Graphene, which is composed of a honeycomb arrangement of carbon atoms, is the focus of intense R&D in the world because of its incredible strength, ability to very efficiently conduct electricity, high degree of chemical stability, the speed at which electrons can move across its surface, and other exotic properties. Some of this research is focused on the use of graphene as a component in computer circuits and display screens, in drug delivery systems, and in solar cells and batteries. Recently, Scientists have enlisted the exotic properties of graphene, to function like the film of an incredibly sensitive camera system in visually mapping tiny electric fields in a liquid. Researchers hope the new method will allow more extensive and precise imaging of the electrical signaling networks in our hearts and brains. The ability to visually depict the strength and motion of very faint electrical fields could also aid in the development of so-called lab-on-a-chip devices that use very small quantities of fluids on a microchip-like platform to diagnose disease or aid in drug development, for example, or that automate a range of other biological and chemical analyses. One of the outstanding problems in studying a large network of cells is understanding how information propagates between them. Here, successful results were achieved with the use of graphene. The idea sprang from a conversation between Feng Wang, a UC Berkeley associate professor of physics in Berkeley Lab's Materials Sciences Division whose research focuses on the control of light-matter interactions at the nanoscale, and Bianxiao Cui, an associate professor of chemistry at Stanford University that specializes in the study of nerve-cell signaling. Researchers found that images could pinpoint an electric field's location along the graphene sheet's surface down to tens of microns (millionths of a meter), and capture its fading strength in a sequence of time steps separated by as few as five milliseconds, or thousandths of a second. The results of this study was published in the journal of Nature Communications (volume 7, Dec. 2016, page 13704).