MnNi2O4-MWCNTs as a nano-electrocatalyst for methanol oxidation reaction

Metal oxides as inexpensive and stable materials in electrochemical processes have been noticed by researchers. In this work, we synthesized a nanocatalyst based on nickel oxide and manganese oxide in the form of binary transition metal oxides (BTMOs), as well as a hybrid of this nanocatalyst with multi-walled carbon nanotubes (MWCNTs) by hydrothermal method. The efficiency of this nanocatalyst was checked for methanol oxidation reaction (MOR). After confirming the successful synthesis of the proposed nanocatalysts with detailed physical characterization and then performing cyclic voltammetry (CV), linear sweep voltammetry (LSV), and electrical impedance spectroscopy (EIS) tests, both MnNi₂O₄ and MnNi₂O₄- MWCNTs nanocatalysts showed relatively good capabilities in the MOR process. The results showed that adding MWCNTs to the nanocatalyst structure, in addition to increasing electrical conductivity, also increases the active surface of the nanocatalyst. Thus MnNi₂O₄- MWCNTs nanocatalyst with an exchange current of 3.74 × 10⁻⁵ mA/cm² and 98% cyclic stability compared to MnNi₂O₄ with an exchange current of 8.61 × 10⁻⁸ mA/cm² and 91% cycle stability, is more efficient in the MOR process. Synthesized nanocatalysts can be cheap, stable, and attractive options for MOR.     

Insights into the effects of Pt and PtOx site for electrocatalytic water gas shift reaction via altering supports and calcinated temperatures

Electrocatalytic water-gas shift reaction (EWGSR) at room temperature and atmospheric pressure is an emerging process for high-pure hydrogen production without an additional H₂ separation procedure. Therefore, developing efficient electrocatalysts of EWGSR is one of the critical factors for its wide applications. Herein, the effects of support and calcinated temperature on the EWGSR performance are highlighted by systematically investigating the Pt/γ-Fe₂O₃, Pt/CeO₂, Pt/TiO₂, and Pt/α-Fe₂O₃ catalysts. The results reveal that the γ-Fe₂O₃ supported Pt catalyst (calcined at 400 °C) exhibits the lowest anodic onset potential and the highest activity compared to these prepared catalysts, and the mass activity is 3.5 times as high as 20% Pt/C. Furthermore, the onset potential for the EWGSR shows a strong correlation with the active O in the amorphous PtOx structures, where the active O atoms can promote the activation of the OH⁻ and reduce the onset potential of the reaction. The significantly enhanced catalytic performance and durability are more responsible for the exposed Pt⁰ and the weak adsorption of CO on the Pt/γ-Fe₂O₃ catalyst. This study provides a new and promising route for designing excellent Pt catalysts for EWGSR in the hope that it can be helpful to the scholars in this orientation.     

A comparison of deep learning models applied to Water Gas Shift catalysts for hydrogen purification

As a consequence of the renewed interest in the Water Gas Shift reaction a great volume of information was produced. Since a traditional method like the reaction kinetics or mechanism are not capable of dealing with all this information, a deep learning model is convenient to explore to make useful predictions of catalysts performance. In the present work some novel features were included, a measure of reducibility, the crystal size, and the catalysts cost. The Principal Component Analysis indicated that the chosen features of the dataset were not redundant and the suggested novel features strongly influenced the most important components. A Random Forest Regressor was optimized and then trained in order to obtain the feature importance. An Artificial Neural Network was employed after a Grid Search optimization. This model was fed with different sizes of datasets in order to determine its effect on the accuracy of the predictions.