ReviewFundamental research2026
Photonic biosensors based on nanoparticle superstructures: from data analysis to artificial intelligence (AI) detection.
Review in Fundamental research, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.