Electrolyte-gated graphene field-effect transistors : modeling and applications

This work presents a model for electrolyte-gated graphene field-effect transistors (EGFETs) that incorporates the effects of the double layer capacitance and the quantum capacitance of graphene. The model is validated through experimental graphene EGFETs, which were fabricated and measured to provide experimental data and extract graphene EGFET parameters such as mobility, minimum carrier concentration, interface capacitance, contact resistance, and effective charged impurity concentration. The proposed graphene EGFET model accurately determines a number of properties necessary for circuit design such as current-voltage characteristics, transconductance, output resistance, and intrinsic gain. The model can also be used to optimize the design of EGFETs. For example, simulated and experimental results show that avoiding the practice of partial channel passivation enhances the transconductance of graphene EGFETs. Graphene EGFETs are fabricated for pH sensing. The location of the Dirac point is measured for pH concentrations varying from 4 to 10. In this range, graphene EGFETs are shown to produce -50.8 mV/pH sensitivity. Graphene EGFETs are also fabricated for use in a real-time polymerase chain reaction (RTPCR) system. RTPCR is run successfully to identify DNA segments thought responsible for the metabolism of clopidogrel, a widely prescribed antiplatelet medication. The graphene EGFETs, however, failed to sense an increase in DNA concentration. Further optimization of the PCR mix is required to ensure that increased DNA concentration lowers the PCR mix pH without rendering the DNA polymerase ineffective. Lastly, graphene EGFETs fabricated for electrogenic cell sensing using the optimized parameters from the newly developed graphene EGFET current-voltage model. Hippocampal mouse neurons were cultured on top of the graphene EGFETs in attempt record action potentials.

Department dc:contributor.department

Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2015

Author dc:creator

• Mackin, Charles Edward

Advisor dc:contributor.advisor

• Tomás Palacios.

• Electrical Engineering and Computer Science.

Statement dc:rights

• M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

http://hdl.handle.net/1721.1/97815

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/97815