Thermally Drawn Multifunctional All‐Hydrogel Fibers for Anti‐Fibrotic and Multimodal Neural Interfaces

Hydrogels have emerged as promising materials for anti-fibrotic neural interfaces due to their mechanical and chemical similarity to biological tissue. However, their use in multimodal platforms remains limited, owing to fabrication challenges in microstructuring multiple functional hydrogels into compact architectures. Here, a hydrogel thermal drawing process (HG-TDP) is presented that enables the co-fabrication of multiple thermoplastically deformable hydrogels into a single, compact, and multifunctional fiber. By optimizing key process parameters, all-hydrogel neural interfaces are developed that minimize gliosis through tissue-like mechanical compliance and enable post-implantation anti-inflammatory drug delivery via the hydrogel-based matrix. These fibers feature the compact integration of diverse hydrogel components, including a step-index optical waveguide, an electrically conductive hydrogel electrode, and a hydrogel-based microfluidic channel within a unified fiber structure. This integration enables multimodal neural interfacing, as demonstrated by high-quality neural signal recording, optogenetic stimulation, and localized chemical modulation of neural circuits. This work offers a scalable route toward compact, fully hydrogel-based neural interfaces that combine multimodal functionality with tissue-friendly, anti-fibrotic properties.

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Publication Details

Journal
Advanced Materials
Published
2025-10-09
DOI
https://doi.org/10.1002/adma.202511634
Citations
4
Primary Topic
Neuroscience and Neural Engineering
Type
article
Field-Weighted Citation Impact
3.64
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article

Thermally Drawn Multifunctional All‐Hydrogel Fibers for Anti‐Fibrotic and Multimodal Neural Interfaces

Young‐Gyun Park, Alan Jung Park, Haider Butt, Yeji Kim et al.
4 citations
Advanced Materials
Neuroscience and Neural Engineering
3.64
article

Thermally Drawn Multifunctional All‐Hydrogel Fibers for Anti‐Fibrotic and Multimodal Neural Interfaces

Young‐Gyun Park, Alan Jung Park, Haider Butt, Yeji Kim, Kum Seok Nam, Kanghyeon Kim, Hyunwoo Yuk, Chanwoong Yoon, Jiheong Kang, Ain Chung, Hye-In Kang, Changhoon Sung, Seongjun Park, Somin Lee
article en
4 citations

Abstract

Hydrogels have emerged as promising materials for anti-fibrotic neural interfaces due to their mechanical and chemical similarity to biological tissue. However, their use in multimodal platforms remains limited, owing to fabrication challenges in microstructuring multiple functional hydrogels into compact architectures. Here, a hydrogel thermal drawing process (HG-TDP) is presented that enables the co-fabrication of multiple thermoplastically deformable hydrogels into a single, compact, and multifunctional fiber. By optimizing key process parameters, all-hydrogel neural interfaces are developed that minimize gliosis through tissue-like mechanical compliance and enable post-implantation anti-inflammatory drug delivery via the hydrogel-based matrix. These fibers feature the compact integration of diverse hydrogel components, including a step-index optical waveguide, an electrically conductive hydrogel electrode, and a hydrogel-based microfluidic channel within a unified fiber structure. This integration enables multimodal neural interfacing, as demonstrated by high-quality neural signal recording, optogenetic stimulation, and localized chemical modulation of neural circuits. This work offers a scalable route toward compact, fully hydrogel-based neural interfaces that combine multimodal functionality with tissue-friendly, anti-fibrotic properties.

Advanced MaterialsVol. 38(9)
Seoul National University (KR), Korea Advanced Institute of Science and Technology (KR), Seoul National University of Education (KR), Khalifa University of Science and Technology (AE), New Generation University College (ET), Seoul National University Hospital (KR), National Medical Center (KR), American Association for Higher Education and Accreditation (US)
Openalex Percentile: Top 6%
Neuroscience and Neural Engineering
3.64
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