Nanoparticle-detecting microlasers to be used to identify viruses

Microlasers that are able to detect minuscule individual nanoparticles may be able to be used to detect viruses.

Researchers from Washington University, St Louis, created the system, which is able to detect and individually count nanoparticles at sizes of 10 nanometers.

Nanoparticles can be between 1 and 100 nanometers in size – with a nanometer being the equivalent to one billionth of a meter.

They are now aiming to develop the technology to be able to detect particles that are smaller than nanoparticles, which includes viruses.

The technology will benefit a number of fields including electronics, acoustics and biomedical applications.


The team that worked on the microlasers created a Raman sensor in a silicon dioxide chip to find the individual particles.

Previously, to identify the nanoparticles they would have had have to include rare ions in the chip to provide optical gain for the laser.

Researcher Sahin Kaya Ozdemir, who worked on the project, said that when generating the Raman laser beam in the resonator it will split into two to and provide a reference for the other beam to sense the particle.

“Our new sensor differs from the earlier whispering gallery sensors in that it relies on Raman gain, which is inherent in silica, thereby eliminating the need for doping the microcavity with gain media, such as rare-earth ions or optical dyes, to boost detection capability.

“This new sensor retains the biocompatibility of silica and could find widespread use for sensing in biological media.”

“It doesn’t matter what kind of wavelength is used, once you have the Raman laser circulating inside and there is a molecule sitting on the circle, when the beam sees the particle it will scatter in all kinds of directions.”

“Initially you have a counterclockwise mode, then a clockwise mode, and by analyzing the characterization of the two split modes, we confirm the detection of nanoparticles.”


The work, which was led by Lan Yang from the university, works in a similar way to that of the whispering gallery in London’s St Paul’s Cathedral.

In this space, which is the inside of the building’s iconic dome, one person can hear a message that is spoken to the wall by another person on the other side.

Unidirectional microlasers were first demonstrated in 2010 by scientists at Harvard University.

Featured image courtesy of Jeff Keyzer. Inline image one courtesy of J. Zhu, B. Peng, S.K. Ozdemir and L. Yang, image two courtesy of Harvard School of Engineering and Applied Sciences.

Weak spots: Helping to predict injuries before they happen

New research from Washington University in the US may one day be used to prevent injuries after repair surgery on knees, shoulders and other tissues, and could even predict problems before they become an issue.

Using their algorithms, the researchers can identify weak spots in muscles, tendons and bones that are prone to tearing or breaking.

Senior investigator and professor of orthopaedic surgery Stavros Thomopoulos said: “Tendons are constantly stretching as muscles pull on them, and bones also bend or compress as we carry out everyday activities.”

He added: “Small cracks or tears can result from these loads and lead to major injuries. Understanding how these tears and cracks develop over time therefore is important for diagnosing and tracking injuries.”

By stretching tissues and tracking the results as their shapes changed or became distorted, Thomopoulos and the team could visualize and predict spots where tissues are weakened.

John J. Boyle, the paper’s first author and a graduate student in biomedical engineering, explains: “If you imagine stretching Silly Putty or a swimming cap with a picture on it, as you pull, the picture becomes distorted. This allows us to track how the material responds to an external force.”

By combining mechanical engineering with image-analysis techniques, Boyle was able to create the algorithms and then test them in different materials as well as in animal models.

The research showed that one of the two new algorithms is 1,000 times more accurate than older methods at quantifying large stretches near tiny tears and cracks. The second algorithm has the ability to predict where they are likely to form.

Professor of mechanical engineering and co-senior investigator on the study Guy Genin commented: “This extra accuracy is critical for quantifying large strains.

“Commercial algorithms that estimate strain often are much less sensitive, and they are prone to detecting noise that can arise from the algorithm itself rather than from the material being examined.

“The new algorithms can distinguish the noise from true regions of large strains.”


Genin added that, while the current study helps with understanding injury and stress on human tissue, the algorithms could also help engineers to identify vulnerable parts of buildings and other structures. According to Genin, our muscles and bones are influenced by the same strains that affect those structures.

He continued: “Whether it’s a bridge or a tendon, it’s vital to understand the ways that physical forces cause structures and tissues to deform so that we can identify the onset of failures and eventually predict them.”

Current imaging techniques, such as MRI and ultrasound, lack the required clarity and resolution so only once the team can get better images of the body’s tissues, will patients be able to see the new algorithms in action.

The group applied for a provisional patent earlier this year. Their research is available online in the Journal of the Royal Society Interface.