Correlation of Electrochemical Performance with Lithium Environments and Cation Dynamics in Li<sub>2</sub>(Mn<sub>1–<i>y</i></sub>Fe<sub><i>y</i></sub>)P<sub>2</sub>O<sub>7</sub> using <sup>6</sup>Li Solid-State NMR
Bibliographic record
Abstract
6 Li solid-state nuclear magnetic resonance (ssNMR) is used here to evaluate a series of Li 2 Mn 1– y Fe y P 2 O 7 cathode materials in an effort to quantify ion exchange rates and diffusion pathways. Magic angle spinning (MAS) NMR of the series of mixed metal pyrophosphates reveals a trade-off between electrochemical performance and well-resolved NMR spectra resulting from the change in electronic structure of the transition metal redox center. In addition, 1D 6 Li selective inversion NMR is employed to characterize Li ion dynamics in the fully Mn substituted member of the pyrophosphate series, where three of the four unique Li resonances are well resolved and labeled AB, C, and D, with AB corresponding to Li ions within one tunnel, and C and D Li ions residing in another. Despite limited inversion efficiency it is found that the utility of this experiment is not compromised so long as the initial magnetization conditions are well-defined. Initial fitting procedures involved the inclusion of all possible exchange pairs, a process which gave rise to consistently negative rate constants for C–AB or D–AB exchange, suggesting negligible exchange between these Li ions. Upon limiting the exchange model to ion exchange processes between the pairs of high and low frequency sites, rate constants of 45 ± 25 and 100 ± 30 Hz were obtained for C–D exchange at room temperature and 350 K respectively. Ion exchange pathways that are revealed by the exchange experiments imply limited mobility across distinct two-dimensional tunnels and slow exchange for within - tunnel ions. These exchange results provide corroboration for the geometrically determined site assignment in the 1-D spectrum, as well as support the notion of limited ion mobility in the Mn-phase resulting in poor electrochemical capability.
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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 | 0.000 |
| 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".