Application of the “confusion principle” to Sn-based materials as negative electrode materials for Li-ion batteries
Bibliographic record
Abstract
The “confusion principle” (Greer. Nature, 366, 303 (1993)) is applied to tin-3d transition metals carbon alloys to obtain a nanostructured negative electrode material. Various Sn–TMs–C samples with TMs = Ti, V, Cr, Mn, Fe, Co, Ni, and Cu (all included with same atomic ratios) were prepared by mechanical milling and by mechanical alloying. Each 10-component alloy sample was examined structurally using X-ray diffraction (XRD) and electrochemically using Li/Sn–TM–C cells. The sample Sn10TMs80C10 showed a nanostructured or amorphous-type XRD pattern, which shows the validity of this principle. XRD patterns of samples prepared with higher Sn atomic content showed crystalline features of Sn-based intermetallics. As expected, a very low specific capacity ( [Formula: see text]100 mAh/g) was observed for the sample Sn10TMs80C10. The sample Sn30TMs30C40 had the highest specific capacity (near 400 mAh/g) of the samples prepared. However, features of Sn aggregation were noticed at cycle number 80 of the latter sample, which are normally detrimental to the capacity retention upon further cycling.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| 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".