The pursuit of sustainable green energy has intensified the demand for self-powered materials, which are capable of efficiently converting ambient mechanical energy into electrical signals. Herein, polyvinylidene fluoride (PVDF) fabricated by electro-assisted fused deposition modeling (E-FDM) 3D printing is a firm candidate due to the exceptional electroactivity and excellent flexibility. Unlike conventional fabrication methods struggling with dimension control, a physics-informed neural network (PINN) framework was developed for the first time to predict and control PVDF fibers. PINN successfully integrates physical interpretability with data-driven flexibility, achieving high precision predictions (R2 = 0.97, MSE = 0.0016, MAE = 0.034) with improved morphological control. In particular, the results showed that PVDF 50 μm fibers exhibited more than 3-fold improvement in piezoelectric β-phase content when compared to the 3D-printed counterparts without the external electric field. Correspondingly, the PVDF sensor achieved an excellent sensitivity of 130.9 mV/kPa and a fast response time of 35 ms. The PINN-based approach offers a scalable manufacturing strategy for the fabrication of PVDF sensors with tailored electromechanical properties, paving the way for flexible electronics and intelligent sensing systems.
Wang et al. (Mon,) studied this question.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: