pH-driven morphological control of Pt-Cu nanocrystals for excellent catalytic activity in methanol oxidation reaction
Source abstract
Abstract
The template sacrifice method is a pivotal strategy for synthesizing noble metal-based nanocrystals with enhanced catalytic properties. A critical limitation of this method lies in the challenges associated with regulating the morphology of the resultant nanocrystals . This study demonstrates that the morphology of PtCu nanocrystals can be effectively modulated by adjusting the pH in the Cu 2 O template sacrifice method. Various PtCu nanocrystals, including PtCu hollow nanoboxes and PtCu nanodendrites of differing sizes, have been successfully synthesized. Mechanistic investigations reveal that reduced pH corresponds to a lower electrode potential of Cu 2 O, which enhances its reducing capacity, thereby intensifying the galvanic replacement reaction with the Pt precursor. The driving force of Kirkendall effect during this process can be systematically manipulated via pH adjustments, leading to the generation of PtCu nanocrystals with diverse morphologies. The synthesized PtCu nanocrystals demonstrate significantly improved methanol oxidation reaction activity compared to commercial Pt/C. Specifically, the PtCu hollow nanoboxes exhibit a methanol oxidation reaction (MOR) activity of 1.93 A mg⁻¹, which is 5.4 times greater than that of the commercial Pt/C. Physical characterizations suggest that the superior catalytic performance of PtCu hollow nanoboxes catalyst is attributed to its increased surface electronic density of Pt resulting from the special hollow and porous morphology. The pH control strategy outlined in this research holds promise for the synthesis of other Cu-based nanoalloys with varying morphologies, and may be applicable to other catalytic systems.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Electrocatalysts for Energy Conversion · Catalytic Processes in Materials Science · Copper-based nanomaterials and applications
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
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