Specific loss power optimization in metal ferrite nanocrystals for radiofrequency stimulation of neurons

Neurons serve as the basic unit of computation within the nervous system. As the nervous system is involved with the encoding, transmission, processing, and decoding of information at every level, characterization of the nervous system is of the utmost interest to neuroscience. However, techniques for probing the nervous system have previously focused primarily of characterizing single cell behavior, which does not provide insight as to the functioning of the system as a whole. This is further complicated by the fact that functional network of neurons are typically spatial interwoven, rendering spatially-limited stimulation techniques ineffective. The desire to characterize the system in its entirety necessitates the development of neuronal probes that can target functional subpopulations of cells. A proposed system for such stimulation is the genetic targeting of neurons via expression of gated ion channels, and the selective stimulation of them using a transmitter-receiver pair. This thesis describes the design and optimization of such a transmitter-receiver pair that activates ion channels via the dissipation of heat. Magnetic losses in superparamagnetic metal ferrite nanocrystals are modeled to determine the optimal operating parameters for dissipation of heat. Optimal nanocrystals are then synthesized via high-temperature thermolysis of a mixed metal oleate precursor, and stabilized in the aqueous phase through functionalization with polyethylene glycol. A solenoid is designed and constructed to serve as a radiofrequency excitation source, and subsequently optimized to maximize the power transfer from solenoid to magnetic nanocrystals. A susceptometer and lock-in amplifier are designed for characterization of colloidal nanocrystals in the aqueous phase. The constructed susceptometer is then used to measure magnetic losses in metal ferrite nanocrystals and compare their performance with the modeled behavior.

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

2013

Author dc:creator

• Lachenmyer, Nathan S. (Nathan Scott)

Advisor dc:contributor.advisor

• Polina 0. Anikeeva.

• 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/1721.1/85475

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/85475