Photonic biosensors based on nanoparticle superstructures: from data analysis to artificial intelligence (AI)...

The superstructures formed by the self-assembly of nanoparticles (NPs) can exhibit unique photonic collective properties (structural color, localized surface plasmon resonance [LSPR]), enhance the interaction between light and matter, and open up new possibilities for photonic sensing. Many photonic biosensors have addressed the limitations of current bioanalytical methods with their non-invasive nature, real-time monitoring, and high sensitivity. In recent years, the construction of photonic biosensors using super-structured materials could further enhance the sensors in terms of sensitivity, processing capacity, ease of use, and miniaturization. Superstructure-based photonic biosensors can analyze complex samples, but their development still needs to overcome limitations related to target binding specificity, long-term stability, and signal decoding efficiency. The development of artificial intelligence (AI) provides new opportunities to solve these problems. Deep learning (DL) algorithms can independently extract multi-dimensional data features such as spectra and images, distinguish weak biological signals from noise, optimize detection parameters, and achieve real-time dynamic calibration. In this review, we provide the photonic collective characteristics of superstructures and the applications of biosensors in intelligent diagnosis. The applications of superstructured photonic sensors in disease diagnosis, drug delivery, and cell imaging are summarized. The colorimetric, fluorescence-based sensor technologies assisted by DL are discussed along with challenges faced in integrating AI with superstructure-based photonic biosensors. As this field continues to evolve, the integration of AI and superstructure-based photonic biosensors will undoubtedly play a pivotal role in shaping the future of medical diagnostics and therapeutic interventions.