A Tailored Bifunctional Electrocatalyst: Boosting Oxygen Reduction/Evolution Catalysis via Electron Transfer Between N-Doped Graphene and Perovskite Oxides

Small ◽  
2018 ◽  
Vol 14 (48) ◽  
pp. 1802767 ◽  
Author(s):  
Yunfei Bu ◽  
Gyutae Nam ◽  
Seona Kim ◽  
Keunsu Choi ◽  
Qin Zhong ◽  
...  
Keyword(s):  
Electron Transfer ◽  
Oxygen Reduction ◽  
Perovskite Oxides ◽  
Bifunctional Electrocatalyst ◽  
Doped Graphene

scholarly journals Cobalt and Nitrogen Co-Doped Graphene-Carbon Nanotube Aerogel as an Efficient Bifunctional Electrocatalyst for Oxygen Reduction and Evolution Reactions

Catalysts ◽  
2018 ◽  
Vol 8 (7) ◽  
pp. 275 ◽  
Author(s):  
Xiaochang Qiao ◽  
Jutao Jin ◽  
Hongbo Fan ◽  
Lifeng Cui ◽  
Shan Ji ◽  
...  
Keyword(s):  
Carbon Nanotube ◽  
Oxygen Reduction ◽  
Bifunctional Electrocatalyst ◽  
Doped Graphene ◽  
Co Doped

scholarly journals Heteroatom (Nitrogen/Sulfur)-Doped Graphene as an Efficient Electrocatalyst for Oxygen Reduction and Evolution Reactions

Catalysts ◽  
2018 ◽  
Vol 8 (10) ◽  
pp. 475 ◽  
Author(s):  
Jian Zhang ◽  
Jia Wang ◽  
Zexing Wu ◽  
Shuai Wang ◽  
Yumin Wu ◽  
...  
Keyword(s):  
Oxygen Reduction ◽  
Carbon Nanomaterials ◽  
Carbon Materials ◽  
Electrochemical Reactions ◽  
Intrinsic Structure ◽  
Bifunctional Electrocatalyst ◽  
Structure And Property ◽  
Energy Conversion And Storage ◽  
Doped Graphene ◽  
And Storage

Carbon nanomaterials are potential materials with their intrinsic structure and property in energy conversion and storage. As the electrocatalysts, graphene is more remarkable in electrochemical reactions. Additionally, heteroatoms doping with metal-free materials can obtain unique structure and demonstrate excellent electrocatalytic performance. In this work, we proposed a facile method to prepare bifunctional electrocatalyst which was constructed by nitrogen, sulfur doped graphene (NSG), which demonstrate superior properties with high activity and excellent durability compared with Pt/C and IrO2 for oxygen reduction (OR) and oxygen evolution (OE) reactions. Accordingly, these phenomena are closely related to the synergistic effect of doping with nitrogen and sulfur by rationally regulating the polarity of carbon in graphene. The current work expands the method towards carbon materials with heteroatom dopants for commercialization in energy-related reactions.

scholarly journals Eco-Friendly Nitrogen-Doped Graphene Preparation and Design for the Oxygen Reduction Reaction

Molecules ◽  
2021 ◽  
Vol 26 (13) ◽  
pp. 3858
Author(s):  
Monica Dan ◽  
Adriana Vulcu ◽  
Sebastian A. Porav ◽  
Cristian Leostean ◽  
Gheorghe Borodi ◽  
...  
Keyword(s):  
Oxygen Reduction Reaction ◽  
Oxygen Reduction ◽  
Rotating Disk ◽  
Reduction Reaction ◽  
Machine Learning Algorithms ◽  
Rotating Disk Electrode ◽  
X Ray ◽  
Synthesis Conditions ◽  
Characterization Techniques ◽  
Doped Graphene

Four N-doped graphene materials with a nitrogen content ranging from 8.34 to 13.1 wt.% are prepared by the ball milling method. This method represents an eco-friendly mechanochemical process that can be easily adapted for industrial-scale productivity and allows both the exfoliation of graphite and the synthesis of large quantities of functionalized graphene. These materials are characterized by transmission and scanning electron microscopy, thermogravimetry measurements, X-ray powder diffraction, X-ray photoelectron and Raman spectroscopy, and then, are tested towards the oxygen reduction reaction by cyclic voltammetry and rotating disk electrode methods. Their responses towards ORR are analysed in correlation with their properties and use for the best ORR catalyst identification. However, even though the mechanochemical procedure and the characterization techniques are clean and green methods (i.e., water is the only solvent used for these syntheses and investigations), they are time consuming and, generally, a low number of materials can be prepared, characterized and tested. In order to eliminate some of these limitations, the use of regression learner and reverse engineering methods are proposed for facilitating the optimization of the synthesis conditions and the materials’ design. Thus, the machine learning algorithms are applied to data containing the synthesis parameters, the results obtained from different characterization techniques and the materials response towards ORR to quickly provide predictions that allow the best synthesis conditions or the best electrocatalysts’ identification.

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