Methane Decomposition for Enhanced Hydrogen Production: Noble Metal Exsolution from Layered Perovskite Oxides

Abstract

This thesis seeks a review on literature and synthesizes effective catalysts for the catalytic decomposition of methane (CDM) to produce hydrogen and high-value carbon, verifying trends in exsolution method and evaluating methane conversion, and hydrogen yield. Based on thorough screening of literature, double-layered perovskites catalysts were synthesized and analyzed using a range of advanced analytical characterizations techniques using X-ray diffraction (XRD), Rietveld refinement, scanning electron microscopy (SEM), transmission electron microscopy (TEM), energy dispersive X-ray spectroscopy (EDX), and X-ray photoelectron spectroscopy (XPS), and surface area and porosity (BET) to verify the structure, morphology, and element compositions of the catalysts. Ruthenium metal loading of 1.85 wt% was employed in successfully synthesized double-layered perovskites PrBaMn2O5+δ to study the effect of finely dispersed nanoparticles on surface covering exsolution in-situ growth technique. The substitution of the B-site with ruthenium confirms the formation of double-layered perovskites PrBaMn1.925Ru0.075O5+δ with a notable expansion in the c-axis using Rietveld refinement analysis. The nanoparticles on the surface demonstrate successful in-situ growth of exsolved particles, expected to enhance catalytic activity by providing additional active sites. The catalysts demonstrate high crystallinity, well-developed perovskite structure, and confirmed exsolution of noble metal ruthenium nanoparticles. High-order metal cation valence state (Mn4+, Mn3+) favors the absorption and emission of oxygen within the Pr (A-site) layer with vacancies. Catalytic decomposition of methane (CDM) experiment was conducted at 600ºC, with gas space velocity of 48000 mL/h gcat. Despite high crystallinity and successful exsolution of ruthenium nanoparticles, methane conversion for PrBaMn2O5+δ and PrBaMn1.925Ru0.075O5+δ catalysts did not demonstrate the anticipated performance in CDM, suggesting potential areas for further optimization and investigation.

Date of Award	6 Dec 2024
Original language	American English
Supervisor	Sivaprakash Sengodan (Supervisor)