Arsenic for high-capacity lithium- and sodium-ion batteries
Bibliographic record
Abstract
We report arsenic (As) as a promising alternative to graphite anode materials in lithium- and sodium-ion batteries (LIBs and SIBs). The electrochemical properties of the As/carbon nanocomposite for both LIBs and SIBs were investigated using experimental and theoretical approaches. The LIBs showed excellent cycling performance, with a reversible capacity of 1306 mA h g-1 (after 100 cycles), which is much higher than that of Li3As (1072 mA h g-1). In the corresponding SIBs, the measured reversible capacity was 750 mA h g-1 (after 200 cycles), which is lower than that of Na3As. Extensive first-principles calculations were performed employing a structure prediction method for crystalline LixAs and NaxAs (x = 1-6) phases, as well as ab initio molecular dynamics simulations for their amorphous phases. In good agreement with the experimental LIB data, our calculations successfully predict the discharge capacity versus voltage curves, showing that the capacity of the amorphous phase reaches up to that of Li4As. In contrast, the SIB exhibited difficulty in reaching the predicted capacity (x = 3.5), probably due to significant volume expansion. Comparison of the theoretical discharge curves with the experimental data provides valuable information for the development of high-performance LIBs and SIBs.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".