A review on lossy mode resonance-based sensors: fundamentals and applications
Lossy mode resonance (LMR) sensors have garnered significant attention over the past 20 years due to their high sensitivity, broad applicability, and multiparameter detection capability. This review systematically summarizes progress in theoretical models, experimental validations, and applications...
| Autores: | , , |
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| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2025 |
| País: | España |
| Institución: | Universidad Pública de Navarra |
| Repositorio: | Academica-e. Repositorio Institucional de la Universidad Pública de Navarra |
| OAI Identifier: | oai:academica-e.unavarra.es:2454/55626 |
| Acceso en línea: | https://hdl.handle.net/2454/55626 |
| Access Level: | acceso abierto |
| Palabra clave: | Optical sensor Lossy mode resonance Modal analysis Thin-film Biosensor Chemical sensor Gas sensor Multiparameter sensing |
| Sumario: | Lossy mode resonance (LMR) sensors have garnered significant attention over the past 20 years due to their high sensitivity, broad applicability, and multiparameter detection capability. This review systematically summarizes progress in theoretical models, experimental validations, and applications during this period. Originating from optical fiber structures and later extended to planar waveguides and integrated circuits, LMR sensors have evolved significantly with the introduction of modeling methods, including geometrical optics and modal analysis. These methods, especially modal analysis, offer a new perspective on LMRs as lossy directional couplers, facilitating deeper understanding of their behavior and guiding sensor design optimization. The performance of LMR-based sensors depends on parameters such as sensitivity and spectral width, which can be optimized to enhance their operation, with applications spanning two primary domains: aqueous media, including biosensors and chemical sensors, and air, with sensors for humidity, gases, and volatile organic compounds (VOCs). In addition, emerging designs, including multi-parameter sensing and integration with other phenomena such as surface plasmon resonances (SPRs) and surface acoustic waves (SAWs), highlight their versatility. Despite challenges like environmental cross-sensitivity and light coupling, advances in temperature compensation and machine learning provide promising pathways for overcoming these limitations, paving the way for next-generation sensing technologies. |
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