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From device metrics to information fidelity: reframing nanophotonic biosensing through mode–interface–measurement–decision
从设备指标到信息保真度:通过模式-界面-测量-决策重新构建纳米光子生物传感
装置指標から情報忠実度まで:モード−界面−測定−意思決定によるナノフォトニックバイオセンシングの再構築
장치 측정치에서 정보 충실성까지: 모드-인터페이스-측정-결정을 통해 나노 광자 바이오센싱을 재구성합니다.
De las métricas del dispositivo a la fidelidad de la información: reestructurar la biosensoría nanofotónica a través de la decisión de modo-interfaz-medición
Des métriques de dispositif à la fidélité de l'information: recadrement de la biosensing nanophotonique par le biais de la décision de mode-interface-mesure
От показателей устройства до верности информации: переформирование нанофотонного биосенсора через режим-интерфейс-измерение-решение
Mengyao Li, Jiaming Lyu
Key Laboratory of the Ministry of Education for Optoelectronic Measurement Technology and Instrument, Beijing Information Science & Technology University, Beijing 100192, China
北京信息科技大学光电测试技术及仪器教育部重点实验室
Opto-Electronic Advances, 25 October 2026
Abstract

Nanophotonic biosensors have evolved from surface plasmon resonance systems into a broad class of platforms that includes localized plasmonic nanostructures, surface-enhanced Raman spectroscopy, metasurfaces, and metamaterial-enabled biosensors. However, the field continues to be characterized primarily by device-level metrics, such as sensitivity, linewidth, quality factor, and limit of detection, which do not fully capture practical performance in biological environments.

In this work, we argue that nanophotonic biosensing should instead be understood as a system-level process of information transfer. To formalize this perspective, we introduce a four-layer framework based on Mode, Interface, Measurement, and Decision (MIMD). Using this framework, we reinterpret representative advances across major nanophotonic biosensing platforms and show that progress depends not only on stronger optical enhancement, but also on improved coordination across the entire sensing chain.

We further propose that the next stage of the field should be guided by layered sensing architectures that integrate robust linear transduction with nonlinear or inelastic channels to achieve molecular specificity and dynamic characterization.
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