Lattice structures are typically made up of a crisscross pattern of beam elements, allowing engineers to distribute material in a more structurally effective way. However, a main challenge in the...
Electrospun polymeric piezoelectric fibers have a considerable potential for shape‐adaptive mechanical energy harvesting and self‐powered sensing in biomedical, wearable, and industrial applications. However, their unsatisfactory piezoelectric performance remains an issue to be overcome. While strategies for increasing the crystallinity of electroactive β phases have thus far been the major focus in realizing enhanced piezoelectric performance, tailoring the fiber morphology can also be a promising alternative. Herein, a design strategy that combines the nonsolvent‐induced phase separation of a polymer/solvent/water ternary system and electrospinning for fabricating piezoelectric poly(vinylidene fluoride‐trifluoroethylene) (P(VDF‐TrFE) fibers with surface porosity under ambient humidity is presented. Notably, electrospun P(VDF‐TrFE) fibers with higher surface porosity outperform their smooth‐surfaced counterparts with a higher β phase content in terms of output voltage and power generation. Theoretical and numerical studies also underpin the contribution of the structural porosity to the harvesting performance, which is attributable to local stress concentration and reduced dielectric constant due to the air in the pores. This porous fiber design can broaden the application prospects of shape‐adaptive energy harvesting and self‐powered sensing based on piezoelectric polymer fibers with enhanced voltage and power performance, as successfully demonstrated in this work by developing a communication system based on self‐powered motion sensing.
Lattice structures are known to have high performance-to-weight ratios because of their highly efficient material distribution in a given volume. However, their inherently large void fraction leads to low mechanical properties compared to the base material, high anisotropy, and brittleness. Most works to date have focused on modifying the spatial arrangement of beam elements to overcome these limitations, but only simple beam geometries are adopted due to the infinitely large design space associated with probing and varying beam shapes. Herein, we present an approach to enhance the elastic modulus, strength, and toughness of lattice structures with minimal tradeoffs by optimizing the shape of beam elements for a suite of lattice structures. A generative deep learning-based approach is employed, which leverages the fast inference of neural networks to accelerate the optimization process. Our optimized lattice structures possess superior stiffness (+59%), strength (+49%), toughness (+106%), and isotropy (+645%) compared to benchmark lattices consisting of cylindrical beams. We fabricate our lattice designs using additive manufacturing to validate the optimization approach; experimental and simulation results show good agreement. Remarkable improvement in mechanical properties is shown to be the effect of distributed stress fields and deformation modes subject to beam shape and lattice type.
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