This article has been reviewed according to Science X's editorial process and policies. Editors have highlighted the following attributes while ensuring the content's credibility: Finding a single cancer cell in a tube of blood is a bit like finding a needle in a haystack. As researchers seek to detect ever-smaller and rarer targets, from viruses to circulating tumor cells in blood, the demand for ultrasensitive, robust sensors that are practical for real-world samples continues to grow.
A major challenge is doing this without adding fluorescent tags or other labels. Most label-free optical sensors work only when the targets pass extremely close to or bind to a tiny sensing area on the device. At low concentrations, many particles never hit that spot, and even when they do, their signals can be drowned out by background noise, limiting detection efficiency and making rare targets especially difficult to find.
This limitation is shared by many optical microsensors, including whispering-gallery-mode (WGM) resonators, among the most sensitive optical sensors ever developed. These devices can detect individual nanoparticles and molecules with exceptional precision, but their sensing region is typically confined to a very small area close to the sensor surface. "Over the past two decades, whispering-gallery-mode-based sensors have demonstrated exceptional capability to detect extremely weak signals across a wide range of sensing applications," said Lan Yang, the Edwin H. & Florence G.
Skinner professor at the Preston M. Green Department of Electrical & Systems Engineering in the McKelvey School of Engineering at Washington University in St. "Conventional approaches have largely relied on interactions occurring very close to the sensor surface.
We wanted to unlock new capabilities for this sensing platform by extending its sensing range beyond the immediate surface, while preserving the exceptional sensitivity that has made these sensors so powerful. This can potentially open entirely new sensing modalities and applications." Many applications require sensors that are both extremely sensitive and capable of monitoring a larger sample volume, without delicate alignment or complex preparation. In a study published in Light: Science & Applications, a team led by Yang reports a new strategy that closes this gap by enabling the detection of particles beyond the immediate surface of the sensors.
Rather than waiting for particles to pass close to or bind to the sensor, the team taught WGM resonators to "listen" for particles as they flow through. The project combined expertise in optical sensing and artificial intelligence, with collaborators from the lab of Chenyang Lu, the Fullgraf professor and director of the AI for Health Institute at WashU, helping to develop machine-learning approaches for signal classification. Instead of relying on particles to bind to the sensor surface, the researchers combine light and sound through a process known as photoacoustics.
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