(2026) Journal of Analytical and Applied Pyrolysis_Promotional effect of CO₂ on low-carbon syngas production from corncob pyrolysis

Kim J.-H.; Moon G.; Lee D.; Kwon E.E.

(Elsevier B.V.) Journal of Analytical and Applied Pyrolysis ISSN: 1652370 Vol.199 Issue. Article No.108053 DOI: 10.1016/j.jaap.2026.108053

The demand for crops to ensure food security has led to the substantial generation of crop residues. Nevertheless, conventional management of crop residues through composting and incineration raises environmental concerns, such as air pollutant emission. To address these concerns, this study explored pyrolysis of corncob, and corncob was selected as a widely available crop residue. CO₂ was employed as a reactant to modify the pyrolysis mechanisms of corncob. The use of CO₂ enhanced syngas production, particularly CO, through the homogeneous interaction (HI) involving CO₂ and corncob-derived thermolysates. The oxidative functional role of CO₂ promoted the oxidation of thermolysates, with a simultaneous reduction of CO₂ into CO. The use of a Ni/Al₂O₃ catalyst with thermal energy at 700 ℃ promoted the oxidative functional role of CO₂, thereby further improving syngas production while using CO₂ as oxidative agent. Varying the CO₂ loading enabled to control the H₂/CO molar ratio by proportionally enhancing CO₂-involved HI. Sustainability assessment revealed that CO₂-mediated catalytic pyrolysis of corncob yielded 6.10 times more energy as syngas than conventional pyrolysis, while achieving net-negative CO₂ emissions, thereby resulting in the production of H₂ and CO as low-carbon syngas. Thus, CO₂-mediated catalytic pyrolysis of corncob showed a potential to produce sufficient energy to operate the corn industry while offsetting its CO₂ emissions. These findings suggest that CO₂-mediated catalytic pyrolysis serves as a pathway for valorizing crop residues into low-carbon syngas while simultaneously reducing the CO₂ emissions. © 2026 Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies.

This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korean Government (MSIT) (No. RS\u22122023-NR077231 ).