Interface Engineering Via Fluorinated Solid Electrolytes for All-Solid-State Li Batteries
Bibliographic record
Abstract
Solid electrolytes (SEs) are vital for all-solid-state batteries (ASSBs) since they replace the flammable liquid electrolytes to make the ASSBs safer and compacter.1 In order to boost the energy density of ASSBs, a practical SE is not only expected possessing high ionic conductivity, but also good compatibility with both cathode and anode to allow the use of high-voltage cathode and Li metal.2, 3 However, most of the developed SEs show limitations on directly contact with either high-voltage cathode materials or Li metal. As such, SE modification is required to address the interfacial issues between SE and electrodes. In this work, fluorinated sulfide- and halide-based SEs are proposed to stabilize the SE/Li metal and SE/high-voltage cathode interfaces, respectively. Our results firstly show that fluorinated argyrodite Li6PS5Cl (LPSCl) can enhance the interfacial stability toward the Li metal anode.4 The in-situ formed interface between Li and LPSCl1−xFx are of highly fluorinated and condense, which enables ultrastable Li plating/stripping behavior over 250 hrs at a high current density of 6.37 mA cm−2 and a cutoff capacity of 5 mAh cm−2. The Li metal treated by the LPSCl1−xFx SE is then demonstrated to deliver good durability and rate capability in full cells. Other than anode side improvement, F is introduced into a superionic conductor Li3InCl6 to widen the oxidation limit to over 6 V (vs. Li/Li+).5 Both experimental and computational results identify that F-containing passivating interphases are generated to contribute to the enhanced oxidation stability of Li3InCl6-xFx and stabilization the surface of cathodes at high cut-off voltages. The optimized composition Li3InCl4.8F1.2 is directly matched with bare high-voltage LiCoO2, enabling ASSBs to stably operate at room temperature at a cut-off voltage of 4.8 V (vs Li/Li+). Our studies provide a new strategy of interface engineering by introducing F in SEs, realizing the good compatibility between SE and electrodes and opening up the applications of ASSBs. Re ferences Manthiram, A., Yu, X. W., Wang, S. F. Lithium battery chemistries enabled by solid-state electrolytes. Nat. Rev . Mater. 2, 1-16 (2017). Wang, C. H., Liang, J. W., Zhao, Y., Zheng, M. T., Li, X. N., Sun, X. L. All-solid-state lithium batteries enabled by sulfide electrolytes: from fundamental research to practical engineering design. Energy Environ. Sci. 14, 2577-2619 (2021). Li, J. C., Ma, C., Chi, M. F., Liang, C. D., Dudney, N. J. Solid Electrolyte: the Key for High-Voltage Lithium Batteries. Adv. Energy Mater. 5, 1401408 (2015). Zhao, F. P., et al. Ultrastable Anode Interface Achieved by Fluorinating Electrolytes for All-Solid-State Li Metal Batteries. ACS Energy Lett. 5, 1035-1043 (2020). Zhang, S. M., et al. Advanced High-Voltage All-Solid-State Li-Ion Batteries Enabled by a Dual-Halogen Solid Electrolyte. Adv. Energy Mater. 11, 2100836 (2021).
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".