Hybrid CMOS-Graphene Sensor Array for Subsecond Dopamine Detection

Bayan Nasri, Ting Wu, Abdullah Alharbi, Kae Dyi You, Mayank Gupta, Sunit P. Sebastian, Roozbeh Kiani, Davood Shahrjerdi

Research output: Contribution to journalArticle

Abstract

We introduce a hybrid CMOS-graphene sensor array for subsecond measurement of dopamine via fast-scan cyclic voltammetry (FSCV). The prototype chip has four independent CMOS readout channels, fabricated in a 65-nm process. Using planar multilayer graphene as biologically compatible sensing material enables integration of miniaturized sensing electrodes directly above the readout channels. Taking advantage of the chemical specificity of FSCV, we introduce a region of interest technique, which subtracts a large portion of the background current using a programmable low-noise constant current at about the redox potentials. We demonstrate the utility of this feature for enhancing the sensitivity by measuring the sensor response to a known dopamine concentration in vitro at three different scan rates. This strategy further allows us to significantly reduce the dynamic range requirements of the analog-to-digital converter (ADC) without compromising the measurement accuracy. We show that an integrating dual-slope ADC is adequate for digitizing the background-subtracted current. The ADC operates at a sampling frequency of 5-10 kHz and has an effective resolution of about 60 pA, which corresponds to a theoretical dopamine detection limit of about 6 nM. Our hybrid sensing platform offers an effective solution for implementing next-generation FSCV devices that can enable precise recording of dopamine signaling in vivo on a large scale.

Original languageEnglish (US)
Article number8187660
Pages (from-to)1192-1203
Number of pages12
JournalIEEE Transactions on Biomedical Circuits and Systems
Volume11
Issue number6
DOIs
StatePublished - Dec 2017

Keywords

  • Biosensor
  • FSCV
  • cyclic voltammetry
  • dopamine
  • dual-slope ADC
  • electrochemical sensor
  • graphene

ASJC Scopus subject areas

  • Biomedical Engineering
  • Electrical and Electronic Engineering

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