Researchers at Penn State have developed a handheld device called VIRRION that can capture and identify viral particles in samples within minutes. The device contains a “forest” of carbon nanotubes that capture and sort viral particles depending on their size, and then users can identify the viruses using Raman spectroscopy. The technique could help to increase the speed and convenience of identifying viral infections, and could be very useful when viral outbreaks strike.
During events such as recent outbreaks of Ebola and Zika, rapidly detecting a virus in patient and environmental samples could help health organizations to employ countermeasures in good time. However, current techniques to detect certain viruses can be cumbersome and lengthy.
To address this, researchers have been developing technology to aid in rapid viral detection, with the goal that a doctor in a community clinic could quickly identify viral infections. The VIRRION device employs a gradient of carbon nanotubes to trap viruses within it, before Raman spectroscopy reveals the identity of the virus.
“We have developed a fast and inexpensive handheld device that can capture viruses based on size,” said Mauricio Terrones, a researcher involved in the study. “Our device uses arrays of nanotubes engineered to be comparable in size to a wide range of viruses. We then use Raman spectroscopy to identify the viruses based on their individual vibration.”
The researchers claim that the VIRRION can detect viruses within minutes, requires only a few milliliters of sample, and is inexpensive and readily portable, potentially making it suitable for remote clinics and even at the point of care.
“Most current techniques require large and expensive pieces of equipment,” said Terrones. “The VIRRION is a few centimeters across. We add gold nanoparticles to enhance the Raman signal so that we are able to detect the virus molecule in very low concentrations. We then use machine learning techniques to create a library of virus types.”
Study in PNAS: A rapid and label-free platform for virus capture and identification from clinical samples
Via: Penn State