In this podcast episode, MRS Bulletin’s Laura Leay interviews Stanford University’s Jennifer Dionne and her PhD student Fareeha Safir and their colleague Amr. Saleh from Cairo University about their work on identifying bacteria in complex samples. Instead of culturing bacteria then identifying them using specific methods such as a polymerase chain reaction test, which takes hours, Dionne’s research group uses Raman spectroscopy combined with machine learning to detect the presence of two specific bacteria in samples that contained red blood cells. The addition of gold nanorods to the samples further enhanced the signal from the bacteria. Another way the research team accelerated the detection of bacteria signal was by building an acoustic bioprinter for the liquid samples: the specialist printer uses focused soundwaves to break the surface tension of a larger droplet, maintaining cell viability. This work was published in a recent issue of Nano Letters.