Non-Destructive and High-Fidelity Sensing of Plant Water Content Enabled by Near-Infrared Luminescent Metal Halides.

Abstract

Real-time, accurate water monitoring is a crucial technical foundation for industrial, environmental, and biological research. However, traditional detection methods typically include destructive processes and suffer from response delays. Near-infrared (NIR) luminescent metal halides offer a novel solution to this challenge, but they still face issues such as ultraviolet excitation and low photoelectric conversion efficiency. Herein, a luminescent material was synthesized based on the blue-light-excited lead-free perovskite Cs₂HfCl₆:Te⁴⁺/Mo⁴⁺, in which energy transfer (ET) from Te⁴⁺ to Mo⁴⁺ enables highly efficient NIR luminescence in the 800-1200 nm wavelength range. Upon encapsulation with a commercial blue light chip, the fabricated NIR light-emitting diode device achieved a photoelectric conversion efficiency of up to 16.1%. By utilizing the absorption characteristics of water molecules in the NIR spectrum and receiving signals via a sensor. An interactive learning process based on a neural network machine learning algorithm was employed, achieving an estimation accuracy of up to 98.6% for plant water content. This non-destructive and precise NIR detection module provides a new solution for the real-time monitoring of crop physiological status and holds broad application prospects in the fields of precision agriculture and plant science.