Selective Detection of Volatile Organic Compounds on Ti2CO2 and Zr2CO2 MXenes for Noninvasive Disease Diagnostics

Driven by the increasing demand for noninvasive diagnostics of chronic conditions, we investigate Ti2CO2 and Zr2CO2 MXene monolayers as potential sensing materials for detecting breath-based volatile organic compounds (VOCs). Using first-principles density functional theory (DFT) in combination with nonequilibrium Green's function (NEGF) formalism, the adsorption behavior and transport characteristics toward clinically relevant VOCs, including 2-pentanone (C5H10O), p-xylene (C8H10), limonene (C10H16), hexanal (C6H12O), and dimethyl sulfide (C2H6S) are systematically explored. The calculated adsorption energies (Eads) reveal predominantly physisorption interactions governed by dispersion and electrostatic forces, ranging from -0.64 to -1.33 eV (Ti2CO2) and -0.69 to -1.21 eV (Zr2CO2). Among the investigated VOCs, limonene exhibits the strongest adsorption on both Ti2CO2 (Eads = -1.33 eV) and Zr2CO2 (Eads = -1.21 eV) monolayers. Meanwhile, 2-pentanone and hexanal display comparatively moderate interactions, while dimethyl sulfide shows the weakest affinity. Notably, hexanal on Zr2CO2 exhibits partial chemisorption through weak Zr-O bond formation. Bader charge, effective band structure, and density of states (DOS) analysis reveal noticeable interfacial charge redistribution and VOC-induced states near the Fermi level. Substantial variation in work function and electrostatic potential, especially for limonene and p-xylene, indicates distinct surface perturbation. Langmuir adsorption model-based thermodynamic analysis predicts excellent room-temperature sensing performance toward limonene at sub-ppb concentrations under varying pressure conditions. Furthermore, NEGF-based transport calculations reveal strong bias-dependent sensitivity, with a maximum response of 87% for limonene on Ti2CO2 and 89.1% for p-xylene on Zr2CO2. These findings establish Ti2CO2 and Zr2CO2 MXenes as promising candidates for selective trace-level VOC sensing in next-generation breath diagnostic sensors.