Dual-metallic site regulation boosts charge storage in zinc-ion hybrid supercapacitors
Bibliographic record
Abstract
Zinc-ion hybrid supercapacitors (ZIHCs) are compelling candidates for next-generation energy storage owing to their intrinsic safety, low cost, and high power density. However, their practical implementation remains hindered by the limited energy density of traditional carbon-based cathodes. Here, we rationally design porous carbon nanofibers embedded with atomically dispersed Zn and Fe dual-metal sites (ZnFe/PCNFs), synthesized via electrospinning followed by controlled carbonization. The introduction of Fe modulates the local electronic structure of Zn centers, thereby facilitating enhanced d -orbital hybridization and stronger ion adsorption through the formation of ZnFeN 6 coordination motifs. Coupled with high surface area and hierarchical porosity, these atomic-level interactions facilitate exceptional ion accessibility and rapid charge-transfer kinetics. As a cathode for ZIHCs, ZnFe/PCNFs deliver a specific capacity of 213 mAh g -1 , exceptional high-rate capability, and long-term cycling stability over 20000 cycles. This work elucidates mechanisms of dual-metal atomic coordination and provides a robust design strategy for high-performance, durable aqueous energy storage systems. Porous carbon nanofibers embedded with atomically dispersed Zn and Fe bimetallic sites (ZnFe/PCNFs) are developed for high-performance zinc-ion hybrid supercapacitors. The unique ZnFeN 6 configuration, combined with high specific surface area, porous architecture, and heteroatom functionalities, facilitates efficient ion adsorption and charge transfer, delivering outstanding electrochemical performance. • ZnFeN6 in ZnFe/PCNFs boosts Zn2+/anion adsorption and charge kinetics. • Hierarchical porous structure of ZnFe/PCNFs enables ultrafast ion diffusion. • In ZnFe/PCNFs, Fe regulates Zn’s electronic structure to enhance ion storage.
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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".