Reactivity and Evolution of Ionic Solid-Electrolyte-Interphases in Battery Electrolytes
Bibliographic record
Abstract
The instability of the solid electrolyte interphase (SEI) on the lithium (Li) metal anode is a major challenge towards improving the Coulombic efficiency (CE) and cycle life of Li batteries. In classical SEI models with mosaic and layered structures, ionic phases (e.g., Li2O and LiF) are enriched closest to the Li|SEI interface, while the outer SEI are highly dependent on electrolytes and typically assigned to less-reduced species, such as semi-carbonates and organic Li salts.1, 2 The formation of SEI on Li is often interpreted as a consequence of reactivity between Li metal and electrolytes; however, there remains a lack of understanding about the interplay between electrolytes and specific SEI phases once they are formed. Some recent computational studies have shed light on the chemical reactivity between thin (~1 nm) single-phase inorganic SEI (Li2O, LiOH, and Li2CO3) on Li metal and DME electrolytes containing LiTFSI or LiFSI salts, using ab initio molecular dynamics (AIMD) calculations;3, 4 experimental insights are still much-needed. Herein, we study two ionic SEI phases, Li2O (Fig. 1a-b) and LiF (Fig. 1c-d), that are nearly ubiquitously found across many native SEIs, and investigate their stability at the SEI|electrolyte interface. We find, by using a combination of electrochemical impedance spectroscopy (EIS), X-ray photoelectron spectroscopy (XPS) and non-destructive X-ray absorption near-edge spectroscopy (XANES), that the ionic SEI|electrolyte interfaces can undergo significant chemical evolution as a function of electrolyte. As shown in Fig. 1a, distinctive lower-frequency semicircles emerged in the Nyquist plots of EIS when 1 M LiTFSI EC/DEC and 1 M LiPF6 EC/DEC were used, indicating significant changes at the interface between the ionic Li2O SEI and carbonate-based electrolytes, especially in the presence of LiPF6 salt. The surface atomic concentration of F also increased significantly from 4 at.% in 1 M LiTFSI DOL/DME to 44 at.% in 1 M LiPF6 EC/DEC, suggesting that a F-rich Li2O|electrolyte interface leads to an additional charge transfer barrier. Non-destructive Li K-edge XANES of the Li2O SEI soaked in 1 M LiPF6 EC/DEC electrolyte displayed a dominant LiF peak at 62.3 eV with an evident blue shift compared with the standard LiF peak at 62.0 eV (Fig. 1b),5 which could be attributed to the solvation of F-rich species at the interface by organic phases from carbonates decomposition, forming an “organic/F-rich” outer SEI layer. Similar organic/F-rich outer SEI was also observed on the LiF SEI soaked in 1 M LiPF6EC/DEC electrolyte, as evidenced by EIS (Fig. 1c), XPS (Fig. 1d) and XANES results. The organic/F-rich outer SEI induced by reactivity between ionic SEI phases and carbonate-based electrolytes can significantly exacerbate the subsequent plating overpotentials. Our experimental results suggest that electrolyte solvent and salt selection is of great importance to optimize transport in ionic-rich Li interfaces, which can have important implications ultimately for SEI stability and thus CE. Figure 1. (a) Nyquist plot of EIS spectra of Li|Li2O SEI in the three different electrolytes, where the thickness L and conductivity σ of the Li2O SEI were acquired by fitting the higher-frequency semi-circle (or the sole semi-circle in the case of DOL/DME).6 (b) Li K-edge XANES fluorescence yield (FLY) of Li|Li2O interface after soaking in EC/DEC solvent or 1 M LiPF6 EC/DEC electrolyte for 20 hours. (c) Nyquist plot of EIS spectra of Li|LiF SEI in the three different electrolytes. (d) C1s and F1s XPS of the surface layer on Li|LiF SEI after soaking in each electrolyte. References: 1. E. Peled, D. Golodnitsky, and G. Ardel, J. Electrochem. Soc., 144 (8), L208-L210 (1997). 2. A. Schechter, D. Aurbach, and H. Cohen Langmuir, 15 (9), 3334-3342 (1999). 3. E. P. Kamphaus, S. Angarita-Gomez, X. P. Qin, M. H. Shao, M. Engelhard, K. T. Mueller, V. Murugesan, and P. B. Balbuena, ACS Appl. Mater. Interfaces, 11 (34), 31467-31476 (2019). 4. E. P. Kamphaus, S. A. Gomez, X. Qin, M. Shao, and P. B. Balbuena, ChemPhysChem, 21 (12), 1310-1317 (2020). 5. D. Wang and L. Zuin, J. Power Sources, 337, 100-109 (2017). 6. R. Guo and B. M. Gallant, Chem. Mater., 32 (13), 5525-5533 (2020). Figure 1
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".