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 (Presented at SPIE BiOS: January 27, 2024; Published: 13 March 2024); https://doi.org/10.1117/12.3003327.1a5423cc-8626-ee11-a999-00505691c5e1.