Unraveling the Nature of Excellent Potassium Storage in Small‐Molecule Se@Peapod‐Like N‐Doped Carbon Nanofibers
Bibliographic record
Abstract
Abstract The potassium–selenium (K–Se) battery is considered as an alternative solution for stationary energy storage because of abundant resource of K. However, the detailed mechanism of the energy storage process is yet to be unraveled. Herein, the findings in probing the working mechanism of the K‐ion storage in Se cathode are reported using both experimental and computational approaches. A flexible K–Se battery is prepared by employing the small‐molecule Se embedded in freestanding N ‐doped porous carbon nanofibers thin film (Se@NPCFs) as cathode. The reaction mechanisms are elucidated by identifying the existence of short‐chain molecular Se encapsulated inside the microporous host, which transforms to K 2 Se by a two‐step conversion reaction via an “all‐solid‐state” electrochemical process in the carbonate electrolyte system. Through the whole reaction, the generation of polyselenides (K 2 Se n , 3 ≤ n ≤ 8) is effectively suppressed by electrochemical reaction dominated by Se 2 molecules, thus significantly enhancing the utilization of Se and effecting the voltage platform of the K–Se battery. This work offers a practical pathway to optimize the K–Se battery performance through structure engineering and manipulation of selenium chemistry for the formation of selective species and reveal its internal reaction mechanism in the carbonate electrolyte.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".