Treehopper-Inspired Passive Electroluminescent Vector Electric Field Sensor with Deep Learning-Enabled High-Precision Reconstruction.

Electric fields are fundamental physical quantities that describe electromagnetic phenomena. Achieving passive, high-precision, vector-resolved sensing is essential for both fundamental electromagnetics and the precise regulation of electrified systems, yet it remains a central technical challenge. Inspired by the bioelectrosensory mechanism of treehoppers, this study designs and realizes a passive electroluminescent vector electric field sensor (PELVEFS) based on a bioinspired dielectric heterostructure. The three-dimensional high-permittivity architecture within the device couples with the external electric field and reshapes it into a direction-dependent surface field distribution, which subsequently drives an electroluminescent (EL) coating to emit optical signals that encode both the magnitude and the direction of the vector field. A shared-weight one-dimensional convolutional neural network inversion scheme then reconstructs the full vector information from the EL spectra, enabling the precise measurement and decoupling of field strength and direction without any external power source. Experimental results demonstrate that the PELVEFS provides a nonoverlapping omnidirectional response across a wide dynamic range of 0.20-1.00 kV/mm, achieving a mean absolute error of 0.015 kV/mm and a mean relative error of 2.2% for field strength, as well as a mean absolute angular error of 3.62° for direction. This work establishes an integrated approach for vector electric field sensing in complex electromagnetic environments, encompassing the entire process from the sensing mechanism and device architecture to data-driven information reconstruction.