Combinatorial Study of Sn[sub 1−x]Co[sub x] (0<x<0.6) and [Sn[sub 0.55]Co[sub 0.45]][sub 1−y]C[sub y] (0<y<0.5) Alloy Negative Electrode Materials for Li-Ion Batteries
Bibliographic record
Abstract
Using combinatorial and high-throughput materials science methods, we have studied thin-film libraries of and alloy negative electrode materials for Li-ion batteries. Over one hundred compositions have been studied carefully by X-ray diffraction and electrochemical methods. The system is found to be amorphous for . For , the amorphous phase coexists with electrochemically inactive crystalline . Amorphous materials with show a specific capacity of , but differential capacity, , vs potential is not stable vs cycling indicating irreversible atomic-scale changes in the alloy, most likely due to tin aggregation. Adding carbon to this system, for example in the library, has a number of positive effects. First, all alloys with are amorphous, with carbon directly incorporated within the amorphous phase. Second, the addition of carbon increases, not decreases, the specific capacity from about for for . Third, compositions with show differential capacity vs potential curves that do not change during charge-discharge cycling, indicating that such alloys are stable on the atomic scale and hence are extremely good candidates for long cycle life. Stability increases with carbon content up to .
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 teacher head, 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".