Accurate Measurements of Li<sup>+</sup> Dynamics in Pressure-Treated Solid Electrolytes by Powder X-ray Diffraction and <sup>7</sup>Li Magnetic Resonance Diffusometry
Bibliographic record
Abstract
Applying fabrication pressure is an inevitable step when preparing solid electrolytes (SEs) for all-solid-state batteries (ASSBs). Utilizing 7 Li diffusometry and relaxometry nuclear magnetic resonance (NMR) measurements, this study demonstrates that the long-range ion transport in sulfide SEs, measured by diffusometry, is slowed by 20% following the application of pressure of 500 MPa. The local range relaxometry measurements show a more modest change. It is notable that the NMR and structural measurements are taken on samples after the applied pressure is removed, while the sample remains in the pellet form. The fact that this change in ion dynamics remains evident even when the pressure is no longer actively applied is an important finding that will impact the consideration of the role of pressure in altering the ion dynamics in ASSBs. Powder X-ray diffraction (PXRD) was performed on powder and compressed thiophosphate Li 10 SnP 2 S 12 (LSnPS) samples to reveal the grain morphology change after pressure treatment. The PXRD analysis reveals the change in the peak shapes of the LSnPS materials, consistent with a significant microstrain imparted to the material by the fabrication pressure. A comparative investigation was performed for the reference ceramic oxide Li 1.5 Al 0.5 Ge 1.5 (PO 4 ) 3 (LAGP) phase, where no significant changes were observed in either the ion dynamics or the micromorphology. This is expected due to the significantly larger Young’s modulus of the oxide relative to that of the sulfide SEs. This study demonstrates the accuracy with which diffusometry and relaxometry NMR can measure changes in ion dynamics under mechanical modification, opening a new window to link macroscopic material engineering with particle-level dynamics and ion transport.
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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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".