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Tom Zajdel, Ph.D. 2018

Electrical Engineering
Advisor: Prof. Maharbiz

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Research Interests: Microbial electrophysiology and bioelectronics.
Job Interests: Academia, post-doc, industry R&D.

Tom J. Zajdel is a PhD Candidate in Electrical Engineering at UC Berkeley, where he designs microsystems that interface with bacterial cells for biosensing. During graduate school, he co-developed "EE40LX: Electronic Interfaces" with Professor Michel Maharbiz, a massive open online course that teaches basic circuit principles, reaching over 80 thousand students worldwide. He is a recipient of the Berkeley Chancellor's Fellowship and the NSF Graduate Research Fellowship. He completed his BS in Electrical and Computer Engineering from The Ohio State University in 2012.

Tethered Bacteria-Based Biosensing [BPN853]
Though the chemotaxis sensing system of emph{Escherichia coli} is known to approach fundamental
physical limits for biosensing, few attempts have been made to co-opt the system as the front end
for a biohybrid sensor. We propose a biohybrid sensor that monitors chemotactic bacterial flagellar
motor (BFM) rotation speed and direction to infer analyte concentration for a low-power, fast, and
sensitive response. We present the design and fabrication of a four point impedimetric array that
uses current injection electrodes to circumvent electrode polarization screening, enabling solution
resistance monitoring within a four-micron by four-micron region. We also demonstrate the first
lithographically patterned silica shaft encoders for the BFM, which utilize localized biotin-avidin
chemistry to selectively bind to the BFM and encode rotation. When these two components are
integrated by bringing the rotating shaft encoders in proximity to the microelectrode array, they
will enable an electrochemical method for observing the BFM. Such an impedance-based biohybrid
sensor obviates the need for a microscope and in principle may be multiplexed and scaled to large
arrays of BFMs, enabling the development of deployable low-power and fast sensing systems that
directly observe the BFM to infer analyte concentration.

Current Active Projects:

     Last Updated: Tue 2017-Feb-14 12:26:42

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