Unraveling the Evolution of Transition Metals during Li Alloying–Dealloying by In-Operando Magnetometry
Bibliographic record
Abstract
In view of the long-standing controversy over the reversibility of transition metals in Sn-based alloys as an anode for Li-ion batteries, an in situ real-time magnetic monitoring method was used to investigate the evolution of Sn–Co alloy during the electrochemical cycling. Sn–Co alloy film anodes with different compositions were prepared via magnetron sputtering without using binders and conductive additives. The magnetic responses showed that the Co particles liberated by Li insertion recombine fully with Sn during the delithiation to reform Sn–Co alloy into stannum-richer phases Sn 7 Co 3 . However, as the Co content increases, it can only recombine partially with Sn into cobalt-richer phases Sn 3 Co 7 . The unconverted Co particles may form a dense barrier layer and prevent the full reaction of Li with all the Sn in the anode, leading to lower capacities. In addition, we also showed that the Fe can recombine with Sn (Sb) during the delithiation in the Sn (Sb)–Fe alloy film anodes by operando magnetometry. These critical results shed light on understanding the reaction mechanism of transition metals and provide valuable insights toward the design of high-performance Sn (Sb)-based alloy anodes.
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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) | 0.000 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".