Ultrahigh‐Capacity and Long‐Life Lithium–Metal Batteries Enabled by Engineering Carbon Nanofiber–Stabilized Graphene Aerogel Film Host
Bibliographic record
Abstract
Abstract A safe, high‐capacity, and long‐life Li metal anode is highly desired due to recent developments in high‐energy‐density Li–metal batteries. However, there are still rigorous challenges associated with the undesirable formation of Li dendrites, lack of suitable host materials, and unstable chemical interfaces. Herein, a carbon nanofiber‐stabilized graphene aerogel film (G‐CNF film), inspired by constructional engineering, is constructed. As the host material for Li deposition, the G‐CNF film features a large surface area, porous structure, and a robust skeleton that can render low local current density. This allows for dendrite‐free Li deposition and mitigation of problems associated with large volume change. Importantly, the G‐CNF film can keep high Li plating/stripping efficiency at nearly 99% for over 700 h with an areal capacity of 10 mA h cm−2 (the specific capacity up to 2588 mA h g−1 based on the total mass of carbon host and Li metal). The symmetric cells can stably run for more than 1000 h with low voltage hysteresis. The full cell with the LiFePO4 cathode also delivers enhanced capacity and lowered overpotential. As two‐in‐one host materials for both cathodes and anodes in Li–O2 batteries, the battery exhibits a capacity of 1.2 mA h cm−2.
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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) | 0.001 | 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".