Nitrogen‐Doped Carbon Nanotubes with Large Interplanar Distances toward Fast Potassium Storage
Bibliographic record
Abstract
Abstract One of the main challenges potassium‐ion batteries (PIBs) face is the lack of structurally stable anodes with high reactivity and fast kinetics for reversible potassium insertion/extraction. Herein, nitrogen‐doped carbon nanotubes (N‐CNTs) are synthesized using a straightforward method assisted by a Co‐based catalyst. The as‐synthesized N‐CNTs possess interlayer distances up to 0.38 nm and nitrogen‐doping content of 2.2 at.%. Compared to the undoped CNTs (U‐CNTs), N‐CNTs exhibit a promising initial specific capacity of 568 mAh g−1 at 0.1 A g−1, as well as excellent long‐term cycling performance of 104, 82, and 76 mAh g−1 at a high current of 0.5, 1, and 2 A g−1 for 500 cycles. The cyclic voltammetry (CV) measurements reveal the potassium storage mechanism of N‐CNTs, which combines the major capacitive mechanisms and the secondary diffusion, and successfully avoids inconspicuous voltage plateau. The kinetic analysis of the galvanostatic intermittent titration technique and ex situ X‐ray photoelectron spectroscopy spectra show fast reaction kinetics and low side effects and degradation for the N‐CNTs anodes during the potassiation/de‐potassiation process. This study provides a straightforward method to synthesize heteroatom‐doped carbonaceous anode materials and achieves superior electrochemical properties with cost‐effectiveness and material sustainability.
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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".