Coffee Ring Effect: Faster Disease Tests
Revolutionary At-Home Test Detects Diseases with Unprecedented Accuracy Using the “Coffee Ring Effect”
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A groundbreaking new diagnostic test, developed by researchers at UC Berkeley, promises to revolutionize at-home health monitoring. Leveraging a phenomenon known as the “coffee ring effect” and advanced nanotechnology, this innovative technology can detect diseases – from COVID-19 to sepsis and even potential cancer biomarkers – with startling speed and accuracy. The research, recently published in Nature Communications, paves the way for accessible, early disease detection, potentially saving countless lives.
From COVID-19 to Cancer: A New Era of Point-of-Care Diagnostics
Inspired by the patterns left behind when coffee dries in a cup, the team, led by Distinguished Professor Liwei Lin, discovered a way to harness the “coffee ring effect” for diagnostic purposes.This effect causes particles in a liquid to concentrate at the edges as the liquid evaporates, creating a ring-like pattern.
“We figured out that we could use this coffee-ring effect to build somthing even better than what we initially set out to create,” explained researcher Behrouzi.The technology centers around tiny particles called plasmonic nanoparticles, which interact with light in a unique way.Here’s how the test works:
- Sample Collection: A user adds a droplet of liquid containing disease-relevant proteins – obtained from a simple cheek or nasal swab – to a specialized membrane.
- Concentration & Drying: as the droplet dries,the coffee ring effect concentrates any disease biomarkers at the perimeter.
- Nanoparticle interaction: A second droplet containing engineered plasmonic nanoparticles is added. These nanoparticles are designed to bind specifically to the disease biomarkers.
- Light Interaction & Detection: If biomarkers are present, the nanoparticles aggregate in distinct patterns, altering how light interacts with the membrane. This change is visible to the naked eye or can be precisely measured using an AI-powered smartphone app.
Unmatched Sensitivity and Speed
The implications of this technology are significant. The test delivers results in under 12 minutes and boasts a sensitivity 100 times greater than existing COVID-19 rapid tests.
“One of the key proteins that we are able to detect with this method is a biomarker of sepsis, a life-threatening inflammatory response to a bacterial infection that can develop rapidly in peopel over 50,” stated Lin. “Every hour is critical, but culturing bacteria to determine the source of the infection can take a few days.Our technique could help doctors detect sepsis in 10 to 15 minutes.”
Early detection is crucial for conditions like sepsis, where rapid intervention dramatically improves outcomes. The technology’s potential extends far beyond infectious diseases. Researchers envision applications in early cancer screening, allowing individuals to monitor for biomarkers without frequent doctor visits.
A Home Testing Kit for the Future
The UC Berkeley team has already developed a prototype of a user-friendly home testing kit. Similar in concept to current at-home COVID-19 tests, the kit incorporates 3D-printed components to ensure accurate sample and droplet placement.
Lin envisions a future where regular health screenings are more accessible. “During the COVID-19 pandemic, we relied on at-home tests to know if we were infected or not,” he said.”I hope that our technology makes it easier and more accessible for people to regularly screen for conditions like prostate cancer without leaving the home.”
This technology represents a significant step towards proactive healthcare, empowering individuals to take control of their health and potentially detect diseases at their earliest, most treatable stages.
Study Co-Authors: Zahra Khodabakhshi fard, Chun-ming Chen, Peisheng He and Megan Teng of UC Berkeley.Source: University of California – Berkeley – https://news.berkeley.edu/2025/07/08/from-covid-to-cancer-new-at-home-test-spots-disease-with-startling-accuracy/
Journal Reference: Behrouzi, K., et al. (2025). Plasmonic coffee-ring biosensing for AI-assisted point-of-care diagnostics. Nature Communications.[https