Graphene has a potential new application of this wonder material for detecting Amyotrophic Lateral Sclerosis (ALS), a progressive brain disorder for which there is currently "no objective diagnostic test".
This novel use is described in the scientific journal Applied Materials & Interfaces of the American Chemical Society.
ALS is characterised by rapid loss of motor neurons controlling skeletal muscles, leading to paralysis.
Graphene consists of a single layer of carbon atoms arranged in a hexagonal lattice, each atom bound to its neighbours by chemical bonds. The elasticity of these bonds produces resonant vibrations, known as phonons.
The foreign molecule affects the vibrational energies of graphene and the changes can be "accurately mapped using Raman spectroscopy", a technique commonly used in chemistry to provide a structural fingerprint by which molecules can be identified.
In their study the UIC team found a distinct change in the vibrational characteristics of graphene when Cerebro-Spinal Fluid (CSF) -- found in the brain and the spinal cord -- from patients with ALS was added to it. The researchers carried out the test using the CSF from 13 people with ALS; three people with multiple sclerosis (MS) and three people with an unknown neuro-degenerative disease.
"The changes in graphene's phonon vibration-energies -- as measured by Raman spectroscopy -- were unique and distinct," Keisham said. "These distinct changes accurately predicted what kind of patient the CSF came from - one with ALS, MS or no neurodegenerative disease."
The authors, however, add this strategy does not analyze the Raman signal of the CSF but rather "looks at the change in the Raman signal from interfaced graphene".
"In summary, we demonstrate a robust system to investigate ALS by using graphene," says the report. "The results suggest that our graphene platform can be used not only to potentially diagnose ALS, but also to monitor its progression and in the future, to study the efficacy of therapeutics," it says.
"Based on our analysis, it can be concluded that this ultrasensitive platform can efficaciously differentiate neurodegenerative diseases although the exact cause for these differences is beyond the scope of this study," the authors conclude.