γ-Graphyne as a Functional 2D Nanoarchitectonics for Room-Temperature Chemiresistive–Potentiometric Sensing Interfaces

The development of highly selective gas sensors operating at room temperature with detection capabilities in the parts-per-billion (ppb) range is one of fundamental and technological interest across diverse fields. Conventional sensor arrays often suffer from signal instability, large device footprints, and high fabrication costs. The emergence of two-dimensional (2D) materials has enabled new paradigms in chemiresistive sensing, leveraging their quantum confinement, high surface-to-volume ratio, and tunable electronic structure. In this study, we present, for the first time, a high-performance chemiresistive sensor based on chemically exfoliated γ-graphyne, a carbon allotrope with sp-sp²-hybridized bonding and an extended π-conjugated system. The material's cross-linked layered structure introduces spatially varying local potential gradients, which enhance charge carrier modulation upon gas molecule adsorption. First-principles density functional theory (DFT) calculations were employed to optimize the graphyne synthesis pathway and to model adsorption energies and charge transfer dynamics. Real-time detection of NO₂ gas at room temperature demonstrates exceptional sensor performance, with a measured response of 1.05 at 25 ppb and an estimated detection limit as low as 0.45 ppb. The device exhibits rapid response (53 s) and recovery (185 s) times, governed by gas-adsorbate interactions and carrier scattering mechanisms. Theoretical models reveal that adsorption of NO₂ induces significant modulation of the local density of states and carrier concentration in graphyne, enhancing its chemiresistive response. Furthermore, we integrated machine learning algorithms with the experimental sensor output to establish a robust gas classification framework. Classifiers trained on sensor data exhibit 100% accuracy across varying concentrations (15-100 ppb) of NO₂ and high selectivity for other interfering gases, validating the discriminatory power of the sensor. This synergistic approach combining quantum mechanical modeling, charge transport physics, and data-driven learning algorithms opens new avenues for designing next-generation miniaturized gas sensors with ultrahigh sensitivity and selectivity.