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13 March 2024 Smartphone-based plasmonic biosensing via asymmetric droplets and deep generative networks
Kamyar Behrouzi, Zahra Khodabakhshi Fard, Chun-Ming Chen, Peisheng He, Fanping Sui, Liwei Lin
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Proceedings Volume PC12832, Optics and Biophotonics in Low-Resource Settings X; PC1283206 (2024) https://doi.org/10.1117/12.3003327
Event: SPIE BiOS, 2024, San Francisco, California, United States
Abstract
Low-cost biosensing methods are crucial for the early detection of diseases. The work presents a smartphone-based detection scheme based on two asymmetrically evaporated droplets on a nanofibrous membrane with the specimen sample and plasmonic nanoparticles, respectively. Leveraging deep learning algorithms, we achieve automatic detection and quantification of a range of proteins based on distinct droplet patterns to differentiate positive/negative cases, including N-protein, PCT, CEA, and PSA at concentrations as low as 10 pg/ml in 12 minutes. The method enhances the droplet pre-concentration and the generation of optically structural patterns via plasmonic particles for improved sensing sensitivity.
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Kamyar Behrouzi, Zahra Khodabakhshi Fard, Chun-Ming Chen, Peisheng He, Fanping Sui, and Liwei Lin "Smartphone-based plasmonic biosensing via asymmetric droplets and deep generative networks", Proc. SPIE PC12832, Optics and Biophotonics in Low-Resource Settings X, PC1283206 (13 March 2024); https://doi.org/10.1117/12.3003327
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KEYWORDS
Biosensing
Plasmonics
Cancer detection
Proteins
Diseases and disorders
Cancer
Diagnostics
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