Hybrid CMOS-Graphene Sensor Array for Sub-Second Dopamine Detection
We introduce a hybrid CMOS-graphene sensor array for sub-second 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...
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Published in | IEEE transactions on biomedical circuits and systems Vol. 11; no. 6; pp. 1192 - 1203 |
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Main Authors | , , , , , , , |
Format | Journal Article |
Language | English |
Published |
01.12.2017
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Online Access | Get full text |
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Summary: | We introduce a hybrid CMOS-graphene sensor array for sub-second 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. |
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Bibliography: | These authors contributed equally to this work. |
ISSN: | 1932-4545 1940-9990 |
DOI: | 10.1109/TBCAS.2017.2778048 |