using support vector machines (SVMs). The proposed system acquires debug knowledge from empirical data; this strategy avoids the difficulties involved in knowledge acquisition in traditional fault diagnosis methods. SVMs provide an optimal separating hyperplane in classification. The optimal solution and generalization ability of SVMs lead to higher diagnostic accuracy, compared to the classical learning approaches such as artificial neural networks (ANNs). An industrial board is used to validate the effectiveness of the proposed system. Extensive simulation results demonstrate that the SVMs-based diagnostic system provides quantifiable improvement over current diagnostic software and an ANN-based diagnostic system.

The rapid development of the Internet of Things depends on wireless devices and their network. Traditional wireless sensing and transmission technology still requires multiple modules for sensing, signal modulation, transmission, and power, making the whole system bulky, rigid, and costly. Here, we proposed a paradigm shift wireless sensing solution based on the breakdown discharge–induced displacement current. Through that, we can combine the abovementioned functional modules in a single unit of self-powered wireless sensing e-sticker (SWISE), which features a small size (down to 9 mm by 9 mm) and long effective transmission distance (>30 m) when compared to existing wireless sensing technologies. Furthermore, SWISEs have functions of multipoint motion sensing and gas detection in fully self-powered manner. This work proposes a solution for flexible self-powered wireless sensing platforms, which shows great potential for implantable and wearable electronics, robotics, health care, infrastructure monitoring, human-machine interface, virtual reality, etc.