Review of Emerging Concepts in SEI Analysis and Artificial SEI Membranes for Lithium, Sodium, and Potassium Metal Battery Anodes
Open accessAbstract
Anodes for lithium metal batteries, sodium metal batteries, and potassium metal batteries are susceptible to failure due to dendrite growth. This review details the structure–chemistry–performance relations in membranes that stabilize the anodes’ solid electrolyte interphase (SEI), allowing for stable electrochemical plating/stripping. Case studies involving Li, Na, and K are presented to illustrate key concepts. “Classical” versus “modern” understandings of the SEI are described, with an emphasis on the new structural insights obtained through novel analytical techniques, including in situ liquid‐secondary ion mass spectroscopy, titration gas chromatography, and tip‐enhanced Raman spectroscopy. This Review highlights diverse approaches for increasing SEI stability, either by inserting a secondary layer between the native SEI and the separator, or by combining the membrane with a native SEI to form a hybrid composite. Exciting and nonintuitive findings are discussed, such as that the metal anode roughness profoundly affects the SEI structure and stability, or that organic artificial SEI‐layers may be more effective than the native inorganic–organic SEIs. Emerging multifunctional architectures are presented, which serve a dual role as metal hosts and metal surface protection layers. Throughout the Review, fruitful future research directions and the critical areas where there is incomplete understanding are discussed.
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Traditional SOH estimation methods based on electrochemical/physical models face limitations in practical applications due to complexity of aging and individual variability.
“These models can accurately simulate the internal behavior of batteries, but due to the complexity of battery aging processes and individual variability, they face many limitations in practical applications.24,25,26,27”
From Battery state of health estimation under fast charging via deep transfer learning (2025) · cites this paper as [26]
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Battery state of health estimation under fast charging via deep transfer learning
Zhao, J., Li, D., Li, Y., Shi, D., Nan, J., & Burke, A. F. (2025) · iScience · doi:10.1016/j.isci.2025.112235 · Cited by 53 · see citing works
- Authors
- Liu, W., Liu, P., & Mitlin, D.
- Affiliations
- Sichuan University (CN)
- Texas Materials Institute (US)
- Corresponding author
- Liu, Wei
- Mitlin, David
- Venue
- Advanced Energy Materials
- Publisher
- Wiley
- Published
- 2020-10-07
- Volume
- 10
- Issue
- 43
- ISSN
- 1614-6832 (ISSN-L)
- 1614-6840
- OpenAlex
- W3092103797
- Cited by
- 687
- References
- 5
- Citations by year
- 2026: 65 · 2025: 156 · 2024: 145 · 2023: 120 · 2022: 129 · 2021: 66 · 2020: 6
- FWCI
- 27.56
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Advanced Energy Materials (publishedVersion, cc-by)
- Funders
- National Natural Science Foundation of China (51702223)
- National Science Foundation
- Division of Civil, Mechanical and Manufacturing Innovation (1911905)
- Topics
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Advanced Battery Materials and Technologies
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Advancements in Battery Materials
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Advanced battery technologies research
- Keywords
- solid electrolyte interphase, lithium metal anode, sodium metal anode, potassium metal anode, dendrite suppression, tip-enhanced Raman spectroscopy, protective layer
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