Proton exchange membrane fuel cells are pivotal to the global energy transition, however, their catalysts exhibit high sensitivity to CO poisoning. CO preferential oxidation (CO-PROX) serves as the core technology for hydrogen purification, and honeycomb ceramics are ideal supports for CO-PROX catalysts. However, raw cordierite honeycomb ceramics (2MgO·2Al₂O₃·5SiO₂) have drawbacks, including a low specific surface area and poor coating adhesion, which limit catalytic performance. Oriented toward cultivating scientific thinking in teaching practice, this study investigates how pretreatment of honeycomb ceramic supports affects catalytic performance in CO-PROX under hydrogen-rich conditions. It aims to enhance support performance through pretreatment optimization and establish an experimental teaching paradigm that progresses from single-factor to multiparameter optimization.

Single-factor experiments were first conducted to screen the reasonable operating ranges of key parameters as a basis for systematic optimization of the pretreatment process. Employing the coating loading rate and catalytic activity (correlated with subsequent T₅₀/T₉₀ indicators) as evaluation criteria, this study investigated the independent effects of acid treatment time (1–3 h), nitric acid concentration (1–3 mol/L), calcination temperature (300–500 ℃), and calcination time (1–3 h). This step excluded support structure damage and ineffective modifications caused by excessive parameter values, and the study then determined the effective range for subsequent multifactor optimization. Based on the results, a response surface methodology (RSM) model was constructed using a four-variable central composite rotatable design. A total of 30 experiments were designed, comprising 16 full-factor points covering different level combinations of the 4 parameters, 8 axial points to expand the response at the parameter boundaries, and 6 center repeat points to evaluate experimental errors. The temperatures at which CO conversion reached 50% (T₅₀) and 90% (T₉₀) were used as response values. The RSM model’s visual analysis function enabled intuitive identification of parameter interactions and facilitated determination of the parameter combination that minimized T₅₀ and T₉₀ to optimal levels. The model fitting effect was verified to ensure consistency between the experimental data and the predicted results. Finally, the pretreatment process parameters were systematically optimized and verified, and model fitting was used to analyze synergistic effects between acid treatment time, acid concentration, calcination temperature, and calcination time to determine the optimal process parameters.

The single-factor experiments revealed that treating the supports with 1 mol/L nitric acid for 2–3 h effectively optimized their specific surface area and surface roughness, thereby improving coating loading rate. Additionally, calcination at 400 ℃ for 1 h enhanced the pore structure and modified the surface chemical state. The RSM-based model demonstrated strong agreement between predicted and experimental values. The optimal process parameters were identified as a 2.5 h treatment with 1 mol/L nitric acid, followed by calcination at 400 ℃ for 1 h, which significantly enhanced catalytic activity. The analysis of the RSM model revealed that acid treatment time, acid concentration, and calcination temperature exhibit notable synergistic effects on catalytic performance, whereas calcination time shows negligible interactions and can thus be optimized independently.

This study offers a reference for process development in catalytic chemical systems and presents an instructional framework to enhance students’ capabilities in multifactor coupling analysis.