Cellulose Nanocrystal and Self-Assembling Lignin Enhanced the PEDOT/PSS/PVA Composite on Mechanical and Self-Powered...

Lignin nanomicelle (LNM) synthesis via deep eutectic solvent (DES) has been optimized from a conventional duration of 2-3 days to a streamlined 12 h procedure utilizing autoclave reactor heating. This approach facilitates the efficient extraction of lignin from straw and its subsequent formation into LNMs via a simultaneous self-assembly mechanism. Integration of these amphiphilic LNMs into a cellulose nanocrystal (CNC) framework, combined with PEDOT: PSS in a poly(vinyl alcohol) (PVA) matrix, yields a self-powered strain sensor characterized by enhanced tensile properties and heightened strain sensitivity. Incorporating carboxyl functional groups from LNMs on the PVA matrix significantly augments the sensor's mechanical strength and elasticity. This is evidenced by achieving Young's modulus of 65.9 MPa and an elongation capacity of 320%, ensuring its efficacy in human motion detection. The synergistic inclusion of CNCs and LNMs amplifies the sensor's gauge factor, thereby augmenting its strain responsiveness. The elevated aspect ratio of CNCs establishes an efficacious electrical network that, in concert with the interaction between CNCs and PEDOT: PSS, diminishes the electrical percolation threshold, culminating in an improved gauge factor of 19, indicative of enhanced strain detection capabilities. Furthermore, the sensor can generate a thermoelectric voltage in response to thermal gradients, with the dynamic structures of LNM improving the conductivity and PEDOT: PSS dispersion within the PVA matrix, thereby optimizing the Seebeck coefficient. After enduring 5000 cycles of 100% strain deformation tests, the sensor demonstrates consistent performance, underscoring its reliability and durability. The fabricated PVA/Gly-LNM/CNCs/PEDOT: PSS composite material has been successfully applied to detect nuanced human gestures, including finger and wrist movements, affirming its potential utility in wearable technology applications.