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Micro and Nanoscale Fabrication and Characterization For Next-Generation Biosensors

  • Author(s): Koo, Bonhye
  • Advisor(s): Monbouquette, Harold G
  • et al.

Pressing performance demands require next-generation biosensors to detect target chemical and biological molecules with higher sensitivity, shorter response times, and lower detection limit. Micro- and nanoscale devices are attractive for a wide range of biosensor applications since at small scale, in addition to being more compact, the device may exhibit improved performance. The benefits include minimization of tissue damage for implantable devices, improved spatial resolution and sensitivity, as well as increased surface charge to mass ratio, which is important for the performance of our novel technology for nucleic acid detection described below. Borrowing from the processing technologies used in the semiconductor industry, we implemented micromachining techniques to fabricate devices at both the micro- and nanoscale. In this dissertation, we present our work on the fabrication and characterization of two next-generation biosensors.

The first device we fabricated is a sequence-specific nucleic acid sensor based on the blockage of a nanopore. Current methods for nucleic acid detection generally rely on polymerase chain reaction (PCR) and fluorescent labeling, however, these methods render the devices slow, expensive, complex, and bulky. In order to address these limitations, a new sensor was fabricated from a single glass wafer, consisting of a glass nanopore in a thin glass membrane. For nanopore sensing, low frequency noise is critical since it limits the discrimination of signal change based on target analyte movement from the fluctuation of noise. To further our understanding of nanopores, we observed how different pore geometries affect noise characteristics, and then compared this newly developed glass nanopore to conventional Si-based nanopores. Based on the analysis, low-noise glass nanopores, suitable for sequence-specific nucleic acid detection, were fabricated. By scaling down the pore diameter to the nano-regime, 1 aM detection of 16S rRNA from Escherichia coli was demonstrated even in the presence of a million-fold background of RNA from Pseudomona putida. This new platform for the PCR-free, optics-free, label-free sequence-specific nucleic acid detection shows the potential to detect pathogens in body fluids, food, or water.

In addition, we developed a new method to transfer enzyme to a microelectrode array on an implantable microprobe, which enables fabrication of better performing microprobes for the sensing of multiple neurochemicals in vivo. Monitoring the release of neurotransmitters in real-time offers valuable information necessary to understand neurological disorders and abnormal behaviors. We employed polydimethylsiloxane (PDMS) stamping to transfer enzyme onto microelectrode array microprobes. A model enzyme, glucose oxidase (GOx), was stamped onto the surface of disk electrodes to test the feasibility of PDMS stamping for biosensor fabrication. The model sensor showed a good combination of performance (29 μA/mM cm2 sensitivity and 4 μM detection limit) proving that PDMS stamping offers a simple and cost-effective enzyme deposition method for construction of electroenzymatic sensors. The next step was to add an alignment function to PDMS stamping to create microprobes with dual sensing (glucose and choline) capabilities for in vivo applications. Two different enzymes, GOx and choline oxidase (ChOx), were selectively transferred onto specific sites in a 4 microelectrode array by PDMS stamping with alignment using a microscope and a custom-built stage. The dual sensor showed improved consistency and performance including sensitivity to choline and to glucose (286 and 117 μA/mM cm2, respectively) as well as low detection limits (3 and 1 μM, respectively). This work demonstrated the ability to immobilize specific enzymes on selected microelectrodes in an array to give a high performance microprobe for simultaneous sensing of two analytes for neuroscience application.

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