The rapid development of portable and wearable electronic devices has led researchers to actively study triboelectric nanogenerators (TENGs) that can provide self-powering capabilities. In this study, we propose a highly flexible and stretchable sponge-type TENG, named flexible conductive sponge triboelectric nanogenerator (FCS-TENG), which consists of a porous structure manufactured by inserting carbon nanotubes (CNTs) into silicon rubber using sugar particles. Nanocomposite fabrication processes, such as template-directed CVD and ice freeze casting methods for fabricating porous structures, are very complex and costly. However, the nanocomposite manufacturing process of flexible conductive sponge triboelectric nanogenerators is simple and inexpensive. In the tribo-negative CNT/silicone rubber nanocomposite, the CNTs act as electrodes, increasing the contact area between the two triboelectric materials, increasing the charge density, and improving charge transfer between the two phases. Measurements of the performance of flexible conductive sponge triboelectric nanogenerators using an oscilloscope and a linear motor, under a driving force of 2–7 N, show that it generates an output voltage of up to 1120 V and a current of 25.6 µA. In addition, by using different weight percentages of carbon nanotubes (CNTs), it is shown that the output power increases with the weight percentage of carbon nanotubes (CNTs). The flexible conductive sponge triboelectric nanogenerator not only exhibits good performance and mechanical robustness but can also be directly used in light-emitting diodes connected in series. Furthermore, its output remains extremely stable even after 1000 bending cycles in an ambient environment. In sum, the results demonstrate that flexible conductive sponge triboelectric nanogenerators can effectively power small electronics and contribute to large-scale energy harvesting.

Air, a widely recognized dielectric material, is employed as a dielectric layer in this study. We present a triboelectric sensor with a laser-induced graphene (LIG) electrode and an air-trapped pad using silicone rubber (SR). A very thin device with a thickness of 1 mm and an effective gap for contact–separation between the films of silicone rubber and polyimide (PI) of 0.6 mm makes the device extremely highly sensitive for very low amplitudes of pressure. The fabrication of LIG as an electrode material on the surface of PI is the key reason for the fabrication of the thin sensor. In this study, we showed that the fabricated air-trapped padded sensor (ATPS) has the capability to generate an output voltage of ~32 V, a short-circuit current of 1.2 µA, and attain a maximum power density of 139.8 mW m⁻². The performance of the ATPS was compared with a replicated device having a hole on the pad, allowing air to pass through during contact–separation. The observed degradation in the electrical output suggests that the trapped air in the pad plays a crucial role in enhancing the output voltage. Therefore, the ATPS emerges as an ultra-sensitive sensor for healthcare sensing applications.

In this study, we propose a module-type triboelectric nanogenerator (TENG) capable of harvesting electricity from a variety of mechanical energy sources and generating power from diverse forms that fit the modular structure of the generator. The potential energy and kinetic energy of water are used for the rotational motion of the generator module, and electricity is generated by the contact/separation generation mode between the two triboelectric surfaces inside the rotating TENG. Through the parametric design of the internal friction surface structure and mass ball, we optimized the output of the proposed structure. To magnify the power, experiments were conducted to optimize the electrical output of the series of the TENG units. Consequently, outputs of 250 V and 11 μA were obtained when the angle formed between the floor and the housing was set at 0° while nitrile was set as the positively charged material and the frequency was set at 7 Hz. The electrical signal generated by the module-type TENG can be used as a sensor to recognize the strength and direction of various physical quantities, such as wind and earthquake vibrations.

In this study, we propose a module-type triboelectric nanogenerator (TENG) capable of harvesting power from a variety of mechanical energy sources. The potential energy and kinetic energy of water are used for the rotational motion of the generator module, and electricity is generated by the contact/separation generation mode between the two triboelectric surfaces inside the rotating TENG. Through the parametric design of the internal friction surface structure and mass ball, we optimized the output of the proposed structure. To magnify the power, experiments were conducted to optimize the electrical output of the series of TENG units. The electrical signal generated by the module-type TENG can be used as a sensor to recognize the strength and direction of various physical quantities, such as wind or earthquake vibrations.

This study presents a gesture-based authentication system utilizing triboelectric nanogenerator (TENG) sensors. As self-powered devices capable of generating high-voltage outputs without external power, TENG sensors are well-suited for low-power IoT sensors and smart device applications. The proposed system recognizes single tap, double tap, and holding gestures. The electrical characteristics of the sensor were evaluated under varying pressure conditions, confirming a linear relationship between applied force and output voltage. These results demonstrate the sensor’s high sensitivity and precision. A threshold-based classification algorithm was developed by analyzing signal features enabling accurate gesture recognition in real time. To enhance the practicality and scalability of the system, the algorithm was further configured to automatically segment raw sensor signals into gesture intervals and assign corresponding labels. From these segments, time-domain statistical features were extracted to construct a training dataset. A random forest classifier trained on this dataset achieved a high classification accuracy of 98.15% using five-fold cross-validation. The system reduces security risks commonly associated with traditional keypad input, offering a user-friendly and reliable authentication interface. This work confirms the feasibility of TENG-based gesture recognition for smart locks, IoT authentication devices, and wearable electronics, with future improvements expected through AI-based signal processing and multi-sensor integration.