Lego-Inspired Reconfigurable Phononic Crystal Sensor for Versatile Liquid Identification

This study proposes a reconfigurable Lego-inspired phononic crystal sensor that has been optimized via a genetic algorithm for the identification of liquids. In contrast to conventional methods, this approach enables tunable band gaps and defect resonance to be used to distinguish liquids in real time. The sensor's modular design facilitates adaptability to specific liquids and frequencies. The sensor's optimization is achieved through a two-step process that maximizes the bandgap and differentiates resonance peaks. Experimental validation confirmed the sensor's capacity for accurate identification of glycerol, ethanol, and mineral oil, thereby demonstrating its versatility.