Implanting CNT Forest onto Carbon Nanosheets as Multifunctional Hosts for High‐Performance Lithium Metal Batteries
Bibliographic record
Abstract
Abstract Lithium (Li) metal anodes are considered an ideal anode for the next‐generation Li batteries with high energy density. However, some intrinsic problems, such as Li dendrite growth and tremendous volume change, inhibit their practical applications. Here, an unstacked microstructure is tailored by planting an N‐doped carbon nanotube (CNT) forest on the surface of biomass‐derived large‐aspect‐ratio N‐doped carbon sheets (CSs) (CS‐CNT), which can effectively overcome the easy aggregation properties of CSs. As the host material for Li metal anode, the N‐doping and unstacked natures of CS‐CNT offer sufficient Li nucleation sites, large surface area, and space for smooth and uniform Li deposition, effectively preventing the formation of dendritic Li. As a result, the cell with this electrode can keep high and stable Coulombic efficiency of 98.8% for over 2000 h, superior to the pure CSs and Cu foil electrodes. Additionally, the symmetric cell exhibits significantly enhanced cycle life up to 1500 h as well as lowered hysteresis. The present study sheds light on the design of unstacked porous carbon materials and offers an opportunity to develop high efficiency Li metal anode.
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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".