Cover
Volume 6, Issue 9 (2026) – 19 articles
Cover Picture: Dielectric barrier discharge (DBD) plasma-coupled photocatalysis offers a promising route for CO2-to-syngas conversion, but rational optimization remains challenging because operating variables are strongly coupled. Here, we develop a small-sample machine-learning framework to predict and optimize a plasma-coupled photocatalytic CO2 conversion system using a Cu-Pd/TiO2 photocatalyst. Using 120 experimental runs, five operating parameters, including discharge power, catalyst dosage, gas flow rate, relative humidity, and light intensity, were evaluated against CO2 conversion, CO yield, and H2 yield. Among the evaluated models, a shared-weight Gradient Boosting Regressor (GBR)-Kernel Ridge Regression (KRR) hybrid model achieved the great predictive performance, with test-set R2 values of 0.947, 0.950, and 0.954 for CO2 conversion, CO yield, and H2 yield, respectively. Permutation importance and SHapley Additive exPlanations (SHAP) analyses identified relative humidity, light intensity, and discharge power as the most influential variables and revealed distinct model-predicted trends across the three outputs. The trained model was further used for constrained optimization under target H2/CO ratios of 1 and 2, followed by experimental validation of the selected operating conditions. Overall, this work establishes a data-driven strategy for interpreting nonlinear plasma-photocatalytic CO2 conversion and provides practical guidance for syngas-ratio regulation under experimentally relevant conditions.
Back Cover
Volume 6, Issue 9 (2026) – 19 articles
Back Cover Picture: Hydrogen production via catalytic decomposition of methane (CDM) offers a CO2-free route for simultaneous hydrogen and solid carbon generation. However, practical implementation remains limited by catalyst deactivation, metal contamination in the carbon product, and inefficient post-reaction purification. In this work, Ni-promoted (La0.75Ca0.25)(Cr0.5Mn0.5)O3-δ (Ni-LCCM) perovskite catalysts prepared via a nitrate-based route were evaluated for CDM. Under thermal CDM at 750 °C without pre-reduction, 050Ni-LCCM achieved a Ni-normalized carbon productivity of up to 11.53 gC·gNi-1. A non-monotonic dependence of intrinsic carbon productivity on Ni loading was observed, with local maxima for 050Ni-LCCM and 150Ni-LCCM. These two experimental compositions were represented by the 2Ni-LCCM and 6Ni-LCCM models in the density functional theory (DFT) calculations. The 2Ni-LCCM and 6Ni-LCCM models showed lower relative rate-limiting activation barriers than those of neighboring configurations. Together with structural characterization, these results suggest that methane decomposition activity is strongly influenced by local Ni configuration, Ni reducibility, and site accessibility rather than by total Ni loading alone. Carbon characterization showed the formation of predominantly nanocrystalline graphitic carbon, including mixed carbon nanostructures such as carbon nanotubes, carbon nanofibers, and carbon nano-onions. Post-reaction purification using 5 M HNO3 reduced the residual inorganic content to 4.20% and 1.41% for carbon products from 050Ni-LCCM and 150Ni-LCCM, respectively, corresponding to thermogravimetric analysis (TGA)-based carbon contents of approximately 95.8% and 98.6%. Overall, the results demonstrate that Ni-LCCM is an effective catalyst system for CDM, in which composition-dependent Ni configurations influence intrinsic carbon productivity while enabling recovery of high-carbon-content solid products after mild acid treatment.
