Journal
ACS APPLIED ENERGY MATERIALS
Volume 3, Issue 6, Pages 5993-6000Publisher
AMER CHEMICAL SOC
DOI: 10.1021/acsaem.0c01059
Keywords
energy storage; supercapacitor; heteroatom-doped carbon; data-driven; machine learning
Funding
- Fluid Interface Reactions, Structures, and Transport (FIRST) Center, an Energy Frontier Research Center - U.S. Department of Energy (DOE), Office of Science, Office of Basic Energy Sciences
- Office of Science of the U.S. Department of Energy [DE-AC02-05CH11231]
Ask authors/readers for more resources
Doping with heteroatoms such as nitrogen and oxygen has been widely practiced to improve the capacitance of carbon electrodes for supercapacitor. However, the role of different heteroatoms and their local atomic configurations on the supercapacitor performance remains elusive, which hampers the rational design of carbon electrodes to achieve high energy density and power density. In this work, machine-learning models are applied to interpret how the surface chemistry affects the inoperando behavior of various carbon electrodes with different structural features such as the specific surface areas of micro- and mesopores. Quantitative descriptions have been established to predict how the configurations of nitrogen-doping and oxygen-doping influence the capacitance and retention rate. The machine-learning models provide insights into the design and possible routes to the synthesis of nitrogen and oxygen co-doped carbon electrodes that maximize the energy storage capacity.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available