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Lego-Inspired Reconfigurable Phononic Crystal Optimization for Adaptive Liquid Sensing through Physics-Informed Genetic Algorithm

  • 신지원 (Jeewon Shin, 서강대학교 일반대학원)

초록 (요약문)

This study presents a reconfigurable phononic crystal (PnC) sensor capable of identifying and analyzing liquid concentrations through a two-stage optimization framework. In the first stage, a Genetic Algorithm (GA) maximized the acoustic bandgap by evolving symmetric lattice configurations. In the second, a Physics-Informed Genetic Algorithm (PIGA) optimized defect placement, generating distinct, composition-dependent resonance modes. The Lego-inspired sensor allows modular tuning of the center frequency based on the liquid’s acoustic properties. Experimental validation with the optimized structure confirmed identification of various liquids via unique defect-mode frequencies. Tests with binary mixtures of glycerol and light mineral oil revealed clear, concentration-dependent spectral shifts. The primary resonance showed a strong linear correlation with molar ratio, while the secondary peak improved sensing robustness. The GA–PIGA strategy, supported by consistent experiments, demonstrates accurate, reliable detection across diverse liquid compositions. Its acoustic tunability and modular architecture underscore potential for real-time, in-line monitoring in multiphase or evolving fluidic environments, offering a versatile platform for next-generation liquid sensing.

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목차

1. Introduction 1
2. Experimental Section 4
2.1 Design and optimization strategy 4
2.2 Experimental setup and Materials 12
2.3 Statistical Analysis 15
3. Results and discussion 18
3.1 Bandgap Optimization 18
3.2 Liquid Identification 24
3.3 Liquid Concentration Sensing 29
3.4 Discussion 34
4. Conclusion 41
References 42

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