Toward Understanding the Reactivity of Garnet-Type Solid Electrolytes with H<sub>2</sub>O/CO<sub>2</sub> in a Glovebox Using X-ray Photoelectron Spectroscopy and Electrochemical Methods
Bibliographic record
Abstract
Chemical stability of garnet-type lithium ion conductors is one of the critical issues in their application in all-solid-state batteries. Here, we conducted quantitative analysis of impurity layers on the garnet-type solid electrolytes, Li 6.5 La 3– x AE x Zr 1.5– x Ta 0.5+ x O 12 ( x = 0 and 0.1; AE = Ca, Sr, and Ba), by means of X-ray photoelectron spectroscopy (XPS) and electrochemical methods. Two complimentary XPS techniques were employed: (i) background analyses by Tougaard’s method and (ii) relative intensity analyses of La 3d/La 4d spectra to determine the surface chemical composition. XPS revealed that even after cleaning by annealing and polishing, the surface is covered by LiOH- and Li 2 CO 3 -based compounds with a thickness of 4–6 nm within 30 min as a result of the reaction with traces of H 2 O (<0.5 ppm) and CO 2 (<5 ppm) in an Ar-filled glovebox. The sensitivity to H 2 O and CO 2 depends on the basicity of dopants. Ba-doped solid electrolytes exhibited the thickest impurity layers compared to Sr- and Ca-doped compounds. A surface cleaning process, consisting of annealing and polishing, effectively reduces the charge-transfer resistance to 10–15 Ω cm 2 because of negligible impurity layers. Highest short-circuit tolerance is obtained for a 700 °C annealed specimen (critical current density: 0.5 mA cm –2 ), which is possibly due to the strengthened grain boundaries by Li 2 CO 3 among grains around its melting point.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".