University of New Hampshire
McNair Scholar, 2019
Major: Bioengineering
Mentor: Dr. Edward Song, Department of Electrical and Computer Engineering
Research Title: Graphene Based Biosensor For The Selective Detection Of Lysozyme And Thrombin
Graphene Based Biosensor For The Selective Detection Of Lysozyme And Thrombin
Currently, commercial detection methods of molecular disease biomarkers are expensive, need trained personnel, and require expensive equipment usually only found in hospitals. Thus, the creation of a marketable, portable device that can detect such biomarkers would have significant impact on the healthcare sector. This research proposes to create such a device by using graphene field effect transistors (GFETs) for the selective detection of the neurotransmitter dopamine, which is an important biomarker in various diseases and disorders such as Parkinson’s or schizophrenia. GFET biosensors have been created and proven to be effective. However, they could benefit from enhanced selectivity, ability to detect at lower concentration levels, and producing more consistent results. By introducing a microfluidic channel, which lowers the amount of fluid needed for each test and increases device sensitivity, it is hypothesized these characteristics will be enhanced. The device will consist of a GFET attached to a microfluidic channel molded into polydimethylsiloxane (PDMS). A solution holding the target molecule flowing through this channel will be in contact with a graphene sheet. Aptamers, which are small single-stranded pieces of nucleic acids capable of binding to proteins, neurotransmitters, or almost any other biological molecule, will be attached to the graphene. When the solution passes through the channel, the dopamine molecules will bind to the aptamers, inducing a voltage change that can be recorded. Thus, the presence of the dopamine in the solution can be verified. The device will be tested for sensitivity, selectivity as well as consistent results. Dozens of these devices will be created and tested so outliers from faulty devices can be ignored in the data.
