Artificial olfaction, i.e., e‐nose, plays a critical function in robotics by mimicking the human olfactory organ that can recognize different smells that correlate with a range of fields, including environment monitoring, disease diagnosis, public security affairs, agricultural production, food industry, etc. The advances in the artificial olfaction (electronic nose) technology and its applications are concisely reviewed herein. Three main elements are investigated and presented, with an emphasis on the emerging sensors and algorithm of the artificial neural network in the relevant fields. The first element is the diverse applications of e‐nose in medical care, food industry, environment monitoring, public security affairs, and agricultural production. The second element is the investigation of the sensors in e‐nose and representative and promising advances, which is the building block of e‐nose through mimicking the olfactory receptors. The third element is the introduction to the algorithm of the artificial neural network to serve in the recognition of the pattern of odors (i.e., their chemical profiles). Promises and challenges of the separately reviewed parts and the combined parts are presented and discussed. Ideas regarding further orientation and development of the e‐nose system are also considered.

Achieving highly accurate responses to external stimuli during human motion is a considerable challenge for wearable devices. The present study leverages the intrinsically high surface‐to‐volume ratio as well as the mechanical robustness of nanostructures for obtaining highly‐sensitive detection of motion. To do so, highly‐aligned nanowires covering a large area were prepared by capillarity‐based mechanism. The nanowires exhibit a strain sensor with excellent gauge factor (≈35.8), capable of high responses to various subtle external stimuli (≤200 µm deformation). The wearable strain sensor exhibits also a rapid response rate (≈230 ms), mechanical stability (1000 cycles) and reproducibility, low hysteresis (<8.1%), and low power consumption (<35 µW). Moreover, it achieves a gauge factor almost five times that of microwire‐based sensors. The nanowire‐based strain sensor can be used to monitor and discriminate subtle movements of fingers, wrist, and throat swallowing accurately, enabling such movements to be integrated further into a miniaturized analyzer to create a wearable motion monitoring system for mobile healthcare.

Weak magnetic nondestructive testing (e.g., metal magnetic memory method) concerns the magnetization variation of ferromagnetic materials due to its applied load and a weak magnetic surrounding them. One key issue on these nondestructive technologies is the magnetomechanical effect for quantitative evaluation of magnetization state from stress–strain condition. A representative phenomenological model has been proposed to explain the magnetomechanical effect by Jiles in 1995. However, the Jiles' model has some deficiencies in quantification, for instance, there is a visible difference between theoretical prediction and experimental measurements on stress–magnetization curve, especially in the compression case. Based on the thermodynamic relations and the approach law of irreversible magnetization, a nonlinear coupled model is proposed to improve the quantitative evaluation of the magnetomechanical effect. Excellent agreement has been achieved between the predictions from the present model and previous experimental results. In comparison with Jiles' model, the prediction accuracy is improved greatly by the present model, particularly for the compression case. A detailed study has also been performed to reveal the effects of initial magnetization status, cyclic loading, and demagnetization factor on the magnetomechanical effect. Our theoretical model reveals that the stable weak magnetic signals of nondestructive testing after multiple cyclic loads are attributed to the first few cycles eliminating most of the irreversible magnetization. Remarkably, the existence of demagnetization field can weaken magnetomechanical effect, therefore, significantly reduces the testing capability. This theoretical model can be adopted to quantitatively analyze magnetic memory signals, and then can be applied in weak magnetic nondestructive testing.

Wound healing is a complex tissue regeneration process involving many changes in multiple physiological parameters. The pH and temperature of a wound site have long been recognized as important biomarkers for assessing wound healing status. For effective wound management, wound dressings integrated with wearable sensors and systems used for continuous monitoring of pH and temperature have received much attention in recent years. Herein, recent advances in the development of wearable pH and temperature sensors and systems based on different sensing mechanisms for wound status monitoring and treatment are comprehensively summarized. Challenges in the areas of sensing performance, infection identification threshold, large-area 3-dimensional detection, and long-term reliable monitoring in current wearable sensors/systems and emerging solutions are emphasized, providing critical insights into the development of wearable sensors and systems for wound healing monitoring and management.

Water−solid triboelectric nanogenerators (TENGs) are insensitive to ambient humidity, providing a wide range of possibilities for designing stable waterenergy-based harvesters and self-powered sensors. However, the wide application of most water−solid TENGs has been limited by low triboelectrification performance. To boost the output performance of water−solid TENGs, a newly structured TENG has been developed by adding a polyimide (PI) as a charge storage intermediate layer between the friction layer and the conducting layer, significantly improving the output performance (1.260 mW), with a 5-fold increase compared to the water−solid TENG without the PI intermediate layer (0.234 mW). This analysis shows that adding an intermediate layer with a high density of electron capture sites to the TENG results in more triboelectric charge being retained, thereby improving the electrical performance of TENG. The electrical performance of TENG is related to the thickness of the PI layer, but this is not a positive correlation. Contact angles and falling heights between the droplet and the device also affect the output performance. Finally, the water−solid PI-TENG we have developed has promise in hydropower harvesting capabilities and can be used to power warning signals on a dark and rainy night to ensure the safety of people.