Computational insights into Li cluster-based gas sensors

Lithium clusters have emerged as promising candidates for gas sensing applications due to their tunable electronic properties and high reactivity. In this study, we systematically investigate the adsorption behaviour of CO, CO₂, CS₂, SO₂, NO, NO₂ H₂S, and NH₃ on Li clusters using Density Functional Theory (DFT). Structural and electronic analysis reveals that increasing cluster size enhances stability through stronger metallic bonding, while odd-numbered clusters exhibit spin polarization, influencing their chemical activity. Adsorption energy calculations confirm that CO, CO₂, H₂S, and NH₃ interact within the optimal energy range (0.2-0.7 eV), ensuring reversible and selective sensing. Sensitivity analysis identifies NH₃ and H₂S as the most detectable gases due to their strong charge redistribution effects. Dipole moment variations correlate with adsorption strength, further reinforcing the role of electrostatic interactions in gas detection. Density of States (DOS) and Reduced Density Gradient (RDG) analyses highlight significant electronic modifications upon gas adsorption, confirming charge transfer and interaction mechanisms. Li clusters demonstrate best performance in selectivity, adsorption energy, and recovery time for CO, CO₂, H₂S, and NH₃, making them highly promising candidates for gas sensing applications. However, competitive adsorption from H₂O, O₂, and O₂ in humid or oxygen-rich environments present challenges for real-world applications. These findings provide key insights into the gas sensing capabilities of Li clusters and pave the way for their optimization in practical sensor development.