Govind Porwal, Haseena K V, S. Sreedhala, Tuhin Suvra Khan, M. Ali Haider* and C. P. Vinod*,
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Deciphering Particle Morphology Effects in Cinnamaldehyde Hydrogenation over Palladium Nanostructures
Chemoselective hydrogenation of α-β unsaturated hydrocarbons is a widely studied chemical transformation. In this study, hydrogenation of cinnamaldehyde (CAL) to the corresponding products, viz hydrocinnamaldehyde (HCAL) and hydrocinnamyl alcohol (HCOL) and cinnamyl alcohol (COL), over the different exposed facets of a Pd-based catalyst is studied. The Pd octahedra having (111) facet shows 90% selectivity toward HCAL with 100% conversion in a short duration (45 min). Pd cube having (100) facet shows selectivity (55%) toward HCOL, while Pd spheres show initial selectivity toward HCAL but to HCOL over a prolonged reaction period. The experimental results are corroborated by density functional theory (DFT) calculations, wherein we observe a lower activation barrier Ea = 51 kJ/mol for HCAL formation on the Pd(111) surface. However, an alternative route through the COL intermediate is more prominent on the Pd(100) surface.
期刊介绍：
）ACS Engineering Au is an open access journal that reports significant advances in chemical engineering applied chemistry and energy covering fundamentals processes and products. The journal's broad scope includes experimental theoretical mathematical computational chemical and physical research from academic and industrial settings. Short letters comprehensive articles reviews and perspectives are welcome on topics that include:Fundamental research in such areas as thermodynamics transport phenomena (flow mixing mass & heat transfer) chemical reaction kinetics and engineering catalysis separations interfacial phenomena and materialsProcess design development and intensification (e.g. process technologies for chemicals and materials synthesis and design methods process intensification multiphase reactors scale-up systems analysis process control data correlation schemes modeling machine learning Artificial Intelligence)Product research and development involving chemical and engineering aspects (e.g. catalysts plastics elastomers fibers adhesives coatings paper membranes lubricants ceramics aerosols fluidic devices intensified process equipment)Energy and fuels (e.g. pre-treatment processing and utilization of renewable energy resources; processing and utilization of fuels; properties and structure or molecular composition of both raw fuels and refined products; fuel cells hydrogen batteries; photochemical fuel and energy production; decarbonization; electrification; microwave; cavitation)Measurement techniques computational models and data on thermo-physical thermodynamic and transport properties of materials and phase equilibrium behaviorNew methods models and tools (e.g. real-time data analytics multi-scale models physics informed machine learning models machine learning enhanced physics-based models soft sensors high-performance computing)
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