Issue 14, 2025

Self-healing and highly adhesive conductive polydimethylsiloxane-based elastomers for chronic epilepsy monitoring

Abstract

Flexible substrate materials with high adhesion, high stretchability, and low impedance are essential to ensure long-term stable acquisition of electrophysiological signals with less tissue inflammation. Polydimethylsiloxane is a promising candidate owing to its inherent flexibility and biocompatibility; however, its poor adhesion to the skin and excessive stiffness of tissue interfaces limit its application in this field. To address these challenges, we developed a flexible electrode system based on crosslinked block polyborosiloxane and carbon nanotube (C-PBS/CNT) elastomers carrying hydroxyl groups through a thiol–ene reaction. The composite exhibits enhanced adhesion to both the skin and skull, high stretchability, and tunable stiffness ranging from 10 to over 200 kPa, enabling adaptability to the long-term monitoring of epileptic activity and other application scenarios. Moreover, the C-PBS/CNT composite elastomer demonstrated excellent self-healing performance owing to its dynamic boronate ester and hydrogen bonds. The packaged C-PBS/CNT electrode demonstrates low impedance for efficient multi-channel acquisition of epileptic activity under humid conditions. These innovations enable a precise analysis of cortical epileptic-activity propagation and provide an essential technological platform for the prediction and treatment of epileptic seizures, paving the way for next-generation wearable biomedical devices.

Graphical abstract: Self-healing and highly adhesive conductive polydimethylsiloxane-based elastomers for chronic epilepsy monitoring

Supplementary files

Article information

Article type
Paper
Submitted
14 Jan 2025
Accepted
24 Feb 2025
First published
03 Mar 2025
This article is Open Access
Creative Commons BY-NC license

Nanoscale, 2025,17, 8624-8633

Self-healing and highly adhesive conductive polydimethylsiloxane-based elastomers for chronic epilepsy monitoring

M. Tang, K. Lei, X. Zhao, X. Hu, Q. He, K. Zhang, X. Ma, H. Ni, Y. Shu and Z. Li, Nanoscale, 2025, 17, 8624 DOI: 10.1039/D5NR00171D

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