Detailed Information

Cited 55 time in webofscience Cited 59 time in scopus
Metadata Downloads

Rational Design of Graphene Derivatives for Electrochemical Reduction of Nitrogen to Ammonia

Authors
Majumder, MandiraSaini, HaneeshDedek, IvanSchneemann, AndreasChodankar, Nilesh R.Ramarao, ViswanathaSantosh, Mysore SridharNanjundan, Ashok KumarKment, StepanDubal, DeepakOtyepka, MichalZboril, RadekJayaramulu, Kolleboyina
Issue Date
23-Nov-2021
Publisher
AMER CHEMICAL SOC
Keywords
graphene; nitrogen reduction reaction (NRR); electrocatalyst; defects; doping; hybrid; graphene derivative; machine learning
Citation
ACS NANO, v.15, no.11, pp 17275 - 17298
Pages
24
Indexed
SCIE
SCOPUS
Journal Title
ACS NANO
Volume
15
Number
11
Start Page
17275
End Page
17298
URI
https://scholarworks.dongguk.edu/handle/sw.dongguk/4157
DOI
10.1021/acsnano.1c08455
ISSN
1936-0851
1936-086X
Abstract
The conversion of nitrogen to ammonia offers a sustainable and environmentally friendly approach for producing precursors for fertilizers and efficient energy carriers. Owing to the large energy density and significant gravimetric hydrogen content, NH3 is considered an apt next-generation energy carrier and liquid fuel. However, the low conversion efficiency and slow production of ammonia through the nitrogen reduction reaction (NRR) are currently bottlenecks, making it an unviable alternative to the traditional Haber-Bosch process for ammonia production. The rational design and engineering of catalysts (both photo- and electro) represent a crucial challenge for improving the efficiency and exploiting the full capability of the NRR. In the present review, we highlight recent progress in the development of graphene-based systems and graphene derivatives as catalysts for the NRR. Initially, the history, fundamental mechanism, and importance of the NRR to produce ammonia are briefly discussed. We also outline how surface functionalization, defects, and hybrid structures (single-atom/multiatom as well as composites) affect the N-2 conversion efficiency. The potential of graphene and graphene derivatives as NRR catalysts is highlighted using pertinent examples from theoretical simulations as well as machine learning based performance predictive methods. The review is concluded by identifying the crucial advantages, drawbacks, and challenges associated with principal scientific and technological breakthroughs in ambient catalytic NRR.
Files in This Item
There are no files associated with this item.
Appears in
Collections
College of Engineering > ETC > 1. Journal Articles

qrcode

Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.

Altmetrics

Total Views & Downloads

BROWSE