Electrochemical oxidation of glycerol to glyceric acid using Iridium-Vanadium (IrV) dual atom catalysts on graphene variants: Experimental and computational approach
Source abstract
Abstract
• Efficient synthesis of IrV dual-atom catalysts (DACs) on rGO. • HAADF-STEM analysis confirmed uniform dispersion of Ir and V atoms without agglomeration. • Metal-metal interaction boosts electron transfer, enhancing catalytic activity. • High selectivity (92.8%) and conversion (72.7%) for glycerol oxidation to glyceric acid. • The computational findings were consistent with the experimental data. Iridium-vanadium (IrV) dual-atom catalysts (DACs) supported on reduced graphene oxide (rGO) were synthesized using a modified solvothermal method for efficient glycerol oxidation to glyceric acid. Characterization by X-ray diffraction (XRD), X-ray photoelectron spectroscopy (XPS), Raman spectroscopy, and high-angle annular dark-field scanning transmission electron microscopy (HAADF-STEM) confirmed the successful reduction of graphene oxide and uniform dispersion of Ir and V atoms without agglomeration. XPS analysis indicated predominantly metallic Ir and V states, with minimal surface oxidation. However, the XPS analysis also showed trace amounts of oxidized species (Ir4 + , Ir3 + , V4 + , and V5 + ) were present, likely due to surface oxidation of the electrocatalyst. The IrV/rGO DAC achieved 92 % selectivity and 73 % glycerol conversion within 10,000 s. Density functional theory (DFT) analysis demonstrated the catalyst’s stability, with a binding energy of −6.69 eV and a Bader charge of + 0.58 |e|. Enhanced electronic structure and adsorption/desorption properties provided superior catalytic performance compared to other catalysts. This makes IrV/rGO an effective material for electrocatalytic glycerol oxidation.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Electrocatalysts for Energy Conversion · Supercapacitor Materials and Fabrication · Catalysis for Biomass Conversion
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
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