December 1 - 6, 2024
Boston, Massachusetts

Event Supporters

2024 MRS Fall Meeting & Exhibit
EN08.03.05

Operando Neutron Imaging-Guided Gradient Design of Li-Ion Solid Conductor for Extremely High Mass-Loading Cathodes

When and Where

Dec 3, 2024
10:30am - 10:45am
Hynes, Level 3, Ballroom C

Presenter(s)

Co-Author(s)

Hongli Zhu1,Tongtai Ji1

Northeastern University1

Abstract

Hongli Zhu1,Tongtai Ji1

Northeastern University1
High mass-loading cathodes are crucial for achieving high energy density in all-solid-state batteries from lab scale to industry. However, as mass-loading increases, electrochemical performance is significantly compromised due to sluggish kinetics. <i>Operando</i> neutron imaging of a high mass-loading NMC 811 cathode of 33 mg/cm<sup>2</sup> (5.0 mAh/cm<sup>2</sup>, 180 µm thick) reveals the lithiation prioritization of the cathode active material (CAM) from the solid electrolyte layer to the current collector side. In addition to the tortuosity, another key limitation to ion transfer in the cathode arises from the mismatch between the uniform distribution of the solid electrolyte (catholyte) in the conventional composite cathode and the non-uniform Li<sup>+</sup> flux generated by the Faraday reaction of CAMs. Therefore, a novel design with a gradient in the catholyte concentration is engineered to match the Li<sup>+</sup> flux distribution, aiming to eliminate the ion transfer obstacle. This innovative approach demonstrates enhanced rate performance, even with ultra-high mass-loading cathodes. A LiCoO<sub>2</sub> composite cathode with 100 mg/cm<sup>2</sup> ultra-high mass-loading exhibited an areal capacity of 10.4 mAh/cm<sup>2</sup> at a current density of 2.25 mA/cm<sup>2</sup>. This work demonstrated an effective gradient design to optimize ion transport in high mass-loading cathodes to overcome the kinetic barrier and achieve high battery performance.

Symposium Organizers

Kelsey Hatzell, Vanderbilt University
Ying Shirley Meng, The University of Chicago
Daniel Steingart, Columbia University
Kang Xu, SES AI Corp

Session Chairs

Rachel Carter
Daniel Steingart

In this Session