Assessing Electrochemical Stability Windows of Li<sub>1+X</sub>Al<sub>x</sub>M<sub>2-X</sub>(PO<sub>4</sub>)<sub>3</sub> (M=Ge,Ti) Nasicon Solid Electrolytes for Their Application in All Solid-State Lithium Batteries
Bibliographic record
Abstract
All-Solid-State Lithium Batteries (ASSLBs) are a new generation of lithium batteries that are developed to meet expectations in terms of safety, stability and high energy density. The liquid electrolyte of conventional Li-ion batteries is replaced in ASSLBs by a safer and more stable solid electrolyte (SE). ASSLBs are promising because they may enable the use of high potential materials as positive electrode and lithium metal as negative electrode. This is only possible through SE stated large electrochemical stability windows (ESW). Nevertheless, values for these electrochemical windows are very divergent in the published literature. Recently, several studies have come to specifically decry the frequent overestimation of SE electrochemical stability windows 1- 4 . Establishing a robust procedure to accurately determine SEs ESW has therefore become crucial. Our work is focused on using an original experimental set up to assess the ESW of two widely investigated NASICON-type SEs Li1.3Al0.3Ti1.7(PO4)3 (LATP) and Li1.5Al0.5Ge1.5(PO4)3 (LAGP). The experimental set-up provides a large contact surface between the SE and the conductive material, which maximizes the redox current signals. A combination of Potentiostatic Intermittent Titration Technique (PITT) and Electrochemical Impedance Spectroscopy (EIS) measurements allowed us to precisely determine the ESW of LATP and LAGP solid electrolytes. Using EIS and physico-chemical characterizations, we attempted to shed light on the degradation mechanisms occurring upon the solid electrolytes’ oxidation. 1 - Y. Tian et al. Energy Environ. Sci. 2017, 10, 1150. 2 - Z. Zhang et al. Energy Environ. Sci. 2018, 11, 1945-1976. 3 - F. Han et al. Adv. Energy Mater. 2016, 6(8), 1501590. 4 - T. Schwietert et al. Nature Mat. 2020, 19, 428–435.
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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 | 0.001 |
| 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".