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PANI-ZnO/Pt-Ru Electrocatalyst for Methanol Oxidation: Synthesis, Characterization, Electrocatalytic Performance and Artificial Neural Network Modeling
Type Article
PANI-ZnO/Pt–Ru electrocatalystNanocompositesMethanol oxidationCyclic voltammetryArtificial neural network
In methanol oxidation, fabrication of novel electrocatalysts consisted of a lower loading of precious metals like platinum and ruthenium as well as possessing great electrocatalytic performance is of interest. One approach is to use an appropriate substrate. In this study, Polyaniline-Zinc oxide (PANI-ZnO) nanocomposites were prepared by polymerization method. Then, PANI-ZnO/Pt–Ru electrocatalyst was fabricated using the synthesized nanocomposites as a platinum and ruthenium (Pt–Ru) substrate. Field Emission Scanning Electron Microscopy (FESEM) was applied to study the morphology of the samples, while X-ray diffraction (XRD) analysis and Fourier Transform Infrared Spectroscopy (FTIR) were used to evaluate their phase structure and chemical groups, respectively. Electrochemical behaviors of fabricated PANI-ZnO/Pt–Ru electrocatalyst in the oxidation of methanol were specified via linear sweep voltammetry (LSV), cyclic voltammetry (CV), and chronoamperometry (CA) methods. The current density of PANI-ZnO/Pt–Ru was 91.14 mA/cm2, which was about 15% greater than the current density of Pt/Ru. This reflects the high catalytic performance of the electrocatalyst in the presence of nanocomposites. The electrochemically active surface area of the PANI-ZnO/Pt–Ru and Pt–Ru electrocatalysts are 37.72 m2/g and 21.22 m2/g, respectively. This increase is due to the specific morphology in the structure of nanocomposites. In general, these findings exhibited that the presence of PANI-ZnO support could enhance the catalytic performance and stability of Pt–Ru electrocatalysts. Besides, artificial neural network (ANN) model was used to predict the rate-determining reaction in methanol oxidation at different electrodes and the results indicated the efficiency of the model for forecasting.
Researchers Atefe Niknejad (First researcher) , Nima Nabiyan (Second researcher) , mohammad soleimani lashkenari (Third researcher) , Mohsen Ghorbani (Fourth researcher)