Plasmonic coffee-ring biosensing for AI-assisted point-of-care diagnostics

A major challenge in addressing global health issues is developing simple, affordable biosensors with high sensitivity and specificity. Significant progress has been made in at-home medical detection kits, especially during the COVID-19 pandemic. Here, we demonstrated a coffee-ring biosensor with ultrahigh sensitivity, utilizing the evaporation of two sessile droplets and the formation of coffee-rings with asymmetric nanoplasmonic patterns to detect disease-relevant proteins as low as 3 pg/ml, under 12 min. Experimentally, a protein-laden droplet dries on a nanofibrous membrane, pre-concentrating biomarkers at the coffee ring. A second plasmonic droplet with functionalized gold nanoshells is then deposited at an overlapping spot and dried, forming a visible asymmetric plasmonic pattern due to distinct aggregation mechanisms. To enhance detection sensitivity, a deep neural model integrating generative and convolutional networks was used to enable quantitative biomarker diagnosis from smartphone photos. We tested four different proteins, Procalcitonin (PCT) for sepsis, SARS-CoV-2 Nucleocapsid (N) protein for COVID-19, Carcinoembryonic antigen (CEA) and Prostate-specific antigen (PSA) for cancer diagnosis, showing a working concentration range over five orders of magnitude. Sensitivities surpass equivalent lateral flow immunoassays by over two orders of magnitude using human saliva samples. The detection principle, along with the device, and materials can be further advanced for early disease diagnostics.

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Publication Details

Journal
Nature Communications
Published
2025-05-17
DOI
https://doi.org/10.1038/s41467-025-59868-y
Citations
17
Primary Topic
Biosensors and Analytical Detection
Type
article
Field-Weighted Citation Impact
11.46

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article

Plasmonic coffee-ring biosensing for AI-assisted point-of-care diagnostics

Michael Mu‐Huo Teng, Peisheng He, Zahra Khodabakhshi Fard, Kamyar Behrouzi et al.
17 citations
Nature Communications
Biosensors and Analytical Detection
11.46
article

Plasmonic coffee-ring biosensing for AI-assisted point-of-care diagnostics

Michael Mu‐Huo Teng, Peisheng He, Zahra Khodabakhshi Fard, Kamyar Behrouzi, Liwei Lin, Chunming Chen
article en
17 citations

Abstract

A major challenge in addressing global health issues is developing simple, affordable biosensors with high sensitivity and specificity. Significant progress has been made in at-home medical detection kits, especially during the COVID-19 pandemic. Here, we demonstrated a coffee-ring biosensor with ultrahigh sensitivity, utilizing the evaporation of two sessile droplets and the formation of coffee-rings with asymmetric nanoplasmonic patterns to detect disease-relevant proteins as low as 3 pg/ml, under 12 min. Experimentally, a protein-laden droplet dries on a nanofibrous membrane, pre-concentrating biomarkers at the coffee ring. A second plasmonic droplet with functionalized gold nanoshells is then deposited at an overlapping spot and dried, forming a visible asymmetric plasmonic pattern due to distinct aggregation mechanisms. To enhance detection sensitivity, a deep neural model integrating generative and convolutional networks was used to enable quantitative biomarker diagnosis from smartphone photos. We tested four different proteins, Procalcitonin (PCT) for sepsis, SARS-CoV-2 Nucleocapsid (N) protein for COVID-19, Carcinoembryonic antigen (CEA) and Prostate-specific antigen (PSA) for cancer diagnosis, showing a working concentration range over five orders of magnitude. Sensitivities surpass equivalent lateral flow immunoassays by over two orders of magnitude using human saliva samples. The detection principle, along with the device, and materials can be further advanced for early disease diagnostics.

Nature CommunicationsVol. 16(1)
University of California, Berkeley (US)
Banatao Institute, National Institutes of Health
Openalex Percentile: Top 1%
Biosensors and Analytical Detection
11.46
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Plasmonic coffee-ring biosensing for AI-assisted point-of-care diagnostics — Michael Mu‐Huo Teng, Peisheng He, et al. · Nature Communications (2025) | TGRS Research Map | TGRS