Mechanically Compliant and Impedance Matching Hydrogel Bioelectronics for Low‐Voltage Peripheral Neuromodulation
In neural biointerfacing technologies, mitigating the mismatch in mechanical and impedance attributes between neural tissues and bioelectronics remains a central challenge for achieving high-efficacy neuromodulation. Here, full-hydrogel bioelectronics that demonstrate superior mechanical compliance and impedance matching with 3D peripheral nerves, allowing for low-voltage vagus nerve stimulation, are reported. By precisely tuning the dimensional parameters through 3D printing, the hydrogel bioelectronics, initially in a 2D planar form in a dehydrated state, can curl spontaneously around nerves and form a seamless interface. During the hydration process, instant, and tough bioadhesion is achieved through a dry crosslinking mechanism, enabling a mechanically robust nerve-electrode interface to resist dynamic yet vigorous deformations of the peripheral nerve systems. The as-formed nerve-electrode interface significantly mitigates the impedance mismatch, in favor of electrical stimulation at a threshold voltage of 10 mV, one order of magnitude lower than that of conventional metallic electrodes. The use of the hydrogel bioelectronics for successful stroke rehabilitation through low-voltage vagus nerve stimulation in a rat model is also demonstrated.
Authors
- Fu‐Cheng Wang (ORCID: https://orcid.org/0000-0001-5011-7934)
- Yuhua Xue (ORCID: https://orcid.org/0000-0002-6117-1132)
- Liangjie Shan
- Linna Guo (ORCID: https://orcid.org/0000-0002-6977-5459)
- Xingmei Chen (ORCID: https://orcid.org/0009-0007-6266-0396)
- Ji Liu (ORCID: https://orcid.org/0000-0001-7171-405X)
- Changjiang Wang (ORCID: https://orcid.org/0000-0002-0425-7976)
- Yafei Wang (ORCID: https://orcid.org/0000-0002-9575-781X)
- Pei Zhang (ORCID: https://orcid.org/0000-0003-0608-5677)
- Yinghui Feng (ORCID: https://orcid.org/0000-0001-9749-4673)
- Dong Li (ORCID: https://orcid.org/0000-0002-3144-5104)
Institutions
- Southern University of Science and Technology (CN)
Publication Details
- Journal
- Advanced Materials
- Published
- 2025-08-29
- DOI
- https://doi.org/10.1002/adma.202511014
- Citations
- 10
- Primary Topic
- Neuroscience and Neural Engineering
- Type
- article
- Field-Weighted Citation Impact
- 9.09
Funders
- National Natural Science Foundation of China
- Development and Reform Commission of Shenzhen Municipality