TY - JOUR
T1 - Machine learning-based design of electrocatalytic materials towards high-energy lithium||sulfur batteries development
AU - Han, Zhiyuan
AU - Chen, An
AU - Li, Zejian
AU - Zhang, Mengtian
AU - Wang, Zhilong
AU - Yang, Lixue
AU - Gao, Runhua
AU - Jia, Yeyang
AU - Ji, Guanjun
AU - Lao, Zhoujie
AU - Xiao, Xiao
AU - Tao, Kehao
AU - Gao, Jing
AU - Lv, Wei
AU - Wang, Tianshuai
AU - Li, Jinjin
AU - Zhou, Guangmin
N1 - Publisher Copyright:
© The Author(s) 2024.
PY - 2024/12
Y1 - 2024/12
N2 - The practical development of Li | |S batteries is hindered by the slow kinetics of polysulfides conversion reactions during cycling. To circumvent this limitation, researchers suggested the use of transition metal-based electrocatalytic materials in the sulfur-based positive electrode. However, the atomic-level interactions among multiple electrocatalytic sites are not fully understood. Here, to improve the understanding of electrocatalytic sites, we propose a multi-view machine-learned framework to evaluate electrocatalyst features using limited datasets and intrinsic factors, such as corrected d orbital properties. Via physicochemical characterizations and theoretical calculations, we demonstrate that orbital coupling among sites induces shifts in band centers and alterations in the spin state, thus influencing interactions with polysulfides and resulting in diverse Li-S bond breaking and lithium migration barriers. Using a carbon-coated Fe/Co electrocatalyst (synthesized using recycled Li-ion battery electrodes as raw materials) at the positive electrode of a Li | |S pouch cell with high sulfur loading and lean electrolyte conditions, we report an initial specific energy of 436 Wh kg−1 (whole mass of the cell) at 67 mA and 25 °C.
AB - The practical development of Li | |S batteries is hindered by the slow kinetics of polysulfides conversion reactions during cycling. To circumvent this limitation, researchers suggested the use of transition metal-based electrocatalytic materials in the sulfur-based positive electrode. However, the atomic-level interactions among multiple electrocatalytic sites are not fully understood. Here, to improve the understanding of electrocatalytic sites, we propose a multi-view machine-learned framework to evaluate electrocatalyst features using limited datasets and intrinsic factors, such as corrected d orbital properties. Via physicochemical characterizations and theoretical calculations, we demonstrate that orbital coupling among sites induces shifts in band centers and alterations in the spin state, thus influencing interactions with polysulfides and resulting in diverse Li-S bond breaking and lithium migration barriers. Using a carbon-coated Fe/Co electrocatalyst (synthesized using recycled Li-ion battery electrodes as raw materials) at the positive electrode of a Li | |S pouch cell with high sulfur loading and lean electrolyte conditions, we report an initial specific energy of 436 Wh kg−1 (whole mass of the cell) at 67 mA and 25 °C.
UR - http://www.scopus.com/inward/record.url?scp=85208689388&partnerID=8YFLogxK
U2 - 10.1038/s41467-024-52550-9
DO - 10.1038/s41467-024-52550-9
M3 - 文章
C2 - 39505845
AN - SCOPUS:85208689388
SN - 2041-1723
VL - 15
JO - Nature Communications
JF - Nature Communications
IS - 1
M1 - 8433
ER -