Stretchable Surface Electrode Arrays Using an Alginate/PEDOT:PSS-Based Conductive Hydrogel for Conformal Brain Interfacing.

brain interface conductive hydrogel electrocorticogram implantable electronics soft electronics stretchable electronics

Journal

Polymers
ISSN: 2073-4360
Titre abrégé: Polymers (Basel)
Pays: Switzerland
ID NLM: 101545357

Informations de publication

Date de publication:
25 Dec 2022
Historique:
received: 14 11 2022
revised: 01 12 2022
accepted: 19 12 2022
entrez: 8 1 2023
pubmed: 9 1 2023
medline: 9 1 2023
Statut: epublish

Résumé

An electrocorticogram (ECoG) is the electrical activity obtainable from the cerebral cortex and an informative source with considerable potential for future advanced applications in various brain-interfacing technologies. Considerable effort has been devoted to developing biocompatible, conformal, soft, and conductive interfacial materials for bridging devices and brain tissue; however, the implementation of brain-adaptive materials with optimized electrical and mechanical characteristics remains challenging. Herein, we present surface electrode arrays using the soft tough ionic conductive hydrogel (STICH). The newly proposed STICH features brain-adaptive softness with Young's modulus of ~9.46 kPa, which is sufficient to form a conformal interface with the cortex. Additionally, the STICH has high toughness of ~36.85 kJ/mm

Identifiants

pubmed: 36616434
pii: polym15010084
doi: 10.3390/polym15010084
pmc: PMC9824691
pii:
doi:

Types de publication

Journal Article

Langues

eng

Subventions

Organisme : National Research Foundation of Korea
ID : 2020R1C1C1005567

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Auteurs

Sungjun Lee (S)

School of Electrical and Electronic Engineering, Yonsei University, Seoul 03722, Republic of Korea.
Center for Bionics of Biomedical Research Institute, Korea Institute of Science and Technology, Seoul 02792, Republic of Korea.

Kyuha Park (K)

Department of Electrical and Computer Engineering, Sungkyunkwan University (SKKU), Suwon 16419, Republic of Korea.

Jeungeun Kum (J)

Center for Bionics of Biomedical Research Institute, Korea Institute of Science and Technology, Seoul 02792, Republic of Korea.
Division of Bio-Medical Science & Technology, KIST School, University of Science and Technology, Seoul 02792, Republic of Korea.

Soojung An (S)

Department of Electrical and Computer Engineering, Sungkyunkwan University (SKKU), Suwon 16419, Republic of Korea.

Ki Jun Yu (KJ)

School of Electrical and Electronic Engineering, Yonsei University, Seoul 03722, Republic of Korea.

Hyungmin Kim (H)

Center for Bionics of Biomedical Research Institute, Korea Institute of Science and Technology, Seoul 02792, Republic of Korea.
Division of Bio-Medical Science & Technology, KIST School, University of Science and Technology, Seoul 02792, Republic of Korea.

Mikyung Shin (M)

Department of Intelligent Precision Healthcare Convergence, Sungkyunkwan University (SKKU), Suwon 16419, Republic of Korea.
Department of Biomedical Engineering, Sungkyunkwan University (SKKU), Suwon 16419, Republic of Korea.

Donghee Son (D)

Department of Electrical and Computer Engineering, Sungkyunkwan University (SKKU), Suwon 16419, Republic of Korea.
Department of Superintelligence Engineering, Sungkyunkwan University (SKKU), Suwon 16419, Republic of Korea.

Classifications MeSH