High-surface-area ZnO nanobowls with Pd nanoparticles: Unraveling the dual role of morphology and electronic modulation for enhanced CH4 sensing

Rapid and reliable detection of methane (CH 4 ), a key clean energy source and potent greenhouse gas, is of great importance for industrial safety and environmental protection. In this work, a high-performance CH 4 gas sensor is developed based on hollow ZnO nanobowls decorated with Pd nanoparticles, synthesized via a template-assisted strategy followed by mild impregnation. The hollow nanobowl architecture features a uniform particle size (~350 nm), ordered mesopores (~3.4 nm), and a high specific surface area of 104.06 m 2  g −1 , which effectively enhances gas diffusion and surface reaction kinetics without relying on complex heterojunctions or bimetallic systems. The introduction of Pd increases the oxygen vacancy content from 29.41% to 41.48%, provides additional active sites, and facilitates electron transport. Moreover, Pd catalyzes O 2 dissociation via a spillover effect and lowers the activation energy for C H bond cleavage in CH 4 , while the work function difference between Pd and ZnO induces a Schottky barrier at the interface, dynamically modulating the sensor conductivity. Benefiting from the synergistic effects of morphological regulation and interfacial electronic modulation, the sensor exhibits a high response of 67% to 1000 ppm CH₄ at an operating temperature of 253.5 °C, along with an ultrafast response/recovery time of 2/10.5 s and a wide detection range of 20–10,000 ppm. In addition, the sensor demonstrates good selectivity and excellent long-term stability, with less than 10% signal fluctuation over 30 days. Building upon the reliable concentration discrimination validated by CNN-1D models, an LSTM-based short-term dynamic prediction framework was further introduced to evaluate temporal early-warning performance under rapid methane concentration variations. The results demonstrate that the proposed sensor–algorithm integration can effectively capture dynamic concentration evolution and accurately predict the 5000 ppm alarm threshold across multiple diffusion rates, highlighting its potential for early-warning applications in confined spaces.