Two-Stage Growth of Solid Electrolyte Interphase on Copper: Imaging and Quantification by <i>Operando</i> Atomic Force Microscopy

The solid electrolyte interphase (SEI) plays a key role in the aging of lithium-ion batteries. The engineering of advanced negative electrode materials to increase battery lifetime relies on accurate models of SEI growth, but quantitative measurement of SEI growth rates remains challenging due to their nanoscale heterogeneity and environmental sensitivity. In this work, using <i>operando</i> electrochemical atomic force microscopy, we track the growth of SEI on copper in a carbonate electrolyte. From <i>operando</i> measurements of SEI thickness and irreversible electrochemical capacity, we directly visualize the dual growth regimes of the SEI, observing an early-stage primary SEI approximately ten times more "electrochemically compact" than later-stage secondary SEI, as quantified via the incremental thickness per charge passed. While primary SEI is responsible for about half of the irreversible capacity lost in a 24 h period, it accounts for only a tenth of thickness. We also show that nanoscale defects on the copper substrate play a key role in determining the nonuniform growth morphology of the SEI, thus providing direct evidence that initial SEI growth is not purely transport-limited. Our experiments reveal that SEI grows by two modes: first reaction-limited nucleation and growth of a dense, passivating primary SEI layer, governed by ion-coupled electron transfer kinetics; and subsequently by diffusion-limited growth of a porous secondary SEI layer, once the primary SEI fully passivates the electrode surface.

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Publication Details

Journal
ACS Nano
Published
2025-03-17
DOI
https://doi.org/10.1021/acsnano.4c16418
Citations
7
Primary Topic
Machine Learning in Materials Science
Type
article
Field-Weighted Citation Impact
3.62

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article

Two-Stage Growth of Solid Electrolyte Interphase on Copper: Imaging and Quantification by Operando Atomic Force Microscopy

Henry L. Thaman, Michael L. Li, Swati Narasimhan, Che-Ning Yeh et al.
7 citations
ACS Nano
Machine Learning in Materials Science
3.62
article

Two-Stage Growth of Solid Electrolyte Interphase on Copper: Imaging and Quantification by Operando Atomic Force Microscopy

Henry L. Thaman, Michael L. Li, Swati Narasimhan, Che-Ning Yeh, Justin A. Rose, William C. Chueh, Andrew R. Akbashev, Norman Jin, Martin Z. Bazant, Xin Xu, Isabel Davidoff
article en
7 citations

Abstract

The solid electrolyte interphase (SEI) plays a key role in the aging of lithium-ion batteries. The engineering of advanced negative electrode materials to increase battery lifetime relies on accurate models of SEI growth, but quantitative measurement of SEI growth rates remains challenging due to their nanoscale heterogeneity and environmental sensitivity. In this work, using <i>operando</i> electrochemical atomic force microscopy, we track the growth of SEI on copper in a carbonate electrolyte. From <i>operando</i> measurements of SEI thickness and irreversible electrochemical capacity, we directly visualize the dual growth regimes of the SEI, observing an early-stage primary SEI approximately ten times more "electrochemically compact" than later-stage secondary SEI, as quantified via the incremental thickness per charge passed. While primary SEI is responsible for about half of the irreversible capacity lost in a 24 h period, it accounts for only a tenth of thickness. We also show that nanoscale defects on the copper substrate play a key role in determining the nonuniform growth morphology of the SEI, thus providing direct evidence that initial SEI growth is not purely transport-limited. Our experiments reveal that SEI grows by two modes: first reaction-limited nucleation and growth of a dense, passivating primary SEI layer, governed by ion-coupled electron transfer kinetics; and subsequently by diffusion-limited growth of a porous secondary SEI layer, once the primary SEI fully passivates the electrode surface.

ACS NanoVol. 19(12)
Massachusetts Institute of Technology (US), Stanford University (US)
Stanford University, Toyota Research Institute, Vehicle Technologies Office
Openalex Percentile: Top 7%
Machine Learning in Materials Science
3.62
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