From Bio-Composite to Intelligent System: A CM-Based Triboelectric Nanogenerator for Self-Powered Smart Packaging and Fruit Recognition

The damage caused by transportation to fresh agricultural products is a major challenge in the logistics field. The traditional packaging systems are passive and cannot provide real-time monitoring functions. Moreover, the acceleration and pressure sensors currently used for collision monitoring are facing issues such as power dependence, high costs, and environmental problems. Frictional electric nanogenerators (TENGs) can convert mechanical stimuli into electrical signals, providing an innovative approach for achieving self-powered and intelligent food packaging systems, with enhanced functionality, sustainability, and interactivity. Here, we prepared a chitosan-gelatin (Csgel)/CNC/MXene (CM) composite bio-film using cellulose nanocrystal (CNC)-bridged MXene hybrid material as the functional filler and Csgel as the matrix. Based on this film, we further constructed a self-powered and environmentally friendly CM-TENG system to monitor the collisions of fruits during transportation packaging. The CM biofilm has broad-spectrum antibacterial properties, with an antibacterial rate of up to 99.99% against Escherichia coli and Staphylococcus aureus. Its antioxidant performance is up to 75.34%, and its UV shielding UPF value is up to 138.08, providing good preservation effects for strawberries. The device's electrical energy conversion efficiency (instantaneous area power density of 5.8   W   m -2 and force sensitivity (24.65   V·N⁻¹)) makes CM-TENG a potential sensor for monitoring the collisions of fresh agricultural products in the packaging during logistics. CM-TENG collects mechanical vibrations during fruit transportation and converts them into real-time electrical signals. The simulation measurement of strawberry transportation indicates that the sensor's electrical output increases with the increase in impact force. This can remind drivers to reduce vibration and minimize mechanical damage to the fruits. Additionally, the characteristic electrical signals generated by different fruit impacts combined with convolutional neural networks (CNN) for recognition have an identification accuracy of up to 98.0% for 8 types of fruits. This study demonstrates that CM-TENG can effectively monitor collision damage during fruit transportation and can identify different fruit types, providing new ideas for intelligent food packaging systems.