First‐principle calculation of distorted T‐carbon as a promising anode for Li‐ion batteries with enhanced capacity, reversibility, and ion migration properties
Bibliographic record
Abstract
Abstract Carbon group element‐based materials are the most widely used anode materials for Li‐ion batteries (LIBs). However, their performance is limited by the low capacity (eg, graphite) or high‐volume changes (eg, Si and Sn). Therefore, exploring high‐performance anode materials is quite appealing and promising. By first‐principle calculations in this study, we found that distorted T‐carbon (DTC) as a desired LIB anode shows properties of the enhanced capacity, decreased volume change, and the increased ion migration. The origin of such improved properties is attributed to the interconnected tunnels and large cavities of the carbon skeleton. The theoretical specific capacity of DTC is found to be 558 mAh/g, which is 1.5 times higher than that of commercial graphite anodes. Interestingly, the volume change of the DTC anode is only 3% at the full‐lithiation state (one‐fifth of that of the commercial graphite anode), which can overcome the pulverization problem in most high‐capacity anode materials and attain a longer cycling lifetime. Both transition state calculations and molecular dynamics simulations demonstrate that the Li‐ion migration barrier is less than 0.1 eV and the Li‐ion vacancy is only 0.2 eV, enabling its promising rate performance. This study provides a new and effective strategy to improve the anode properties of LIBs.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".