Discovery of Calcium‐Metal Alloy Anodes for Reversible Ca‐Ion Batteries
Bibliographic record
Abstract
Abstract Ca‐ion batteries (CIBs) show promise to achieve the high energy density required by emerging applications like electric vehicles because of their potentially improved capacities and high operating voltages. The development of CIBs is hindered by the failure of traditional graphite and calcium metal anodes due to the intercalation difficulty and the lack of efficient electrolytes. Recently, a high voltage (4.45 V) CIB cell using Sn as the anode has been reported to achieve a remarkable cyclability (>300 cycles). The calciation of Sn is observed to end at Ca 7 Sn 6 , which is surprising, since higher Ca‐content compounds are known (e.g., Ca 2 Sn). Here, the Sn electrochemical calciation reaction process is investigated computationally and the reaction driving force as a function of Ca content is explored using density functional theory (DFT) calculations. This exploration allows the identification of threshold voltages which govern the limits of the calciation process. This information is then used to design a four‐step screening strategy and high‐throughput DFT is utilized to search for anode materials with higher properties. Many metalloids (Si, Sb, Ge), (post‐)transition metals (Al, Pb, Cu, Cd, CdCu 2 ) are predicted to be promising inexpensive anode candidates and warrant further experimental investigations.
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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 | 0.001 | 0.000 |
| Open science | 0.001 | 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".