Softening, Conformable, and Stretchable Conductors for Implantable Bioelectronics Interfaces
Neural implantable devices serve as electronic interfaces facilitating communication between the body and external electronic systems. These bioelectronic systems ideally possess stable electrical conductivity, flexibility, and stretchability to accommodate dynamic movements within the body. However, achieving both high electrical conductivity and mechanical compatibility remains a challenge. Effective electrical conductors tend to be rigid and stiff, leading to a substantial mechanical mismatch with bodily tissues. On the other hand, highly stretchable polymers, while mechanically compatible, often suffer from limited compatibility with lithography techniques and reduced electrical stability. Therefore, there exists a pressing need to develop electromechanically stable neural interfaces that enable precise communication with biological tissues. In this study, a polymer that is softening, flexible, conformal, and compatible with lithography to microfabricate perforated thin-film architectures was utilized. These architectures offer stretchability and improved mechanical compatibility. Three distinct geometries were evaluated both mechanically and electrically under in-vitro conditions that simulate physiological environments. Notably, the Peano structure demonstrates minimal changes in resistance, varying less than 1.5× even when subjected to almost 150% strain. Furthermore, devices exhibit a maximum mechanical elongation before fracture, reaching 220%. Finally, the application of multi-electrode spinal cord leads employing titanium nitride for neural stimulation in rat models was demonstrated.
Authors
- Melanie Ecker (ORCID: https://orcid.org/0000-0002-0603-6683)
- Joseph J. Pancrazio (ORCID: https://orcid.org/0000-0001-8276-3690)
- Pedro Emanuel Rocha-Flores (ORCID: https://orcid.org/0000-0003-1106-1434)
- Hongbing Lu (ORCID: https://orcid.org/0000-0003-4268-7245)
- Jason B. Carmel (ORCID: https://orcid.org/0000-0002-3933-3692)
- Ahmet S. Asan (ORCID: https://orcid.org/0000-0003-1461-4209)
- Chandani Chitrakar (ORCID: https://orcid.org/0000-0003-2956-9527)
- Ankit Parikh (ORCID: https://orcid.org/0000-0003-0107-5533)
- Aldo Garcia‐Sandoval (ORCID: https://orcid.org/0000-0002-6616-1179)
- Alexandra Joshi‐Imre (ORCID: https://orcid.org/0000-0002-4271-1623)
- James R. McIntosh (ORCID: https://orcid.org/0000-0002-3970-1619)
- Ovidio Rodríguez-Lopez (ORCID: https://orcid.org/0000-0002-7790-8979)
- Walter Voit (ORCID: https://orcid.org/0000-0003-0135-0531)
- Yao Ren
Institutions
- University of North Texas (US)
- The University of Texas at Dallas (US)
- Columbia University (US)
Publication Details
- Journal
- Advanced Materials Technologies
- Published
- 2024-12-04
- DOI
- https://doi.org/10.1002/admt.202401047
- Citations
- 8
- Primary Topic
- Neuroscience and Neural Engineering
- Type
- article
- Field-Weighted Citation Impact
- 2.15
Funders
- National Science Foundation
- National Institute of Neurological Disorders and Stroke