A Microfluidic Platform Based on Laser-Induced Graphene Electrodes and Machine Learning for Real-Time Skeletal Muscle Analysis

Abstract

Laser-induced graphene (LIG) is a novel, low-cost material with excellent electrical properties that has recently gained increasing attention in bioengineering for both sensing and actuation applications. However, its integration into light microscopy-compatible platforms for in vitro biological studies, such as lab-on-chip systems, is often hindered by complex and potentially invasive techniques for transferring it into final substrates. In this work, we propose a novel approach for the direct fabrication of LIG on polystyrene substrates using commercial polyimide adhesive tape, CO2 laser irradiation, and a simple peel-off process, enabling the production of fully in vitro-compatible devices. The material is comprehensively characterized through scanning electron microscopy (SEM), Raman spectroscopy, electrical resistivity measurements, finite element method (FEM) simulations, and machine learning based analysis. The resulting LIG electrodes are integrated into a muscle-on-chip microfluidic device, where they successfully generated electrical stimuli, inducing contractions in differentiated myotubes. These contractions are monitored by time-lapse microscopy and quantitatively assessed using video analysis, demonstrating the tissue response in phase with electrical stimulation.