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• W4321170031 abstract "Abstract The synthesis of syngas (H 2 : CO=2) via catalytic partial oxidation of methane (CPOM) is studied over noble metal doped Ni−CeO 2 bimetallic catalysts for CPOM reaction. The catalysts were synthesized via a controlled deposition approach and were characterized using XRD, BET‐surface area analysis, H 2 ‐TPR, TEM, Raman and TGA analysis. The catalysts were experimentally and computationally studied for their activity, selectivity, and long‐term stability. Although the pure 5Ni/CeO2 catalyst showed high initial activity (∼90%) of CH 4 conversion, it rapidly deactivates around 20% of its initial activity within 140 hours of TOS. Doping of Ni/CeO 2 catalyst with noble metal was found to be coke resistant with the best‐performing Ni−Pt/CeO 2 catalyst showed ∼95% methane conversion with >90% selectivity at a temperature of 800 °C, having exceptional stability for about 300 hours of time‐on‐stream (TOS). DFT studies were performed to calculate the activation barrier for the C−H activation of methane over the Ni, Ni 3 Pt, Ni 3 Pd, and Ni 3 Ru (111) surfaces showed nearly equal activation energy over all the studied surfaces. DFT studies showed high coke formation tendency of the pure Ni (111) having a very small C−C coupling activation barrier (14.2 kJ/mol). In contrast, the Ni 3 Pt, Ni 3 Pd, and Ni 3 Ru (111) surfaces show appreciably higher C−C coupling activation barrier (∼70 kJ/mol) and hence are more resistant against coke formation as observed in the experiments. The combined experimental and DFT study showed Ni−Pt/CeO 2 as a promising CPOM catalyst for producing syngas with high conversion, selectivity and long‐term stability suited for future industrial applications." @default.
• W4321170031 created "2023-02-18" @default.
• W4321170031 creator A5003911688 @default.
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• W4321170031 date "2023-03-02" @default.
• W4321170031 modified "2023-10-06" @default.
• W4321170031 title "Improving the Coke Resistance of Ni‐Ceria Catalysts for Partial Oxidation of Methane to Syngas: Experimental and Computational Study" @default.
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• W4321170031 doi "https://doi.org/10.1002/asia.202201298" @default.
• W4321170031 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/36797847" @default.
• W4321170031 hasPublicationYear "2023" @default.
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