High-performance flexible strain sensors with a low cost, simple structure, and large-scale fabrication methods have a high demand in soft robotics, wearable devices, and health monitoring. Here, a direct-ink-writing-based 3D printing method, which fabricates structural layers in an efficient, layered manner, was developed to fabricate a stretchable and flexible strain sensor composed of carbon black/silicone elastomer (CB/PDMS) composites as the strain-sensing elements and electrodes. As the sensing element, the CB/PDMS composite had a sensitivity of 5.696 in the linear strain detection range of 0 to 60%, with good stability and low hysteresis. The flexible strain sensor demonstrates potential in monitoring various human motions, including large deformation motions of the human body, and muscle motions with facial micro-expressions.

Respiratory monitoring is crucial for evaluating health status and identifying potential respiratory diseases such as respiratory failure, bronchitis, and pneumonia. Humidity sensors play a significant role in this regard, and efforts are being made to improve their performance. However, achieving ideal sensor parameters such as sensitivity, detection range, and response speed is challenging. In this work, we propose a flexible preparation method for a double-layer humidity sensor using PDMS as a substrate and a GNP/MWCNT composite material as a sensor element. This sensor exhibits high sensitivity (1.4 RH-1), a wide detection range (20–90%), ultra-fast response (0.35 s) and recovery (2.5 s), high repetitiveness (500 cycles), good long-term stability, and excellent flexibility. Due to these advantages, this sensor has potential applications in real-time clinical and home medical care, such as accurate human respiratory monitoring and non-invasive skin humidity monitoring. Hence, this humidity sensor can be a powerful tool to monitor respiratory moisture levels for diagnosing and treating respiratory diseases effectively.

Pitaya fruit is a significant agricultural commodity in southern China. The traditional method of determining the ripeness of pitaya by humans is inefficient, it is therefore of the utmost importance to utilize precision agriculture and smart farming technologies in order to accurately identify the ripeness of pitaya fruit. In order to achieve rapid recognition of pitaya targets in natural environments, we focus on pitaya maturity as the research object. During the growth process, pitaya undergoes changes in its shape and color, with each stage exhibiting significant characteristics. Therefore, we divided the pitaya into four stages according to different maturity levels, namely Bud, Immature, Semi-mature and Mature, and we have designed a lightweight detection and classification network for recognizing the maturity of pitaya fruit based on the YOLOv8n algorithm, namely GSE-YOLO (GhostConv SPPELAN-EMA-YOLO). The specific methods include replacing the convolutional layer of the backbone network in the YOLOv8n model, incorporating attention mechanisms, modifying the loss function, and implementing data augmentation. Our improved YOLOv8n model achieved a detection and recognition accuracy of 85.2%, a recall rate of 87.3%, an F1 score of 86.23, and an mAP50 of 90.9%, addressing the issue of false or missed detection of pitaya ripeness in intricate environments. The experimental results demonstrate that our enhanced YOLOv8n model has attained a commendable level of accuracy in discerning pitaya ripeness, which has a positive impact on the advancement of precision agriculture and smart farming technologies.

The extensive water pollution caused by production activities is a key issue that needs to be addressed in the aquaculture industry. The dynamic monitoring of water quality is essential for understanding water quality and the growth of fish fry. Here, a low-cost, low-noise, real-time monitoring and automatic feedback biomimetic robotic fish was proposed for the dynamic monitoring of multiple water quality parameters in aquaculture. The biomimetic robotic fish achieved a faster swimming speed and more stable posture control at a swing angular velocity of 16 rad/s by using simulation analysis. A fast swimming speed (0.4 m/s) was achieved through the control of double-jointed pectoral and caudal fins, exhibiting various types of movements, such as straight swimming, obstacle avoidance, turning, diving, and surfacing. As a demonstration of application, bionic robotic fish were placed in a lake for on-site water sampling and parameter detection. The relative average deviations in water quality parameters, such as water temperature, acidity and alkalinity, and turbidity, were 1.25%, 0.07%, and 0.94%, respectively, meeting the accuracy requirements for water quality parameter detection. In the future, bionic robotic fish are beneficial for monitoring water quality, fish populations, and behaviors, improving the efficiency and productivity of aquaculture, and also providing interesting tools and technologies for science education and ocean exploration.

Water pollution seriously affects human health. Accurate and rapid detection and timely treatment of toxic substances in water are urgently needed. A stacked multilayer electrostatic printing technique was developed for making nanofiber-based microfluidic chips for water-quality testing. Nanofiber membrane matrix structures for microfluidic devices were fabricated by electrospinning. A hydrophobic barrier was then printed through electrostatic wax printing. This process was repeatedly performed to create three-dimensional nanofiber-based microfluidic analysis devices (3D-µNMADs). Flexible printing enabled one-step fabrication without the need for additional alignment or adhesive bonding. Practical applications of 3D-µNMADs include a colorimetric platform to quantitatively detect iron ion concentrations in water. There is also great potential for personalized point-of-care testing. Overall, the devices offer simple fabrication processes, flexible prototyping, potential for mass production, and multi-material integration.