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Implantable neural probes with monolithically integrated CNTFET arrays for multimodal monitoring
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  • Published: 14 December 2025

Implantable neural probes with monolithically integrated CNTFET arrays for multimodal monitoring

  • Jie Xia1,
  • Luxi Zhang1,
  • Shengming Wang1,
  • Yanlan Yu2,
  • Li Ding  ORCID: orcid.org/0000-0003-4310-99573,
  • Fan Zhang  ORCID: orcid.org/0000-0002-4480-21581,
  • Shaomin Zhang  ORCID: orcid.org/0000-0001-6311-59461,
  • Jikui Luo  ORCID: orcid.org/0000-0003-0310-24431,
  • Yan Yan Shery Huang  ORCID: orcid.org/0000-0003-2619-730X4,
  • Luigi Occhipinti  ORCID: orcid.org/0000-0002-9067-25344,
  • Gang Pan  ORCID: orcid.org/0000-0002-4049-61811,
  • Zhen Cao  ORCID: orcid.org/0000-0002-5440-36281,5,
  • Guoqing Ding  ORCID: orcid.org/0000-0003-3156-36182 &
  • …
  • Shurong Dong  ORCID: orcid.org/0000-0002-8715-70721,2 

Nature Communications , Article number:  (2025) Cite this article

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We are providing an unedited version of this manuscript to give early access to its findings. Before final publication, the manuscript will undergo further editing. Please note there may be errors present which affect the content, and all legal disclaimers apply.

Subjects

  • Bionanoelectronics
  • Sensors

Abstract

The implantable neural probe for simultaneous recording of various brain signals is one of the key technologies for neurological science and clinics that is yet to be broken through. Here, we introduce an implantable neural probe with integrated carbon nanotube field-effect transistors which is able to perform multimodal recording of electrical and chemical signals of the brain under magnetic resonance imaging (MRI). We demonstrate here a simultaneous measurement of an electrophysiological signal with high signal-to-noise ratio up to 40.34 dB and calcium concentration with a detection limit down to 0.47 nM. We use our neural probes to detect neural activity in rats and results reveal that changes in Ca²⁺ concentration occur concurrently with the epileptiform local field potential events, providing an alternative method for accurate detection of epilepsy. Our work may provide a powerful means for the future studies of brain and holds great potential for practical diagnostic applications.

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Data availability

All data supporting the findings of this study are available within the article and its supplementary files. Any additional requests for information can be directed to, and will be fulfilled by, the corresponding authors. Source data are provided with this paper.

Code availability

The custom code used in this study is available on Zenodo under the https://doi.org/10.5281/zenodo.17547948 (2025)55.

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Acknowledgements

Shurong Dong, Gang Pan, Jikui Luo, Shaomin Zhang et al. would like to thank STI2030-Major Projects (No. 2021ZD0200401). Shurong Dong would like to thank Zhejiang Province high level talent special support plan (No. 2022R52042), Zhejiang Province Key R & D programs (No. 2024C03001, No. 2024C03007). Zhen Cao would like to thank Zhejiang Province Leading Geese Plan (No. 2024C03217). Yanlan Yu would like to thank Medical Interdisciplinary Innovation Program 2024, Zhejiang University School of Medicine. The authors would like to express their gratitude to Prof. Tawfique Hasan (Department of Engineering, University of Cambridge, UK) for the collaboration and technical consultations. We also thank Jingyao Chen, Qiong Huang, Chengcheng Zhang, and Yajun Yu from the core facility platform of Zhejiang University School of Medicine for their technical support, and Xu Bin from Zhejiang University 7 T Brain Imaging Research Center for assistance. We further acknowledge Hangzhou Rong brain Technology Co., Ltd. for assisting with the LFP collection, and process engineers from Haijiexing Technology Co., Ltd. (Suzhou, China) for sharing their expertise inlaser internal modification and providing relevant equipment.

Author information

Authors and Affiliations

  1. The State Key Lab of Brain-Machine Intelligence, Zhejiang University, Hangzhou, China

    Jie Xia, Luxi Zhang, Shengming Wang, Fan Zhang, Shaomin Zhang, Jikui Luo, Gang Pan, Zhen Cao & Shurong Dong

  2. Department of Urology, Sir Run Run Shaw Hospital, School of Medicine, Zhejiang University, Hangzhou, China

    Yanlan Yu, Guoqing Ding & Shurong Dong

  3. Key Laboratory for the Physics and Chemistry of Nanodevices and Center for Carbon-based Electronics, School of Electronics, Peking University, Beijing, China

    Li Ding

  4. Department of Engineering, University of Cambridge, Cambridge, UK

    Yan Yan Shery Huang & Luigi Occhipinti

  5. Zhejiang Provincial Engineering Research Center of Innovative Instruments for Precise Pathogen Detection, Hangzhou, China

    Zhen Cao

Authors
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Contributions

J.X. and S.D. developed the methodology, acquired the data, and wrote the manuscript. L.Z., Y.Y. and F.Z. conducted the animal experiments. S.W. and L.D. performed data analysis. S.Z., L.D. and G.D. contributed to methodology development and manuscript revision. S.D. supervised the project. G.P. and S.D. provided funding and supervised the study. Z.C. contributed to manuscript revision and supervision. J.L., Y.Y.S.H. and L.O. contributed to manuscript revision.

Corresponding authors

Correspondence to Gang Pan, Zhen Cao, Guoqing Ding or Shurong Dong.

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Xia, J., Zhang, L., Wang, S. et al. Implantable neural probes with monolithically integrated CNTFET arrays for multimodal monitoring. Nat Commun (2025). https://doi.org/10.1038/s41467-025-67535-5

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  • Received: 07 March 2025

  • Accepted: 03 December 2025

  • Published: 14 December 2025

  • DOI: https://doi.org/10.1038/s41467-025-67535-5

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