<p>Metal-organic frameworks (MOFs) possess high sensitivity as chemiresistive gas sensing materials at room temperature (RT), while their applications are severely hampered by the issues of slow recovery and poor recyclability. To address these challenges, a strategy integrating DFT calculation with experimental synthesis is developed to identify and remove non-essential active units with excessive interactions to gas molecules, thereby enhancing sensing recovery and recyclability. In a case study, DFT calculations reveal that one-dimensional (1D) {Cu₄I₃}_<i>n</i>^<i>n</i>+ chains in a K-MOF, K-CuI-K-INA (HINA = isonicotinic acid), exhibit strong affinity to NO₂ while 2D {Cu₄I₅}_<i>n</i>^<i>n</i>- layers show moderate interactions. Thus, by modulating the structure features of CuI motifs in M-CuI-K-INA (M = Na⁺, K⁺, and Cs⁺, HINA = isonicotinic acid) type of MOFs, their NO₂ sensing recovering and recycling performances are consequently optimized. Particularly, Cs-CuI-K-INA with solely 2D {Cu₄I₅}_<i>n</i>^<i>n</i>- layers possesses the best sensing recovery and recycle abilities, meanwhile maintains the excellent sensitivity and selectivity among these RT NO₂ sensing materials presented in the current study. This approach paves the way for chemiresistive sensing materials with improved recoverability and recyclability while retaining high sensitivity and selectivity.</p>