Differentiating Lithium Ion Hopping Rates in Vanadium Phosphate versus Vanadium Fluorophosphate Structures Using 1D <sup>6</sup>Li Selective Inversion NMR
Bibliographic record
Abstract
The electrochemical performance of lithium ion batteries is strongly correlated with the ion dynamics within the electrode structures. This study characterizes Li ion hopping rates and energy barriers in the layered phase, Li 5 V(PO 4 ) 2 F 2, using 6 Li selective inversion (SI) NMR measurements. Li 5 V(PO 4 ) 2 F 2 has six crystallographically distinct lithium sites giving the possibility of fifteen exchange partners between nonequivalent lithium environments. Here, 6 Li 1D SI measurements over a variable temperature range were used to quantify the time scales and energy barriers of ion mobility for several ion pairs observed to participate in ion hopping. The rates determined in this material are similar in range to the previously determined rates found in tavorite Li 2 VPO 4 F yet considerably slower than results from both α-Li 3 V 2 (PO 4 ) 3 and α-Li 3 Fe 2 (PO 4 ) 3 . A detailed analysis of the structural features that enhance or inhibit fast ion mobility is discussed. This includes a consideration of the bond valence density maps of the diffusion pathway. Comparison of the ion mobilities in the phosphates and fluorophosphates shows how the gains in redox potential come at the expense of fast ion mobility, meaning that any improvements to the energy output of the lithium ion battery through higher voltage may be compromised due to slow charge/discharge rates.
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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".