Nanomechanical elasticity and fracture studies of lithium phosphate (LPO) and lithium tantalate (LTO) solid-state electrolytes
Bibliographic record
Abstract
All-solid-state batteries (ASSBs) have attracted much attention due to their enhanced energy density and safety as compared to traditional liquid-based batteries. However, cyclic performance depreciates due to microcrack formation and propagation at the interface of the solid-state electrolytes (SSEs) and electrodes. Herein, we studied the elastic and fracture behavior of atomic layer deposition (ALD) synthesized glassy lithium phosphate (LPO) and lithium tantalate (LTO) thin films as promising candidates for SSEs. The mechanical behavior of ALD prepared SSE thin films with a thickness range of 5 nm to 30 nm over suspended single-layer graphene was studied using an atomic force microscope (AFM) film deflection technique. Scanning transmission electron microscopy (STEM) coupled with AFM was used for microstructural analysis. LTO films exhibited higher stiffness and higher fracture forces as compared to LPO films. Fracture in LTO films occurred directly under the indenter in a brittle fashion, while LPO films failed by a more complex fracture mechanism including significant plastic deformation prior to the onset of complete fracture. The results and methodology described in this work open a new window to identify the potential influence of SSEs mechanical performance on their operation in flexible ASSBs.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".