# Commercialization of Enzyme Modified Carbon-Fiber Electrodes Paired with Voltammetry for Simultaneous Real-Time Monitoring of Electroactive and Non-Electroactive Species at Discrete Brain Locations

> **NIH NIH R43** · PINNACLE TECHNOLOGY, INC · 2020 · $445,038

## Abstract

ABSTRACT
Commercially available biosensors are designed to measure only one molecule at a time at a given recording
site. This is a problem because chemical signals in the brain do not work in isolation; rather, neurotransmission
involves many chemical species simultaneously released and little is known about how specific
neurochemicals fluctuate relative to one another. Understanding these relationships is critical to the
development of drugs and treatments for a wide range of neurological disorders. The Sombers Lab has
established the feasibility of using fast-scan cyclic voltammetry (FSCV) and carbon-fiber microelectrodes for
the simultaneous detection of rapid dopamine fluctuations and those of non-electroactive species, such as
glucose, at the same recording site. This is done with higher spatial and temporal resolution than currently
available methods. The goal of this Lab to Marketplace: Tools for Brain and Behavioral Research SBIR is to
translate and commercialize the technology developed by the Sombers team at North Carolina State
University. The first goal is to transfer the core technology for the co-detection of dopamine and glucose from
the Sombers laboratory to Pinnacle Technology, a company that has developed, manufactured and sold
biosensors and electrochemical measurement systems worldwide. Pinnacle-produced sensors will be fully
characterized and detailed specifications for the technology (sensitivity, linear range, shelf-life and benchmarks
for in vivo performance) will be outlined. The second goal is to develop training tools and software to minimize
the learning curve associated with the proper implementation, characterization and analysis of FSCV in
research or pre-clinical applications. This will be accomplished by modifying existing Pinnacle software to
create an intuitive platform for acquisition and analysis of voltammetry data using the commercial probes.
Finally, high production value training videos will be created and made freely available on the Pinnacle
website. These will detail experimental procedures for all aspects of in vivo voltammetry including probe
calibration, surgical procedures, and data acquisition and analysis protocols. Overall, this project is innovative,
because it departs from the status quo by utilizing the redox activity inherent to enzymatically generated H2O2
to identify targeted non-electroactive species, even in the presence of electroactive molecules that are typically
excluded as interferents. It is significant, because it combines two state-of-the-art and well-characterized
technologies for neurochemical monitoring in a clever, straightforward, and unprecedented manner to provide
the community with an established tool that can be used to study the role of glucose in complex physiological
processes ranging from basic endocrine function to motivation. It promises to have a transformative effect on
neuroscience by allowing researchers interested in diverse aspects of brain function to better...

## Key facts

- **NIH application ID:** 9903459
- **Project number:** 5R43MH119870-02
- **Recipient organization:** PINNACLE TECHNOLOGY, INC
- **Principal Investigator:** DAVID A JOHNSON
- **Activity code:** R43 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $445,038
- **Award type:** 5
- **Project period:** 2019-04-01 → 2022-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9903459

## Citation

> US National Institutes of Health, RePORTER application 9903459, Commercialization of Enzyme Modified Carbon-Fiber Electrodes Paired with Voltammetry for Simultaneous Real-Time Monitoring of Electroactive and Non-Electroactive Species at Discrete Brain Locations (5R43MH119870-02). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/9903459. Licensed CC0.

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