Global warming, driven by greenhouse gases like CH
4 and CO
2, necessitates efficient catalytic conversion to syngas. Herein, Ni containing different molecular sieve nanomaterials are investigated for dry reforming of methane (DRM). The reduced catalysts are characterized by surface area porosity,
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Global warming, driven by greenhouse gases like CH
4 and CO
2, necessitates efficient catalytic conversion to syngas. Herein, Ni containing different molecular sieve nanomaterials are investigated for dry reforming of methane (DRM). The reduced catalysts are characterized by surface area porosity, X-ray diffraction, Raman infrared spectroscopy, CO
2 temperature-programmed desorption techniques, and transmission electron microscopy. The active sites over each molecular sieve remain stable under oxidizing gas CO
2 during DRM. The reduced 5Ni/CBV10A catalyst, characterized by the lowest silica–alumina ratio, smallest surface area and pore volume, and narrow 8-ring connecting channels, generated the maximum number of active sites on its outer surface. In contrast, the reduced-5Ni/CBV3024E catalyst, with the highest silica–alumina ratio, more than double the surface area and pore volume, 12-ring sinusoidal porous channels, and smallest Ni crystallite, produced the highest H
2 output (44%) after 300 min of operation at 700 °C, with a CH
4:CO
2 = 1:1, P = 1 atom, gas hour space velocity (GHSV) = 42 L gcat
−1 h
−1. This performance was achieved despite having 25% fewer initial active sites, suggesting that a larger fraction of these sites is stabilized within the pore channels, leading to sustained catalytic activity. Using central composite design and response surface methodology, we successfully optimized the process conditions for the 5Ni/CBV3024E catalyst. The optimized conditions yielded a desirable H
2 to CO ratio of 1.00, with a H
2 yield of 91.92% and a CO yield of 89.16%, indicating high efficiency in gas production. The experimental results closely aligned with the predicted values, demonstrating the effectiveness of the optimization approach.
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