Enhancing the Output of Self-Powered Cellulose-Based Humidity Sensors by Embedding Covalent Organic Frameworks

Humidity exerts a profound influence on human life, making the monitoring of atmospheric humidity critically important. To address the growing demand for low-power, lightweight, and environmentally friendly humidity sensors, increasing research efforts are focused on the development of self-powered humidity sensors. Self-powered humidity sensors based on the moist-electric effect can directly generate electrical signals through the adsorption and desorption behavior of water molecules in the air, without requiring external mechanical contact. However, current humidity sensors based on the moist-electric effect suffer from issues such as weak electrical output and an inability to produce sustained electrical output, limiting their practical application. This study uniformly disperses a covalent organic framework (COF) and cellulose nanofibers (CNF) to form a novel humidity-sensitive hybrid aerogel. Using the COF/CNF aerogel as a substrate, a humidity sensor operating via the moist-electric effect was fabricated. This sensor can be used for monitoring human respiration under different states and for noncontact sensing of finger movements. COF exhibits high hydrolytic stability and abundant polar functional groups. When combined with CNF, it forms synergistic effects, offering advantages of uniform dispersion and enhanced moisture absorption. This not only enables the device to achieve exceptional humidity response linearity across its sensing range (R² = 0.991), but also significantly enhances the device's electrical output intensity, achieving a high open-circuit voltage of 0.84 V and a sensitivity reaching 14 mV/% RH. The TMEG-based humidity sensor maintains a stable voltage output of 0.55-0.6 V for at least 5 h under environmental humidity conditions. Benefiting from its high output and robust output stability, this aerogel platform provides a practical route toward self-powered humidity sensing for potential environmental monitoring and low-power sensing.