Nickel, Manganese, and Cobalt Dissolution from Ni-Rich NMC and Their Effects on NMC622-Graphite Cells

Roland Jung,Fabian Linsenmann,Rowena Thomas,Johannes Wandt,Sophie Solchenbach,Filippo Maglia,Christoph Stinner,Moniek Tromp,Hubert A. Gasteiger

Journal of the Electrochemical Society（2019）SCI 3区

Tech Univ Munich | BMW Grp | Univ Amsterdam

Cited 250|Views18

Abstract

Transition metal dissolution from the cathode active material and its deposition on the anode causes significant cell aging, studied most intensively for manganese. Owing to their higher specific energy, the current focus is shifting towards nickel-rich layered LiNixMnyCozO2 (NMC, x + y + z = 1) with x > 0.5, so that the effect of Ni dissolution on cell degradation needs to be understood. This study investigates the dissolution of transition metals from a NMC622 cathode and their subsequent deposition on a graphite anode using operando X-ray absorption spectroscopy. We show that in NMC622-graphite cells transition metals dissolve nearly stoichiometrically at potentials > 4.6 V, highlighting the significance of investigating Ni dissolution/deposition. Using NMC622-graphite full-cells with electrolyte containing the bis(trifluoromethane) sulfonimide (TFSI) salts of either Ni, Mn, or Co, we compare the detrimental impact of these metals on cell performance. Using in-situ and ex-situ XRD, we show that the aging mechanism induced by all three metals is the loss of cycleable lithium in the solid electrolyte interface (SEI) of the graphite. This loss is larger in magnitude when Mn is present in the electrolyte compared to Ni and Co, which we ascribe to a higher activity of deposited Mn towards SEI decomposition in comparison to Ni and Co. (C) The Author(s) 2019. Published by ECS.

Translated text

Bibtex

AI Read Science

AI Summary

AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.

Example

Background

Key content

Introduction

Methods

Results

Related work

Fund

Key content

Pretraining has recently greatly promoted the development of natural language processing (NLP)
We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance

Try using models to generate summary,it takes about 60s

Must-Reading Tree

Example

Generate MRT to find the research sequence of this paper