Theoretical insight into the catalytic activities of oxygen reduction reaction on transition metal–N4 doped graphene

2018 ◽  
Vol 42 (12) ◽  
pp. 9620-9625 ◽  
Author(s):  
Cong Yin ◽  
Hao Tang ◽  
Kai Li ◽  
Yuan Yuan ◽  
Zhijian Wu
Keyword(s):  
Transition Metal ◽  
Oxygen Reduction Reaction ◽  
Oxygen Reduction ◽  
Reduction Reaction ◽  
Catalytic Activities ◽  
Doped Graphene ◽  
Theoretical Insight ◽  
Insight Into

The ORR (OER) activities are expressed as the function of ΔGads(O) on the M–N4-C composites and the best component can be predicted.

Computational screening of efficient graphene-supported transition metal single atom catalysts toward the oxygen reduction reaction

2020 ◽  
Vol 8 (37) ◽  
pp. 19319-19327 ◽  
Author(s):  
Lei Li ◽  
Rao Huang ◽  
Xinrui Cao ◽  
Yuhua Wen
Keyword(s):  
Transition Metal ◽  
Oxygen Reduction Reaction ◽  
Oxygen Reduction ◽  
Reduction Reaction ◽  
Single Atom ◽  
Nitrogen Doped ◽  
Single Atoms ◽  
Computational Screening ◽  
Nitrogen Doped Graphene ◽  
Doped Graphene

Transition metal single atoms anchored on nitrogen-doped graphene toward the oxygen reduction reaction have been screened.

scholarly journals Probing the activity of transition metal M and heteroatom N4 co-doped in vacancy fullerene (M–N4–C64, M = Fe, Co, and Ni) towards the oxygen reduction reaction by density functional theory

RSC Advances ◽  
2021 ◽  
Vol 11 (5) ◽  
pp. 3174-3182
Author(s):  
Siwei Yang ◽  
Chaoyu Zhao ◽  
Ruxin Qu ◽  
Yaxuan Cheng ◽  
Huiling Liu ◽  
...  
Keyword(s):  
Density Functional Theory ◽  
Transition Metal ◽  
Oxygen Reduction Reaction ◽  
Oxygen Reduction ◽  
Density Functional ◽  
Reduction Reaction ◽  
Functional Theory ◽  
Co Doped

In this study, a novel type oxygen reduction reaction (ORR) electrocatalyst is explored using density functional theory (DFT); the catalyst consists of transition metal M and heteroatom N4 co-doped in vacancy fullerene (M–N4–C64, M = Fe, Co, and Ni).

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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