Compact Solvation Enables Sub‐Minute Sodium‐Ion Storage: A Data‐Driven Perspective

M Mingxu Wang C Chenyu Tang (Electrical Engineering Division, Department of Engineering, University of Cambridge) J Jinyu Yang Z Ziyue Li H Hao Du (Institute of Materials Research, Tsinghua Shenzhen International Graduate School) Q Qin Li H Haoran Ji X Xinjie Li Y Yan Lu F Fang Fang M Mao Su (Shanghai Artificial Intelligence Laboratory 2 , Shanghai 200232,) J Jiafeng Ruan F Fei Wang

Abstract

ABSTRACT Extremely fast charging (XFC) of batteries holds significant importance in the era of intelligent technologies, yet the intricate role of electrolyte properties in determining XFC behavior remains obscure. Furthermore, conventional theories and in situ characterization techniques fails to elucidate electrochemical behaviors under sub‐minute‐level charging conditions. Herein, we report a data‐driven approach for analyzing the effect of each independent physical and solvation property on sub‐minute‐level sodium‐ion storage behavior. Causal graph analysis reveals that the size of the solvation clusters shows the strongest negative correlation with ultrafast Na + storage in graphite. The optimized compact solvation electrolyte demonstrates an astonishing extreme current density of 250 A g −1 (corresponding to a power density of 46.78 kW kg −1 graphite ) and an unprecedented cycle life of 100 000 cycles. Most notably, the graphite||Na 4 Fe 3 (PO 4 ) 2 P 2 O 7 full batteries achieve stable sub‐minute‐level charging and discharging, exhibiting ultrahigh rate capabilities (up to 200 C, ∼3.4 s per charge) and ultra‐stable cycle performance (24 000 cycles at 50 C, ∼29 s per charge). This work provides a promising pathway for the development of XFC battery, pioneering an innovative assessment strategy for next‐generation electrolytes.

Article Details

Volume / Issue Vol. 38, Issue 39
Published July 01, 2026
ISSN 0935-9648
Publisher Unknown Publisher

Journal Info

Advanced Materials

Unknown Publisher

ISSN: 0935-9648 Physical Sciences

Authors (13)

M

Mingxu Wang

C

Chenyu Tang

Electrical Engineering Division, Department of Engineering, University of Cambridge

J

Jinyu Yang

Z

Ziyue Li

H

Hao Du

Institute of Materials Research, Tsinghua Shenzhen International Graduate School

Q

Qin Li

H

Haoran Ji

X

Xinjie Li

Y

Yan Lu

F

Fang Fang

M

Mao Su

Shanghai Artificial Intelligence Laboratory 2 , Shanghai 200232,

J

Jiafeng Ruan

F

Fei Wang