Machine Learning Guided Broadleaf‐Inspired Reconfigurable Triboelectric Array for Space‐Efficient Ocean Current Energy Harvesting and Antifouling

ABSTRACT Ocean currents represent a stable source of blue energy. Flag‐type triboelectric nanogenerators (F‐TENGs), which have been widely investigated for harvesting energy from low‐velocity ocean currents, generally rely on bluff body to induce vortex. This dependence limits their spatial utilization efficiency. Inspired by broadleaf, a structural reconfiguration F‐TENG (SRF‐TENG) array are developed for ocean current energy harvesting and antifouling. Under ocean current excitation, the reconfigurable component of the generator bends into a U‐shape. It undergoes oscillation and generates a vortex street wake with concentrated vortical structures. This configuration replaces the bluff body, reduces the space of energy harvesting, and creates room for efficiency improvement in energy collection. The power density of the SRF‐TENG increases by over 65.5% compared to traditional F‐TENGs at 0.45 m s −1 . In the array guided by an interpretable machine learning framework, the downstream‐row harvesters produce a root‐mean‐square current 48.2% higher than that of the isolated device. The harvested electricity is used to drive a carbon‐fiber antifouling unit, achieving inactivation efficiencies of 98.00% for Chlorella vulgaris , 99.07% for Shewanella sp. and 99.06% for Pseudomonas aeruginosa . This work integrates bioinspired structural design, data‐driven array deployment and self‐powered antifouling into a compact blue energy platform.