Data for: Neurochemical Profiling in Urine: Multiplexed Detection of Dopamine and Serotonin Using ML-Integrated Laser-Induced Graphene Biosensors

Simultaneous detection of dopamine (DA) and serotonin (SER) is critical for monitoring neurological health, but remains challenging in urine due to matrix complexity, low concentrations, and overlapping oxidation potentials. We developed a laser-induced graphene (LIG)-based electrochemical sensor optimized for multiplexed DA and SER detection in undiluted human urine. Optimization of laser passes and electrode size improved sensor performance, with 2-pass LIG increasing electroactive surface area by ~77%, reducing LOD by ~1.8× in differential pulse voltammetry (DPV) and ~4.5× in square wave voltammetry (SWV). Nafion coating further suppressed interferents and improved LOD by almost 10-fold compared to uncoated electrodes. While sub-micromolar LODs were achieved for both analytes using single-mode voltammetry, to better meet clinical needs in a wider concentration range, we integrated multimodal voltammetry with machine learning (ML) by training models on features from DPV and SWV data. ML analysis reduced LODs to 5 nM for both DA and SER, representing >60-fold and >120-fold improvements over single-mode voltammetry. This low-cost, ML-powered sensor enables highly sensitive and stable neurochemical monitoring, offering strong potential for neuroscience research and clinical applications.