Bamboo‐Like Nitrogen‐Doped Carbon Nanotube Forests as Durable Metal‐Free Catalysts for Self‐Powered Flexible Li–CO<sub>2</sub> Batteries
Bibliographic record
Abstract
Abstract The Li–CO2 battery is a promising energy storage device for wearable electronics due to its long discharge plateau, high energy density, and environmental friendliness. However, its utilization is largely hindered by poor cyclability and mechanical rigidity due to the lack of a flexible and durable catalyst electrode. Herein, flexible fiber‐shaped Li–CO2 batteries with ultralong cycle‐life, high rate capability, and large specific capacity are fabricated, employing bamboo‐like N‐doped carbon nanotube fiber (B‐NCNT) as flexible, durable metal‐free catalysts for both CO2 reduction and evolution reactions. Benefiting from high N‐doping with abundant pyridinic groups, rich defects, and active sites of the periodic bamboo‐like nodes, the fabricated Li–CO2 battery shows outstanding electrochemical performance with high full‐discharge capacity of 23 328 mAh g−1, high rate capability with a low potential gap up to 1.96 V at a current density of 1000 mA g−1, stability over 360 cycles, and good flexibility. Meanwhile, the bifunctional B‐NCNT is used as the counter electrode for a fiber‐shaped dye‐sensitized solar cell to fabricate a self‐powered fiber‐shaped Li–CO2 battery with overall photochemical–electric energy conversion efficiency of up to 4.6%. Along with a stable voltage output, this design demonstrates great adaptability and application potentiality in wearable electronics with a breath monitor as an example.
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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) | 0.000 | 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".