Characterization of Disordered Li<sub>(1+<i>x</i>)</sub>Ti<sub>2<i>x</i></sub>Fe<sub>(1–3<i>x</i>)</sub>O<sub>2</sub> as Positive Electrode Materials in Li-Ion Batteries Using Percolation Theory
Bibliographic record
Abstract
Recent theoretical and experimental works have shown that disordered positive electrode materials can function well in lithium cells. This work explores the solid solution series Li (1+ x ) Ti 2 x Fe (1–3 x ) O 2 (0 ≤ x ≤ 0.333) and compares the measured specific capacity variation with x to a recent theoretical model. The samples have varying degrees of cation disordering between lithium and transition metal layers that is dependent on x . The materials were characterized using induced coupled plasma optical emission spectroscopy, scanning electron microscopy, X-ray diffraction, and X-ray absorption spectroscopy (XAS) to quantify the degree of disorder and predict electrochemical performance. The specific capacities of lithium-limited samples (0 ≤ x ≤ 0.13) were found to agree very well with the recently proposed percolation theory model, whereas redox-limited samples (0.13 ≤ x ≤ 0.29) yielded slightly higher than expected capacities due to oxygen redox compensation characterized by oxygen K-edge XAS studies. Capacity retention was found to increase with lithium content. The voltage vs specific capacity relations for this set of materials do not suggest practicality, so this work is primarily of academic interest, but it suggests that more disordered materials should be explored.
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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) | 0.000 | 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".