The Impact of the Addition of Rare Earth Elements to Si[sub 1−x]Sn[sub x] Negative Electrode Materials for Li-Ion Batteries
Bibliographic record
Abstract
The impact of the addition of cerium and lanthanum-rich misch metal (MM, a mixture of rare earth elements) to silicon-tin alloys was studied using combinatorial and high-throughput materials science methods. Combinatorial libraries of , , and were prepared using the Dalhousie University combinatorial sputtering systems. The addition of MM increases the crystallization temperature of amorphous alloys, which makes their preparation in the amorphous state more likely by rapid solidification techniques like melt-spinning. Increasing the atomic fraction of MM in both and amorphous alloys decreases the reversible specific capacity of the alloys for lithium, but does not negatively impact capacity retention. Attractive compositions were identified in pseudoternary system that: (1) remain amorphous during charge-discharge cycling between 0.005 and vs ; (2) show excellent capacity retention; (3) have specific capacities as large as ; and (4) may be compatible with rapid solidification techniques for the production of amorphous materials. In particular, amorphous alloys containing tin and MM exhibited excellent capacity retention and reversible specific capacities near .
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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 | 0.001 | 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".