3D‐Printable, Honeycomb‐Inspired Tissue‐Like Bioelectrodes for Patient‐Specific Neural Interface
The unique gyral patterns of the human brain demand patient-specific neural interfaces to achieve precise neuromodulation, mitigate adverse tissue responses, and optimize therapeutic efficacy and safety. One-size-fits-all, conventional rigid electrocorticography (ECoG) electrodes, standardized for mass production through lithographic techniques, exhibit limited conformability to the brain's heterogeneous cortical topography. This mechanical mismatch results in poor electrode-tissue contact, signal loss, and foreign body responses. To address these limitations, we present an integrated novel platform, synergizing MRI-based anatomical mapping, finite element analysis (FEA)-optimized mechanical design, and direct ink writing (DIW) 3D printing to fabricate electrodes customized to individual gyral patterns. The resulting honeycomb-inspired printable gel electrode (HiPGE) employs a bioinspired honeycomb architecture with ultra-soft hydrogels, engineered to match the bending stiffness of brain tissue (0.1-10 kPa) while maintaining cost-efficiency and long-term durability. This mechanical congruence ensures exceptional cortical conformability and adaptive interfacing, circumventing the geometric and material limitations of traditional rigid electrodes. By combining patient-specific design with scalable fabrication, our platform establishes a transformative framework for neural interface engineering, enhancing precision, biocompatibility, and functional performance in neuromodulation therapies and neuroprosthetic applications.
Authors
- L. Huang
- Xiaoai Chen
- Samuel Cramer
- Xinyi Wang (ORCID: https://orcid.org/0000-0002-5367-2404)
- Salahuddin Ahmed (ORCID: https://orcid.org/0000-0002-0666-8496)
- Marzia Momin
- Jiashu Ren
- Basma AlMahmood
- Nanyin Zhang
- Tao Zhou
- Hyunjin Lee (ORCID: https://orcid.org/0000-0002-5860-1963)
- Luyi Feng
- Sulin Zhang
Institutions
- Pennsylvania State University (US)
Publication Details
- Journal
- Advanced Materials
- Published
- 2026-03-14
- DOI
- https://doi.org/10.1002/adma.202516291
- Primary Topic
- Neuroscience and Neural Engineering
- Type
- article
- Field-Weighted Citation Impact
- 0.00