A disposable and ecofriendly starch paper was used to fabricate a triboelectric nanogenerator (TENG) for the sensing of human perspiration. Using cost-effective and commercially accessible materials, the starch paper-based TENG (S-TENG) can be achieved through a rapid and simple fabrication method. The output performance varies with the absorbed water content, which can be utilized for human perspiration sensing. The starch structure can be broken down in water within 4 min. The proposed S-TENGs have a considerable potential in the field of green wearable electronics.

Recent years, triboelectric nanogenerators (TENGs) have attracted increased attention from researchers worldwide. Owing to their conductivity and triboelectric characteristics, metal materials can be made as both triboelectric materials and conductive electrodes. However, the surface of typical metals (such as copper, aluminum, and iron) is likely to be corroded when the sweat generated by human-body movement drops on the surface of TENGs, as this corrosion is detrimental to the output performance of TENGs. In this work, we proposed a novel corrosion-resistant copper–nickel based TENG (CN-TENG). Copper–nickel alloy conductive tape and polytetrafluoroethylene (PTFE) tape played the role of the triboelectric materials, and polymethyl methacrylate (PMMA) was utilized as the supporting part. The conductive copper–nickel alloy tape also served as a conductive electrode. The open-circuit voltage (V_OC) and short-circuit current (I_SC) can arrive at 196.8 V and 6 μA, respectively. Furthermore, peak power density values of 45 μW/cm² were realized for the CN-TENG. A series of experiments confirmed its corrosion-resistant property. The approximate value of V_OC for the fabricated TENG integrated into the shoe reached 1500 V, which is capable of driving at least 172 high-power LEDs in series. The results of this research provide a workable method for supporting corrosion-resistant self-powered wearable electronics.

Biosensors are a new type of sensor that utilize biologically sensitive materials and microbially active analytes to measure a variety of biological signals. The purpose of monitoring is achieved by combining these sensitive materials with analytes such as proteins, cells, viruses, and bacteria, inducing changes in their physical or chemical properties. The use of nanomaterials in fabricating surface acoustic wave (SAW) biosensors is particularly noteworthy for the label-free detection of organisms due to their compact size, portability, and high sensitivity. Recent advancements in the manufacturing techniques of SAW biosensors have significantly enhanced sensor performance and reliability. These techniques not only ensure precise control over sensor dimensions and material properties but also facilitate scalable and cost-effective production processes. As a result, SAW biosensors are poised to become powerful tools for various clinical and rapid detection applications.

Al-Sn-Al wafer bonding is a new semiconductor manufacturing technology that plays an important role in device manufacturing. Optimization of the bonding process and testing of the bonding strength remain key issues. However, using only physical experiments to study the above problems presents difficulties such as repeating many experiments, high costs, and low efficiency. Deep learning algorithms can quickly simulate complex physical correlations by training large amounts of data, which is a good solution to the difficulties in studying wafer bonding. Therefore, this paper proposes the use of deep learning models (2-layer CNN and 50-layer ResNet) to achieve autonomous recognition of bonding strengths corresponding to different bonding conditions, and the results from a comparative test set show that the ResNet model has an accuracy of 99.17%, outperforming the CNN model with an accuracy of 91.67%. Then, the identified images are analyzed using the Canny edge detector, which showed that the fracture surface morphology of the wafer is a hole-shaped structure, with the smaller the area of hole movement on the wafer surface, the higher the bonding strength. In addition, the effects of bonding time and bonding temperature on bonding strength are verified, showing that relatively short bonding times and relatively low bonding temperatures resulted in better wafer bonding strength. This research demonstrates the potential of using deep learning to accelerate wafer bonding strength identification and process condition optimization.

This paper sets out a simple and affordable method for the manufacture of a water sensor involving drawing marks on paper using a flexible pencil. The sensor indicates flexible endurance variations upon exposure to water or a drying surface. It can be employed for the detection of water substances in ethanol. In addition, an experiment suggests the usefulness of the sensor for the sensing of breath. This shows the immense potential applications of the sensor for wearable and also health-inspecting electronic gadgetry.