3D printed stretchable triboelectric nanogenerator fibers and devices - Tong, Yuxin; Feng, Ziang; Kim, Jongwoon; Robertson, John L.; Jia, Xiaoting; Johnson, Blake N. (Nano energy, 2020)
Triboelectric generators and sensors have a great potential as self-powered wearable devices for energy harvesting, biomedical monitoring, and recording human activity. Here, we report a process for 3D printing stretchable membranes, meshes, and hollow 3D structures on planar, rotating, and non-planar anatomical substrates using elastomeric metal-core triboelectric nanogenerator (TENG) fibers. The triboelectric performance of single 3D-printed elastomeric metal-core silicone-copper (Cu) (cladding-core) fibers and 3D-printed membranes was quantified by cyclic loading tests, which showed maximum power densities of 31.39 and 23.94 mW m−2, respectively. The utility of the flexible silicone-Cu TENG fibers and 3D printing process was demonstrated through applications to wearable mechanosensors for organ and human activity monitoring, specifically, monitoring of perfused organs and speech recognition in the absence of sound production by the speaker (i.e., ‘silent speech’), respectively. 3D-printed wearable triboelectric mechanosensors, in the form of stretchable form-fitting meshes and membranes, in combination with machine-learning signal processing algorithms, enabled real-time monitoring of perfusion-induced kidney edema and speech recognition in the absence of sound production by human subjects (99% word classification accuracy). Overall, this work expands the conductive and functional materials palette for 3D printing and encourages the use of 3D-printed triboelectric devices for self-powered sensing applications in biomanufacturing, medicine, and defense. [Display omitted] •Coaxial micro-extrusion process for production of flexible silicone-copper triboelectric nanogenerator (TENG) fibers.•3D printing of stretchable triboelectric constructs and devices, including form-fitting wearable systems.•Triboelectric-based real-time mechanosensing of organ edema and speech recognition in the absence of sound production by the speaker using machine learning techniques.