Combinatorially Prepared [LiF]<sub>1−<i>x</i></sub>Fe<sub><i>x</i></sub> Nanocomposites for Positive Electrode Materials in Li-Ion Batteries
Bibliographic record
Abstract
Nanocomposites of lithium fluoride and transition metals are promising new positive electrode materials for lithium-ion batteries. Libraries of [LiF] 1− x [Fe] x (0 < x < 1) nanocomposites were prepared by combinatorial cosputtering of lithium fluoride and iron. The sputtered libraries were characterized by X-ray diffraction (XRD) and 57 Fe Mössbauer effect spectroscopy to determine their micro- or nanostructure. At the Fe-rich end of the library ( x ≈ 0.9), a broad Fe (110) Bragg peak appears in the XRD patterns. Mössbauer spectra show that most of the Fe atoms are located within large Fe grains while a small number of Fe atoms are located in the interfacial region between Fe grains and small LiF regions. At the LiF-rich end of the library ( x ≈ 0.1), the LiF (111) peak was observed in the XRD pattern. Large LiF grains prevent the Fe atoms from aggregating, so the Fe grain sizes are so small that they exhibit paramagnetic properties in Mössbauer spectra. Some of the Fe atoms are also located in the interfacial region between small paramagnetic Fe grains and LiF grains. In the middle of the library ( x ≈ 0.5), XRD patterns do not show any Fe or LiF Bragg peaks, while Mössbauer spectra show that about half of the Fe atoms are aggregated into small grains, while the other half of Fe atoms are in the interfacial region. The electrochemical activities of the libraries were investigated using 64 electrode combinatorial electrochemical cells heated at 70 °C. The differential capacity versus potential curves show that lithium fluoride (very small x ) and iron (very large x ) do not show electrochemical activity, as expected, but that nanocomposites of lithium fluoride and iron exhibit significant electrochemical activities. When the LiF:Fe ratio is near 3, the second discharge capacity is about 620 mA h/g at 70 °C.
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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) | 0.001 | 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) | 0.002 | 0.001 |
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".