Stabilized carbon coating on microelectrodes for scalable and interoperable neurotransmitter sensing

Real-time monitoring of neurotransmitters is essential in driving basic neuroscience understandings and creating treatments for various brain disorders. However, current neurotransmitter sensing devices are highly limited in their spatiotemporal resolution and ability to integrate with neuronal recording. Here, we introduce a unique carbon coating approach to achieve high-performance voltammetry electrodes with extraordinary scalability and interoperability. Surprisingly, we discovered that mild annealing drastically improves the electrochemical stability of graphene-based carbon coating, enabling the transformation of conventional neuroelectrodes into fast-scan-cyclic-voltammetry-stable carbon sensors. We successfully validated sub-second detection of nanomolar dopamine in vivo using carbon-coated microelectrodes (CCMs) in rodents and demonstrated arrays of one hundred CCMs with high yield and uniformity. Furthermore, we developed a dual-modal neural probe that integrates the CCM with electrophysiological recording sites, allowing us to demonstrate that dopamine fluctuation in the ventral striatum of awake rats strongly correlates with the high gamma power in the brain with sub-second-level precision. Together, these advances pave the way for spatiotemporally scalable and multiplexed brain interfacing, with also broad applicability in electrochemical-related diagnostic and interventional approaches.

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Institutions

Publication Details

Journal
Nature Communications
Published
2025-04-07
DOI
https://doi.org/10.1038/s41467-025-58388-z
Citations
14
Primary Topic
Electrochemical sensors and biosensors
Type
article
Field-Weighted Citation Impact
10.23

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article

Stabilized carbon coating on microelectrodes for scalable and interoperable neurotransmitter sensing

Jyun-you Liou, Jaehyeon Ryu, Anand Iyer, Wen Gu et al.
14 citations
Nature Communications
Electrochemical sensors and biosensors
10.23
article

Stabilized carbon coating on microelectrodes for scalable and interoperable neurotransmitter sensing

Jyun-you Liou, Jaehyeon Ryu, Anand Iyer, Wen Gu, Hui Fang, Dongyeol Jang, Matthijs A. A. van der Meer, Yieljae Shin, Gen Li, Yongli Qi, Hongtao Ma, Yi Qiang, Tianyu Bai
article en
14 citations

Abstract

Real-time monitoring of neurotransmitters is essential in driving basic neuroscience understandings and creating treatments for various brain disorders. However, current neurotransmitter sensing devices are highly limited in their spatiotemporal resolution and ability to integrate with neuronal recording. Here, we introduce a unique carbon coating approach to achieve high-performance voltammetry electrodes with extraordinary scalability and interoperability. Surprisingly, we discovered that mild annealing drastically improves the electrochemical stability of graphene-based carbon coating, enabling the transformation of conventional neuroelectrodes into fast-scan-cyclic-voltammetry-stable carbon sensors. We successfully validated sub-second detection of nanomolar dopamine in vivo using carbon-coated microelectrodes (CCMs) in rodents and demonstrated arrays of one hundred CCMs with high yield and uniformity. Furthermore, we developed a dual-modal neural probe that integrates the CCM with electrophysiological recording sites, allowing us to demonstrate that dopamine fluctuation in the ventral striatum of awake rats strongly correlates with the high gamma power in the brain with sub-second-level precision. Together, these advances pave the way for spatiotemporally scalable and multiplexed brain interfacing, with also broad applicability in electrochemical-related diagnostic and interventional approaches.

Nature CommunicationsVol. 16(1)
Dartmouth College (US), Northeastern University (US), Cornell University (US), Neurological Surgery (US)
National Science Foundation, Dartmouth College, National Institutes of Health, Division of Electrical, Communications and Cyber Systems
Openalex Percentile: Top 2%
Electrochemical sensors and biosensors
10.23
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