Lithium diffusion in <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>Li</mml:mi><mml:mi>Mn</mml:mi><mml:mi mathvariant="normal">P</mml:mi><mml:msub><mml:mrow><mml:mi mathvariant="normal">O</mml:mi></mml:mrow><mml:mn>4</mml:mn></mml:msub></mml:math> detected with <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:msup><mml:mi>μ</mml:mi><mml:mo>±</mml:mo></mml:msup><mml:mi>SR</mml:mi></mml:math>
Bibliographic record
Abstract
Positive-and negative-muon spin rotation and relaxation ( SR) was first used to investigate fluctuations of nuclear magnetic fields in an olivine-type battery material, LiMnPO 4 , in order to clarify the diffusive species, namely, to distinguish between a + hopping among interstitial sites and Li + ions diffusing in the LiMnPO 4 lattice. Muon diffusion can only occur in + SR, because the implanted -forms a stable muonic atom at the lattice site, and therefore any change in linewidth measured with -SR must be due to Li + diffusion. Since the two measurements exhibit a similar increase in the field fluctuation rate with temperature above 100 K, it is confirmed that Li + ions are in fact diffusing. The diffusion coefficient of Li + at 300 K and its activation energy were estimated to be 1.4(3) 10 -10 cm 2 /s and 0.19(3) eV, respectively. Such combined SR measurements are thus shown to be a suitable tool for detecting ion diffusion in solid-state energy materials.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.100 | 0.030 |
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".