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Related Experiment Video

Updated: May 1, 2026

A Label-free Technique for the Spatio-temporal Imaging of Single Cell Secretions

A Label-free Technique for the Spatio-temporal Imaging of Single Cell Secretions

Published on: November 23, 2015

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

Kamyar Behrouzi^1,2, Zahra Khodabakhshi Fard³, Chun-Ming Chen⁴

¹Department of Mechanical Engineering, University of California, Berkeley, CA, USA. kbehrouzi@berkeley.edu.

Nature Communications

|May 17, 2025

Summary

This summary is machine-generated.

This study introduces a novel coffee-ring biosensor for ultra-sensitive protein detection. The advanced biosensor achieves high sensitivity and specificity for early disease diagnostics using smartphone imaging.

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Area of Science:

Biomedical Engineering
Nanotechnology
Analytical Chemistry

Background:

Developing sensitive and affordable biosensors is crucial for global health.
At-home medical detection kits have advanced significantly, particularly post-COVID-19.

Purpose of the Study:

To demonstrate a novel coffee-ring biosensor with ultrahigh sensitivity for disease-relevant protein detection.
To develop a quantitative diagnostic method using smartphone imaging and deep neural networks.

Main Methods:

Utilized coffee-ring effect from evaporating sessile droplets on a nanofibrous membrane.
Employed functionalized gold nanoshells to create asymmetric nanoplasmonic patterns for biomarker pre-concentration.
Integrated a deep neural model (generative and convolutional networks) for quantitative analysis of smartphone images.

Main Results:

Detected disease proteins as low as 3 pg/ml within 12 minutes.
Achieved sensitivities over two orders of magnitude higher than lateral flow immunoassays using saliva samples.
Demonstrated detection across a five-order magnitude concentration range for four different proteins (PCT, SARS-CoV-2 N protein, CEA, PSA).

Conclusions:

The coffee-ring biosensor offers a highly sensitive and rapid method for disease biomarker detection.
The integrated deep learning model enables quantitative diagnosis from simple smartphone images.
This technology holds potential for advancing early disease diagnostics and at-home testing.