Laser-Induced Graphene Interfaces with Controlled Electrical Conductivity, Topography and Wettability for Biomedical Applications

Abstract

Graphene-based materials hold great potential for the development of neural interfaces; however, conventional fabrication techniques often involve costly and intricate processes, limiting their scalability and practical implementation. In contrast, laser-induced graphene (LIG) provides a highly scalable, cost-effective, and direct laser-writing technique for the fabrication of nanostructured graphene-like sheets. LIG enables the rapid and accessible production of customizable substrates without the need for complex processing or expensive precursors. Moreover, its versatility allows for precise control over laser parameters, allowing the fine-tuning of critical physicochemical properties such as electrical conductivity, wettability, and surface roughness. This adaptability makes LIG an attractive platform for engineering graphene-based biomaterials, particularly for neural interfaces, where surface characteristics influence key biological responses, including cell adhesion, proliferation, and differentiation. In this study, we engineered and characterized three distinct LIG substrates with tailored topographies, defined patterns, and controlled physicochemical properties, assessing their stability under biological environments. Systematic analysis of wettability, surface roughness, mechanical and electrical properties revealed that these parameters remain stable under physiological conditions. Furthermore, preliminary biocompatibility assays using neural-like cells demonstrate encouraging results. Notably variations in laserinduced patterning significantly influenced cellular behavior, with specific topographies enhancing adhesion and promoting guided cellular alignment. These findings highlight the critical role of surface architecture in modulating cell responses, reinforcing the potential of these substrates for neuro-biomedical applications. Our work highlights the potential of LIG as a tunable and scalable strategy for the development of next-generation neural interfaces and pave the way for future studies aimed at harnessing LIG’s versatility for next-generation neural interfaces.