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Exploring the Fundamental Roles of Functionalized Ligands in Platinum@Metal–Organic Framework Catalysts

Wenlei Zhang, Wenlei Zhang, Wenlan Ji, Linjie Li, Peishan Qin, Islam E. Khalil, Zhida Gu, Peng Wang, Hongfeng Li, Yun Fan, Zhen Ren, Yu Shen, Weina Zhang, Weina Zhang, Yu Fu, Fengwei Huo

2020ACS Applied Materials & Interfaces39 citationsDOI

Abstract

The metal nodes, functionalized ligands, and uniform channels of metal–organic frameworks (MOFs) are typically utilized to regulate the catalytic properties of metal nanoparticles (MNPs). However, though the ligand functionalization could impact the properties of the metal nodes and channels, which might further regulate the catalytic activity and selectivity of MNPs, related research in the design of MNP/MOF catalysts was usually neglected. Herein, we synthesized a series of Pt@UiO-66 composites (Pt@UiO-66-NH2, Pt@UiO-66-SO3H, and Pt@UiO-66) with slightly different organic ligands, which enhanced steric hindrance and contributed to multipathway electron transfer in selective hydrogenation of linear citronellal. The selectivity toward citronellol was gradually improved along with the increased size of functional groups (hydrogen, amino groups, and sulfo groups) on organic ligands, which enhanced steric hindrance provided by channels. In addition, the X-ray photoelectron spectroscopy measurements also revealed that the electronic state of Pt NPs was regulated through multipathway electron transfer from Pt NPs to metal nodes, between organic ligands and Pt NPs/metal nodes. Our research proved that the ligand functionalization altered physiochemical properties of the channels and metal nodes, further together managing the catalytic performance of Pt NPs through enhanced steric hindrance and multi-pathway electron transfer.

Topics & Concepts

Materials scienceCatalysisPlatinumMetal-organic frameworkMetalNanotechnologyChemical engineeringOrganic chemistryMetallurgyAdsorptionChemistryEngineeringMetal-Organic Frameworks: Synthesis and ApplicationsNanocluster Synthesis and ApplicationsMachine Learning in Materials Science
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