Exploring the effect of carbon support dimensionality on FeN₄-based acetone sensors

This inclusive innovation study presents a computational investigation into the design of biosensors capable of detecting volatile organic compound (VOC) biomarkers. Density functional theory (DFT) calculations were employed to examine the adsorption behavior of acetone, a clinically relevant biomarker, compared to common interfering atmospheric molecules (N₂, CO₂, and H₂O) on FeN₄-doped nanostructures. Three distinct FeN₄-doped nanostructures were considered, including a 0-dimensional fullerene (F-FeN₄), a 1-dimensional carbon nanotube (C-FeN₄), and a 2-dimensional graphene (G-FeN₄). Ab initio molecular dynamics (AIMD) simulations confirmed the thermodynamic stability of all three systems under ambient conditions. Adsorption energy analysis revealed that G-FeN₄ exhibits the strongest interaction with acetone, with a calculated adsorption energy of -0.82 eV. While F-FeN₄ and C-FeN₄ demonstrated slightly superior recovery times and broader detection ranges, G-FeN₄ displayed a pronounced current suppression within the voltage window of 0-2 V, highlighting its superior suitability as an acetone sensor relative to its 0D and 1D counterparts. Moreover, the presence of interfering air molecules N₂, CO₂, and H₂O) further enhances the adsorption energy of acetone, suggesting cooperative effects that may improve sensor selectivity under realistic operating conditions.