journal article·2025-05Open access

High‐Performance MXene/PEDOT‐PSS Microscale Fiber Electrodes for Neural Recording and Stimulation

Shuchun Gou, Peixuan Li, Shu Yang, Guoqiang Bi, Zhanhong Du

Advanced Functional Materials· Volume 35· Issue 20

Abstract

Abstract The pursuit of advanced neural interfaces hinges on developing electrode materials with enhanced cathodic charge storage capacity (CSCc), charge injection capacity (CIC), and low impedance. 2D nanomaterials, particularly MXenes, have emerged as leading candidates due to their outstanding electrical properties. However, current fabrication strategies have not fully leveraged the potential of MXene, limiting the performance gains in neural recording and stimulation applications. Here, a novel microscale fiber electrode (MPP) engineered from Ti₃C₂ MXene and PEDOT‐PSS using a two‐step solidification wet spinning process is presented. These 30 µm fibers exhibit a conductivity of (2.16 ± 1.46) × 10⁵ S m−1, low interfacial impedance, and high CSCc and CIC, achieving substantial improvements in signal fidelity and stimulation efficiency. The MPP electrodes demonstrate robust electrochemical stability, biocompatibility, and magnetic resonance imaging (MRI) compatibility, excelling across various modalities, including electroencephalography (EEG), electromyography (EMG), electrocardiography (ECG), cortical recordings, and subthalamic nucleus deep brain stimulation (STN‐DBS). These results mark a significant step toward scalable, high‐performance neural interfaces that can transform neurotherapeutic applications.
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Research context

OpenAlex topics: Neuroscience and Neural Engineering · Advanced Memory and Neural Computing · MXene and MAX Phase Materials

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

Affiliations

Brain Cognition and Brain Disease Institute CAS Key Laboratory of Brain Connectome and Manipulation Guangdong Provincial Key Laboratory of Brain Connectome and Behavior Shenzhen Institute of Advanced Technology (SIAT) Chinese Academy of Sciences (CAS) Shenzhen 518055 China

Shenzhen‐Hong Kong Institute of Brain Science‐Shenzhen Fundamental Research Institutions Shenzhen 518055 China

Faculty of Life and Health Sciences Shenzhen University of Advanced Technology Shenzhen 518055 China

University of Chinese Academy of Sciences Beijing 100049 China

Center for Integrative Imaging Hefei National Laboratory for Physical Sciences at the Microscale and School of Life Sciences USTC Hefei 230027 China

Funding

National Natural Science Foundation of China · National Key Research and Development Program of China

References (51)

Show source-provided references
1. 10.1002/adfm.20230122310.1002/adfm.202301223 ↗
2. 10.1002/adfm.20170433510.1002/adfm.201704335 ↗
3. 10.1109/10.6103810.1109/10.61038 ↗
4. 10.1002/adma.20180053410.1002/adma.201800534 ↗
5. 10.1039/D1NR07226A10.1039/D1NR07226A ↗
6. C.Boehler F.Oberueber T.Stieglitz M.Asplund presented at2015 7th Int. IEEE/EMBS Conf. on Neural Engineering (NER) Montpellier France April 2015.
7. 10.1016/j.isci.2022.10453910.1016/j.isci.2022.104539 ↗
8. J.Schulte M. M.Hofert I. G.Vasilaş T.Stieglitz 2022 44th Annual Int. Conf. of the IEEE Engineering in Medicine & Biology Society (EMBC) presented atGlasgow Scotland UK July 2022.
9. 10.1152/jn.00711.201110.1152/jn.00711.2011 ↗
10. 10.1126/science.abf158110.1126/science.abf1581 ↗
11. 10.1021/acsnano.4c0579710.1021/acsnano.4c05797 ↗
12. 10.1002/adfm.20220477210.1002/adfm.202204772 ↗
13. 10.1039/D1BM00526J10.1039/D1BM00526J ↗
14. 10.1039/D2EN00527A10.1039/D2EN00527A ↗
15. 10.1002/adfm.20231277010.1002/adfm.202312770 ↗
16. 10.1021/acsnano.8b0601410.1021/acsnano.8b06014 ↗
17. 10.1126/scitranslmed.abf862910.1126/scitranslmed.abf8629 ↗
18. 10.1038/s41565-023-01570-510.1038/s41565-023-01570-5 ↗
19. 10.1038/s41467-020-15570-910.1038/s41467-020-15570-9 ↗
20. 10.3390/ijms2516861510.3390/ijms25168615 ↗
21. 10.1002/smll.20180473210.1002/smll.201804732 ↗
22. 10.1038/s41467-020-16671-110.1038/s41467-020-16671-1 ↗
23. 10.1021/acsnano.0c1025510.1021/acsnano.0c10255 ↗
24. 10.1016/j.carbon.2022.08.04510.1016/j.carbon.2022.08.045 ↗
25. 10.1021/acscentsci.9b0121710.1021/acscentsci.9b01217 ↗
26. 10.1039/C7TA07999K10.1039/C7TA07999K ↗
27. 10.1021/acsnano.5b0106010.1021/acsnano.5b01060 ↗
28. 10.1016/j.mattod.2023.08.00310.1016/j.mattod.2023.08.003 ↗
29. 10.1021/acs.chemmater.0c0035910.1021/acs.chemmater.0c00359 ↗
30. 10.1016/j.biomaterials.2011.04.05110.1016/j.biomaterials.2011.04.051 ↗
31. 10.1021/acs.nanolett.8b0445610.1021/acs.nanolett.8b04456 ↗
32. 10.1186/s42234-024-00154-510.1186/s42234-024-00154-5 ↗
33. 10.1073/pnas.230677712010.1073/pnas.2306777120 ↗
34. 10.1039/D3BM01114C10.1039/D3BM01114C ↗
35. 10.1021/acs.chemrestox.0c0023210.1021/acs.chemrestox.0c00232 ↗
36. 10.1186/s12951-022-01590-810.1186/s12951-022-01590-8 ↗
37. 10.1002/adfm.20231090810.1002/adfm.202310908 ↗
38. 10.1016/j.msec.2019.02.00810.1016/j.msec.2019.02.008 ↗
39. 10.1016/j.bios.2014.09.00410.1016/j.bios.2014.09.004 ↗
40. 10.1016/S0165-0173(98)00056-310.1016/S0165-0173(98)00056-3 ↗
41. 10.1016/j.neuroscience.2019.01.03910.1016/j.neuroscience.2019.01.039 ↗
42. 10.1016/j.jneumeth.2014.12.01010.1016/j.jneumeth.2014.12.010 ↗
43. 10.3389/fnins.2019.0032410.3389/fnins.2019.00324 ↗
44. 10.1038/s41592-023-02034-310.1038/s41592-023-02034-3 ↗
45. 10.1126/science.abq259410.1126/science.abq2594 ↗
46. 10.1371/journal.pone.021679610.1371/journal.pone.0216796 ↗
47. 10.1002/adma.20230495610.1002/adma.202304956 ↗
48. 10.1093/cercor/bhn14910.1093/cercor/bhn149 ↗
49. 10.1016/j.actbio.2017.02.01010.1016/j.actbio.2017.02.010 ↗
50. 10.1152/jn.00808.200710.1152/jn.00808.2007 ↗
51. 10.1039/D0MH01656J10.1039/D0MH01656J ↗