# Integrated microfluidic electrochemical neural probe with monolithic sampling, in-situ calibration, and online detection of neurochemicals for increased long-term performance

> **NIH NIH F31** · UNIVERSITY OF ILLINOIS AT URBANA-CHAMPAIGN · 2022 · $46,752

## Abstract

Project Summary
 Detection of neurochemical transients within the brain have led to better understanding of how underlying neural
circuits function and correlate to behavior, and have contributed to the development of several therapeutic drugs and
treatments targeted to a multitude of neurological disorders, neurodegenerative diseases, consequences of stroke, and injury.
Fast-scan cyclic voltammetry (FSCV) is one of the most common methods of neurochemical sensing, as it provides 1.) sub-
second temporal resolution to capture chemical transmission, diffusion, and uptake events, 2.) physiologically relevant
chemical resolution at ~10-100 nM limits of detection, and 3.) sub-micrometer spatial resolution to target specific brain
regions of interest by sweeping working electrode voltage and measuring the resultant faradaic current from oxidizing and
reducing analytes at their specific redox potentials. However, FSCV selectivity is limited for molecules with similar redox
potentials, such as dopamine and ascorbic acid. FSCV also suffers in long-term performance, as implanted electrodes
foul due to adsorption of cells, degradatory enzymes, and redox intermediates. This limits FSCV to practical
experimental time windows of approximately 90 seconds and functional implant use to just a couple of months. Furthermore,
the fast scan rates used in FSCV result in background capacitive currents that are several orders of magnitude higher than
the desired signals. This necessitates background subtraction, which results in an inability to detect basal concentrations,
limiting traditional FSCV to transient measurements in-vivo. Calibration also cannot be performed in-situ and is
traditionally done in a beaker before implantation and after removal of the probe, leading to questionable measurement
accuracy requiring confirmation by other methods such as mass spectrometry.
 The innovation of this proposal is to develop a miniaturized neurochemical sampling probe with integrated
electrodes within its microfluidic channels for sensitive, on-line detection while improving long-term sensor
performance by leveraging mature silicon microfabrication techniques. The envisioned device can 1.) sense
neurochemicals on-line with considerably reduced electrode fouling and drift by physically separating the sampling
site and harsh inflammatory response from the detection electrode site, and introducing flow to combat analyte adsorption
and increase sensitivity; and 2.) demonstrate in-situ calibration and regeneration of the electrodes through sophisticated
microfluidic design, combatting eventual fouling and drift. Aim 1 focuses on electrochemical flow-cell optimization of
sensitivity and selectivity with in-vivo validation. Aim 2 focuses on valve-less flow redirection for electrode calibration and
regeneration in-situ with demonstration of long-term in-vivo sensing. Successful completion of this project results in an
electrochemical neural probe capable of enhanced long-term and ...

## Key facts

- **NIH application ID:** 10466133
- **Project number:** 1F31NS126018-01A1
- **Recipient organization:** UNIVERSITY OF ILLINOIS AT URBANA-CHAMPAIGN
- **Principal Investigator:** Christopher Kenji Brenden
- **Activity code:** F31 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $46,752
- **Award type:** 1
- **Project period:** 2022-12-13 → 2025-05-12

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10466133

## Citation

> US National Institutes of Health, RePORTER application 10466133, Integrated microfluidic electrochemical neural probe with monolithic sampling, in-situ calibration, and online detection of neurochemicals for increased long-term performance (1F31NS126018-01A1). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10466133. Licensed CC0.

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