Controlled synthesis of MOF-derived hollow and yolk–shell nanocages for improved water oxidation and selective ethylene glycol reformation
Source abstract
Abstract
Delicately designed metal–organic framework (MOF)-derived nanostructured electrocatalysts are essential for improving the reaction kinetics of the oxygen evolution reaction and tuning the selectivity of small organic molecule oxidation reactions. Herein, novel oxalate-modified hollow CoFe-based layered double hydroxide nanocages (h-CoFe-LDH NCs) and yolk–shell [email protected] nanocages ([email protected] NCs) are developed through an etching–doping reconstruction strategy from a Co-based MOF precursor (ZIF-67). The distinctive nanostructures, along with the incorporation of the secondary metal element and intercalated oxalate groups, enable h-CoFe-LDH NCs and [email protected] NCs to expose more active sites with high intrinsic activity. The resultant h-CoFe-LDH NCs exhibit outstanding OER activity with an overpotential of only 278 mV to deliver a current density of 50 mA cm−2. Additionally, controlling the reconstruction degree enables the formation of [email protected] NCs with a yolk–shell nanocage nanostructure, which show outstanding electrocatalytic performance for the selective ethylene glycol oxidation reaction (EGOR) toward formate, with a Faradaic efficiency of up to 91%. Consequently, a hybrid water electrolysis system integrating the EGOR and the hydrogen evolution reaction using Pt/C||[email protected] NCs is explored for energy-saving hydrogen production, requiring a cell voltage 127 mV lower than water electrolysis to achieve a current density of 50 mA cm−2. This work demonstrates a feasible way to design advanced MOF-derived electrocatalysts toward enhanced electrocatalytic reactions.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Electrocatalysts for Energy Conversion · Advanced battery technologies research · Fuel Cells and Related Materials
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
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